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Fixed point results for ℑ-Contractions in JS-generalized metric spaces with an application
The goal of this work is to establish ℑ-contractions and to show some novel fixed point theorems for these contractive conditions in the setting of generalized metric spaces in the sense of Jleli and Samet. Finally, using proven fixed point results, an existence result for a solution of the RLC circuit’s current differential equation is established.
Reliability evaluation method and system for the ventilation door cylinder based on Bayes Monte Carlo simulation
Top-down effects on translucency perception in relation to shape cues
It is well established that object shape perception significantly influences the perception of translucency. However, how object shape cues such as motion and binocular disparity affect the perception of translucency in rich environments, like virtual reality or real visual environments, remains unclear. This study aims to psychophysically measure the extent to which multiple object shape cues influence the perception of translucency. Additionally, we examined whether top-down factors, such as changes in cognitive attitude caused by the sequence of experiments, affect translucency perception. The results revealed that while motion and binocular disparity enhance translucency perception, this effect is confined to situations where shape cues are poor. Moreover, the effect became particularly pronounced when the experiments began with weak specular reflection stimuli, followed by the experiments using stimuli with specular reflection. In the case of translucent objects without specular reflection, strong shape information cannot be derived solely from shading patterns. These findings thus suggest that top-down factors related to shape modulate the influence of shape cues on translucency perception.
Model-based iterative reconstruction with adaptive regularization for artifact reduction in electron tomography
Abstract Obtaining high-quality 3D reconstructions from electron tomography of crystalline particles embedded in lighter support elements is crucial for various material systems such as catalysts for fuel cell applications. However, significant challenges arise due to the limited tilt range, sparse and low signal-to-noise ratio of the measurements. In addition, small metal particles can cause strong streaking and shading artifacts in the 3D reconstructions when using conventional reconstruction algorithms due to the presence of Bragg diffraction and the large scattering cross-section difference between the materials of the particles and the background support regions. These artifacts lead to errors in the downstream characterization affecting extraction of critical features such as the size of the metal particles, their distribution and the volume of the lighter support regions. In this paper, we present a two-stage algorithm based on metal artifact reduction, utilizing model-based iterative reconstruction methods with adaptive adjustment of regularization parameters. Our approach yields high-quality 3D reconstructions compared to traditional algorithms, accurately capturing both the metal particles as well as the background support. We demonstrate the effectiveness of our algorithm through simulated and experimental bright-field electron tomography data, showing significant improvements in reconstruction quality compared to traditional methods.
Identification of biomarkers in Alzheimer’s disease and COVID-19 by bioinformatics combining single-cell data analysis and machine learning algorithms
Background Since its emergence in 2019, COVID-19 has become a global epidemic. Several studies have suggested a link between Alzheimer’s disease (AD) and COVID-19. However, there is little research into the mechanisms underlying these phenomena. Therefore, we conducted this study to identify key genes in COVID-19 associated with AD, and evaluate their correlation with immune cells characteristics and metabolic pathways. Methods Transcriptome analyses were used to identify common biomolecular markers of AD and COVID-19. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were performed on gene chip datasets (GSE213313, GSE5281, and GSE63060) from AD and COVID-19 patients to identify genes associated with both conditions. Gene ontology (GO) enrichment analysis identified common molecular mechanisms. The core genes were identified using machine learning. Subsequently, we evaluated the relationship between these core genes and immune cells and metabolic pathways. Finally, our findings were validated through single-cell analysis. Results The study identified 484 common differentially expressed genes (DEGs) by taking the intersection of genes between AD and COVID-19. The black module, containing 132 genes, showed the highest association between the two diseases according to WGCNA. GO enrichment analysis revealed that these genes mainly affect inflammation, cytokines, immune-related functions, and signaling pathways related to metal ions. Additionally, a machine learning approach identified eight core genes. We identified links between these genes and immune cells and also found a association between EIF3H and oxidative phosphorylation. Conclusion This study identifies shared genes, pathways, immune alterations, and metabolic changes potentially contributing to the pathogenesis of both COVID-19 and AD.
Photodynamic therapy in non-surgical treatment of periodontitis
Active tuberculosis in household contacts of bacteriologically confirmed pulmonary tuberculosis patients: A multicenter study finding the ‘Missed One’ in Central Ethiopia
Background There was a ‘missing millions’ gap between the incidence of tuberculosis (TB) cases and the notified cases. In many TB high-burden countries, only about 25% of household contacts (HHCs) completing household TB evaluation and 20–89% of eligible contacts did not adhere to TB screening. The study was conducted to assess the yield of door-to-door TB household contact investigation among household contact of bacteriologically confirmed pulmonary TB cases in central Ethiopia. Methods This cross-sectional study was carried out in selected health facilities of central Ethiopia from January 1, 2023 to December 3, 2023.All sequential voluntary bacteriologically confirmed TB patients and their HHCs without discrimination by age were included in the study. Xpert Ultra assay and TB culture were used to investigate active TB from sputum sample. Spearman’s correlation analysis was used to determine the correlation between the index case cycle threshold value and the corresponding HHCs. Multivariable logistic regression analysis was done to investigate the associated risk factors for active TB in HHCs. Results Among 967 HHCs claimed by 303 index cases (259 drug susceptible TB (DS-TB) and 44 multi-drug resistance TB (MDR/RR-TB)), 902(93.07%) HHCs had received baseline symptom-based TB evaluation. Presumptive TB was identified in 20.17% (182) of the evaluated HHCs and 13(1.44%) were diagnosed with active TB. Eleven HHCs (7.24%; 95% CI: 3.85–12.9%) from DS-TB index case contacts and 2 (6.67%; 95% CI: 1.16–23.51) from MDR/RR-TB indexes HHCs were found to be MTB detected Rifampicin resistance not detected cases. The Xpert ultra assay results revealed an 84.62% (95% CI: 57.77–95.68) Rifampicin drug resistance concordance between the index case and the corresponding HHC. Active TB was significantly associated with night sweating and sharing a bed with the index patient, P-value < 0.05. Conclusion Home-to-home TB contact screening have high active TB yield and implementable in both rural and urban areas of the nation only by mentoring and motivating the health extension workers. Proximity to bacteriologically confirmed TB patient for long time exposes household contacts for active TB. Scheduling convenient times and last-mile service delivery to contacts is very important to address the missed active TB cases in the community.
Deep learning-based classification of diffusion-weighted imaging-fluid-attenuated inversion recovery mismatch
Leveraging natural language processing for efficient information extraction from breast cancer pathology reports: Single-institution study
Background Pathology reports provide important information for accurate diagnosis of cancer and optimal treatment decision making. In particular, breast cancer has known to be the most common cancer in women worldwide. Objective For the data extraction of breast cancer pathology reports in a single institute, we assessed the accuracy of methods between regular expression and natural language processing (NLP). Methods A total of 1,215 breast cancer pathology reports were annotated for NLP model development. As NLP models, we considered three BERT models with specific vocabularies including BERT-basic, BioBERT, and ClinicalBERT. K-fold cross-validation was used to verify the performance of the BERT model. The results between the regular expression and the BERT model were compared using the named entity recognition (NER) techniques. Results Among three BERT models, BioBERT was the most accurate parsing model (average performance = 0.99901) for breast cancer pathology when set to k = 5. BioBERT also had the lowest error rate for all items in the breast cancer pathology report compared to other BERT models (accuracy for all variables ≥ 0.9). Therefore, we finally selected BioBERT as the NLP model. When comparing the results of BioBERT and regular expressions using NER, we identified that BioBERT was more accurate than regular expression method, especially for some items such as intraductal component (BioBERT: 1.0, RegEx: 0.1644), lymph node (BioBERT: 0.9886, RegEx: 0.4792), and lymphovascular invasion (BioBERT: 0.9918, RegEx: 0.3759). Conclusions Our results showed that the NLP model, BioBERT, had higher accuracy than regular expression, suggesting the importance of BioBERT in the processing of breast cancer pathology reports.
Evaluating the effect of 0.125% atropine on foveal microvasculature using optical coherence tomography angiography
Reply to: Should we be careful with exercise in post-exertional malaise after Long COVID?
Analysis of differential expression of matrix metalloproteinases and defensins in the nasopharyngeal milieu of mild and severe COVID-19 cases
Introduction A subset of COVID-19 disease patients suffers a severe form of the illness; however, underlying early pathophysiological mechanisms associated with the severe form of COVID-19 disease remain to be fully understood. Several studies showed the association of COVID-19 disease severity with the changes in the expression profile of various matrix metalloproteinases (MMPs) and defensins (DA). However, the link between the changes in the expression of MMPs and DA in the nasopharyngeal milieu during early phases of infection and disease severity remains poorly understood. Therefore, we performed differential gene expression analysis of MMPs and DA in the nasopharyngeal swab samples collected from normal (COVID-19 negative), mild, and severe COVID-19 cases and examined the association between MMP and DA expression and disease severity. Material and method A total of 118 previously collected nasopharyngeal samples from mild and severe COVID-19 patients (as per the WHO criteria) and 10 healthy individuals (COVID-19 negative, controls) were used in this study. A real-time qPCR assay was used to determine the viral loads and assess the mRNA expression of MMPs and DA. One-way ANOVA was applied to perform multiple comparisons (estimate differences) in MMPs and defensin gene expression in the normal vs mild vs severe groups. In addition, a multivariable logistic regression analysis was carried out with all the variables from the data set using ‘severity’ as the outcome variable. Results Our results showed that as compared to controls, DA1, DA3, and DA4 expression was significantly (p < 0.05) upregulated in the mild group, whereas the expression of DA6 was significantly downregulated in both mild and severe groups (p-value < 0.05). Similarly, compared to controls, the expression of MMP1 and MMP7 was significantly downregulated in both mild and severe groups, whereas MMP2 expression was upregulated in the mild group (p-value < 0.05). Additionally, the regression analysis showed that the expression of MMP1, MMP2, and MMP9 was significantly associated with the severity of the disease. Conclusion The early detection of changes in the expression of MMPs and defensins may act as a useful biomarker/predictor for possible severe COVID-19 disease, which may be useful in the clinical management of patients to reduce COVID-19-associated morbidity and mortality.
Expression profile and characterization of respiratory burst oxidase homolog genes in rice under MeJA, SA and Xoo treatments
Bioturbation in the hadal zone
Abstract The hadal zone, >6 km deep, remains one of the least understood ecosystems on Earth. We address bioturbational structures in sediment cores from depths exceeding 7.5 km, collected during the IODP Expedition 386 in the Japan Trench. Micro-CT imaging on 20 core sections allowed to identify biogenic sedimentary structures (incipient trace fossils) and their colonization successions within gravity flow deposits. Their frequency, and consequent changes in substrate consistency, oxygenation and organic matter delivery and remineralization controlled the endobenthic colonization. The gravity-flow beds show recurring bioturbation successions: The initial colonization is characterized by deposit-feeding structures such as Phycosiphon, Nereites and Artichnus generating typically 20 cm thick intensively bioturbated fabrics. The final colonization stage comprises slender spiral, lobate and deeply penetrating straight and ramifying burrow systems such as Gyrolithes, Pilichnus and Trichichnus, interpreted to include burrows of microbe farming and chemosymbiotic invertebrates. The main factor precluding colonization is soupy substrate. Organic matter degradation and post-event upward expansion of the anoxic zone drive the change from deposit feeding to microbe-dependent feeding strategies.
Inverse association between prognostic nutritional index and kidney stone prevalence: A population-based study
Background Kidney stones frequently occur due to metabolic disorders, dietary habits, and lifestyle influences. The Prognostic Nutritional Index, which reflects an individual’s nutritional condition, might be associated with kidney stone prevalence. This study examines the association between PNI and kidney stone prevalence in US adults. Methods The study used data from the National Health and Nutrition Examination Survey database from 2009–2018 and excluded pregnant women, and individuals who lacked data on kidney stones, or had incomplete Prognostic Nutritional Index data. Independent associations between Prognostic Nutritional Index and kidney stones were investigated by multivariate logistic regression and subgroup analyses, in addition to exploring nonlinear associations using smoothed curves and threshold effects. Results A total of 13,835 participants aged ≥ 20 years were included, with a kidney stone prevalence of 8.48%. An inverse association was observed between the Prognostic Nutritional Index and kidney stone prevalence (OR = 0.97, 95% CI = 0.96–0.98, P < 0.001). This relationship was not significantly modified by race, education, marital status, or comorbidities such as hypertension, diabetes, and hyperlipidemia. However, sex and total cholesterol levels influenced the association. Stratified analysis showed a significant negative association in men (OR = 0.98, 95% CI = 0.96–0.99, P = 0.031), but not in women. A nonlinear relationship was identified in individuals with total cholesterol ≥ 5.2 mmol/L, with a significant negative association below the inflection point of 57 (OR = 0.96, P = 0.012) and a positive association above it (OR = 1.11, P = 0.03). These findings suggest that the Prognostic Nutritional Index is inversely associated with kidney stones, particularly in men and those with high cholesterol levels. Conclusion The Prognostic Nutritional Index was negatively associated with the risk of kidney stones, particularly in men and individuals with high cholesterol levels below the identified inflection point, suggesting that tailored nutritional management may be crucial for these subgroups.
Clusterin inhibits lipopolysaccharide induced liver injury
Associations of long-term exposure to nitrogen oxides with all-cause and cause-specific mortality
Transcriptomic analysis of benznidazole-resistant Trypanosoma cruzi clone reveals nitroreductase I-independent resistance mechanisms
The enzyme nitroreductase I (NTRI) has been implicated as the primary gene responsible for resistance to benznidazole (Bz) and nifurtimox in Trypanosoma cruzi. However, Bz-resistant T. cruzi field isolates carrying the wild-type NTR-I enzyme suggest that additional mechanisms independent of this enzyme may contribute to the resistance phenotype. To investigate these alternative mechanisms, in this paper, we pressured a Trypanosoma cruzi clone with a high Bz concentration over several generations to select Bz-resistant clones. Surprisingly, we found a highly drug-resistant clone carrying a wild-type NTRI. However, the knockout of this gene using CRISPR-Cas9 in the sensitive clone showed that NTRI indeed induces resistance to Bz and supports the idea that the resistant one exhibits mechanisms other than NTRI. To explore these new mechanisms, we performed an RNA-seq analysis, which revealed genes involved in metabolic pathways related to oxidative stress, energy metabolism, membrane transporters, DNA repair, and protein synthesis. Our results support the idea that resistance to benznidazole is a multigenic trait. A Deeper understanding of these genes is essential for developing new drugs to treat Chagas disease.