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17 pages, 580 KiB  
Article
Screening for Alcohol Use Disorder Among Hospitalised Patients: Learning from a Retrospective Cohort Study in Secondary Care
by Mohsan Subhani, Dipaka Rani Nath, Usman Talat, Aqsa Imtiaz, Amardeep Khanna, Awais Ali, Guruprasad P. Aithal, Stephen D. Ryder and Joanne R. Morling
J. Clin. Med. 2024, 13(24), 7617; https://doi.org/10.3390/jcm13247617 - 13 Dec 2024
Viewed by 493
Abstract
Background: Excessive alcohol consumption is among the leading causes of hospitalisation in high-income countries and contributes to over 200 medical conditions. We aimed to determine the prevalence and characteristics of alcohol use disorder (AUD), describe the distribution of AUD in ICD-10 discharge diagnosis [...] Read more.
Background: Excessive alcohol consumption is among the leading causes of hospitalisation in high-income countries and contributes to over 200 medical conditions. We aimed to determine the prevalence and characteristics of alcohol use disorder (AUD), describe the distribution of AUD in ICD-10 discharge diagnosis groups and ascertain any relationship between them in secondary care. Methods: The study group was a retrospective cohort of adult patients admitted to Nottingham University Hospital (NUH) between 4 April 2009 and 31 March 2020. Uni- and multivariable analysis was performed to determine the relationship between AUD and covariable high-risk characteristics and describe the distribution of AUD in ICD-10 discharge diagnosis groups defined by an alcohol-attributable fraction. Results: A total of 44,804 patients (66,440 admissions) were included, with a mean age of 63.1 years (SD ± 19.9); of these, 48.0% (n = 20,863) were male and 71.2% were (n = 30,994) white. AUDIT-C was completed in 97.1% (n = 43,514) of patients, and identified 16.5% (n = 7164) as having AUD, while 2.1% (n = 900) were found to be alcohol-dependent. In patients with AUD, 4.0% (n = 283) had an ICD-10 diagnosis that was alcohol-specific and 17.5% (n = 1255) were diagnosed with alcohol-related disorders; the remainder were not diagnosed with either disorder. Two-thirds (64.7%) of the patients with AUD had associated mental and behavioural disorders. Multivariable logistic regression analysis revealed that patients aged 60–69 had the highest risk of AUD (OR 4.19, 95% CI 3.53–4.99). Being single (OR 1.18, 95% CI 1.11–1.26) and a history of emergency admission (OR 1.21, 95% CI 1.14–1.29) were associated with increased odds of AUD. Conversely, females compared to males (OR 0.34, 95% CI 0.35–0.39), individuals from minority ethnic backgrounds compared to white Caucasians (OR 0.39, 95% CI 0.35–0.45), and those from more deprived areas (IMD quintile 1: OR 0.79, 95% CI 0.74–0.86) had lower odds of AUD. Conclusions: One in six admitted patients had AUD, with a higher risk in males, ages 60–69, and emergency admissions. Mental disorders are highly prevalent among hospitalised patients with AUD. The performance of the AUDIT-C score varied among hospitalised patients based on their ICD-10 diagnosis, which should be considered when implementing universal alcohol screening in these settings. Full article
(This article belongs to the Special Issue Long-Term Clinical Strategies for Psychiatric Rehabilitation)
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Figure 1
<p>Participants flow diagram: Alcohol use disorder (AUD) was defined by an AUDIT-C score of 5–12 and split into three categories: increased risk, high-risk, and alcohol-dependent. An AUDIT score of 0–4 was deemed low risk for AUD. * Unconscious patients, critically unwell patients, and patients with cognitive impairment.</p>
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<p>Top ten inpatient speciality of care: low risk versus alcohol use disorder (AUD).</p>
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39 pages, 12168 KiB  
Article
Plugging-In Caledonia: Location and Utilisation of Public Electric Vehicle Chargers in Scotland
by Kathleen Davies, Edward Hart and Stuart Galloway
World Electr. Veh. J. 2024, 15(12), 570; https://doi.org/10.3390/wevj15120570 - 11 Dec 2024
Viewed by 533
Abstract
Electrification of private cars is a key mechanism for reducing transport emissions and achieving net zero. Simultaneously, the development of public electric vehicle (EV) charging networks is essential for an equitable transition to EVs. This paper develops and analyses an extensive, nationally representative [...] Read more.
Electrification of private cars is a key mechanism for reducing transport emissions and achieving net zero. Simultaneously, the development of public electric vehicle (EV) charging networks is essential for an equitable transition to EVs. This paper develops and analyses an extensive, nationally representative dataset of EV-charging sessions taking place on a key public charging network in Scotland between 2022 and 2024 to gain insights that can support the development of public charging infrastructure. Data were collated from 2786 chargers and analysed to establish a detailed characterisation of the network’s organisation and utilisation. The network considered is government-owned and was fundamental to the Scottish rollout of public chargers. Key insights from our analysis of the developed dataset include quantified disparities between urban and rural charger use-time behaviours, with the most rural areas tending to have charging activity more concentrated towards the middle of the day; an analysis of the numbers of deployed chargers in areas of greater/lesser deprivation; utilisation disparities between charger technologies, with 35% of slower chargers being used at least once daily compared to 86% of rapid/ultra-rapid chargers; and demonstration that charging tariff introductions resulted in a 51.3% average decrease in sessions. The implications of our findings for policy and practice are also discussed. Full article
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<p>The number of chargers (left); and number of chargers per 100,000 people (right), on the ChargePlace Scotland public EV-charging network as of March 2024, including data on population and the number of ChargePlace Scotland chargers (reprinted from Refs. [<a href="#B35-wevj-15-00570" class="html-bibr">35</a>,<a href="#B38-wevj-15-00570" class="html-bibr">38</a>]) and thematic layer representing local authority boundaries (reprinted from Ref. [<a href="#B39-wevj-15-00570" class="html-bibr">39</a>]).</p>
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<p>Map of Scotland’s local authority areas (reprinted from Ref. [<a href="#B43-wevj-15-00570" class="html-bibr">43</a>]), including thematic layer representing local authority boundaries (reprinted from Ref. [<a href="#B39-wevj-15-00570" class="html-bibr">39</a>]).</p>
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<p>Flowchart outlining the dataset creation process, including cleansing of raw EV charging-session data from ChargePlace Scotland and the addition of supplementary geographical indicators.</p>
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<p>Flowchart detailing the dataset analysis processes conducted and their related research questions that they are conducted to address.</p>
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<p>The total number of EV-charging sessions taking place on the ChargePlace Scotland public network between October 2022 and March 2024.</p>
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<p>The absolute number of EV chargers on the ChargePlace Scotland public network found in each Urban–Rural Classification, where a lower classification value generally indicates a more urban area, and a higher value generally indicates a more rural area.</p>
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<p>The number of EV chargers per 100,000 people on the ChargePlace Scotland public network found in each Urban–Rural Classification, where a lower classification value generally indicates a more urban area, and a higher value generally indicates a more rural area.</p>
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<p>The absolute number of EV chargers on the ChargePlace Scotland public network found in each Scottish Index of Multiple Deprivation quintile, where a lower quintile generally indicates a more deprived area, and a higher quintile generally indicates a less deprived area.</p>
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<p>The absolute number of EV chargers on the ChargePlace Scotland public network found in each Geographical Accessibility Index quintile, where a lower quintile generally indicates a less accessible area, and a higher quintile generally indicates a more accessible area.</p>
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<p>The average number of sessions taking place each day of the week on the ChargePlace Scotland public EV-charging network for all areas and for Urban–Rural Classification 1 and 8 areas and first and fifth quintiles of the Scottish Index of Multiple Deprivation and Geographical Accessibility Index areas.</p>
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<p>Trends in start time of charging sessions on the ChargePlace Scotland public EV-charging network for all sessions, and sessions in Urban–Rural Classifications 1 and 8.</p>
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<p>Trends in start time of charging sessions on the ChargePlace Scotland public EV-charging network for all sessions, and sessions in first and fifth Scottish Index of Multiple Deprivation quintiles.</p>
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<p>Trends in start time of charging sessions on the ChargePlace Scotland public EV-charging network for all sessions, and sessions in first and fifth Geographical Accessibility Index quintiles.</p>
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<p>Trends in start time of charging sessions for nine chargers in East Lothian on the ChargePlace Scotland public EV-charging network before and after introduction of a time-of-use tariff, with the average plug-in time marked by a vertical black dotted line.</p>
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<p>The number of sessions taking place in Dundee City, along with the number of chargers observed. Dundee City already had a tariff in place during this time period.</p>
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<p>The number of sessions taking place in North Lanarkshire, along with the number of chargers observed. North Lanarkshire introduced a tariff on 4 January 2023, illustrated by the vertical black dotted line.</p>
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<p>The number of sessions taking place in East Renfrewshire, with the number of chargers observed. East Renfrewshire introduced a tariff on 1 October 2023, illustrated by the vertical black dotted line.</p>
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<p>The number of sessions taking place on chargers within 10 km of North Lanarkshire borders that were free to use at the time of charging tariff introduction for North Lanarkshire council-owned chargers (the date of tariff introduction is represented by the vertical black dotted line).</p>
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<p>The number of sessions taking place on chargers within 10 km of Renfrewshire borders that were free to use at the time of charging tariff introduction for Renfrewshire council-owned chargers (the date of tariff introduction is represented by the vertical black dotted line).</p>
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<p>The number of sessions taking place on chargers within 10 km of Perth and Kinross borders that were free to use at the time of charging tariff introduction for Perth and Kinross council-owned chargers (the date of tariff introduction is represented by the vertical black dotted line).</p>
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<p>In total, 35% of AC chargers (<b>left donut chart</b>) experience at least one daily session, while 86% of rapid/ultra-rapid chargers (<b>right donut chart</b>) experience at least one daily session.</p>
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<p>The number of chargers per Urban–Rural Classification that are within the top 5% most utilised AC chargers by average daily sessions.</p>
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<p>The number of chargers per Geographical Accessibility Index quintile that are within the top 5% most utilised AC chargers by average daily sessions.</p>
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<p>The number of chargers per Scottish Index of Multiple Deprivation quintile that are within the top 5% most utilised AC chargers by average daily sessions.</p>
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<p>The number of chargers per Urban–Rural Classification that are within the top 5% most utilised rapid chargers by average daily sessions.</p>
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<p>The number of chargers per Geographical Accessibility Index quintile that are within the top 5% most utilised rapid chargers by average daily sessions.</p>
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<p>The number of chargers per Scottish Index of Multiple Deprivation quintile that are within the top 5% most utilised rapid chargers by average daily sessions.</p>
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<p>Positions of all chargers in the dataset (<b>left</b>), the top 5% most utilised rapid chargers by their average daily sessions (<b>middle</b>), and the top 5% most utilised AC chargers by their average daily sessions (<b>right</b>), including thematic layer representing local authority boundaries (reprinted from Ref. [<a href="#B39-wevj-15-00570" class="html-bibr">39</a>]).</p>
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<p>Summary of the key implications the results of this study have for policy and transport planning for three key stakeholders—power systems, charge point operators, and governance.</p>
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<p>The number of EV chargers per 100,000 people on the ChargePlace Scotland public network found in each Scottish Index of Multiple Deprivation quintile, where a lower quintile generally indicates a more deprived area, and a higher quintile generally indicates a less deprived area.</p>
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<p>The number of EV chargers per 100,000 people on the ChargePlace Scotland public network found in each Geographical Accessibility Index quintile, where a lower quintile generally indicates a less accessible area, and a higher quintile generally indicates a more accessible area.</p>
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14 pages, 1873 KiB  
Article
The Impact of Socioeconomic Status and Comorbidities on Non-Melanoma Skin Cancer Recurrence After Image-Guided Superficial Radiation Therapy
by Liqiao Ma, Michael Digby, Kevin Wright, Marguerite A. Germain, Erin M. McClure, Francisca Kartono, Syed Rahman, Scott D. Friedman, Candace Osborne and Alpesh Desai
Cancers 2024, 16(23), 4037; https://doi.org/10.3390/cancers16234037 - 1 Dec 2024
Viewed by 827
Abstract
Background: Non-melanoma skin cancers (NMSCs) are the most common cancers in the United States. Image-guided superficial radiation therapy (IGSRT) is an effective treatment for NMSCs. Patient comorbidities and socioeconomic status (SES) are known contributors to health disparities. However, the impact of comorbidities or [...] Read more.
Background: Non-melanoma skin cancers (NMSCs) are the most common cancers in the United States. Image-guided superficial radiation therapy (IGSRT) is an effective treatment for NMSCs. Patient comorbidities and socioeconomic status (SES) are known contributors to health disparities. However, the impact of comorbidities or SES on the outcomes of IGSRT-treated NMSCs has not yet been studied. This study evaluated freedom from recurrence in IGSRT-treated NMSCs stratified by SES and the number of comorbidities. Methods: This large retrospective cohort study evaluated associations between SES (via Area Deprivation Index (ADI)) or comorbidity (via Charlson Comorbidity Index (CCI)) and 2-, 4-, and 6-year year freedom from recurrence in patients with IGSRT-treated NMSC (n = 19,988 lesions). Results: Freedom from recurrence in less (ADI ≤ 50) vs. more (ADI > 50) deprived neighborhoods was 99.47% vs. 99.61% at 6 years, respectively (p = 0.2). Freedom from recurrence in patients with a CCI of 0 (low comorbidity burden) vs. a CCI of ≥7 (high comorbidity burden) was 99.67% vs. 99.27% at 6 years, respectively (p = 0.9). Conclusions: This study demonstrates that there are no significant effects of SES or comorbidity burden on freedom from recurrence in patients with IGSRT-treated NMSC. This supports the expansion of IGSRT in deprived neighborhoods to increase access to care, and IGSRT should be a consideration even in patients with a complex comorbidity status. Full article
(This article belongs to the Special Issue Advance Research in Imaging-Guided Cancer Therapy)
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<p>Freedom from recurrence over time of non-melanoma skin cancer treated with image-guided superficial radiation therapy by Area Deprivation Index (ADI) score. ADI ≤ 50 represents advantaged neighborhoods (high SES), and ADI &gt; 50 represents disadvantaged neighborhoods. The “At Risk” value represents the sample size at the corresponding year of follow-up. The “Events” value represents the number of NMSC lesions that have recurred by the corresponding year of follow-up. The <span class="html-italic">p</span> value of 0.2 indicates that freedom from recurrence of the ADI &gt; 50 group compared with the ADI ≤ 50 is not statistically significant.</p>
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<p>Freedom from recurrence over time of non-melanoma skin cancer treated with image-guided superficial radiation therapy by Charlson Comorbidity Index (CCI) score. Higher CCI scores represent higher comorbidity burdens. The “At Risk” value represents the sample size at the corresponding year of follow-up. The “Events” value represents the number of NMSC lesions that have recurred by the corresponding year of follow-up. The <span class="html-italic">p</span> value of 0.9 indicates that the differences in freedom from recurrence between CCI groups are not statistically significant.</p>
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<p>Freedom from recurrence over time of non-melanoma skin cancer treated with image-guided superficial radiation therapy by Charlson Comorbidity Index (CCI) scores 0–6+. Higher CCI scores represent higher comorbidity burdens. The “At Risk” value represents the sample size at the corresponding year of follow-up. The “Events” value represents the number of NMSC lesions that have recurred by the corresponding year of follow-up. The <span class="html-italic">p</span> value of 0.9 indicates that the differences in freedom from recurrence between CCI groups are not statistically significant.</p>
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30 pages, 1387 KiB  
Review
Adipose Tissues Have Been Overlooked as Players in Prostate Cancer Progression
by Kia T. Liermann-Wooldrik, Elizabeth A. Kosmacek and Rebecca E. Oberley-Deegan
Int. J. Mol. Sci. 2024, 25(22), 12137; https://doi.org/10.3390/ijms252212137 - 12 Nov 2024
Viewed by 2602
Abstract
Obesity is a common risk factor in multiple tumor types, including prostate cancer. Obesity has been associated with driving metastasis, therapeutic resistance, and increased mortality. The effect of adipose tissue on the tumor microenvironment is still poorly understood. This review aims to highlight [...] Read more.
Obesity is a common risk factor in multiple tumor types, including prostate cancer. Obesity has been associated with driving metastasis, therapeutic resistance, and increased mortality. The effect of adipose tissue on the tumor microenvironment is still poorly understood. This review aims to highlight the work conducted in the field of obesity and prostate cancer and bring attention to areas where more research is needed. In this review, we have described key differences between healthy adipose tissues and obese adipose tissues, as they relate to the tumor microenvironment, focusing on mechanisms related to metabolic changes, abnormal adipokine secretion, altered immune cell presence, and heightened oxidative stress as drivers of prostate cancer formation and progression. Interestingly, common treatment options for prostate cancer ignore the adipose tissue located near the site of the tumor. Because of this, we have outlined how excess adipose tissue potentially affects therapeutics’ efficacy, such as androgen deprivation, chemotherapy, and radiation treatment, and identified possible drug targets to increase prostate cancer responsiveness to clinical treatments. Understanding how obesity affects the tumor microenvironment will pave the way for understanding why some prostate cancers become metastatic or treatment-resistant, and why patients experience recurrence. Full article
(This article belongs to the Section Molecular Oncology)
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<p>Adipose tissue composition changes with obesity. Healthy adipose tissue is well maintained and regulated. Stem cells and a few anti-inflammatory immune cells are present. Obese adipose tissue has fewer mature adipocytes, but the adipocytes are larger in size. There is excess ROS and limited vascularization in obese adipose tissue. Many pro-inflammatory cells are present, and the metabolism and cytokine secretions are dysregulated, increased (↑) and decreased (↓).</p>
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<p>Adipose tissue and cancer are in constant crosstalk with one another. Adiponectin secreted by adipose tissue inhibits mTOR and in turn blocks proliferation, while leptin activates the JAK-STAT pathway to promote proliferation. Adipose tissue undergoes lipolysis secreting fatty acids that activate PPAR-γ, initiating angiogenesis in tumor cells. Immune cells from adipose tissue secrete pro-tumor factors aiding the tumor in evading the immune system. Adipose tissue also secretes cytokines such as CXCL12 and CCL7 to induce prostate cancer cell migration. Prostate cancer also releases cytokines such as IL-1B, which activates COX-2 and PGE2. PGE2 then cycles back to the tumor to promote migration and invasion through the cAMP-PKA/PI3K/AKT pathway. MiRNA-130 and TNF-α from cancer cells promote the dedifferentiation of adipocytes into cancer-associated adipocytes (CAAs). CAAs undergo enhanced lipolysis, feeding the cancer cells fatty acids.</p>
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<p>Prostate cancer treatments’ effect on adipose and cancer. Prostate cancer is commonly treated with ADT, chemotherapies, and radiotherapy, all of which interact with adipose tissue: (<b>A</b>) ADT treatment induces oxidative stress and inflammation in adipose tissue. This causes an increase in adipose tissue, cholesterol, oxidative stress, and inflammation, leading to tumor progression. Obesity-related proteins THEM6 and YAP1 are associated with ADT resistance in prostate cancer by increasing cellular lipid content and activating the UPR. (<b>B</b>) Chemotherapy is often lipophilic and, therefore, easily sequestered and metabolized by adipocytes containing the AKR protein, reducing the availability of the drug for prostate tumors. Chemotherapy induces dysregulated cytokine secretion from adipose tissue, such as an increased release of IGF-1, causing the upregulation of TUBB2B, which is associated with chemo-resistance. (<b>C</b>) Radiation to adipose tissue decreases lipogenic gene expression and increases ROS and lipolysis. Both ROS and lipolysis lead to the release of unsaturated fatty acids from the adipocytes, which cancer cells use to activate pro-cancer pathways. Additionally, radiation activates FTO, which demethylates mRNA to promote radioresistance.</p>
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19 pages, 8117 KiB  
Article
Effects of Glutamine or Glucose Deprivation on Inflammation and Tight Junction Disruption in Yak Rumen Epithelial Cells
by Ziqi Yue, Junmei Wang, Rui Hu, Quanhui Peng, Hongrui Guo, Huawei Zou, Jianxin Xiao, Yahui Jiang and Zhisheng Wang
Animals 2024, 14(22), 3232; https://doi.org/10.3390/ani14223232 - 12 Nov 2024
Viewed by 688
Abstract
Yak is a special free-ranging cattle breed in the plateau areas of Qinghai and Tibet. Pasture withering in cold-season pastures results in energy deficiency in yaks, which undermines the rumen epithelial barrier. However, the leading factor causing rumen epithelial injury remains unknown. Glutamine [...] Read more.
Yak is a special free-ranging cattle breed in the plateau areas of Qinghai and Tibet. Pasture withering in cold-season pastures results in energy deficiency in yaks, which undermines the rumen epithelial barrier. However, the leading factor causing rumen epithelial injury remains unknown. Glutamine (Gln), a conditionally essential amino acid, is insufficient under pathological conditions. Glucose (GLU) is an important energy source. Thus, we explored the effects of Gln or GLU deprivation on the barrier function of yak rumen epithelial cells and investigated the underlying mechanisms, as well as the differences in rumen epithelial barrier function between Gln deprivation (Gln-D) and GLU deprivation (GLU-D). In previous work, we constructed the yak rumen epithelial cells (YRECs) line by transferring the human telomerase reverse transcriptase gene (hTERT) and simian virus 40 large T antigen (SV40T) into primary YRECs. The YRECs were exposed to normal, Gln-D, GLU-D, and serum replacement (SR) media for 6, 12, and 24 h. Our data displayed that cell viability and tight junction protein expression in the SR group were not significantly changed compared to the normal group. Whereas, compared with the SR group, Gln-D treated for more than 12 h reduced cell viability and proliferation, and GLU-D treated for more than 12 h damaged the cell morphology and reduced cell viability and proliferation. The cell proliferation and cell viability were decreased more in GLU-D than in Gln-D. In addition, Gln-D treated for more than 12 h disrupted YREC cellular partially tight junctions by inducing oxidative stress and inflammation, and GLU-D treated for more than 12 h disrupted YREC cellular tight junctions by inducing apoptosis, oxidative stress, and inflammation. Compared with Gln-D, GLU-D more significantly induced cell injury and reduced tight junction protein levels. Our results provided evidence that GLU-D induced damage through the p38 mitogen-activated protein kinase (p38 MAPK)/c-junN-terminal kinase (JNK) signaling pathway, which was more serious than Gln-D treated for more than 12 h. Full article
(This article belongs to the Section Animal Nutrition)
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<p>Effects of Gln-D or GLU-D on cell morphology in YRECs at 6 h, 12 h, and 24 h. Cell morphology was observed by HE staining after Gln-D or GLU-D, and the images were taken under a microscope (100×). Scale bars represent 200 μm. HE = hematoxylin–eosin. CON, control; Gln-D, glutamine deprivation; GLU-D, glucose deprivation; SR, serum replacement.</p>
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<p>Effects of Gln-D or GLU-D on cell viability and proliferation in YRECs at 6 h, 12 h, and 24 h. (<b>A</b>) Cell viability of YRECs assayed by CCK-8. (<b>B</b>) EDU-positive cells were detected by Image J. (<b>C</b>–<b>E</b>) Cell proliferation of YRECs assayed by EDU, and the images were taken under a fluorescence microscope (100×). Scale bars represent 100 μm. Data, expressed as the rate of control cells at each time point, were expressed as means ± SD, n = 3 independent experiments. Different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05). CON, control; Gln-D, glutamine deprivation; GLU-D, glucose deprivation; SR, serum replacement. EDU = 5-ethynyl-2′-deoxyuridine.</p>
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<p>Effects of Gln-D or GLU-D on apoptosis in YRECs. (<b>A</b>–<b>C</b>) The mRNA levels of <span class="html-italic">caspase-3</span>, <span class="html-italic">Bax</span>, and <span class="html-italic">Bcl-2</span> at 6 h, 12 h, and 24 h. (<b>D</b>,<b>E</b>) The protein levels of Bcl-2, Bax, and Cleaved-caspase-3, GAPDH were used as a loading control at 12 h. (<b>F</b>,<b>G</b>) The protein levels of Bcl-2, Bax, and Cleaved-caspase-3 GAPDH were used as a loading control at 24 h. Data, expressed as the rate of control cells at each time point, were expressed as means ± SD, n = 3 independent experiment. Different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05). CON, control; Gln-D, glutamine deprivation; GLU-D, glucose deprivation; SR, serum replacement. Bax = B-cell lymphoma 2-associated X protein, Bcl-2 = B-cell lymphoma 2, GAPDH = glyceraldehyde-3-phosphatedehydrogenase.</p>
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<p>Effects of Gln-D or GLU-D on oxidative stress in YRECs at 6 h, 12 h, and 24 h. (<b>A</b>) T-AOC level. (<b>B</b>) GSH-PX concentration. (<b>C</b>) SOD concentration. (<b>D</b>) MDA concentration. (<b>E</b>,<b>F</b>) The ROS level in YRECs at 24 h was tested by flow cytometry and analyzed by FlowJo software. (<b>G</b>–<b>N</b>) The mRNA levels of <span class="html-italic">NQO1</span>, <span class="html-italic">GPX4</span>, <span class="html-italic">GPX1</span>, <span class="html-italic">HO-1</span>, <span class="html-italic">CAT</span>, <span class="html-italic">SOD-2</span>, <span class="html-italic">Nrf2</span>, and <span class="html-italic">Keap1</span>. Data, expressed as the rate of control cells at each time point, were expressed as means ± SD, n = 3 independent experiments. Different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05). CON, control; Gln-D, glutamine deprivation; GLU-D, glucose deprivation; SR, serum replacement. NQO1 = NAD(P)H Dehydrogenase Quinone 1; GPX4 = glutathione peroxidase 4; GPX1 = glutathione peroxidase 1; HO-1 = heme oxygenase 1; CAT = catalase; SOD2 = superoxide dismutase 2; Nrf2 = nuclear factor-erythroid 2-related factor 2; Keap1 = Kelch-like-ECH-associated protein 1; ROS = reactive oxygen species; T-AOC = total antioxidant capacity; GSH-PX = glutathione peroxidase; SOD = superoxide dismutase; MDA = malondialdehyde.</p>
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<p>Effects of Gln-D or GLU-D on the inflammation reaction in YRECs. (<b>A</b>–<b>E</b>) The mRNA levels of <span class="html-italic">IL-1β</span>, <span class="html-italic">IL-6</span>, <span class="html-italic">TNF-α</span>, <span class="html-italic">NF-κB</span>, and <span class="html-italic">IL-10</span> at 6 h, 12 h, and 24 h. (<b>F</b>,<b>H</b>) Phosphorylated NF-κB p65 and IκB expression levels at 12 h. (<b>G</b>,<b>I</b>) Phosphorylated NF-κB p65 and IκB expression levels at 24 h. Immunoblots were captured and quantified using Image J software, and then the normalized values were calculated and presented as ratios of phosphorylated proteins relative to total proteins. Data, expressed as the rate of control cells at each time point, were expressed as means ± SD, n = 3 independent experiments. Different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05). CON, control; Gln-D, glutamine deprivation; GLU-D, glucose deprivation; SR, serum replacement. IL-1β = interleukin-1β, IL-6 = interleukin-6, TNF-α = tumor necrosis factor-α, NF-κB p65= nuclear factor-κB p65, IL-10 = interleukin-10, p-NF-κB p65 = phospho-NF-κB p65, IκB = inhibitor of NF-κB; p-IκB = phospho-IκB; GAPDH = glyceraldehyde-3-phosphatedehydrogenase.</p>
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<p>Effects of Gln-D or GLU-D on the tight junction in YRECs. (<b>A</b>–<b>F</b>) The mRNA expression levels of <span class="html-italic">ZO-1</span>, <span class="html-italic">ZO-2</span>, <span class="html-italic">Occludin</span>, <span class="html-italic">claudin-1</span>, <span class="html-italic">claudin-4</span>, and <span class="html-italic">JAM-A</span> at 6 h, 12 h, and 24 h. (<b>G</b>,<b>I</b>) The protein expression levels of claudin-1, claudin-4, Occludin, and ZO-1, GAPDH were used as a loading control at 12 h. (<b>H</b>,<b>J</b>) The protein expression levels of claudin-1, claudin-4, Occludin, and ZO-1, GAPDH were used as a loading control at 24 h. (<b>K</b>,<b>L</b>) The fluorescence localization of ZO-1 at 24 h (200×). Data, expressed as the rate of control cells at each time point, were expressed as means ± SD, n = 3 independent experiments. Different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05). CON, control; Gln-D, glutamine deprivation; GLU-D, glucose deprivation; SR, serum replacement. ZO-1 = zonula occludens 1, ZO-2 = zonula occludens 2, JAM-A = junctional adhesion molecule-A; GAPDH = glyceraldehyde-3-phosphatedehydrogenase.</p>
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<p>Effects of Gln-D or GLU-D on the MAPK signaling pathway in YRECs. (<b>A</b>–<b>C</b>) The mRNA expression levels of <span class="html-italic">p38 MAPK</span>, <span class="html-italic">JNK</span>, and <span class="html-italic">ERK1/2</span> at 6 h, 12 h, and 24 h. (<b>D</b>,<b>E</b>) Phosphorylated p38 MAPK, JNK, and ERK1/2 expression levels at 12 h. (<b>F</b>,<b>G</b>) Phosphorylated p38 MAPK, JNK, and ERK1/2 expression levels at 24 h. Immunoblots were captured and quantified using Image J software, and then the normalized values were calculated and presented as ratios of phosphorylated proteins relative to total proteins. Data, expressed as the rate of control cells at each time point, were expressed as means ± SD, n = 3 independent experiments. Different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05). CON, control; Gln-D, glutamine deprivation; GLU-D, glucose deprivation; SR, serum replacement. p38 MAPK = p38 mitogen-activated protein kinase; JNK = c-junN-terminal kinase; ERK1/2 = extracellular signal-regulated 1/2; GAPDH = glyceraldehyde-3-phosphatedehydrogenase.</p>
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<p>p38 MAPK inhibitor (SB203580) and JNK inhibitor (SP600125) reversed the inflammation and tight junction induced by Gln-D or GLU-D in YRECs at 24 h. The YRECs were pretreated with 10 μM of p38 MAPK inhibitor (SB203580) or JNK inhibitor (SP600125) for 1 h. (<b>A</b>–<b>C</b>) p38 MAPK inhibitor (SB203580) reversed the inflammation and tight junction induced by Gln-D or GLU-D in YRECs at 24 h. Phosphorylated p38 MAPK, NF-κB p65, and IκB expression levels. The protein expression levels of ZO-1 and Occludin. GAPDH was used as a loading control at 24 h. (<b>D</b>–<b>F</b>) JNK inhibitor (SP600125) reversed the inflammation and tight junction induced by Gln-D or GLU-D in YRECs at 24 h. Phosphorylated JNK, NF-κB p65, and IκB expression levels. The protein expression levels of ZO-1 and Occludin. GAPDH was used as a loading control at 24 h. Immunoblots were captured and quantified using Image J software, and then the normalized values were calculated and presented as ratios of phosphorylated proteins relative to total proteins. Data, expressed as the percent of control cells, were expressed as means ± SD, n = 4 independent experiments. Different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05). CON, control; Gln-D, glutamine deprivation; GLU-D, glucose deprivation; SR, serum replacement. p38 MAPK = p38 mitogen-activated protein kinase; JNK = c-junN-terminal kinase; NF-κB p65 = nuclear factor-κB p65; p-NF-κB p65 = phospho-NF-κB p65, IκB = inhibitor of NF-κB; p-IκB = phospho-IκB; ZO-1 = zonula occludens 1; GAPDH = glyceraldehyde-3-phosphatedehydrogenase.</p>
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<p>Effects of Gln-D or GLU-D on inflammation and tight junction disruption in yak rumen epithelial cells at 24 h.</p>
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12 pages, 1519 KiB  
Article
Culex quinquefasciatus Density Associated with Socioenvironmental Conditions in a Municipality with Indeterminate Transmission of Lymphatic Filariasis in Northeastern Brazil
by Amanda Xavier, Cristine Bonfim, Pablo Cantalice, Walter Barbosa Júnior, Filipe Santana da Silva, Vítor Régis, André Sá and Zulma Medeiros
Pathogens 2024, 13(11), 985; https://doi.org/10.3390/pathogens13110985 - 11 Nov 2024
Viewed by 540
Abstract
Lymphatic filariasis (LF) is a neglected tropical disease associated with poverty and poor environmental conditions. With the inclusion of vector control activities in LF surveillance actions, there is a need to develop simple methods to identify areas with higher mosquito density and thus [...] Read more.
Lymphatic filariasis (LF) is a neglected tropical disease associated with poverty and poor environmental conditions. With the inclusion of vector control activities in LF surveillance actions, there is a need to develop simple methods to identify areas with higher mosquito density and thus a higher consequent risk of W. bancrofti transmission. An ecological study was conducted in Igarassu, which is in the metropolitan region of Recife, Pernambuco, Brazil. The mosquitoes were captured in 2060 houses distributed across 117 census tracts. The vector density index (VDI), which measures the average number of lymphatic-filariasis-transmitting mosquitoes per number of houses collected in the risk stratum, was constructed. Moreover, the social deprivation indicator (SDI) was constructed and calculated through principal component factor analysis. An average of 242 female C. quinquefasciatus were found in the high-risk stratum, while the average in the low-risk stratum was 108. The overall VDI was 6.8 mosquitoes per household. The VDI for the high-risk stratum was 13.2 mosquitoes per household, while for the low/medium-risk stratum, it was 5.2. This study offers an SDI for the density of C. quinquefasciatus mosquitoes, which can help reduce the costs associated with data collection and allows for identifying priority areas for vector control actions. Full article
(This article belongs to the Section Parasitic Pathogens)
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<p>Spatial representation of the variables used in the social deprivation indicator, Igarassu, 2022.</p>
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<p>Spatial representation of the social deprivation indicator, Igarassu, 2022.</p>
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18 pages, 2031 KiB  
Article
Impact of Socioeconomic Deprivation on Care Quality and Surgical Outcomes for Early-Stage Non-Small Cell Lung Cancer in United States Veterans
by Steven Tohmasi, Daniel B. Eaton, Brendan T. Heiden, Nikki E. Rossetti, Ana A. Baumann, Theodore S. Thomas, Martin W. Schoen, Su-Hsin Chang, Nahom Seyoum, Yan Yan, Mayank R. Patel, Whitney S. Brandt, Bryan F. Meyers, Benjamin D. Kozower and Varun Puri
Cancers 2024, 16(22), 3788; https://doi.org/10.3390/cancers16223788 - 11 Nov 2024
Viewed by 1004
Abstract
Background: Socioeconomic deprivation has been associated with higher lung cancer risk and mortality in non-Veteran populations. However, the impact of socioeconomic deprivation on outcomes for non-small cell lung cancer (NSCLC) in an integrated and equal-access healthcare system, such as the Veterans Health [...] Read more.
Background: Socioeconomic deprivation has been associated with higher lung cancer risk and mortality in non-Veteran populations. However, the impact of socioeconomic deprivation on outcomes for non-small cell lung cancer (NSCLC) in an integrated and equal-access healthcare system, such as the Veterans Health Administration (VHA), remains unclear. Hence, we investigated the impact of area-level socioeconomic deprivation on access to care and postoperative outcomes for early-stage NSCLC in United States Veterans. Methods: We conducted a retrospective cohort study of patients with clinical stage I NSCLC receiving surgical treatment in the VHA between 1 October 2006 and 30 September 2016. A total of 9704 Veterans were included in the study and assigned an area deprivation index (ADI) score, a measure of socioeconomic deprivation incorporating multiple poverty, education, housing, and employment indicators. We used multivariable analyses to evaluate the relationship between ADI and postoperative outcomes as well as adherence to guideline-concordant care quality measures (QMs) for stage I NSCLC in the preoperative (positron emission tomography [PET] imaging, appropriate smoking management, pulmonary function testing [PFT], and timely surgery [≤12 weeks after diagnosis]) and postoperative periods (appropriate surveillance imaging, smoking management, and oncology referral). Results: Compared to Veterans with low socioeconomic deprivation (ADI ≤ 50), those residing in areas with high socioeconomic deprivation (ADI > 75) were less likely to have timely surgery (multivariable-adjusted odds ratio [aOR] 0.832, 95% confidence interval [CI] 0.732–0.945) and receive PET imaging (aOR 0.592, 95% CI 0.502–0.698) and PFT (aOR 0.816, 95% CI 0.694–0.959) prior to surgery. In the postoperative period, Veterans with high socioeconomic deprivation had an increased risk of 30-day readmission (aOR 1.380, 95% CI 1.103–1.726) and decreased odds of meeting all postoperative care QMs (aOR 0.856, 95% CI 0.750–0.978) compared to those with low socioeconomic deprivation. There was no association between ADI and overall survival (adjusted hazard ratio [aHR] 0.984, 95% CI 0.911–1.062) or cumulative incidence of cancer recurrence (aHR 1.047, 95% CI 0.930–1.179). Conclusions: Our results suggest that Veterans with high socioeconomic deprivation have suboptimal adherence to care QMs for stage I NSCLC yet do not have inferior long-term outcomes after curative-intent resection. Collectively, these findings demonstrate the efficacy of an integrated, equal-access healthcare system in mitigating disparities in lung cancer survival that are frequently present in other populations. Future VHA policies should continue to target increasing adherence to QMs and reducing postoperative readmission for socioeconomically disadvantaged Veterans with early-stage NSCLC. Full article
(This article belongs to the Section Cancer Survivorship and Quality of Life)
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<p>Association between area deprivation index and adherence to quality metrics assessing access to preoperative care. Models adjust for area deprivation index score, age, race, sex, body mass index, smoking status at surgery, Charlson–Deyo Comorbidity Index score, American Society of Anesthesiologists class, preoperative forced expiratory volume in one second, number of prescription medications in the year prior to surgery, distance lived from treatment facility, annual hospital case load, tumor histology, tumor grade, tumor location, tumor size, year of operation. Abbreviations used: ADI—area deprivation index; aOR—adjusted odds ratio; CI—95% confidence interval; PET—positron emission tomography; PFT—pulmonary function testing; Preop—preoperative; QMs—quality measures.</p>
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<p>Adjusted Kaplan–Meier survival analysis stratified by area deprivation index (ADI) score.</p>
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<p>Fine–Gray competing risk analysis examining cumulative incidence of cancer recurrence based on area deprivation index (ADI) score.</p>
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<p>Association between area deprivation index and adherence to quality metrics assessing access to postoperative care. Models adjust for area deprivation index score, age, race, sex, body mass index, smoking status at surgery, Charlson–Deyo Comorbidity Index score, American Society of Anesthesiologists class, preoperative forced expiratory volume in one second, number of prescription medications in the year prior to surgery, distance lived from treatment facility, annual hospital case load, tumor histology, tumor grade, tumor location, tumor size, year of operation, surgical approach, lung resection type, adequate intraoperative lymph node sampling, adherence to all preoperative care quality measures (yes versus no). Abbreviations used: ADI—area deprivation index; aOR—adjusted odds ratio; CI—95% confidence interval; Postop—postoperative; QMs—quality measures.</p>
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9 pages, 1043 KiB  
Article
Construct Validity of a Wearable Inertial Measurement Unit (IMU) in Measuring Postural Sway and the Effect of Visual Deprivation in Healthy Older Adults
by Luca Ferrari, Gianluca Bochicchio, Alberto Bottari, Alessandra Scarton, Francesco Lucertini and Silvia Pogliaghi
Biosensors 2024, 14(11), 529; https://doi.org/10.3390/bios14110529 - 1 Nov 2024
Viewed by 822
Abstract
Inertial Motor sensors (IMUs) are valid instruments for measuring postural sway but their ability to detect changes derived from visual deprivation in healthy older adults requires further investigations. We examined the validity and relationship of IMU sensor-derived postural sway measures compared to force [...] Read more.
Inertial Motor sensors (IMUs) are valid instruments for measuring postural sway but their ability to detect changes derived from visual deprivation in healthy older adults requires further investigations. We examined the validity and relationship of IMU sensor-derived postural sway measures compared to force plates for different eye conditions in healthy older adults (32 females, 33 males). We compared the relationship of the center of mass and center of pressure (CoM and CoP)-derived total length, root means square (RMS) distance, mean velocity, and 95% confidence interval ellipse area (95% CI ellipse area). In addition, we examined the relationship of the IMU sensor in discriminating between open- (EO) and closed-eye (EC) conditions compared to the force plate. A significant effect of the instruments and eye conditions was found for almost all the variables. Overall, EO and EC variables within (force plate r, from 0.38 to 0.78; IMU sensor r, from 0.36 to 0.69) as well as between (r from 0.50 to 0.88) instruments were moderately to strongly correlated. The EC:EO ratios of RMS distance and 95% CI ellipse area were not different between instruments, while there were significant differences between total length (p = 0.973) and mean velocity (p = 0.703). The ratios’ correlation coefficients between instruments ranged from moderate (r = 0.65) to strong (r = 0.87). The IMU sensor offers an affordable, valid alternative to a force plate for objective, postural sway assessment. Full article
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<p>A comparison of the mean between ratios and correlation plots is displayed. * indicates statistical significance difference between means. Ratios were calculated by dividing the closed-eye values by the open-eye ones.</p>
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<p>A comparison of the mean between ratios and correlation plots is displayed. * indicates statistical significance difference between means. Ratios were calculated by dividing the closed-eye values by the open-eye ones.</p>
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15 pages, 1415 KiB  
Review
Amino Acid Deprivation in Glioblastoma: The Role in Survival and the Tumour Microenvironment—A Narrative Review
by Keven Du, Leila Grocott, Giulio Anichini, Kevin O’Neill and Nelofer Syed
Biomedicines 2024, 12(11), 2481; https://doi.org/10.3390/biomedicines12112481 - 29 Oct 2024
Viewed by 882
Abstract
Background: Glioblastoma is the most common and aggressive primary brain tumour, characterised by its invasive nature and complex metabolic profile. Emerging research highlights the role of amino acids (AAs) in glioblastoma metabolism, influencing tumour growth and the surrounding microenvironment. Methods: This narrative review [...] Read more.
Background: Glioblastoma is the most common and aggressive primary brain tumour, characterised by its invasive nature and complex metabolic profile. Emerging research highlights the role of amino acids (AAs) in glioblastoma metabolism, influencing tumour growth and the surrounding microenvironment. Methods: This narrative review synthesises recent pre-clinical studies focusing on the metabolic functions of AAs in glioblastoma. Key areas include the effects of AA deprivation on tumour growth, adaptive mechanisms, and the tumour microenvironment. Results: The effects related to arginine, glutamine, methionine, and cysteine deprivation have been more extensively reported. Arginine deprivation in arginine-auxotrophic glioblastomas induces apoptosis and affects cell adhesion, while glutamine deprivation disrupts metabolic pathways and enhances autophagy. Methionine and cysteine deprivation impact lipid metabolism and ferroptosis. Tumour adaptive mechanisms present challenges, and potential compensatory responses have been identified. The response of the microenvironment to AA deprivation, including immune modulation, is critical to determining therapeutic outcomes. Conclusions: Targeting AA metabolism offers a promising approach for glioblastoma treatment, with potential targeted drugs showing clinical promise. However, the complexity of tumour adaptive mechanisms and their impact on the microenvironment necessitates further research to optimise combination therapies and improve therapeutic efficacy. Full article
(This article belongs to the Special Issue Mechanisms and Novel Therapeutic Approaches for Gliomas)
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<p>Schematics of the physiological role of glutamine and some pathways which are suspected to be affected by its deprivation in glioblastoma (see text for more details). GLS—Glutaminase; CR—Cystine Reductase; GCL—Glutamine–Cysteine Ligase. Picture created with BioRender.com.</p>
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<p>Conflicting effects of methionine deprivation in glioblastoma (see text for more details). The reduced expression of IL1RN, an antagonist of the IL1 receptor, causes cell cycle arrest and reduced proliferation. However, methionine deprivation also causes increased expression of CXCL8, which theoretically should attract neutrophils but also increases glycerophospholipid metabolism, giving a survival advantage to the cancer cells under stress conditions. Picture created with BioRender.com.</p>
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11 pages, 444 KiB  
Article
Ethnicity and Socioeconomic Disparities in Clinical Trial Participation for Ovarian Cancer: A Retrospective Observational Study in London
by Karim H. El-Shakankery, Joanna Kefas, Kieran Palmer, Andrew Houston, Uma Mukherjee, Kangbo Gao, Weiteen Tan, Shanthini M. Crusz, Michael J. Flynn, Jonathan A. Ledermann, Michelle Lockley, Mary McCormack, Nicola MacDonald, Shibani Nicum, Michael John Devlin and Rowan E. Miller
Cancers 2024, 16(21), 3590; https://doi.org/10.3390/cancers16213590 - 24 Oct 2024
Viewed by 869
Abstract
Background: Ethnic and socioeconomic disparities in cancer outcomes are exacerbated by clinical trial underrepresentation. This study aims to identify inequalities in ethnicity and socioeconomic features among ovarian cancer clinical trial participants in two London cancer centres. Methods: All ovarian cancer patients treated between [...] Read more.
Background: Ethnic and socioeconomic disparities in cancer outcomes are exacerbated by clinical trial underrepresentation. This study aims to identify inequalities in ethnicity and socioeconomic features among ovarian cancer clinical trial participants in two London cancer centres. Methods: All ovarian cancer patients treated between 2017 and 2022 were included. Patients participating in clinical trials were classified as the trial population (TP); the remainder were considered the non-trial population (NTP). Data on disease characteristics and sociodemographic features, including ethnicity and Indices of Multiple Deprivation (IMD) deciles, were accessed from electronic patient records. Results: Of the 892 patients, 212 (24%) were enrolled in trials: 87 in Phase II, 103 in Phase III, and 21 in prospective, non-investigational medicinal product trials. The TP were more likely to be of White ethnicity (72.6% vs. 57.5%; p < 0.001), younger (mean age 58 vs. 60; p = 0.003), living in less deprived areas (most deprived tercile: 21.2% vs. 34.0%; p = 0.004), and English-speaking (95.8% vs. 90.9%; p = 0.041). In the multivariate analysis, White ethnicity (p < 0.0001), age (p = 0.003), IMD decile (p = 0.007), and interpreter requirement (p = 0.037) were independent predictors of trial participation. Conclusions: Ethnic and socioeconomic inequalities affect trial participation, potentially worsening health disparities in ovarian cancer patients. Strategies to overcome trial recruitment barriers for underserved groups are needed to improve the equity of care. Full article
(This article belongs to the Special Issue Oncology: State-of-the-Art Research in UK, 2nd Edition)
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<p>Forest plot demonstrating the independent associations of each predictor with trial participation. Age is treated as a continuous variable, with the odds ratio reflecting the increasing likelihood of trial participation with every increasing year of age. The IMD tertile is treated as ordinal, and the remaining variables are treated as binary.</p>
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18 pages, 536 KiB  
Article
The Impact of Social Environment Perception on Relative Deprivation among Residents in Rural Tourism Destinations
by Mengxue Wu, Yan Yan and Deyi Kong
Sustainability 2024, 16(20), 8937; https://doi.org/10.3390/su16208937 - 16 Oct 2024
Viewed by 962
Abstract
The sustainable development of rural tourism requires not only active participation from the government and enterprises but is also closely tied to the attitudes of local residents. This study, grounded in the theories of relative deprivation and social comparison, focuses on the residents [...] Read more.
The sustainable development of rural tourism requires not only active participation from the government and enterprises but is also closely tied to the attitudes of local residents. This study, grounded in the theories of relative deprivation and social comparison, focuses on the residents living near the Jinshi Gorge Scenic Area in Shangluo City. We constructed a structural equation model to explore how residents’ perceptions of the social environment in rural tourism influence their sense of relative deprivation, enhance their happiness, and ultimately promote the sustainable development of rural tourism. The study’s findings reveal the following: (1) that demographic characteristics, including age, education level, and annual income, significantly influence residents’ perceptions of their social environment, particularly their sense of group identity, social support, and feelings of inequality. (2) Levels of relative deprivation vary significantly across different demographic groups. (3) There is a strong positive correlation between individual cognitive relative deprivation and individual emotional relative deprivation. Similarly, group cognitive relative deprivation positively predicts group emotional relative deprivation. (4) Experiences of discrimination, feelings of inequality, and strength of group identity emerge as significant predictors of both individual and group-level cognitive and emotional relative deprivation. (5) Social support has a significant negative effect on individual cognition, individual emotions, group cognition, and group emotional relative deprivation. Full article
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<p>Hypothetical model.</p>
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17 pages, 7146 KiB  
Article
Agricultural Productivity and Multidimensional Poverty Reduction in Colombia: An Analysis of Coffee, Plantain, and Corn Crops
by Jaime Andrés Betancourt, Gloria Yaneth Florez-Yepes and Yeison Alberto Garcés-Gómez
Earth 2024, 5(4), 623-639; https://doi.org/10.3390/earth5040032 - 11 Oct 2024
Viewed by 983
Abstract
This article presents the correlation between the Multidimensional Poverty Index (MPI) and the area planted, production in tons, and productive yield for various crops in Colombia from 2018 to 2021. The aim of this study is to determine the relationship between agricultural productivity [...] Read more.
This article presents the correlation between the Multidimensional Poverty Index (MPI) and the area planted, production in tons, and productive yield for various crops in Colombia from 2018 to 2021. The aim of this study is to determine the relationship between agricultural productivity and multidimensional poverty in Colombia, focusing on the cultivation of coffee, plantain, and corn. The methodology employed included a literature review through a bibliometric analysis to understand the relationships between the MPI and agricultural production. In the second stage, the agricultural sector statistics for the years 2018 to 2021 and the MPI by regions and departments of Colombia during the same period was systematized. Finally, a quantitative statistical analysis was conducted to establish the correlation of the MPI with the area planted, production in tons, and productive yield for coffee, plantain, and corn crops in Colombia. The MPI identifies those who are deprived in 50% or more of the index’s dimensions as living in extreme poverty. The results show that higher productive yields in the crops analyzed correspond to a lower MPI. Coffee crops have an MPI below 50%; plantain crops have an MPI between 20% and 50%, and for mechanized corn crops, the data show an MPI between 20% and 30%. This demonstrates that coffee, plantain, and corn crops represent an alternative for reducing the MPI in Colombia. Full article
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<p>Methodology applied for this study.</p>
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<p>Documentation by year (own research with data from the Scopus database).</p>
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<p>Documentation by thematic area (own research with data from the Scopus database).</p>
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<p>Correlations of descriptors related to the Multidimensional Poverty Index. The red cluster shows the link between agriculture and climate change. Green focuses on social aspects such as health and education. Blue encompasses environmental and economic factors, including industry and pollution. Finally, yellow groups methodological terms to measure poverty. The map illustrates the complexity of poverty and its relationship to agricultural, social, environmental and economic aspects.</p>
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<p>Correlation between the Multidimensional Poverty Index, climate change, agriculture, and the Malmquist Productivity Index.</p>
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<p>Distribution of the production of different crops in Colombia in tons.</p>
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<p>Yield of coffee cultivation concerning the MPI-R from 2018 to 2021.</p>
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<p>Relationship between productive yield of coffee cultivation and the MPI-R.</p>
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<p>Productive yield of coffee vs. MPI-R levels.</p>
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<p>Productive yield of plantains vs. MPI-R levels.</p>
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<p>Productive yield of technified corn vs. MPI-R levels.</p>
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<p>Correlations between the variables area, production, yield, and the Multidimensional Rural Poverty Index (MPI-Rural) in Colombia for coffee, plantain, and technified corn crops for the period 2018 to 2021. (*** <math display="inline"><semantics> <mrow> <mi>p</mi> <mtext>-</mtext> <mi>v</mi> <mi>a</mi> <mi>l</mi> <mi>u</mi> <mi>e</mi> <mo>&lt;</mo> <mn>0.0005</mn> </mrow> </semantics></math>, ** <math display="inline"><semantics> <mrow> <mi>p</mi> <mtext>-</mtext> <mi>v</mi> <mi>a</mi> <mi>l</mi> <mi>u</mi> <mi>e</mi> <mo>&lt;</mo> <mn>0.005</mn> </mrow> </semantics></math>, * <math display="inline"><semantics> <mrow> <mi>p</mi> <mtext>-</mtext> <mi>v</mi> <mi>a</mi> <mi>l</mi> <mi>u</mi> <mi>e</mi> <mo>&lt;</mo> <mn>0.05</mn> </mrow> </semantics></math>).</p>
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16 pages, 2712 KiB  
Article
Population-Level Exposure to PM2.5, NO2, Greenness (NDVI), Accessible Greenspace, Road Noise, and Rail Noise in England
by Sophia Garkov, Lorraine Dearden and Ai Milojevic
Atmosphere 2024, 15(10), 1197; https://doi.org/10.3390/atmos15101197 - 8 Oct 2024
Viewed by 786
Abstract
Air pollution, greenspace and noise are interrelated environmental factors with the potential to influence human health outcomes. Research has measured these exposures in diverse ways across the globe, but no study has yet performed a country-wide analysis of air pollution, greenspace, and noise [...] Read more.
Air pollution, greenspace and noise are interrelated environmental factors with the potential to influence human health outcomes. Research has measured these exposures in diverse ways across the globe, but no study has yet performed a country-wide analysis of air pollution, greenspace, and noise in England. This study examined cross-sectional PM2.5, NO2, greenness, accessible greenspace, road noise, and rail noise exposure data at all residential postcodes in England (n = 1,227,681). Restricted cubic spline models were fitted between each environmental exposure and a measure of socioeconomic status, the Index of Multiple Deprivation (IMD) rank. Population-weighted exposures by IMD deciles, urbanicity, and region were subsequently estimated. Restricted cubic spline models were also fitted between greenness and each other environmental exposure in the study. The results show some evidence of inequalities in exposure to air pollutants, greenspace, and noise across England. Notably, there is a socioeconomic gradient in greenness, NO2, PM2.5, and road noise in London. In addition, NO2, PM2.5, and road noise exposure decrease as greenness increases in urban areas. Concerningly, almost all air pollution estimates in our study exceed international health guidelines. Further research is needed to elucidate the socioeconomic patterns and health impacts of air pollution, greenspace, and noise over time. Full article
(This article belongs to the Special Issue Research on Air Pollution and Human Exposures)
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<p>Associations among air pollution, greenspace (GS), noise, and deprivation. IMD rank percentile varies from 0 (most deprived) to 100 (least deprived). Restricted cubic splines were predicted using all available data. Grey dots represent a 0.01% random sample of all residential postcodes. Shading around the fitted splines indicates the 95% confidence intervals. (<b>A</b>) Red (urban), green (rural), and black (national); (<b>B</b>) red (London), green (other regions), and black (national).</p>
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<p>Associations between air pollution, accessible greenspace (GS), and noise and greenness (NDVI). NDVI varies from −0.08 (low greenness) to 0.92 (high greenness). Restricted cubic splines were predicted using all available data. Grey dots represent a 0.01% random sample of all residential postcodes. Shading around fitted splines indicates 95% confidence intervals. (<b>A</b>) Red (urban), green (rural), and black (national); (<b>B</b>) red (London), green (other regions), and black (national).</p>
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<p>Distribution of air pollution, greenspace (GS), and noise exposure at all residential postcodes with measurements by urbanicity. Shaded areas indicate density with a normal distribution fitted in solid lines. PM<sub>2.5</sub> (n = 1,227,681), NO<sub>2</sub> (n = 1,227,681), NDVI (n = 1,218,956), accessible GS (n = 1,227,681), road noise (n = 1,071,970), and rail noise (n = 209,040).</p>
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<p>Associations between air pollution, greenness (NDVI), and noise and accessible greenspace (GS, log-transformed). Log of accessible GS varied from 0 (close proximity) to 9.01 (further). Restricted cubic splines were predicted using all available data. Grey dots represent a 0.01% random sample of all residential postcodes. Shading around the fitted splines indicates the 95% confidence intervals. (<b>1</b>) Red (urban), green (rural), and black (national); (<b>2</b>) red (London), green (other regions), and black (national).</p>
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<p>Associations between air pollution, greenness (NDVI), and noise and accessible greenspace (GS, log-transformed). Log of accessible GS varied from 0 (close proximity) to 9.01 (further). Restricted cubic splines were predicted using all available data. Grey dots represent a 0.01% random sample of all residential postcodes. Shading around the fitted splines indicates the 95% confidence intervals. (<b>1</b>) Red (urban), green (rural), and black (national); (<b>2</b>) red (London), green (other regions), and black (national).</p>
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16 pages, 357 KiB  
Article
Mitigating Health Disparities among the Elderly in China: An Analysis of the Roles of Social Security and Family Support from a Perspective Based on Relative Deprivation
by Guozhang Yan, Lianyou Li, Muhammad Tayyab Sohail, Yanan Zhang and Yahui Song
Sustainability 2024, 16(18), 7973; https://doi.org/10.3390/su16187973 - 12 Sep 2024
Viewed by 907
Abstract
The joint involvement of family and society in elderly care is a crucial factor in improving the health status of older adults and narrowing health disparities, which are essential for achieving sustainable development goals. However, the interactions between these entities and their mechanisms [...] Read more.
The joint involvement of family and society in elderly care is a crucial factor in improving the health status of older adults and narrowing health disparities, which are essential for achieving sustainable development goals. However, the interactions between these entities and their mechanisms of influence require further investigation. By utilizing data from the China Longitudinal Aging Social Survey (CLASS) spanning 2014 to 2016 and employing the Kakwani index of individual relative deprivation in conjunction with a two-way fixed-effects model for unbalanced panel data, in this study, we investigated the mechanisms through which social elderly care security and familial support influence health inequalities among the elderly. The findings reveal that only senior benefits (=−0.009, p < 0.05) significantly mitigate relative health deprivation in this population. Enrollment in pension insurance amplifies the sense of relative health deprivation among the elderly, but this effect becomes insignificant after controlling for temporal effects. Both economic support (=−0.002, p < 0.05) and emotional support (=−0.004, p < 0.01) from offspring significantly reduce the level of relative health deprivation among the elderly. Mechanism testing results indicate that individual attitudes towards aging serve as a mediator in the relationship between relative health deprivation and preferential treatment, economic support, and emotional support. The results of further heterogeneity tests suggest that the impact of various elderly support models on relative health deprivation differs by age, gender, and residential area.These findings confirm that support from both society and family plays a crucial role in achieving sustainable health outcomes for the elderly. Consequently, it is recommended to enhance the social elderly care security system, bolster familial support functions, cultivate positive individual attitudes towards aging, and address health inequalities among the elderly in accordance with their distinct characteristics, thereby improving their quality of life and sense of fulfillment, and contributing to the broader goals of sustainable development. Full article
(This article belongs to the Section Health, Well-Being and Sustainability)
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<p>Theoretical analysis framework.</p>
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<p>Pathway diagram.</p>
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23 pages, 10725 KiB  
Article
Leveraging Geospatial Information to Map Perceived Tenure Insecurity in Urban Deprivation Areas
by Esaie Dufitimana, Jiong Wang and Divyani Kohli-Poll Jonker
Land 2024, 13(9), 1429; https://doi.org/10.3390/land13091429 - 4 Sep 2024
Viewed by 741
Abstract
Increasing tenure security is essential for promoting safe and inclusive urban development and achieving Sustainable Development Goals. However, assessment of tenure security relies on conventional census and survey statistics, which often fail to capture the dimension of perceived tenure insecurity. This perceived tenure [...] Read more.
Increasing tenure security is essential for promoting safe and inclusive urban development and achieving Sustainable Development Goals. However, assessment of tenure security relies on conventional census and survey statistics, which often fail to capture the dimension of perceived tenure insecurity. This perceived tenure insecurity is crucial as it influences local engagement and the effectiveness of policies. In many regions, particularly in the Global South, these conventional methods lack the necessary data to adequately measure perceived tenure insecurity. This study first used household survey data to derive variations in perceived tenure insecurity and then explored the potential of Very-High Resolution (VHR) satellite imagery and spatial data to assess these variations in urban deprived areas. Focusing on the city of Kigali, Rwanda, the study collected household survey data, which were analysed using Multiple Correspondence Analysis to capture variations of perceived tenure insecurity. In addition, VHR satellite imagery and spatial datasets were analysed to characterize urban deprivation. Finally, a Random Forest regression model was used to assess the relationship between variations of perceived tenure insecurity and the spatial characteristics of urban deprived areas. The findings highlight the potential of geospatial information to estimate variations in perceived tenure insecurity within urban deprived contexts. These insights can inform evidence-based decision-making by municipalities and stakeholders in urban development initiatives. Full article
(This article belongs to the Special Issue Digital Earth and Remote Sensing for Land Management)
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<p>Map of Kigali city and the selected sites.</p>
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<p>Steps and process followed by the study.</p>
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<p>Characteristics of physical environment of neigborhoods across the study sites.</p>
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<p>Responses according to housing materials, building shapes, sizes, and access to basic amenities.</p>
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<p>Tenure rights based on land and/or property documentation, acquisition methods, and duration of occupation.</p>
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<p>Perceptions of respondents on tenure (in)security.</p>
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<p>Scatter plot of respondents in 2-dimensional space on the first and second dimension of MCA.</p>
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<p>Squared correlation indicators with the first dimension of MCA.</p>
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<p>The variation of perceived tenure insecurity across the study sites. A illustrates site of Gatsata (3), b illustrates sites of Kimisagara (2) and Gitega (1).</p>
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<p>Example of land cover classification results from the model (<b>Left</b>), GLCM texture features (<b>Right</b>).</p>
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<p>Variable importance based on image-based spatial characteristics extracted at the buffer of 20 m.</p>
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<p>Variable importance based on image-based spatial characteristics and additional spatial at the buffer of 25 m.</p>
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