搜索结果：SLAS

The porcine epidemic diarrhea virus (PEDV) causes a gastrointestinal disease generating mortality rates approaching 100% in piglets worldwide. The S glycoprotein of PEDV is the main target for the development of vaccines. Two vaccines approved by the Ministry of Agriculture and Rural Development are used in Mexico: the first vaccine is based on an inactivated virus isolated more than a decade ago, whereas the second vaccine is based on mRNA technology. The most important tool for controlling PEDV outbreaks is vaccination; however, coronaviruses are characterized by the accumulation of multiple mutations, which compromise the immune response elicited by outdated vaccines. In this work, we classified the Mexican strains of PEDV reported so far in GenBank, according to their genotypes. Subsequently, we searched for B and T cell epitopes conserved in Mexican PEDV strains using bioinformatic tools. In addition, we explored whether these epitopes can induce allergies, autoimmunity, and/or toxic effects. Next, we determined the localization of B cell epitopes in the S glycoprotein using the protein crystal and protein modeling of several S glycoproteins. Finally, we carried out molecular docking analysis to assess whether these T cell epitopes could interact with the peptide-binding groove of the Swine Leukocyte Antigens (SLAs). Five conserved B cell epitopes were found to be exposed on the surface of the S glycoprotein, whereas several promiscuous CTL and HTL epitopes were bound, with low free energy, to the peptide-binding grooves of SLA-I and SLA-II, respectively. The best epitopes were used to generate a plasmid carrying the sequence to produce a recombinant protein. This plasmid was used for transfection experiments in PK-15 cell culture. The B cell epitopes reported here were recognized by the sera from pigs infected with PEDV but not by the sera from uninfected animals. These results justify future evaluations of the ability of these epitopes to stimulate cytokine production by T cells, antibody generation, and their neutralizing activity.

Plasma proteomics and metabolomics snapshots reveal a molecular signature in circulation delineating pathophysiology of major and minor neurocognitive disorder. To identify new cues to disease aetiology and diagnostic approach, we applied plasma proteomics and metabolomics profiling platforms to samples collected in a population-based study of the Singapore Longitudinal Ageing Studies Wave 2 (SLAS-2). In this longitudinal study, blood samples were analysed with standard clinical chemistry, plasma proteomics (Sengenics) and metabolomics (Nightingale) panels. Participants were followed up for the development of mild cognitive impairment (MCI) and dementia for 3-5 years. Of the total 1,892 molecules in all assay types, 463 demonstrated significant associations with baseline prevalent MCI and dementia. We trained an automatic linear modelling of predictors for follow-up new-onset MCI and dementia. The best model consists of 10 variables including ZSCAN18, PRKD3, SPANXN4, DDX43, saturated fatty acids, PPP3CA, NFATC4, IL-8, PAK6, and PDGFB. In terms of molecular function, these molecular markers are involved in immunological dysfunction and inflammatory reaction, protein coding, lipids, DNA-binding transcription factor activity, and nervous system development. In conclusion, our current research has identified an omics signature linked to new-onset mild cognitive disorder and dementia, which we hope can help enhance the accuracy of their diagnosis using circulating blood samples.

Contract management and quoting processes are mission-critical operations in modern enterprises, yet they remain prone to inefficiencies, compliance risks, and human error-particularly in IT and SaaS sectors where contract complexity is high. This paper introduces the Artificial Intelligence Contract Risk and Intelligence Model (AICRIM), a simulation-validated, prototype-level framework that integrates generative AI (GPT-4) and BERT-based semantic matching into Oracle Configure-Price-Quote (CPQ) systems to perform real-time compliance and risk assessment during SaaS and cloud deal negotiations. AICRIM autonomously identifies anomalies in data privacy clauses, service-level agreements (SLAs), and pricing terms by interpreting unstructured contract text and benchmarking it against GDPR, HIPAA, and ISO 27,001 regulatory standards. The framework is evaluated through structured simulation experiments over a corpus of 500 synthetic SaaS contract documents, using manual legal review and rule-based NLP as baselines. Simulation experiments demonstrate a 27-32% reduction in contract errors, a 38.2% reduction in deal cycle time, and a compliance detection accuracy of 92.1%, with statistically significant improvements over both baselines (p&#x2009;<&#x2009;0.01). These results are simulation-derived and serve as indicative upper-bound estimates pending real-world validation. AICRIM also embeds AI governance controls-AES-256 encryption, bias monitoring, and structured audit trails-to ensure ethical and auditable deployment. By extending Oracle CPQ via REST API event hooks, this model provides enterprises with a scalable, reproducible approach to contract risk automation.

This paper introduces a sophisticated, scalable testing system that integrates observability-driven automation with AI-augmented proactive quality engineering to tackle contemporary software delivery difficulties. The suggested system enhances PreventativeTestPro, an open-source, hybrid testing platform that combines black-box and white-box methodologies, by incorporating an innovative observability-based test orchestration layer. The platform utilizes logs, metrics, events, and traces alongside browser and server-side monitoring to promptly identify anomalies, enhance test case selection, and automate the creation of functional, performance, and security test suites. A distinctive characteristic is the incorporation of large language models (LLMs) to provide root cause insights and autonomously construct new test cases based on production behaviors and identified abnormalities, thus providing adaptive regression coverage and intelligent remediation. The system facilitates concurrent test execution with instantaneous AI-driven log analysis, fostering a continuous feedback loop between operations and testing. It has been validated in several enterprise scenarios, including microservices-based SaaS platforms and SAP BTP ecosystems. Empirical findings from four production deployments and a beta group of 49 engineers indicate a decrease of up to 30% in mean time to resolution, over 95% compliance with SLAs, and substantial improvements in both test coverage and defect traceability. The effortless connection with industry-standard tools illustrates its plug-and-play capability. This research presents a comprehensive, tool-independent, and forward-looking quality engineering methodology consistent with agile and DevOps principles. Future endeavors encompass dynamic anomaly classification through machine learning, extension to mobile and user experience-oriented systems, and augmented large language model capabilities for domain-specific test development and failure forecasting.

The glomerulus is the filtering unit of the kidney, and the glomerular filtration barrier (GFB) is responsible for filtering waste, retaining plasma protein, and maintaining fluid balance. Drug-induced nephrotoxicity is characterized by dysfunction of the GFB and is a main obstacle in the new therapeutic screening process. This paper presents the simple and robust SLAS (Society of Laboratory Automation and Society) standard format-based microfluidic GFB-on-a-chip (GFBoC). We formed and cultured the GFB by aligning human glomerular mesangial cells (gMCs), podocytes, and glomerular endothelial cells (gECs) on each side of a conventional transwell membrane as the glomerular basement membrane (GBM). This provides facile loading/unloading of the GFB transwell into the microfluidic chip, enabling its cultivation under pump-/tubing-less perfusion flow and additional off-chip bioanalysis. It also allows various and multiple experiments in parallel in a conventional incubator at moderate operation complexity. Glomerular selective permeability of the GFB was characterized by filtration and leakage of the representative macromolecule, albumin, via the GFB, while the drug doxorubicin affected the GFB during cultivation in the GFBoC. This demonstrated that the GFBoC has potential as a simple, robust, and efficient platform for the multiple testing of nephrotoxicity and kidney disease drugs in parallel.

Humans can adapt their gait to minimize energy cost when given sufficient exposure to novel energy landscapes. However, it remains unclear whether broad experience in one energy landscape is sufficient to initiate continuous optimization when encountering similar but distinct conditions. In this study, we used visual biofeedback to guide 15 participants to broadly explore walking gaits with different step length asymmetries (SLAs) while walking on a split-belt treadmill with a large split-belt ratio. We subsequently tested how participants adapted their walking patterns during free exploration trials at the large split-belt ratio and at a new, smaller split-belt ratio. Our results showed that during guided exploration, participants were exposed to energetically favorable conditions. However, participants did not self-select walking patterns that minimized their metabolic cost during either free exploration trial. When first exposed to the smaller split-belt ratio, participants immediately adjusted their leg swing distances and belt contact times while maintaining similar step lengths. Throughout this trial, they continued adapting by significantly increasing step lengths on the fast belt. Taken together, our results suggest that participants actively modified their gait strategy when exposed to an energy landscape distinct from the one in which they gained broad experience.

The accumulation of pathological bronchial secretions compromises ventilation and oxygenation in critically ill patients and may lead to atelectasis or secondary infection in severe cases, making timely identification and removal of pathological secretions essential during intensive care and surgical anesthesia. Conventional manual bronchoscopic assessment depends heavily on operator experience, lacks real-time reliability, and fails to meet clinical requirements for efficient and precise intervention. To address this limitation, this study proposes an Edge-Aware Dual-Scale Transformer (EADST) for intelligent and automated bronchial secretion recognition based on Canny edge features. Bronchoscopic image data from 50 critically ill pneumonia patients, including both normal physiological and pathological secretions, were preprocessed by grayscale enhancement and Canny edge detection to generate structural representations of secretion regions, which were subsequently processed through a patch embedding module for edge-aware feature mapping, a dual-scale attention module for capturing both global semantic and local structural dependencies, and an edge-aware feed-forward network to adaptively enhance critical channels, followed by a back-end classification head for real-time pathological secretion discrimination. All experiments were conducted under a unified Canny feature representation, and the proposed framework was evaluated against several classic state-of-the-art (SOTA) models, including VGG-16, ResNet-50, EfficientNet, MobileNet, and Vision Transformer (ViT). Experimental results demonstrate that EADST achieves superior accuracy (89.2%) and robustness in pathological secretion recognition on edge-derived features, indicating that attention-driven and edge-adaptive feature modeling effectively enhances bronchoscopic visual perception and providing a promising foundation for intelligent, real-time bronchoscopic pathological secretion aspiration decision support systems.

Cardiac complications arising from diabetes mellitus manifest as structural and functional myocardial alterations independent of traditional cardiovascular risk factors. The intricate molecular underpinnings driving disease evolution and the spectrum of cellular diversity remain inadequately characterized. Our investigation sought to systematically elucidate the transcriptional architecture and intercellular signaling frameworks in diabetes-associated myocardial dysfunction through integrated omics methodologies. We executed parallel bulk and single-cell transcriptomic profiling of cardiac specimens from diabetic disease models. Gene expression disparities were determined via DESeq2 employing stringent thresholds (|log&#x2082;FC| > 1; FDR < 0.05). Quality control applied specific thresholds (200-6000 genes/cell, <20% mitochondrial content), with clustering resolution optimized at 0.8 for main cell types and 1.2 for fibroblast subclustering. Single-cell datasets underwent Seurat-based processing with t-distributed stochastic neighbor embedding for population delineation. WGCNA employed soft-thresholding (R&#xb2; > 0.85), minimum module size of 30 genes, and merge cut height of 0.25. Co-expression module detection within fibroblast subsets was achieved through weighted correlation network construction. Functional annotation leveraged GO and KEGG repositories. CellChat analysis incorporated permutation-based significance testing (n = 100, p < 0.05) with CellPhoneDB validation. Intercellular signaling topology was reconstructed using CellChat, emphasizing macrophage migration inhibitory factor circuitry. Transcriptional profiling unveiled 2000 dysregulated transcripts against 28,840 stable genes, demonstrating substantial reprogramming during pathogenesis. Single-cell resolution exposed profound cellular heterogeneity encompassing myocytes, endothelium, fibroblasts, myeloid cells, and specialized populations including metabolic coordinators and stress-activated subsets. Granular fibroblast dissection revealed 21 molecularly distinct subtypes (designated M1-M21), underscoring remarkable intra-lineage diversity. Enrichment analyses highlighted perturbations in matrix architecture, inflammatory cascades, and proliferative control. Network analysis identified co-regulated gene clusters governing matrix remodeling, inflammation, and metabolic homeostasis. Communication mapping positioned MIF signaling as a pivotal intercellular coordination axis, with stress-responsive cells functioning as nodal integrators throughout disease progression. This integrative multi-platform investigation provides comprehensive molecular characterization of diabetes-induced cardiac pathology, revealing extensive cellular heterogeneity and intricate communication networks that extend previous single-cell cardiac studies.

The Stacks platform is a reconfigurable open microfluidic cell culture device that enables integrated analysis of multi-culture systems. Effective utilization of this platform requires repeated fluid exchanges for culture maintenance as well as experimental interventions and analysis. The initial operating protocols of the Stacks platform incorporated conventional manual liquid exchanges for cell culture that are performed using standard single-channel pipettes to remove and replace individual 10 &#xb5;L liquid droplets. While functional, this conventional Pipette Method (PM) for fluid exchange can be time-consuming and carries risk for intra- and inter-user variability in fluid exchange volumes. In order to improve the efficiency and reproducibility of ultra-low-volume fluid exchanges for the Stacks platform, we have developed a novel fluid removal device, called the Stacks Insert System (SIS), which is comprised of a base and insert component. In this manuscript, we describe the design of the SIS and compare its efficiency and reproducibility to fluid exchanges utilizing the conventional PM. We demonstrate that the SIS reduces fluid removal time by greater than 14-fold when compared to the conventional PM method while increasing uniformity of fluid volume removal, measured as total fluid volume removed per device. We propose the SIS protocol as an improved method to perform fluid removal for the integrated Stacks multi-culture platform. Furthermore, the principles of the SIS can be adapted to increase efficiency and reproducibility for other microfluidic and tissue chip platforms.

Biomolecular condensates (BCs) are membraneless organelles which play roles in key biological functions such as RNA metabolism, signal transduction and DNA repair, reflecting their importance in cellular organization and function. The dysregulation of condensate self-assembly and its internal material properties due to aberrant phase separation has been linked to neurodegeneration, cancers, viral infections, and cardiac diseases. Consequently, there is growing interest in the discovery and development of therapeutic molecules, referred to as condensate modifiers (c-mods), that specifically target BCs and/or their components which are associated with disease. In this perspective, we first provide readers with a brief overview of the possible modes of action of c-mods and the strategies underlying their design for effective targeting of BCs. Next, we highlight the role of traditional computer-aided drug discovery (CADD) in synergy with modern AI/ML methods in targeting BCs as illustrated in recent studies. Finally, we discuss the physicohemical features of the condensate microenvironment and c-mods that enable the favorable partitioning of the latter, thereby opening new avenues for targeting "undruggable" proteins within the condensate microenvironment. We conclude by providing an overview of the challenges that remain to successfully integrate experiment and computation, and discuss potential strategies to overcome them.

Antibody-drug conjugate (ADC) is a novel type of targeted systemic therapy that is changing the current landscape of tumor treatment. By integrating the specificity of antibodies with the cytotoxic effects of their payloads, ADCs facilitate precise tumor killing. In solid tumors such as breast cancer, non-small cell lung cancer (NSCLC), and urothelial carcinoma, ADCs have demonstrated significant efficacy with acceptable toxicity profiles. Colorectal cancer (CRC) ranks as the third most prevalent tumor globally, and there remains an unmet clinical need for targeted treatment options. The approval of T-DXd for HER2-positive (IHC3+) CRC marks ADCs' entry into this treatment arena. This article will focus on the clinical performance of ADCs and explore design considerations and future directions for ADCs in CRC. Significance statement ADCs, a targeted treatment strategy that has emerged over the past two decades, are rapidly evolving. we believe that ADCs will offer more targeted treatment options for CRC patients in the future. This review focuses on the clinical performance of ADCs and explores design considerations and future directions for ADCs in CRC. This study provides a reference for researchers to understand and learn ADC.

To address the challenges of hardware integration and system complexity in laboratory automation, this work introduces a universal platform built on two key innovations. First, a standardized instruction framework unifies the control of multi-brand robots by converting complex operations into simple, tabular instructions. Second, a zero-code natural language interface, powered by OpenAI's GPTs platform, translates user commands into executable workflows, which are reviewed by trained domain scientists before execution, with an automated validation mechanism providing an additional safeguard. The platform's performance was validated through complex, multi-device experiments, including an automated Cell Counting Kit-8 (CCK-8) cell viability assay, which yielded results highly consistent with those of manual operations. With a 99.0% success rate in translating natural language test instructions, this work demonstrates a practical framework to assist domain scientists in multi-robot laboratory automation.

This study was to optimize the current methods for identifying and predicting the risk of critical illness in patients with connective tissue disease-associated interstitial lung disease (CTD-ILD). First, 200 patients diagnosed with CTD-ILD were included, and detailed demographic, serological, and imaging data were collected. Second, a risk identification and prediction framework was constructed based on multivariate logistic regression and machine learning algorithms (random forest (RF) and convolutional neural network (CNN)) to identify significant determinants of critical illness. Finally, the overall performance of each model was evaluated using K-fold cross-validation and external validation procedures. A feature ablation experiment was conducted based on the optimal random forest model to validate the independent contribution of each core predictor. The results showed that the logistic regression, random forest (RF), and CNN models were all successfully constructed and validated, among which the RF model demonstrated the best overall performance, with an accuracy of 85.7%, an area under the curve (AUC) of 0.88, a sensitivity of 83.5%, and a specificity of 88.2%. The ablation experiment confirmed that each feature had independent predictive value, with the most significant decline in model performance observed after the removal of IL&#x2011;6. Among them, the individual AUC value of interleukin-6 (IL-6) reached 0.981. Significant risk factors included patient age, C-reactive protein (CRP) level, presence of honeycomb lung on imaging, and the ratio of arterial oxygen partial pressure to inhaled oxygen concentration (PaO2/FiO2). The model in this study demonstrated satisfactory predictive ability and stability in both internal and external validation phases. The random forest model performed excellently in predicting the likelihood of critical illness in patients with CTD-ILD.

Colorimetric assays offer a low-cost, accessible means of diagnostic testing but often suffer from subjective interpretation and variability caused by inconsistent imaging conditions. To address these challenges, we present HueTools, a comprehensive image processing software that enables quantitative development of paper-based colorimetric assays using smartphone imaging. HueTools integrates a mobile app, and a web platform to standardize image capture, apply precise color correction, and predictive modeling of analyte concentration using interpretable machine learning. The system supports a complete workflow: from image acquisition and color calibration to region-of-interest (ROI) selection, signal extraction, and statistical analysis. The system enables perceptually grounded color analysis using the CIELAB space and provides quantitative metrics, including LoB, LoD, and LoQ via interpretable ensemble-based customization models. HueTools was validated using a lateral flow dipstick luteinizing hormone (LH) and vertical flow alanine transaminase (ALT) assays. The results demonstrate HueTools' ability to reduce human error, improve assay reproducibility, and provide feedback for optimizing assay design. It also supports seamless transitions between field testing and lab analysis, allowing researchers to capture images on-site and perform in-depth analysis remotely. HueTools offers a hardware-independent, cloud-based solution for assay developers, streamlining workflows while minimizing costs associated with dedicated readers. Its accessibility, automation, and cross-platform compatibility make it well-suited for research and development of colorimetric point-of-care diagnostics, especially in resource-limited settings.

Adenosine monophosphate deaminase 2 (AMPD2) catalyzes the conversion of adenosine monophosphate (AMP) to inosine monophosphate and is believed to play a significant role in nucleotide metabolism, energy homeostasis, and immune oncology. Three primary AMPD isozymes, designated as M (muscle), L (liver), and E (erythrocyte) forms, have been identified. However, due to the high similarity of the catalytic site's amino acid sequence and structural topologies, most reported orthosteric inhibitors exhibit minimal selectivity toward AMPD isozymes. There is therefore a significant need for selective AMPD2 inhibitors as tools for validating the biological roles of AMPD2. In this study, we hypothesized that allosteric AMPD2 inhibitors would show selectivity towards other AMPD isoforms and used an X-ray fragment screening approach to identify these inhibitors. Consequently, we identified compound 5, which showed the capacity to bind to a previously uncharacterized allosteric site. The pharmacophore search based on structural information around 5 and the following X-ray screening also identified 6 and 8, which bind to the same site as 5. The merging of the initial fragment hit 5 with the secondary fragment hit 6 yielded 7, which was further merged with 8, resulting in the successful generation of 9. Moreover, the optimization of 9 led to more potent selective inhibitors 10g and 10h. Our results suggest that the fragment merging method using X-ray screening and pharmacophore searching provides effective opportunities to improve the affinity of the fragment hits. In addition, we believe that these selective compounds could be used as tool compounds for studying the biological roles of AMPD2.

暂无摘要（点击查看详情）

Intrahepatic cholangiocarcinoma (ICC) is an aggressive hepatobiliary malignancy characterized by a complex pathogenesis and poor prognosis. Telomere dysfunction and cellular senescence are involved in the pathogenesis of various types of cancers. However, the prognostic significance of telomere- and aging-related genes in ICC remains unclear. This study aimed to identify such genes with prognostic significance, and construct a prognostic risk model for ICC. We screened prognostic genes associated with telomere-aging in patients using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We then integrated 10 base machine learning algorithms, and generated 101 model configurations via parameter optimization and algorithm combination strategies, to construct and validate a prognostic risk model. The clinical prognostic value was analyzed using a nomogram. Immune infiltration, drug sensitivity, immune responses, and subgroups were analyzed based on telomere-aging-related prognostic genes. We also validated core gene expression in cell lines and tissue samples using the quantitative reverse-transcription polymerase chain reaction (qRT-PCR) and enrolled 76 patients with ICC to identify the prognostic value of key genes. We constructed a prognostic model using the telomere-aging-related prognostic genes BET1L, RAD50, ANXA1, and AURKA. Survival analysis revealed a significant difference in overall survival between high- and low-risk groups. The expression of these genes was significantly increased in ICC cell lines and tissues. High BET1L expression was significantly associated with lymph node metastasis, tumor-node-metastasis (TNM) stage, tumor differentiation, and a poor prognosis for patients with ICC. Knocking down BET1L significantly reduced the proliferative ability of HUCCT1 cells. We established a risk model comprising the telomere-aging-related prognostic genes BET1L, RAD50, ANXA1, and AURKA to predict the prognosis of patients with ICC. Elevated BET1L expression indicated a poor prognosis for patients with ICC, and low BET1L expression inhibited HUCCT1 cell proliferation.

Direct measurement of drug-target engagement in cells is a critical component of early drug discovery. Many existing cellular target engagement approaches require prior determination of protein melting temperatures, prolonged incubation steps, or detection formats with limited compatibility with real-time instrumentation. Here, we describe a real-time temperature-series MICRO-TAG assay that integrates fluorescence-based cellular target engagement with programmable thermal cycling on the Applied Biosystems QuantStudio Real-Time PCR System. The assay employs an RNase-based FRET substrate to generate real-time fluorescence signals that reflect ligand-induced thermodynamic stabilization of protein targets in intact cells. By implementing a ramp-hold-detect temperature program, the workflow eliminates the need for melting-temperature scouting and enables streamlined assay setup with quantitative signal acquisition across temperature series. Using MAPK1 as a representative target, we demonstrate reproducible real-time signal separation between ligand-treated and control samples and show that both single-concentration binding assessment and EC50 determination can be achieved within a single experimental run. This Application Note outlines the assay rationale, materials, protocol configuration, instrument programming, and interpretation of representative data.