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Stringent selection drives convergence toward omicron-like SARS-CoV-2 receptor-binding motifs
Abstract In vitro protein evolution can provide powerful insights into the amino acid sequences that underlie key biological functions. Here, we use this to explore the evolutionary trajectories of the SARS-CoV-2 spike protein receptor-binding motif (RBM) binding the human angiotensin-converting enzyme 2 (ACE2), an essential first step in viral infection. Applying stringent selection pressures starting from the Wuhan or another non-Omicron variant protein-coding sequence results in rapid convergence towards Omicron characteristic mutations and its sub-lineages. Conversely, under mild selection, only some Omicron-like mutations are selected, however at lower frequencies and with incomplete representation. Stringent selection results in fewer, but dominant, non-synonymous mutations mirroring Omicron mutations and their variations within its sub-lineages. Notably, initiating evolution from Omicron itself results in maintenance of Omicron-defining mutations under both conditions. This evolutionary pattern parallels global SARS-CoV-2 mutation trends as well as in silico simulations, emphasizing the critical role of receptor-binding constraints in shaping viral adaptation. Mutations primarily associated with immune evasion are not selected by in vitro evolution. Our findings demonstrate the predictive capacity of in vitro evolution, suggesting Omicron RBM to be the humanized binding motif, emerging from high-stringency selection, superimposed on milder background pressures.
Comparative AI-optimized HPLC–DAD strategy for the simultaneous determination of ranolazine, amlodipine, and diltiazem with pharmacotherapeutic relevance and multi-trait sustainability assessment
Abstract A robust and reliable HPLC–DAD method was developed and validated for the simultaneous determination of ranolazine, diltiazem, and amlodipine, a drug combination relevant to the concurrent management of chronic angina and hypertension. Preliminary screening of chromatographic conditions was supported by an AI-assisted platform and subsequently refined through conventional experimental optimization. The proposed method was successfully applied to bulk materials and combined dosage forms. It was further extended to spiked rat plasma to verify its applicability for monitoring ranolazine in the presence of co-administered diltiazem and amlodipine. Chromatographic separation was achieved on a C18 column using an isocratic mobile phase consisting of buffer (pH 5), acetonitrile, and methanol (60:30:10, v/v) at a flow rate of 1.0 mL/min. The developed method provided efficient separation with satisfactory peak symmetry and resolution under optimized conditions. The AI-assisted predictions showed good agreement with experimentally optimized parameters, supporting their utility as a preliminary tool to guide method development. The method was validated in accordance with established guidelines, demonstrating satisfactory linearity, accuracy, precision, and selectivity within the studied concentration range. The analytical performance and environmental impact were evaluated and compared with previous methods using the Multi-Color Assessment tool, yielding an overall Whiteness Score of 64.8% overwhelming the other reported methods. In addition, the alignment of the AI-optimized method with sustainability principles was assessed using the Need, Quality, and Sustainability tool and compared with conventional optimization. The results demonstrate that the proposed method provides a reliable and environmentally considerate analytical approach for the simultaneous quantification of the studied drug combination in pharmaceutical formulations with demonstrated applicability in biological matrices.
Spatial predictors of response to chemo-immunotherapy in microsatellite stable metastatic colorectal cancer
Model-free sliding mode control for PMSM based on adaptive super-twisting reaching law
RAISE: A computational tool for evaluating sarbecovirus spillover potential
XGBoost-based model for predicting five-year survival in gastric cancer using clinical indicators
Coupled cross-sectional and longitudinal non-negative matrix factorization reveals dominant brain aging trajectories in 48,949 individuals
Synergistic interface engineering of Ni-integrated Co-ZIF67 on two-demensional MXene for enhanced oxygen evolution reaction
Retinoic acid drives cell fate specification, maturation and retinal regionality in human retinal organoids
Performance enhancement of a sugarcane bagasse-fired steam power plant through flue gas-assisted drying: a case study of Metehara Sugar Factory, Ethiopia
Abstract This study investigates the performance enhancement of a sugarcane bagasse-fired steam power plant at the Metehara Sugar Factory in Ethiopia by integrating flue-gas-assisted biomass drying. The primary objective is to evaluate how reducing the high as-received moisture content (~ 46%) of bagasse improves fuel quality and overall plant performance. A thermodynamic model of the existing simple Rankine cycle was developed using Engineering Equation Solver (EES) with IAPWS-IF97 water–steam properties. The model was applied to assess plant performance under two operating conditions: a base case using as-received bagasse and an upgraded case incorporating flue gas-assisted drying. Key operating parameters, including turbine inlet pressure (2 MPa) and condenser pressure (95 kPa), were maintained constant, while turbine inlet temperature varied based on combustion conditions. In the base case, the plant generates 9.03 MW of net power with a thermal efficiency of 22.56%. With the integration of flue gas-assisted drying, the reduction in moisture content increases the lower heating value and combustion temperature, leading to improved steam conditions. As a result, net power output increases to 16.61 MW, and thermal efficiency improves to 27.35%, representing an efficiency gain of 4.8 percentage points without additional fuel input. The results demonstrate that utilizing waste heat from boiler exhaust gases for biomass drying significantly enhances energy recovery and power generation capacity. This approach provides a practical and low-cost solution for improving the performance of biomass-fired power plants and supports more efficient utilization of available energy resources in the sugar industry.
Comparative study of pathology of various organs of rhesus macaques exposed to two different doses of acute total-body radiation
Abstract Acute, whole-body exposure to ionizing radiation has the potential to induce widespread injury, compromising multiple organ systems and predisposing the individual to a variety of pathologies. For medical countermeasures to advance towards regulatory approval, the United States Food and Drug Administration Animal Rule requires the use of validated large animal models of acute radiation syndrome (ARS). The rhesus macaque ( Macaca mulatta ) serves as a primary model for therapeutic development due to its close genetic, anatomic, and physiological homology to humans. Characterizing the nature of ARS is critical prior to drug development as it provides detailed insights into the system-specific responses to radiation exposure. In this study, we assessed the effects of 5.8 and 6.5 Gy total-body irradiation (TBI) on multiple organ systems in male and female rhesus macaques through histological evaluations of various tissues, clinical parameters, complete blood counts, and serum biochemistry. A total of 31 nonhuman primates were used to investigate the injury cascade of ARS. Our results demonstrate that TBI induces consistent lung injury along with gastrointestinal and hematopoietic alterations. Of note, severe splenic depletion in non-surviving animals, alongside unexpected tissue-specific responses such as reversed trends in sternum cellularity, were observed. Hematopoietic suppression was dose-dependent, with profound leukopenia, neutropenia, and thrombocytopenia, while red blood cell parameters declined more gradually. Serum biochemistry further revealed dynamic changes in various renal, hepatic, and pancreatic markers. Comparisons between 5.8 and 6.5 Gy exposures, as well as between males and females, revealed differences in injury severity and recovery, underscoring the need to account for both dose and sex in ARS model characterization.
First-principles investigation of the structural, optoelectronic, and thermoelectric properties of Sr2MgXO6 (X = S, Se) double perovskite materials
Association between periodontal status and salivary interleukin-6 levels in normal-weight and obese individuals with and without prediabetes
Intelligent virtual agents in psychotherapy: a safety evaluation across high-risk mental health scenarios
Abstract The growing burden of mental illness and limited access to evidence-based psychotherapy have increased interest in artificial intelligence (AI)–driven conversational agents as potential supports for mental health care. In this exploratory pilot study, we examined the safety and feasibility of an intelligent virtual agent (IVA) designed to simulate psychotherapeutic interactions, with a focus on high-risk situations involving suicidality and substance use. Two licensed psychotherapists engaged in scripted interactions with the IVA across 12 predefined scenarios addressing suicidality and substance abuse. The IVA was powered by GPT-4omni and embedded in a Unity-based avatar. After each interaction, testers evaluated acceptance, usability, and human–robot interaction. Two independent psychotherapists rated the IVA’s responses using a structured scale assessing guideline adherence, risk recognition, help provision, de-escalation, and empathy. No real patients were involved; all interactions were simulated for safety testing purposes. The IVA showed preliminary indications of good usability and generally empathic responses. However, problematic responses occurred in 29% of conversations, with 12.5% rated as highly critical. Responses rated as “critical” or “highly critical” referred to outputs that failed to provide adequate support, showed insufficient risk recognition, or included ethically problematic suggestions. Key concerns included inadequate recognition of risk, normalization of substance use, and insufficient referral to crisis resources, particularly in scenarios involving underage alcohol access and suicide-related inquiries. In this small, expert-based pilot safety evaluation, the findings suggest that although AI-based agents may improve access to mental health support, rigorous safety evaluation, clinical oversight, and robust safeguards are essential prior to clinical deployment. No clinical conclusions can be drawn from this simulated study.
Solvent-free and green synthesis of fused pyrimidine derivatives catalyzed by pillar[5]arene-based Lewis acid ionic liquid
Glucagon-like peptide-1 (GLP-1) levels are associated with acute kidney injury after cardiac surgery
Abstract Acute kidney injury (AKI) is a frequent and clinically relevant complication after cardiac surgery. Early prediction remains challenging because serum creatinine is a delayed and insensitive marker of renal dysfunction. Glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) are incretin hormones with established metabolic functions and emerging roles in cardiovascular, renal, and critical illness–related pathophysiology, and their perioperative association with AKI remains incompletely characterised. In this prospective single-centre observational study, 107 adults undergoing elective cardiac surgery were enrolled. Serum GLP-1 and GIP were measured at four time points (preoperatively, immediately postoperatively, and on postoperative days 1 and 4). AKI was defined according to KDIGO criteria. Subgroup analyses excluded patients with chronic kidney disease (CKD). Postoperative AKI occurred in 21 patients (19.6%), predominantly KDIGO stage 1. These patients had longer ICU stays (5 [2–12] vs. 3 [1–10] days; p < 0.001), higher SAPS II scores (35 [28–39] vs. 29 [25–33]; p = 0.027), and higher SOFA scores on postoperative days 1 and 4 (both p < 0.05). Preoperative GLP-1 levels were significantly elevated in patients who developed AKI (38.3 [30–52] vs. 27.4 [16.7–48.9] pmol/L; p = 0.04), whereas baseline creatinine and GIP did not differ. The association between GLP-1 and AKI remained significant after excluding patients with CKD. Preoperative endogenous GLP-1 levels were associated with postoperative AKI and were elevated earlier in the perioperative course compared with serum creatinine, particularly in patients without CKD. These findings suggest that GLP-1 may reflect early renal stress or vulnerability and warrant further investigation in larger cohorts.
Mechanisms of employability development for applied talents in the artificial intelligence era
Hyperspectral image classification network based on multiscale spatial-spectral fusion and semantic enhancement encoder
Abstract Hyperspectral image classification has become a pivotal task in remote sensing data processing. However, current deep learning-based methods still face challenges in effectively extracting discriminative joint spatial-spectral features, especially when handling complex spatial structures and highly variable spectral responses. To address this issue, a novel hyperspectral image classification network (MSNet) based on multiscale spatial-spectral fusion (MSSF) and semantic enhancement encoder (SEE) is proposed. First, principal component analysis is employed to reduce the dimensionality of hyperspectral image, preserving essential spectral information while mitigating noise interference. Second, the proposed MSSF extracts multiscale spatial features through a dedicated spatial branch while simultaneously mining discriminative spectral information via a parallel spectral branch. Feature fusion is subsequently employed to achieve synergistic interaction between spatial and spectral representations, effectively capturing joint characteristics of land covers across multiple scales. Third, the designed SEE incorporates a multi-head attention mechanism to explicitly model global feature dependencies, thereby enhancing the representation of semantically critical regions. Extensive experiments have been conducted on the Pavia University and Salinas datasets. Experimental results demonstrate that MSNet achieves outstanding performance, with overall accuracy reaching 95.68% and 96.84%, respectively, surpassing existing mainstream methods and validating its effectiveness.