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Search Results (1,841)

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17 pages, 1214 KiB  
Article
Meta-Analysis of the Relationship Between Green Entrepreneurial Orientation and Sustainable Firm Performance
by Resul Öztürk, Mehtap Öztürk and Zeynep Kızılkan
Sustainability 2024, 16(24), 11224; https://doi.org/10.3390/su162411224 (registering DOI) - 21 Dec 2024
Viewed by 336
Abstract
The purpose of this study is to examine the relationship between green entrepreneurial orientation and sustainable firm performance. In order to examine this relationship, a meta-analysis method was used, and analyses were carried out with a Comprehensive Meta-Analysis Software (CMA) v4 package program. [...] Read more.
The purpose of this study is to examine the relationship between green entrepreneurial orientation and sustainable firm performance. In order to examine this relationship, a meta-analysis method was used, and analyses were carried out with a Comprehensive Meta-Analysis Software (CMA) v4 package program. In the study, a sample of 23 articles, 42 effect sizes, and 6666 enterprises was reached through a systematic literature review. The studies included in the research were accessed by searching the keywords “green entrepreneurial orientation” and “sustainable firm performance” from Web of Science, EBSCO Host, Scopus, and Google Scholar databases, and only articles without any year limit were included. Throughout the study, statistical analyses were performed on Fisher z values and conducted under the random effects model. The effect size, heterogeneity, and publication bias analyses of green entrepreneurial orientation and sustainable firm performance and its sub-dimensions were tested separately, and the findings were interpreted by converting them into correlation coefficients. As a result of the analyses, it was found that the relationship between green entrepreneurial orientation and sustainable firm performance is positive and highly significant (p < 0.05). In addition, the relationship between financial, environmental, social, sustainable, entrepreneurial, and green innovation performance, which is the sub-dimensions of sustainable firm performance, and green entrepreneurial orientation, was found to be high and significant (p < 0.05). However, it was concluded that there is no significant relationship between green innovation performance, which is another dimension of sustainable firm performance, and green entrepreneurial orientation. Moderator analyses revealed that sector and continent have a moderating effect on the relationship between green entrepreneurship orientation and sustainable firm performance. Full article
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<p>Theoretical Model.</p>
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<p>PRISMA Flow Diagram. Source: [<a href="#B39-sustainability-16-11224" class="html-bibr">39</a>].</p>
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<p>Number of Studies by Year.</p>
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<p>Number of Studies on the Sub-dimensions of SFP.</p>
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<p>Funnel Plot of Standard Error by Fisher’s Z.</p>
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14 pages, 2900 KiB  
Article
Laser-Based Length-Measuring Board for the Measurement of Infant Body Length from Outside an Incubator: Proposal and Assessment of a Model
by Luís Pereira-da-Silva, Rafael B. Henriques, Daniel Virella, Andreia Mascarenhas, Ana Luísa Papoila, Marta Alves and Horácio Fernandes
Children 2024, 11(12), 1544; https://doi.org/10.3390/children11121544 - 19 Dec 2024
Viewed by 236
Abstract
Introduction: Opening the incubator side wall to insert a non-sterile length-measuring device carries the risk of microbial contamination and thermal instability for preterm infants. To reduce this inconvenience, a laser-based length-measuring board is proposed to measure body length from outside the incubator. Methods: [...] Read more.
Introduction: Opening the incubator side wall to insert a non-sterile length-measuring device carries the risk of microbial contamination and thermal instability for preterm infants. To reduce this inconvenience, a laser-based length-measuring board is proposed to measure body length from outside the incubator. Methods: This device has two laser-line-shaped cursors which can be pointed to opposite ends of a segment to be measured. It is attached to the outer side of one of the incubator’s side walls in such a manner as to ensure that its axis is parallel to the longitudinal axis of the segment. To validate the measurements made with this model, a calibrated caliper consisting of a conventional rigid length-measuring board with a resolution of 0.05 mm was constructed to serve as a reference. Crown–heel length was measured in a sample of 45 infants, including 32 preterm and 13 term infants of corrected gestational age at the time of measurement. Results: Good intra-observer variability was obtained. Near-perfect statistical agreement was found between measurements with both devices, with concordance correlation coefficients of 0.994 (95% CI: 0.990; 0.996) in preterm infants and 0.994 (95% CI: 0.988, 0.998) in infants at term. The clinical relevance of the agreement between measurements was assessed by a Bland–Altman plot, and the difference may reach clinical relevance (up to 1 cm) but without evidence of proportional bias. Conclusion: The proposed validated laser-based length-measuring board offers a suitable alternative to conventional length-measuring boards for contactless measurement of infant body length. Full article
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Figure 1

Figure 1
<p>Laser-based length-measuring board: (A) linear spirit level for horizontal alignment; (B) mobile laser support carriage; (C) linear spirit level for vertical alignment.</p>
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<p>Schematic representation of the laser-based length-measuring board (upper view) including the calibrated axis with two measuring scales, including a calibrated ruler (5) with two-millimeter scales (6 and 7), the mobile laser support carriages (9), the two linear spirit levels (11 and 12), the two vacuum suction cups (14), and the spring switch (16).</p>
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<p>Schematic representation of the laser-based length-measuring board for contactless measurement of body length or body segments from outside an incubator (upper view): the length board (1); the infant (2); the incubator (3); and laser sights (4).</p>
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<p>Caliper consisting of a rigid conventional metal length-measuring board, built to validate the laser-based length-measuring board, with a Vernier scale (A) with a resolution of 0.05 mm.</p>
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<p>Scatterplot comparing the caliper and laser-based length-measuring board in the crown–heel length measurements of infants at preterm age. Each point corresponds to the individual measurements made on a subject. The black diagonal line is the identity line (x = y). The red line corresponds to the reduced major regression line.</p>
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<p>Bland-Altman plot shows the paired differences in cm between the measurement methods against their averages in the crown–heel length measurements of infants at preterm age. Circles correspond to the individual pairs of observations. The dotted line shows the mean bias (−0.061), and dashed lines represent the limits of agreement (upper limit 0.977; lower limit −1.099). The solid black line corresponds to perfect agreement.</p>
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<p>Scatterplot comparing the caliper and laser-based length-measuring board in the crown–heel length measurements of infants at term. Each point corresponds to the individual measurements made on a subject. The black diagonal line is the identity line (x = y). The red line corresponds to the reduced major regression line.</p>
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<p>Bland-Altman plot showing the paired differences in cm between the measuring methods against their averages in the crown–heel length measurements of infants at term. Circles correspond to the individual pairs of observations. The dotted line shows the mean bias (−0.170), and dashed lines represent the limits of agreement (upper limit 1.035; lower limit −1.374). The solid black line corresponds to perfect agreement.</p>
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21 pages, 12281 KiB  
Systematic Review
The Effect of Hepatic Surgical Margins of Colorectal Liver Metastases on Prognosis: A Systematic Review and Meta-Analysis
by Daniel Paramythiotis, Eleni Karlafti, Dimitrios Tsavdaris, Fani Apostolidou Kiouti, Anna-Bettina Haidich, Aristeidis Ioannidis, Stavros Panidis and Antonios Michalopoulos
J. Clin. Med. 2024, 13(24), 7776; https://doi.org/10.3390/jcm13247776 - 19 Dec 2024
Viewed by 290
Abstract
Introduction: Colorectal cancer is the third most common malignancy, with around half of patients developing liver metastases. Hepatectomy is the preferred treatment, but its success depends on several factors, including surgical margins. Various surgical margins have been suggested to achieve optimal results. This [...] Read more.
Introduction: Colorectal cancer is the third most common malignancy, with around half of patients developing liver metastases. Hepatectomy is the preferred treatment, but its success depends on several factors, including surgical margins. Various surgical margins have been suggested to achieve optimal results. This systematic review and meta-analysis aim to explore the impact of negative surgical margins ranging from 1 to 10 mm, and >10 mm on survival, with the objective of identifying optimal surgical margins. Methods: A systematic literature search was conducted on the MEDLINE, Scopus, and Cochrane databases. The six included studies that examined the effect of surgical margins at the aforementioned distances on patient survival. Studies were assessed for risk of bias using the Quality in Prognosis Studies tool. Statistical analysis was performed using SPSS software. Results: The results of the meta-analysis revealed the superiority of wider surgical margins (>10) on overall survival compared to smaller margins (1–10 mm), as the HR was calculated to be 1.38 [1.10; 1.73]. Specifically, negative margins between 1 and 10 mm are linked to a 38% increased risk of mortality compared to margins larger than 10 mm. The low heterogeneity indicates consistent findings across studies, and the statistically significant hazard ratio underscores the importance of aiming for larger surgical margins to enhance patient outcomes. In the subgroup that included only studies in which patients received neoadjuvant therapy, the HR was 1.48 [1.06; 2.07], further emphasizing the importance of ensuring negative surgical margins in today’s era. Conclusions: In summary, this systematic review and meta-analysis highlights the impact of surgical margin width on the survival of patients with colorectal liver metastases, as well as the importance of margin optimization in surgical management strategies. Full article
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<p>PRISMA 2020 flow chart.</p>
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<p>Quality assessment of included studies using the QUIPS tool: bar graph depicting risk of bias levels, with gray indicating low risk, yellow representing moderate risk, and red signifying high risk of bias across evaluated domains.</p>
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<p>Forest plot summarizing the results of the meta-analysis on the effect of hepatic surgical margins on prognosis in colorectal liver metastases. The pooled hazard ratio (HR) from six studies [<a href="#B35-jcm-13-07776" class="html-bibr">35</a>,<a href="#B36-jcm-13-07776" class="html-bibr">36</a>,<a href="#B37-jcm-13-07776" class="html-bibr">37</a>,<a href="#B38-jcm-13-07776" class="html-bibr">38</a>,<a href="#B39-jcm-13-07776" class="html-bibr">39</a>,<a href="#B40-jcm-13-07776" class="html-bibr">40</a>] indicates that patients with margins between 1 and 10 mm have a 38% higher hazard of poorer survival outcomes compared to those with margins &gt;10 mm (HR: 1.38 [1.10; 1.73]). Moderate heterogeneity is observed (I<sup>2</sup> = 56%). The red line represents the prediction interval, showing the expected range of true effects in a future study. The dotted vertical line represents the pooled hazard ratio (HR = 1.38) as the central point of the random-effects model estimate.</p>
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<p>Funnel plot assessing publication bias in the meta-analysis. The slight asymmetry suggests potential publication bias, as smaller studies with non-significant or positive effects of larger margins appear to be underreported or unpublished. The dotted vertical line represents the pooled effect estimate from the meta-analysis.</p>
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<p>Forest plot from the subgroup analysis assessing the impact of surgical margins greater than 10 mm on the prognosis of patients with colorectal liver metastases (CRLM) in the era of neoadjuvant treatments [<a href="#B37-jcm-13-07776" class="html-bibr">37</a>,<a href="#B38-jcm-13-07776" class="html-bibr">38</a>,<a href="#B39-jcm-13-07776" class="html-bibr">39</a>,<a href="#B40-jcm-13-07776" class="html-bibr">40</a>]. The pooled hazard ratio (HR) from four studies indicates a significant survival benefit for patients with margins &gt;10 mm (HR: 1.48 [1.06; 2.07]). The prediction interval of [0.37; 5.97] suggests variability in individual study results, and substantial heterogeneity (I<sup>2</sup> = 72%, τ<sup>2</sup> = 0.0758, <span class="html-italic">p</span> = 0.01) indicates differences in study designs or populations that may influence the overall effect. The red line represents the prediction interval, showing the expected range of true effects in a future study. The dotted vertical line represents the pooled hazard ratio (HR = 1.48) as the central point of the random-effects model estimate.</p>
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<p>Funnel plot assessing publication bias in the subgroup analysis. The slight asymmetry suggests potential publication bias or small-study effects, indicating that studies with negative or non-significant results may be underreported, potentially influencing the interpretation of the data. The dotted vertical line represents the pooled effect estimate from the meta-analysis.</p>
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17 pages, 7699 KiB  
Systematic Review
Long Non-Coding RNAs as Diagnostic Biomarkers for Ischemic Stroke: A Systematic Review and Meta-Analysis
by Jianwei Pan, Weijian Fan, Chenjie Gu, Yongmei Xi, Yu Wang and Peter Wang
Genes 2024, 15(12), 1620; https://doi.org/10.3390/genes15121620 - 18 Dec 2024
Viewed by 291
Abstract
Ischemic stroke is a serious cerebrovascular disease, highlighting the urgent need for reliable biomarkers for early diagnosis. Recent reports suggest that long non-coding RNAs (lncRNAs) can be potential biomarkers for ischemic stroke. Therefore, our study seeks to investigate the potential diagnostic value of [...] Read more.
Ischemic stroke is a serious cerebrovascular disease, highlighting the urgent need for reliable biomarkers for early diagnosis. Recent reports suggest that long non-coding RNAs (lncRNAs) can be potential biomarkers for ischemic stroke. Therefore, our study seeks to investigate the potential diagnostic value of lncRNAs for ischemic stroke by analyzing existing research. A comprehensive literature search was conducted across the PubMed, ScienceDirect, Wiley Online Library, and Web of Science databases for articles published up to July 10, 2024. Statistical analyses were performed using Stata 17.0 software to calculate pooled sensitivity, specificity, positive likelihood ratio (PLR), diagnostic odds ratio (DOR), negative likelihood ratio (NLR), and area under the curve (AUC). Heterogeneity was explored with the Cochran-Q test and the I2 statistical test, and publication bias was assessed with Deeks’ funnel plot. A total of 44 articles were included, involving 4302 ischemic stroke patients and 3725 healthy controls. Results demonstrated that lncRNAs H19, GAS5, PVT1, TUG1, and MALAT1 exhibited consistent trends across multiple studies. The pooled sensitivity of lncRNAs in the diagnosis of ischemic stroke was 79% (95% CI: 73–84%), specificity was 88% (95% CI: 77–94%), PLR was 6.63 (95% CI: 3.11–14.15), NLR was 0.23 (95% CI: 0.16–0.33), DOR was 28.5 (95% CI: 9.88–82.21), and AUC was 0.88 (95% CI: 0.85–0.90). Furthermore, the results of subgroup analysis indicated that lncRNA H19 had superior diagnostic performance. LncRNAs demonstrated strong diagnostic accuracy in distinguishing ischemic stroke patients from healthy controls, underscoring their potential as reliable biomarkers. Because most of the articles included in this study originate from China, large-scale, high-quality, multi-country prospective studies are required to further validate the reliability of lncRNAs as biomarkers for ischemic stroke. Full article
(This article belongs to the Special Issue The Epigenetic Roles of lncRNAs)
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Figure 1
<p>Flow diagram of study search and selection.</p>
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<p>Risk of bias assessment of eligible studies using QUADAS-2. (<b>A</b>) Summary of bias risk items in the QUADAS-2 quality assessment. (<b>B</b>) Percentile of risk of bias in the QUADAS-2 quality assessment [<a href="#B18-genes-15-01620" class="html-bibr">18</a>,<a href="#B19-genes-15-01620" class="html-bibr">19</a>,<a href="#B21-genes-15-01620" class="html-bibr">21</a>,<a href="#B25-genes-15-01620" class="html-bibr">25</a>,<a href="#B28-genes-15-01620" class="html-bibr">28</a>,<a href="#B30-genes-15-01620" class="html-bibr">30</a>,<a href="#B32-genes-15-01620" class="html-bibr">32</a>,<a href="#B38-genes-15-01620" class="html-bibr">38</a>,<a href="#B41-genes-15-01620" class="html-bibr">41</a>,<a href="#B43-genes-15-01620" class="html-bibr">43</a>,<a href="#B51-genes-15-01620" class="html-bibr">51</a>,<a href="#B52-genes-15-01620" class="html-bibr">52</a>,<a href="#B53-genes-15-01620" class="html-bibr">53</a>,<a href="#B56-genes-15-01620" class="html-bibr">56</a>,<a href="#B57-genes-15-01620" class="html-bibr">57</a>,<a href="#B58-genes-15-01620" class="html-bibr">58</a>,<a href="#B59-genes-15-01620" class="html-bibr">59</a>].</p>
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<p>(<b>A</b>) Forest plot showing the pooled sensitivity and specificity of lncRNAs in diagnosing ischemic stroke. Squares represent individual studies, while line segments indicate the 95% confidence interval (CI) for each study. The center of the diamond and the red dashed line represent the pooled effect size, and the width of the diamond corresponds to the 95% CI of the pooled results. (<b>B</b>) Summary receiver operating characteristic (SROC) curve with the 95% confidence and prediction contours. The <span class="html-italic">Y</span>-axis represents sensitivity, and the <span class="html-italic">X</span>-axis represents specificity. Numbers represent individual studies, and the curves depict combined diagnostic performance.</p>
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<p>Fagan nomogram (<b>A</b>) and likelihood ratio scattergram (<b>B</b>) are illustrated. (<b>A</b>) If two values are known, the nomogram can be used to calculate a third value. (<b>B</b>) The ordinate represents the positive likelihood ratio, indicating the likelihood of a positive result in a patient compared to a non-patient. The abscissa represents the negative likelihood ratio, indicating the likelihood of a negative result in a patient compared to a non-patient.</p>
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<p>Deeks’ funnel plot for publication bias analysis. A <span class="html-italic">p</span>-value &gt; 0.05 indicates no significant publication bias.</p>
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<p>LncRNAs as diagnostic markers for ischemic stroke. Created using <a href="https://BioRender.com" target="_blank">https://BioRender.com</a> (accessed on 3 December 2024).</p>
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31 pages, 6491 KiB  
Systematic Review
Obesity, Physical Activity, and Cancer Incidence in Two Geographically Distinct Populations; The Gulf Cooperation Council Countries and the United Kingdom—A Systematic Review and Meta-Analysis
by Christine Gaskell, Stuart Lutimba, Ghizlane Bendriss and Eiman Aleem
Cancers 2024, 16(24), 4205; https://doi.org/10.3390/cancers16244205 - 17 Dec 2024
Viewed by 333
Abstract
Background: The relationship between obesity, physical activity, and cancer has not been well studied across different countries. The age-standardized rate of cancer in the UK is double–triple that in the Gulf Cooperation Council Countries (GCCCs). Here, we study the association between obesity, physical [...] Read more.
Background: The relationship between obesity, physical activity, and cancer has not been well studied across different countries. The age-standardized rate of cancer in the UK is double–triple that in the Gulf Cooperation Council Countries (GCCCs). Here, we study the association between obesity, physical activity, and cancer incidence with the aim to elucidate cancer epidemiology and risk factors in two geographically, ethnically, and climatically different parts of the world. Methods: Our systematic search (from 2016 to 2023) in PubMed, EMBASE, Scopus, and APA PsycINFO databases resulted in 64 studies totaling 13,609,578 participants. The Cochrane risk of bias tool, GRADE, R programming language, and the meta package were used. Results: Significant associations between obesity and cancer were found in both regions, with a stronger association in the UK (p ≤ 0.0001) than the GCCCs (p = 0.0042). While physical inactivity alone did not show a statistically significant association with cancer incidence, the pooled hazard ratio analysis revealed that the presence of both obesity and physical inactivity was associated with a significantly higher cancer incidence. The most common types of cancer were breast cancer in the UK and colorectal cancer across the GCCCs. Conclusion: Although both regions share similarities, advanced healthcare systems, genetic characteristics, dietary habits, and cultural practices may influence cancer incidence and types. Full article
(This article belongs to the Special Issue Obesity and Cancers)
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<p>Flow chart of study selection for inclusion in the meta-analysis (PRISMA flow chart).</p>
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<p>Forest plot of all studies showing the association of obesity and cancer incidence in both GCCCs and the UK. The random-effects model was used to adjust for heterogeneity. The black squares and lines represent the confidence intervals of the individual studies, the grey squares represent the study weight, and the grey diamond represents the pooled HR. CI, confidence interval, GCCCs, Gulf Cooperation Council Countries, HR, hazard ratio, SE, standard error, TE, treatment effect, UK, United Kingdom [<a href="#B40-cancers-16-04205" class="html-bibr">40</a>,<a href="#B41-cancers-16-04205" class="html-bibr">41</a>,<a href="#B42-cancers-16-04205" class="html-bibr">42</a>,<a href="#B43-cancers-16-04205" class="html-bibr">43</a>,<a href="#B44-cancers-16-04205" class="html-bibr">44</a>,<a href="#B45-cancers-16-04205" class="html-bibr">45</a>,<a href="#B46-cancers-16-04205" class="html-bibr">46</a>,<a href="#B47-cancers-16-04205" class="html-bibr">47</a>,<a href="#B48-cancers-16-04205" class="html-bibr">48</a>,<a href="#B49-cancers-16-04205" class="html-bibr">49</a>,<a href="#B50-cancers-16-04205" class="html-bibr">50</a>,<a href="#B51-cancers-16-04205" class="html-bibr">51</a>,<a href="#B52-cancers-16-04205" class="html-bibr">52</a>,<a href="#B53-cancers-16-04205" class="html-bibr">53</a>,<a href="#B54-cancers-16-04205" class="html-bibr">54</a>,<a href="#B55-cancers-16-04205" class="html-bibr">55</a>,<a href="#B56-cancers-16-04205" class="html-bibr">56</a>,<a href="#B57-cancers-16-04205" class="html-bibr">57</a>,<a href="#B58-cancers-16-04205" class="html-bibr">58</a>,<a href="#B59-cancers-16-04205" class="html-bibr">59</a>,<a href="#B60-cancers-16-04205" class="html-bibr">60</a>,<a href="#B61-cancers-16-04205" class="html-bibr">61</a>,<a href="#B62-cancers-16-04205" class="html-bibr">62</a>,<a href="#B63-cancers-16-04205" class="html-bibr">63</a>,<a href="#B64-cancers-16-04205" class="html-bibr">64</a>,<a href="#B65-cancers-16-04205" class="html-bibr">65</a>,<a href="#B66-cancers-16-04205" class="html-bibr">66</a>,<a href="#B67-cancers-16-04205" class="html-bibr">67</a>,<a href="#B68-cancers-16-04205" class="html-bibr">68</a>,<a href="#B69-cancers-16-04205" class="html-bibr">69</a>,<a href="#B70-cancers-16-04205" class="html-bibr">70</a>,<a href="#B71-cancers-16-04205" class="html-bibr">71</a>,<a href="#B72-cancers-16-04205" class="html-bibr">72</a>,<a href="#B73-cancers-16-04205" class="html-bibr">73</a>,<a href="#B74-cancers-16-04205" class="html-bibr">74</a>,<a href="#B75-cancers-16-04205" class="html-bibr">75</a>,<a href="#B76-cancers-16-04205" class="html-bibr">76</a>,<a href="#B77-cancers-16-04205" class="html-bibr">77</a>,<a href="#B78-cancers-16-04205" class="html-bibr">78</a>,<a href="#B79-cancers-16-04205" class="html-bibr">79</a>,<a href="#B80-cancers-16-04205" class="html-bibr">80</a>,<a href="#B81-cancers-16-04205" class="html-bibr">81</a>,<a href="#B82-cancers-16-04205" class="html-bibr">82</a>,<a href="#B83-cancers-16-04205" class="html-bibr">83</a>,<a href="#B84-cancers-16-04205" class="html-bibr">84</a>,<a href="#B85-cancers-16-04205" class="html-bibr">85</a>,<a href="#B86-cancers-16-04205" class="html-bibr">86</a>,<a href="#B87-cancers-16-04205" class="html-bibr">87</a>,<a href="#B88-cancers-16-04205" class="html-bibr">88</a>,<a href="#B89-cancers-16-04205" class="html-bibr">89</a>,<a href="#B90-cancers-16-04205" class="html-bibr">90</a>,<a href="#B91-cancers-16-04205" class="html-bibr">91</a>,<a href="#B92-cancers-16-04205" class="html-bibr">92</a>,<a href="#B93-cancers-16-04205" class="html-bibr">93</a>,<a href="#B94-cancers-16-04205" class="html-bibr">94</a>,<a href="#B95-cancers-16-04205" class="html-bibr">95</a>,<a href="#B96-cancers-16-04205" class="html-bibr">96</a>,<a href="#B97-cancers-16-04205" class="html-bibr">97</a>,<a href="#B98-cancers-16-04205" class="html-bibr">98</a>,<a href="#B99-cancers-16-04205" class="html-bibr">99</a>,<a href="#B100-cancers-16-04205" class="html-bibr">100</a>,<a href="#B101-cancers-16-04205" class="html-bibr">101</a>,<a href="#B102-cancers-16-04205" class="html-bibr">102</a>].</p>
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<p>Funnel plot of all studies included in the meta-analysis. The <span class="html-italic">x</span>-axis displays the study estimated effect size with inverse hazard ratio (In(HR)), and the <span class="html-italic">y</span>-axis represents a measure of study precision, with standard error. The dots represent the effect sizes from individual studies plotted against their precision while the dashed lines signify the expected distribution of these studies. The distribution of the studies observed in the funnel plot could be due to the heterogeneity of the studies.</p>
Full article ">Figure 4
<p>Forest plot of 42 studies showing the association of obesity and the incidence of cancer in the UK. The random-effects model was used to adjust for heterogeneity. The black squares and lines represent the confidence intervals of the individual studies, the grey squares represent the study weight, and the diamond represents the pooled HR. CI, confidence interval, HR, hazard ratio, SE, standard error, TE: treatment effect [<a href="#B40-cancers-16-04205" class="html-bibr">40</a>,<a href="#B41-cancers-16-04205" class="html-bibr">41</a>,<a href="#B42-cancers-16-04205" class="html-bibr">42</a>,<a href="#B43-cancers-16-04205" class="html-bibr">43</a>,<a href="#B44-cancers-16-04205" class="html-bibr">44</a>,<a href="#B45-cancers-16-04205" class="html-bibr">45</a>,<a href="#B47-cancers-16-04205" class="html-bibr">47</a>,<a href="#B48-cancers-16-04205" class="html-bibr">48</a>,<a href="#B49-cancers-16-04205" class="html-bibr">49</a>,<a href="#B57-cancers-16-04205" class="html-bibr">57</a>,<a href="#B58-cancers-16-04205" class="html-bibr">58</a>,<a href="#B59-cancers-16-04205" class="html-bibr">59</a>,<a href="#B60-cancers-16-04205" class="html-bibr">60</a>,<a href="#B61-cancers-16-04205" class="html-bibr">61</a>,<a href="#B62-cancers-16-04205" class="html-bibr">62</a>,<a href="#B63-cancers-16-04205" class="html-bibr">63</a>,<a href="#B64-cancers-16-04205" class="html-bibr">64</a>,<a href="#B65-cancers-16-04205" class="html-bibr">65</a>,<a href="#B66-cancers-16-04205" class="html-bibr">66</a>,<a href="#B67-cancers-16-04205" class="html-bibr">67</a>,<a href="#B68-cancers-16-04205" class="html-bibr">68</a>,<a href="#B69-cancers-16-04205" class="html-bibr">69</a>,<a href="#B70-cancers-16-04205" class="html-bibr">70</a>,<a href="#B71-cancers-16-04205" class="html-bibr">71</a>,<a href="#B72-cancers-16-04205" class="html-bibr">72</a>,<a href="#B73-cancers-16-04205" class="html-bibr">73</a>,<a href="#B74-cancers-16-04205" class="html-bibr">74</a>,<a href="#B75-cancers-16-04205" class="html-bibr">75</a>,<a href="#B76-cancers-16-04205" class="html-bibr">76</a>,<a href="#B77-cancers-16-04205" class="html-bibr">77</a>,<a href="#B78-cancers-16-04205" class="html-bibr">78</a>,<a href="#B79-cancers-16-04205" class="html-bibr">79</a>,<a href="#B81-cancers-16-04205" class="html-bibr">81</a>,<a href="#B82-cancers-16-04205" class="html-bibr">82</a>,<a href="#B83-cancers-16-04205" class="html-bibr">83</a>,<a href="#B86-cancers-16-04205" class="html-bibr">86</a>,<a href="#B87-cancers-16-04205" class="html-bibr">87</a>,<a href="#B88-cancers-16-04205" class="html-bibr">88</a>,<a href="#B90-cancers-16-04205" class="html-bibr">90</a>,<a href="#B91-cancers-16-04205" class="html-bibr">91</a>,<a href="#B92-cancers-16-04205" class="html-bibr">92</a>].</p>
Full article ">Figure 5
<p>Forest plot showing the association of obesity and the incidence of cancer of 22 studies of the GCCCs (<b>A</b>), of 9 studies of the GCCCs excluding Saudi Arabia (<b>B</b>) and of 13 studies of Saudi Arabia (<b>C</b>). The random-effects model was used to adjust for heterogeneity. The black squares and lines represent the confidence intervals of the individual studies, the grey squares represent the study weight, and the grey diamond represents the pooled HR. CI, Confidence interval, GCCCs, Gulf cooperation countries council, HR, hazard ratio, SE, standard error, TE: treatment effect [<a href="#B46-cancers-16-04205" class="html-bibr">46</a>,<a href="#B50-cancers-16-04205" class="html-bibr">50</a>,<a href="#B51-cancers-16-04205" class="html-bibr">51</a>,<a href="#B52-cancers-16-04205" class="html-bibr">52</a>,<a href="#B53-cancers-16-04205" class="html-bibr">53</a>,<a href="#B54-cancers-16-04205" class="html-bibr">54</a>,<a href="#B55-cancers-16-04205" class="html-bibr">55</a>,<a href="#B56-cancers-16-04205" class="html-bibr">56</a>,<a href="#B80-cancers-16-04205" class="html-bibr">80</a>,<a href="#B84-cancers-16-04205" class="html-bibr">84</a>,<a href="#B85-cancers-16-04205" class="html-bibr">85</a>,<a href="#B89-cancers-16-04205" class="html-bibr">89</a>,<a href="#B93-cancers-16-04205" class="html-bibr">93</a>,<a href="#B94-cancers-16-04205" class="html-bibr">94</a>,<a href="#B95-cancers-16-04205" class="html-bibr">95</a>,<a href="#B96-cancers-16-04205" class="html-bibr">96</a>,<a href="#B97-cancers-16-04205" class="html-bibr">97</a>,<a href="#B98-cancers-16-04205" class="html-bibr">98</a>,<a href="#B99-cancers-16-04205" class="html-bibr">99</a>,<a href="#B100-cancers-16-04205" class="html-bibr">100</a>,<a href="#B101-cancers-16-04205" class="html-bibr">101</a>,<a href="#B102-cancers-16-04205" class="html-bibr">102</a>].</p>
Full article ">Figure 6
<p>Forest plot illustrating the association between cancer incidence and age group (40–60). The black squares and lines represent the confidence intervals of the individual studies; the grey squares represent the study weight. The diamond at the bottom of the plot represents the overall pooled effect size, with its width reflecting the 95% CI. CI, confidence interval, HR, hazard ratio, SE, standard error [<a href="#B43-cancers-16-04205" class="html-bibr">43</a>,<a href="#B51-cancers-16-04205" class="html-bibr">51</a>,<a href="#B65-cancers-16-04205" class="html-bibr">65</a>,<a href="#B68-cancers-16-04205" class="html-bibr">68</a>,<a href="#B70-cancers-16-04205" class="html-bibr">70</a>,<a href="#B74-cancers-16-04205" class="html-bibr">74</a>,<a href="#B81-cancers-16-04205" class="html-bibr">81</a>,<a href="#B84-cancers-16-04205" class="html-bibr">84</a>,<a href="#B87-cancers-16-04205" class="html-bibr">87</a>,<a href="#B88-cancers-16-04205" class="html-bibr">88</a>,<a href="#B90-cancers-16-04205" class="html-bibr">90</a>,<a href="#B91-cancers-16-04205" class="html-bibr">91</a>,<a href="#B96-cancers-16-04205" class="html-bibr">96</a>,<a href="#B99-cancers-16-04205" class="html-bibr">99</a>,<a href="#B101-cancers-16-04205" class="html-bibr">101</a>].</p>
Full article ">Figure 7
<p>Meta-regression bubble plot showing the relationship between mean participant age and log hazard ratio (effect size). Each bubble represents a study, with bubble size proportional to the study’s weight in the meta-analysis. The solid line indicates the regression line, while the dashed lines represent the 95% confidence interval.</p>
Full article ">Figure 8
<p>Forest plots show the association of gender and incidence of cancer for females and males [<a href="#B40-cancers-16-04205" class="html-bibr">40</a>,<a href="#B41-cancers-16-04205" class="html-bibr">41</a>,<a href="#B42-cancers-16-04205" class="html-bibr">42</a>,<a href="#B43-cancers-16-04205" class="html-bibr">43</a>,<a href="#B44-cancers-16-04205" class="html-bibr">44</a>,<a href="#B45-cancers-16-04205" class="html-bibr">45</a>,<a href="#B46-cancers-16-04205" class="html-bibr">46</a>,<a href="#B47-cancers-16-04205" class="html-bibr">47</a>,<a href="#B48-cancers-16-04205" class="html-bibr">48</a>,<a href="#B49-cancers-16-04205" class="html-bibr">49</a>,<a href="#B50-cancers-16-04205" class="html-bibr">50</a>,<a href="#B51-cancers-16-04205" class="html-bibr">51</a>,<a href="#B52-cancers-16-04205" class="html-bibr">52</a>,<a href="#B53-cancers-16-04205" class="html-bibr">53</a>,<a href="#B54-cancers-16-04205" class="html-bibr">54</a>,<a href="#B55-cancers-16-04205" class="html-bibr">55</a>,<a href="#B56-cancers-16-04205" class="html-bibr">56</a>,<a href="#B57-cancers-16-04205" class="html-bibr">57</a>,<a href="#B58-cancers-16-04205" class="html-bibr">58</a>,<a href="#B59-cancers-16-04205" class="html-bibr">59</a>,<a href="#B60-cancers-16-04205" class="html-bibr">60</a>,<a href="#B61-cancers-16-04205" class="html-bibr">61</a>,<a href="#B62-cancers-16-04205" class="html-bibr">62</a>,<a href="#B63-cancers-16-04205" class="html-bibr">63</a>,<a href="#B64-cancers-16-04205" class="html-bibr">64</a>,<a href="#B65-cancers-16-04205" class="html-bibr">65</a>,<a href="#B66-cancers-16-04205" class="html-bibr">66</a>,<a href="#B67-cancers-16-04205" class="html-bibr">67</a>,<a href="#B68-cancers-16-04205" class="html-bibr">68</a>,<a href="#B69-cancers-16-04205" class="html-bibr">69</a>,<a href="#B70-cancers-16-04205" class="html-bibr">70</a>,<a href="#B71-cancers-16-04205" class="html-bibr">71</a>,<a href="#B72-cancers-16-04205" class="html-bibr">72</a>,<a href="#B73-cancers-16-04205" class="html-bibr">73</a>,<a href="#B74-cancers-16-04205" class="html-bibr">74</a>,<a href="#B75-cancers-16-04205" class="html-bibr">75</a>,<a href="#B76-cancers-16-04205" class="html-bibr">76</a>,<a href="#B77-cancers-16-04205" class="html-bibr">77</a>,<a href="#B78-cancers-16-04205" class="html-bibr">78</a>,<a href="#B79-cancers-16-04205" class="html-bibr">79</a>,<a href="#B80-cancers-16-04205" class="html-bibr">80</a>,<a href="#B81-cancers-16-04205" class="html-bibr">81</a>,<a href="#B82-cancers-16-04205" class="html-bibr">82</a>,<a href="#B83-cancers-16-04205" class="html-bibr">83</a>,<a href="#B84-cancers-16-04205" class="html-bibr">84</a>,<a href="#B85-cancers-16-04205" class="html-bibr">85</a>,<a href="#B86-cancers-16-04205" class="html-bibr">86</a>,<a href="#B87-cancers-16-04205" class="html-bibr">87</a>,<a href="#B88-cancers-16-04205" class="html-bibr">88</a>,<a href="#B89-cancers-16-04205" class="html-bibr">89</a>,<a href="#B90-cancers-16-04205" class="html-bibr">90</a>,<a href="#B91-cancers-16-04205" class="html-bibr">91</a>,<a href="#B92-cancers-16-04205" class="html-bibr">92</a>,<a href="#B93-cancers-16-04205" class="html-bibr">93</a>,<a href="#B94-cancers-16-04205" class="html-bibr">94</a>,<a href="#B95-cancers-16-04205" class="html-bibr">95</a>,<a href="#B96-cancers-16-04205" class="html-bibr">96</a>,<a href="#B97-cancers-16-04205" class="html-bibr">97</a>,<a href="#B98-cancers-16-04205" class="html-bibr">98</a>,<a href="#B99-cancers-16-04205" class="html-bibr">99</a>,<a href="#B100-cancers-16-04205" class="html-bibr">100</a>,<a href="#B101-cancers-16-04205" class="html-bibr">101</a>,<a href="#B102-cancers-16-04205" class="html-bibr">102</a>].</p>
Full article ">Figure 9
<p>Forest plot showing the association of obesity and the incidence of breast and gastrointestinal cancer types. Both types of cancer had a statistically significant association with obesity. The diamond at the bottom of the plot represents the overall pooled HR. CI, confidence interval, HR, hazard ratio, SE, standard error [<a href="#B40-cancers-16-04205" class="html-bibr">40</a>,<a href="#B43-cancers-16-04205" class="html-bibr">43</a>,<a href="#B45-cancers-16-04205" class="html-bibr">45</a>,<a href="#B47-cancers-16-04205" class="html-bibr">47</a>,<a href="#B48-cancers-16-04205" class="html-bibr">48</a>,<a href="#B51-cancers-16-04205" class="html-bibr">51</a>,<a href="#B53-cancers-16-04205" class="html-bibr">53</a>,<a href="#B54-cancers-16-04205" class="html-bibr">54</a>,<a href="#B55-cancers-16-04205" class="html-bibr">55</a>,<a href="#B56-cancers-16-04205" class="html-bibr">56</a>,<a href="#B60-cancers-16-04205" class="html-bibr">60</a>,<a href="#B61-cancers-16-04205" class="html-bibr">61</a>,<a href="#B63-cancers-16-04205" class="html-bibr">63</a>,<a href="#B64-cancers-16-04205" class="html-bibr">64</a>,<a href="#B66-cancers-16-04205" class="html-bibr">66</a>,<a href="#B67-cancers-16-04205" class="html-bibr">67</a>,<a href="#B68-cancers-16-04205" class="html-bibr">68</a>,<a href="#B69-cancers-16-04205" class="html-bibr">69</a>,<a href="#B70-cancers-16-04205" class="html-bibr">70</a>,<a href="#B72-cancers-16-04205" class="html-bibr">72</a>,<a href="#B73-cancers-16-04205" class="html-bibr">73</a>,<a href="#B74-cancers-16-04205" class="html-bibr">74</a>,<a href="#B76-cancers-16-04205" class="html-bibr">76</a>,<a href="#B77-cancers-16-04205" class="html-bibr">77</a>,<a href="#B78-cancers-16-04205" class="html-bibr">78</a>,<a href="#B79-cancers-16-04205" class="html-bibr">79</a>,<a href="#B80-cancers-16-04205" class="html-bibr">80</a>,<a href="#B81-cancers-16-04205" class="html-bibr">81</a>,<a href="#B82-cancers-16-04205" class="html-bibr">82</a>,<a href="#B84-cancers-16-04205" class="html-bibr">84</a>,<a href="#B85-cancers-16-04205" class="html-bibr">85</a>,<a href="#B86-cancers-16-04205" class="html-bibr">86</a>,<a href="#B87-cancers-16-04205" class="html-bibr">87</a>,<a href="#B88-cancers-16-04205" class="html-bibr">88</a>,<a href="#B93-cancers-16-04205" class="html-bibr">93</a>,<a href="#B94-cancers-16-04205" class="html-bibr">94</a>,<a href="#B96-cancers-16-04205" class="html-bibr">96</a>,<a href="#B97-cancers-16-04205" class="html-bibr">97</a>,<a href="#B98-cancers-16-04205" class="html-bibr">98</a>,<a href="#B102-cancers-16-04205" class="html-bibr">102</a>].</p>
Full article ">Figure 10
<p>Forest plot showing the results of a mixed-effects meta-analysis model, synthesizing data from 64 studies using the Restricted Maximum Likelihood (REML) method to estimate variance components. The model fit statistics include a log likelihood of −22.5703, deviance of 45.1406, Akaike Information Criterion (AIC) of 51.1406, Bayesian Information Criterion (BIC) of 57.5220, and a Corrected AIC (AICc) of 51.5544. Heterogeneity measures indicate substantial variability among studies, with τ<sup>2</sup> (residual heterogeneity) at 0.0194 (SE = 0.0064), <span class="html-italic">I</span><sup>2</sup> at 80.29%, and H<sup>2</sup> at 5.07. A significant residual heterogeneity is evident from the Q_E statistic (Q_E(<span class="html-italic">df</span> = 62) = 193.2016, <span class="html-italic">p</span> ≤ 0.0001). However, the moderator effect of physical exercise is not significant (Q_M(<span class="html-italic">df</span> = 1) = 0.0266, <span class="html-italic">p</span> = 0.8705) [<a href="#B40-cancers-16-04205" class="html-bibr">40</a>,<a href="#B41-cancers-16-04205" class="html-bibr">41</a>,<a href="#B42-cancers-16-04205" class="html-bibr">42</a>,<a href="#B43-cancers-16-04205" class="html-bibr">43</a>,<a href="#B44-cancers-16-04205" class="html-bibr">44</a>,<a href="#B45-cancers-16-04205" class="html-bibr">45</a>,<a href="#B46-cancers-16-04205" class="html-bibr">46</a>,<a href="#B47-cancers-16-04205" class="html-bibr">47</a>,<a href="#B48-cancers-16-04205" class="html-bibr">48</a>,<a href="#B49-cancers-16-04205" class="html-bibr">49</a>,<a href="#B50-cancers-16-04205" class="html-bibr">50</a>,<a href="#B51-cancers-16-04205" class="html-bibr">51</a>,<a href="#B52-cancers-16-04205" class="html-bibr">52</a>,<a href="#B53-cancers-16-04205" class="html-bibr">53</a>,<a href="#B54-cancers-16-04205" class="html-bibr">54</a>,<a href="#B55-cancers-16-04205" class="html-bibr">55</a>,<a href="#B56-cancers-16-04205" class="html-bibr">56</a>,<a href="#B57-cancers-16-04205" class="html-bibr">57</a>,<a href="#B58-cancers-16-04205" class="html-bibr">58</a>,<a href="#B59-cancers-16-04205" class="html-bibr">59</a>,<a href="#B60-cancers-16-04205" class="html-bibr">60</a>,<a href="#B61-cancers-16-04205" class="html-bibr">61</a>,<a href="#B62-cancers-16-04205" class="html-bibr">62</a>,<a href="#B63-cancers-16-04205" class="html-bibr">63</a>,<a href="#B64-cancers-16-04205" class="html-bibr">64</a>,<a href="#B65-cancers-16-04205" class="html-bibr">65</a>,<a href="#B66-cancers-16-04205" class="html-bibr">66</a>,<a href="#B67-cancers-16-04205" class="html-bibr">67</a>,<a href="#B68-cancers-16-04205" class="html-bibr">68</a>,<a href="#B69-cancers-16-04205" class="html-bibr">69</a>,<a href="#B70-cancers-16-04205" class="html-bibr">70</a>,<a href="#B71-cancers-16-04205" class="html-bibr">71</a>,<a href="#B72-cancers-16-04205" class="html-bibr">72</a>,<a href="#B73-cancers-16-04205" class="html-bibr">73</a>,<a href="#B74-cancers-16-04205" class="html-bibr">74</a>,<a href="#B75-cancers-16-04205" class="html-bibr">75</a>,<a href="#B76-cancers-16-04205" class="html-bibr">76</a>,<a href="#B77-cancers-16-04205" class="html-bibr">77</a>,<a href="#B78-cancers-16-04205" class="html-bibr">78</a>,<a href="#B79-cancers-16-04205" class="html-bibr">79</a>,<a href="#B80-cancers-16-04205" class="html-bibr">80</a>,<a href="#B81-cancers-16-04205" class="html-bibr">81</a>,<a href="#B82-cancers-16-04205" class="html-bibr">82</a>,<a href="#B83-cancers-16-04205" class="html-bibr">83</a>,<a href="#B84-cancers-16-04205" class="html-bibr">84</a>,<a href="#B85-cancers-16-04205" class="html-bibr">85</a>,<a href="#B86-cancers-16-04205" class="html-bibr">86</a>,<a href="#B87-cancers-16-04205" class="html-bibr">87</a>,<a href="#B88-cancers-16-04205" class="html-bibr">88</a>,<a href="#B89-cancers-16-04205" class="html-bibr">89</a>,<a href="#B90-cancers-16-04205" class="html-bibr">90</a>,<a href="#B91-cancers-16-04205" class="html-bibr">91</a>,<a href="#B92-cancers-16-04205" class="html-bibr">92</a>,<a href="#B93-cancers-16-04205" class="html-bibr">93</a>,<a href="#B94-cancers-16-04205" class="html-bibr">94</a>,<a href="#B95-cancers-16-04205" class="html-bibr">95</a>,<a href="#B96-cancers-16-04205" class="html-bibr">96</a>,<a href="#B97-cancers-16-04205" class="html-bibr">97</a>,<a href="#B98-cancers-16-04205" class="html-bibr">98</a>,<a href="#B99-cancers-16-04205" class="html-bibr">99</a>,<a href="#B100-cancers-16-04205" class="html-bibr">100</a>,<a href="#B101-cancers-16-04205" class="html-bibr">101</a>,<a href="#B102-cancers-16-04205" class="html-bibr">102</a>].</p>
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17 pages, 1581 KiB  
Article
The Influence of the Spatial Co-Registration Error on the Estimation of Growing Stock Volume Based on Airborne Laser Scanning Metrics
by Marek Lisańczuk, Krzysztof Mitelsztedt and Krzysztof Stereńczak
Remote Sens. 2024, 16(24), 4709; https://doi.org/10.3390/rs16244709 - 17 Dec 2024
Viewed by 506
Abstract
Remote sensing (RS)-based forest inventories are becoming increasingly common in forest management. However, practical applications often require subsequent optimisation steps. One of the most popular RS-based forest inventory methods is the two-phase inventory with regression estimator, commonly referred to as the area-based approach [...] Read more.
Remote sensing (RS)-based forest inventories are becoming increasingly common in forest management. However, practical applications often require subsequent optimisation steps. One of the most popular RS-based forest inventory methods is the two-phase inventory with regression estimator, commonly referred to as the area-based approach (ABA). There are many sources of variation that contribute to the overall performance of this method. One of them, which is related to the core aspect of this method, is the spatial co-registration error between ground measurements and RS data. This error arises mainly from the imperfection of the methods for positioning the sample plots under the forest canopy. In this study, we investigated how this positioning accuracy affects the area-based growing stock volume (GSV) estimation under different forest conditions and sample plot radii. In order to analyse this relationship, an artificial co-registration error was induced in a series of simulations and various scenarios. The results showed that there were minimal differences in ABA inventory performance for displacements below 4 m for all stratification groups except for deciduous sites, where sub-metre plot positioning accuracy was justified, as site- and terrain-related factors had some influence on GSV estimation error (r up to 0.4). On the other hand, denser canopy and spatially homogeneous stands mitigated the negative aspects of weaker GNSS positioning capabilities under broadleaved forest types. In the case of RMSE, the results for plots smaller than 400 m2 were visibly inferior. The BIAS behaviour was less strict in this regard. Knowledge of the actual positioning accuracy as well as the co-registration threshold required for a particular stand type could help manage and optimise fieldwork, as well as better distinguish sources of statistical uncertainty. Full article
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<p>Distribution of GSV estimation error due to co-registration shift and plot area.</p>
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11 pages, 644 KiB  
Systematic Review
Cueing Interventions for Gait and Balance in Parkinson’s Disease: A Scoping Review of Current Evidence
by Federica Giorgi, Danilo Donati and Roberto Tedeschi
Appl. Sci. 2024, 14(24), 11781; https://doi.org/10.3390/app142411781 - 17 Dec 2024
Viewed by 373
Abstract
Background: Cueing interventions, which utilize external auditory, visual, or somatosensory stimuli, are increasingly used to improve motor performance in individuals with Parkinson’s disease (PD). This review explores the effectiveness of cueing on gait, balance, and quality of life outcomes in PD. Methods: A [...] Read more.
Background: Cueing interventions, which utilize external auditory, visual, or somatosensory stimuli, are increasingly used to improve motor performance in individuals with Parkinson’s disease (PD). This review explores the effectiveness of cueing on gait, balance, and quality of life outcomes in PD. Methods: A scoping review of six studies was conducted, focusing on the impact of cueing interventions on gait parameters, balance stability, and functional outcomes in PD patients. Studies were evaluated for methodological quality using the PEDro scale, and risk of bias was assessed with RoB 2. Results: Cueing interventions consistently improved gait parameters, with five studies showing significant increases in step length. The results for walking speed were more varied, with some studies reporting statistically significant gains while others found non-significant or mixed outcomes. Balance improvements were noted in dynamic balance measures, though static balance effects were less consistent. Two studies observed long-term benefits at follow-up, particularly when interventions were structured and supervised. The quality of life improvements were limited, with only one study measuring this outcome and showing no significant changes. Conclusions: Cueing interventions demonstrate potential for enhancing gait and dynamic balance in PD, though effects on quality of life remain uncertain. Early and structured implementation of cueing, especially with auditory stimuli, may support functional gains in PD management. Further research is required to establish optimal cueing protocols and long-term benefits. Full article
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<p>Preferred reporting items for systematic reviews and meta-analyses 2020 (PRISMA) flow-diagram.</p>
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14 pages, 691 KiB  
Article
Efficacy of a Dietary Supplement Extracted from Persimmon (Diospyros kaki L.f.) in Overweight Healthy Adults: A Randomized, Double-Blind, Controlled Clinical Trial
by Silvia Pérez-Piñero, Juan Carlos Muñoz-Carrillo, Jon Echepare-Taberna, Cristina Herrera-Fernández, Macarena Muñoz-Cámara, Vicente Ávila-Gandía and Francisco Javier López-Román
Foods 2024, 13(24), 4072; https://doi.org/10.3390/foods13244072 - 17 Dec 2024
Viewed by 410
Abstract
A single-center, randomized, double-blind, and placebo-controlled clinical trial assessed the efficacy in improving body composition and in weight management of a dietary supplement consisting of 400 mg of a standardized extract of the persimmon fruit (Diospyros kaki L.f.) in adult subjects with [...] Read more.
A single-center, randomized, double-blind, and placebo-controlled clinical trial assessed the efficacy in improving body composition and in weight management of a dietary supplement consisting of 400 mg of a standardized extract of the persimmon fruit (Diospyros kaki L.f.) in adult subjects with a BMI between 25 and 34.99 kg/m2 administered for 120 consecutive days. In total, 36 participants were assigned to the placebo group and 35 to the experimental group (registered at ClinicalTrials.gov (NCT05750342)). Primary analysis focused on overweight subjects (placebo, n = 26; experimental, n = 23). In this group, fat mass expressed in kg and percentage evaluated by both dual-energy X-ray absorptiometry (DEXA) and bioelectrical impedance analysis (BIA) decreased significantly (between-group differences p < 0.001) in those receiving the persimmon extract as compared with the placebo. No significant reduction in lean mass was observed, suggesting that the muscle mass was maintained during fat loss. The use of the investigational product improved classic anthropometric parameters to a statistically significantly greater extent than the placebo, including body weight, BMI, and waist and abdominal circumference (p < 0.001), in the overweight group. In the overall population, similar improvements were observed, with significant between-group differences (p < 0.001) in fat mass reduction and improvements in body composition. Changes in the biochemical lipidic, glycemic, and anti-inflammatory profile were not found, except for between-group significant differences (p < 0.001) in decreases in tumor necrosis factor-alpha (TNFα) and increases in total antioxidant capacity (TAC) in favor of the experimental condition. There was a significant increase in fecal fat excretion in the experimental group at the end of the study in subjects with low fecal fat (9%) at baseline. Consumption of the investigational product vs. placebo improved the quality of life, with significantly greater scores in the total score and the mental health component of the SF-12 questionnaire. The persimmon extract was safe and well tolerated. Full article
(This article belongs to the Section Food Nutrition)
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<p>Flow chart of the study population.</p>
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10 pages, 508 KiB  
Article
Risk of Surgical Site Infection in Posterior Spine Surgery Using Different Closing Techniques: A Retrospective Study of Two Neurosurgical Centers
by Granit Molliqaj, Sara Lener, Michele Da Broi, Aria Nouri, Nalla Silva Baticam, Karl Schaller, Claudius Thomé, Pierre-Pascal Girod and Enrico Tessitore
J. Clin. Med. 2024, 13(24), 7675; https://doi.org/10.3390/jcm13247675 - 16 Dec 2024
Viewed by 331
Abstract
Objectives: To determine whether a closed dressing protocol reduces the surgical site infections (SSI) rate compared to conventional closing techniques. Methods: Patients who underwent lumbar spine surgery at two neurosurgical centers were retrospectively included from June 2015 to December 2019. Data on patients, [...] Read more.
Objectives: To determine whether a closed dressing protocol reduces the surgical site infections (SSI) rate compared to conventional closing techniques. Methods: Patients who underwent lumbar spine surgery at two neurosurgical centers were retrospectively included from June 2015 to December 2019. Data on patients, general risk factors, and surgical risk factors for SSI were collected. Patients were subdivided into two groups: a Closed Protocol where the Dermabond® ± Prineo® dressing system was used, and a Conventional Protocol, namely sutures or staples. Statistical analysis was undertaken to compare the infection rates among the different closure techniques. Results: Altogether, 672 patients were included. In the whole cohort, 157 (23.36%) underwent skin closure with staples, 122 (18.15%) with sutures, 98 (14.58%) with intracutaneous sutures, 78 (11.61%) with Dermabond®, and 217 (32.29%) with Demabond® + Prineo®. The overall infection rate was 2.23% (n = 15). Skin suture had the highest infection rate (4.10%), while the lowest was Dermabond® (1.28%) and Dermabond® + Prineo® (1.4%), though the difference was not significant. Risk factors for SSI included higher BMI (29.46 kg/m2 vs. 26.96 kg/m2, p = 0.044), other sites infection (20.00% vs. 2.38%, p = 0.004), and a higher national nosocomial infections surveillance score (p = 0.003). Conclusions: This study showed that a closed protocol with the use of adhesive dressing with or without mesh had a slight tendency to lower infection rates compared to conventional protocol with sutures or staples, although no statistically significant difference was found between the closure techniques. Larger randomized studies are needed to investigate this potential benefit avoiding selection bias. Full article
(This article belongs to the Section Orthopedics)
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<p>Flow diagram illustrating the patients’ selection process and the further subdivisions.</p>
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33 pages, 7006 KiB  
Article
Suitability of Mechanics-Based and Optimized Machine Learning-Based Models in the Shear Strength Prediction of Slender Beams Without Stirrups
by Abayomi B. David, Oladimeji B. Olalusi, Paul O. Awoyera and Lenganji Simwanda
Buildings 2024, 14(12), 3946; https://doi.org/10.3390/buildings14123946 - 11 Dec 2024
Viewed by 450
Abstract
Accurate shear capacity estimation for reinforced concrete (RC) beams without stirrups is essential for reliable structural design. Traditional code-based methods, primarily empirical, exhibit variability in predicting shear strength for these beams. This paper assesses the effectiveness of mechanics-based and optimized machine learning (ML) [...] Read more.
Accurate shear capacity estimation for reinforced concrete (RC) beams without stirrups is essential for reliable structural design. Traditional code-based methods, primarily empirical, exhibit variability in predicting shear strength for these beams. This paper assesses the effectiveness of mechanics-based and optimized machine learning (ML) models for predicting shear strength in stirrup-less, slender beams using a dataset of 784 tests. Seven ML models—artificial neural network (ANN), support vector machine (SVM), decision tree (DT), random forest (RF), AdaBoost, gradient boosting (GBR), and extreme gradient boosting (XGB)—were compared against three mechanics-based models: the Tran’s NLT Model (2020), the Multi-Action Shear Model (MASM), and the Compression Chord Capacity Model (CCC). Among the ML models, XGB and GBR demonstrated the highest predictive accuracy, with coefficients of determination (R2) of 0.974 and 0.966, respectively, indicating strong correlation with experimental data. Performance metrics such as mean absolute error (MAE) and root mean squared error (RMSE) showed that XGB and GBR consistently outperformed other models, yielding lower error margins. Statistical analysis revealed minimal bias and variability in the predictions of XGB and GBR, with a coefficient of variation (CoV) of 14%, ensuring high reliability. The NLT model, the most accurate of the mechanical-based models, achieved a mean of 1.02 and a CoV of 16% for its model error, demonstrating reasonable prediction reliability but falling behind XGB and GBR in accuracy. With Shapley additive explanations (SHAPs), the beam width and depth were identified as primary predictors of shear strength, providing critical insights for enhancing design and construction practises. Full article
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<p>Heatmap with annotated correlation matrix.</p>
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<p>Frequency distribution of input parameters.</p>
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<p>Relationship between input parameters and target variable.</p>
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<p>Comparison of experimental shear strength (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>u</mi> <mo>,</mo> <mi>e</mi> <mi>x</mi> <mi>p</mi> </mrow> </msub> </mrow> </semantics></math>) to ML-predicted shear strength (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>u</mi> <mo>,</mo> <mi>M</mi> <mi>L</mi> </mrow> </msub> </mrow> </semantics></math>).</p>
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<p>Bar charts displaying the metrics score of ML models’ shear results.</p>
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<p>Box plots showing (<b>a</b>) model errors for training prediction and (<b>b</b>) model errors for test predictions.</p>
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<p>SHAP summary plot for XGB model.</p>
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<p>SHAP dependence plots for XGB shear model.</p>
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<p>SHAP dependence plots for XGB shear model.</p>
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<p>SHAP dependence plots for XGB shear model.</p>
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<p>SHAP waterfall plot for XGB shear model.</p>
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<p>SHAP bar plot for XGB shear model.</p>
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<p>Partial dependence plot of input features used in XGB shear model.</p>
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<p>Line chart overlaying the experimental shear prediction with the (<b>a</b>) Tran [<a href="#B61-buildings-14-03946" class="html-bibr">61</a>] model and (<b>b</b>) XGB prediction.</p>
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<p>Probability distribution function and probability plot.</p>
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<p>Probability distribution function and probability plot.</p>
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14 pages, 4665 KiB  
Systematic Review
Tunnel Technique in Bone Augmentation Procedures for Dental Implant Rehabilitation: A Systematic Review
by Stefano Sivolella, Giulia Brunello, Dario Azeglio Castagna, Francesco Cavallin and Ugo Consolo
Dent. J. 2024, 12(12), 405; https://doi.org/10.3390/dj12120405 - 11 Dec 2024
Viewed by 570
Abstract
Background/Objectives: This systematic review aimed to compare the tunnel technique for pre-implant bone regeneration with traditional flap techniques also involving a crestal incision, in terms of procedure success, graft healing, postoperative course, patient satisfaction, and implant follow-up. Methods: A systematic search [...] Read more.
Background/Objectives: This systematic review aimed to compare the tunnel technique for pre-implant bone regeneration with traditional flap techniques also involving a crestal incision, in terms of procedure success, graft healing, postoperative course, patient satisfaction, and implant follow-up. Methods: A systematic search was conducted on MEDLINE/PubMed, EMBASE, and the Cochrane Central Register of Controlled Trials following PRISMA guidelines, searching for comparative prospective and retrospective studies in English, published between January 2002 and April 2024. The population of interest consisted of patients with edentulous ridge atrophy requiring pre-implant bone regeneration. The primary outcome was the success of the procedure. The secondary outcomes included complications, patient comfort, graft resorption, bone gain, primary implant stability, implant success/survival, peri-implant bone level change, and operative time. The risk of bias was assessed using RoB2 and ROBINS-I. Results: The search and selection process yielded one randomized controlled trial and three comparative observational studies, all with serious/high risk of bias. A narrative synthesis was conducted due to the small number of studies and the heterogeneity in key features. The tunnel technique might provide some advantages in terms of the success of the procedure, but the findings were not statistically significant. Conflicting findings or non-significant differences were reported in terms of the secondary outcomes. Conclusions: This review suggested some potential advantages of the tunnel technique for bone augmentation over traditional techniques involving a crestal incision, but the limited quality and amount of data precluded any definitive conclusions. Full article
(This article belongs to the Special Issue Bone Augmentation in Dentistry)
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<p>Representative clinical case of tunnel flap application belonging to the author’s (S.S.) original research data. (<b>a</b>) Preoperative CT demonstrating right upper alveolar process atrophy associated with edentulism. (<b>b</b>) Single buccal incision mesial to the antrostomy area. (<b>c</b>) Full-thickness flap elevated and heterologous bone graft in place at the end of the procedure. (<b>d</b>) Postoperative CT scan at 5 months with the graft in place. (<b>e</b>) Implants placed in the bone graft and prosthesis in place. (<b>f</b>) Intraoral view of the prosthesis on implants.</p>
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<p>Flowchart of selection process.</p>
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19 pages, 1944 KiB  
Systematic Review
Comparisons of Intravesical Treatments with Mitomycin C, Gemcitabine, and Docetaxel for Recurrence and Progression of Non-Muscle Invasive Bladder Cancer: Updated Systematic Review and Meta-Analysis
by Jubin E. Matloubieh, David Hanelin and Ilir Agalliu
Cancers 2024, 16(24), 4125; https://doi.org/10.3390/cancers16244125 - 10 Dec 2024
Viewed by 575
Abstract
Background: Non-muscle-invasive bladder cancer (NMIBC) comprises about 75% of all bladder cancers. Although NMIBC is treatable, it poses significant costs and burdens to patients due to high recurrence rates. We conducted an updated meta-analysis of studies that evaluated the efficacy of and outcomes [...] Read more.
Background: Non-muscle-invasive bladder cancer (NMIBC) comprises about 75% of all bladder cancers. Although NMIBC is treatable, it poses significant costs and burdens to patients due to high recurrence rates. We conducted an updated meta-analysis of studies that evaluated the efficacy of and outcomes after treatment with mitomycin C (MMC), gemcitabine (GEM), and docetaxel (DOCE) for NMIBC recurrence and progression. Methods: We searched the PubMed and Cochrane databases for observational cohort studies and randomized clinical trials (RCT) conducted between 2009 and 2022 that assessed the efficacy of GEM, DOCE, or MMC, alone or in combination, regarding NMIBC outcomes. A total of 49 studies that met the inclusion criteria were reviewed for their quality, sample size, outcomes, and potential for bias, and relevant data were extracted for the meta-analysis. Separate meta-analyses were performed to assess the risks of recurrence or progression when comparing GEM/DOCE or MMC vs. other treatments. Study heterogeneity was assessed by I2 statistics. Results: Among 31 studies comparing GEM or MMC to other treatments for NMIBC recurrence, there were statistically significant risk reductions of 24% for GEM (pooled relative risk (RR) of 0.76; 95% confidence interval (CI) 0.64–0.87) and 37% for MMC (pooled RR = 0.63; 95% CI 0.58–0.68). Recurrence-free survival (RFS) for GEM or MMC alone was 69.5% (95% CI 66.6–72.3%) and 67.2% (95% CI 66.2–68.2%), respectively. Studies assessing the combination of treatments had a pooled RFS of 44.6% (95% CI 40.4–48.7%). Fewer studies examined the risk of NMIBC progression, with large variability and inconclusive results across them. Conclusions: Our findings corroborate recent guidelines indicating that both GEM and MMC are effective treatments that reduce tumor recurrence and improve survival of NMIBC, although with large variability across the studies. Fewer studies evaluated DOCE treatment, with inconclusive results. Women and minorities were generally underrepresented, raising concerns about the generalizability of the findings and highlighting the importance of including a broader patient population in future RCTs. Full article
(This article belongs to the Special Issue Systematic Reviews and Meta-Analyses of Genitourinary Cancers)
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<p>Flowchart of study selection for this updated systematic review and meta-analysis.</p>
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<p>Forest plot of studies comparing the efficacy of gemcitabine (GEM) and/or docetaxel (DOCE) vs. other treatments in relation to the risks of recurrence (<b>a</b>) and of high-grade disease persistence and progression (<b>b</b>) for non-muscle-invasive bladder cancer (NMIBC). (<b>a</b>) NMIBC recurrence. Summary pooled RR = 0.76 (95% CI 0.64–0.87)—favors gemcitabine (GEM). Overall heterogeneity: I<sup>2</sup> = 80.4% (<span class="html-italic">p</span> &lt; 0.0001). Included studies comparing GEM alone (<span class="html-italic">n</span>=13) or a combination of GEM/BCG (<span class="html-italic">n</span> = 1) or GEM/DOCE (<span class="html-italic">n</span> = 1) to other treatments. Studies were weighted by their sample size; studies with a larger number of patients, indicated by larger grey boxes, received greater weights. (<b>b</b>) High-grade NMIBC persistence and progression. Summary pooled RR = 0.60 (95% CI 0.22–0.97)—favors GEM. Overall heterogeneity: I<sup>2</sup> = 57% (<span class="html-italic">p</span> = 0.03). Included studies comparing GEM or GEM/BCG to other treatments that reported risks of progression or high-grade disease persistence. Bohle et al.’s RCT study [<a href="#B24-cancers-16-04125" class="html-bibr">24</a>] was removed from this analysis due to an unstable RR of 3.0 with a very large 95% CI (0.32–28.45).</p>
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<p>Forest plot of studies comparing the efficacy of gemcitabine (GEM) and/or docetaxel (DOCE) vs. other treatments in relation to the risks of recurrence (<b>a</b>) and of high-grade disease persistence and progression (<b>b</b>) for non-muscle-invasive bladder cancer (NMIBC). (<b>a</b>) NMIBC recurrence. Summary pooled RR = 0.76 (95% CI 0.64–0.87)—favors gemcitabine (GEM). Overall heterogeneity: I<sup>2</sup> = 80.4% (<span class="html-italic">p</span> &lt; 0.0001). Included studies comparing GEM alone (<span class="html-italic">n</span>=13) or a combination of GEM/BCG (<span class="html-italic">n</span> = 1) or GEM/DOCE (<span class="html-italic">n</span> = 1) to other treatments. Studies were weighted by their sample size; studies with a larger number of patients, indicated by larger grey boxes, received greater weights. (<b>b</b>) High-grade NMIBC persistence and progression. Summary pooled RR = 0.60 (95% CI 0.22–0.97)—favors GEM. Overall heterogeneity: I<sup>2</sup> = 57% (<span class="html-italic">p</span> = 0.03). Included studies comparing GEM or GEM/BCG to other treatments that reported risks of progression or high-grade disease persistence. Bohle et al.’s RCT study [<a href="#B24-cancers-16-04125" class="html-bibr">24</a>] was removed from this analysis due to an unstable RR of 3.0 with a very large 95% CI (0.32–28.45).</p>
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<p>Forest plot of studies comparing the efficacy of mitomycin C (MMC) vs. other treatments in relation to the risks of recurrence (<b>a</b>) and of high-grade disease persistence and progression (<b>b</b>) for non-muscle-invasive bladder cancer (NMIBC). (<b>a</b>) NMIBC recurrence. Summary pooled RR = 0.63 (95% CI 0.58–0.68)—favors MMC. Overall heterogeneity: I<sup>2</sup> = 78% (<span class="html-italic">p</span> &lt; 0.0001). Studies included comparing MMC alone or MMC and BCG therapy to other treatments. Studies with a larger sample size (i.e., number of patients), as indicated by larger grey boxes, received greater weight. This meta-analysis was highly influenced by a large study [<a href="#B49-cancers-16-04125" class="html-bibr">49</a>] that contributed 61% of all weight. (<b>b</b>) High-grade NMIBC persistence and progression: summary pooled RR = 1.19 (95% CI 0.30–2.07). Overall heterogeneity: I<sup>2</sup> = 0% (<span class="html-italic">p</span> = 0.58).</p>
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<p>Recurrence-free survival (RFS) during follow-up of NMIBC patients receiving either gemcitabine (GEM) alone (<b>a</b>), mitomycin C (MMC) alone (<b>b</b>), or the combination of treatments (<b>c</b>). (<b>a</b>) GEM treatment: summary pooled RFS = 69.5% (95% CI 66.6–72.3%). Overall heterogeneity: I<sup>2</sup> = 92% (<span class="html-italic">p</span> &lt; 0.0001). Studies included evaluating RFS among patients receiving GEM treatment only. Studies with a larger number of patients, as indicated by larger grey boxes, received greater weights. (<b>b</b>) MMC treatment: summary pooled RFS = 67.2% (95% CI 66.2–68.2%). Overall heterogeneity: I<sup>2</sup> = 86.6% (<span class="html-italic">p</span> &lt; 0.0001). Studies included evaluating RFS among patients receiving MMC alone or MMC/BCG treatment. (<b>c</b>) Combination of treatments: summary pooled RFS = 44.6% (95% CI 40.4–48.7%). Overall heterogeneity: I<sup>2</sup> = 56% (<span class="html-italic">p</span> = 0.033). Studies included evaluating RFS among NMIBC patients receiving various combinations of treatments (e.g., GEM/BCG, GEM/MMC or GEM/DOCE). Studies with a larger number of patients, as indicated by larger grey boxes, received greater weights.</p>
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<p>Recurrence-free survival (RFS) during follow-up of NMIBC patients receiving either gemcitabine (GEM) alone (<b>a</b>), mitomycin C (MMC) alone (<b>b</b>), or the combination of treatments (<b>c</b>). (<b>a</b>) GEM treatment: summary pooled RFS = 69.5% (95% CI 66.6–72.3%). Overall heterogeneity: I<sup>2</sup> = 92% (<span class="html-italic">p</span> &lt; 0.0001). Studies included evaluating RFS among patients receiving GEM treatment only. Studies with a larger number of patients, as indicated by larger grey boxes, received greater weights. (<b>b</b>) MMC treatment: summary pooled RFS = 67.2% (95% CI 66.2–68.2%). Overall heterogeneity: I<sup>2</sup> = 86.6% (<span class="html-italic">p</span> &lt; 0.0001). Studies included evaluating RFS among patients receiving MMC alone or MMC/BCG treatment. (<b>c</b>) Combination of treatments: summary pooled RFS = 44.6% (95% CI 40.4–48.7%). Overall heterogeneity: I<sup>2</sup> = 56% (<span class="html-italic">p</span> = 0.033). Studies included evaluating RFS among NMIBC patients receiving various combinations of treatments (e.g., GEM/BCG, GEM/MMC or GEM/DOCE). Studies with a larger number of patients, as indicated by larger grey boxes, received greater weights.</p>
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<p>Recurrence-free survival (RFS) during follow-up of NMIBC patients receiving either gemcitabine (GEM) alone (<b>a</b>), mitomycin C (MMC) alone (<b>b</b>), or the combination of treatments (<b>c</b>). (<b>a</b>) GEM treatment: summary pooled RFS = 69.5% (95% CI 66.6–72.3%). Overall heterogeneity: I<sup>2</sup> = 92% (<span class="html-italic">p</span> &lt; 0.0001). Studies included evaluating RFS among patients receiving GEM treatment only. Studies with a larger number of patients, as indicated by larger grey boxes, received greater weights. (<b>b</b>) MMC treatment: summary pooled RFS = 67.2% (95% CI 66.2–68.2%). Overall heterogeneity: I<sup>2</sup> = 86.6% (<span class="html-italic">p</span> &lt; 0.0001). Studies included evaluating RFS among patients receiving MMC alone or MMC/BCG treatment. (<b>c</b>) Combination of treatments: summary pooled RFS = 44.6% (95% CI 40.4–48.7%). Overall heterogeneity: I<sup>2</sup> = 56% (<span class="html-italic">p</span> = 0.033). Studies included evaluating RFS among NMIBC patients receiving various combinations of treatments (e.g., GEM/BCG, GEM/MMC or GEM/DOCE). Studies with a larger number of patients, as indicated by larger grey boxes, received greater weights.</p>
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Systematic Review
Clinical Efficacy of Cysteamine Application for Melasma: A Meta-Analysis
by Bing-Qi Wu, Yen-Jen Wang, Chang-Cheng Chang, Tzong-Yuan Juang, Yung-Hsueh Huang and Ying-Chuan Hsu
J. Clin. Med. 2024, 13(23), 7483; https://doi.org/10.3390/jcm13237483 - 9 Dec 2024
Viewed by 642
Abstract
Background: Melasma is a challenging, acquired hyperpigmentary disorder. The gold standard treatment is Kligman’s formulation, which contains hydroquinone, tretinoin, and dexamethasone, but its long-term use is limited by the risk of exogenous ochronosis. Cysteamine, a tyrosinase inhibitor, reduces melanocyte activity and melanin production, [...] Read more.
Background: Melasma is a challenging, acquired hyperpigmentary disorder. The gold standard treatment is Kligman’s formulation, which contains hydroquinone, tretinoin, and dexamethasone, but its long-term use is limited by the risk of exogenous ochronosis. Cysteamine, a tyrosinase inhibitor, reduces melanocyte activity and melanin production, showing strong depigmenting effects in patients resistant to Kligman’s formulation. Nonetheless, clinical studies have yielded inconsistent efficacy results. This meta-analysis aimed to assess the efficacy of cysteamine in treating melasma and to identify potential factors that may impact its therapeutic outcomes. Methods: A systematic search of PubMed, Embase, Web of Science, and CENTRAL, from the earliest record until August 2024, was conducted. Randomized controlled trials and quasi-randomized design studies related to topical cysteamine on melasma patients were included. The primary outcome was MASI or mMASI assessment after treatments. The current meta-analysis was conducted with a random-effects model. Subgroup analyses and meta-regressions were performed based on baseline MASI, disease duration of melasma, patient age, and sample size of the included studies. Funnel plots and Duval and Tweedie’s trim and fill method were adopted to assess the publication bias. Results: Eight studies were included for quantitative analysis. The analysis of MASI after topical cysteamine demonstrated a significant decrease compared to the placebo (p = 0.002). Compared to other melasma treatments, cysteamine did not show superior efficacy in mMASI (p = 0.277). The treatment efficacy of hydroquinone, modified Kligman’s formula, and tranexamic acid mesotherapy for melasma was not statistically different when compared to cysteamine (p = 0.434). Further analyses showed no benefit when allowing extended cysteamine application time (p < 0.0001). The meta-regression revealed the efficacy of cysteamine decreased as the duration of melasma increased (coefficient = 0.38, p = 0.0001, R2 = 0.99). The funnel plot displayed some asymmetry. The trim and fill method suggested the adjusted effect size was 0.607 (95% CI = −0.720 to 1.935). Conclusions: Cysteamine exhibited efficacy in treating melasma patients; however, its depigmentation effect was comparable to hydroquinone-based regimens, tranexamic acid mesotherapy, and modified Kligman’s formula. Using cysteamine in patients with a short duration of melasma may result in better efficacy. Full article
(This article belongs to the Special Issue Skin Diseases: From Diagnosis to Treatment)
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<p>PRISMA 2020 flowchart for the current meta-analysis.</p>
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<p>Summary of quality assessment for the studies included in the current meta-analysis using version 2 of the Cochrane risk-of-bias tool for randomized trials [<a href="#B7-jcm-13-07483" class="html-bibr">7</a>,<a href="#B8-jcm-13-07483" class="html-bibr">8</a>,<a href="#B14-jcm-13-07483" class="html-bibr">14</a>,<a href="#B15-jcm-13-07483" class="html-bibr">15</a>,<a href="#B18-jcm-13-07483" class="html-bibr">18</a>,<a href="#B31-jcm-13-07483" class="html-bibr">31</a>,<a href="#B32-jcm-13-07483" class="html-bibr">32</a>,<a href="#B33-jcm-13-07483" class="html-bibr">33</a>].</p>
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<p>(<b>a</b>)<b>.</b> Forest plot presenting mean difference in depigmentation efficacy between cysteamine and placebo in patients with melasma. (<b>b</b>)<b>.</b> Forest plot presenting mean difference in depigmentation efficacy between cysteamine and other treatments in patients with melasma [<a href="#B7-jcm-13-07483" class="html-bibr">7</a>,<a href="#B8-jcm-13-07483" class="html-bibr">8</a>,<a href="#B14-jcm-13-07483" class="html-bibr">14</a>,<a href="#B15-jcm-13-07483" class="html-bibr">15</a>,<a href="#B18-jcm-13-07483" class="html-bibr">18</a>,<a href="#B31-jcm-13-07483" class="html-bibr">31</a>,<a href="#B32-jcm-13-07483" class="html-bibr">32</a>,<a href="#B33-jcm-13-07483" class="html-bibr">33</a>].</p>
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<p>In the efficacy of cysteamine compared to other treatments, a sensitivity analysis was performed using the one-study-removal method. The main result remained consistent without significant changes after excluding any of the included trials [<a href="#B7-jcm-13-07483" class="html-bibr">7</a>,<a href="#B15-jcm-13-07483" class="html-bibr">15</a>,<a href="#B18-jcm-13-07483" class="html-bibr">18</a>,<a href="#B31-jcm-13-07483" class="html-bibr">31</a>,<a href="#B32-jcm-13-07483" class="html-bibr">32</a>,<a href="#B33-jcm-13-07483" class="html-bibr">33</a>].</p>
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<p>The forest plot of subgroup analysis using different melasma treatments versus cysteamine as the moderator. The treatment efficacy of hydroquinone, modified Kligman’s formula, and tranexamic acid mesotherapy for melasma was not statistically different when compared to cysteamine. The Cochran’s Q test for the effect sizes difference among melasma treatment subgroups was insignificant (<span class="html-italic">p</span> = 0.434) [<a href="#B7-jcm-13-07483" class="html-bibr">7</a>,<a href="#B15-jcm-13-07483" class="html-bibr">15</a>,<a href="#B18-jcm-13-07483" class="html-bibr">18</a>,<a href="#B31-jcm-13-07483" class="html-bibr">31</a>,<a href="#B32-jcm-13-07483" class="html-bibr">32</a>,<a href="#B33-jcm-13-07483" class="html-bibr">33</a>].</p>
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<p>The forest plot of subgroup analysis is based on the permitted extended cysteamine application. In the study conducted by Lima et al., participants were asked to apply the cream for 15 min on the first night and gradually extend the duration up to 2 h. The Cochran’s Q test for the effect size differences among subgroups was significant (<span class="html-italic">p</span> &lt; 0.0001) [<a href="#B7-jcm-13-07483" class="html-bibr">7</a>,<a href="#B15-jcm-13-07483" class="html-bibr">15</a>,<a href="#B18-jcm-13-07483" class="html-bibr">18</a>,<a href="#B31-jcm-13-07483" class="html-bibr">31</a>,<a href="#B32-jcm-13-07483" class="html-bibr">32</a>,<a href="#B33-jcm-13-07483" class="html-bibr">33</a>].</p>
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<p>Meta-regression of difference in means on disease duration. The coefficient was 0.38 with <span class="html-italic">p</span> = 0.0001. Sachdev et al.’s study was omitted during the meta-regression due to the absence of exact disease duration in their trial.</p>
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<p>The funnel plot of included studies showed asymmetric distribution. The trim and fill method suggested the adjusted effect size was 0.607 (95% CI = −0.720 to 1.935).</p>
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21 pages, 1655 KiB  
Systematic Review
The Relationship Between Adolescent Orthodontic Treatment and Temporomandibular Disorders: A Systematic Review with Meta-Analysis
by Seorin Jeong, Myeong-Kwan Jih, Ji-Won Ryu, Jong-Mo Ahn and Hyun-Jeong Park
Appl. Sci. 2024, 14(23), 11430; https://doi.org/10.3390/app142311430 - 9 Dec 2024
Viewed by 472
Abstract
The relationship between fixed orthodontic treatment and the development of temporomandibular disorders (TMDs) in adolescents has been a topic of considerable debate. This systematic review and meta-analysis aimed to evaluate the impact of fixed orthodontic treatment on the prevalence of TMDs in adolescents. [...] Read more.
The relationship between fixed orthodontic treatment and the development of temporomandibular disorders (TMDs) in adolescents has been a topic of considerable debate. This systematic review and meta-analysis aimed to evaluate the impact of fixed orthodontic treatment on the prevalence of TMDs in adolescents. A comprehensive literature search was conducted using PubMed, Web of Science, EMBASE, Google Scholar, and the Cochrane Library, yielding 886 records. After duplicate removal, 665 records were screened, and 8 studies were assessed for eligibility. Following quality assessment using the Joanna Briggs Institute checklist, 4 studies were included in the final analysis. Data were analyzed using a random-effects model in RevMan software. The meta-analysis revealed an overall odds ratio of 0.75 (95% CI: 0.37–1.51, p = 0.42), indicating no statistically significant association between fixed orthodontic treatment and the risk of developing TMDs. Substantial heterogeneity was observed (I2 = 73%), attributed to variations in study designs, populations, and outcome measures. The risk of bias analysis highlighted concerns in several domains, particularly selection bias and measurement of outcomes. While confounding bias and missing data bias were generally well-controlled, deviations in intervention and inconsistent outcome measurements were noted across the studies. These findings suggest that fixed orthodontic treatment does not significantly alter the risk of developing TMDs in adolescents. However, the substantial heterogeneity and potential biases across the included studies emphasize the need for further high-quality, standardized research to confirm these results and provide clearer clinical guidance. Full article
(This article belongs to the Special Issue Recent Advances in Pediatric Orthodontics and Pediatric Dentistry)
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<p>The PRISMA flowchart.</p>
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<p>Forest plot of the meta-analysis [<a href="#B33-applsci-14-11430" class="html-bibr">33</a>,<a href="#B34-applsci-14-11430" class="html-bibr">34</a>,<a href="#B42-applsci-14-11430" class="html-bibr">42</a>,<a href="#B45-applsci-14-11430" class="html-bibr">45</a>].</p>
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<p>ROB domains [<a href="#B33-applsci-14-11430" class="html-bibr">33</a>,<a href="#B34-applsci-14-11430" class="html-bibr">34</a>,<a href="#B42-applsci-14-11430" class="html-bibr">42</a>,<a href="#B45-applsci-14-11430" class="html-bibr">45</a>].</p>
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<p>ROB domains [<a href="#B33-applsci-14-11430" class="html-bibr">33</a>,<a href="#B34-applsci-14-11430" class="html-bibr">34</a>,<a href="#B42-applsci-14-11430" class="html-bibr">42</a>,<a href="#B45-applsci-14-11430" class="html-bibr">45</a>].</p>
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16 pages, 945 KiB  
Review
Hands-On Versus Hands-Off Treatment of Hip-Related Nonspecific Musculoskeletal Diseases: A Systematic Review
by Giulia Franceschi, Irene Scotto, Filippo Maselli, Firas Mourad and Marco Gallotti
J. Funct. Morphol. Kinesiol. 2024, 9(4), 262; https://doi.org/10.3390/jfmk9040262 - 7 Dec 2024
Viewed by 736
Abstract
Background/Objectives: A manual approach combined with therapeutic exercise versus therapeutic exercise alone is a debated issue in the literature. The American College of Rheumatology guidelines “conditionally recommended against” manual therapy for the management of hip osteoarthritis. Manual therapy followed by exercise, instead, appears [...] Read more.
Background/Objectives: A manual approach combined with therapeutic exercise versus therapeutic exercise alone is a debated issue in the literature. The American College of Rheumatology guidelines “conditionally recommended against” manual therapy for the management of hip osteoarthritis. Manual therapy followed by exercise, instead, appears to lead to a faster return to sport than exercise alone for adductor groin pain. There is a need to understand which is the most effective treatment in the management of hip nonspecific musculoskeletal diseases. The aim of this systematic review is to determine which is the most effective treatment between manual therapy combined with therapeutic exercise and therapeutic exercise alone in subjects with hip nonspecific musculoskeletal diseases. Methods: This systematic review complies with the guidelines of the 2020 Prisma Statement. The databases consulted were Pubmed, Cinahl, and Web Of Science. The search was conducted from October 2004 to November 2023. The search string was developed following the PICO model. Free terms or synonyms (e.g., manual therapy, exercise therapy, hip disease, effectiveness) and Medical Subject Headings terms were combined with Boolean operators (AND, OR, NOT). The risk-of-bias assessment was conducted using Version 2 of the Cochrane risk-of-bias tool for randomized controlled trials and the Newcastle Ottawa Scale for observational studies. A qualitative analysis of the results was conducted through narrative synthesis of key concepts. When possible, quantitative analysis was conducted through statistical parameters. Results: Ten articles were analyzed. Results show no differences between the interventions analyzed. Preliminary evidence seems to favor the combined intervention for the outcomes of pain, ROM, and patient satisfaction, with other studies claiming an absence of differences. Only one study claims that therapeutic exercise alone is more effective for quality of life. Preliminary evidence seems to show that manual therapy does not seem to bring any benefit in addition to therapeutic exercise in mid- and long-term functionality, especially for hip osteoarthritis. Conclusions: There seems to be no difference in effectiveness between manual therapy combined with therapeutic exercise and therapeutic exercise alone in individuals with hip nonspecific musculoskeletal diseases. Full article
(This article belongs to the Special Issue Role of Exercises in Musculoskeletal Disorders—7th Edition)
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<p>PRISMA statement flow chart.</p>
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<p>Rob-2 score evaluation.</p>
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