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Artificial intelligence and the wellbeing of workers
Author Correction: Metamaterials with asymmetric transmission effect based on magnetic field manipulation
BRCA1 expression in ovarian cancer and its relationship with clinicopathological characteristics and prognosis
Early warnings are too late when parameters change rapidly
Validation of albumin platelet product as a non-invasive fibrosis staging tool in patients with chronic HCV-related liver disease
Abstract Background and aim: The Albumin Platelet Product (APP) has emerged as a promising non-invasive biomarker for fibrosis staging in chronic liver disease (CLD). This cross-sectional study aims to evaluate the effectiveness of APP compared to established non-invasive markers of fibrosis in an Egyptian cohort with HCV-related CLD. Methods: 580 participants were assessed across different fibrosis stages (F0-F4) to analyze the relationship between APP and liver fibrosis. APP was compared with FIB-4 and APRI scores for diagnostic performance. Results: The study included 580 patients with HCV-related CLD (mean age: 37.6 ± 9.66 years; 74.3% males). APP proved superiority in identifying liver cirrhosis (F4) at Cut-off values ≤ 0.59 with 81% sensitivity, 63.6% specificity (p < 0.001). APP showed a significant correlation with fibrosis stages, with an AUC of 0.920 (95% CI: 0.888–0.953) for distinguishing F4 from F0-F3 surpassing both FIB4 and APRI scores. However, FIB-4 proved superiority in distinguishing advanced fibrosis (F ≥ 3) with AUC of 0.899 (95% CI: 0.871–0.927) compared to APP with AUC of 0.87 (95% CI: 0.84–0.90), respectively. Multivariate analysis confirmed APP as an independent predictor of fibrosis (OR: 0.997, 95% CI: 0.995–0.998; p < 0.001). Conclusion: APP showed the highest performance in predicting cirrhosis, suggesting its potential as a simple, non-invasive marker for identifying patients with advanced liver disease. Its integration into clinical practice may enhance early detection and risk stratification in chronic HCV-related fibrosis. However, further multicenter, longitudinal studies are required to validate its efficacy across diverse populations and other liver disease etiologies.
Recognizing American Sign Language gestures efficiently and accurately using a hybrid transformer model
Abstract Gesture recognition plays a vital role in computer vision, especially for interpreting sign language and enabling human–computer interaction. Many existing methods struggle with challenges like heavy computational demands, difficulty in understanding long-range relationships, sensitivity to background noise, and poor performance in varied environments. While CNNs excel at capturing local details, they often miss the bigger picture. Vision Transformers, on the other hand, are better at modeling global context but usually require significantly more computational resources, limiting their use in real-time systems. To tackle these issues, we propose a Hybrid Transformer-CNN model that combines the strengths of both architectures. Our approach begins with CNN layers that extract detailed local features from both the overall hand and specific hand regions. These CNN features are then refined by a Vision Transformer module, which captures long-range dependencies and global contextual information within the gesture. This integration allows the model to effectively recognize subtle hand movements while maintaining computational efficiency. Tested on the ASL Alphabet dataset, our model achieves a high accuracy of 99.97%, runs at 110 frames per second, and requires only 5.0 GFLOPs—much less than traditional Vision Transformer models, which need over twice the computational power. Central to this success is our feature fusion strategy using element-wise multiplication, which helps the model focus on important gesture details while suppressing background noise. Additionally, we employ advanced data augmentation techniques and a training approach incorporating contrastive learning and domain adaptation to boost robustness. Overall, this work offers a practical and powerful solution for gesture recognition, striking an optimal balance between accuracy, speed, and efficiency—an important step toward real-world applications.
Green and white analytical approach for parallel quantification of gabapentin and methylcobalamin in medicinal products using inventive RP-HPLC technique
Retraction Note: Unraveling the influence of TiO2 nanoparticles on growth, physiological and phytochemical characteristics of Mentha piperita L. in cadmium-contaminated soil
Randomised land use pathway generation allows efficient multi-outcome appraisal
Daily briefing: Space shots from the largest digital camera in the world
Cryogenic mouse tissue homogenization as an alternative to fresh-frozen biopsy use for genomics, transcriptomics, proteomics and metabolomics
Abstract The classical approach of using adjacent pieces of fresh-frozen tissue for various omics analysis from the same sample possesses a risk of biological mismatch between arising from intrinsic tissue heterogeneity. We propose an alternative approach of tissue cryogenic pulverization and lyophilization before distribution for omics studies for a more reliable analysis. Here, we compare individual omics layer readouts from fresh-frozen adjacent tissue pieces and homogenized powder in mouse brain, kidney, and liver. Genomics, transcriptomics, proteomics, and metabolomics analyses showed comparable RNA integrity, DNA methylation, and coverage of transcripts, proteins, and metabolites across both methods. Moreover, the homogenized-lyophilized powder usage led to reduced heterogeneity between biological replicates. We conclude that the cryogenically pulverized-lyophilized tissue approach not only maintains a critical molecular feature coverage and quality but also provides a homogenous basis for various omics analysis enhancing reproducibility, sample transport, storage and enabling multi omics base on one and the same tissue aliquot.
Trump team vows to improve kids’ health: scientists are sceptical
K36-based inhibitor analogs as potential therapeutics against SARS-CoV-2 main protease (Mpro): a computational investigation
Abstract The global pandemic caused by SARS-CoV-2 has underscored the critical necessity for effective antiviral therapies. The viral main protease (Mpro), crucial for viral replication, has emerged as a promising therapeutic target. In the present study, the inhibitory potential of ten drug-like compounds (KL1-KL10), designed as derivatives of the parent inhibitor K36, against Mpro, has been computationally investigated. To elucidate the binding affinities and interactions of the suggested drugs with the Mpro active site, molecular docking and molecular dynamics (MD) simulations till 500 nanoseconds have been applied. Our results revealed that many suggested inhibitors exhibited enhanced binding affinities compared to the parent inhibitor K36. Among these, KL7 displayed the most favourable binding characteristics, with a docking score of -13.54 and MM-PBSA binding energy of -34.57 kJ/mol, surpassing that of K36. Molecular dynamics simulations demonstrated persistent binding of these compounds to Mpro, with RMSD values ranging from 0.5 to 2.0 nm, suggesting their potential as effective inhibitors. These findings suggest that the proposed ligands hold promise as potential scaffolds for developing potent antiviral drugs against COVID-19.
Will Gates and other funders save massive public health database at risk from Trump cuts?
Author Correction: Emergence of highly virulent multidrug and extensively drug resistant Escherichia coli and Klebsiella pneumoniae in buffalo subclinical mastitis cases
Evidence for a transgenerational mutational signature from ionizing radiation exposure in humans
Abstract The existence of transgenerational effects of radiation exposure on the human germline remains controversial. Evidence for transgenerational biomarkers are of particular interest for populations, who have been exposed to higher than average levels of ionizing radiation (IR). This study investigated signatures of parental exposure to IR in offspring of former German radar operators and Chernobyl cleanup workers, focusing on clustered de novo mutations (cDNMs), defined as multiple de novo mutations (DNMs) within 20 bp. We recruited 110 offspring of former German radar operators, who were likely to have been exposed to IR (Radar cohort, exposure = 0–353 mGy), and reanalyzed sequencing data of 130 offspring of Chernobyl cleanup workers (CRU, exposure = 0–4080 mGy) from Yeager, et al. In addition, we analyzed whole genome trio data of 1275 offspring from unexposed families (Inova cohort). We observed on average 2.65 cDNMs (0.61 adjusted for the positive predictive value (PPV)) per offspring in the CRU cohort, 1.48 (0.34 PPV) in the Radar cohort and 0.88 (0.20 PPV) in the Inova cohort. Although under the condition that the proportion of true mutations is low in this analysis, this represented a significant increase ( $$\:p<0.005$$ ) of cDNMs counts, that scaled with paternal exposure to IR ( $$\:p<0.001$$ ). Our findings corroborate that cDNMs are a potential transgenerational biomarker of paternal IR exposure.
Eco-friendly spectrophotometric quantification of the potential combination of mirabegron and tamsulosin
Abstract Recently, men with overactive bladder have been prescribed mirabegron and tamsulosin for the treatment of benign prostatic hyperplasia. Highly efficient and environmentally sustainable spectrophotometric methods have been developed for the accurate determination of mirabegron and tamsulosin in their pure forms as well as within pharmaceutical formulations. This study presents three effective and simple spectrophotometric methods for the simultaneous quantification of mirabegron and tamsulosin. The current protocols have demonstrated validation for linearity across concentration ranges of 3–20 µg/mL for mirabegron and 2–40 µg/mL for tamsulosin, utilizing dual wavelength, ratio difference, and derivative ratio techniques. The coefficients of determination exceeded 0.999. The validation of these methodologies was conducted in accordance with the guidelines set forth by the International council for Harmonization (ICH). Quality control laboratories may utilize existing techniques to identify the binary combination because of their high accuracy and cheap cost. The evaluation of the environmental sustainability of the established approaches was conducted using AGREE, GAPI, MOGAPI and whiteness revealing their notable eco-friendliness. The proposed method was deemed practical after the evaluation carried out with the Blue Applicability Grade Index (BAGI) assessment.