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14 pages, 632 KiB  
Article
Impacts of Interleukin-10 Promoter Genotypes on Prostate Cancer
by Yu-Ting Chin, Chung-Lin Tsai, Hung-Huan Ma, Da-Chuan Cheng, Chia-Wen Tsai, Yun-Chi Wang, Hou-Yu Shih, Shu-Yu Chang, Jian Gu, Wen-Shin Chang and Da-Tian Bau
Life 2024, 14(8), 1035; https://doi.org/10.3390/life14081035 - 20 Aug 2024
Viewed by 386
Abstract
Prostate cancer (PCa) is a multifactorial disease influenced by genetic, environmental, and immunological factors. Genetic polymorphisms in the interleukin-10 (IL-10) gene have been implicated in PCa susceptibility, development, and progression. This study aims to assess the contributions of three IL-10 promoter [...] Read more.
Prostate cancer (PCa) is a multifactorial disease influenced by genetic, environmental, and immunological factors. Genetic polymorphisms in the interleukin-10 (IL-10) gene have been implicated in PCa susceptibility, development, and progression. This study aims to assess the contributions of three IL-10 promoter single nucleotide polymorphisms (SNPs), A-1082G (rs1800896), T-819C (rs3021097), and A-592C (rs1800872), to the risk of PCa in Taiwan. The three IL-10 genotypes were determined using PCR-RFLP methodology and were evaluated for their contributions to PCa risk among 218 PCa patients and 436 non-PCa controls. None of the three IL-10 SNPs were significantly associated with the risks of PCa (p all > 0.05) in the overall analyses. However, the GG at rs1800896 combined with smoking behavior was found to significantly increase the risk of PCa by 3.90-fold (95% confidence interval [95% CI] = 1.28–11.89, p = 0.0231). In addition, the rs1800896 AG and GGs were found to be correlated with the late stages of PCa (odds ratio [OR] = 1.90 and 6.42, 95% CI = 1.05–3.45 and 2.30–17.89, p = 0.0452 and 0.0003, respectively). The IL-10 promoter SNP, A-1082G (rs1800896), might be a risk factor for PCa development among smokers and those at late stages of the disease. These findings should be validated in larger and more diverse populations. Full article
(This article belongs to the Section Physiology and Pathology)
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<p>Physical map for <span class="html-italic">IL-10</span> rs1800896, rs3021097, and rs1800872 promoter polymorphic sites.</p>
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21 pages, 18750 KiB  
Article
Development of Ex Vivo Analysis for Examining Cell Composition, Immunological Landscape, Tumor and Immune Related Markers in Non-Small-Cell Lung Cancer
by Elena G. Ufimtseva, Margarita S. Gileva, Ruslan V. Kostenko, Vadim V. Kozlov and Lyudmila F. Gulyaeva
Cancers 2024, 16(16), 2886; https://doi.org/10.3390/cancers16162886 - 20 Aug 2024
Viewed by 490
Abstract
NSCLC is a very aggressive solid tumor, with a poor prognosis due to post-surgical recurrence. Analysis of the specific tumor and immune signatures of NSCLC samples is a critical step in prognostic evaluation and management decisions for patients after surgery. Routine histological assays [...] Read more.
NSCLC is a very aggressive solid tumor, with a poor prognosis due to post-surgical recurrence. Analysis of the specific tumor and immune signatures of NSCLC samples is a critical step in prognostic evaluation and management decisions for patients after surgery. Routine histological assays have some limitations. Therefore, new diagnostic tools with the capability to quickly recognize NSCLC subtypes and correctly identify various markers are needed. We developed a technique for ex vivo isolation of cancer and immune cells from surgical tumor and lung tissue samples of patients with NSCLC (adenocarcinomas and squamous cell carcinomas) and their examination on ex vivo cell preparations and, parallelly, on histological sections after Romanovsky–Giemsa and immunofluorescent/immunochemical staining for cancer-specific and immune-related markers. As a result, PD-L1 expression was detected for some patients only by ex vivo analysis. Immune cell profiling in the tumor microenvironment revealed significant differences in the immunological landscapes between the patients’ tumors, with smokers’ macrophages with simultaneous expression of pro- and anti-inflammatory cytokines, neutrophils, and eosinophils being the dominant populations. The proposed ex vivo analysis may be used as an additional diagnostic tool for quick examination of cancer and immune cells in whole tumor samples and to avoid false negatives in histological assays. Full article
(This article belongs to the Section Methods and Technologies Development)
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Figure 1

Figure 1
<p>Representative images demonstrate isolation of cancer cells (<b>a</b>) from surgically resected tumor sample sq1 (weight 0.07 g), which is indicated by the red arrow in the Petri dish 5 cm in diameter, (<b>b</b>) in the cell suspension after separating fibrotic tissue in a sieve and (<b>c</b>–<b>f</b>) their analysis on (<b>c</b>,<b>e</b>) cell smears and (<b>d</b>,<b>f</b>) the ex vivo cell preparations after (<b>c</b>,<b>d</b>) Romanovsky–Giemsa staining and (<b>e</b>,<b>f</b>) the immunofluorescence assay with specific antibodies to different NSCLC and fibroblast markers (green and red signals). Nuclei are stained by DAPI (blue signal). (<b>e</b>,<b>f</b>) Colocalization of the markers is (<b>e</b>) yellow and (<b>f</b>) magenta (in the nuclei) signals on confocal immunofluorescent images. The scale bars are (<b>c</b>,<b>d</b>) 10, (<b>e</b>) 20, and (<b>f</b>) 5 μm.</p>
Full article ">Figure 2
<p>Representative images after Romanovsky–Giemsa staining demonstrate that the differentiation and specific features of the patients’ adenocarcinoma cells and their clusters can be defined not only (<b>e</b>–<b>l</b>) on the histological sections, but also (<b>a</b>–<b>d</b>) on the ex vivo cell preparations obtained from the same tumor samples. (<b>e</b>–<b>h</b>) Close-ups of the parts of the images (<b>i</b>–<b>l</b>). The scale bars are (<b>i</b>) 5, (<b>a</b>–<b>d</b>,<b>j</b>–<b>l</b>) 10, and (<b>e</b>–<b>h</b>) 50 μm.</p>
Full article ">Figure 3
<p>Representative confocal merged immunofluorescent or immunochemical images demonstrate the expression of lung (<b>A</b>) adenocarcinoma- and (<b>B</b>) squamous-cell-carcinoma-specific markers and (<b>C</b>) proliferation marker Ki-67 in cancer cells both on the ex vivo cell preparations and, in parallel, on the histological sections obtained from the same tumor samples, while (<b>C</b>) PD-L1 expression is detected (<b>u</b>,<b>w</b>) in some lung squamous cell carcinoma cells only by ex vivo analysis. Cells and their nuclei are stained with appropriate specific antibodies (green and red signals or brown staining) and DAPI (blue signal), respectively. Localization of some markers in the nuclei is magenta signal. (<b>t</b>) Red arrow indicates the anaphase of mitosis. Green arrows indicate the PD-L1-positive cancer cells, as solitary and in clusters. The scale bars are (<b>a</b>,<b>b</b>,<b>h</b>,<b>j</b>,<b>q</b>,<b>t</b>) 5, (<b>d</b>,<b>f</b>,<b>g</b>,<b>k</b>–<b>p</b>,<b>r</b>,<b>w</b>) 10, and (<b>c</b>,<b>e</b>,<b>i</b>,<b>s</b>,<b>u</b>,<b>v</b>,<b>x</b>) 20 μm.</p>
Full article ">Figure 3 Cont.
<p>Representative confocal merged immunofluorescent or immunochemical images demonstrate the expression of lung (<b>A</b>) adenocarcinoma- and (<b>B</b>) squamous-cell-carcinoma-specific markers and (<b>C</b>) proliferation marker Ki-67 in cancer cells both on the ex vivo cell preparations and, in parallel, on the histological sections obtained from the same tumor samples, while (<b>C</b>) PD-L1 expression is detected (<b>u</b>,<b>w</b>) in some lung squamous cell carcinoma cells only by ex vivo analysis. Cells and their nuclei are stained with appropriate specific antibodies (green and red signals or brown staining) and DAPI (blue signal), respectively. Localization of some markers in the nuclei is magenta signal. (<b>t</b>) Red arrow indicates the anaphase of mitosis. Green arrows indicate the PD-L1-positive cancer cells, as solitary and in clusters. The scale bars are (<b>a</b>,<b>b</b>,<b>h</b>,<b>j</b>,<b>q</b>,<b>t</b>) 5, (<b>d</b>,<b>f</b>,<b>g</b>,<b>k</b>–<b>p</b>,<b>r</b>,<b>w</b>) 10, and (<b>c</b>,<b>e</b>,<b>i</b>,<b>s</b>,<b>u</b>,<b>v</b>,<b>x</b>) 20 μm.</p>
Full article ">Figure 4
<p>Representative images after Romanovsky–Giemsa staining demonstrate the different types of immune cells detected on the ex vivo cell preparations and, in parallel, on histological sections obtained from the same tumor samples (<b>a</b>–<b>l</b>) for tobacco smokers and (<b>m</b>–<b>x</b>) non-smoking patients. Red and green arrows indicate macrophages, as solitary and in clusters, with denser dark inclusions in the cytoplasm (smokers’ macrophages) and without them, respectively. Yellow and brown arrows indicate (<b>n</b>,<b>t</b>,<b>u</b>) neutrophils and (<b>b</b>,<b>e</b>) eosinophils, respectively, as solitary and in clusters. (<b>e</b>) The granules of eosinophils are visualized with DAB substrate. The scale bars are (<b>a</b>–<b>d</b>,<b>f</b>–<b>x</b>) 10 and (<b>e</b>) 50 μm.</p>
Full article ">Figure 5
<p>Differences in the number of immune cells in the TME between different NSCLC subtypes are found for the patients without tumor eosinophilia. The total number of immune cells (all types) is expressed as the percentage of the total number of the patients’ cells (cancer and immune) examined on the ex vivo cell preparations for adenocarcinoma ad1, ad3-ad8 (<span class="html-italic">n</span> = 7) and squamous cell carcinoma sq1, sq3, sq4 (<span class="html-italic">n</span> = 3) samples. Data are expressed as the means ± SEM. * <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">Figure 6
<p>Most macrophages express all markers studied (<b>A</b>,<b>B</b>) in the tumor microenvironment of all patients’ samples and (<b>A</b>) in the lung tissue samples of the smoking NSCLC patients, whereas (<b>B</b>) alveolar macrophages only with CD14 expression are identified in the lung tissues for some non-smoking patients. The number of the marker-positive macrophages expressed as the percentage of the total number of the macrophages analyzed on the ex vivo cell preparations.</p>
Full article ">Figure 6 Cont.
<p>Most macrophages express all markers studied (<b>A</b>,<b>B</b>) in the tumor microenvironment of all patients’ samples and (<b>A</b>) in the lung tissue samples of the smoking NSCLC patients, whereas (<b>B</b>) alveolar macrophages only with CD14 expression are identified in the lung tissues for some non-smoking patients. The number of the marker-positive macrophages expressed as the percentage of the total number of the macrophages analyzed on the ex vivo cell preparations.</p>
Full article ">
12 pages, 568 KiB  
Review
Association between Accelerated Biological Aging, Diet, and Gut Microbiome
by Shweta Sharma, Anna Prizment, Heather Nelson, Lin Zhang, Christopher Staley, Jenny N. Poynter, Gokul Seshadri, Aidan Ellison and Bharat Thyagarajan
Microorganisms 2024, 12(8), 1719; https://doi.org/10.3390/microorganisms12081719 - 20 Aug 2024
Viewed by 544
Abstract
Factors driving accelerated biological age (BA), an important predictor of chronic diseases, remain poorly understood. This study focuses on the impact of diet and gut microbiome on accelerated BA. Accelerated Klemera–Doubal biological age (KDM-BA) was estimated as the difference between KDM-BA and chronological [...] Read more.
Factors driving accelerated biological age (BA), an important predictor of chronic diseases, remain poorly understood. This study focuses on the impact of diet and gut microbiome on accelerated BA. Accelerated Klemera–Doubal biological age (KDM-BA) was estimated as the difference between KDM-BA and chronological age. We assessed the cross-sectional association between accelerated KDM-BA and diet/gut microbiome in 117 adult participants from the 10,000 Families Study. 16S rRNA sequencing was used to estimate the abundances of gut bacterial genera. Multivariable linear mixed models evaluated the associations between accelerated KDM-BA and diet/gut microbiome after adjusting for family relatedness, diet, age, sex, smoking status, alcohol intake, and BMI. One standard deviation (SD) increase in processed meat was associated with a 1.91-year increase in accelerated KDM-BA (p = 0.04), while one SD increase in fiber intake was associated with a 0.70-year decrease in accelerated KDM-BA (p = 0.01). Accelerated KDM-BA was positively associated with Streptococcus and negatively associated with Subdoligranulum, unclassified Bacteroidetes, and Burkholderiales. Adjustment for gut microbiome did not change the association between dietary fiber and accelerated KDM-BA, but the association with processed meat intake became nonsignificant. These cross-sectional associations between higher meat intake, lower fiber intake, and accelerated BA need validation in longitudinal studies. Full article
(This article belongs to the Special Issue Gut Microbiota and Nutrients, 2nd Edition)
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Figure 1
<p>A Circos plot showing a linear association between accelerated KDM-BA (Accel_BA) and bacterial genera significantly associated with KDM-BA after adjustment for covariates (chronological age, sex, BMI, alcohol intake, and smoking status). Bacterial genera are positioned around the plot. Blue arrows symbolize positive linear relationships, indicating that an increase in accelerated KDM-BA corresponds to an increased relative abundance of the associated bacterial genus. Conversely, red arrows signify negative linear relationships, suggesting a decrease in the relative abundance of the bacterial genus with increasing accelerated KDM-BA. The directional arrow represents the strength and magnitude, and the broad arrow indicates stronger associations between the variables.</p>
Full article ">
8 pages, 4098 KiB  
Article
Dietary Isoflavones Intake and Gastric Cancer
by Arianna Natale, Federica Fiori, Maria Parpinel, Claudio Pelucchi, Eva Negri, Carlo La Vecchia and Marta Rossi
Nutrients 2024, 16(16), 2771; https://doi.org/10.3390/nu16162771 - 20 Aug 2024
Viewed by 562
Abstract
Dietary isoflavones have been associated with a lower risk of gastric cancer (GC), but the evidence for this association is still limited. We investigated the association between isoflavone intake and GC risk using data from a case–control study including 230 incident, histologically confirmed [...] Read more.
Dietary isoflavones have been associated with a lower risk of gastric cancer (GC), but the evidence for this association is still limited. We investigated the association between isoflavone intake and GC risk using data from a case–control study including 230 incident, histologically confirmed GC cases and 547 controls with acute, non-neoplastic conditions. Dietary information was collected through a validated food frequency questionnaire (FFQ) and isoflavone intake was estimated using ad hoc databases. We estimated the odds ratios (OR) and the corresponding 95% confidence intervals (CI) of GC using logistic regression models, including terms for total energy intake and other major confounders. The OR for the highest versus the lowest tertile of intake was 0.65 (95%CI = 0.44–0.97, p for trend = 0.04) for daidzein, 0.75 (95%CI = 0.54–1.11, p for trend = 0.15) for genistein, and 0.66 (95%CI = 0.45–0.99, p for trend = 0.05) for total isoflavones. Stratified analyses by sex, age, education, and smoking showed no heterogeneity. These findings indicate a favorable effect of dietary isoflavones on GC. Full article
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Figure 1
<p>Odds ratios <sup>a</sup> (OR) of gastric cancer and corresponding 95% confidence intervals (C) for the highest versus the lowest tertile of isoflavone intake in the strata of selected characteristics. Italy 1997–2007. <b><sup>a</sup></b> Derived from the logistic regression model adjusting for sex, age, education, year of interview, smoking status, and total energy intake.</p>
Full article ">
23 pages, 17866 KiB  
Article
Design of Small-Size Lithium-Battery-Based Electromagnetic Induction Heating Control System
by Yuanjie Liang, Shihao Song, Bocheng Xu, Zhuangzhuang Li, Xuelin Li, Zonglai Mo and Jun Li
Electronics 2024, 13(16), 3287; https://doi.org/10.3390/electronics13163287 - 19 Aug 2024
Viewed by 488
Abstract
This paper presents the design and optimization of a small-size electromagnetic induction heating control system powered by a 3.7 V–900 mAh lithium battery and featuring an LC series resonant full-bridge inverter circuit, which can be used for small metal material heating applications, such [...] Read more.
This paper presents the design and optimization of a small-size electromagnetic induction heating control system powered by a 3.7 V–900 mAh lithium battery and featuring an LC series resonant full-bridge inverter circuit, which can be used for small metal material heating applications, such as micro medical devices. The effects of the resonant capacitance, inductor wire diameter, heating tube material, and wall thickness were studied to maximize the heating rate of the workpiece and simultaneously reduce the temperature rise of the NMOS transistor. The optimal circuit configuration meeting the design requirements was finally identified by comparing the operational parameters and NMOS transistor loss under different circuit conditions. Validation experiments were conducted on designed electromagnetic induction smoking devices. The results indicate that under an output current of 4.6 A, the heating tube can reach the temperature target of 250 °C within 11 s, and all NMOS transistors stay below 50 °C in a 5 min heating process. Full article
(This article belongs to the Special Issue Analog and Mixed Circuit: Design and Applications)
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Figure 1

Figure 1
<p>Structure of full-bridge LLC series resonant circuit for industrial induction heating control system.</p>
Full article ">Figure 2
<p>(<b>a</b>) The structure of the full-bridge LC series resonant circuit for a small induction heating control system. (<b>b</b>) The waveform when the <span class="html-italic">U<sub>AB</sub></span> duty cycle is 50% and the power factor angle is φ.</p>
Full article ">Figure 3
<p>(<b>a</b>) Small electromagnetic induction heating control system’s overall structural frame. (<b>b</b>) Heating non-combustible vape control system’s PCB circuit boards. (<b>c</b>) Relative positions and dimensions of induction coils, heating tubes, and temperature-holding layers.</p>
Full article ">Figure 4
<p>The circuit diagram of the full-bridge NMOS driver for the small-size electromagnetic induction heating control system powered by a 3.7 V lithium battery.</p>
Full article ">Figure 5
<p>The frequency phase-locked circuit diagram for the small-size electromagnetic induction heating control system powered by a 3.7 V lithium battery.</p>
Full article ">Figure 6
<p>LC series resonant circuit operation process and NMOS drive pulses and related important waveforms during power adjustment process.</p>
Full article ">Figure 7
<p>Under the initial conditions of an inductor coil wire diameter of 1.17 mm, a resonant capacitor of 2.2 uF, and a heating tube with a 0.1 mm wall thickness made of pure iron, variations in system parameters with the operation time are shown. (<b>a</b>) The variation in the DC-side current I<sub>DC</sub> and the temperature of the heating tube over time. (<b>b</b>) The variation in the inverter frequency f and the inverter voltage duty cycle D with time.</p>
Full article ">Figure 8
<p>Figures showing the turn-off process of <span class="html-italic">Q1</span>. (<b>a</b>) The changes in the gate-to-source voltage <span class="html-italic">V<sub>GS</sub></span> and gate drive current <span class="html-italic">I<sub>g</sub></span> during the turn-off process of <span class="html-italic">Q1</span>. (<b>b</b>) The variations in the drain-to-source voltage <span class="html-italic">V<sub>DS</sub></span> and drain current <span class="html-italic">I<sub>DS</sub></span> with time during the turn-off process of <span class="html-italic">Q1</span>.</p>
Full article ">Figure 9
<p>The analysis of various loss factors for NMOSs <span class="html-italic">Q1</span> and <span class="html-italic">Q4</span>. (<b>a</b>) The conduction loss of Q1 varies with the duty cycle of its driving voltage for different <span class="html-italic">I<sub>DC</sub></span> and <span class="html-italic">R<sub>DS(on)</sub></span>. (<b>b</b>) The trend of the NMOS’s driving loss with increasing inverter frequency <span class="html-italic">f</span> under different gate total charges <span class="html-italic">Q<sub>g</sub></span>. (<b>c</b>) The variation in <span class="html-italic">Q1</span>’s turn-off current with the driving voltage duty cycle for different <span class="html-italic">I<sub>DC</sub></span>. (<b>d</b>) <span class="html-italic">Q1</span>’s turn-off loss changes with the turn-off current under different <span class="html-italic">t<sub>off</sub></span> and <span class="html-italic">f</span>.</p>
Full article ">Figure 10
<p>(<b>a</b>) Trends of the number of wire strands and coil DC resistance with increasing wire diameter. (<b>b</b>) The trend of the equivalent inductance of the coil with increasing wire diameter with no load as well as under various load conditions.</p>
Full article ">Figure 11
<p>The experimental setup. (<b>a</b>) The experimental platform for the performance testing of a small induction heating control system. (<b>b</b>) The output waveforms from the oscilloscope. (<b>c</b>) Three experimental materials: iron, 430 stainless steel, and 1J50 iron–nickel soft magnetic alloy. (<b>d</b>) Induction coil wires with different diameters. (<b>e</b>) The appearance of different wall thicknesses of heating tubes. (<b>f</b>) The temperature distribution of the heating tube and the arrangement of K-type thermocouples.</p>
Full article ">Figure 12
<p>Experimental results under initial circuit conditions with a coil wire diameter of 1.17 mm and a 0.1 mm thick pure iron heating tube. (<b>a</b>) The DC-side current <span class="html-italic">I<sub>DC</sub></span> and inverter frequency <span class="html-italic">f</span> during the heating and temperature-holding periods as the capacitance increases. (<b>b</b>) NMOS Q1 and <span class="html-italic">Q4</span> losses and the heating rate of the heating tube during the heating and temperature-holding periods as the capacitance increases. (<b>c</b>) The trend of the average heating rate of the heater with increasing DC-side current. (<b>d</b>) The current withstand and limit values of coils correspond to the DC-side current in relation to the coil wire diameter.</p>
Full article ">Figure 13
<p>The effects of the wire diameter on NMOS losses and heating rates in induction heating systems. (<b>a</b>) Losses of <span class="html-italic">Q1</span> and <span class="html-italic">Q4</span> and heating rate variation with wire diameter for a pure iron heating tube (0.1 mm wall thickness). (<b>b</b>) Losses of <span class="html-italic">Q1</span> and <span class="html-italic">Q4</span> and heating rate variation with the wire diameter for a 1J50 soft magnetic alloy heating tube (0.1 mm wall thickness). (<b>c</b>) Losses of <span class="html-italic">Q1</span> and <span class="html-italic">Q4</span> and heating rate variation with the wire diameter for a 430 stainless steel heating tube (0.1 mm wall thickness). (<b>d</b>) A comparison of the heating rate and losses of <span class="html-italic">Q1</span> and <span class="html-italic">Q4</span> at optimized conditions (points a, b, c).</p>
Full article ">Figure 13 Cont.
<p>The effects of the wire diameter on NMOS losses and heating rates in induction heating systems. (<b>a</b>) Losses of <span class="html-italic">Q1</span> and <span class="html-italic">Q4</span> and heating rate variation with wire diameter for a pure iron heating tube (0.1 mm wall thickness). (<b>b</b>) Losses of <span class="html-italic">Q1</span> and <span class="html-italic">Q4</span> and heating rate variation with the wire diameter for a 1J50 soft magnetic alloy heating tube (0.1 mm wall thickness). (<b>c</b>) Losses of <span class="html-italic">Q1</span> and <span class="html-italic">Q4</span> and heating rate variation with the wire diameter for a 430 stainless steel heating tube (0.1 mm wall thickness). (<b>d</b>) A comparison of the heating rate and losses of <span class="html-italic">Q1</span> and <span class="html-italic">Q4</span> at optimized conditions (points a, b, c).</p>
Full article ">Figure 14
<p>The impact of wall thickness on NMOS losses and heating rates in induction heating systems. (<b>a</b>) Changes in <span class="html-italic">Q1</span> and <span class="html-italic">Q4</span> losses and heating rate with increasing wall thickness for the pure iron heating tube. (<b>b</b>) Changes in <span class="html-italic">Q1</span> and <span class="html-italic">Q4</span> losses and the heating rate with increasing wall thickness for a 1J50 soft magnetic alloy heating tube. (<b>c</b>) Changes in <span class="html-italic">Q1</span> and <span class="html-italic">Q4</span> losses and the heating rate with increasing wall thickness for a 430 stainless steel heating tube. (<b>d</b>) A comparison of heating rate and <span class="html-italic">Q1</span> and <span class="html-italic">Q4</span> losses during the holding period at optimization points e, f, and g.</p>
Full article ">Figure 14 Cont.
<p>The impact of wall thickness on NMOS losses and heating rates in induction heating systems. (<b>a</b>) Changes in <span class="html-italic">Q1</span> and <span class="html-italic">Q4</span> losses and heating rate with increasing wall thickness for the pure iron heating tube. (<b>b</b>) Changes in <span class="html-italic">Q1</span> and <span class="html-italic">Q4</span> losses and the heating rate with increasing wall thickness for a 1J50 soft magnetic alloy heating tube. (<b>c</b>) Changes in <span class="html-italic">Q1</span> and <span class="html-italic">Q4</span> losses and the heating rate with increasing wall thickness for a 430 stainless steel heating tube. (<b>d</b>) A comparison of heating rate and <span class="html-italic">Q1</span> and <span class="html-italic">Q4</span> losses during the holding period at optimization points e, f, and g.</p>
Full article ">Figure 15
<p>A performance analysis of NMOS losses and heating dynamics in induction heating systems. (<b>a</b>) NMOS loss values (<span class="html-italic">Q1</span>, <span class="html-italic">Q4</span>) during heating and temperature holding at 6 optimization points. (<b>b</b>) The heating time and NMOS temperature rise (<span class="html-italic">Q1</span>, <span class="html-italic">Q4</span>) after 5 min of operation at 6 optimization points.</p>
Full article ">Figure 16
<p>The thermal analysis of NMOSs and the heating tube in the induction heating system. (<b>a</b>) Infrared temperature distribution of NMOSs after running the device for 1 min, 2 min, 3 min, 4 min, and 5 min, respectively. (<b>b</b>) A 3D thermogram of NMOSs after running the device for 5 min. (<b>c</b>) The trends of the NMOSs as well as the heating tube temperature with the device running time.</p>
Full article ">
15 pages, 1810 KiB  
Review
The Association between Telomere Length and Head and Neck Cancer Risk: A Systematic Review and Meta-Analysis
by Dimitrios Andreikos, Efthymios Kyrodimos, Athanassios Kotsinas, Aristeidis Chrysovergis and Georgios X. Papacharalampous
Int. J. Mol. Sci. 2024, 25(16), 9000; https://doi.org/10.3390/ijms25169000 - 19 Aug 2024
Viewed by 484
Abstract
Telomeres play a crucial role in maintaining chromosomal integrity and regulating the number of cell divisions and have been associated with cellular aging. Telomere length (TL) has been widely studied in manifold cancer types; however, the results have been inconsistent. This systematic review [...] Read more.
Telomeres play a crucial role in maintaining chromosomal integrity and regulating the number of cell divisions and have been associated with cellular aging. Telomere length (TL) has been widely studied in manifold cancer types; however, the results have been inconsistent. This systematic review and meta-analysis aims to analyze the evidence on the association between TL and head and neck cancer (HNC) risk. We comprehensively searched the literature in PubMed, Cochrane Library, and Scopus and identified nine eligible studies, which yielded 11 datasets. The odds ratios (ORs) and 95% confidence intervals (CIs) were used to ascertain the strength of the association. On the basis of the median TL, we defined two groups, short TL and long TL, with the latter being the reference group. Our analysis found a significant relationship between short TL and increased HNC risk (OR 1.38, 95% CI: 1.10–1.73, p = 0.005), while significant heterogeneity among the studies was noted. The subgroup analysis on HNC subtypes revealed a significant association between short TL and oral cancers (OR 2.08, 95% CI: 1.23–3.53, p = 0.007). Additionally, subgroup analysis indicates that adjustments for age, sex, and smoking did not affect the significance of our findings. In conclusion, our meta-analysis found evidence for an association between short TL and HNC risk, which could indicate that TL might act as a potential biomarker for HNC risk, but high-quality prospective studies are imperative to validate our findings. Full article
(This article belongs to the Section Molecular Oncology)
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<p>PRISMA flow diagram of the meta-analysis.</p>
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<p>Forest plot representing the meta-analysis of the relationship between short TL and the risk for HNC using a random effects model [<a href="#B37-ijms-25-09000" class="html-bibr">37</a>,<a href="#B38-ijms-25-09000" class="html-bibr">38</a>,<a href="#B39-ijms-25-09000" class="html-bibr">39</a>,<a href="#B40-ijms-25-09000" class="html-bibr">40</a>,<a href="#B41-ijms-25-09000" class="html-bibr">41</a>,<a href="#B42-ijms-25-09000" class="html-bibr">42</a>,<a href="#B43-ijms-25-09000" class="html-bibr">43</a>,<a href="#B44-ijms-25-09000" class="html-bibr">44</a>,<a href="#B45-ijms-25-09000" class="html-bibr">45</a>]. Abbreviations: HNC = head and neck cancer; SE = standard error; CI = confidence interval; TL = telomere length. The black rhombus represents the combined OR, its width representing the 95% CI. The horizontal lines with the red square represent the 95% CI.</p>
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<p>Forest plot representing the subgroup analysis of the relationship between short TL and the risk for HNC, categorized by HNC subtype [<a href="#B37-ijms-25-09000" class="html-bibr">37</a>,<a href="#B38-ijms-25-09000" class="html-bibr">38</a>,<a href="#B39-ijms-25-09000" class="html-bibr">39</a>,<a href="#B40-ijms-25-09000" class="html-bibr">40</a>,<a href="#B41-ijms-25-09000" class="html-bibr">41</a>,<a href="#B42-ijms-25-09000" class="html-bibr">42</a>,<a href="#B43-ijms-25-09000" class="html-bibr">43</a>,<a href="#B44-ijms-25-09000" class="html-bibr">44</a>,<a href="#B45-ijms-25-09000" class="html-bibr">45</a>]. Abbreviations: HNC = head and neck cancer; SE = standard error; CI = confidence interval; TL = telomere length; OCC = oral cavity cancer. The black rhombus represents the combined OR, its width representing the 95% CI. The horizontal lines with the red square represent the 95% CI.</p>
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<p>Forest plot representing the subgroup analysis of the relationship between short TL and the risk for HNC, categorized by source of DNA sample [<a href="#B37-ijms-25-09000" class="html-bibr">37</a>,<a href="#B38-ijms-25-09000" class="html-bibr">38</a>,<a href="#B39-ijms-25-09000" class="html-bibr">39</a>,<a href="#B40-ijms-25-09000" class="html-bibr">40</a>,<a href="#B41-ijms-25-09000" class="html-bibr">41</a>,<a href="#B42-ijms-25-09000" class="html-bibr">42</a>,<a href="#B43-ijms-25-09000" class="html-bibr">43</a>,<a href="#B44-ijms-25-09000" class="html-bibr">44</a>,<a href="#B45-ijms-25-09000" class="html-bibr">45</a>]. Abbreviations: HNC = head and neck cancer; SE = standard error; CI = confidence interval; TL = telomere length; PBL = peripheral blood leukocytes. The black rhombus represents the combined OR, its width representing the 95% CI. The horizontal lines with the red square represent the 95% CI.</p>
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<p>Forest plot representing the subgroup analysis of the relationship between short TL and the risk for HNC, categorized by study region [<a href="#B37-ijms-25-09000" class="html-bibr">37</a>,<a href="#B38-ijms-25-09000" class="html-bibr">38</a>,<a href="#B39-ijms-25-09000" class="html-bibr">39</a>,<a href="#B40-ijms-25-09000" class="html-bibr">40</a>,<a href="#B41-ijms-25-09000" class="html-bibr">41</a>,<a href="#B42-ijms-25-09000" class="html-bibr">42</a>,<a href="#B43-ijms-25-09000" class="html-bibr">43</a>,<a href="#B44-ijms-25-09000" class="html-bibr">44</a>,<a href="#B45-ijms-25-09000" class="html-bibr">45</a>]. Abbreviations: HNC = head and neck cancer; SE = standard error; CI = confidence interval; TL = telomere length. The black rhombus represents the combined OR, its width representing the 95% CI. The horizontal lines with the red square represent the 95% CI.</p>
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<p>Forest plots representing the subgroup analysis of the relationship between short TL and the risk for HNC, categorized by factors the ORs have been adjusted for [<a href="#B37-ijms-25-09000" class="html-bibr">37</a>,<a href="#B38-ijms-25-09000" class="html-bibr">38</a>,<a href="#B39-ijms-25-09000" class="html-bibr">39</a>,<a href="#B40-ijms-25-09000" class="html-bibr">40</a>,<a href="#B41-ijms-25-09000" class="html-bibr">41</a>,<a href="#B42-ijms-25-09000" class="html-bibr">42</a>,<a href="#B43-ijms-25-09000" class="html-bibr">43</a>,<a href="#B44-ijms-25-09000" class="html-bibr">44</a>,<a href="#B45-ijms-25-09000" class="html-bibr">45</a>]. Abbreviations: HNC = head and neck cancer; SE = standard error; CI = confidence interval; TL = telomere length. The black rhombus represents the combined OR, its width representing the 95% CI. The horizontal lines with the red square represent the 95% CI.</p>
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<p>Forest plot representing the subgroup analysis of the relationship between short TL and the risk for HNC, categorized by sample size [<a href="#B37-ijms-25-09000" class="html-bibr">37</a>,<a href="#B38-ijms-25-09000" class="html-bibr">38</a>,<a href="#B39-ijms-25-09000" class="html-bibr">39</a>,<a href="#B40-ijms-25-09000" class="html-bibr">40</a>,<a href="#B41-ijms-25-09000" class="html-bibr">41</a>,<a href="#B42-ijms-25-09000" class="html-bibr">42</a>,<a href="#B43-ijms-25-09000" class="html-bibr">43</a>,<a href="#B44-ijms-25-09000" class="html-bibr">44</a>,<a href="#B45-ijms-25-09000" class="html-bibr">45</a>]. Abbreviations: HNC = head and neck cancer; SE = standard error; CI = confidence interval; TL = telomere length. The black rhombus represents the combined OR, its width representing the 95% CI. The horizontal lines with the red square represent the 95% CI.</p>
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<p>Funnel Plot of the studies included in the meta-analysis [<a href="#B37-ijms-25-09000" class="html-bibr">37</a>,<a href="#B38-ijms-25-09000" class="html-bibr">38</a>,<a href="#B39-ijms-25-09000" class="html-bibr">39</a>,<a href="#B40-ijms-25-09000" class="html-bibr">40</a>,<a href="#B41-ijms-25-09000" class="html-bibr">41</a>,<a href="#B42-ijms-25-09000" class="html-bibr">42</a>,<a href="#B43-ijms-25-09000" class="html-bibr">43</a>,<a href="#B44-ijms-25-09000" class="html-bibr">44</a>,<a href="#B45-ijms-25-09000" class="html-bibr">45</a>].</p>
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14 pages, 727 KiB  
Review
Factors Affecting the Effectiveness of DIBH (Deep Inspiratory Breath Hold) in Patients with Left Breast Cancer: A Narrative Review
by Edyta Hanczyk, Dawid Piecuch, Szymon Kopcial and Joanna Jonska-Gmyrek
Appl. Sci. 2024, 14(16), 7287; https://doi.org/10.3390/app14167287 - 19 Aug 2024
Viewed by 807
Abstract
Deep Inspiratory Breath Hold (DIBH) has become a valuable technique in left-breast cancer radiotherapy, offering the possibility to reduce radiation exposure to organs at risks (OARs) and minimize the risk of cardiac complications. This treatment method involves stopping the breathing of patients during [...] Read more.
Deep Inspiratory Breath Hold (DIBH) has become a valuable technique in left-breast cancer radiotherapy, offering the possibility to reduce radiation exposure to organs at risks (OARs) and minimize the risk of cardiac complications. This treatment method involves stopping the breathing of patients during irradiation in order to temporarily distance the heart from the radiation field, which reduces potential cardiac risks and other complications. To identify factors that may affect the effectiveness of DIBH treatment, we analyzed the most important 5-year studies published in the PubMed database. Research shows that DIBH reduces the radiation dose to the heart and lungs. However, the effectiveness of DIBH is determined by a variety of factors, including the patient’s training, cooperation, anatomical features, age, and choice of radiotherapy technique. Additionally, cardiovascular risk factors, such as diabetes, smoking, and hypertension, can be impactful to the effectiveness and potential complications of DIBH. Moreover, if a patient has a substantial level of depression or anxiety, then they may be potentially disqualified from the DIBH treatment method. In addition to this, a lack of consent and/or fear may also disqualify a patient from DIBH treatment. Careful patient selection, comprehensive training, and optimization of treatment parameters are essential to maximize the benefits of DIBH whilst minimizing any potential side effects. DIBH enhancement techniques, such as IMRT and VMAT, also have an important role to play. The purpose of this narrative review article is to summarize the factors affecting the efficacy and side effects of DIBH in radiation therapy for left-breast cancer, with the aim of optimizing its clinical application while minimizing side effects. Patients who are likely to benefit most from DIBH are young women in good medical condition, able to cooperate with the procedure, and with smaller breasts. The increase in the estimated 10-year patient survival is significantly influenced by cardiovascular problems, so patients without diabetes and metabolic syndrome, and non-smokers, will benefit the most. An estimated 50–70% of breast cancer patients are likely to benefit from DIBH, and in the best case, it can result in a 50% reduction in the risk of cardiac problems after photodynamic therapy (PDT). Full article
(This article belongs to the Special Issue Novel Approaches in Radio- and Chemotherapy and Clinical Applications)
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<p>PRISMA flowchart study selection process. Identification of studies via databases and registers.</p>
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17 pages, 3057 KiB  
Article
Green Tea Polyphenol Nanoparticles Reduce Anxiety Caused by Tobacco Smoking Withdrawal in Rats by Suppressing Neuroinflammation
by Alaa M. Hammad, Lujain F. Alzaghari, Malek Alfaraj, Vanessa Lux and Suhair Sunoqrot
Toxics 2024, 12(8), 598; https://doi.org/10.3390/toxics12080598 - 18 Aug 2024
Viewed by 494
Abstract
Repeated exposure to tobacco smoke causes neuroinflammation and neuroplasticity, which correlates with smoking withdrawal-induced anxiety. The purpose of this study was to investigate the anticipated involvement of antioxidant-rich nanoparticles (NPs) prepared by oxidation-triggered polymerization of green tea catechins in impacting these effects in [...] Read more.
Repeated exposure to tobacco smoke causes neuroinflammation and neuroplasticity, which correlates with smoking withdrawal-induced anxiety. The purpose of this study was to investigate the anticipated involvement of antioxidant-rich nanoparticles (NPs) prepared by oxidation-triggered polymerization of green tea catechins in impacting these effects in a rat model of tobacco smoke exposure. Exposure to tobacco smoke was carried out for 2 h a day, 5 days a week, for a total of 36 days. Weekly behavioral tests were conducted prior to recommencing the exposure. Following a 20-day exposure period, rats were administered either distilled water or green tea (GT) NPs (20 mg/kg, orally) for an additional 16 days. Our findings revealed that tobacco smoke exposure induced anxiety-like behavior indicative of withdrawal, and this effect was alleviated by GT NPs. Tobacco smoke exposure caused a marked increase in the relative mRNA and protein expression of nuclear factor-kappa B (NF-κB) and reduced the relative mRNA and protein expression of brain-derived neurotrophic factor (BDNF) in the hippocampus (HIP) and hypothalamus (HYP) brain subregions. The intervention of GT NPs effectively inhibited these effects. Our findings demonstrate the potent protective role of GT NPs in reducing withdrawal-induced anxiety-like behavior, neuroinflammation, and neuroplasticity triggered by tobacco smoke exposure. Full article
(This article belongs to the Special Issue Toxicity of Central Nervous System (CNS) Modulators)
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<p>Timeline of the experiment involving tobacco smoke exposure, GT NP intervention, and behavioral testing.</p>
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<p>Characterization of GT NPs by (<b>A</b>) SEM, (<b>B</b>) FT-IR, (<b>C</b>) UV-vis, and (<b>D</b>) DPPH radical scavenging activity.</p>
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<p>Results of the behavioral tests carried out on the animals before (baseline) and throughout tobacco smoke exposure. (<b>A</b>) Distance travelled in the open field (OF); (<b>B</b>) time spent in the center of the OF; (<b>C</b>) time spent in the illuminated compartment of the light and dark box (LDB); (<b>D</b>) time spent in the open arms of the elevated plus maze (EPM); (<b>E</b>) number of crossings of the EPM. Data presented as means ± SEM (<span class="html-italic">n</span> = 6); * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001 based on two-way ANOVA followed by Bonferroni’s multiple-comparison test. Results show increased anxiety following 4 weeks of cigarette smoke exposure, and 16 days’ treatment with GT NPs attenuated this effect.</p>
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<p>Relative mRNA expression of (<b>A</b>) <span class="html-italic">nf-κb</span> and (<b>B</b>) <span class="html-italic">bdnf</span> in the hippocampus (HIP) brain region after tobacco smoke exposure and intervention with GT NPs. Data expressed as means ± SEM (n = 5); ** <span class="html-italic">p</span> &lt; 0.01 and *** <span class="html-italic">p</span> &lt; 0.001 based on one-way ANOVA followed by Tukey’s multiple-comparison test. Results show a significant increase in the gene expression of <span class="html-italic">nf-κb</span> and a significant decrease in <span class="html-italic">bdnf</span> gene expression following cigarette smoke exposure, which was attenuated by 16 days of treatment with GT NPs.</p>
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<p>Expression levels of relative mRNA of (<b>A</b>) <span class="html-italic">nf-κb</span> and (<b>B</b>) <span class="html-italic">bdnf</span> in the hypothalamus (HYP) brain region after tobacco smoke exposure and intervention with GT NPs. Data expressed as means ± SEM (n = 5); * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 based on one-way ANOVA followed by Tukey’s multiple-comparison test. Results show a significant increase in the gene expression of <span class="html-italic">nf-κb</span> and a significant decrease in <span class="html-italic">bdnf</span> gene expression following cigarette smoke exposure, which was attenuated by 16 days of treatment with GT NPs.</p>
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<p>Protein expression of (<b>A</b>) NF-κB and (<b>B</b>) BDNF in the hippocampus (HIP) brain region after tobacco smoke exposure and intervention with GT NPs. Data expressed as means ± SEM (n = 5); * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 based on one-way ANOVA followed by Tukey’s multiple-comparison test. Results show a significant increase in the protein expression of NF-κB and a significant decrease in BDNF protein expression following cigarette smoke exposure, which was attenuated by 16 days of treatment with GT NPs.</p>
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<p>Protein expression of (<b>A</b>) NF-κB and (<b>B</b>) BDNF in the hypothalamus (HYP) brain region after cigarette smoke exposure and treatment with GT NPs. Data expressed as means ± SEM (n = 5); * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001 based on one-way ANOVA followed by Tukey’s multiple-comparison test. Results show a significant increase in the protein expression of NF-κB and a significant decrease in BDNF protein expression following cigarette smoke exposure, which was attenuated by 16 days of treatment with GT NPs.</p>
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16 pages, 1041 KiB  
Article
Correlation of the Walk Score and Environmental Perceptions with Perceived Neighborhood Walkability: The Quantile Regression Model Approach
by Suin Jin and Eun Jung Kim
Sustainability 2024, 16(16), 7074; https://doi.org/10.3390/su16167074 - 18 Aug 2024
Viewed by 641
Abstract
The walk score, which is widely used as an index of walkability, does not include pedestrian’s perception, so there is a limit to explaining the level of perceived walkability in a neighborhood. The purpose of this study is to examine how an objectively [...] Read more.
The walk score, which is widely used as an index of walkability, does not include pedestrian’s perception, so there is a limit to explaining the level of perceived walkability in a neighborhood. The purpose of this study is to examine how an objectively measured walk score and subjectively measured environmental perceptions correlate with perceived neighborhood walkability. This study conducted a survey on 371 participants aged 18 or older living in Daegu, South Korea to examine perceived neighborhood walkability and perception of the built environment. We measured the walk score based on participants’ location using a geographic information system. We used the quantile regression model, whereby we investigated the effects of explanatory variables (e.g., the walk score, perceptions of the built environment) by classifying perceived neighborhood walkability by quantile into Q10, Q25, Q50, Q75, and Q90. The walk score had a positive association with people with low perceived neighborhood walkability (Q10), but a negative association with people with high perceived neighborhood walkability (Q90). Regarding views of the built environment, in most quantiles, people perceived the environment as walkable if there were abundant green spaces and diverse alternative routes. Conversely, odors, smoke, hills, and stairs impeded walkability. This indicates that along with an objective walkability index, perceptions of the built environment play an important role in determining perceived neighborhood walkability. This implies that our results can help identify appropriate policies to promote walkability for citizens. Full article
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<p>Study areas and the respondents’ home locations.</p>
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<p>Quantile regression estimates for the walk score and perceptions of the built environment. Note: Vertical axes show coefficient estimates of independent variables in the quantile regression model. Horizontal axes depict quantiles of the dependent variable. The dashed black line denotes the quantile regression coefficient estimation. The grey area shows the 95% confidence intervals of the coefficients. The solid red horizontal line indicates the OLS coefficient, and the red dashed line embodies the 95% confidence interval of the OLS estimation.</p>
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17 pages, 1014 KiB  
Article
Metabolic Changes Following Smoking Cessation in Patients with Type 2 Diabetes Mellitus
by Stamatina Driva, Aliki Korkontzelou, Serena Tonstad, Nikolaos Tentolouris, Eleni Litsiou, Vasiliki Vasileiou, Alice G. Vassiliou, Vassiliki Saltagianni and Paraskevi Katsaounou
Biomedicines 2024, 12(8), 1882; https://doi.org/10.3390/biomedicines12081882 - 17 Aug 2024
Viewed by 806
Abstract
Background: Smoking cessation is crucial for reducing complications of type 2 diabetes mellitus (T2DM), but associated weight gain can worsen glycemic control, discouraging quitting attempts. Varenicline, a partial agonist of α4β2 nicotinic receptors, aids smoking cessation. This study examines the effects of varenicline [...] Read more.
Background: Smoking cessation is crucial for reducing complications of type 2 diabetes mellitus (T2DM), but associated weight gain can worsen glycemic control, discouraging quitting attempts. Varenicline, a partial agonist of α4β2 nicotinic receptors, aids smoking cessation. This study examines the effects of varenicline on body weight and metabolic parameters in patients with T2DM and prediabetes. Methods: Fifty-three patients were enrolled, of which 32 successfully quit smoking after a three-month course of varenicline and were examined after an additional month with no medication. Measurements taken at baseline, 2.5 months, and 4 months included body weight, blood pressure, resting metabolic rate (RMR), glycated hemoglobin (HbA1c), fasting glucose, blood lipids, C-reactive protein (CRP), appetite-related hormones, and physical activity. Results: Post-treatment, there were no significant changes in body weight, blood pressure, RMR, or glycemic control. Total (CHOL) and low-density lipoprotein (LDL-C) cholesterol decreased significantly at 4 months of the study (from 168 to 156 mg/dL, p = 0.013, and from 96 to 83 mg/dL, p = 0.013, respectively). Leptin levels increased (from 11 to 13.8 ng/dL, p = 0.004), as did glucagon-like peptide-1 (GLP-1) levels (from 39.6 to 45.8 pM, p = 0.016) at 4 months of follow-up. The percentage of participants who reported moderate-intensity activity increased from 28% to 56%, while those reporting high-intensity activity increased from 19% to 22%, respectively (p = 0.039). Conclusions: Our study showed that smoking cessation with varenicline in smokers with T2DM and prediabetes led to significant improvements in lipid profile, significant increase in plasma leptin and GLP-1 levels, and increased physical activity, without significant weight gain. Thus, smoking cessation without weight gain or deteriorated glycemic control is feasible for these smokers, with added benefits to lipid profiles, GLP-1 regulation, and physical activity. Full article
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<p>Study Flow Chart.</p>
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<p>The box plots show the median and quartiles, and the whisker caps of the box plots show the mean 5th and 95th percentile values. A Wilcoxon matched-pair signed-rank test yielded the results. (<b>a</b>) The plasma levels of leptin at baseline (0), 2.5 months, and 4 months were 11 (5.4; 33.7), 17 (7.6; 39.4), and 13.8 (7.5; 44) ng/L, respectively. (<b>b</b>) Fasting GLP-1 levels at baseline (0), 2.5 months, and 4 months were 39.6 (27.7; 58.5), 41.8 (29.3; 70.7), and 45.8 (31.1; 69.1) pM, respectively. * <span class="html-italic">p</span> values with Wilcoxon matched-pair signed-rank test. Data are presented as median (interquartile range).</p>
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<p>International Physical Activity Questionnaire (IPAQ 2002) and comparative data for self-reported level of physical activity intensity (low, moderate, high). * <span class="html-italic">p</span> values with McNemar–Bowker test (crosstabulation).</p>
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11 pages, 1506 KiB  
Article
Color Modifications of a Maxillofacial Silicone Elastomer under the Effect of Cigarette Smoke
by Anca Irina Gradinariu, Alexandru-Constantin Stoica, Alexandra Bargan, Carmen Racles, Carmen Gabriela Stelea and Victor Vlad Costan
Materials 2024, 17(16), 4089; https://doi.org/10.3390/ma17164089 - 17 Aug 2024
Viewed by 398
Abstract
Although it is known (from the observations of medical professionals) that cigarette smoke negatively affects maxillofacial prostheses, especially through staining/discoloration, systematic research in this regard is limited. Herein, the color modifications of M511 maxillofacial silicone, unpigmented and pigmented with red or skin tone [...] Read more.
Although it is known (from the observations of medical professionals) that cigarette smoke negatively affects maxillofacial prostheses, especially through staining/discoloration, systematic research in this regard is limited. Herein, the color modifications of M511 maxillofacial silicone, unpigmented and pigmented with red or skin tone pigments, covered with mattifiers, or with makeup and mattifiers, and directly exposed to cigarette smoke, were investigated by spectrophotometric measurements in the CIELab and RGB color systems. The changes in color parameters are comparatively discussed, showing that the base silicone material without pigmentation and coating undergoes the most significant modifications. Visible and clinically unacceptable changes occurred after direct exposure to only 20 cigarettes. By coating and application of makeup, the material is more resistant to color changes, which suggests that surface treatments provide increased protection to adsorption of the smoke components. The dynamic water vapor sorption (DVS) measurements indicate a decrease of the sorption capacity in pigmented versus unpigmented elastomers, in line with the changes in color parameters. Full article
(This article belongs to the Special Issue Advances in Biomaterials: Synthesis, Characteristics and Applications)
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<p>Dynamic vapor sorption isotherms of uncoated samples (“s” stands for sorption and “d” for desorption branch).</p>
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<p>The modification of different color parameters of unpigmented and red samples after being exposed to the smoke of 20 cigarettes.</p>
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<p>The Pearson correlation matrix for: (<b>A</b>) the CIELab system and (<b>B</b>) RGB system.</p>
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<p>Schematic representation of the addition cross-linking, the presumptive structures of the main silicone components in the maxillofacial elastomer, and sample coding.</p>
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14 pages, 2440 KiB  
Article
Ilex paraguariensis Extracts: A Source of Bioelements and Biologically Active Compounds for Food Supplements
by Elżbieta Rząsa-Duran, Bożena Muszyńska, Agnieszka Szewczyk, Katarzyna Kała, Katarzyna Sułkowska-Ziaja, Joanna Piotrowska, Włodzimierz Opoka and Agata Kryczyk-Poprawa
Appl. Sci. 2024, 14(16), 7238; https://doi.org/10.3390/app14167238 - 17 Aug 2024
Viewed by 462
Abstract
Ilex paraguariensis, commonly known as yerba mate, is a plant belonging to the holly genus Ilex and the Aquifoliaceae family, indigenous to South America, and is used for the production of yerba mate. Yerba mate is renowned for its abundance of essential [...] Read more.
Ilex paraguariensis, commonly known as yerba mate, is a plant belonging to the holly genus Ilex and the Aquifoliaceae family, indigenous to South America, and is used for the production of yerba mate. Yerba mate is renowned for its abundance of essential nutrients and bioactive compounds. Based on test results, it can be assumed that the selection of raw material for the preparation of extracts as well as the extraction method significantly influence the final content of biologically active compounds in the extracts. Consequently, this variability impacts the ultimate concentration of biologically active substances within the end product, potentially influencing human consumption. The present study aimed to quantify and compare the content of selected biological active compounds in supplements and products containing I. paraguariensis extracts, along with organic yerba mate dried through a smoke-free process, available in the European market (P-1–P-10). The evaluation focused on antioxidant substances such as neochlorogenic acid, chlorogenic acid, cryptochlorogenic acid, 4-feruloylquinic acid, isochlorogenic acid, rutoside astragalin, and caffeine. Additionally, the concentration of specific macro and trace elements was ascertained. The antioxidant compound makeup differs between methanol-extracted samples and aqueous extracts. In both cases, methanol extracts, particularly those in instant and traditional herb forms, showed the highest content of organic compounds with antioxidant properties (such as phenolic compounds and caffeine). The highest content of chlorogenic acid was detected in both methanol (14.7412 mg/g d.w.) and water (8.3120 mg/g d.w.) extracts in product P-4. The caffeic acid content ranged from 0.1491 mg/g d.w. to 1.7938 mg/g d.w. in methanol extracts and from 0.0760 mg/g d.w. to 0.4892 mg/g d.w. in water extracts. The neochlorogenic acid content ranged from 2.6869 to 23.9750 mg/g d.w. in ethanol extracts and from 0.4529 to 10.2299 mg/g d.w. in water extracts. Therefore, the traditional preparation of yerba mate as a water infusion does not fully exploit the raw material’s potential. Among the tested products, only the dietary supplement in capsule form contained protocatechuic acid, which was not present in any other tested products. Conversely, compounds characteristic of yerba mate found in other preparations were absent in this supplement. The caffeine content was also the lowest in this product. The determined content of active substances did not consistently match the declarations made by producers if stated on the packaging. Full article
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<p>A representative chromatogram of the analyzed <span class="html-italic">Ilex paraguariensis</span> product samples: (<b>A</b>)—the methanol extract from sample P-1 (1—protocatechuic acid, 2—caffeine); (<b>B</b>)—the methanol extract from sample P-9 (2—caffeine, 3—neochlorogenic acid, 4—chlorogenic acid, 5—caffeic acid, 6—4-feruloylquinic acid, 7—isochlorogenic acid, 8—rutoside, 9—astragalin).</p>
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<p>Structural formulas of bioactive substances analyzed in <span class="html-italic">Ilex paraguariensis</span> product samples: 1—protocatechuic acid, 2—neochlorogenic acid, 3—chlorogenic acid, 4—caffeic acid, 5—4-feruloylquinic acid, 6—isochlorogenic acid.</p>
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<p>Concentration of caffeine, rutoside and astragalin in P-1–P-10 products [mg/g].</p>
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31 pages, 1404 KiB  
Review
The Current Molecular and Cellular Landscape of Chronic Obstructive Pulmonary Disease (COPD): A Review of Therapies and Efforts towards Personalized Treatment
by Luke A. Farrell, Matthew B. O’Rourke, Matthew P. Padula, Fernando Souza-Fonseca-Guimaraes, Gaetano Caramori, Peter A. B. Wark, Shymali C. Dharmage and Phillip M. Hansbro
Proteomes 2024, 12(3), 23; https://doi.org/10.3390/proteomes12030023 - 16 Aug 2024
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Abstract
Chronic obstructive pulmonary disease (COPD) ranks as the third leading cause of global illness and mortality. It is commonly triggered by exposure to respiratory irritants like cigarette smoke or biofuel pollutants. This multifaceted condition manifests through an array of symptoms and lung irregularities, [...] Read more.
Chronic obstructive pulmonary disease (COPD) ranks as the third leading cause of global illness and mortality. It is commonly triggered by exposure to respiratory irritants like cigarette smoke or biofuel pollutants. This multifaceted condition manifests through an array of symptoms and lung irregularities, characterized by chronic inflammation and reduced lung function. Present therapies primarily rely on maintenance medications to alleviate symptoms, but fall short in impeding disease advancement. COPD’s diverse nature, influenced by various phenotypes, complicates diagnosis, necessitating precise molecular characterization. Omics-driven methodologies, including biomarker identification and therapeutic target exploration, offer a promising avenue for addressing COPD’s complexity. This analysis underscores the critical necessity of improving molecular profiling to deepen our comprehension of COPD and identify potential therapeutic targets. Moreover, it advocates for tailoring treatment strategies to individual phenotypes. Through comprehensive exploration-based molecular characterization and the adoption of personalized methodologies, innovative treatments may emerge that are capable of altering the trajectory of COPD, instilling optimism for efficacious disease-modifying interventions. Full article
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<p>A visual representation of the research performed at Centre for Inflammation, Centenary Institute and University of Technology Sydney, Faculty of Science and the relevance to COPD. This was created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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<p>A visual representation of the cascading impact carbonyl groups exerts over lung immune function, driving inflammation and forming a disease state. This was created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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<p>Macrophage differentiation pathways from M0 to M1 and M2 subtypes, with associated triggers for the phenotype shift. This was created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>.</p>
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17 pages, 1339 KiB  
Review
Exploring the Multifactorial Landscape of Penile Cancer: A Comprehensive Analysis of Risk Factors
by Ugo Amicuzi, Marco Grillo, Marco Stizzo, Michelangelo Olivetta, Simone Tammaro, Luigi Napolitano, Pasquale Reccia, Luigi De Luca, Andrea Rubinacci, Giampiero Della Rosa, Arturo Lecce, Paola Coppola, Salvatore Papi, Francesco Trama, Lorenzo Romano, Carmine Sciorio, Lorenzo Spirito, Felice Crocetto, Celeste Manfredi, Francesco Del Giudice, Matteo Ferro, Bernardo Rocco, Octavian Sabin Tataru, Raffaele Balsamo, Giuseppe Lucarelli, Dario Del Biondo and Biagio Baroneadd Show full author list remove Hide full author list
Diagnostics 2024, 14(16), 1790; https://doi.org/10.3390/diagnostics14161790 - 16 Aug 2024
Viewed by 618
Abstract
Penile cancer, while rare, is a critical public health issue due to its profound impact on patients and the complexities of its management. The disease’s multifactorial etiology includes risk factors such as HPV infection, poor hygiene, smoking, genetic predispositions, and socioeconomic determinants. This [...] Read more.
Penile cancer, while rare, is a critical public health issue due to its profound impact on patients and the complexities of its management. The disease’s multifactorial etiology includes risk factors such as HPV infection, poor hygiene, smoking, genetic predispositions, and socioeconomic determinants. This article provides a comprehensive review and analysis of these diverse risk factors, aiming to enhance understanding of the disease’s underlying causes. By elucidating these factors, the article seeks to inform and improve prevention strategies, early detection methods, and therapeutic interventions. A nuanced grasp of the multifactorial nature of penile cancer can enable healthcare professionals to develop more effective approaches to reducing incidence rates and improving patient outcomes. Full article
(This article belongs to the Special Issue Diagnosis and Management of Andrological Diseases)
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<p>Overview of the clinical classification of the TNM for penile cancer.</p>
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<p>Overview of the clinical classification of the TNM for penile cancer (section).</p>
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<p>Natural history of penile cancer.</p>
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14 pages, 1466 KiB  
Article
The Role of Triglyceride/HDL Ratio, Triglyceride–Glucose Index, and Pan-Immune-Inflammation Value in the Differential Diagnosis of Acute Coronary Syndrome and Predicting Mortality
by Murat Bilgin, Emre Akkaya and Recep Dokuyucu
J. Clin. Med. 2024, 13(16), 4832; https://doi.org/10.3390/jcm13164832 - 16 Aug 2024
Viewed by 473
Abstract
Objectives: We aimed to evaluate the predictive importance of various clinical and laboratory parameters in the differential diagnosis of Acute Coronary Syndrome (ACS). Understanding these predictors is critical for improving diagnostic accuracy, guiding therapeutic decisions, and ultimately enhancing patient outcomes. Methods: The study [...] Read more.
Objectives: We aimed to evaluate the predictive importance of various clinical and laboratory parameters in the differential diagnosis of Acute Coronary Syndrome (ACS). Understanding these predictors is critical for improving diagnostic accuracy, guiding therapeutic decisions, and ultimately enhancing patient outcomes. Methods: The study included a total of 427 patients diagnosed with ACS, comprising 142 with unstable angina, 142 with non-ST elevation myocardial infarction (NSTEMI), and 143 with ST elevation myocardial infarction (STEMI). The data were collected from medical records of patients treated at a tertiary care hospital between January 2020 and December 2024. In addition to other biochemical parameters, triglyceride/HDL ratio (THR), triglyceride–glucose index (TGI), and Pan-Immune-Inflammation Value (PIV) were calculated and compared. Results: THR, TGI, PIV, and mortality rate were statistically higher in the STEMI group (p = 0.034, p = 0.031, p = 0.022, p = 0.045, respectively). The risk factors were found to be significantly associated with STEMI in the multiple logistic regression analysis and included age, total cholesterol, triglycerides, diabetes mellitus, smoking, cTnI, LVEF, THR, TGI, and PIV. High THR increases the risk of STEMI (AUC = 0.67, 95% CI: 0.62–0.72, p = 0.020). High THR increases the risk of mortality in ACS patients (AUC = 0.70, 95% CI: 0.65–0.75, p = 0.004). THRs above 3.5 are associated with higher risk. Sensitivity is 75% and specificity is 60%. High TGI increases the risk of mortality in ACS patients (AUC = 0.73, 95% CI: 0.68–0.78, p = 0.007). TGIs above 8.5 are associated with higher risk. Sensitivity is 78% and specificity is 63%. High PIVs increase the risk of mortality in ACS patients (AUC = 0.75, 95% CI: 0.70–0.80, p = 0.009). PIVs above 370 are associated with higher risk. Sensitivity is 80% and specificity is 65%. The combination of TGI, THR, PIV, and cTnI has the highest predictive capability over individual parameters for STEMI and mortality. Conclusions: We found that age, total cholesterol, triglycerides, cTnI, THR, TGI, and PIV increase, low LVEF, presence of diabetes mellitus, and smoking have predictive values for STEMI and mortality in patients with ACS. Unlike the studies in the literature, this is the first study in which cTnI, THR, TGI, and PIV values were evaluated together in ACS and mortality prediction. Full article
(This article belongs to the Special Issue Acute Coronary Syndromes: Focus on Precision Medicine)
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<p>Study design of patients with ACS.</p>
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<p>ROC analysis results in patients with STEMI.</p>
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<p>ROC analysis results in patients with mortality in patients with ACS.</p>
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