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Development and validation of a prognostic nomogram model for severe osteomyelitis patients
Many-body van der Waals interactions in multilayer structures studied by atomic force microscopy
Heterozygous mutation in BRCA2 induces accelerated age-dependent decline in sperm quality with male subfertility in rats
Exosomes derived from hUC-MSCs exhibit ameliorative efficacy upon previous cesarean scar defect via orchestrating β-TrCP/CHK1 axis
Radiocarbon monoxide indicates increasing atmospheric oxidizing capacity
Data mining and analysis of adverse events of Vedolizumab based on the FAERS database
Genome-scale evolution in local populations of wild chimpanzees
Chemical looping synthesis of amines from N2 via iron nitride as a mediator
Study on the evolution characteristics and influencing factors of frost heave on kaolin clay during the horizontal freezing process
Validity and reliability of the step test to estimate maximal oxygen consumption in pediatric population
Deep learning and genome-wide association meta-analyses of bone marrow adiposity in the UK Biobank
Abstract Bone marrow adipose tissue is a distinct adipose subtype comprising more than 10% of fat mass in healthy humans. However, the functions and pathophysiological correlates of this tissue are unclear, and its genetic determinants remain unknown. Here, we use deep learning to measure bone marrow adiposity in the femoral head, total hip, femoral diaphysis, and spine from MRI scans of approximately 47,000 UK Biobank participants, including over 41,000 white and over 6300 non-white participants. We then establish the heritability and genome-wide significant associations for bone marrow adiposity at each site. Our meta-GWAS in the white population finds 67, 147, 134, and 174 independent significant single nucleotide polymorphisms, which map to 54, 90, 43, and 100 genes for the femoral head, total hip, femoral diaphysis, and spine, respectively. Transcriptome-wide association studies, colocalization analyses, and sex-stratified meta-GWASes in the white participants further resolve functional and sex-specific genes associated with bone marrow adiposity at each site. Finally, we perform a multi-ancestry meta-GWAS to identify genes associated with bone marrow adiposity across the different bone regions and across ancestry groups. Our findings provide insights into BMAT formation and function and provide a basis to study the impact of BMAT on human health and disease.
Harnessing CRISPR interference to resensitize laboratory strains and clinical isolates to last resort antibiotics
AbstractThe global race against antimicrobial resistance requires novel antimicrobials that are not only effective in killing specific bacteria, but also minimize the emergence of new resistances. Recently, CRISPR/Cas-based antimicrobials were proposed to address killing specificity with encouraging results. However, the emergence of target sequence mutations triggered by Cas-cleavage was identified as an escape strategy, posing the risk of generating new antibiotic-resistance gene (ARG) variants. Here, we evaluated an antibiotic re-sensitization strategy based on CRISPR interference (CRISPRi), which inhibits gene expression without damaging target DNA. The resistance to four antibiotics, including last resort drugs, was significantly reduced by individual and multi-gene targeting of ARGs in low- to high-copy numbers in recombinant E. coli. Escaper analysis confirmed the absence of mutations in target sequence, corroborating the harmless role of CRISPRi in the selection of new resistances. E. coli clinical isolates carrying ARGs of severe clinical concern were then used to assess the robustness of CRISPRi under different growth conditions. Meropenem, colistin and cefotaxime susceptibility was successfully increased in terms of MIC (up to > 4-fold) and growth delay (up to 11 h) in a medium-dependent fashion. ARG repression also worked in a pathogenic strain grown in human urine, as a demonstration of CRISPRi-mediated re-sensitization in host-mimicking media. This study laid the foundations for further leveraging CRISPRi as antimicrobial agent or research tool to selectively repress ARGs and investigate resistance mechanisms.
Study on discrete prediction model for mechanical behavior of buried pipelines under the influence of differential frost heave
One-year mortality and re-admission rate by disease etiology in National Heart Failure Registry of India
Innovative machine learning approaches for indoor air temperature forecasting in smart infrastructure
AbstractEfficient energy management and maintaining an optimal indoor climate in buildings are critical tasks in today’s world. This paper presents an innovative approach to surrogate modeling for predicting indoor air temperature (IAT) in buildings, leveraging advanced machine learning techniques. At the core of this study is the application of Long Short-Term Memory (LSTM) networks for time-series modeling, which significantly enhances the capture of temporal dependencies in temperature predictions. The proposed LSTM with RWCV (Rolling Window Cross-Validation) offers significant advantages over a usual LSTM in time-series tasks, particularly due to its ability to adapt to new data trends through the rolling window mechanism. It provides more robust and generalizable forecasts in dynamic environments, prevents overfitting through dropout and cross-validation, and improves model evaluation with temporal integrity. In contrast, traditional LSTM models are better suited for static, non-evolving datasets and may not handle dynamic time-series data effectively. To rigorously assess model performance, a comprehensive evaluation framework is developed, incorporating metrics such as mean square error (MSE) and the coefficient of determination (R²). Additionally, a novel cumulative error analysis method is introduced enabling real-time monitoring and model adjustment to maintain predictive accuracy over time. Test results demonstrate that model losses on the test dataset are only marginally higher than those on the training dataset, indicating robust generalization capabilities. Loss values range from 0.0004709 to 0.02819861, depending on building operating conditions. A comparative analysis reveals that Adaboost and Gradient Boosting models outperform linear regression, highlighting their potential for achieving energy-efficient and comfortable indoor climate management in buildings. The findings underscore the efficacy of the proposed approach for IAT prediction and point towards further research possibilities in dataset expansion and model optimization to enhance building climate management and energy conservation.
A cost-utility analysis of adding SGLT2 inhibitors for the management of type 2 diabetes with chronic kidney disease in Thailand
Physiological premature aging of ovarian blood vessels leads to decline in fertility in middle-aged mice
Investigating the relationship between built environment and urban vitality using big data
Distribution and risk assessment of microplastics in a source water reservoir, Central China
AbstractThe current researches on microplastics in different water layers of reservoirs remains limited. This study aims to investigate the microplastics in different water layers within a source water reservoir. Results revealed that the abundance of microplastics ranged from 2.07 n/L to 14.28 n/L (reservoir, water) and 3 to 7.02 n/L (river, water), while varied from 350 to 714 n/kg(dw) (reservoir, sediment) and 299 to 1360 n/kg(dw) (river, sediment). The average abundance in surface, middle, and bottom water were 6.83 n/L, 6.30 n/L, and 6.91 n/L respectively. Transparent fibrous smaller than < 0.5 mm were identified as the predominant fraction with Polypropylene and Polyethylene being the prevalent polymer types. Additionally, the pollution load index, hazard index, and pollution risk index were calculated for different layers and sediments. Results showed that surface water exhibited a moderate level of risk while the sediments posed a low level of risk. Both the middle and bottom water showed elevated levels of risk due to higher concentrations of polymers with significant toxicity indices. This study presents novel findings on the distribution of microplastics in different water layers, providing crucial data support for understanding the migration patterns of microplastics in source water reservoirs and facilitating pollution prevention efforts.