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In this study, the Xgboost method is employed for TOC estimation in mixed carbonate and siliciclastic shale from the Hashan area, Junggar Basin. The results show that this approach is effective for TOC estimation in this area although the model performance is not very excellent with a correlation coefficient of 0.54 between measured TOC and predicted TOC values, likely due to a small samples dataset. Therefore, the PCA method is applied to debase dimension of well log data from five dimensional to two-dimensional data, which enhances the correlation coefficient between the predicted and measured TOC from 0.54 to 0.68. Based on the model, the isopleth maps of TOC distributions in Fengcheng Formation were redrawn showing two shale oil exploration targets, which likely correspond to two depositional centers of this strata. All the same, the model in this work provides reliable data for shale oil evaluation in the study area and a good example under similar geological setting.

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The effect of delignification on the adsorption capacity of loofah sponge-based immobilized metal affinity chromatography adsorbents was investigated with recombinant His-tagged trehalose synthase as the model protein. Pretreatments with [EMIM][Ac] ionic liquid at 80 °C for 5 h and with sodium chlorite/acetic acid at 80 °C for 2 h were found effective for the removal of lignin, leading to a loss in biomass of 15.7% and 25.2%, respectively. Upon delignification, the metal chelating capacities of the loofah sponge-based adsorbents prepared with 5-h ionic liquid pretreatment (712 ± 82 µmole Cu(II)/g) and with 2-h sodium chlorite/acetic acid pretreatment (1012 ± 18 µmole Cu(II)/g) were 38% and 97% higher than that of the control (514 ± 55 µmole Cu(II)/g), adsorbent prepared with untreated loofah sponge, respectively. Results of protein adsorption study indicated that the Co(II)-loaded adsorbent prepared with 2-h sodium chlorite/acetic acid pretreatment exhibited the highest adsorption capacity and selectivity for the recombinant His-tagged trehalose synthase, giving a purification product with a specific activity of 7.62 U/mg protein. The predicted maximum adsorption capacity of the delignified loofah sponge-based adsorbent, 2.04 ± 0.14 mg/g, was 73% higher than that of the control.

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Introduction: Esophagectomy patients who experience unplanned ICU admission (UIA) may experience a heavier economic burden and worse clinical outcomes than those who experience routine intensive care unit (ICU) admission. The aim of this study was to identify the risk factors for postoperative UIA in patients who underwent esophagectomy. Methods: We retrospectively included patients with esophageal cancer who underwent esophagectomy. The characteristics of postoperative UIA were described, and univariable and multivariable analyses were performed based on the logistic regression model. Furthermore, a recursive partitioning analysis was adopted to stratify the patients according to the risk of UIA. Results: A total of 628 patients were included in our final analysis, among whom 57 (9.1%) had an UIA. The patients in the UIA cohort had a higher rate of in-hospital mortality (P<0.001), longer hospital stay (P<0.001), and higher associated costs (P<0.001). Multivariable analysis showed that hybrid/open esophagectomy (OR=4.366, 95% CI=2.142 to 8.897, P<0.001), operation time (OR=1.006, 95% CI=1.002 to 1.011, P=0.007), intraoperative blood transfusion (OR=3.118, 95% CI=1.249 to 7.784, P=0.015) and the prognostic nutrition index (PNI) (OR=0.779, 95% CI=0.724 to 0.838, P<0.001) were independently associated with UIA. Conclusions: We identified several critical independent perioperative risk factors that may increase the risk of UIA following esophagectomy, and the above risk factors should be the focus of attention to reduce the incidence of postoperative UIA.

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Background: The intricate relationship among gut microbiota, serum metabolites, and immunophenotypes may significantly impact myocarditis. However, direct causal links between these domains and myocarditis are not well understood. Methods: The study performed Mendelian randomization (MR) analysis using genetic data from public sources. Exposure data included 211 gut microbiota, 486 serum metabolites, and 731 immunophenotypes from Mibiogen, the Metabolomics GWAS server, and GWAS catalog databases. Single nucleotide polymorphisms (SNPs) were selected as instrumental variables based on established criteria. Myocarditis data from GWAS (427,911 participants, 24, 180, 570 SNPs) were used as the outcome variable. MR analysis was conducted using Inverse Variance Weighting (IVW), with Cochran's Q test for heterogeneity and Egger's intercept to assess horizontal pleiotropy. Results: 9 gut microbiota, 10 serum metabolites, and 2 immunophenotypes were negatively associated with myocarditis risk. In contrast, 5 gut microbiota, 12 serum metabolites, and 7 immunophenotypes were positively associated with myocarditis risk (all, P < 0.05). Sensitivity analyses confirmed the stability of these results. Conclusion: This MR study suggests that gut microbiota, serum metabolites, and immunophenotypes may causally influence myocarditis risk. These findings provide genetic evidence for myocarditis etiology and could inform future precision prevention and treatment strategies.

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OBJECTIVE: This study aimed to investigate the potential causal association between Sleep Apnea Syndrome (SAS) and Depression, focusing on the roles of gut microbiota, serum metabolites, and inflammatory factors in these conditions. METHODS: Mendelian Randomization (MR) analysis was performed using data from genome-wide association studies to assess 211 types of gut microbiota, 1400 serum metabolites, and 91 inflammatory factors as potential contributing factors. Causal inference was conducted using the Inverse Variance Weighted (IVW) method, with additional robustness checks through Cochran's Q test, MR-Egger regression intercept test, MR-PRESSO global test, and leave-one-out analysis. RESULTS: The MR analysis indicated a positive correlation between the risk of SAS and Depression (OR = 1.12, 95 % CI: 1.05-1.19, P < 0.001), with a reciprocal analysis showing a similar positive correlation between Depression and the risk of SAS (OR = 1.19, 95 % CI: 1.07-1.31, P = 0.001). Additionally, causal associations were identified between 15 types of gut microbiota, 36 serum metabolites, and 2 inflammatory factors with SAS, and between 11 types of gut microbiota, 23 serum metabolites, and 3 inflammatory factors with Depression (IVW, all P < 0.05). The robustness of these findings was confirmed through the MR-Egger regression intercept test and MR-PRESSO global test. CONCLUSION: This study provides epidemiological evidence of a bidirectional causal association between SAS and Depression, emphasizing the potential roles of gut microbiota, serum metabolites, and inflammatory factors in the pathogenesis of these disorders. These findings may inform the development of new therapeutic strategies.

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The overuse of antibiotics has resulted in a surge of drug-resistant bacteria, making the pursuit of natural antimicrobials an urgent and significant trend. Encapsulation and nanoparticulation are effective ways to enhance the antibacterial properties of natural drugs. In this study, we encapsulated tannic acid (TA) with chitosan (CS) and poly (lactide-co-glycolide) (PLGA) using the emulsion-solvent evaporation method to enhance the antimicrobial effect of TA. We prepared a bilayer membrane spherical nanoemulsion of TA-PLGA-CS (TPC) with uniform size of 559.87 ± 1.16 nm, and zeta potential of 59.53 ± 1.07 mV. TPC could be stably stored for 90 days at 4°C without affecting the properties of the emulsion, and the minimum bactericidal concentration against four strains of Escherichia coli (E. coli) remained unchanged for 60 d. The results indicated that TPC enhanced the inhibitory effect of TA against E. coli. Scanning electron microscope images revealed that TPC treatment caused damage to the bacterial cell membrane. In addition, in vivo experiments indicated that TPC exhibited a superior therapeutic effect on artificial colibacillosis in chickens infested with Avian pathogenic Escherichia coli, as evidenced by the changes in body weight and a reduction bacterial load in heart. Furthermore, TPC reversed the down-regulation of catalase, glutathione peroxidase1 (GPX1), and GPX7 gene expression levels in intestinal tissues. Compared to the model group, TPC treatment elevated serum glutathione peroxidase activities and lowered myeloperoxidase and lactate dehydrogenase levels, offering antioxidant protection that was slightly better than that of doxycycline hydrochlorid group. In summary, we prepared a novel TA antimicrobial preparation with significant antioxidant potential and inhibitory effect against E. coli both in vitro and in vivo.

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FAM136A promotes the progression and metastasis of various tumors. However, there are few studies on the role of FAM136A in esophageal cancer (ESCA). The TCGA, GTEx, and GEO databases are employed to analyze the expression of FAM136A in ESCA, and qPCR and TMA experiments are performed for validation. Enrichment analyzes are performed to investigate the association of FAM136A expression with immune features, m6A modification, alternative splicing, cuproptosis, and the ceRNA network via bioinformatics analysis. FAM136A is highly expressed in ESCA and correlated with lymph node metastasis and overall survival (OS). Bioinformatics analysis suggested that FAM136A may participate in the following processes to promote ESCA development and progression: 1) Promotion of mast cells infiltration to influence the ESCA immune microenvironment, 2) HNRNPC upregulation to regulate m6A modification, 3) ALYREF upregulation to increase the occurrence of retained intron (RI) events, 4) CDK5RAP1 upregulation to achieve inhibition of tumor cell apoptosis, and 5) promotion of ESCA progression through the lncRNA SNHG15/hsa-miR-29c-3p/FAM136A ceRNA network. FAM136A is a potential biomarker for ESCA diagnosis and treatment response evaluation, and the underlying mechanisms may be associated with immune infiltration, m6A modification, alternative splicing, cuproptosis, and the ceRNA regulatory network.

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BACKGROUND: Alopecia areata is an autoimmune hair loss disorder with an incompletely understood etiology. Although trace elements, serum metabolites, and inflammatory factors are implicated in the disease, the potential causal relationships between these factors and alopecia areata require further investigation. METHODS: This study employed Mendelian randomization (MR), utilizing data from genome-wide association studies, to explore the causal relationships between 15 trace elements, 1400 serum metabolites, and 91 inflammatory factors and alopecia areata. The analysis was conducted using the inverse variance weighted (IVW) method complemented by various sensitivity analyses, including Cochran's Q test, MR-Egger regression intercept test, MR-PRESSO global test, and leave-one-out analysis, to assess the robustness of the results. RESULTS: MR analysis indicated a negative correlation between copper levels and the risk of developing alopecia areata (odds ratio = 0.86, 95% confidence interval: 0.75-0.99, p = 0.041). Additionally, causal relationships were identified between 15 serum metabolites and 6 inflammatory factors and the risk of alopecia areata (IVW, all p values < 0.05). CONCLUSION: This study provides genetic evidence of the relationships between trace elements, serum metabolites, and alopecia areata, underscoring the potential value of targeted therapeutic strategies and preventive measures. Future research should expand to diverse populations and further explore the specific roles of these biomarkers in the disease mechanism.

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Elliptical beams (EBs), an essential family of structured light, have been investigated theoretically due to their intriguing mathematical properties. However, their practical application has been significantly limited due to the inability to determine all their physical quantities, particularly the ellipticity factor, a unique parameter for EBs of different families. In this paper, to our knowledge, we proposed the first high-accuracy approach that can effectively distinguish EBs with an ellipticity factor difference of 0.01, equivalent to 99.9% field similarities. The method is based on a transformer deep learning (DL) network, and the accuracy has reached 99% for two distinct families of exemplified EBs. To prove that the high performance of this model can dramatically extend the practical aspect of EBs, we used EBs as information carriers in free-space optical communication for an image transmission task, and an error bit rate as low as 0.22% is achieved. Advancing the path of such a DL approach will facilitate the research of EBs for many practical applications such as optical imaging, optical sensing, and quantum-related systems.

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Previous studies have found a possible causal relationship between triglycerides and lipid-lowering drugs and valvular disease. The aim of this study was to explore the potential causal relationship between triglycerides and lipid-lowering drugs and valvular disease using Mendelian randomization (MR) analysis. Data sets associated with triglycerides (441,016 participants and 12,321,875 single nucleotide polymorphisms [SNPs]) and cholesterol-lowering drugs (209,638 participants and 9851,867 SNPs) were retrieved from the Genome-Wide Association Study (GWAS) database. A total of 297 and 49 SNPs significantly associated with triglycerides and cholesterol-lowering drugs, respectively (P < 5 × 10-8), were identified. Similarly, data sets for non-rheumatic valve diseases (NVDs) (361,194 participants and 10,080,950 SNPs) were obtained from the GWAS database. Inverse variance weighting was used as the primary method for calculating the odds ratio (OR) and 95% confidence intervals (CI). The MR-Egger, weighted median, and weighted mode analyses were also used to test the robustness of the main results. The MR-Egger intercept test and the MR-PRESSO test were used to evaluate horizontal pleiotropy. Inverse variance weighted (IVW) results showed that both triglyceride and cholesterol-lowering medication were positively associated with NVDs (OR = 1.001, 95% CI 1.000-1.0012, P = 0.006; OR = 1.007, 95% CI 1.003-1.010; P = 0.002). This study suggests that both triglyceride and cholesterol-lowering medications are positively associated with NVDs, suggesting that lowering triglyceride levels or the use of cholesterol-lowering medications may reduce the incidence of NVDs. However, larger samples are required for further validation.

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Porcine epidemic diarrhea virus (PEDV) results in severe economic losses to the swine industry due to its widespread prevalence and high mortality. Currently, there is no effective treatment against PEDV. New antiviral therapies are urgently needed to control this highly contagious pathogen. In this research, the anti-PEDV activity and mechanism of Dehydroevodiamine (DHED) were investigated in vitro. Our results showed that DHED exerted satisfactory anti-PEDV activity by ameliorating cytopathic effects (CPEs), reducing virus titer, and inhibiting PEDV N protein expression and gene transcription dose-dependently. The antiviral mechanism of DHED is related to its inhibition of the entry, replication, and assembly stages of PEDV life cycle. In addition, DHED can regulate the MAPK signaling pathway, and suppress phosphorylated ERK1/2 activation, thus exerting antiviral effects. In conclusion, our research confirmed the anti-PEDV activity and mechanism of DHED, preliminarily providing a new strategy for anti-PEDV drug development.

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Male animals often display higher levels of aggression than females. However, the neural circuitry mechanisms underlying this sexually dimorphic aggression remain elusive. Here, we identify a hypothalamic-amygdala circuit that mediates male-biased aggression in mice. Specifically, the ventrolateral part of the ventromedial hypothalamus (VMHvl), a sexually dimorphic region associated with eliciting male-biased aggression, projects densely to the posterior substantia innominata (pSI), an area that promotes similar levels of attack in both sexes of mice. Although the VMHvl innervates the pSI unidirectionally through both excitatory and inhibitory connections, it is the excitatory VMHvl-pSI projections that are strengthened in males to promote aggression, whereas the inhibitory connections that reduce aggressive behavior are strengthened in females. Consequently, the convergent hypothalamic input onto the pSI leads to heightened pSI activity in males, resulting in male-biased aggression. Our findings reveal a sexually distinct excitation-inhibition balance of a hypothalamic-amygdala circuit that underlies sexually dimorphic aggression.

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Subsequently to the publication of the above article, the authors have realized that, in Fig. 1A, the incorrect image was uploaded to show the ultrastructure of exos isolated from plasma and examined using transmission electron microscopy (essentially, the image in question had already appeared in an article published by the same research group in Journal of Cellular and Molecular Medicine). In addition,  the '+' and '-' signs for the 'Cell lysis' experiments shown underneath the gels in Fig. 1B were incorporated the wrong way around. The revised version of Fig. 1, showing the correct image in Fig. 1A and the correct labels in Fig. 1B, is shown below. Note that the errors made in assembling this figure did not have a major impact on either the results or the conclusions reported in this paper. The authors are grateful to the Editor of Molecular Medicine Reports for allowing them this opportunity to publish a corrigendum, and apologize to the readership of the Journal for any inconvenience caused. [Molecular Medicine Reports 27: 124, 2023; DOI: 10.3892/mmr.2023.13010].

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Behavioral studies play a crucial role in unraveling the mechanisms underlying brain function. Recent advances in optogenetics, neuronal typing and labeling, and circuit tracing have facilitated the dissection of the neural circuitry involved in various important behaviors. The identification of monosynaptic connections, both upstream and downstream of specific neurons, serves as the foundation for understanding complex neural circuits and studying behavioral mechanisms. However, the practical implementation and mechanistic understanding of monosynaptic connection tracing techniques and functional identification remain challenging, particularly for inexperienced researchers. Improper application of these methods and misinterpretation of results can impede experimental progress and lead to erroneous conclusions. In this paper, we present a comprehensive description of the principles, specific operational details, and key steps involved in tracing anterograde and retrograde monosynaptic connections. We outline the process of functionally identifying monosynaptic connections through the integration of optogenetics and electrophysiological techniques, providing practical guidance for researchers.

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BACKGROUND: Atherosclerosis is a chronic inflammatory disease characterized by abnormal lipid deposition in the arteries. Programmed cell death is involved in the inflammatory response of atherosclerosis, but PANoptosis, as a new form of programmed cell death, is still unclear in atherosclerosis. This study explored the key PANoptosis-related genes involved in atherosclerosis and their potential mechanisms through bioinformatics analysis. METHODS: We evaluated differentially expressed genes (DEGs) and immune infiltration landscape in atherosclerosis using microarray datasets and bioinformatics analysis. By intersecting PANoptosis-related genes from the GeneCards database with DEGs, we obtained a set of PANoptosis-related genes in atherosclerosis (PANoDEGs). Functional enrichment analysis of PANoDEGs was performed and protein-protein interaction (PPI) network of PANoDEGs was established. The machine learning algorithms were used to identify the key PANoDEGs closely linked to atherosclerosis. Receiver operating characteristic (ROC) analysis was used to assess the diagnostic potency of key PANoDEGs. CIBERSORT was used to analyze the immune infiltration patterns in atherosclerosis, and the Spearman method was used to study the relationship between key PANoDEGs and immune infiltration abundance. The single gene enrichment analysis of key PANoDEGs was investigated by GSEA. The transcription factors and target miRNAs of key PANoDEGs were predicted by Cytoscape and online database, respectively. The expression of key PANoDEGs was validated through animal and cell experiments. RESULTS: PANoDEGs in atherosclerosis were significantly enriched in apoptotic process, pyroptosis, necroptosis, cytosolic DNA-sensing pathway, NOD-like receptor signaling pathway, lipid and atherosclerosis. Four key PANoDEGs (ZBP1, SNHG6, DNM1L, and AIM2) were found to be closely related to atherosclerosis. The ROC curve analysis demonstrated that the key PANoDEGs had a strong diagnostic potential in distinguishing atherosclerotic samples from control samples. Immune cell infiltration analysis revealed that the proportion of initial B cells, plasma cells, CD4 memory resting T cells, and M1 macrophages was significantly higher in atherosclerotic tissues compared to normal tissues. Spearman analysis showed that key PANoDEGs showed strong correlations with immune cells such as T cells, macrophages, plasma cells, and mast cells. The regulatory networks of the four key PANoDEGs were established. The expression of key PANoDEGs was verified in further cell and animal experiments. CONCLUSIONS: This study evaluated the expression changes of PANoptosis-related genes in atherosclerosis, providing a reference direction for the study of PANoptosis in atherosclerosis and offering potential new avenues for further understanding the pathogenesis and treatment strategies of atherosclerosis.

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Given their good antitumor effects, epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) are standard first-line therapy for EGFR-sensitive mutations, including exon 19 deletions and exon 21 L858R mutations. EGFR fusion mutations and EGFR amplification are very rare in non-small cell lung cancer (NSCLC). We describe 2 patients with NSCLC harboring EGFR fusion mutations (EGFR-MACF1 and EGFR-GNAT3) combined with EGFR amplification. Both patients received EGFR-TKI treatment, and 1 of them showed an antitumor response.

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BACKGROUND: The cardiovascular effects of metformin continue to be a subject of debate within the medical community. METHODS: The Mendelian randomization (MR) study used data from genome-wide association studies (GWAS) to explore the causal association with six diseases that are associated with bimatoprost treatment and myocardial infarction, chronic heart failure, atrial fibrillation, hypertrophic and dilated cardiomyopathy, and valvular disease. Genome-wide significant single nucleotide polymorphisms (SNPs), that are associated with metformin use were selected as the instrumental variables. To determine the causal relationship between metformin use and various cardiovascular diseases, MR analysis was conducted, employing methods such as Instrumental Variable Weighting (IVW). RESULTS: The IVW analysis demonstrated a positive association between metformin treatment and the risk of myocardial infarction (OR = 22.67, 95% CI 3.22-34.01; P = 0.002). Conversely, metformin treatment exhibited a negative association with the risk of developing valvular disease (OR = 0.98, 95% CI 0.95-1.00; P = 0.046) and hypertrophic cardiomyopathy (OR = 0.01, 95% CI 0.00-0.22; P = 0.016). Multiple test correction found that metformin treatment was causally associated with the risk of both hypertrophic cardiomyopathy (PFDR = 0.048) and myocardial infarction (PFDR = 0.012). The analysis revealed limited heterogeneity in the individual results, absence of pleiotropy evidence, and indications of stability in the findings. CONCLUSION: The MR study discovered from a genetic standpoint that metformin may lower the risk of hypertrophic cardiomyopathy and valvular heart disease, yet it could elevate the risk of myocardial infarction.

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BACKGROUND: The pathogenesis of cardiac arrhythmias is multifaceted, encompassing genetic, environmental, hemodynamic, and various causative factors. Emerging evidence underscores a plausible connection between gut flora, serum metabolites, and specific types of arrhythmias. Recognizing the role of host genetics in shaping the microbiota, we employed two-sample Mendelian randomization analyses to investigate potential causal associations between gut flora, serum metabolites, and distinct arrhythmias. METHODS: Mendelian randomization methods were deployed to ascertain causal relationships between 211 gut flora, 575 serum metabolites, and various types of arrhythmias. To ensure the reliability of the findings, five complementary Mendelian randomization methods, including inverse variance weighting methods, were employed. The robustness of the results was scrutinized through a battery of sensitivity analyses, incorporating the Cochran Q test, leave-one-out test, and MR-Egger intercept analysis. RESULTS: Eighteen gut flora and twenty-six serum metabolites demonstrated associations with the risk of developing atrial fibrillation. Moreover, ten gut flora and fifty-two serum metabolites were linked to the risk of developing supraventricular tachycardia, while eight gut flora and twenty-five serum metabolites were associated with the risk of developing tachycardia. Additionally, six gut flora and twenty-one serum metabolites exhibited associations with the risk of developing bradycardia. CONCLUSION: This study revealed the potential causal relationship that may exist between gut flora, serum metabolites and different cardiac arrhythmias and highlights the need for further exploration. This study provides new perspectives to enhance diagnostic and therapeutic strategies in the field of cardiac arrhythmias.

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A fundamental understanding of the electroanalytical activity of transition metal sulfide electrocatalysts, especially the origin of the electrocatalytic reactivity on the surface sites of heterostructures with multiple crystalline phases, is essential for the design of low-cost and highly efficient nonprecious metal electrocatalysts for further scientific and technological achievements. Herein, we injected P into NiS and occupied the S sites through a doping strategy. The redistributed electronic structure induced the construction of heterostructures, which significantly improved the structure and chemical state of electrochemically inert NiS. The phase-change mechanism between NiS and NiS2 synergistically catalyzes Pb(II), while the P and S sites jointly lose electrons. Moreover, the constructed heterojunction sensor shows the a sensitivity of 83.43 µA µM-1 to Pb(II) with a theoretical limit of detection of 48 nM, as well as excellent stability, reproducibility, and anti-interference ability. The accurate detection in real water further reveals the potential of this sensor for practical applications. This study provides a guiding strategy for improving electrochemically inert materials to design highly active electrocatalytic interfaces, which has important implications for the development of highly efficient electrode-sensitive materials similar to precious metals to achieve accurate electrical analysis.

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PURPOSE: Development and assessment the deep learning weakly supervised algorithm for the classification and detection pneumonia via X-ray. METHODS: This retrospective study analyzed two publicly available dataset that contain X-ray images of pneumonia cases and normal cases. The first dataset from Guangzhou Women and Children's Medical Center. It contains a total of 5,856 X-ray images, which are divided into training, validation, and test sets with 8:1:1 ratio for algorithm training and testing. The deep learning algorithm ResNet34 was employed to build diagnostic model. And the second public dataset were collated by researchers from Qatar University and the University of Dhaka along with collaborators from Pakistan and Malaysia and some medical doctors. A total of 1,300 images of COVID-19 positive cases, 1,300 normal images and 1,300 images of viral pneumonia for external validation. Class activation map (CAM) were used to location the pneumonia lesions. RESULTS: The ResNet34 model for pneumonia detection achieved an AUC of 0.9949 [0.9910-0.9981] (with an accuracy of 98.29% a sensitivity of 99.29% and a specificity of 95.57%) in the test dataset. And for external validation dataset, the model obtained an AUC of 0.9835[0.9806-0.9864] (with an accuracy of 94.62%, a sensitivity of 92.35% and a specificity of 99.15%). Moreover, the CAM can accurately locate the pneumonia area. CONCLUSION: The deep learning algorithm can accurately detect pneumonia and locate the pneumonia area based on weak supervision information, which can provide potential value for helping radiologists to improve their accuracy of detection pneumonia patients through X-ray images.

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