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Comparing the motion of dark matter and standard model particles on cosmological scales
Preclinical efficacy and mechanisms of statin-loaded polymeric nanocapsules: a meta-analysis of tumor lipid metabolism inhibition
Adaptive multi-omics integration framework for breast cancer survival analysis
Dexamethasone enhances intestinal glucose absorption and TMPRSS2 expression with implications for hyperglycaemia and infection risk
The impact of social exclusion on malevolent creativity among Chinese university students: the roles of empathy, moral disengagement, and teacher support
An impaired glycolysis induces ATP deficiency and reduced cell respiration in stem cells of patients with autism spectrum disorders
System log anomaly detection based on contrastive learning and retrieval augmented
Conscientiousness personality is associated with increased thyroid nodule risk and sleep quality plays a masking role in this association
Targeting asparagine and cysteine in SARS-CoV-2 variants and human pro-inflammatory mediators to alleviate COVID-19 severity; a cross-section and in-silico study
Abstract To date, COVID-19 continues to pose a global health challenge, with substantial morbidity, mortality, and long-term post-COVID-19 complications threatening public health resilience. During the early pandemic, the IL-6 inhibitor (tocilizumab) was the widely used approved immunotherapy for critically ill patients; however, a subset of ICU cases exhibited normal interleukin-6 (IL-6) levels and failed to respond. We hypothesized that interleukin-17 (IL-17), which acts synergistically with IL-6, contributes to cytokine storm progression and severe inflammation. Our study uniquely integrates a clinical cross-sectional analysis with advanced in-silico modelling, directly linking patient-derived biomarker, radiological, and statistical data to molecular-level mechanisms of COVID-19 severity. Serum IL-17 was significantly elevated in critical versus moderate COVID-19 cases, with a threshold of 187.9 ng/mL predicting poor outcomes by ROC analysis. Logistic regression identified age and monocytes as independent predictors of severity, supporting a combined biomarker approach for improving the prognosis and clinical outcomes. Radiological findings, including ground-glass opacities and consolidations, alongside hematological abnormalities, were more frequent in critical cases. Computational docking revealed key amino acid residues—particularly asparagine (Asn) and cysteine (Cys)—as structural determinants shared by SARS-CoV-2 spike protein and human inflammatory mediators (IL-17R, IL-6R, CD41/CD61, CD47/SIRP). Asparaginase (ASNase) targeted critical residues such as the invariant gate residue “Asn343” and Cys213 of spike protein, Asn240 of IL-17R, and Asn136 of IL-6R. Several phytochemicals, including phytic acid and amygdalin, as well as synthetic agents such as candesartan, remdesivir, and enalapril, were found to preferentially bind to cysteine (Cys) residues—and, to a lesser extent, asparagine (Asn) residues—within key binding interfaces, in addition to targeting B-cell epitopes. This conserved residue preference supports the rationale for a dual-action therapeutic strategy in which asparaginase (ASNase) is combined with selected plant-derived ligands to simultaneously disrupt viral entry mechanisms and attenuate the inflammatory signalling. This dual-perspective approach not only identified IL-17 and IL-6 as independent severity predictors but also revealed conserved Asn and Cys motifs as critical therapeutic targets, leading to novel strategies—such as ASNase, synthetic agents and phytochemical combinations—for simultaneously blocking viral entry and modulating hyperinflammatory pathways. These findings warrant rigorous experimental and clinical validation to facilitate translation into effective therapeutic interventions.
Reconfigurable intelligent surface-assisted N-ary Alamouti
Federated nnU-Net for privacy-preserving medical image segmentation
Abstract The nnU-Net framework has played a crucial role in medical image segmentation and has become the gold standard in multitudes of applications targeting different diseases, organs, and modalities. However, so far it has been used primarily in a centralized approach where the collected data is stored in the same location where nnU-Net is trained. This centralized approach has various limitations, such as potential leakage of sensitive patient information and violation of patient privacy. Federated learning has emerged as a key approach for training segmentation models in a decentralized manner, enabling collaborative development while prioritising patient privacy. In this paper, we propose FednnU-Net, a plug-and-play, federated learning extension of the nnU-Net framework. To this end, we contribute two federated methodologies to unlock decentralized training of nnU-Net, namely, Federated Fingerprint Extraction (FFE) and Asymmetric Federated Averaging (AsymFedAvg). We conduct a comprehensive set of experiments demonstrating high and consistent performance of our methods for breast, cardiac and fetal segmentation based on a multi-modal collection of 6 datasets representing samples from 18 different institutions. To democratize research as well as real-world deployments of decentralized training in clinical centres, we publicly share our framework at https://github.com/faildeny/FednnUNet .
Application of multipoint flexible optical probe for intra-abdominal visceral perfusion monitoring
Kinematic modeling of musculoskeletal systems considering muscle inertia for monoarticular muscles
In silico design of natural-based structures as drug candidates to inhibit ROS1 protein
Association between smoking status and complete blood cell parameters in baseline data from the Fasa adults cohort study
Improved convergent cross mapping method for causal inference based on decomposition of the Lorenz trajectory
Genomic analysis of Listeria monocytogenes diversity over a 10-year period in Uruguay
Simultaneous detection of respiratory virus RNA on environmental surfaces in a university setting by a sensitive Surface 3-Step PCR platform
Abstract Most respiratory viruses like SARS-CoV-2 spread through aerosols and fomites, remaining viable in the air and on surfaces. The present study aims to detect simultaneously by a multiplex molecular approach (Surface 3-step PCR platform), the presence of the three major co-circulating respiratory viruses (SARS-CoV-2, Flu A/B and RSV A/B) from inert surface samples in non-healthcare environments on a university setting in Central Italy. In total, 400 environmental surface swabs were collected during the study period in a three time point longitudinal program (T1, the end of the first semester: November-December 2023, weeks 48–49; T2, the extraordinary exam session: January 2024, weeks 2–4; T3, the start of the 2nd semester: February 2024, weeks 8–9) among which 62 (16%) were positive for viral RNA and with the positive rate that dropped from 20% (25/125) to 8% (10/130). The frequency of environmental contamination was higher in small classrooms (30/135, 22%) than in medium (15/105, 14%) and large (13/115, 11%) ones. Here, we describe the use of a novel rapid and sensitive combined multistep molecular platform involving two process controls, one synthetic RNA added directly to the sample and one endogenous human, able to detect low copy numbers of viral RNA targets in high-touch surfaces, with high sensitivity (98% of valid results). This study shows the potential as an effective solution to apply targeted interventions to prevent the spread of the airborne infections within the university community.