SLAS Technology最新文献

Artificial intelligence (AI) holds immense potential to revolutionize drug discovery, yet its widespread adoption within scientific enterprises faces significant hurdles. Key challenges include ensuring user-friendliness, managing complex workflows, and integrating diverse datasets. To address these issues, we propose a novel framework that leverages the familiar Electronic Lab Notebook (ELN) paradigm. By formalizing AI workflows as ELN protocols and treating AI execution as ELN experiments, the proposed system provides a scalable, traceable, and user-oriented deployment strategy that aligns with existing laboratory practices. This ELN-based framework adheres to FAIR principles, enhancing data findability, accessibility, interoperability, and reusability. By mirroring the intuitive ELN interface, our solution empowers bench chemists to easily access and utilize cutting-edge AI tools, enabling them to move beyond purely synthetic roles and fully engage as medicinal chemists. This allows chemists to design compounds with real-time consideration of synthetic feasibility and to actively contribute to the drug design process with their practical expertise, thereby accelerating drug discovery efforts and maximizing the return on AI investments.

With the increasing aging population, the health management of older people has become an increasingly important issue. The author aims to meet the health needs of older people by designing a wearable device and a vital sign monitoring system based on optical sensing technology. The author analyzed the system's requirements and functions, provided the overall design architecture, and logically divided the system into the following functional modules: wireless environment monitoring, elderly physiological parameter collection, indoor area positioning, network node management, and the cloud platform. The functions that each module needs to implement were analyzed. Using the STM32F103C8T6 microcontroller as the core control device, combined with DS18B20 and MAX30102 sensors, it collects wrist temperature, heart rate, and blood oxygen data. The MPU6050 gyroscope is used to detect changes in human body deflection angle and acceleration, serving as the basis for fall detection. LCD screens and buttons are used to achieve human-computer interaction. The test results show that the average error between wrist temperature and forehead temperature is about 0.2°C, and the mistake with armpit temperature is about 0.6°C; In terms of heart rate measurement, the photodegradation method can obtain reliable human heart rate and blood oxygen signals, and the system measurement values match well with the heart rate image output by PGG; In terms of fall detection, the fusion of deflection angle and acceleration changes can improve the accuracy of fall detection to 95.2%. This system has the characteristics of small size, high precision, low power consumption, and ease of wear, and offers a wide range of application prospects.

Objective: To explore the application value of DTI combined with DTT in brain function and volume in children with autism.

Methods: A total of 616 children with autism diagnosed from January 2020 to December 2022 (ASD group) and 91 healthy children with age and gender matching (control group) were included in the study. All subjects underwent DTI and DTT examinations. The DTI-DTT examination was conducted to analyze the Fractional Anisotropy (FA) values of key brain regions such as the corpus callosum and internal capsule, and the correlation and diagnostic efficacy were analyzed with the scores of the Autism Behavior Checklist (ABC).

Results: The total scores of ABC and each factor in the ASD group were significantly higher than those in the control group (p<0.05). The FA values of the knee joint and compressed part of the corpus callosum as well as the anterior and posterior limbs of the internal capsule in the ASD group were significantly higher than those in the control group (p<0.05). Relevant analysis showed that the FA values of the anterior and posterior limbs of the capsule in the ASD group were moderately positively correlated with the scores of sensory and body movement factors in the ABC scale (p<0.01). The FA values of the knee and compression parts of the corpus callosum were also moderately positively correlated with the communication and connection factor scores (p<0.01). ROC curve analysis indicated that the FA values of the above-mentioned brain regions had a high diagnostic value for ASD (AUC values were all >0.64).

Conclusion: The combination of DTI and DTT effectively reveals the microstructure abnormalities of the main white matter pathways (such as the corpus callosum and internal capsule) in children with autism. These abnormalities are significantly correlated with specific behavioral symptoms. This combined imaging technology provides important neuroimaging evidence for the early objective diagnosis and rehabilitation intervention of children with autism.

Heart failure (HF) remains a major contributor to global morbidity and mortality, characterized by complex pathological processes including inflammation and aberrant intercellular communication. Shenfu Injection (SFI), a traditional Chinese herbal preparation, shows beneficial clinical outcomes in HF, but the precise molecular basis governing its effects on the cardiac microenvironment is not fully elucidated. We integrated single-cell RNA sequencing (scRNA-seq), bulk transcriptomics, and machine learning to investigate the cellular landscape, intercellular communication networks, and key apoptosis-related genes in HF. Cell-cell interaction analyses were performed to dissect signaling dynamics. The cardioprotective effects of SFI were validated in a murine HF model. scRNA-seq revealed a pro-inflammatory microenvironment characterized by immune cell activation and elevated apoptosis, particularly in fibroblast populations. Cell-cell communication analysis highlighted a dramatic increase in intercellular signaling activity in HF, with pro-inflammatory pathways like MAPK being central to this pathological crosstalk. Through LASSO regression and pathway analysis, LUM (Lumican) was identified as a fibroblast-specific, apoptosis-related hub gene. SFI treatment significantly downregulated LUM expression and the associated p38/p53 pathway, thereby limiting cardiomyocyte apoptosis, reducing inflammatory cytokine levels (TNF-α, IL-6), improving cardiac performance, and alleviating myocardial injury. This study identifies LUM as a critical regulator at the intersection of apoptosis, inflammation, and cellular communication in HF. We demonstrate that SFI exerts its cardioprotective effects by modulating the LUM-mediated signaling axis, thereby disrupting pathological intercellular signaling and mitigating inflammation. These findings offer novel mechanistic insights, positioning the LUM-centric inflammatory communication network as a potential therapeutic target for HF.

The article details combined portable sample preparation and colorimetric detection using loop-mediated isothermal amplification (RT-LAMP) of Citrus tristeza virus (CTV) from citrus leaves. The sample preparation employs an OmniLyse micro-homogenizer and cellulose paper disks to extract and isolate total nucleic acids in less than 15 minutes. Primer and dimethyl sulfoxide (DMSO) concentrations were optimized to minimize RT-LAMP assay reaction times and maximize the delay in the appearance of false positives due to non-specific amplification, respectively. The CTV primers were assessed against a panel of 24 CTV-positive isolates and 6 non-CTV pathogen isolates. To adapt the protocol for cold-chain-free field deployment, a lyophilized RT-LAMP reagent mix was developed and its rehydration solution was optimized to minimize false positives. In a greenhouse setting, the micro-homogenizer successfully extracted and isolated nucleic acids from CTV-positive trees, followed by the lyophilized RT-LAMP assay positively detecting the pathogen within 35 minutes. This study establishes the feasibility of quick and portable CTV detection without access to laboratory equipment, paving the way for larger-scale field studies, comprehensive validation of assay performance, and potential extension to other plant pathogens.

Bayesian optimization is efficient even with a small amount of data and is used in engineering and in science, including biology and chemistry. In Bayesian optimization, a parameterized model with an uncertainty is fitted to explain the experimental data, and then the model suggests parameters that would most likely improve the results. Batch Bayesian optimization reduces the processing time of optimization by parallelizing experiments. However, batch Bayesian optimization cannot be applied if the number of parallelized experiments is limited by the cost or scarcity of equipment; in such cases, sequential methods require an unrealistic amount of time. In this study, we developed pipelining Bayesian optimization (PipeBO) to reduce the processing time of optimization even with a limited number of parallel experiments. PipeBO was inspired by the pipelining of central processing unit architecture, which divides computational tasks into multiple processes. PipeBO was designed to achieve experiment parallelization by overlapping various processes of the experiments. PipeBO uses the results of completed experiments to update the parameters of running parallelized experiments. PipeBO was mathematically formulated by modeling experiments as multiple processes with asynchronous result arrival, enabling partial model updates in a pipelined fashion. Using the Black-Box Optimization Benchmarking, which consists of 24 benchmark functions, we compared PipeBO with the sequential Bayesian optimization methods. PipeBO reduced the average processing time of optimization to about 56% for the experiments that consisted of two processes or even less for those with more processes for 20 out of the 24 functions. Overall, PipeBO parallelizes Bayesian optimization in experimental equipment-limited situations so that efficient optimization can be achieved.

This study investigates the potential of Pseudomonas songnenensis (P. songnenensis) in degrading β-lactam antibiotics through enzymatic hydrolysis by β-lactamase (β-Lase). Faecal soil samples were collected from ten poultry farms in Tamil Nadu, India, between June and July 2023. Each housing 10,000-50,000 birds and is routinely administered antibiotics. Among the bacterial isolates obtained, strain 18 showed the highest degradation activity. Molecular docking analysis revealed stable enzyme-antibiotic interactions, with Amoxicillin showing the strongest binding affinity due to multiple hydrogen bonds. The β-Lase enzyme effectively hydrolyses the β-lactam ring, breaking the amide bond and rendering antibiotics inactive. This stepwise degradation mechanism contributes to reducing antibiotic persistence in the environment and offers insights into microbial-driven bioremediation strategies. The findings highlight the novelty of using P. songnenensis for antibiotic degradation and emphasise its potential application in mitigating antibiotic pollution in livestock farming and food production systems.

Lung diseases such as pneumonia, tuberculosis, COVID-19, and lung cancer remain significant global health challenges that demand rapid and accurate diagnosis to improve patient outcomes. This study proposes NASNet-ViT, a novel deep learning framework that integrates the powerful convolutional feature extraction of NASNet with the global attention mechanisms of the Vision Transformer (ViT). To enhance diagnostic precision, a multi-stage preprocessing pipeline, termed MixProcessing, is introduced, combining wavelet transform decomposition, adaptive histogram equalization, and morphological filtering to improve image quality and feature clarity. The proposed NASNet-ViT model classifies lung images into five categories, normal, lung cancer, COVID-19, pneumonia, and tuberculosis achieving outstanding performance metrics: 98.9% accuracy, 0.99 sensitivity, 0.989 F1-score, and 0.987 specificity. Compared to established architectures such as MixNet-LD, D-ResNet, MobileNet, and ResNet50, NASNet-ViT demonstrates superior accuracy while maintaining a lightweight model size of only 25.6 MB and fast inference time of 12.4 seconds, making it practical for deployment in real-time, resource-constrained clinical environments. This research advances the field of medical image analysis by offering a robust and scalable AI solution capable of supporting clinicians in timely and precise lung disease diagnosis.