Search Results (43,962)

SARS-CoV-2 main protease (M^pro) is essential for viral polyprotein processing and represents a prime target for antiviral drug discovery. However, most available screening strategies rely on computational predictions or cell-free biochemical approaches that provide limited functional context and often require specialized instrumentation, while mammalian cell-based models remain costly and require high biosafety levels. Accordingly, there remains a shortage of simple, rapid, and biosafe functional screening tools suitable for early-stage prioritization of potential M^pro inhibitors, particularly those derived from natural sources and in urgent situations such as the COVID-19 pandemic. In this study, a bacterial colorimetric reporter assay was developed that directly links SARS-CoV-2 M^pro activity to β-galactosidase function in Escherichia coli. To the best of our knowledge, the developed assay represents the first bacterial colorimetric model for functional detection of SARS-CoV-2 M^pro inhibition based on a phenotypic readout. The assay enables the rapid visual detection of protease inhibition on X-gal-containing medium and provides a cost-effective and biosafe platform for prioritizing candidate inhibitors, under standard laboratory conditions, prior to further validation. Due to its bacterial expression context, this assay is intended for functional screening to provide the most promising candidate compounds and/or extracts for subsequent biochemical or mammalian cell-based validation; it is not intended to determine quantitative potency or to replace further validation approaches. It should be noted that the selective compound uptake in E. coli restricts the range of chemical compositions that can be evaluated using this method. Therefore, proof-of-concept application was demonstrated using pomegranate juice, a representative natural inhibitor source, rather than most currently known specific M^pro inhibitors. In addition, other plant-derived preparations, including rhubarb, grape, and red/black currant juices, were tested demonstrating the assay’s applicability to diverse natural matrices. Full article

The utilization of polymer-based additive manufacturing processes for the production of functional components, consumer goods, spare parts, etc., has increased thanks to recent technological advances. The Arburg Plastic Freeforming (APF) process is a promising AM technology, in which standard plastic granules are deployed, and droplets are discharged along a track instead of using continuously extruded straws, unlike other filament-based processes, to the benefit of various industries that require good mechanical properties while maintaining dimensional precision. Due to the round shape of the droplets and tracks, however, defects such as voids can occur between individual paths during processing, which affect, most notably, mechanical properties. The electrical/ferroelectric properties of conductive/electroactive polymers are also affected. This study focuses on determining the optimal form factor for processing a special grade polyvinylidene fluoride (PVDF) material whilst other parameters, along with the ones ascertained in previous work, are kept constant. Along with tensile tests, X-ray computed microtomography (µ-CT) and scanning electron microscopy (SEM) analyses are implemented, particularly to observe microstructural porosity. Electrical properties and possible piezoelectric behavior are investigated via an originally adapted analytical method. The results provide important insights into the APF process and printing high-performance plastics with individual features, expanding the potential for further applications. Full article

Synthesized by expressing natural collagen sequences in specific hosts, recombinant collagen exhibits multiple advantages, encompassing a higher content of bioactive domains, enhanced antioxidant activity, the absence of viral pathogens, favorable hydrophilicity, reproducible production, and low immunogenicity. Consequently, it has found extensive use in applications ranging from biomaterials and pharmaceuticals to skincare. This review systematically explores various expression systems for recombinant collagen, including those utilizing Escherichia coli, Pichia pastoris, plants, insect baculovirus, and mammalian cells. It provides a detailed comparison of their differences and commonalities in terms of production efficiency, post-translational modification capability, and cost-effectiveness. Key separation and purification techniques for recombinant collage-notably precipitation, affinity chromatography, ion-exchange chromatography, and gel filtration chromatography are further introduced, with an in-depth analysis of the applicable scenarios and purification outcomes for each method. Finally, the review comprehensively summarizes the characterization methods for both the physicochemical properties and biological functions of recombinant collagen. For physicochemical properties, techniques covered include scanning electron microscopy, micro-differential thermal analysis, circular dichroism spectroscopy, SDS-PAGE, mass spectrometry, and Fourier-transform infrared spectroscopy. For biological functions, the focus is on its roles and the corresponding assessment methods in processes such as cell proliferation, migration, adhesion, and wound healing. Building upon this comprehensive overview, current challenges facing recombinant collagen are identified, and future directions are proposed, emphasizing the need to reduce R&D costs, refine testing methods for cosmetic products, and improve safety evaluation protocols to advance the field. Full article

Thermoresponsive chitosan hydrogels hold significant promise for advancing biomedical technologies, yet their frequent reliance on petroleum-based polymers raises biosafety and environmental concerns. The present study utilized a molecular functionalization strategy to transform chitosan into thermoresponsive alkylated chitosan (ICS). The ICS was subsequently covalently crosslinked to construct a fully degradable, all-chitosan thermoresponsive hydrogel (TR-ICS_gel), showcasing the effective integration of structural design and functionality. By adjusting the ICS concentration, TR-ICS_gels with varying volume phase transition temperatures (VPTTs) were obtained. Above the VPTT, strengthened alkyl chain hydrophobic interactions triggered hydrogel dehydration and pronounced, reversible shrinkage–swelling. The hydrogel maintained a stable swelling response over 20 consecutive temperature-stimulus cycles. Further investigation was conducted on the effects of ionic strength and small-molecule solvents on the thermoresponsive behavior of TR-ICS_gel. Soil burial and buffer solution tests demonstrated that the hydrogel underwent almost complete degradation within 27 and 15 days, respectively, and the degradation rate could be regulated by the ICS concentration. The TR-ICS_gel’s all-chitosan framework ensured excellent biocompatibility, with cell viability maintained above 95%. This study presents a strategy for developing fully bio-based, degradable smart hydrogels, enhancing biosafety and environmental friendliness. Moreover, these results provide crucial performance data and theoretical support for their practical application. Full article

Hand rehabilitation in neurologic conditions such as stroke and cerebral palsy traditionally emphasizes repetitive task practice with visually observable feedback, despite motor impairment arising largely from abnormal neuromuscular activation. We present a platform that leverages noninvasive measurements of brain and muscle activity for neurofeedback-guided movement training. Trainees first learn to control EEG during movement preparation, followed by reciprocal control of finger muscle EMG during exoskeleton-assisted movement. We describe the platform design and two feasibility studies. Five neurotypical individuals learned to use EEG and EMG to drive an exoskeleton to grasp and release a virtual ball in a single session. They achieved a mean success rate of 65%, demonstrating improved movement latency (9%) and task completion time (6%) across the session. One individual post-stroke trained with the platform across eight sessions and exhibited improvements on the Box and Blocks Test, the Action Research Arm Test, and the Wolf Motor Function Test. These results demonstrate the feasibility of multi-level, neurofeedback training that targets neural activation throughout movement, rather than movement outcome alone. By explicitly engaging both cortical and muscular control signals, this paradigm offers a promising new direction for hand rehabilitation following neurologic injury. Full article

The physiological relevance of in vitro models is limited because conventional two-dimensional cell culture systems are unable to replicate the structural and functional complexity of native tissues. Extracellular matrix (ECM)-mimetic hydrogels have become important platforms for tissue engineering applications. This work developed hybrid hydrogels that mimic important biochemical and mechanical characteristics of cardiac tissue by combining decellularized bovine pericardium-derived (dBP) ECM, gellan gum (GG), and spermine (SPM). Although dBP offers tissue-specific biological cues, processing compromises its mechanical integrity. This limitation was overcome by adding GG, whose ionic gelation properties were optimized using DMEM and SPM. The hydrogels’ mechanical, biological, physicochemical, and structural characteristics were all evaluated. Under physiologically simulated conditions, the formulations showed quick gelation and long-term stability; scanning electron microscopy revealed an interconnected, ECM-like porous microarchitecture. While uniaxial compression testing provided Young’s modulus values comparable to native myocardium, rheological analysis revealed a concentration-dependent increase in storage modulus with increasing SPM content. H9C2 cardiomyoblasts were used in cytocompatibility studies to confirm that cell viability, morphology, and cytoskeletal organization were all preserved. All of these findings support the potential application of dBP−GG−SPM hydrogels in advanced in vitro cardiac models by showing that they successfully replicate important characteristics of cardiac ECM. Full article

Fluorinated greenhouse gases (FGGs) are classified as worldwide pollutants and have a high global warming potential compared to other greenhouse gases. Detecting the existence and concentration of new and older refrigerant gases is crucial for assessing system functionality and determining whether they can be recycled or need to be disposed of. Additional justifications for the necessity of quantitative measurements of these gases include the manufacturing of air conditioning components; leak detection is conducted to ensure they are free of leaks. Classical laboratory Fast Fourier transform spectrometers enable the detection and measurement of substances while being delicate, unwieldy, and costly, and typically requiring a skilled technician to operate them. For the estimation of refrigerants in the field, a portable, user-friendly, and cost-effective detection device must be deployed. This article provides an in-depth analysis of the categorization of refrigerant gases using an Internet of Things (IoT) gas detection device. The functionality in effectively differentiating between important refrigerant gases, like R-32 and R-134a, with low delay, is demonstrated through practical tests. With the portable device, this study utilizes Fourier-Transformed infrared spectra measured from the refrigerants R-32 and R-134a, collected using a custom-made 3D-printed tubular reactor equipped with two BaF₂ windows, suitable for use in the beamline of a Bruker IR Spectrometer. Calibration was performed by exposing the infrared sensor to controlled gas environments with varying amounts of refrigerant gases using accurately produced gas mixtures. Following the on-field analysis of the reclaimed refrigerants, the obtained data was immediately processed, and both the data and the results were uploaded to an IoT platform, making them available to business-to-business (B2B) clients. The functionality of the device is demonstrated. Full article

In this study, we investigate how computationally simplified activation functions affect predictive performance, inference latency, and energy usage in long short-term memory-based temperature prediction for wind turbine generator bearings. We tested three different types of long short-term memory (LSTM) cells, along with bidirectional LSTM (biLSTM) networks, to determine their effectiveness in modeling dynamic changes in gearbox bearing temperatures. We compared several activation-function variants, focusing on variants that are either computationally simple or known to give good performance in deep recurrent networks. The results show that the best-performing architectures achieved root mean squared errors (RMSEs) between $0.0798$ and $0.0822$, corresponding to coefficients of determination in the range of $R^2=0.84$–$0.85$. When applied across five turbines, the best-performing architectures (peephole and bidirectional) achieved root mean squared errors of $0.0898$, $0.0882$, and $0.042$, respectively. The best activation function-enhanced variant (the peephole) improved accuracy by approximately $3\%$ while maintaining low model complexity. These findings provide a practical and efficient solution for embedded predictive maintenance systems, providing high accuracy without incurring the computational cost of deeper or bidirectional architectures. Full article

Essential hypertension is a leading cause of chronic kidney disease (CKD) and is frequently complicated by hypertensive nephropathy, characterized by nephroangiosclerosis and increased intrarenal vascular resistance, assessable by renal resistive index (RRI). Oxidative stress and endothelial dysfunction contribute to CKD progression, and the Mediterranean diet (MD) has been associated with a more favorable oxidative and endothelial profile, although data linking diet to renal microcirculation in hypertensive nephropathy remain limited. The aim of this study is to evaluate the relationship between RRI, oxidative stress, endothelial function, and adherence to the Mediterranean diet in patients with essential hypertension and hypertensive nephropathy. We performed a cross-sectional single-center study and we enrolled 99 patients with essential hypertension, hypertensive nephropathy, and CKD stages G1–G4 (KDIGO). All patients underwent laboratory testing, measurement of oxidative stress markers (sNOX2-dp, H₂O₂) and endothelial function (NO), renal ultrasound with interlobar RRI assessment, and PREDIMED questionnaire for MD adherence. A significant direct correlation was observed between RRI and oxidative stress markers (sNOX2-dp and H₂O₂) (p = 0.002, r = 0.302; p = 0.002, r = 0.322), while a significant inverse correlation was found between RRI and the endothelial function marker (NO) (p = 0.013, r = −0.302). The correlation between RRI and PREDIMED questionnaire scores did not reach statistical significance, but there was a trend toward an inverse association (p = 0.06, r = −0.18). In addition, a significant inverse correlation was observed between RRI and eGFR (p = 0.005, r = −0.27), consistent with published data. We also found a significant inverse correlation between sNOX2-dp and PREDIMED scores (p = 0.034, r = −0.21); no statistically significant correlations with H₂O₂ and NO were observed in this analysis. Higher intrarenal vascular resistance is associated with heightened oxidative stress, impaired endothelial function, and lower eGFR. Adherence to the Mediterranean diet is linked to lower NOX2-mediated oxidative stress, supporting a potential association between higher MD adherence and lower NOX2-related oxidative stress. These findings are hypothesis-generating and require confirmation in adequately powered longitudinal and interventional studies before any clinical inference on CKD progression can be made. Full article

Interferometric Synthetic Aperture Radar (InSAR) is an advanced imaging geodesy technique for detecting and characterizing surface deformation with high spatial resolution and broad spatial coverage. However, as an inherently post-event observation method, InSAR suffers from limited capability for near-real-time and short-term updates of deformation time series. In this paper, we proposed a data-driven adaptive framework for deformation prediction based on a hybrid deep learning method to accurately predict the InSAR-derived deformation time series and take the Xi’erguazi−Mawo landslide complex (XMLC) as a case study. The InSAR-derived time series was initially decomposed into trend and periodic components with a two-step decomposition process, which were thereafter modeled separately to enhance the characterization of motion kinematics and prediction accuracy. After retrieving the observations from the multi-temporal InSAR method, two-step signal decomposition was then performed using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variational Mode Decomposition (VMD). The decomposed trend and periodic components were further evaluated using statistical hypothesis testing to verify their significance and reliability. Compared with the single-decomposition model, the further decomposition leads to an overall improvement in prediction accuracy, i.e., the Mean Absolute Errors (MAEs) and the Root Mean Square Errors (RMSEs) are reduced by 40–49% and 36–42%, respectively. Subsequently, the Radial Basis Function (RBF) neural network and the proposed CNN-BiLSTM-SelfAttention (CBS) models were constructed to predict the trend and periodic variations, respectively. The CNN and self-attention help to extract local features in time series and strengthen the ability to capture global dependencies and key fluctuation patterns. Compared with the single network model in prediction, the MAEs and RMSEs are reduced by 22–57% and 4–33%, respectively. Finally, the two predicted components were integrated to generate the fused deformation prediction results. Ablation experiments and comparative experiments show that the proposed method has superior ability. Through rapid and accurate prediction of InSAR-derived deformation time series, this research could contribute to the early-warning systems of slope instabilities. Full article

Background: Gustatory dysfunction represents an underrecognized complication that may influence dietary behaviors and metabolic control. Previous investigations have suggested alterations in taste in patients with diabetes, yet the relationship between disease duration and specific taste modalities remains incompletely characterized. Aim: This study aimed to (i) compare orosensory detection thresholds for lipid and sweet tastes between patients with recent type 2 diabetes mellitus (rT2DM) (duration ≤ 5 years) and chronic type 2 diabetes mellitus (cT2DM) (duration > 5 years), and (ii) determine whether diabetes duration is associated with alterations in chemosensory function in a North African population. Methods: A cross-sectional comparative pilot study was conducted at the National Institute of Nutrition and Food Technology in Tunis, Tunisia, from April to June 2021. Sixty-seven patients with type 2 diabetes mellitus (T2DM) receiving oral antidiabetic medication were recruited through systematic sampling and divided into two groups: rT2DM (n = 30, duration ≤ 5 years) and cT2DM (n = 37, duration > 5 years). Orosensory detection thresholds for lipid taste were assessed using eight ascending concentrations of linoleic acid ranging from 0.018 to 12 mmol/L. In contrast, sweet taste thresholds were evaluated using a sucrose concentration series ranging from 0.01 to 5 mmol/L. The three-alternative forced-choice method with an ascending-concentration presentation was employed for both taste modalities. Detection thresholds were defined as the lowest concentration at which participants correctly identified the taste quality. Results: Patients with cT2DM exhibited significantly elevated orosensory detection thresholds compared to those with rT2DM for both taste modalities tested. The median linoleic acid detection threshold was 6.000 mmol/L in cT2DM versus 0.058 mmol/L in rT2DM (p < 0.001), representing a 107-fold increase in detection threshold. For sweet taste, the median sucrose detection threshold was 1.0 mmol/L in cT2DM compared with 0.5 mmol/L in rT2DM (p < 0.001), indicating a 2-fold increase in the threshold. In the overall patient cohort, the duration of diabetes was positively correlated with both fat taste perception thresholds (r = 0.657, p < 0.001) and sweet taste perception thresholds (r = 0.466, p < 0.001). However, when analyses were performed by diabetes duration-based subgroups, these correlations were observed only for fat taste perception in cT2DM, with no statistically significant correlations found in rT2DM. In multivariate linear regression analyses adjusted for age, body mass index, and sex/gender, the duration of diabetes remained independently associated with fat and sweet taste perception. Conclusions: Extended T2DM duration is associated with substantial elevations in orosensory detection thresholds for both lipid and sweet tastes in a North African population. These findings suggest that disease chronicity may contribute to chemosensory impairment, potentially influencing dietary preferences and metabolic control in patients with diabetes. Full article

Ecosystem vulnerability assessment is paramount for local environmental stability and lasting economic progress. This study selects Hubei Province as the research area, applying multi-source spatiotemporal datasets spanning the period 2000–2023. A pressure–state–response (PSR) framework, incorporating 14 distinct indicators, was developed. The selection criteria for these indicators adhered to principles of scientific rigor, all-encompassing scope, statistical representativeness, and practical applicability. The chosen indicators effectively encompass natural, anthropogenic, and socio-economic drivers, aligning with the specific ecological attributes and key vulnerability factors pertinent to Hubei Province. The analytic network process (ANP) method and entropy weighting (EW) method were integrated to ascertain comprehensive weights, thereby computing the ecological vulnerability index (EVI). In the meantime, we analyzed temporal and spatial EVI shifts. Spatial autocorrelation analysis, the geodetic detector, the Theil–Sen median, the Mann–Kendall trend test, and the Grey–Markov model were employed to elucidate spatial distribution, driving factors, and future trends. Results indicate that Hubei Province exhibited mild ecological vulnerability from 2000 to 2023, but with a notable deteriorating trend: extreme vulnerability areas expanded from 0.34% to 0.94%, while moderate and severe vulnerability zones also increased. Eastern regions demonstrate elevated vulnerability, but they were lower in the west, correlating with human activity intensity. The global Moran’s I index ranged from 0.8579 to 0.8725, signifying a significant positive spatial correlation of ecological vulnerability, with the highly vulnerable areas concentrated in regions with intense human activities, while the less vulnerable areas are located in ecologically intact areas. Habitat quality index and carbon sinks emerged as key drivers, possibly stemming from the forest–wetland composite ecosystem’s high dependence on water conservation, biodiversity maintenance, and carbon storage functions. Future projections based on Grey–Markov models indicate that ecological fragility in Hubei Province will exhibit an upward trend, with ecological conservation pressures continuing to intensify. This research offers a preliminary reference basis of grounds for ecological zoning, as well as sustainable regional development in Hubei Province, while also providing a theoretical and practical framework for constructing an ecological security pattern within the Yangtze River Economic Belt (YREB) and facilitating ecological governance in analogous river basins globally, thereby contributing to regional sustainable development goals. Full article

This paper introduces LLM4ATS , a framework integrating large language models, RAG, and closed-loop verification to automatically generate highly reliable automotive automated test scripts from natural language descriptions. Addressing the complex linguistic structure, strict rules, and strong dependency on the in-vehicle communication database inherent in ATS scripts, LLM4ATS innovatively employs fine-grained line-level generation and a rule-guided iterative refinement mechanism. The framework first enhances prompt context by retrieving relevant information from constructed syntax and case knowledge bases via RAG. Subsequently, each generated script line undergoes rigorous verification through a two-stage validator: initial syntax validation followed by semantic compliance checks against the communication database for signal paths and value domains. Any errors trigger structured feedback, driving iterative refinement by the large language model until fully compliant scripts are produced. This paper evaluated the framework’s effectiveness on real ATS datasets, testing models including GPT-3.5, GPT-4, Qwen2.5-7B, and Qwen2.5-72B-Instruct. Experimental results demonstrate that compared to zero-shot and few-shot baseline methods, the LLM4ATS framework significantly improves generation quality and pass rates across all models. Notably, the strongest GPT-4 model achieved a script pass rate of 91% with LLM4ATS, up from 42% in zero-shot mode, and validated functional effectiveness on a specified in-vehicle hardware platform (Chery Fengyun T28 dashboard). At the same time, expert manual evaluations confirmed the superior performance of the generated scripts in correctness, readability, and compliance with industry standards. Full article

Background: We describe the post-mortem analysis of a CARD11 variant allele, p.Ser995Leu, identified in fraternal twins who died in early infancy with no identifiable cause of death. CARD11 variants through varied inheritance models can alter immune function through loss- or gain-of-function mechanisms, involving distinct protein domains; yet the significance of GUK domain variants remains poorly characterized. Twin autopsies showed non-specific findings, such as pulmonary macrophage accumulation and splenic white pulp expansion, but without infection or structural abnormalities. Methods: Whole-exome sequencing, performed as part of molecular autopsies, identified the shared CARD11 p.Ser995Leu variant, previously classified as a variant of uncertain significance (VUS). We assessed evolutionary conservation across CARD family proteins and species and predicted functional impact using in silico tools, which estimate the likelihood that a variant is deleterious. AlphaFold-based structural modeling emphasized qualitative biophysical assessment. Using epidemiological data, population allele frequency, and Bayesian ACMG variant classification, we assessed competing hypotheses under an autosomal dominant model. Results: The p.Ser995Leu substitution affects a conserved, surface-exposed β-sheet within the GUK domain. While CADD scores exceeded 20, other predictive algorithms offered only partial support of pathogenicity. Structural modeling suggested a potential GUK domain destabilization. Integrating genetic, pathologic, immunologic, and probabilistic modeling, we propose a biologically plausible model in which the variant, like other GUK variants, may alter NF-κB or other signaling pathways and is likely pathogenic. Conclusions: While the CARD11 p.Ser995Leu variant’s contribution to disease is uncertain without functional validation or parental testing, and phenotypic findings are non-specific, the presence of an ultra-rare GUK domain variant in both twins, combined with in silico and statistical modeling, supports its interpretation as likely pathogenic or high risk. The results highlight the challenges of data-limited post-mortem variant interpretation. Full article

This protocol details an optimized step-by-step procedure for performing the Seahorse XF Cell Mito Stress Test on human umbilical vein endothelial cells (HUVECs) using the Agilent Seahorse XF Pro Analyzer. Designed to address practical challenges often overlooked in standard manuals, the method preserves the native adherent state of HUVECs—a key in vitro model in vascular aging (VA) research—enabling real-time, label-free measurement of mitochondrial respiration and glycolytic function without cell detachment. The workflow is presented chronologically, covering instrument preparation, cell seeding, compound loading, assay execution, and post-assay normalization, with integrated notes and troubleshooting tips refined through hands-on experience based on the official manuals. This protocol aims to set up a detailed, rearranged standard workflow to improve experimental efficiency, reduce operator error, and support reproducible and well-organized metabolic profiling of HUVECs in aging and cardiovascular studies. Full article