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22 pages, 8285 KiB  
Article
Hole-Free Symmetric Complementary Sparse Array Design for High-Precision DOA Estimation
by He Ma, Libao Liu, Zhihong Gan, Yang Gao and Xingpeng Mao
Remote Sens. 2024, 16(24), 4711; https://doi.org/10.3390/rs16244711 - 17 Dec 2024
Abstract
Direction of arrival (DOA) estimation plays a critical role in remote sensing, where it aids in identifying and tracking multiple targets across complex environments, from atmospheric monitoring to resource mapping. Leveraging difference covariance array (DCA) for DOA estimation has become prevalent, particularly with [...] Read more.
Direction of arrival (DOA) estimation plays a critical role in remote sensing, where it aids in identifying and tracking multiple targets across complex environments, from atmospheric monitoring to resource mapping. Leveraging difference covariance array (DCA) for DOA estimation has become prevalent, particularly with sparse arrays capable of resolving more targets than the number of sensors. This paper proposes a new hole-free sparse array configuration for remote sensing applications to achieve improved DOA estimation performance using DCA. By symmetrically placing a minimum redundancy array (MRA) and its complementary MRA on both sides of a sparse uniform linear array (ULA), this configuration maximizes degrees of freedom (DOFs) and minimizes mutual coupling effects. Expressions for calculating sensor positions and optimal element allocation methods to maximize DOFs are derived. Simulation experiments in various scenarios have shown the advantages of the proposed array in DOA estimation, including a strong ability to estimate multi-targets, high angular resolution, low estimation error, and strong robustness to mutual coupling. Full article
Show Figures

Figure 1

Figure 1
<p>Schematic diagram of the SC_MRA.</p>
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<p>An illustration of a 14-sensor NA configuration containing a 7-sensor dense ULA and a 7-sensor sparse ULA.</p>
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<p>An illustration of a 14-sensor NSC_MRA configuration containing a 4-sensor MRA, a 3-sensor CMRA, and a sparse 7-sensor ULA.</p>
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<p>An illustration of a 20-sensor NSC_MRA configuration with optimal DOFs under the constraint of the total number of array sensors.</p>
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<p>The array configurations and non-negative covariance array weight functions of 15-sensor arrays. (<b>a</b>) NA; (<b>b</b>) ENA; (<b>c</b>) OSENA; (<b>d</b>) ANA; (<b>e</b>) INA; and (<b>f</b>) the proposed method (NSC_MRA).</p>
Full article ">Figure 6
<p>The magnitudes of the mutual coupling matrices of 15-sensor arrays and their respective coupling leakage. (<b>a</b>) NA; (<b>b</b>) ENA; (<b>c</b>) OSENA; (<b>d</b>) ANA; (<b>e</b>) INA; and (<b>f</b>) the proposed method (NSC_MRA).</p>
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<p>Spectrum of SS-MUSIC for 15-sensor arrays without mutual coupling when <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>51</mn> </mrow> </semantics></math> sources are located at <math display="inline"><semantics> <mrow> <mfenced open="[" close="]" separators="|"> <mrow> <mo>−</mo> <mn>60</mn> <mo>°</mo> <mo>:</mo> <mn>2.4</mn> <mo>°</mo> <mo>:</mo> <mn>60</mn> <mo>°</mo> </mrow> </mfenced> </mrow> </semantics></math>, SNR <math display="inline"><semantics> <mrow> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> dB, and <math display="inline"><semantics> <mrow> <mi>L</mi> <mo>=</mo> <mn>1000</mn> </mrow> </semantics></math>. (<b>a</b>) NA; (<b>b</b>) ENA; (<b>c</b>) OSENA; (<b>d</b>) ANA; (<b>e</b>) INA; and (<b>f</b>) the proposed method (NSC_MRA).</p>
Full article ">Figure 8
<p>Spectrum of SS-MUSIC for 15-sensor arrays without mutual coupling when <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math> targets are located at <math display="inline"><semantics> <mrow> <mo>[</mo> <mo>−</mo> <mn>0.3</mn> <mo>°</mo> <mo>,</mo> <mn>0.3</mn> <mo>°</mo> <mo>]</mo> </mrow> </semantics></math>, SNR = 0 dB, and <math display="inline"><semantics> <mrow> <mi>L</mi> <mo>=</mo> <mn>1000</mn> </mrow> </semantics></math>.</p>
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<p>Probability of correct detections vs. SNR for different array configurations without mutual coupling when <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math> targets are located at <math display="inline"><semantics> <mrow> <mo>[</mo> <mo>−</mo> <mn>0.3</mn> <mo>°</mo> <mo>,</mo> <mn>0.3</mn> <mo>°</mo> <mo>]</mo> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>15</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>L</mi> <mo>=</mo> <mn>1000</mn> </mrow> </semantics></math>. The acceptable angle error is set to 0.05°.</p>
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<p>RMSE (in degrees) curves vs. SNR for different arrays in <a href="#remotesensing-16-04711-t002" class="html-table">Table 2</a> without mutual coupling when <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>21</mn> </mrow> </semantics></math> targets are located at <math display="inline"><semantics> <mrow> <mfenced open="[" close="]" separators="|"> <mrow> <mo>−</mo> <mn>60</mn> <mo>°</mo> <mo>:</mo> <mn>6</mn> <mo>°</mo> <mo>:</mo> <mn>60</mn> <mo>°</mo> </mrow> </mfenced> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>15</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>L</mi> <mo>=</mo> <mn>1000</mn> </mrow> </semantics></math>.</p>
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<p>RMSE (in degrees) curves vs. the number of snapshots for different arrays in <a href="#remotesensing-16-04711-t002" class="html-table">Table 2</a> without mutual coupling when <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>21</mn> </mrow> </semantics></math> targets are located at <math display="inline"><semantics> <mrow> <mfenced open="[" close="]" separators="|"> <mrow> <mo>−</mo> <mn>60</mn> <mo>°</mo> <mo>:</mo> <mn>6</mn> <mo>°</mo> <mo>:</mo> <mn>60</mn> <mo>°</mo> </mrow> </mfenced> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>15</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi mathvariant="normal">S</mi> <mi mathvariant="normal">N</mi> <mi mathvariant="normal">R</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> dB.</p>
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<p>RMSE (in degrees) curves vs. SNR for different array configurations without mutual coupling when <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>21</mn> </mrow> </semantics></math> targets are located at <math display="inline"><semantics> <mrow> <mo>[</mo> <mo>−</mo> <mn>60</mn> <mo>°</mo> <mo>:</mo> <mn>6</mn> <mo>°</mo> <mo>:</mo> <mn>60</mn> <mo>°</mo> <mo>]</mo> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>15</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>L</mi> <mo>=</mo> <mn>1000</mn> </mrow> </semantics></math>.</p>
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<p>Probability of correct detections vs. SNR for different array configurations without mutual coupling when <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>21</mn> </mrow> </semantics></math> targets are located at <math display="inline"><semantics> <mrow> <mo>[</mo> <mo>−</mo> <mn>60</mn> <mo>°</mo> <mo>:</mo> <mn>6</mn> <mo>°</mo> <mo>:</mo> <mn>60</mn> <mo>°</mo> <mo>]</mo> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>15</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>L</mi> <mo>=</mo> <mn>1000</mn> </mrow> </semantics></math>. The acceptable angle error is set to 0.15°.</p>
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<p>RMSE (in degrees) curves vs. the number of snapshots for different array configurations without mutual coupling when K = 21 targets are located at <math display="inline"><semantics> <mrow> <mo>[</mo> <mo>−</mo> <mn>60</mn> <mo>°</mo> <mo>:</mo> <mn>6</mn> <mo>°</mo> <mo>:</mo> <mn>60</mn> <mo>°</mo> <mo>]</mo> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>15</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi mathvariant="normal">S</mi> <mi mathvariant="normal">N</mi> <mi mathvariant="normal">R</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> dB, and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>c</mi> </mrow> <mrow> <mi>l</mi> </mrow> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>.</p>
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<p>RMSE (in degrees) curves vs. <math display="inline"><semantics> <mrow> <mi>N</mi> </mrow> </semantics></math> for different array configurations without mutual coupling when <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>21</mn> </mrow> </semantics></math> targets are located at <math display="inline"><semantics> <mrow> <mo>[</mo> <mo>−</mo> <mn>60</mn> <mo>°</mo> <mo>:</mo> <mn>6</mn> <mo>°</mo> <mo>:</mo> <mn>60</mn> <mo>°</mo> <mo>]</mo> </mrow> </semantics></math>, SNR <math display="inline"><semantics> <mrow> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> dB, and <math display="inline"><semantics> <mrow> <mi>L</mi> <mo>=</mo> <mn>1000</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 16
<p>RMSE (in degrees) curves vs. SNR for different array configurations in the presence of mutual coupling. <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>21</mn> </mrow> </semantics></math> targets are located at <math display="inline"><semantics> <mrow> <mo>[</mo> <mo>−</mo> <mn>60</mn> <mo>°</mo> <mo>:</mo> <mn>6</mn> <mo>°</mo> <mo>:</mo> <mn>60</mn> <mo>°</mo> <mo>]</mo> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>15</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>L</mi> <mo>=</mo> <mn>1000</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msup> <mrow> <msub> <mi>c</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.2</mn> <mi>e</mi> </mrow> <mrow> <mi>j</mi> <mi>π</mi> <mo>/</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>c</mi> </mrow> <mrow> <mi>l</mi> </mrow> </msub> <mo>=</mo> <msub> <mrow> <mi>c</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mo>/</mo> <mi>l</mi> </mrow> </semantics></math>.</p>
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<p>RMSE (in degrees) curves vs. the number of snapshots for different array configurations in the presence of mutual coupling. <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>21</mn> </mrow> </semantics></math> targets are located at <math display="inline"><semantics> <mrow> <mo>[</mo> <mo>−</mo> <mn>60</mn> <mo>°</mo> <mo>:</mo> <mn>6</mn> <mo>°</mo> <mo>:</mo> <mn>60</mn> <mo>°</mo> <mo>]</mo> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>15</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi mathvariant="normal">S</mi> <mi mathvariant="normal">N</mi> <mi mathvariant="normal">R</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> dB, <math display="inline"><semantics> <mrow> <msup> <mrow> <msub> <mi>c</mi> <mn>1</mn> </msub> <mo>=</mo> <mn>0.2</mn> <mi>e</mi> </mrow> <mrow> <mi>j</mi> <mi>π</mi> <mo>/</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>c</mi> </mrow> <mrow> <mi>l</mi> </mrow> </msub> <mo>=</mo> <msub> <mrow> <mi>c</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mo>/</mo> <mi>l</mi> </mrow> </semantics></math>.</p>
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<p>RMSE (in degrees) curves vs. <math display="inline"><semantics> <mrow> <mi>a</mi> </mrow> </semantics></math> for different array configurations when <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>21</mn> </mrow> </semantics></math> targets are located at <math display="inline"><semantics> <mrow> <mo>[</mo> <mo>−</mo> <mn>60</mn> <mo>°</mo> <mo>:</mo> <mn>6</mn> <mo>°</mo> <mo>:</mo> <mn>60</mn> <mo>°</mo> <mo>]</mo> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>15</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>L</mi> <mo>=</mo> <mn>1000</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msup> <mrow> <msub> <mi>c</mi> <mn>1</mn> </msub> <mo>=</mo> <mi>a</mi> <mo>×</mo> <mn>0.2</mn> <mi>e</mi> </mrow> <mrow> <mi>j</mi> <mi>π</mi> <mo>/</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>c</mi> </mrow> <mrow> <mi>l</mi> </mrow> </msub> <mo>=</mo> <msub> <mrow> <mi>c</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mo>/</mo> <mi>l</mi> </mrow> </semantics></math>.</p>
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13 pages, 1003 KiB  
Article
Predictive Molecular Biomarkers of Bladder Cancer Identified by Next-Generation Sequencing—Preliminary Data
by Aleksander Myszka, Marek Ciesla, Aleksandra Siekierzynska, Anna Sendera, Constantina Constantinou, Pawel Karpinski, Grzegorz Wysiadecki and Krzysztof Balawender
J. Clin. Med. 2024, 13(24), 7701; https://doi.org/10.3390/jcm13247701 - 17 Dec 2024
Abstract
Background: The majority of patients with bladder cancer suffer from tumour recurrence. Identifying prognostic factors for tumour recurrence is crucial for treatment and follow-up in affected patients. The study aimed to assess the impact of somatic mutations in bladder cancer on patient outcomes [...] Read more.
Background: The majority of patients with bladder cancer suffer from tumour recurrence. Identifying prognostic factors for tumour recurrence is crucial for treatment and follow-up in affected patients. The study aimed to assess the impact of somatic mutations in bladder cancer on patient outcomes and tumour recurrence. Methods: The study group comprised 46 patients with urothelial bladder cancers referred for transurethral resection of the tumour. A molecular study on tumour-derived DNA was performed using next-generation sequencing. Somatic mutations were screened in 50 genes involved in carcinogenesis. Results: We identified 81 variants in 23 genes, including 54 pathogenic mutations, 18 likely pathogenic variants, and 9 variants of unknown significance. The most frequently mutated genes were FGFR3, PIK3CA, and TP53 in 52%, 35%, and 24% of tumours, respectively. The average tumour-free survival was significantly longer in cases with mutations in the PIK3CA gene (p = 0.02), and mutations in the PIK3CA gene were associated with a decreased risk of tumour recurrence (Hazard Ratio = 0.26; 95% CI: 0.11–0.62; p = 0.018). Conclusions: The PIK3CA gene was shown to be a predictive marker of a low risk of bladder tumour recurrence. Molecular screening of bladder cancers supported predictive biomarkers of tumour recurrence and showed that tumour-free survival is molecularly determined. Full article
(This article belongs to the Section Oncology)
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Figure 1

Figure 1
<p>The prevalence of variants identified in bladder tumours of studied patients.</p>
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<p>Comparison of cancer-free survival between patients with somatic mutations (+) and patients without somatic mutations (−) in the <span class="html-italic">PIK3CA</span> gene: (<b>a</b>) recurrence-free survival in patients with mutations compared to patients without mutation; (<b>b</b>) impact of mutations in the <span class="html-italic">PIK3CA</span> gene on cancer-free survival probability of bladder cancer patients.</p>
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<p>Hierarchical Cluster Analysis covering age of onset, cancer-free survival and a number of pathogenic mutations. Clusters differ significantly with respect to survival (<span class="html-italic">p</span> &lt; 0.001). Blue and green lines indicate extracted clusters. The red dotted line indicates the cutoff level set at 50% of the length of the longest bond in the dendrogram and determines the number of clusters.</p>
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18 pages, 5181 KiB  
Article
Knoevenagel Condensation Catalyzed by Biogenic Carbonates for the Solvent-Free Synthesis of 3-(Furan-2-yl)acrylonitrile Derivatives
by Eliana Yasmín Mesa Castro, Andrés Felipe Monroy Ramírez, José Jobanny Martínez, Juan-Carlos Castillo and Gerardo Andrés Caicedo Pineda
Catalysts 2024, 14(12), 927; https://doi.org/10.3390/catal14120927 - 16 Dec 2024
Viewed by 215
Abstract
Calcium and barium carbonates were synthesized via biologically induced mineralization using Bacillus subtilis. The biogenic materials were characterized by using infrared and Raman spectroscopy, scanning electron microscopy, energy-dispersive X-ray spectroscopy, and powder X-ray diffraction. These biogenic carbonates were then tested as basic [...] Read more.
Calcium and barium carbonates were synthesized via biologically induced mineralization using Bacillus subtilis. The biogenic materials were characterized by using infrared and Raman spectroscopy, scanning electron microscopy, energy-dispersive X-ray spectroscopy, and powder X-ray diffraction. These biogenic carbonates were then tested as basic heterogenous catalysts for the solvent-free Knoevenagel reaction between 5-HMF derivatives and active methylene compounds, producing 3-(furan-2-yl)acrylonitrile derivatives in 71–87% yields. Optimal catalytic performance was achieved with a 50:50 Ca:Ba ratio, attributed to the synergistic interaction between baritocalcite and vaterite, which enhances the availability of active basic sites and surface interactions. This method offers operational simplicity, reduced reaction times, good yields, excellent (E)-selectivity, and minimal catalyst loading. Full article
(This article belongs to the Special Issue Advances in Catalytic Conversion of Biomass)
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Figure 1

Figure 1
<p>Kinetic behavior of <span class="html-italic">Bacillus subtilis</span> during a 48 h process: (<b>a</b>) pH variation, (<b>b</b>) optical density, (<b>c</b>) °Brix, and (<b>d</b>) conductivity.</p>
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<p>Infrared spectra of precipitates from assays with different Ca:Ba ratios.</p>
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<p>Raman spectra of precipitates obtained from assays with varying Ca:Ba ratios.</p>
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<p>SEM images of precipitates with corresponding diameters at various Ca:Ba ratios: (<b>a</b>) 100:0, (<b>b</b>) 75:25, (<b>c</b>) 50:50, (<b>d</b>) 25:75, and (<b>e</b>) 0:100. The numerical values (µm) represent the diameters of the carbonate agglomerates observed in the precipitates from each assay.</p>
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<p>SEM/EDX microchemical analysis of precipitates from experiments with varying Ca:Ba ratios: (<b>a</b>) 100:0, (<b>b</b>) 75:25, (<b>c</b>) 50:50, (<b>d</b>) 25:75, and (<b>e</b>) 0:100.</p>
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<p>SEM/EDX mapping of precipitates from experiments with different Ca:Ba ratios: (<b>a</b>) 75:25, (<b>b</b>) 50:50, (<b>c</b>) 25:75, and (<b>d</b>) 0:100.</p>
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<p>Diffractograms of precipitates obtained from experiments with different Ca:Ba ratios.</p>
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<p>Mineralogical composition of precipitates with various Ca:Ba ratios.</p>
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<p>Solvent-free synthesis of Knoevenagel adducts <b>3a</b>–<b>e</b> catalyzed by a mixture of Ca:Ba carbonates in a 50:50 ratio.</p>
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<p>The proposed mechanism for the synthesis of 3-(furan-2-yl)acrylonitrile derivatives <b>3</b> catalyzed by a mixture of Ca:Ba carbonates in a 50:50 ratio.</p>
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27 pages, 9031 KiB  
Article
Novel Quinoline- and Naphthalene-Incorporated Hydrazineylidene–Propenamide Analogues as Antidiabetic Agents: Design, Synthesis, and Computational Studies
by Osama Alharbi, Wael H. Alsaedi, Mosa Alsehli, Saif H. Althagafi, Hussam Y. Alharbi, Yazeed M. Asiri, Ramith Ramu and Mohammed Al-Ghorbani
Pharmaceuticals 2024, 17(12), 1692; https://doi.org/10.3390/ph17121692 - 15 Dec 2024
Viewed by 445
Abstract
Background: Type 2 diabetes has become a significant global health challenge. Numerous drugs have been developed to treat the condition, either as standalone therapies or in combination when glycemic control cannot be achieved with a single medication. As existing treatments often come with [...] Read more.
Background: Type 2 diabetes has become a significant global health challenge. Numerous drugs have been developed to treat the condition, either as standalone therapies or in combination when glycemic control cannot be achieved with a single medication. As existing treatments often come with limitations, there is an increasing focus on creating novel therapeutic agents that offer greater efficacy and fewer side effects to better address this widespread issue. Methods: The methylene derivatives 3a,b were coupled with phenyl/ethyl isothiocyanate in the basic medium, and dimethyl sulfate was subsequently added. Further, 5ad were reacted with the quinoline/naphthalene hydrazides 6a,b. The target compounds 7ag were subjected to the in vitro enzyme inhibition studies on α-glucosidase, α-amylase, and aldose reductase. Results: 7g exerted remarkable inhibitory effects on α-glycosidase [Inhibitory Concentration (IC50): 20.23 ± 1.10 µg/mL] and α-amylase (17.15 ± 0.30 µg/mL), outperforming acarbose (28.12 ± 0.20 µg/mL for α-glycosidase and 25.42 ± 0.10 µg/mL for α-amylase), and exhibited a strong inhibition action on aldose reductase (12.15 ± 0.24 µg/mL), surpassing quercetin (15.45 ± 0.32 µg/mL) and the other tested compounds. In a computational study, 7g demonstrated promising binding affinities (−8.80, −8.91 kcal/mol) with α-glycosidase and α-amylase, compared to acarbose (−10.87, −10.38 kcal/mol) for α-glycosidase and α-amylase. Additionally, 7g had strong binding with aldose reductase (−9.20 kcal/mol) in comparison to quercetin (−9.95 kcal/mol). Molecular dynamics (MDs) simulations demonstrated that 7g remained stable over a 100 ns simulation period, and the binding free energy estimates remained consistent throughout this time. Conclusions: We reported the modification of quinoline and naphthalene rings to hydrazineylidene–propenamides 7ag using various synthetic approaches. 7g emerged as a leading candidate, exhibiting greater inhibition of α-glycosidase, α-amylase, and aldose reductase. These findings underscore their potential as essential molecules for the development of innovative antidiabetic treatments. Full article
(This article belongs to the Section Medicinal Chemistry)
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Figure 1

Figure 1
<p>(<b>a</b>) Function of α-amylase and α-glucosidase. (<b>b</b>) Function of aldose reductase.</p>
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<p>Fundamental pharmacophoric features of the amide linker in antidiabetic drugs.</p>
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<p>Design of the target compound <b>7g</b>.</p>
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<p>Compound <b>7g</b> interacting with active site amino acids of α-glucosidase are represented in two dimensions (<b>A</b>), and acarbose interacting with the active site amino acids of α-glucosidase are represented in two dimensions (<b>B</b>).</p>
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<p>Compound <b>7g</b> interacting with active site amino acids of α-amylase are represented in two dimensions (<b>A</b>), and acarbose interacting with the active site amino acids of α-amylase are represented in two dimensions (<b>B</b>).</p>
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<p>Compound <b>7g</b> interacting with active site amino acids of aldose reductase are represented in two dimensions (<b>A</b>), and acarbose interacting with the active site amino acids of aldose reductase are represented in two dimensions (<b>B</b>).</p>
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<p>(<b>a</b>) The validation of molecular docking by the superimposition of the original and re-docked structures of the co-crystal ligand Zenerastat. Green: original compound from PDB. Red: re-docked compound. (<b>b</b>) The validation of molecular docking by the superimposition of the original and re-docked structures of the co-crystal ligand GLC 601. Pink: original compound from PDB. Brown: re-docked compound.</p>
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<p>Trajectories depicting the different properties—RMSD (Å), the radius of gyration, intra hydrogen bonds, molecular surface area, solvent accessible surface area, and the polar surface area of compound <b>7g</b> when complexed with α-glucosidase.</p>
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<p>MD trajectories of α-glucosidase complexed with compound <b>7g</b> and acarbose. (<b>A</b>) RMSD of α-glucosidase. (<b>B</b>) RMSF of α-glucosidase. (<b>C</b>) RMSD of α-glucosidase–compound <b>7g</b>. (<b>D</b>) RMSF of α-glucosidase. (<b>E</b>) RMSF of compound <b>7g</b>. (<b>F</b>) RMSD of α-glucosidase–acarbose. (<b>G</b>) RMSF of α-glucosidase. (<b>H</b>) RMSF of acarbose. The pink color in Figures (<b>C</b>,<b>F</b>) indicates the RMSD of compound <b>7g</b> and acarbose, and the blue color indicates the protein (α-glucosidase) RMSD. The blue color in Figures (<b>B</b>,<b>D</b>,<b>G</b>) indicates the RMSF of the protein i.e., in this case, it is α-glucosidase. The pink color in Figures (<b>E</b>,<b>H</b>) indicates the RMSF of the compound <b>7g</b> and acarbose respectively. The green color in Figures (<b>D</b>,<b>G</b>) indicates Proteins (amino acid residues) that interact with the ligand, which are marked with green-colored vertical bars.</p>
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<p>Trajectories depicting the different properties—RMSD (Å), the radius of gyration, intra hydrogen bonds, molecular surface area, solvent accessible surface area, and the polar surface area of compound <b>7g</b> when complexed with α-amylase.</p>
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<p>MD trajectories of α-amylase complexed with compound <b>7g</b> and acarbose. (<b>A</b>) RMSD of α-amylase. (<b>B</b>) RMSF of α-amylase. (<b>C</b>) RMSD of α-amylase–compound <b>7g</b>. (<b>D</b>) RMSF of α-amylase (<b>E</b>) RMSF of compound <b>7g</b>; (<b>F</b>) RMSD of α-amylase–acarbose (<b>G</b>) RMSF of α-amylase (<b>H</b>) RMSF of acarbose. The pink color in Figures (<b>C</b>,<b>F</b>) indicates the RMSD of compound <b>7g</b> and acarbose, and the blue color indicates the protein (α-glucosidase) RMSD. The blue color in Figures (<b>B</b>,<b>D</b>,<b>G</b>) indicates the RMSF of the protein i.e., in this case, it is α-glucosidase. The pink color in Figures (<b>E</b>,<b>H</b>) indicates the RMSF of the compound <b>7g</b> and acarbose respectively. The green color in Figures (<b>D</b>,<b>G</b>) indicates Proteins (amino acid residues) that interact with the ligand, which are marked with green-colored vertical bars.</p>
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<p>Trajectories depicting the different properties—RMSD (Å), the radius of gyration, intra hydrogen bonds, molecular surface area, solvent accessible surface area, and the polar surface area of compound <b>7g</b> when complexed with aldose reductase.</p>
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<p>MD trajectories of aldose reductase complexed with compound <b>7g</b> and quercetin. (<b>A</b>) RMSD of aldose reductase. (<b>B</b>) RMSF of aldose reductase. (<b>C</b>) RMSD of aldose reductase–compound <b>7g</b>. (<b>D</b>) RMSF of aldose reductase. (<b>E</b>) RMSF of compound <b>7g</b>. (<b>F</b>) aldose reductase–quercetin RMSD. (<b>G</b>) RMSF of aldose reductase. (<b>H</b>) RMSF of quercetin. The pink color in Figures (<b>C</b>,<b>F</b>) indicates the RMSD of compound <b>7g</b>, and the blue color indicates the protein RMSD. The blue color in Figures (<b>B</b>,<b>D</b>,<b>G</b>) indicates the RMSF of the protein i.e., in this case, it is aldose reductase. The pink color in Figures (<b>E</b>,<b>H</b>) indicates the RMSF of the compound <b>7g</b> and quercetin respectively. The green color in Figure (<b>D</b>) indicates Proteins (amino acid residues) that interact with the ligand, which are marked with green-colored vertical bars.</p>
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<p>ADMET radar analysis of compound <b>7d</b>.</p>
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<p>The initial examination of the SAR of the derivatives <b>5a</b>–<b>g</b>.</p>
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<p>Synthesis of quinoline and naphthalene derivatives <b>7a</b>–<b>g</b>.</p>
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16 pages, 9681 KiB  
Article
Transient Slope: A Metric for Assessing Heterogeneity from the Dielectrophoresis Spectrum
by Emmanuel Egun, Tia Wilson, Zuri A. Rashad, Rominna Valentine and Tayloria N. G. Adams
Biophysica 2024, 4(4), 695-710; https://doi.org/10.3390/biophysica4040045 - 14 Dec 2024
Viewed by 323
Abstract
Cellular heterogeneity, an inherent feature of biological systems, plays a critical role in processes such as development, immune response, and disease progression. Human mesenchymal stem cells (hMSCs) exemplify this heterogeneity due to their multi-lineage differentiation potential. However, their inherent variability complicates clinical use, [...] Read more.
Cellular heterogeneity, an inherent feature of biological systems, plays a critical role in processes such as development, immune response, and disease progression. Human mesenchymal stem cells (hMSCs) exemplify this heterogeneity due to their multi-lineage differentiation potential. However, their inherent variability complicates clinical use, and there is no universally accepted method for detecting and quantifying cell population heterogeneity. Dielectrophoresis (DEP) has emerged as a powerful electrokinetic technique for characterizing and manipulating cells based on their dielectric properties, offering label-free analysis capabilities. Quantitative information from the DEP spectrum, such as transient slope, measure cells’ transition between negative and positive DEP behaviors. In this study, we employed DEP to estimate transient slope of various cell populations, including relatively homogeneous HEK-293 cells, heterogeneous hMSCs, and cancer cells (PC3 and DU145). Our analysis encompassed hMSCs derived from bone marrow, adipose, and umbilical cord tissue, to capture tissue-specific heterogeneity. Transient slope was assessed using two methods, involving linear trendline fitting to different low-frequency regions of the DEP spectrum. We found that transient slope serves as a reliable indicator of cell population heterogeneity, with more heterogeneous populations exhibiting lower transient slopes and higher standard deviations. Validation using cell morphology, size, and stemness further supported the utility of transient slope as a heterogeneity metric. This label-free approach holds promise for advancing cell sorting, biomanufacturing, and personalized medicine. Full article
(This article belongs to the Collection Feature Papers in Biophysics)
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<p>Schematic overview of transient slope analysis. (<b>a</b>) Cell polarization in dielectrophoresis (DEP) characterization of cells. The cells will either attract to areas of high electric field strength (positive DEP) or be repelled to areas of low electric field strength (negative DEP). (<b>b</b>) DEP spectrum generated from cell analysis. Two transient slope methods (<b>c</b>) sampling fewer data points and (<b>d</b>) sampling more data points. The blue circles and schematic outlined with blue dashed box represent hypothetical homogeneous cell population. The red circles and the schematic outlined with red dashed box represent hypothetical heterogeneous cell population. (<b>e</b>) Dot plot of transient slope of hypothetical homogeneous and heterogeneous cell populations. Homogeneous cell populations expected to have higher transient slope and heterogeneous cell populations expected to have lower transient slope. Note: this figure is a conceptual schematic and does not represent actual experimental data. Graphics in this figure were created with Biorender.com.</p>
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<p>Comparison of two transient slope methods. (<b>a</b>,<b>e</b>) Method 1 transient slope trendline fitted to DEP spectrum defined by wide frequency range, 10 kHz–20 MHz. (<b>b</b>,<b>f</b>) Method 2 transient slope trendline fitted to DEP spectrum defined by narrow frequency range, 2 kHz–255 kHz. (<b>c</b>,<b>g</b>) Transient slopes and (<b>d</b>,<b>h</b>) trendline R<sup>2</sup> values for method 1 and method 2. (<b>a</b>–<b>d</b>) Depicts the collected data from a homogenous cell sample. (<b>e</b>–<b>h</b>) Depicts the data collected from a heterogeneous cell sample. The average R<sup>2</sup> value for method 1 and method 2 for the homogeneous cell sample is 0.941 and 0.903, respectively. The average R<sup>2</sup> value for method 1 and method 2 for the heterogeneous cell sample is 0.904 and 0.906, respectively. Error bars in (<b>c</b>,<b>g</b>) are standard deviation. For statistical significance: ** <span class="html-italic">p</span> &lt; 0.01 and **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Transient slope of differentiated human mesenchymal stem cells (hMSCs). (<b>a</b>) Violin plot representation of transient slope. (<b>b</b>) Standard deviation of transient slopes. For statistical significance: **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Transient slopes of HEK-293, PC3, DU145, and hMSCs. (<b>a</b>) Violin plot representation of transient slope. (<b>b</b>) Standard deviation of transient slopes. For statistical significance: **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Cell area assessment of (<b>a</b>) HEK-293, (<b>b</b>) DU145, (<b>c</b>) PC3, and (<b>d</b>) hMSCs. (<b>e</b>) Violin plot of the cell area. (<b>f</b>) Standard deviation of the cell area. The scale bar is 400 µm. For statistical significance: ** <span class="html-italic">p</span> &lt; 0.01 and **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Transient slope of PC3, DU145, and AT-hMSCs, BM-hMSCs, and UC-hMSCs tested in a 300 µS/cm DEP buffer solution. (<b>a</b>) Violin plot representation of transient slope. (<b>b</b>) Standard deviation of transient slopes. For statistical significance: * <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.</p>
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<p>Histogram analysis of the transient slope of (<b>a</b>) HEK-293 cells, (<b>b</b>) PC3 cells, (<b>c</b>), DU145 cells, (<b>d</b>) AT-hMSCs, (<b>e</b>) BM-hMSCs, and (<b>f</b>) UC-hMSCs. A Gaussian distribution is plotted with the bins of transient slope values.</p>
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<p>Stemness assessment of PC3 cells, DU145 cells, AT-hMSCs, BM-hMSCs, and UC-hMSCs. Immunofluorescent staining of SOX2 (top row) and NANOG (bottom row) with quantification. Images were processed for improved resolution, brightness, and contrast. The scale bar is 800 µm. For statistical significance: * <span class="html-italic">p</span> &lt; 0.05.</p>
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18 pages, 8857 KiB  
Article
De Novo Regeneration of Cannabis sativa cv. Cheungsam and Evaluation of Secondary Metabolites of Its Callus
by S. M. Ahsan, Da Bin Kwon, Md. Injamum-Ul-Hoque, Md. Mezanur Rahman, Inhwa Yeam and Hyong Woo Choi
Horticulturae 2024, 10(12), 1331; https://doi.org/10.3390/horticulturae10121331 - 12 Dec 2024
Viewed by 381
Abstract
Cannabis sativa L. cv. ‘Cheungsam’ is an industrial hemp plant of Republic of Korea origin, primarily cultivated for fiber and seed production. In vitro seed germination and tissue culture are valuable tools for developing various biotechnological techniques. In the present study, we aimed [...] Read more.
Cannabis sativa L. cv. ‘Cheungsam’ is an industrial hemp plant of Republic of Korea origin, primarily cultivated for fiber and seed production. In vitro seed germination and tissue culture are valuable tools for developing various biotechnological techniques. In the present study, we aimed to develop a tissue culture process for hemp plants using Cheungsam as a model plant and examine the secondary metabolites produced from its callus. We also developed a method to prepare pathogen-free seedlings from field-derived seeds using hydrogen peroxide (H2O2) solution as a liquid germination medium. Treating seedlings with removed seed coat in 3% H2O2 significantly reduced the contamination rate. Callus formation and de novo organogenesis of shoots and roots from callus were successfully achieved using cotyledon and leaf tissues prepared from the pathogen-free seedlings. The most effective in vitro regeneration results were obtained using the Murashige and Skoog (MS) medium supplemented with certain targeted growth regulators. An optimal combination of 0.5 mg/L thidiazuron (TDZ) and 1.0 mg/L 1-naphthalene acetic acid proved highly effective for callus induction. The addition of 0.5 mg/L TDZ in the MS medium significantly stimulated shoot proliferation, while robust root development was best supported by MS medium supplemented with 2.5 mg/L indole-3-butyric acid for both cotyledon and leaf explants. Finally, gas chromatography–mass spectrometry (GC–MS) analysis of ethanol extract from Cheungsam leaf callus revealed the presence of different secondary metabolites, including 9-octadecenamide, methyl salicylate, dodecane, tetradecane, and phenol, 2,4-bis-(1,1-dimethylethyl). This study provides a comprehensive de novo regeneration protocol for Cheungsam plants and insight into the secondary metabolite profiles of its callus. Full article
(This article belongs to the Special Issue Innovative Micropropagation of Horticultural and Medicinal Plants)
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<p>Pathogen-free in vitro seedling preparation from Cheungsam seeds. (<b>A</b>) Enhanced germination rates of Cheungsam seeds germinated in different volumes of 1% H<sub>2</sub>O<sub>2</sub>. (<b>B</b>) Reduced contamination rates of seedlings germinated in different volumes of 1% H<sub>2</sub>O<sub>2</sub>. Contamination rates were assessed 2 weeks after transferring the sterilized seedlings to MS medium. (<b>C</b>) Reduced contamination rates of seedlings additionally sterilized with different volumes of 1% H<sub>2</sub>O<sub>2</sub> after removing the seed coat. (<b>D</b>) Reduced contamination rates of seedlings with removed seed coat, additionally sterilized with 3% H<sub>2</sub>O<sub>2</sub> for different durations (1, 2, 3, and 4 h). (<b>E</b>) Representative images of seedlings with removed seed coat sterilized with 3% H<sub>2</sub>O<sub>2</sub> for different durations (1, 2, 3, and 4 h). Pictures were taken 2 weeks after transferring the sterilized seedlings to MS medium. CON: water control (Scale bar in all panels = 2 cm). Different letters above the bars indicate a statistically significant difference at <span class="html-italic">p</span> &lt; 0.05 according to the Tukey HSD test.</p>
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<p>Callus formation and de novo organogenesis of the excised leaf of Cheungsam plants. (<b>A</b>) Pathogen-free seedlings germinated in H<sub>2</sub>O<sub>2</sub> solution as a liquid germination medium and grown on MS medium. Red circle: Leaf tissue used for callus formation. Scale bar = 0.2 cm. (<b>B</b>–<b>D</b>) Callus induced from leaf explants. Scale bar = 0.2 cm. Initial swelling and formation of callus from leaf explants (4-week-old, (<b>B</b>)). Green nodular photosynthetic and friable callus (red arrows, 5-week-old, (<b>C</b>)). The initial stage of shoot regeneration from photosynthetic callus (red arrow, 6-week-old). (<b>D</b>). Scale bar = 0.2 cm. (<b>E</b>,<b>F</b>) De Novo shoot morphogenesis from leaf-induced callus on shoot induction medium (SIM) containing 0.5 mg/L TDZ. Scale bar = 0.2 cm. (<b>G</b>) De Novo root morphogenesis from leaf-induced callus on root induction medium (RIM) containing 2.5 mg/L IBA. Scale bar = 1 cm. Regenerated roots are indicated by the red arrow.</p>
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<p>Callus formation and de novo organogenesis of excised cotyledon tissues from Cheungsam plants. (<b>A</b>) Pathogen-free seedlings germinated in H<sub>2</sub>O<sub>2</sub> solution as a liquid germination medium and grown on MS medium. Red circle: Cotyledon tissue used for callus formation. Scale bar = 0.2 cm. (<b>B</b>) Callus induced from the cotyledon explants 7 days after incubation on CIM. Initial swelling and formation of callus from cotyledon explant. Green nodular photosynthetic and friable calli are indicated by red arrows. Scale bar = 0.2 cm. (<b>C</b>,<b>D</b>). De Novo shoot morphogenesis from cotyledon-induced callus on SIM containing 0.5 mg/L TDZ. Scale bar = 0.2 cm. (<b>E</b>) De Novo root morphogenesis from cotyledon-induced callus on RIM containing 2.5 mg/L IBA. Regenerated roots are indicated by the red arrow. Scale bar = 1 cm.</p>
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<p>GC–MS total ion chromatogram (TIC) obtained from the analyses of ethanol extract of Cheungsam callus. TIC was generated from the analysis of ethanol extract of one-month-old Cheungsam leaf callus.</p>
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14 pages, 3068 KiB  
Article
Catalytic Oxidation of Acetone over MnOx-SiO2 Catalysts: An Effective Approach to Valorize Rice Husk Waste
by Mauricio Cardoso, Patrice Portugau, Carolina De Los Santos, Ricardo Faccio, Hilario Vidal, José Manuel Gatica, María del Pilar Yesté, Jorge Castiglioni and Martin Torres
Materials 2024, 17(24), 6069; https://doi.org/10.3390/ma17246069 - 12 Dec 2024
Viewed by 309
Abstract
Rice husk, a byproduct of rice production, poses significant environmental challenges due to disposal issues, while the emission of volatile organic compounds into the atmosphere further exacerbates these concerns. This study addresses both problems by exploring the potential of texturally enhanced SiO2 [...] Read more.
Rice husk, a byproduct of rice production, poses significant environmental challenges due to disposal issues, while the emission of volatile organic compounds into the atmosphere further exacerbates these concerns. This study addresses both problems by exploring the potential of texturally enhanced SiO2, derived from Uruguayan rice husk, as a catalytic support for manganese oxides in the combustion of volatile organic compounds. SiO2 was synthesized from rice husk ash using a sustainable, acid-free pretreatment method, yielding a notably high silica purity of 96.5%—a level comparable to or exceeding previously reported values, highlighting the high silica quality inherent in Uruguayan rice husk. The catalytic activity was evaluated using acetone as a model volatile organic compound, achieving up to 90% conversion with 30 wt.% manganese oxide at 300 °C, with CO2 as the primary product. Furthermore, a 24 h stability test demonstrated consistent performance, maintaining a conversion rate of around 95.6 ± 2.5%. These findings suggest that high-purity SiO2 derived from Uruguayan rice husk, with its sustainability benefits, offers an effective solution for acetone removal when supporting an active phase such as manganese oxides, addressing both rice husk disposal and volatile organic compound emissions. Full article
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Graphical abstract

Graphical abstract
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<p>Thermogravimetric analysis results obtained for (<b>a</b>) RH (TGA showed in black and DrTGA in blue) and (<b>b</b>) RHS and manganese acetate, which is the precursor of bulk MnO<sub>x</sub>.</p>
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<p>X-ray powder diffraction patterns for bulk MnO<sub>x</sub>, Mn40RHS, Mn30RHS, Mn10RHS, and RHS. Peaks associated with Mn<sub>2</sub>O<sub>3</sub> (*) and peaks associated with Mn<sub>3</sub>O<sub>4</sub> (<sup>o</sup>).</p>
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<p>Nitrogen adsorption–desorption isotherms of the catalyst samples (<b>a</b>) and pore size distribution curves obtained through NLDFT (<b>b</b>).</p>
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<p>FT-IR spectra for the catalysts.</p>
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<p>SEM micrographs and EDS mapping obtained for (<b>a</b>,<b>b</b>) RHS; (<b>c</b>,<b>d</b>) Mn10RHS; (<b>e</b>,<b>f</b>) Mn30RHS; (<b>g</b>,<b>h</b>) Mn40RHS; (<b>i</b>,<b>j</b>) MnO<sub>x</sub>. All figures were taken using a magnification of ×800.</p>
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<p>H2-TPR for bulk MnOx, Mn40RHS, and RHS.</p>
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<p><span class="html-italic">Light-off</span> curves obtained for acetone oxidation.</p>
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<p>Stability test performed to Mn30RHS during 24 h for acetone.</p>
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<p>Synthesis process flow to obtain MnXRHS catalysts.</p>
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16 pages, 5505 KiB  
Article
Free Vibration Analysis of Hydraulic Quick Couplings Considering Fluid–Structure Interaction Characteristics
by Yuchao Liu, Fei Ma, Xiaoguang Geng, Songyuan Wang, Zhihong Zhou and Chun Jin
Actuators 2024, 13(12), 515; https://doi.org/10.3390/act13120515 - 11 Dec 2024
Viewed by 307
Abstract
As an important component of the automatic hydraulic quick coupling device (AHQCD) on rescue vehicles, the hydraulic quick couplings (HQCs) are used to rapidly dock hydraulic lines and transport fluid while changing and operating hydraulic working tools. However, during tool operation at rescue [...] Read more.
As an important component of the automatic hydraulic quick coupling device (AHQCD) on rescue vehicles, the hydraulic quick couplings (HQCs) are used to rapidly dock hydraulic lines and transport fluid while changing and operating hydraulic working tools. However, during tool operation at rescue sites, pressure pulsations at multiple frequencies in the hydraulic lines can coincide with the natural frequencies of the HQCs, potentially causing resonance that severely affects the stability of fluid conveying and damages the connection of hydraulic lines accidentally. To investigate the natural frequencies of the HQCs with upstream and downstream lines, the characteristics of fluid–structure interaction were considered between the poppets and the fluid in this study, and an equivalent stiffness model of the fluid domain was derived based on the fluid compressibility. A dynamic model, along with 6-DOF equations for the system, was established, and the natural frequencies and mode vectors were determined by free vibration analysis. In addition, the effects of working pressure, air content, and stiffness of the springs on the natural frequency of the HQC system were analyzed. The results show the natural frequency increases with a higher working pressure and lower air content, while the effect of spring stiffness on natural frequencies varies with different modes. Furthermore, the proposed model is validated by experimental pressure signals, showing good agreement, with an average error of 2.7% for the first-order natural frequency. This paper presents a theoretical method for improving the stability of fluid transport when operating various hydraulic tools under complex rescue conditions. Full article
(This article belongs to the Section Control Systems)
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<p>Schematic of the operational process of the AHQCD.</p>
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<p>The detailed schematic of internal structure of the HQCs. 1, 6—Female body; 2, 12—Mounting plate; 3—Female poppet; 4—Inner sleeve; 5—Central stem; 7, 17—Circlip; 8, 9, 10, 11—Sealing system; 13, 16—Male body; 14—Male poppet; 15—Guide; 18, 21—Connector adaptors; 19, 22—Hose assembly; 20—Fixed plate.</p>
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<p>The coupling between the fluid and the solid within the HQCs. The blue-shaded boxes indicate the fluid units.</p>
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<p>Dynamic model of the HQCs with connecting hoses.</p>
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<p>Schematic of equivalent stiffness for the fluid unit in contact with the solid unit.</p>
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<p>Variation curves of the first four natural frequencies at different working pressures.</p>
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<p>Changes in the first four natural frequencies with different air contents.</p>
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<p>Changes in the first four natural frequencies with different stiffnesses of female springs.</p>
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<p>Variation curves of the first four natural frequencies with different stiffnesses of male springs.</p>
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<p>Experimental test system: (<b>a</b>) schematic of the experimental system, (<b>b</b>) picture of the hydraulic test platform.</p>
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<p>The experimental time series of pressure <span class="html-italic">p</span><sub>3</sub> and amplitude–frequency characteristic curves.</p>
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<p>Changing curves of natural frequencies of the docking system at different pressures: (<b>a</b>) the first order <span class="html-italic">f</span><sub>n1</sub>, (<b>b</b>) the second order <span class="html-italic">f</span><sub>n2</sub>, (<b>c</b>) the third order <span class="html-italic">f</span><sub>n3</sub>.</p>
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<p>Images of the heavy-duty rescue vehicle and its testing operation.</p>
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21 pages, 4080 KiB  
Review
MicroRNA-Based Liquid Biopsy for Cervical Cancer Diagnostics and Treatment Monitoring
by Maria A. Kepsha, Angelika V. Timofeeva, Vasiliy S. Chernyshev, Denis N. Silachev, Elena A. Mezhevitinova and Gennadiy T. Sukhikh
Int. J. Mol. Sci. 2024, 25(24), 13271; https://doi.org/10.3390/ijms252413271 - 10 Dec 2024
Viewed by 747
Abstract
Despite prevention strategies, cervical cancer remains a significant public health issue. Human papillomavirus plays a critical role in its development, and early detection is vital to improve patient outcomes. The incidence of cervical cancer is projected to rise, necessitating better diagnostic tools. Traditional [...] Read more.
Despite prevention strategies, cervical cancer remains a significant public health issue. Human papillomavirus plays a critical role in its development, and early detection is vital to improve patient outcomes. The incidence of cervical cancer is projected to rise, necessitating better diagnostic tools. Traditional screening methods like the cytological examination and human papillomavirus testing have limitations in sensitivity and reproducibility. Liquid-based cytology offers some improvements, but the need for more reliable and sensitive techniques persists, particularly for detecting precancerous lesions. Liquid biopsy is a non-invasive method that analyzes cancer-derived products in biofluids like blood, offering potential for real-time monitoring of tumor progression, metastasis, and treatment response. It can be based on detection of circulating tumor cells (CTCs), circulating free DNA (cfDNA), and microRNAs (miRNAs). This review particularly underlines the potential of microRNAs, which are transported by extracellular vesicles. Overall, this article underscores the importance of continued research into non-invasive diagnostic methods like liquid biopsy to enhance cervical cancer screening and treatment monitoring. Full article
(This article belongs to the Section Molecular Pathology, Diagnostics, and Therapeutics)
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<p>Illustration summarizing the stages of CC progression with comparison to a normal cervix and highlighting biofluids that can potentially be used for liquid biopsy. HPV—human papillomavirus; LSIL—low-grade squamous intraepithelial lesions; HSIL—high-grade squamous intraepithelial lesions; CTCs—circulating tumor cells; EVs—extracellular vesicles; ccfRNA—cell-free circulating RNA; ccfDNA—cell-free circulating DNA.</p>
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<p>Illustration depicting liquid biopsy where blood or cervicovaginal lavage is obtained from a patient to isolate and characterize EVs, miRNA, and/or IncRNA for eventual diagnostics. EVs—extracellular vesicles.</p>
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<p>Cellular localization of experimentally validated gene-targets protein products of CC microRNA markers presented in <a href="#ijms-25-13271-t001" class="html-table">Table 1</a> and <a href="#ijms-25-13271-t002" class="html-table">Table 2</a>, generated by the FunRich (Version 3.1, last accessed on 1 September 2024) program.</p>
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<p>Organ expression of target genes for CC miRNA markers. Data are generated using the FunRich (Version 3.1, last accessed on 1 September 2024) tool. The red diamonds indicate the main sites of metastasis for the CC.</p>
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<p>Molecular function of gene-targets protein products for CC miRNA markers. Data are generated using the FunRich (Version 3.1, last accessed on 1 September 2024) tool.</p>
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<p>Signaling pathways formed by gene-targets protein products for CC miRNA markers. Data are generated by using the FunRich (Version 3.1, last accessed on 1 September 2024) tool.</p>
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23 pages, 5822 KiB  
Article
Reverb and Noise as Real-World Effects in Speech Recognition Models: A Study and a Proposal of a Feature Set
by Valerio Cesarini and Giovanni Costantini
Appl. Sci. 2024, 14(23), 11446; https://doi.org/10.3390/app142311446 - 9 Dec 2024
Viewed by 476
Abstract
Reverberation and background noise are common and unavoidable real-world phenomena that hinder automatic speaker recognition systems, particularly because these systems are typically trained on noise-free data. Most models rely on fixed audio feature sets. To evaluate the dependency of features on reverberation and [...] Read more.
Reverberation and background noise are common and unavoidable real-world phenomena that hinder automatic speaker recognition systems, particularly because these systems are typically trained on noise-free data. Most models rely on fixed audio feature sets. To evaluate the dependency of features on reverberation and noise, this study proposes augmenting the commonly used mel-frequency cepstral coefficients (MFCCs) with relative spectral (RASTA) features. The performance of these features was assessed using noisy data generated by applying reverberation and pink noise to the DEMoS dataset, which includes 56 speakers. Verification models were trained on clean data using MFCCs, RASTA features, or their combination as inputs. They validated on augmented data with progressively increasing noise and reverberation levels. The results indicate that MFCCs struggle to identify the main speaker, while the RASTA method has difficulty with the opposite class. The hybrid feature set, derived from their combination, demonstrates the best overall performance as a compromise between the two. Although the MFCC method is the standard and performs well on clean training data, it shows a significant tendency to misclassify the main speaker in real-world scenarios, which is a critical limitation for modern user-centric verification applications. The hybrid feature set, therefore, proves effective as a balanced solution, optimizing both sensitivity and specificity. Full article
(This article belongs to the Special Issue Spatial Audio and Sound Design)
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<p>Timeline of speaker recognition regarding models (above) and input features. ZCR = zero-crossing rate; PLP = Perceptual Linear Predictive; RNN = Recurrent Neural Network; DNN = Deep Neural Network (generic).</p>
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<p>The methodology pipeline that leads to the trained models for each speaker (ID), starting with the non-labeled DEMOS dataset that is then prepared for one-vs.-all classification, augmented with noise and reverb, and transformed into feature matrices for MFCCs, RASTA data, and merged.</p>
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<p>Sensitivity (class 1 recall) progression for the SVM classifier. This starts from the performances on the raw dataset (no noise), and then shows a decay with progressively noisier data and then progressively more reverberated validation data.</p>
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<p>Sensitivity (class 1 recall) progression for the SVM classifier. Starts from the performances on the raw dataset (no noise), then shows a decay with progressively noisier data then progressively more reverberated validation data.</p>
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<p>Per-class recall decay with noisy test data using the MFCC set of features—top 50 for a single selected speaker. Class 1 recall is sensitivity; class 2 recall is specificity.</p>
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<p>Per-class recall decay with noisy test data using the RASTA set of features—top 50 for a single selected speaker. Class 1 recall is sensitivity; class 2 recall is specificity.</p>
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<p>Per-class recall decay with noisy test data using the merged set of features (RASTA + MFCC)—top 50 for a single selected speaker. Class 1 recall is sensitivity; class 2 recall is specificity.</p>
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<p>Per-class recall decay with reverberated test data using the MFCC set of features—top 50 for a single selected speaker. Class 1 recall is sensitivity; class 2 recall is specificity.</p>
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<p>Per-class recall decay with reverberated test data using the RASTA set of features—top 50 for a single selected speaker. Class 1 recall is sensitivity; class 2 recall is specificity.</p>
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<p>Per-class recall decay with reverberated test data using the merged set of features (MFCC + RASTA)—top 50 for a single selected speaker.</p>
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9 pages, 1190 KiB  
Article
Simple and Selective Determination of Free Chlorine in Aqueous Solutions by an Electrophilic Aromatic Substitution Reaction Followed by Liquid Chromatography Coupled with Mass Spectrometry
by Avital Shifrovitch, Moran Madmon, Tamar Shamai Yamin and Avi Weissberg
Organics 2024, 5(4), 614-622; https://doi.org/10.3390/org5040032 - 9 Dec 2024
Viewed by 504
Abstract
We developed a selective technique to rapidly measure free chlorine, which is the sum of elemental chlorine (Cl2), hypochlorous acid (HOCl), and hypochlorite (OCl) in water samples via an electrophilic aromatic substitution reaction hyphenated with liquid chromatography-electrospray ionization tandem [...] Read more.
We developed a selective technique to rapidly measure free chlorine, which is the sum of elemental chlorine (Cl2), hypochlorous acid (HOCl), and hypochlorite (OCl) in water samples via an electrophilic aromatic substitution reaction hyphenated with liquid chromatography-electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS). Sample preparation involved derivatization at 25 °C for 15 min with 3,4,5-trimethoxyphenylacetic acid (TMPAA) in an aqueous solution prior to analysis. Several parameters were evaluated to determine the optimized reaction and for the production of informative MS/MS spectrum of the derivatization product, 2-chloro-3,4,5-trimethoxyphenylacetic acid (Cl-TMPAA). The resulting Cl-TMPAA derivative displayed an informative ESI-MS/MS spectrum characterized by product ions at m/z 232.0142, 200.0245, and 185.0009 from the precursor ion at m/z 259.0379. The linear dynamic range of the method (0.1–10 µg/mL) was fitted to concentration levels relevant to forensic toxicology issues. Compared with other analytical techniques, this newly established LC-MS-based method demonstrated specificity, simplicity, and rapidity. This method enables the detection of free chlorine for forensic investigations in criminal cases. Full article
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<p>Reaction scheme for free chlorine derivatized by 2-(3,4,5-trimethoxyphenyl)acetamide (TMPA) (<b>a</b>) and 2-(3,4,5-trimethoxyphenyl)acetic acid (TMPAA) the final selected derivatizing agent (<b>b</b>).</p>
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<p>Orbitrap-ESI-MS/MS chromatograms of the Cl derivative generated after derivatization of bleach at a concentration of 100 ng/mL with TMPAA at a normalized collision energy of 35 eV.</p>
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<p>Plausible structures for the product ions observed in the ESI-MS/MS spectra of the derivatizing agent after chlorination (Cl-TMPAA (<b>a</b>)) and the derivatization agent prior to chlorination (<b>b</b>).</p>
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<p>Extracted ion chromatograms (EICs) of the main MRM transition (<span class="html-italic">m</span>/<span class="html-italic">z</span> −259 &gt; 200) of the mono-chlorinated derivative (Cl-TMPAA, RT = 1.6 min) in water (LC-MS grade, red) and tap water (blue). The sample was fortified to achieve a concentration of 1 µg/mL in an Eppendorf vial, followed by derivatization and shaking for 15 min prior to LC-MS/MS analysis.</p>
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<p>Extracted ion chromatograms (EICs) of the three dominant MRM transitions (<span class="html-italic">m</span>/<span class="html-italic">z</span> −259 &gt; 200, <span class="html-italic">m</span>/<span class="html-italic">z</span> −261 &gt; 202 and <span class="html-italic">m</span>/<span class="html-italic">z</span> −259 &gt; 185) of the mono-chlorinated derivative (Cl-TMPAA, RT = 1.6 min). Spiking was carried out to obtain a concentration of 0.1 µg/mL (LOQ level, S/N ≥ 10, in blue), and the mixture was stirred for 15 min before LC-MS/MS analysis. The negative control sample (in red) was prepared in the same manner.</p>
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12 pages, 1033 KiB  
Article
Systematic Intravenous Administration of Autologous Mesenchymal Stem Cells Is Safe
by Takaaki Matsuoka, Takaaki Itohara, Yurie Hara and Nana Kobayashi
J. Clin. Med. 2024, 13(23), 7460; https://doi.org/10.3390/jcm13237460 - 7 Dec 2024
Viewed by 590
Abstract
Background: Mesenchymal stem cells (MSCs) have drawn significant attention for their regenerative potential and therapeutic applicability across a range of conditions, including cardiovascular diseases and age-related frailty. Despite extensive preclinical studies, there remain gaps in understanding the long-term safety and efficacy of [...] Read more.
Background: Mesenchymal stem cells (MSCs) have drawn significant attention for their regenerative potential and therapeutic applicability across a range of conditions, including cardiovascular diseases and age-related frailty. Despite extensive preclinical studies, there remain gaps in understanding the long-term safety and efficacy of MSC therapy in humans. This study aimed to assess the safety of intravenous MSC administration, evaluate the mean major adverse cardiac and cerebrovascular event (MACCE)-free period, and identify potential risk factors for MACCE development in patients receiving MSC therapy for various indications. Methods: A retrospective observational study was conducted on 2504 patients (mean age: 54.09 ± 11.65 years) who received intravenous adipose-derived MSC (AD-MSC) therapy between October 2014 and December 2023 at the Omotesando Helene Clinic, Tokyo, Japan. Patients received MSC doses ranging from 100 million to 2 billion cells, with the majority receiving 1–2 billion cells per treatment. Statistical analyses included multivariate Cox proportional hazards regression and Kaplan–Meier survival analysis to evaluate MACCE risk factors and event-free duration. Results: Over the follow-up period, the MACCE rate was exceptionally low at 0.2%. Multivariate analysis identified age as a significant risk factor for MACCE (hazard ratio: 1.127; 95% CI: 1.0418–1.219; p = 0.0029), while sex and MSC dose showed no significant association. Minor adverse events occurred in 0.8% of patients, with no severe adverse events reported. The study found MSC therapy to be safe, with a low adverse event rate and minimal risk of MACCE. Conclusions: This study demonstrates the safety of intravenous MSC therapy in a large cohort of patients, with a low incidence of MACCE and minimal adverse effects. Age was the only significant predictor of MACCE risk. Further prospective randomized studies are needed to validate these findings and explore the potential of MSC therapy in reducing MACCE risk and improving clinical outcomes across diverse indications. Full article
(This article belongs to the Section Clinical Research Methods)
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<p>Kaplan–Meier survival curve showing the major adverse cardiac and cerebrovascular events (MACCE)-free status over time.</p>
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<p>Hazard ratio and 95% confidence interval (CI). Forest plot evaluation of variables predicting MACCE-free status in the study population. Error bars represent the 95% confidence intervals. **: <span class="html-italic">p</span>-value less than 0.05 was considered statistically significant.</p>
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<p>Receiver operating characteristic curve. The diagonal red line represents the reference line.</p>
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13 pages, 1363 KiB  
Systematic Review
Minimally Invasive Versus Open Distal Gastrectomy for Locally Advanced Gastric Cancer: Trial Sequential Analysis of Randomized Trials
by Alberto Aiolfi, Matteo Calì, Francesco Cammarata, Federica Grasso, Gianluca Bonitta, Antonio Biondi, Luigi Bonavina and Davide Bona
Cancers 2024, 16(23), 4098; https://doi.org/10.3390/cancers16234098 - 6 Dec 2024
Viewed by 581
Abstract
Background. Minimally invasive distal gastrectomy (MIDG) has been shown to be associated with improved short-term outcomes compared to open distal gastrectomy (ODG) in patients with locally advanced gastric cancer (LAGC). The impact of MIDG on long-term patient survival remains debated. Aim was to [...] Read more.
Background. Minimally invasive distal gastrectomy (MIDG) has been shown to be associated with improved short-term outcomes compared to open distal gastrectomy (ODG) in patients with locally advanced gastric cancer (LAGC). The impact of MIDG on long-term patient survival remains debated. Aim was to compare the MIDG vs. ODG effect on long-term survival. Methods. Systematic review and trial sequential analysis (TSA) of randomized controlled trials (RCTs). Web of Science, Scopus, MEDLINE, the Cochrane Central Library, and ClinicalTrials.gov were queried. Hazard ratio (HR) and 95% confidence intervals (CI) were used as pooled effect size measures. Five-year overall (OS) and disease-free survival (DFS) were primary outcomes. Results. Five RCTs were included (2835 patients). Overall, 1421 (50.1%) patients underwent MIDG and 1414 (49.9%) ODG. The ages ranged from 48 to 70 years and 63.4% were males. The pooled 5-year OS (HR = 0.86; 95% CI 0.70–1.04; I2 = 0.0%) and 5-year DFS (HR = 1.03; 95% CI 0.87–1.23; I2 = 0.0%) were similar for MIDG vs. ODG. The TSA shows a cumulative z-curve without crossing the monitoring boundaries line (Z = 1.96), thus suggesting not conclusive 5-year OS and DFS results because the total information size was not sufficient. Conclusions. MIDG and ODG seem to have equivalent 5-year OS and DFS in patients with LAGC. However, the cumulative evidence derived from the TSA showed that the actual information size is not sufficient to provide conclusive data. Full article
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<p>The preferred reporting items for systematic reviews and meta-Analyses (PRISMA) diagram.</p>
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<p>(<b>A</b>). Forrest plot for 5-year OS. HR: hazard ratio; 95% CI: confidence interval. (<b>B</b>). Trial sequential analysis for 5-year OS. X axis: Information size (number of patients); Y axis: Cumulative Z score; AIS: Accrued information size; Green line: Z=1.96; Red line: Monitoring boundary line; Blue line: Trial sequential analysis line [<a href="#B12-cancers-16-04098" class="html-bibr">12</a>,<a href="#B13-cancers-16-04098" class="html-bibr">13</a>,<a href="#B14-cancers-16-04098" class="html-bibr">14</a>,<a href="#B24-cancers-16-04098" class="html-bibr">24</a>].</p>
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<p>(<b>A</b>). Forrest plot for 5-year DFS. HR: hazard ratio; 95% CI: confidence interval. (<b>B</b>). Trial sequential analysis for 5-year DFS. X axis: Information size (number of patients); Y axis: Cumulative Z score; AIS: Accrued information size; Green line: Z=1.96; Red line: Monitoring boundary line; Blue line: Trial sequential analysis line [<a href="#B13-cancers-16-04098" class="html-bibr">13</a>,<a href="#B14-cancers-16-04098" class="html-bibr">14</a>,<a href="#B16-cancers-16-04098" class="html-bibr">16</a>,<a href="#B21-cancers-16-04098" class="html-bibr">21</a>,<a href="#B24-cancers-16-04098" class="html-bibr">24</a>].</p>
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16 pages, 4494 KiB  
Article
Methylophiopogonanone A Inhibits Ferroptosis in H9c2 Cells: An Experimental and Molecular Simulation Study
by Yanqing Wang, Xi Zhao, Ban Chen, Shaoman Chen, Yongbai Liang, Dongfeng Chen and Xican Li
Molecules 2024, 29(23), 5764; https://doi.org/10.3390/molecules29235764 - 6 Dec 2024
Viewed by 323
Abstract
In this study, homoisoflavone methylophiopogonanone A (MOA) was investigated for its inhibitory effect on ferroptosis of H9c2 cells using a set of cellular assays, such as BODIPY-probed and H2DCFDA-probed flow cytometry analyses, cell counting kit-8 analysis (CCK-8), and lactate dehydrogenase (LDH) [...] Read more.
In this study, homoisoflavone methylophiopogonanone A (MOA) was investigated for its inhibitory effect on ferroptosis of H9c2 cells using a set of cellular assays, such as BODIPY-probed and H2DCFDA-probed flow cytometry analyses, cell counting kit-8 analysis (CCK-8), and lactate dehydrogenase (LDH) release analysis. All these cellular assays adopted Fer-1 as the positive control. Subsequently, MOA and Fer-1 were subjected to two antioxidant assays, i.e., 2-phenyl-4,4,5,5-tetramethylimidazoline-1-oxyl 3-oxide radical (PTIO)-scavenging and 2,2′-azinobis(3-ethylbenzo-thiazoline-6-sulfonic acid radical (ABTS•+)-scavenging. Finally, MOA, along with Fer-1, were systematically analyzed for molecular docking and dynamics simulations using a set of software tools. The experimental results revealed that MOA could inhibit ferroptosis of H9c2 cells but did not effectively scavenge PTIO and ABTS•+ free radicals. Two molecular simulation methods or algorithms suggested that MOA possessed similar binding affinity and binding free energy (∆Gbind) to Fer-1. Visual analyses indicated various hydrophobic interactions between MOA and one of the seven enzymes, including superoxide dismutase (SOD), dihydroorotate dehydrogenase (DHODH), ferroportin1 (FPN), ferroptosis suppressor protein 1 (FSP1), glutathione peroxidase 4 (GPX4), nicotinamide adenine dinucleotide phosphate (NADPH), and solute carrier family 7 member 11 (SLC7A11). Based on these experimental and molecular simulation results, it is concluded that MOA, a homoisoflavonoid with meta-di-OHs, can inhibit ferroptosis in H9c2 cells. Its inhibitory effect is mainly attributed to the regulation of enzymes rather than direct free radical scavenging. The regulation of enzymes primarily depends on hydrophobic interactions rather than H-bond formation. During the process, flexibility around position 9 allows MOA to adjust to the enzyme binding site. All these findings provide foundational information for developing MOA and its derivatives as potential drugs for myocardial diseases. Full article
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<p>Structures of small molecules with ferroptosis inhibition or antioxidant potential. (<b>A</b>) Natural antioxidants from the previous studies; (<b>B</b>) natural antioxidant in the present study; (<b>C</b>) synthetic ferroptosis inhibitors in both the present and previous studies; (<b>D</b>) natural non-antioxidant from the previous study; (<b>E</b>) natural antioxidant and ferroptosis inhibitor from the previous study.</p>
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<p>The main results of cellular experiments for the inhibitory effect on ferroptosis of MOA. (<b>A</b>) BODIPY-probed flow cytometry analysis; (<b>B</b>) H<sub>2</sub>DCFDA-probed flow cytometry analysis; (<b>C</b>) cell counting kit-8 (CCK-8) analysis; (<b>D</b>) lactate dehydrogenase (LDH) release analysis. (<b>E</b>,<b>F</b>) The dose–response curves and IC<sub>50</sub> values of MOA and Fer-1 in the LAD release analysis. H9c2 cells treated with 10 µM of erastin in the presence of 1–100 µM MOA (<b>E</b>); H9c2 cells treated with 10 µM of erastin in the presence of 0.1–10 µM Fer-1 (<b>F</b>). Control group: H9c2 cells without erastin treatment; erastin group: 10 µM of erastin-treated H9c2 cells; Fer-1 group: H9c2 cells treated with 10 µM of erastin + 1 µM Fer-1; MOA 10 µM group: H9c2 cells treated with 10 µM of erastin + 10 µM MOA; MOA 50 µM group: H9c2 cells treated with 10 µM of erastin + 50 µM MOA. Data are presented as means ± SDs from three independent experiments. ### <span class="html-italic">p</span> &lt; 0.001, #### <span class="html-italic">p</span> &lt; 0.0001, compared with the control group; ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001 compared with the erastin group.</p>
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<p>The dose–response curves and IC<sub>50</sub> values of MOA, Fer-1, and Trolox in two antioxidant assays. (<b>A</b>) ABTS<sup>•+</sup>-scavenging assay; (<b>B</b>) PTIO<sup>•</sup>-scavenging assay. (Each value was expressed as a mean ± SD, n = 3).</p>
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<p>Results of the RMSD analysis obtained from the molecular dynamics simulation. The binding of (<b>A</b>) MOA to SOD; (<b>B</b>) MOA to DHODH; (<b>C</b>) MOA to FPN; (<b>D</b>) MOA to FSP1; (<b>E</b>) MOA to GPX4; (<b>F</b>) MOA to NADPH; and (<b>G</b>) MOA to SLC7A11. The simulation was conducted using the Gromacs 2020.6 package, and the results were plotted using Origin 2017 software.</p>
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<p>Results of the RMSD analysis obtained from the molecular dynamics simulation. The binding of (<b>A</b>) MOA to SOD; (<b>B</b>) MOA to DHODH; (<b>C</b>) MOA to FPN; (<b>D</b>) MOA to FSP1; (<b>E</b>) MOA to GPX4; (<b>F</b>) MOA to NADPH; and (<b>G</b>) MOA to SLC7A11. The simulation was conducted using the Gromacs 2020.6 package, and the results were plotted using Origin 2017 software.</p>
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<p>Construction of pharmacophore models using the MOA binding and complex structure. The binding of (<b>A</b>) MOA to SOD; (<b>B</b>) MOA to DHODH; (<b>C</b>) MOA to FPN; (<b>D</b>), MOA to FSP1; (<b>E</b>) MOA to GPX4; (<b>F</b>) MOA to NADPH; (<b>G</b>) MOA to SLC7A11. The simulation was conducted using the Gromacs 2020.6 package.</p>
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<p>Visualized results of the interactions of methylophiopogonanone (MOA) and Fer-1 with the amino acid residues of seven ferroptosis enzymes. The binding of (<b>A</b>) MOA to SOD; (<b>B</b>) MOA to DHODH; (<b>C</b>) MOA to FPN; (<b>D</b>) MOA to FSP1; (<b>E</b>) MOA to GPX4; (<b>F</b>) MOA to NADPH; (<b>G</b>) MOA to SLC7A11. (<b>A’</b>), Fer-1 to SOD; (<b>B’</b>), Fer-1 to DHODH; (<b>C’</b>), Fer-1 to FPN; (<b>D’</b>), Fer-1 to FSP; (<b>E’</b>), Fer-1 to GPX4; (<b>F’</b>), Fer-1 to NADPH; (<b>G’</b>), Fer-1 to SLC7A11. The molecular dynamics simulation was conducted using Gromacs 2020.6 package.</p>
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<p>Visualized results of the interactions of methylophiopogonanone (MOA) and Fer-1 with the amino acid residues of seven ferroptosis enzymes. The binding of (<b>A</b>) MOA to SOD; (<b>B</b>) MOA to DHODH; (<b>C</b>) MOA to FPN; (<b>D</b>) MOA to FSP1; (<b>E</b>) MOA to GPX4; (<b>F</b>) MOA to NADPH; (<b>G</b>) MOA to SLC7A11. (<b>A’</b>), Fer-1 to SOD; (<b>B’</b>), Fer-1 to DHODH; (<b>C’</b>), Fer-1 to FPN; (<b>D’</b>), Fer-1 to FSP; (<b>E’</b>), Fer-1 to GPX4; (<b>F’</b>), Fer-1 to NADPH; (<b>G’</b>), Fer-1 to SLC7A11. The molecular dynamics simulation was conducted using Gromacs 2020.6 package.</p>
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14 pages, 1262 KiB  
Article
Emerging Non-Breast Implant-Associated Lymphomas: A Systematic Review
by Arianna Di Napoli, Santo Fruscione, Sergio Mazzola, Rosalba Amodio, Giorgio Graziano, Rita Mannino, Maurizio Zarcone, Giorgio Bertolazzi, Nicole Bonaccorso, Martina Sciortino, Daniele Domenico De Bella, Alessandra Savatteri, Miriam Belluzzo, Chiara Alba Norrito, Rosario Sparacino, Paolo Contiero, Giovanna Tagliabue, Claudio Costantino and Walter Mazzucco
Cancers 2024, 16(23), 4085; https://doi.org/10.3390/cancers16234085 - 5 Dec 2024
Viewed by 425
Abstract
Background: Medical devices used for functional or esthetic purposes improve health and quality of life; however, they are not risk-free. Anaplastic large-cell lymphoma (ALCL), associated with breast implants, is a well-known and recognized distinct lymphoma entity. More recently, additional lymphomas have been reported [...] Read more.
Background: Medical devices used for functional or esthetic purposes improve health and quality of life; however, they are not risk-free. Anaplastic large-cell lymphoma (ALCL), associated with breast implants, is a well-known and recognized distinct lymphoma entity. More recently, additional lymphomas have been reported in relation to prosthesis other than breast implants, as these allow the pericyte to develop into a clone that undergoes a maturation process, progressing toward full malignancy. Methods: We performed a systematic review with a descriptive analysis of data extracted from primary studies following PRISMA guidelines, including the search string “(IMPLANT* OR PROSTHES*) AND LYMPHOM*” in the PubMed, Scopus, Embase, and Google-Scholar databases. Data such as patient sex, age, implant site, prosthesis material, and lymphoma type were analyzed. Statistical methods, including Student’s t-test and Fisher’s exact test, were employed to compare lymphoma characteristics, with significance set at a p-value < 0.05. Results: From a total of 5992 studies, we obtained 43 case reports and series on a total of 52 patients diagnosed with prosthesis-associated lymphomas. The majority of implant-related lymphoma cases (85%) were of the B-cell type, mostly fibrin-associated large B-cell lymphoma (LBCL). This lymphoma type was more associated with biological (non-human-derived biological tissue), metallic, and synthetic implants (synthesized from non-organic components) (p-value = 0.007). Patients with ALCL had equal frequencies of metal and silicone prostheses (37.5%, 3 cases each), followed by synthetic prostheses (25%, 2 cases). ALCL cases were most common at skeletal (50%) and muscular-cutaneous sites (25%), whereas B-cell lymphomas were predominantly found in cardiovascular implants (50%), followed by skeletal (27%) and muscular-cutaneous (21%) sites. Death attributed to lymphoma took place in 67% of the cases, mostly LBCL occurring in cardiovascular sites. Conclusions: Because the included studies were limited to case reports and series, a potential non-causal link might have been documented between different implant materials, implant sites and lymphoma types. This underscores the importance of further comprehensive research and monitoring of non-breast implants. Full article
(This article belongs to the Section Systematic Review or Meta-Analysis in Cancer Research)
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<p>Flow diagram for the selection of the primary studies on lymphoma associated with prosthesis (other than BIA-ALCL) included in the study.</p>
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<p>Description of the cases extracted from the selected primary studies, by cell lineage, lymphoma type, and molecular characterization (EBV for LBCL; ALK for ALCL). Legend: LBCL: large B-cell lymphoma; ALCL: anaplastic large-cell lymphoma; ND: not defined.</p>
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<p>Bar chart showing lymphoma types by implant material.</p>
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<p>Bar chart showing lymphoma types by site of implant.</p>
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