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19 pages, 1742 KiB  
Article
Nutritional Benefits and Consumer Acceptance of Maize Chips Combined with Alternative Flours
by Jesús Rodríguez-Miranda, Meliza Peña, Miriam Rivera and Jason Donovan
Foods 2025, 14(5), 864; https://doi.org/10.3390/foods14050864 (registering DOI) - 3 Mar 2025
Abstract
This study evaluated the nutritional composition, techno-functional properties, and sensory acceptance of tortilla chips made from alternative flours derived from local ingredients, including maize, beet, flaxseed, bean, and chia. Three blends were assessed: maize with beans, maize with beet, and maize with chia–flaxseed. [...] Read more.
This study evaluated the nutritional composition, techno-functional properties, and sensory acceptance of tortilla chips made from alternative flours derived from local ingredients, including maize, beet, flaxseed, bean, and chia. Three blends were assessed: maize with beans, maize with beet, and maize with chia–flaxseed. Significant differences (p < 0.05) were observed in the flours’ moisture, ash, protein, lipid, and mineral content. Flaxseed flour exhibited the highest protein content (40.03 g/100 g), while chia flour was notable for its lipid (32.25 g/100 g) and fiber (38.51 g/100 g) content. Bean and chia flour were rich in iron and zinc. Sensory evaluations, conducted with 300 consumers in Honduras, revealed general acceptance of all blends, with maize chips enriched with chia–flaxseed showing the highest preference (47.2%). Approximately 50% of participants reported consuming tortilla chips weekly, prioritizing taste, freshness, and price. Notably, over 40% expressed willingness to pay a premium for more nutritious, baked options. These results underscore the potential of alternative flours to enhance local diets and foster healthier eating habits. Moreover, the positive consumer response highlights a significant market opportunity for small and medium-sized enterprises (SMEs), promoting awareness of nutrition and public health in Honduras. Full article
(This article belongs to the Topic Consumer Behaviour and Healthy Food Consumption)
Show Figures

Figure 1

Figure 1
<p>Photographs of the chips evaluated.</p>
Full article ">Figure 2
<p>Importance of maize in the diet: (<b>A</b>) Participation in food purchasing, (<b>B</b>) Food purchasing decision. (<b>C</b>) Principal maize products in the family diet, (<b>D</b>) Places that sell maize products, (<b>E</b>) Chip consumption frequency, (<b>F</b>) Principal attributes in the purchasing decision.</p>
Full article ">Figure 3
<p>Consumer willingness to purchase: (<b>A</b>) Willingness to purchase, (<b>B</b>) Willingness to pay.</p>
Full article ">
21 pages, 30064 KiB  
Article
Spatial Transcriptomics Reveals Novel Mechanisms Involved in Perineural Invasion in Pancreatic Ductal Adenocarcinomas
by Vanessa Lakis, Noni L Chan, Ruth Lyons, Nicola Blackburn, Tam Hong Nguyen, Crystal Chang, Andrew Masel, Nicholas P. West, Glen M. Boyle, Ann-Marie Patch, Anthony J. Gill and Katia Nones
Cancers 2025, 17(5), 852; https://doi.org/10.3390/cancers17050852 - 1 Mar 2025
Viewed by 146
Abstract
Background: Pancreatic ductal adenocarcinoma (PDAC) has a high incidence of perineural invasion (PNI), a pathological feature of the cancer invasion of nerves. PNI is associated with a poor prognosis, local recurrence and cancer pain. It has been suggested that interactions between nerves and [...] Read more.
Background: Pancreatic ductal adenocarcinoma (PDAC) has a high incidence of perineural invasion (PNI), a pathological feature of the cancer invasion of nerves. PNI is associated with a poor prognosis, local recurrence and cancer pain. It has been suggested that interactions between nerves and the tumor microenvironment (TME) play a role in PDAC tumorigenesis. Methods: Here, we used Nanostring GeoMx Digital Spatial Profiler to analyze the whole transcriptome of both cancer and nerve cells in the microenvironment of PNI and non-PNI foci from 13 PDAC patients. Conclusions: We identified previously reported pathways involved in PNI, including Axonal Guidance and ROBO-SLIT Signaling. Spatial transcriptomics highlighted the role of PNI foci in influencing the immune landscape of the TME and similarities between PNI and nerve injury response. This study revealed that endocannabinoid and polyamine metabolism may contribute to PNI, cancer growth and cancer pain. Key members of these pathways can be targeted, offering potential novel research avenues for exploring new cancer treatment and/or pain management options in PDAC. Full article
(This article belongs to the Special Issue Tumor Microenvironment: Intercellular Communication)
Show Figures

Figure 1

Figure 1
<p>Representative images of regions collected in this study. (<b>a</b>) H&amp;E image of sample used in this study with circled regions containing PNI and cancer or nerves (non-PNI). (<b>b</b>–<b>d</b>) Black squares in specimen indicate regions selected for GeoMx. (<b>e</b>) Zoomed image of region selected in H&amp;E in (<b>b</b>) (PNI region). (<b>f</b>) Same region presented in (<b>e</b>), showing GeoMx DSP ROI and segmented AOIs in terms of PanCK and PGP9.5 positivity for collection of tags. Cancer (arrowhead) and nerve (arrow) compartments in PNI focus. (<b>g</b>) H&amp;E of cancer compartment (non-PNI) indicated in (<b>c</b>) (square), cancer away from any visible nerve. (<b>h</b>) GeoMx DSP ROI showing segmented AOI in terms of PanCK positivity (purple) for collection of tags. (<b>i</b>) H&amp;E image of uninvolved nerve (non-PNI) away from tumor microenvironment indicated in (<b>d</b>) (square), with no visible cancer invasion. (<b>j</b>) GeoMx DSP of collected ROI. Regions (<b>e</b>–<b>j</b>) are examples of regions where gene expression was measured; selection of regions was based on visual inspection of adjacent H&amp;E and PanCK or PGP9.5 positivity for collection of expression tags.</p>
Full article ">Figure 2
<p>Expression measured in 74 regions collected by DSP GeoMx. (<b>a</b>) Principal component analysis (PCA) of normalized expression of regions profiled in cancer and nerve compartments that pass QC (n = 74). Gene counts showed separations of cancer and nerve regions. Each dot represents expression of region profiled and is colored by location in PNI or non-PNI foci. Circles indicate 95th percentile of expression. (<b>b</b>) PCA, same data presented in (<b>a</b>), colored by patient from whom samples were collected. (<b>c</b>) Normalized expression of full ROIs collected from nerve compartments (n = 17) in PNI or non-PNI foci. PCA separated nerves with PNI from non-PNI (without visual signs of invasion). (<b>d</b>) Same data presented in (<b>c</b>), colored by patient from whom samples were collected. (<b>e</b>) Normalized expression of AOIs (areas segmented by PanCK positivity) collected from cancer compartments (n = 40) in PNI or non-PNI foci. (<b>f</b>) Same data presented in (<b>e</b>), colored by patient from whom samples were collected.</p>
Full article ">Figure 3
<p>Differentially expressed genes and pathways enriched in cancer compartment of PNI foci. (<b>a</b>) Volcano plot showing differentially expressed genes in PNI cancer compartment compared with non-PNI foci. Each dot represents gene, with red dots indicating genes significantly up-regulated (adjusted <span class="html-italic">p</span>-value &lt; 0.20 and fold change &gt; 1.4), dark blue dots indicating genes that are significantly down-regulated (adjusted <span class="html-italic">p</span>-value &lt; 0.20 and fold change &lt; −1.4) and black dots indicating genes differentially expressed (adjusted <span class="html-italic">p</span>-value &lt; 0.20) with fold change between −1.4 and 1.4. Pink and light blue dots are genes with fold change &gt;1.4 and fold change &lt; −1.4, respectively, but are not statistically significant (adjusted <span class="html-italic">p</span>-value &gt; 0.20). Dotted lines represent threshold for statistical significance (adjusted <span class="html-italic">p</span>-value &lt; 0.20) and threshold for fold change (fold change &gt; 1.4 or &lt;−1.4). Gene symbols for some significantly differentially expressed genes are shown (for complete list, see <a href="#app1-cancers-17-00852" class="html-app">Table S3</a>). (<b>b</b>) Pathways predicted to be activated or inhibited by up-regulated genes in PNI cancer compartment (n = 288), (for full list of pathways, see <a href="#app1-cancers-17-00852" class="html-app">Table S4</a>). Pathways were obtained using IPA and up-regulated genes. (<b>c</b>) MGLL gene expression in cancer AOIs (PanCK-positive) in PNI and non-PNI regions (adjusted <span class="html-italic">p</span> = 0.091). (<b>d</b>) SAT1 gene expression in cancer AOIs (PanCK-positive) in PNI and non-PNI regions (adjusted <span class="html-italic">p</span> = 0.035).</p>
Full article ">Figure 4
<p>Immunohistochemistry (IHC) for MGLL. (<b>a</b>) Representative histology (H&amp;E staining) images of regions selected to measure immunoreactivity of MGLL in cancer cells in PNI and non-PNI foci. Arrow indicates nerve. IHC double immunofluorescence of morphological regions selected with PanCK and MGLL staining. (<b>b</b>) Mean intensity of MGLL in PanCK-positive regions (PNI and non-PNI foci). (<b>c</b>) Number of cells detected/region. (<b>d</b>) Percentage of cells with positive expression of MGLL. IHC was performed in 8 PDAC samples and quantification of MGLL immunoreactivity performed in 59 regions (31 PNI and 28 non-PNI). Indicated <span class="html-italic">p</span>-values from T-test.</p>
Full article ">Figure 5
<p>Differentially expressed genes and pathways enriched in nerve compartment of PNI foci. (<b>a</b>) Volcano plot showing differentially expressed genes in PNI nerve compartment compared with non-PNI foci. Each dot represents gene, with red dots indicating genes that are significantly up-regulated (adjusted <span class="html-italic">p</span>-value &lt; 0.20 and fold change &gt; 1.4), dark blue dots indicating genes that are significantly down-regulated (adjusted <span class="html-italic">p</span>-value &lt; 0.20 and fold change &lt; −1.4) and black dots indicating genes that are significantly differentially expressed (adjusted <span class="html-italic">p</span>-value &lt; 0.20) with fold change between −1.4 and 1.4. Pink and light blue dots are genes with fold change &gt; 1.4 and fold change &lt; −1.4, respectively, but are not statistically significant (adjusted <span class="html-italic">p</span>-value &gt; 0.20). Dotted lines represent threshold for statistical significance (adjusted <span class="html-italic">p</span>-value &lt; 0.20) and threshold for fold change (fold change &gt; 1.4 or &lt;−1.4). Gene symbols for significantly differentially expressed genes are shown (for complete list, see <a href="#app1-cancers-17-00852" class="html-app">Table S5</a>). (<b>b</b>) Pathways predicted to be activated by up-regulated genes (n = 178) (for full list of pathways, see <a href="#app1-cancers-17-00852" class="html-app">Table S6</a>). Pathways were obtained using IPA. (<b>c</b>) Normalized gene expression of Nestin (NES) (adjusted <span class="html-italic">p</span> = 0.009). (<b>d</b>) GAP43’s normalized expression in nerves with PNI and non-PNI evidence (adjusted <span class="html-italic">p</span> = 0.025).</p>
Full article ">Figure 6
<p>Immunohistochemistry (IHC) of Nestin. (<b>a</b>) Representative histology (H&amp;E staining) images of regions selected to measure immunoreactivity of Nestin in nerves with PNI and non-PNI foci. Arrow indicates nerve fibers. IHC double immunofluorescence of morphological regions selected in H&amp;E with Nestin and S100 staining. (<b>b</b>) Mean intensity of Nestin in S100-positive regions in nerve areas in PNI and non-PNI foci. (<b>c</b>) Number of cells detected/region. (<b>d</b>) Percentage of cells with positive expression of Nestin. IHC was performed in 7 PDAC samples and quantification performed in 41 regions (25 PNI and 16 non-PNI). Indicated <span class="html-italic">p</span>-values from T-test.</p>
Full article ">Figure 7
<p>Receptor–ligand expression in cancer and nerve compartments in PNI and non-PNI foci. Scatterplots of normalized gene expression of significant differentially expressed ligand–receptor pathways (LPRs), obtained using BulkSignalR (adjusted <span class="html-italic">p</span> value &lt; 0.20) with superimposed linear model line. (<b>a</b>) EPHA2 (receptor) and EFNA1 (ligand) expression in cancer compartment of PNI (purple) and non-PNI (green). (<b>b</b>) BMPR2 (receptor) and GDF7 (ligand) expression in nerve compartment of PNI (purple) and non-PNI (green). (<b>c</b>) CD47 (receptor) and THBS2 (ligand) expression in nerve compartment of PNI (purple) and non-PNI (green).</p>
Full article ">
23 pages, 2631 KiB  
Article
Impact of Auxiliary Information and Measurement Errors on Mean Estimation with Mixture Optional Enhanced Trust (MOET) Randomized Response Model
by Michael Parker, Sat Gupta and Sadia Khalil
Axioms 2025, 14(3), 183; https://doi.org/10.3390/axioms14030183 - 28 Feb 2025
Viewed by 138
Abstract
Randomized response technique (RRT) surveys are designed to secure honest answers to sensitive questions. In this study, we consider the important issue of measurement error (ME). While non-response, a common culprit for survey inaccuracy, is a lesser issue in RRT studies because they [...] Read more.
Randomized response technique (RRT) surveys are designed to secure honest answers to sensitive questions. In this study, we consider the important issue of measurement error (ME). While non-response, a common culprit for survey inaccuracy, is a lesser issue in RRT studies because they are conducted through face-to-face interviews, measurement error is of particular significance. RRT models are generally more complex than other survey methods, sometimes requiring that respondents follow ordered instructions, draw cards from decks, and/or perform simple mathematical calculations. All of these steps can result in measurement errors, and when such error is high, estimation efficiency will suffer. In this study, we consider the impact of measurement error on a Mixture Optional Enhanced Trust (MOET) RRT model proposed in 2024, and we propose new estimators for this model that take measurement error into account. We also study the extent to which measurement error can be tolerated before it is so large that it overwhelms and undermines the benefit that RRT was implemented to yield in the first place (the reduction in or elimination of social desirability bias-related untruthfulness). We also draw attention to a surprising finding—that the presence of measurement error inadvertently serves to provide additional scrambling, thereby leading to an increase in privacy. Full article
(This article belongs to the Special Issue New Perspectives in Mathematical Statistics)
Show Figures

Figure 1

Figure 1
<p>MOET model decision tree diagram.</p>
Full article ">Figure 2
<p>MSE of basic and ratio mean estimators when impacted by measurement error. Scenario values: A = 0.95, W = 0.90, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">p</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> = 0.15, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">p</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> = 0.85, α = 0.15, <span class="html-italic">n</span> = 500, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>µ</mi> </mrow> <mrow> <mi>Y</mi> </mrow> </msub> </mrow> </semantics></math> = 10, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>µ</mi> </mrow> <mrow> <mi>R</mi> </mrow> </msub> </mrow> </semantics></math> = 10, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>µ</mi> </mrow> <mrow> <mi>X</mi> </mrow> </msub> </mrow> </semantics></math> = 10, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>µ</mi> </mrow> <mrow> <mi>S</mi> </mrow> </msub> </mrow> </semantics></math> = 0, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>µ</mi> </mrow> <mrow> <mi>T</mi> </mrow> </msub> </mrow> </semantics></math> = 1, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>µ</mi> </mrow> <mrow> <mi>U</mi> </mrow> </msub> </mrow> </semantics></math> = 0, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>µ</mi> </mrow> <mrow> <mi>V</mi> </mrow> </msub> </mrow> </semantics></math> = 0, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">σ</mi> </mrow> <mrow> <mi>Y</mi> </mrow> </msub> </mrow> </semantics></math> = 5, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">σ</mi> </mrow> <mrow> <mi>R</mi> </mrow> </msub> </mrow> </semantics></math> = 5, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">σ</mi> </mrow> <mrow> <mi>X</mi> </mrow> </msub> </mrow> </semantics></math> = 5, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">σ</mi> </mrow> <mrow> <mi>S</mi> </mrow> </msub> </mrow> </semantics></math> = 1, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">σ</mi> </mrow> <mrow> <mi>T</mi> </mrow> </msub> </mrow> </semantics></math> = 1, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">σ</mi> </mrow> <mrow> <mi>U</mi> </mrow> </msub> </mrow> </semantics></math>: [0, 2], <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">σ</mi> </mrow> <mrow> <mi>V</mi> </mrow> </msub> </mrow> </semantics></math>: [0, 2], <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">ρ</mi> </mrow> <mrow> <mi>X</mi> <mi>Y</mi> </mrow> </msub> </mrow> </semantics></math> = 0.75.</p>
Full article ">Figure 3
<p>Inequality relationship between <math display="inline"><semantics> <mrow> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mrow> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>V</mi> </mrow> </msub> </mrow> <mrow> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>X</mi> </mrow> </msub> </mrow> </mfrac> </mstyle> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ρ</mi> </mrow> <mrow> <mi>X</mi> <mi>Y</mi> </mrow> </msub> </mrow> </semantics></math> that indicates whether the use of auxiliary information will improve estimation.</p>
Full article ">Figure 4
<p>Probability that the MOET basic estimator’s estimate is closer to <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>µ</mi> </mrow> <mrow> <mi mathvariant="normal">Y</mi> </mrow> </msub> </mrow> </semantics></math> than a direct survey estimate. Scenario values: <math display="inline"><semantics> <mrow> <mi>A</mi> <mo>=</mo> <mn>0.95</mn> <mo>,</mo> <mi>W</mi> <mo>=</mo> <mn>0.90</mn> <mo>,</mo> <msub> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.15</mn> <mo>,</mo> <mo> </mo> <mi>p</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mo>=</mo> <mn>0.85</mn> <mo>,</mo> <mo> </mo> <msub> <mrow> <mi>p</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> <mo>=</mo> <mn>0.15</mn> <mo>,</mo> <msub> <mrow> <mo> </mo> <mi>μ</mi> </mrow> <mrow> <mi>Y</mi> </mrow> </msub> <mo>=</mo> <mn>10</mn> <mo>,</mo> <msub> <mrow> <mo> </mo> <mi>μ</mi> </mrow> <mrow> <mi>X</mi> </mrow> </msub> <mo>=</mo> <mn>10</mn> <mo>,</mo> <mo> </mo> <msub> <mrow> <mi>μ</mi> </mrow> <mrow> <mi>R</mi> </mrow> </msub> </mrow> </semantics></math> = 10, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>μ</mi> </mrow> <mrow> <mi>S</mi> </mrow> </msub> <msub> <mrow> <mo>=</mo> <mn>0</mn> <mo>,</mo> <mi>μ</mi> </mrow> <mrow> <mi>T</mi> </mrow> </msub> <mo>=</mo> <mn>1</mn> <mo>,</mo> <msub> <mrow> <mi>μ</mi> </mrow> <mrow> <mi>U</mi> </mrow> </msub> <mo>=</mo> <mn>0</mn> <mo>,</mo> <msub> <mrow> <mi>μ</mi> </mrow> <mrow> <mi>V</mi> </mrow> </msub> <mo>=</mo> <mn>0</mn> <mo>,</mo> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>Y</mi> </mrow> </msub> <mo>=</mo> <mn>5</mn> <mo>,</mo> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>R</mi> </mrow> </msub> <mo>=</mo> <mn>5</mn> <mo>,</mo> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>S</mi> </mrow> </msub> <mo>=</mo> <mn>1</mn> <mo>,</mo> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>T</mi> </mrow> </msub> <mo>=</mo> <mrow> <mn>1</mn> <mo>,</mo> <mo> </mo> </mrow> <mi>n</mi> <mo>=</mo> <mn>250</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 5
<p>Privacy loss <math display="inline"><semantics> <mrow> <mfenced separators="|"> <mrow> <mi mathvariant="sans-serif-italic">∇</mi> <mi>L</mi> </mrow> </mfenced> </mrow> </semantics></math> across a range of percentages due to auxiliary information <math display="inline"><semantics> <mrow> <mfenced separators="|"> <mrow> <mi>ϕ</mi> </mrow> </mfenced> </mrow> </semantics></math> in two scenarios. Scenario values:<math display="inline"><semantics> <mrow> <mo> </mo> <mi>A</mi> <mo>=</mo> <mn>0.95</mn> <mo>,</mo> <mo> </mo> <mi>W</mi> <mo>=</mo> <mn>0.90</mn> <mo>,</mo> <mo> </mo> <mi>α</mi> <mo>=</mo> <mn>0.15</mn> <mo>,</mo> <mo> </mo> <msub> <mi>p</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.85</mn> <mo>,</mo> <mo> </mo> <msub> <mrow> <mi>p</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> <mo>=</mo> <mn>0.15</mn> <mo>,</mo> <mo> </mo> <msub> <mrow> <mi>μ</mi> </mrow> <mrow> <mi>Y</mi> </mrow> </msub> <mo>=</mo> <mn>10</mn> <mo>,</mo> <msub> <mrow> <mo> </mo> <mi>μ</mi> </mrow> <mrow> <mi>X</mi> </mrow> </msub> <mo>=</mo> <mn>10</mn> <mo>,</mo> <mo> </mo> <msub> <mrow> <mi>μ</mi> </mrow> <mrow> <mi>R</mi> </mrow> </msub> </mrow> </semantics></math> = 10, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>μ</mi> </mrow> <mrow> <mi>S</mi> </mrow> </msub> </mrow> </semantics></math> = 0, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>μ</mi> </mrow> <mrow> <mi>T</mi> </mrow> </msub> <mo>=</mo> <mn>1</mn> <mo>,</mo> <msub> <mrow> <mi>μ</mi> </mrow> <mrow> <mi>U</mi> </mrow> </msub> <mo>=</mo> <mn>0</mn> <mo>,</mo> <msub> <mrow> <mi>μ</mi> </mrow> <mrow> <mi>V</mi> </mrow> </msub> <mo>=</mo> <mn>0</mn> <mo>,</mo> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>V</mi> </mrow> </msub> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mo> </mo> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>S</mi> </mrow> </msub> <mo>=</mo> <mn>1</mn> <mo>,</mo> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>T</mi> </mrow> </msub> <mo>=</mo> <mrow> <mn>1</mn> <mo>,</mo> <mo> </mo> </mrow> <mi>n</mi> <mo>=</mo> <mrow> <mn>500</mn> <mo>;</mo> <mo> </mo> <mi>standard</mi> <mo> </mo> </mrow> <mi mathvariant="normal">s</mi> <mrow> <mi>cenario</mi> <mo>:</mo> <mo> </mo> </mrow> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>Y</mi> </mrow> </msub> <mo>=</mo> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>X</mi> </mrow> </msub> <mo>=</mo> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>R</mi> </mrow> </msub> </mrow> </semantics></math> = 5, <math display="inline"><semantics> <mrow> <mo>*</mo> <mrow> <mo> </mo> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mi>cenario</mi> <mo> </mo> <mn>2</mn> <mo>:</mo> <mo> </mo> </mrow> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>Y</mi> </mrow> </msub> <mo>=</mo> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>X</mi> </mrow> </msub> <mo>=</mo> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>R</mi> </mrow> </msub> <mo>=</mo> <mn>2</mn> <mo>.</mo> </mrow> </semantics></math></p>
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<p>Unified measure <math display="inline"><semantics> <mrow> <mfenced separators="|"> <mrow> <msup> <mrow> <mi>δ</mi> </mrow> <mrow> <mi>a</mi> </mrow> </msup> </mrow> </mfenced> </mrow> </semantics></math> across a range of measurement errors [0, 2] at 3 levels of privacy loss due to auxiliary information <math display="inline"><semantics> <mrow> <mfenced separators="|"> <mrow> <mi>ϕ</mi> </mrow> </mfenced> </mrow> </semantics></math>. Scenario values: <math display="inline"><semantics> <mrow> <mi>A</mi> <mo>=</mo> <mn>0.95</mn> <mo>,</mo> <mi>W</mi> <mo>=</mo> <mn>0.90</mn> <mo>,</mo> <msub> <mrow> <mi>α</mi> <mo>=</mo> <mn>0.15</mn> <mo>,</mo> <mi>p</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mo>=</mo> <mn>0.85</mn> <mo>,</mo> <msub> <mrow> <mi>p</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> <mo>=</mo> <mn>0.15</mn> <mo>,</mo> <msub> <mrow> <mi>μ</mi> </mrow> <mrow> <mi>Y</mi> </mrow> </msub> </mrow> </semantics></math> = 10, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>μ</mi> </mrow> <mrow> <mi>X</mi> </mrow> </msub> </mrow> </semantics></math> = 10, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>μ</mi> </mrow> <mrow> <mi>R</mi> </mrow> </msub> <mo>=</mo> <mn>10</mn> <mo>,</mo> <mo> </mo> <msub> <mrow> <mi>μ</mi> </mrow> <mrow> <mi>S</mi> </mrow> </msub> <mo>=</mo> <mn>0</mn> <mo>,</mo> <mo> </mo> <mo> </mo> <msub> <mrow> <mi>μ</mi> </mrow> <mrow> <mi>T</mi> </mrow> </msub> <mo>=</mo> <mn>1</mn> <mo>,</mo> <msub> <mrow> <mi>μ</mi> </mrow> <mrow> <mi>U</mi> </mrow> </msub> <mo>=</mo> <mn>0</mn> <mo>,</mo> <msub> <mrow> <mi>μ</mi> </mrow> <mrow> <mi>V</mi> </mrow> </msub> <mo>=</mo> <mn>0</mn> <mo>,</mo> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>Y</mi> </mrow> </msub> <mo>=</mo> <mn>5</mn> <mo>,</mo> <mo> </mo> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>X</mi> </mrow> </msub> <mo>=</mo> <mn>5</mn> <mo>,</mo> <mo> </mo> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>R</mi> </mrow> </msub> <mo>=</mo> <mn>5</mn> <mo>,</mo> <mo> </mo> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>U</mi> </mrow> </msub> <mo>=</mo> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>V</mi> </mrow> </msub> <mo>,</mo> <mo> </mo> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>S</mi> </mrow> </msub> <mo>=</mo> <mn>1</mn> <mo>,</mo> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>T</mi> </mrow> </msub> <mo>=</mo> <mrow> <mn>1</mn> </mrow> </mrow> </semantics></math>, <span class="html-italic">n</span> = 500.</p>
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21 pages, 6600 KiB  
Article
Strategic Deployment of a Single Mobile Weather Radar for the Enhancement of Meteorological Observation: A Coverage-Based Location Problem
by Bikram Parajuli and Xin Feng
Remote Sens. 2025, 17(5), 870; https://doi.org/10.3390/rs17050870 - 28 Feb 2025
Viewed by 344
Abstract
Mobile weather radars have been routinely deployed to acquire high-quality meteorological data for research purposes, particularly for monitoring rapidly evolving weather phenomena at low altitudes. However, identifying an optimal location for mobile weather radar deployment is a complex challenge, as it requires consideration [...] Read more.
Mobile weather radars have been routinely deployed to acquire high-quality meteorological data for research purposes, particularly for monitoring rapidly evolving weather phenomena at low altitudes. However, identifying an optimal location for mobile weather radar deployment is a complex challenge, as it requires consideration of operational safety, data quality, and environmental constraints. In this study, we introduce a framework using a coverage-based location problem to solve the strategic deployment of a single mobile weather radar. This approach aims to enhance weather observation while accounting for the deployment space’s safety constraints and geospatial characteristics. The proposed location problem is solved optimally using the geometric branch-and-bound algorithm and heuristically using swarm-based optimization algorithms. The implementation relies entirely on open-source Python packages, allowing the work to be verified, replicated, and expanded upon by the broader scientific community. Results demonstrate that exact solution methods are ideal when ample time is available for decision-making and optimal deployment locations are desired. In contrast, heuristic algorithms can efficiently identify multiple near-optimal deployment locations, making them highly suitable for rapid decision-making and evaluating alternative deployment options. Moreover, the findings highlight the potential of quantitative decision-making techniques in improving the effectiveness of mobile radar positioning, thereby contributing to efficient weather observation, forecasting, and better-informed emergency response strategies. Full article
(This article belongs to the Special Issue Recent Advances in Precipitation Radar)
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<p>Flowchart for BTST algorithm.</p>
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<p>(<b>a</b>) Initial triangles generated using Delauney triangulation, (<b>b</b>) triangles removed from within the obstacles, and (<b>c</b>) division of big triangle into 4 smaller triangles.</p>
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<p>Flowchart for PSO algorithm.</p>
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<p>Vector representation of position update for particles in SPSO (<b>a</b>) and CoGPSO (<b>b</b>).</p>
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<p>Study area and CM1-simulated reflectivity.</p>
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<p>Study area with supercell thunderstorm (<b>left</b>), and optimal location for radar deployment (<b>right</b>).</p>
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<p>Optimal radar deployment locations in unconstrained continuous space identified by (<b>a</b>) 10 runs of BTST, (<b>b</b>) 1000 runs of SPSO, and (<b>c</b>) 1000 runs of CoGPSO for a 25 km radius. The optimal locations are marked by black stars, while the corresponding effective radar coverage areas are represented by black circles. The upper panels show spatial location for each method’s optimal solutions. The lower panels present histograms illustrating the distribution of objective values obtained from BTST (<b>a</b>), SPSO (<b>b</b>), and CoGPSO (<b>c</b>).</p>
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<p>Supercell thunderstorm and bounded continuous feasible area (<b>left</b>), and optimal location for radar deployment (<b>right</b>).</p>
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<p>Optimal radar deployment locations in constrained continuous space identified by (<b>a</b>) 10 runs of BTST, (<b>b</b>) 1000 runs of SPSO, and (<b>c</b>) 1000 runs of CoGPSO for a 25 km radius. The optimal locations are marked by black stars, while the corresponding effective radar coverage areas are represented by black circles. The upper panels show the spatial location for each method’s optimal solutions. The lower panels present histograms illustrating the distribution of objective values obtained from BTST (<b>a</b>), SPSO (<b>b</b>), and CoGPSO (<b>c</b>).</p>
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<p>Supercell thunderstorm and bounded continuous feasible area and holes (<b>left</b>), and optimal location for radar deployment (<b>right</b>).</p>
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<p>Optimal radar deployment locations in constrained continuous space with obstacles identified by (<b>a</b>) 10 runs of BTST, (<b>b</b>) 1000 runs of SPSO, and (<b>c</b>) 1000 runs of CoGPSO for a 25 km radius. The optimal locations are marked by black stars, while the corresponding effective radar coverage areas are represented by black circles. The upper panels show spatial location for each method’s optimal solutions in the feasible area. The lower panels present histograms illustrating the distribution of objective values obtained from BTST (<b>a</b>), SPSO (<b>b</b>), and CoGPSO (<b>c</b>).</p>
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31 pages, 543 KiB  
Review
Recent Insights into the Pathogenesis, Diagnostics, and Treatment of BK Virus Infections in Children After Hematopoietic Stem Cell Transplantation
by Mislav Peras, Ernest Bilić and Ivana Mareković
Pathogens 2025, 14(3), 236; https://doi.org/10.3390/pathogens14030236 - 28 Feb 2025
Viewed by 186
Abstract
BK polyomavirus (BKPyV) is a pathogen responsible for infectious complications in hematopoietic stem cell transplant (HSCT) recipients. This review aims to give an insight into recent data about the structure and genomic organization, epidemiology, clinical manifestations, diagnosis, and current treatment options of BKPyV [...] Read more.
BK polyomavirus (BKPyV) is a pathogen responsible for infectious complications in hematopoietic stem cell transplant (HSCT) recipients. This review aims to give an insight into recent data about the structure and genomic organization, epidemiology, clinical manifestations, diagnosis, and current treatment options of BKPyV infections in children after HSCT. News regarding viral replication and pathogenesis include the generation of miRNA, new mechanisms of viral shedding by releasing infectious particles via extracellular vesicles, and human bladder microvascular endothelial cells probably acting as viral reservoirs enabling low-level viral replication and persistence. In studies conducted over the past five years, BKPyV hemorrhagic cystitis (BKPyV-HC) has a prevalence rate of 4 to 27% in children undergoing HSCT. Diagnostics still has unsolved dilemmas like whole blood or plasma samples as well as the standardization of molecular methods to allow for reporting in international units. In terms of treatment, new approaches have been used in the past five years, including the use of mesenchymal stem cells (MSCs), virus-specific T cells (VSTs), and recombinant human keratinocyte growth factor (rH-KGF), although the efficacy of some of these treatments has only been documented in isolated studies. This complication continues to pose a substantial clinical challenge, characterized by an absence of effective preventive and therapeutic measures. Full article
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<p>Schematic presentation of BK polyomavirus genome. Abbreviations: BKPyV, BK polyomavirus; NCCR, non-coding control region.</p>
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15 pages, 255 KiB  
Review
New Treatment Strategies in Advanced Epidermal Growth Factor Receptor-Driven Non-Small Cell Lung Cancer: Beyond Single Agent Osimertinib
by Paolo Maione, Valentina Palma, Giuseppina Pucillo and Cesare Gridelli
Cancers 2025, 17(5), 847; https://doi.org/10.3390/cancers17050847 - 28 Feb 2025
Viewed by 169
Abstract
Osimertinib has been the standard treatment for advanced Epidermal Growth Factor Receptor (EGFR)-driven non-small cell lung cancer (NSCLC) for many years. However, even with remarkable response rate, progression-free survival (PFS) and survival benefit as compared to the old generation EGFR tyrosine kinase inhibitors [...] Read more.
Osimertinib has been the standard treatment for advanced Epidermal Growth Factor Receptor (EGFR)-driven non-small cell lung cancer (NSCLC) for many years. However, even with remarkable response rate, progression-free survival (PFS) and survival benefit as compared to the old generation EGFR tyrosine kinase inhibitors (TKIs) gefitinib and erlotinib, treatment outcomes for these subsets of patients remain a challenge. Recently, in order to go beyond osimertinib, new treatment strategies have been developed. In particular, in the FLAURA 2 phase III randomized trial, the combination of platin-based chemotherapy and osimertinib showed impressive PFS benefits as compared to single-agent osimertinib. Furthermore, in the MARIPOSA phase III randomized study, the combination of the anti-EGFR and anti-MET monoclonal antibody amivantamab combined with the new anti-EGFR TKI lazertinib demonstrated remarkable PFS benefit as compared to single agent osimertinib. This paper will discuss these new treatment options and potential selection criteria for personalized treatment of patients. Full article
25 pages, 1118 KiB  
Review
Current Treatment Strategies for Multiple Myeloma at First Relapse
by Evangelos Mavrothalassitis, Konstantinos Triantafyllakis, Panagiotis Malandrakis, Maria Gavriatopoulou, Martina Kleber and Ioannis Ntanasis-Stathopoulos
J. Clin. Med. 2025, 14(5), 1655; https://doi.org/10.3390/jcm14051655 - 28 Feb 2025
Viewed by 144
Abstract
Multiple myeloma (MM), the second most common hematologic cancer, remains an incurable malignancy, characterized by an initial response to therapy followed by successive relapses. The upfront treatment typically involves induction therapy, autologous stem cell transplantation for eligible patients, and long-term maintenance therapy. It [...] Read more.
Multiple myeloma (MM), the second most common hematologic cancer, remains an incurable malignancy, characterized by an initial response to therapy followed by successive relapses. The upfront treatment typically involves induction therapy, autologous stem cell transplantation for eligible patients, and long-term maintenance therapy. It is important to note that the anticipated duration of myeloma response diminishes with each subsequent relapse. Therefore, the first relapse represents a critical juncture in treatment, where refractoriness to key drug classes emerges as a significant challenge. Addressing the optimal management in this setting requires careful consideration of disease biology, prior therapies, and patient-specific factors to optimize outcomes. Cilta-cel, a chimeric antigen receptor T-cell construct, has emerged as the most promising therapeutic option at first relapse, resulting in long-term remissions with a significant treatment-free interval. However, availability and accessibility are not universal and treatment logistics are complex. Triplet regimens based on carfilzomib, pomalidomide or selinexor, remain the cornerstone of treatment at first relapse, whereas the optimal combination is based on refractoriness to prior drugs, especially anti-CD38 monoclonal antibodies and lenalidomide, and patient comorbidities. With the rapidly expanding therapeutic landscape, clinicians face increasing complexity in selecting the most appropriate regimens for individual patients. This review aims to guide clinicians through these evolving options by consolidating evidence-based strategies and highlighting emerging therapies, ensuring a personalized approach to managing first-relapse MM. Full article
(This article belongs to the Special Issue Multiple Myeloma: Advances in Diagnosis and Treatment)
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<p>Proposed therapeutic algorithm for patients with multiple myeloma at first relapse who are anti-CD38 sensitive/naive based on disease refractoriness to prior treatments.</p>
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<p>Proposed therapeutic algorithm for patients with multiple myeloma at first relapse who are anti-CD38 refractory based on disease refractoriness to prior treatments.</p>
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31 pages, 1968 KiB  
Review
Breast Cancer and Tumor Microenvironment: The Crucial Role of Immune Cells
by Tânia Moura, Paula Laranjeira, Olga Caramelo, Ana M. Gil and Artur Paiva
Curr. Oncol. 2025, 32(3), 143; https://doi.org/10.3390/curroncol32030143 - 28 Feb 2025
Viewed by 169
Abstract
Breast cancer is the most common type of cancer in women and the second leading cause of death by cancer. Despite recent advances, the mortality rate remains high, underlining the need to develop new therapeutic approaches. The complex interaction between cancer cells and [...] Read more.
Breast cancer is the most common type of cancer in women and the second leading cause of death by cancer. Despite recent advances, the mortality rate remains high, underlining the need to develop new therapeutic approaches. The complex interaction between cancer cells and the tumor microenvironment (TME) is crucial in determining tumor progression, therapy response, and patient prognosis. Understanding the role of immune cells in carcinogenesis and tumor progression can help improve targeted therapeutic options, increasing the likelihood of a favorable prognosis. Therefore, this review aims to critically analyze the complex interaction between tumor cells and immune cells, emphasizing the clinical and therapeutic implications. Additionally, we explore advances in immunotherapies, with a focus on immune checkpoint inhibitors. Full article
(This article belongs to the Special Issue Advances in Immunotherapy for Breast Cancer)
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<p>The interaction between various cells that make up the immune system and the tumor microenvironment. cDC—conventional dendritic cells; IL—interleukin; M1—M1 macrophages; M2—M2 macrophages; MDSCs—myeloid-derived suppressor cells, MIP-1-alpha—macrophages inflammatory protein-1-α; MMP-9—matrix metalloproteinases-9; N1—N1 neutrophils; N2—N2 neutrophils; NK—natural killer cells; pDC—plasmacytoid dendritic cells; Tc—cytotoxic T cells; Th1—H helper cells 1; TNF-α—tumor necrosis factor alpha; Treg—regulatory T cells; VEGF—vascular endothelial growth factor. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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<p>A schematic representation of cellular interactions in the breast cancer tumor microenvironment. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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<p>The immunogenicity of breast cancer subtypes. Schematic representation of the immune components within the TME of breast cancer, highlighting the different immune cell populations across different breast cancer subtypes. Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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<p>Current treatment regimens for different breast cancer subtypes and their limitations. The figure summarizes the main therapeutic approaches for Luminal (hormone receptor-positive), HER2+, and triple negative breast cancer, highlighting chemotherapy, targeted therapy, endocrine therapy, and immunotherapy where applicable. The associated limitations of each approach, such as resistance mechanisms and treatment-related toxicities, are also outlined. Created by <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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27 pages, 1578 KiB  
Article
The Hedging Strategies of Enterprises in the European Union Allowances Market—Implementation Actions for Sustainable Development
by Małgorzata Błażejowska, Anna Czarny, Iwona Kowalska, Andrzej Michalczewski and Paweł Stępień
Sustainability 2025, 17(5), 2099; https://doi.org/10.3390/su17052099 - 28 Feb 2025
Viewed by 211
Abstract
The pursuit of sustainable development in the implementation of EU energy policy concerns, among other things, the area of trading greenhouse gas emission allowances. The increasing price volatility in the European Union Allowances (EUA) market necessitates the implementation of hedging strategies to minimize [...] Read more.
The pursuit of sustainable development in the implementation of EU energy policy concerns, among other things, the area of trading greenhouse gas emission allowances. The increasing price volatility in the European Union Allowances (EUA) market necessitates the implementation of hedging strategies to minimize the impact of price risk on the operational performance of European enterprises. An intriguing research goal (both in terms of cognitive and practical applications) was to compare the effectiveness of hedging strategies for purchasing EUA in three scenarios: (1) without hedging; (2) hedging based on an unconditional instrument; and (3) hedging based on a conditional instrument. The analysis was conducted on a theoretical-comparative variant and on the example of an entity operating in the real economy. The research objectives were supported by the following methods: 1. Data collection, which included a review of the literature on hedging EUA purchases in the context of connections with financial risk management theories and corporate responsibility, as well as connections with EU ETS policy regulations. 2. Data processing, which involved a quantitative analysis of data mainly from the ICE Endex exchange and its historical quotations (2016–September 2024), including the determination of option pricing using the Black–Scholes model. 3. Expert judgment was used to justify the time frames adopted for the research. The findings revealed that the use of hedging in EUA purchases was effective and led to a reduction in the overall cost of acquisition throughout the analyzed period. The effectiveness of hedging based on an unconditional instrument, such as a futures contract, was higher than that of hedging based on a conditional instrument, such as an option. The results obtained provide a good basis for continuing research on the effectiveness of EUA hedging in extreme scenarios and in conditions of increased volatility. This research approach is justified by the upcoming dismantling of climate initiatives starting in 2025, related to the USA’s withdrawal from the Paris Agreement. Full article
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<p>The purchase price for the buyer of a CO<sub>2</sub> emission allowance forward contract. Source: own study based on [<a href="#B94-sustainability-17-02099" class="html-bibr">94</a>].</p>
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<p>The exchange rate for the seller holding a put option on CO<sub>2</sub> emission allowances Source: own study based on [<a href="#B94-sustainability-17-02099" class="html-bibr">94</a>].</p>
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<p>The purchase price for a buyer holding a call option on CO<sub>2</sub> emission allowances. Source: own study based on [<a href="#B94-sustainability-17-02099" class="html-bibr">94</a>].</p>
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<p>Average EUA purchase price in the studied chemical company compared to the prices from the analyzed variants in the years 2016–2024 (up to September). Source: own study based on market data [<a href="#B81-sustainability-17-02099" class="html-bibr">81</a>,<a href="#B82-sustainability-17-02099" class="html-bibr">82</a>].</p>
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13 pages, 2416 KiB  
Review
Insights into the Interaction Between Clostridioides difficile and the Gut Microbiome
by Dimitra Mougiou, Georgia Gioula, Lemonia Skoura, Cleo Anastassopoulou and Melania Kachrimanidou
J. Pers. Med. 2025, 15(3), 94; https://doi.org/10.3390/jpm15030094 - 28 Feb 2025
Viewed by 97
Abstract
Clostridioides difficile (C. difficile) is a significant healthcare-associated pathogen that is predominantly caused by antibiotic-induced microbiota disturbance. Antibiotics decrease microbial diversity, resulting in C. difficile colonization and infection. Clostridium difficile infection (CDI) manifests through toxins A and B, causing diarrhea and [...] Read more.
Clostridioides difficile (C. difficile) is a significant healthcare-associated pathogen that is predominantly caused by antibiotic-induced microbiota disturbance. Antibiotics decrease microbial diversity, resulting in C. difficile colonization and infection. Clostridium difficile infection (CDI) manifests through toxins A and B, causing diarrhea and colitis. Antibiotic usage, old age, and hospitalization are significant risk factors. A healthy gut microbiota, which is dominated by Firmicutes and Bacteroidetes, provides colonization resistance to C. difficile due to competition for nutrients, creating inhibitory substances and stimulating the immune response. Antibiotic-induced dysbiosis decreases resistance, allowing C. difficile spores to transform into vegetative forms. Patients with CDI have decreased gut microbiota diversity, with a decrease in beneficial bacteria, including Bacteroidetes, Prevotella, and Bifidobacterium, and a rise in harmful bacteria like Clostridioides and Lactobacillus. This disparity worsens the infection’s symptoms and complicates therapy. Fecal Microbiota Transplantation (FMT) has emerged as a potential therapy for recurrent CDI by restoring gut microbiota diversity and function. Comprehending the connection between gut microbiota and CDI pathogenesis is critical for establishing effective preventive and treatment plans. Maintaining a healthy gut microbiota through careful antibiotic use and therapeutic options such as FMT can help in the management and prevention of CDI. Full article
(This article belongs to the Special Issue Personalized Medicine in Infectious Diseases)
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<p>Composition of gut microbiota. <span class="html-italic">Firmicutes</span> and <span class="html-italic">Bacteroidetes</span> are the most represented phyla. <span class="html-italic">Proteobacteria</span>, <span class="html-italic">Fusobacteria</span>, <span class="html-italic">Verrucomicrobia</span>, and <span class="html-italic">Actinobacteria</span>. Created in BioRender.</p>
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<p>CDI pathogenesis. Created in BioRender.</p>
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<p>Gut microbiome in health and in CDI. Created in BioRender.</p>
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12 pages, 987 KiB  
Article
Effectiveness and Toxicity of Cemiplimab Therapy for Advanced Cutaneous Squamous Cell Skin Cancer in a Community Oncology Practice
by Tina Fung, Wolfram Samlowski and Raul Meoz
Cancers 2025, 17(5), 823; https://doi.org/10.3390/cancers17050823 - 27 Feb 2025
Viewed by 176
Abstract
Background: The immune checkpoint inhibitor cemiplimab has significant clinical activity in unresectable and metastatic cutaneous squamous cell carcinomas. There are limited real-world data available to assess the outcome of cemiplimab treatment in patients in a community practice setting. Methods: We conducted a retrospective [...] Read more.
Background: The immune checkpoint inhibitor cemiplimab has significant clinical activity in unresectable and metastatic cutaneous squamous cell carcinomas. There are limited real-world data available to assess the outcome of cemiplimab treatment in patients in a community practice setting. Methods: We conducted a retrospective analysis of treatment outcomes following cemiplimab treatment (350 mg IV every 3 weeks) of squamous cell skin cancer. An exploratory analysis was performed to evaluate patient subsets, including patients with locally advanced disease, regional or distant metastases, and “too numerous to count” primaries. Another small group of patients who did not respond to the initial four doses of cemiplimab were evaluated following added radiotherapy. Results: Of the 36 patients treated, 22 (61.1%) achieved complete remission, 10 (27.8%) experienced a partial response, 3 (8.3%) had stable disease, and 1 (2.8%) developed progressive disease. The median progression-free survival for the entire cohort was over 33 months. Overall, cemiplimab was well-tolerated, with no hospitalizations due to treatment-related toxicity. Conclusions: Cemiplimab produced complete remissions in over 60% of patients with locally advanced and metastatic squamous cell skin cancers, allowing elective treatment discontinuation. Addition of radiotherapy in cemiplimab-refractory patients appeared to increase tumor responsiveness. In contrast, patients with TNTC primary tumors frequently develop new primary skin cancers. Thus, improved treatment options for this patient subset are still needed. Full article
(This article belongs to the Special Issue Immunotherapy for Skin Cancers)
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<p>Overall progression-free survival of cemiplimab-treated cutaneous squamous cell carcinoma patients.</p>
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<p>Progression-free survival of cemiplimab-treated cutaneous squamous cell carcinoma patient subsets.</p>
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<p>Progression-free survival of cemiplimab-treated cutaneous squamous cell carcinoma patients treated with radiotherapy added to cemiplimab treatment.</p>
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23 pages, 21443 KiB  
Article
Strategic Approaches to Sustainable Rural Development by Harnessing Endogenous Resources to Improve Residents’ Quality of Life
by Romulus Iagăru, Nicolae Concioiu, Anca Șipoș, Pompilica Iagăru, Achim Daniel Băluță and Andrei Vasile
Land 2025, 14(3), 491; https://doi.org/10.3390/land14030491 - 26 Feb 2025
Viewed by 176
Abstract
The sustainable development of the Romanian countryside follows strategies outlined in the Common Agricultural Policy. This policy aims to ensure the sustainability of agricultural and non-agricultural businesses, improving inhabitants’ quality of life. Achieving sustainable development is the objective of every rural locality in [...] Read more.
The sustainable development of the Romanian countryside follows strategies outlined in the Common Agricultural Policy. This policy aims to ensure the sustainability of agricultural and non-agricultural businesses, improving inhabitants’ quality of life. Achieving sustainable development is the objective of every rural locality in Romania. This is accomplished by determining the state of endogenous resources and identifying potential conservation and sustainable exploitation alternatives by developing relevant strategic options. The purpose of this research is to develop relevant strategic options for the sustainable rural development of Gușoeni Commune, Vâlcea County, by using the case study methodology and involving stakeholders and community members. In this study, we develop an integrated and dynamic model based on information from a secondary analysis of statistical data and the specialized literature, with the help of the PESTEL (political, economic, social, technological, ecological, legislative), SWOT (strengths, weaknesses, opportunities, and threats), problem tree, objective tree, and DFPSIR (drivers, pressure, status, impact, response) diagnostic models. Full article
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<p>The schematic structure of the field research.</p>
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<p>The problem tree of sustainable rural development in Gușoeni Commune.</p>
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<p>Objective tree for sustainable rural development in Gușoeni Commune.</p>
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<p>DFPSIR framework for sustainable rural development.</p>
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17 pages, 1302 KiB  
Article
DNA Methylation and Demethylation in Triple-Negative Breast Cancer: Associations with Clinicopathological Characteristics and the Chemotherapy Response
by Kateryna Tarhonska, Mateusz Wichtowski, Thomas Wow, Agnieszka Kołacińska-Wow, Katarzyna Płoszka, Wojciech Fendler, Izabela Zawlik, Sylwia Paszek, Alina Zuchowska and Ewa Jabłońska
Biomedicines 2025, 13(3), 585; https://doi.org/10.3390/biomedicines13030585 - 26 Feb 2025
Viewed by 182
Abstract
Objectives: Triple-negative breast cancer (TNBC) is an aggressive cancer subtype with limited treatment options due to the absence of estrogen, progesterone receptors, and HER2 expression. This study examined the impact of DNA methylation and demethylation markers in tumor tissues on TNBC patients’ response [...] Read more.
Objectives: Triple-negative breast cancer (TNBC) is an aggressive cancer subtype with limited treatment options due to the absence of estrogen, progesterone receptors, and HER2 expression. This study examined the impact of DNA methylation and demethylation markers in tumor tissues on TNBC patients’ response to neoadjuvant chemotherapy (NACT) and analyzed the correlation between 5-methylcytosine (5-mC) and 5-hydroxymethylcytosine (5-hmC) and clinicopathological characteristics, offering new insights into the predictive value of these epigenetic markers. Methods: The study included 53 TNBC female patients, 19 of whom received neoadjuvant chemotherapy (NACT) before surgery. Global DNA methylation and demethylation levels were quantified using an ELISA-based method to measure 5-mC and 5-hmC content in DNA isolated from pre-treatment biopsy samples (in patients undergoing NACT) and postoperative tissues (in patients without NACT). Results: In patients who received NACT, those with disease progression had significantly higher pretreatment levels of 5-hmC (p = 0.028) and a trend toward higher 5-mC levels (p = 0.054) compared to those with pathological complete response, partial response, or stable disease. Higher 5-mC and 5-hmC levels were significantly associated with higher tumor grade (p = 0.039 and p = 0.017, respectively). Additionally, a positive correlation was observed between the Ki-67 proliferation marker and both 5-mC (rS = 0.340, p = 0.049) and 5-hmC (rS = 0.341, p = 0.048) levels in postoperative tissues. Conclusions: Our study highlights the potential of global DNA methylation and demethylation markers as predictors of tumor aggressiveness and chemotherapy response in TNBC. Further research in larger cohorts is necessary to validate these markers’ prognostic and predictive value. Full article
(This article belongs to the Special Issue Molecular Research in Breast Cancer)
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<p>Correlations between markers of DNA methylation (5-mC) and markers of DNA demethylation (5-hmC) in the collected tumor samples, including samples from all patients (<b>a</b>), biopsies collected from patients before neoadjuvant chemotherapy (<b>b</b>) and surgical samples from patients not treated with chemotherapy (<b>c</b>).</p>
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<p>Correlation between Ki-67 and markers of DNA methylation/demethylation (5-mC/5-hmC) in the collected tumor samples, including samples from all patients (<b>a</b>,<b>d</b>), biopsies collected from patients before neoadjuvant chemotherapy (<b>b</b>,<b>e</b>) and surgical samples from patients not treated with chemotherapy (<b>c</b>,<b>f</b>).</p>
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<p>Levels of markers of DNA methylation (5-mC) and DNA demethylation (h-mC) in the collected tissue samples according to tumor grade, including samples from all TNBC patients (<b>a</b>,<b>d</b>), biopsies collected from TNBC patients before neoadjuvant chemotherapy (<b>b</b>,<b>e</b>) and surgical samples from TNBC patients not treated with neoadjuvant chemotherapy (<b>c</b>,<b>f</b>). Group differences were analyzed with the Mann–Whitney U test. Data are shown as raw values, with medians and interquartile ranges.</p>
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<p>Pretreatment levels of markers of (<b>a</b>) DNA methylation (5-mC) and (<b>b</b>) DNA demethylation (h-mC) measured in biopsies collected from TNBC patients undergoing neoadjuvant chemotherapy, stratified by disease progression. The group without progression (“No”) included patients with a complete pathological response, partial response or stable disease. Group differences were analyzed with the Mann–Whitney U test. Data are shown as raw values, with medians and interquartile ranges.</p>
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22 pages, 2561 KiB  
Review
Recent Advances in the Development and Efficacy of Anti-Cancer Vaccines—A Narrative Review
by Kajetan Kiełbowski, Paulina Plewa, Jan Zadworny, Estera Bakinowska, Rafał Becht and Andrzej Pawlik
Vaccines 2025, 13(3), 237; https://doi.org/10.3390/vaccines13030237 - 25 Feb 2025
Viewed by 362
Abstract
Immunotherapy is an established and efficient treatment strategy for a variety of malignancies. It aims to boost the anticancer properties of one’s own immune system. Several immunotherapeutic options are available, but immune checkpoint blockers represent the most widely known and investigated. Anticancer vaccines [...] Read more.
Immunotherapy is an established and efficient treatment strategy for a variety of malignancies. It aims to boost the anticancer properties of one’s own immune system. Several immunotherapeutic options are available, but immune checkpoint blockers represent the most widely known and investigated. Anticancer vaccines represent an evolving area of immunotherapy that stimulate antigen-presenting cells, cytotoxic responses of CD8+ T cells, and the presence of memory T cells, among others. Over the years, different approaches for anticancer vaccines have been studied, such as mRNA and DNA vaccines, together with dendritic cell- and viral vector-based vaccines. Recently, an accumulating number of clinical studies have been performed to analyze the safety and potential efficacy of these agents. The aim of this review is to summarize recent advances regarding different types of therapeutic anticancer vaccines. Furthermore, it will discuss how recent advances in preclinical models can enhance clinical outcomes. Full article
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<p>mRNA-based cancer vaccines utilize lipid encapsulation to enter the antigen-presenting cells. After the release of the cargo, mRNA is translated into peptides which can be presented to stimulate immune responses targeted at presented antigens. DNA vaccines, on the other hand, are gene-expressing plasmids. These molecules need to enter nucleus for transcription before they can undergo the translation. Created in BioRender. Kiełbowski, K. (2025) <a href="https://BioRender.com/f25r737" target="_blank">https://BioRender.com/f25r737</a>.</p>
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<p>Individualized cancer vaccines are developed based on the tumor mutational status and the presence of neoantigens. Encapsulated mRNA molecules can enter antigen-presenting cells where a release of cargo occurs. The translation and presentation of neoantigens stimulate immune responses that are targeted at cancer cells, thus providing cytotoxic activity. Created in BioRender. Kiełbowski, K. (2025) <a href="https://BioRender.com/i22w075" target="_blank">https://BioRender.com/i22w075</a>.</p>
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<p>(<b>A</b>,<b>B</b>) Representation of mechanisms that suppress the activity of T cells. (<b>C</b>) The use of mRNA-based and adenovirus-based cancer vaccines that utilize immunogenic PD-L1 and mouse anti-CTLA-4 monoclonal antibodies. Created in BioRender. Kie&amp;ðwski, K. (2025) <a href="https://BioRender.com/h84t864" target="_blank">https://BioRender.com/h84t864</a>.</p>
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<p>Dendritic cell-based vaccines can be prepared by isolating dendritic cell themselves as well as monocytes and CD34+ hematopoietic stem cells and inducing their differentiation towards dendritic cells. Subsequently, these cells can be stimulated with cancer antigens. Reintroduction of stimulated dendritic cells promotes cytotoxic immune reactions. Created in BioRender. Kiełbowski, K. (2025) <a href="https://BioRender.com/h52n415" target="_blank">https://BioRender.com/h52n415</a>.</p>
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38 pages, 1398 KiB  
Review
Available Treatments for Autism Spectrum Disorder: From Old Strategies to New Options
by Liliana Dell’Osso, Chiara Bonelli, Federico Giovannoni, Francesca Poli, Leonardo Anastasio, Gianluca Cerofolini, Benedetta Nardi, Ivan Mirko Cremone, Stefano Pini and Barbara Carpita
Pharmaceuticals 2025, 18(3), 324; https://doi.org/10.3390/ph18030324 - 25 Feb 2025
Viewed by 317
Abstract
Autism spectrum disorder (ASD) is a condition that is gaining increasing interest in research and clinical fields. Due to the improvement of screening programs and diagnostic procedures, an increasing number of cases are reaching clinical attention. Despite this, the available pharmacological options for [...] Read more.
Autism spectrum disorder (ASD) is a condition that is gaining increasing interest in research and clinical fields. Due to the improvement of screening programs and diagnostic procedures, an increasing number of cases are reaching clinical attention. Despite this, the available pharmacological options for treating ASD-related symptoms are still very limited, and while a wide number of studies are focused on children or adolescents, there is a need to increase research about the treatment of ASD in adult subjects. Given this framework, this work aims to review the available literature about pharmacological treatments for ASD, from older strategies to possible new therapeutic targets for this condition, which are often poorly responsive to available resources. The literature, besides confirming the efficacy of the approved drugs for ASD, shows a lack of adequate research for several psychopharmacological treatments despite possible promising results that need to be further investigated. Full article
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<p>Selected pharmacological articles.</p>
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<p>Selected non pharmacological articles.</p>
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