Sleep disordered breathing (SDB) is a common sleep disorder with an increasing prevalence. The current gold standard for diagnosing SDB is polysomnography (PSG), but existing PSG techniques have some limitations, such as long manual interpretation times, a lack of data quality control, and insufficient monitoring of gas metabolism and hemodynamics. Therefore, there is an urgent need in China\'s sleep clinical applications to develop a new intelligent PSG system with data quality control, gas metabolism assessment, and hemodynamic monitoring capabilities. The new system, in terms of hardware, detects traditional parameters like nasal airflow, blood oxygen levels, electrocardiography (ECG), electroencephalography (EEG), electromyography (EMG), electrooculogram (EOG), and includes additional modules for gas metabolism assessment via end-tidal CO 2 and O 2 concentration, and hemodynamic function assessment through impedance cardiography. On the software side, deep learning methods are being employed to develop intelligent data quality control and diagnostic techniques. The goal is to provide detailed sleep quality assessments that effectively assist doctors in evaluating the sleep quality of SDB patients.
睡眠呼吸障碍疾病（sleep disordered breathing, SDB）是常见的睡眠疾病，其患病率逐年上升。目前SDB诊断的金标准为多导睡眠监测（polysomnography, PSG），但现有的PSG监测技术存在人工判读时间长、缺乏数据质控、缺少气体代谢及血流动力学监测等问题。因此，研制具有数据质控、气体代谢及血流动力学监测功能的新型智能PSG是我国睡眠临床应用中迫切需要解决的问题。硬件方面，新系统检测鼻气流、血氧、心电、脑电、肌电、眼电等传统参数，且新增评估气体代谢功能的呼气末CO 2、O 2浓度及评估血流动力学功能的心阻抗检测模块。软件方面，基于深度学习方法研究智能数据质控、智能疾病诊断技术。目标是输出详细的睡眠质量评价报告，以有效地辅助医生充分评估SDB患者的睡眠质量。.

UNASSIGNED: Cancer remains one of the leading causes of mortality globally, with conventional chemotherapy often resulting in severe side effects and limited effectiveness. Recent advancements in bioinformatics and machine learning, particularly deep learning, offer promising new avenues for cancer treatment through the prediction and identification of anticancer peptides.
UNASSIGNED: This study aimed to develop and evaluate a deep learning model utilizing a two-dimensional convolutional neural network (2D CNN) to enhance the prediction accuracy of anticancer peptides, addressing the complexities and limitations of current prediction methods.
UNASSIGNED: A diverse dataset of peptide sequences with annotated anticancer activity labels was compiled from various public databases and experimental studies. The sequences were preprocessed and encoded using one-hot encoding and additional physicochemical properties. The 2D CNN model was trained and optimized using this dataset, with performance evaluated through metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC).
UNASSIGNED: The proposed 2D CNN model achieved superior performance compared to existing methods, with an accuracy of 0.87, precision of 0.85, recall of 0.89, F1-score of 0.87, and an AUC-ROC value of 0.91. These results indicate the model\'s effectiveness in accurately predicting anticancer peptides and capturing intricate spatial patterns within peptide sequences.
UNASSIGNED: The findings demonstrate the potential of deep learning, specifically 2D CNNs, in advancing the prediction of anticancer peptides. The proposed model significantly improves prediction accuracy, offering a valuable tool for identifying effective peptide candidates for cancer treatment.
UNASSIGNED: Further research should focus on expanding the dataset, exploring alternative deep learning architectures, and validating the model\'s predictions through experimental studies. Efforts should also aim at optimizing computational efficiency and translating these predictions into clinical applications.

Proteins, as the primary executors of physiological activity, serve as a key factor in disease diagnosis and treatment. Research into their structures, functions, and interactions is essential to better understand disease mechanisms and potential therapies. DeepMind\'s AlphaFold2, a deep-learning protein structure prediction model, has proven to be remarkably accurate, and it is widely employed in various aspects of diagnostic research, such as the study of disease biomarkers, microorganism pathogenicity, antigen-antibody structures, and missense mutations. Thus, AlphaFold2 serves as an exceptional tool to bridge fundamental protein research with breakthroughs in disease diagnosis, developments in diagnostic strategies, and the design of novel therapeutic approaches and enhancements in precision medicine. This review outlines the architecture, highlights, and limitations of AlphaFold2, placing particular emphasis on its applications within diagnostic research grounded in disciplines such as immunology, biochemistry, molecular biology, and microbiology.

As nanoscale materials with the function of catalyzing substrates through enzymatic kinetics, nanozymes are regarded as potential alternatives to natural enzymes. Compared to protein-based enzymes, nanozymes exhibit attractive characteristics of low preparation cost, robust activity, flexible performance adjustment, and versatile functionalization. These advantages endow them with wide use from biochemical sensing and environmental remediation to medical theranostics. Especially in biomedical diagnosis, the feature of catalytic signal amplification provided by nanozymes makes them function as emerging labels for the detection of biomarkers and diseases, with rapid developments observed in recent years. To provide a comprehensive overview of recent progress made in this dynamic field, here an overview of biomedical diagnosis enabled by nanozymes is provided. This review first summarizes the synthesis of nanozyme materials and then discusses the main strategies applied to enhance their catalytic activity and specificity. Subsequently, representative utilization of nanozymes combined with biological elements in disease diagnosis is reviewed, including the detection of biomarkers related to metabolic, cardiovascular, nervous, and digestive diseases as well as cancers. Finally, some development trends in nanozyme-enabled biomedical diagnosis are highlighted, and corresponding challenges are also pointed out, aiming to inspire future efforts to further advance this promising field.

Recently, topics related to targeted gene therapy and diagnosis have become increasingly important in disease research. The progression of many diseases is associated with specific gene signaling pathways. Therefore, the identification of precise gene targets in various diseases is crucial for the development of effective treatments. Laminin subunit beta 3 (LAMB3), a component of laminin 5, functions as an adhesive protein in the extracellular matrix and plays a vital role in regulating cell proliferation, migration, and cell cycle in certain diseases. Previous studies have indicated that LAMB3 is highly expressed in numerous tumorous and non-tumorous conditions, including renal fibrosis; squamous cell carcinoma of the skin, thyroid, lung, pancreatic, ovarian, colorectalr, gastric, breast, cervical, nasopharyngeal, bladder, prostate cancers; and cholangiocarcinoma. Conversely, it is underexpressed in other conditions, such as hepatocellular carcinoma, epidermolysis bullosa, and amelogenesis imperfecta. Consequently, LAMB3 may serve as a molecular diagnostic and therapeutic target for various diseases through its involvement in critical gene signaling pathways. This paper reviews the research status of LAMB3 and its role in related diseases.

The identification of anticancer peptides (ACPs) is crucial, especially in the development of peptide-based cancer therapy. The classical models such as Split Amino Acid Composition (SAAC) and Pseudo Amino Acid Composition (PseAAC) lack the incorporation of feature representation. These advancements improve the predictive accuracy and efficiency of ACP identification. Thus, the effort of this research is to propose and develop an advanced framework based on feature extraction. Thus, to achieve this objective herein we propose an Extended Dipeptide Composition (EDPC) framework. The proposed EDPC framework extends the dipeptide composition by considering the local sequence environment information and reforming the CD-HIT framework to remove noise and redundancy. To measure the accuracy, we have performed several experiments. These experiments were employed using four famous machine learning (ML) algorithms named; Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and K Nearest Neighbor (KNN). For comparisons, we have used accuracy, specificity, sensitivity, precision, recall, and F1-Score as evaluation criteria. The reliability of the proposed framework is further evaluated using statistical significance tests. As a result, the proposed EDPC framework exhibited enhanced performance than SAAC and PseAAC, where the SVM model delivered the highest accuracy of 96. 6% and significant enhancements in specificity, sensitivity, precision, and F1-score over multiple datasets. Due to the incorporation of enhanced feature representation and the incorporation of local and global sequence profiles proposed EDPC achieves higher classification performance. The proposed frameworks can deal with noise and also duplicating features. These are accompanied by a wide range of feature representations. Finally, our proposed framework can be used for clinical applications where ACP identification is essential. Future works will include extending to a larger variety of datasets, incorporating tertiary structural information, and using deep learning techniques to improve the proposed EDPC.

Metabolomics and artificial intelligence (AI) form a synergistic partnership. Metabolomics generates large datasets comprising hundreds to thousands of metabolites with complex relationships. AI, aiming to mimic human intelligence through computational modeling, possesses extraordinary capabilities for big data analysis. In this review, we provide a recent overview of the methodologies and applications of AI in metabolomics studies in the context of systems biology and human health. We first introduce the AI concept, history, and key algorithms for machine learning and deep learning, summarizing their strengths and weaknesses. We then discuss studies that have successfully used AI across different aspects of metabolomic analysis, including analytical detection, data preprocessing, biomarker discovery, predictive modeling, and multi-omics data integration. Lastly, we discuss the existing challenges and future perspectives in this rapidly evolving field. Despite limitations and challenges, the combination of metabolomics and AI holds great promises for revolutionary advancements in enhancing human health.

UNASSIGNED: Healthcare is crucial to patient care because it provides vital services for maintaining and restoring health. As healthcare technology evolves, cutting-edge tools facilitate faster diagnosis and more effective patient treatment. In the present age of pandemics, the Internet of Things (IoT) offers a potential solution to the problem of patient safety monitoring by creating a massive quantity of data about the patient through the linked devices around them and then analyzing it to estimate the patient\'s current status. Utilizing the IoT-based meta-heuristic algorithm allows patients to be remotely monitored, resulting in timely diagnosis and improved care. Meta-heuristic algorithms are successful, resilient, and effective in solving real-world enhancement, clustering, predicting, and grouping. Healthcare organizations need an efficient method for dealing with big data since the prevalence of such data makes it challenging to analyze for diagnosis. The current techniques used in medical diagnostics have limitations due to imbalanced data and the overfitting issue.
UNASSIGNED: This study introduces the particle swarm optimization and convolutional neural network to be used as a meta-heuristic optimization method for extensive data analysis in the IoT to monitor patients\' health conditions.
UNASSIGNED: Particle Swarm Optimization is used to optimize the data used in the study. Information for a diabetes diagnosis model that includes cardiac risk forecasting is collected. Particle Swarm Optimization and Convolutional Neural Networks (PSO-CNN) results effectively make illness predictions. Support Vector Machine has been used to predict the possibility of a heart attack based on the classification of the collected data into projected abnormal and normal ranges for diabetes.
UNASSIGNED: The results of the simulations reveal that the PSO-CNN model used to predict diabetic disease increased in accuracy by 92.6%, precision by 92.5%, recall by 93.2%, F1-score by 94.2%, and quantization error by 4.1%.
UNASSIGNED: The suggested approach could be applied to identify cancer cells.

Phytopythium helicoides, which belongs to the algae (Chromista), Oomycota, Pythiales, Pythiaceae and Phytophthora, is a quarantine pathogen that causes brown rot of fruits, stem rot and root rot, along with other symptoms that can damage several tree species in urban landscaping. Therefore, disease management requires rapid and accurate diagnosis. The present study used recombinase polymerase amplification (RPA) in conjunction with the CRISPR/Cas12a system to identify P. helicoides. The test exhibited high specificity and sensitivity and could detect 10 pg.µL-1 of P. helicoides genomic DNA at 37 ℃ within 20 minutes. The test results were visible by excitation of fluorophores by blue light. This groundbreaking test is able to detect P. helicoides in artificially inoculated Rhododendron leaves. The RPA-CRISPR/Cas12a detection assay developed in this study is characterized by its sensitivity, efficiency, and convenience. Early detection and control of P. helicoides is crucial for the protection of urban green cover species.

Smart healthcare has advanced the medical industry with the integration of data-driven approaches. Artificial intelligence and machine learning provided remarkable progress, but there is a lack of transparency and interpretability in such applications. To overcome such limitations, explainable AI (EXAI) provided a promising result. This paper applied the EXAI for disease diagnosis in the advancement of smart healthcare. The paper combined the approach of transfer learning, vision transformer, and explainable AI and designed an ensemble approach for prediction of disease and its severity. The result is evaluated on a dataset of Alzheimer\'s disease. The result analysis compared the performance of transfer learning models with the ensemble model of transfer learning and vision transformer. For training, InceptionV3, VGG19, Resnet50, and Densenet121 transfer learning models were selected for ensembling with vision transformer. The result compares the performance of two models: a transfer learning (TL) model and an ensemble transfer learning (Ensemble TL) model combined with vision transformer (ViT) on ADNI dataset. For the TL model, the accuracy is 58 %, precision is 52 %, recall is 42 %, and the F1-score is 44 %. Whereas, the Ensemble TL model with ViT shows significantly improved performance i.e., 96 % of accuracy, 94 % of precision, 90 % of recall and 92 % of F1-score on ADNI dataset. This shows the efficacy of the ensemble model over transfer learning models.