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Isolation and characterization of a Chlamydia muridarum tc0237 mutant from a genetic screen that is attenuated in epithelial cells
Chlamydia are obligate intracellular bacterial pathogens that infect a wide range of vertebrate hosts. Despite having highly conserved genomes, closely related Chlamydia species can exhibit distinct host and tissue tropisms. The host tropisms of the human pathogen Chlamydia trachomatis and the closely related mouse pathogen Chlamydia muridarum are influenced by their ability to evade host immune responses, particularly those mediated by interferon gamma. However, there is evidence that tissue tropism is driven by additional poorly understood host and Chlamydia factors. In this study, we used a forward genetic approach to investigate the mechanisms that mediate C. muridarum tissue tropism. We conducted a tropism screen using a randomly mutagenized C. muridarum library and murine cell lines representing different tissues. We identified a mutant isolate whose growth was restricted in murine rectal and oviduct epithelial cells in an interferon gamma-independent manner. This phenotype was mapped to a missense mutation in tc0237, a gene that mediates the affinity of C. muridarum for cultured human epithelial cells. Our analysis of growth dynamics showed that the tc0237 mutant exhibits a developmental delay in rectal epithelial cells. Together, these results suggest that TC0237 plays a role in C. muridarum tissue tropism.
Development and evaluation of deep neural networks for the classification of subtypes of renal cell carcinoma from kidney histopathology images
Abstract Kidney cancer is a leading cause of cancer-related mortality, with renal cell carcinoma (RCC) being the most prevalent form, accounting for 80–85% of all renal tumors. Traditional diagnosis of kidney cancer requires manual examination and analysis of histopathology images, which is time-consuming, error-prone, and depends on the pathologist’s expertise. Recently, deep learning algorithms have gained significant attention in histopathology image analysis. In this study, we developed an efficient and robust deep learning architecture called RenalNet for the classification of subtypes of RCC from kidney histopathology images. The RenalNet is designed to capture cross-channel and inter-spatial features at three different scales simultaneously and combine them together. Cross-channel features refer to the relationships and dependencies between different data channels, while inter-spatial features refer to patterns within small spatial regions. The architecture contains a CNN module called multiple channel residual transformation (MCRT), to focus on the most relevant morphological features of RCC by fusing the information from multiple paths. Further, to improve the network’s representation power, a CNN module called Group Convolutional Deep Localization (GCDL) has been introduced, which effectively integrates three different feature descriptors. As a part of this study, we also introduced a novel benchmark dataset for the classification of subtypes of RCC from kidney histopathology images. We obtained digital hematoxylin and eosin (H&E) stained WSIs from The Cancer Genome Atlas (TCGA) and acquired region of interest (ROIs) under the supervision of experienced pathologists resulted in the creation of patches. To demonstrate that the proposed model is generalized and independent of the dataset, it has experimented on three well-known datasets. Compared to the best-performing state-of-the-art model, RenalNet achieves accuracies of 91.67%, 97.14%, and 97.24% on three different datasets. Additionally, the proposed method significantly reduces the number of parameters and FLOPs, demonstrating computationally efficient with 2.71 × $$10^9$$ FLOPs & 0.2131 × $$10^6$$ parameters.
Structure-based identification of bioactive compounds as trace amine-associated receptor 1 agonists for the therapeutic management of major depressive disorder
The global burden of major depressive disorder (MDD) drives ongoing efforts to develop safer and more targeted treatment strategies. Modern advances have identified trace amine-associated receptor 1 (TAAR1) as a promising non-monoaminergic target with demonstrated efficacy in treating neuropsychiatric conditions, including MDD. Discovering TAAR1 agonists holds promise for modulating neuropsychiatric disorders while potentially reducing the common side effects associated with conventional therapies. This study employed a structure-based virtual screening approach to identify potential TAAR1 agonists from the IMPPAT database, a curated collection of Indian medicinal plant-derived bioactive phytoconstituents. The initial filtering was done on the compounds based on Lipinski’s rule of five, which was followed by molecular docking, PAINS screening, pharmacokinetic evaluation, and bioactivity predictions. Through this integrative screening approach, we discovered two promising phytochemicals, Bianthraquinone and Peimisine, demonstrating strong binding affinities and favorable drug-like properties. Detailed interaction analysis revealed that both compounds formed stable hydrogen bonds, hydrophobic contacts, and π-π stacking interactions with key residues within the TAAR1 binding pocket, contributing to their high binding stability and receptor specificity. All-atom molecular dynamics simulations, MM-PBSA, and essential dynamics analyses affirmed that they were stable and exhibited favorable conformational interactions. These findings highlight the therapeutic potential of naturally derived TAAR1 agonists and support their further exploration as next-generation antidepressants, laying the foundation for future experimental and clinical development.
Research on multiple paths of transportation system resilience building in digital contexts
Experts’ content validation of the parosmia, phantosmia, and anosmia test (PARPHAIT): A qualitative study
Background The parosmia, phantosmia, and anosmia test (PARPHAIT) has previously been developed as a tool for capturing quantitative and qualitative symptoms of olfactory dysfunction. Its content validity was evaluated in a patient sample, from a statistical point of view through an exploratory factor analysis, and now in a panel of experts. Based on these evaluations, we present the most recent version. The aim of this study was to evaluate the content of the novel PARPHAIT in an expert panel. Methods This was a qualitative interview study with experts in the field of olfaction. The study was done in an international research community on olfactory dysfunction. Thirteen participants (mean age 49.7, 53.8% men) with expertise in the field of smell were interviewed about PARPHAIT’s content, format, and applicability. Participants were selected based on their experience in the field of smell and invited to a digital interview. Results Suggested improvements of PARPHAIT were provided and evaluated. Alterations were done to the formulation of items and introductory text (i.e., instructions and definitions), as well as aspects covered, and the structure and design of the questionnaire. Conclusions PARPHAIT was considered a clear, user-friendly tool suitable for a clinical assessment context. Improvements were made based on experts’ feedback, leading to a final version of the tool. However, some aspects of PARPHAIT remain open for consideration (e.g., response and scoring design) and more work remains to reach consensus on how the PARPHAIT best can capture symptoms of olfactory dysfunction.
GIS-based accessibility analysis of urban park green space landscape
Genetic structure of different ethnic populations at the frontotemporal dementia risk loci
Background and purpose Frontotemporal dementia (FTD) is a devastating neurodegenerative disorder affecting behavior, language, and cognition. It has a complex and still poorly understood genetic basis. The prevalence of FTD and other neurodegenerative disorders varies in populations of different ethnicities. This study aimed to analyze the genetic structure of different ethnic populations at FTD risk loci and provide insights into possible genetic factors underlying the above variation. Methods The data of single-nucleotide polymorphisms (in total 32) with genome-wide significance were extracted from the GWAS Database. The individual genotype data were retrieved from the 1000 Genomes Phase 3 Project. We analyzed several standard parameters of population genetic structure and computed a composite polygenic risk score. In total, five major ethnic superpopulations and 26 subpopulations were analyzed. Results All populations were significantly differentiated (P << 10−5) at the FTD risk loci. Ethnic populations manifested clear differences in the enrichment/depletion patterns of the risk alleles as evidenced by heatmaps. The population-specific unweighted genetic risk scores were relatively low and averaged at 0.091 ± 0.078. The scores differed significantly at the super- and subpopulation levels. Conclusions The results suggest that the major ethnic groups and their subpopulations differ by the allelic and genotypic structure at the FTD risk loci. This may be one of the key factors explaining the different prevalence of FTD across populations. However, currently available data on the epidemiology and genetics of FTD warrant further research.
Prediction of hospital mortality in patients with left-sided infective endocarditis using a score in the first hours of admission
Automated 1D Helmholtz coil design for cell biology: Weak magnetic fields alter cytoskeleton dynamics
Evidence of the biological impacts of weak magnetic fields have been reported for more than fifty years. However, research progress on such effects has been hampered by a lack of systematics in most experiments. Efforts to increase the systematics in such cell biology experiments must include the capability of producing fields that can be automatically adjusted and that are stable throughout an experiment’s duration, usually operating inside an incubator. Here, we report on the design of a fully automated 1D Helmholtz coil setup that is internally water cooled, thus eliminating any confounding effects caused by temperature fluctuations. The coils also allow cells to be exposed to magnetic fields from multiple directions through automated controlled rotation. Preliminary data, acquired with the coils placed inside an incubator and on a rat vascular smooth muscle cell line, confirm previous reports that both microtubule and actin polymerization and dynamics are altered by weak magnetic fields.
Characterizing tomato genotypes in the varied climates of north-western Himalayas and implications for environmental resilience using GGE Biplot analyses
Correction: Multidimensional well-being and income inequality in Central and Eastern Europe: A comparative analysis of CEE North and CEE Continental countries
Effect of operating conditions on the bursting performances of cross-grooved domed rupture disc
Correction: Analyzing cold hardiness (Based on DTA) of one-year-old branches of peaches
Impact of straw return and nitrogen fertilizer on photosynthesis and yield of red kidney beans
Global trends in smoking-attributable rheumatoid arthritis burden: Insights from GBD 2021
Background Smoking is one of the most significant environmental risk factors for Rheumatoid Arthritis (RA). However, there is a lack of research examining the impact of smoking trends on the RA disease burden globally. Methods This study utilized the Global Burden of Disease (GBD) 2021 database to analyze the burden of RA attributable to smoking. Five key indicators were examined: Deaths, Disability-Adjusted Life Years (DALYs), Years Lived with Disability (YLDs), Years of Life Lost (YLLs), and the Socio-Demographic Index (SDI). The analysis was stratified by age, sex, year, and region. Additionally, smoking prevalence and tobacco use data from 2000 to 2021 were extracted from the World Health Organization (WHO) to evaluate trends in smoking and RA burden. Results From 1990 to 2021, while the age-standardized Smoking Attributable Fraction for RA burden metrics generally declined globally, alongside decreasing age-standardized rates (ASR) of smoking-attributable burden in many regions, the absolute global number of both deaths and DALYs due to smoking-attributable RA paradoxically increased (deaths: from 1,792–2,264; DALYs: from 145,727–215,780; all 95% Uncertainty Intervals provided in text). Significant disparities were observed: high-income regions demonstrated greater reductions in smoking-attributable burden than low- and middle-income regions. Males and older populations experienced higher burdens across all metrics. Moderate SDI countries had the highest smoking-attributable age-standardized Deaths and YLLs rate (e.g., Deaths 0.04 per 100,000 population), whereas high SDI countries showed higher YLDs rate (e.g., 3.5 per 100,000 population). Conclusions This study highlights the persistent impact of smoking on the global RA burden and underscores the critical role of tobacco control policies in alleviating this burden. Tailored interventions for high-burden regions (e.g., Eastern Europe and East Asia) and high-risk populations (e.g., middle-aged and older males) are essential. Strengthening early interventions and resource allocation in low- and middle-income regions and enhancing long-term RA management in high-income regions are crucial steps to further reduce the global RA burden.
A decentralized privacy-preserving XR system for 3D medical data visualization using hybrid biometric cryptosystem
Abstract In the era of digital healthcare, accurate and secure 3D visualization of medical data is critical for collaborative surgical planning. Traditional centralized systems suffer from security vulnerabilities and lack of depth cues necessary for accurate visualization of complex anatomy. We present a decentralized Extended Reality (XR)-based framework integrating a Hybrid Biometric Cryptosystem (HBC), hierarchical redactable blockchain, and InterPlanetary File System (IPFS)-based storage to address these limitations. The HBC combines leveled Homomorphic Encryption (HE) and Fuzzy Vault (FV) schemes for privacy-preserving multimodal biometric authentication. A hierarchical blockchain ensures tamper-resistance, consensus-based redactions, and secure access control. Photorealistic, spatially registered 3D models of brain MRI data are rendered in Augmented Reality (AR) and Mixed Reality (MR), enabling intuitive surgical planning. Edge caching accelerates data retrieval, enabling real-time interaction. Real-world deployment on Android and HoloLens 2 platforms demonstrates the clinical utility and robustness of the proposed framework. Security analysis confirms resistance to security threats such as replay, spoofing, etc, and unauthorized redactions. We achieve Equal Error Rates (EER) of 0.53% in AR and 0.68% in MR environments, with average authentication latency under 530 ms. A structured user study involving 40 clinicians confirms the system’s clinical utility, usability, and compliance with GDPR (General Data Protection Regulation) and HIPAA (Health Insurance Portability and Accountability Act) regulations. Therefore, the proposed framework offers a scalable, secure, and immersive platform for collaborative medical data visualization in digital healthcare.
Breakfast consumption patterns and associated factors among adolescent high-school students in Tullo District, Eastern Ethiopia
Background There is growing proof to recommend eating breakfast has positive health and school-related outcomes for adolescents, including improved performance, attention, brain development, and physical growth. However, there is a dearth of evidence on the comprehensive understanding of breakfast consumption patterns and associated factors. Therefore, this study aimed to assess breakfast consumption patterns and their associated factors among adolescent high school students in the Tullo district, Eastern Ethiopia. Methods An institution-based cross-sectional study design was conducted among 405 randomly selected adolescent high school students in the Tullo District, Eastern Ethiopia, from October 09–29, 2023. A self-administered questionnaire was utilized to collect the data. Epidata version 4.6 and SPSS Statistics version 27.0.1 were used for data entry and analysis, respectively. Both bivariable and multivariable logistic regression analyses were performed to identify the factors associated with breakfast consumption patterns. An adjusted odds ratio (AOR) with a 95% confidence interval (CI) was calculated to determine the strength of the association, and a p-value of 0.05 was used to determine statistical significance. Result Nearly half, 46.2% (95% CI: 41.5, 51.4), of participants had irregular breakfast consumption (skipped). Being female (AOR = 5.28; 95% CI: 2.69, 10.36), family size of >5 (AOR = 4.76; 95% CI: 2.41, 9.36), being a rural resident (AOR = 3.34; 95% CI: 1.78, 6.25), no formal maternal education (AOR = 3.89; 95% CI: 2.09, 7.22), chewing khat (AOR = 3.13; 95% CI: 1.59, 6.16), cigarette smoking (AOR = 3.06; 95% CI: 1.02, 9.17), and eating disorders (AOR = 6.54; 95% CI: 2.19, 19.43) were significantly associated with irregular breakfast consumption patterns among adolescents. Conclusion The findings of this study showed that the prevalence of irregular breakfast consumption (breakfast skipping) among adolescent high school students was high. Being female, rural residency, no formal maternal education, current smoking of cigarettes, current khat chewing, and eating disorders were identified as factors associated with breakfast consumption patterns. Given that almost half of adolescents in Tullo District skip breakfast, several modifiable factors associated with this practice, focused interventions are essential.
Human exposure to harmful urban traffic noise pollution levels: a case study from seoul, South Korea
Health inequities in functional limitation among Mexican older adults: An intersectional approach
Functional limitation represents a major health concern among older adults, with its incidence increased based on personal characteristics such as being a woman, having minor levels of education, and lower socioeconomic status, leading to health inequities. Addressing these inequities requires comprehensive frameworks like intersectionality to provide a broader perspective. This study analyzes health inequities in functional limitation among Mexican older adults using data from the 2021 round of the Mexican Health and Aging Study (MHAS) within an intersectional framework. The Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) technique, recognized as the gold standard in quantitative intersectionality research, was employed. Six variables were assessed: age, sex, education, social engagement, economic status, and access to health services. The results indicate that age, social engagement, and economic status were the main variables that explain functional limitation. Enhancing social engagement emerges as a practical short-term strategy to improve functionality and reduce inequities. Contrary to prior evidence, sex was not directly associated with functional limitation. Therefore, higher rates of functionality loss previously reported in the literature may not simply be linked to being a woman but rather to the societal implications of being a woman in contemporary contexts. Similarly, access to health services did not show a significant relationship with functional limitation despite the health system being a critical intermediate social determinant of health with the potential to address inequities. This research underscores the importance of intersectionality in understanding inequality, offering a nuanced perspective on overlapping systems of oppression and privilege to address disparities in Mexican older adults.