Glycogen storage disease type Ⅱ （GSD Ⅱ）， also known as Pompe disease， is a common autosomal recessive lysosomal storage disease with predominantly muscle tissue involvement， and it is caused by defects in the GAA gene which encode acid α-D-glucosidase in lysosomes. According to the age of onset and the main organs involved， it is classified into infant-onset Pompe disease （IOPD） and late-onset Pompe disease（LOPD）. The diagnosis of this disease depends on the reduction in GAA enzyme activity， the detection of GAA gene mutations， and muscle tissue biopsy， and early diagnosis and treatment are crucial for prognosis. Recombinant human GAA（rhGAA） enzyme replacement therapy prepared by the gene recombination technology is currently the main disease-modifying treatment method for Pompe disease， among which the earliest drug alglucosidase α has shown good efficacy in improving muscle strength and respiratory function and prolonging survival time， and the new-generation rhGAA drugs avalglucosidase α and cipaglucosidase alfa provide new options， especially for patients with poor outcomes and severe symptoms. Substrate ablation therapy and gene therapy are still under exploration， and disease-modifying therapies combined with nutritional and exercise therapies and multidisciplinary long-term management will achieve twice the result with half the effort.
Diagnosis

Mitochondrial diseases are a group of hereditary disorders characterized by impaired oxidative phosphorylation in the mitochondrial respiratory chain caused by defects in either mitochondrial DNA or nuclear DNA， and such diseases have complex and diverse clinical manifestations and often involve multiple organs and systems， with the main manifestation of lesions in the nervous system and muscles due to their high energy demands. At present， there is still a lack of effective therapies for most mitochondrial diseases， and therefore， multidisciplinary management is essential in clinical practice， integrating various therapeutic approaches to provide personalized treatment regimens for patients with mitochondrial diseases. The primary treatment principle involves the timely correction of pathological and physiological abnormalities through pharmacological interventions， dietary modifications， and exercise management， along with the prompt treatment of system-specific impairments and the prevention of potential complications.
Diagnosis

Hypertension is a major risk factor for cardiovascular diseases. Controlling blood pressure can reduce the incidence of cardiovascular events and mortality. The patients with hypertension are mainly treated with antihypertensive drugs. For the patients who can't achieve the target blood pressure with a single drug, comprehensive treatment strategies become particularly important. Chinese patent medicines are prepared by modern extraction and processing technology based on the basic theory of traditional Chinese medicine(TCM). Due to the stable antihypertensive effect, target organ protection, and synergistic effect with western medicine, Chinese patent medicines are becoming one of the effective options for the treatment of hypertension. At present, there are many systematic reviews on the treatment of hypertension with Chinese patent medicines, which makes it difficult for health policy makers and health service providers to choose the best evidence for the treatment. Umbrella review can integrate multiple systematic reviews to comprehensively assess the quality of evidence and potential bias, thereby providing high-quality evidence-based medicine basis for formulating clinical guidelines and optimizing treatment strategies. In this study, the systematic reviews/Meta-analysis of Chinese patent medicines in the treatment of essential hypertension were systematically searched. Sixty-nine articles were included for the umbrella review. Literature information was extracted, and the corrected covered area(CCA) was calculated to quantitatively evaluate the overlap degree of original studies in systematic reviews/Meta-analysis. The risk of bias in systematic reviews(ROBIS) tool and Cochrane RoB tool 2.0 were used to assess the risk of bias of the included studies. A Measure Tool to Assess Systematic Reviews 2(AMSTAR 2) was used to evaluate the methodological quality of systematic reviews/Meta-analysis. The quality of evidence was evaluated based on the Grade of Recommendations Assessment, Development and Evaluation(GRADE). The results showed that the Chinese patent medicines in the categories of treating wind, resolving stasis, and reinforcing healthy Qi were effective in lowering blood pressure. The Chinese patent medicines for resolving stasis combined with conventional treatment can lower blood pressure and the levels of low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglyceride, and total cholesterol in the treatment of hypertension complicated with coronary heart disease and hypertension complicated with left ventricular hypertrophy. Moreover, the combined therapy can recover the interventricular septal thickness, left ventricular posterior wall thickness, left ventricular mass index, left ventricular end diastolic diameter, and left ventricular ejection fraction in the case of left ventricular hypertrophy. The Chinese patent medicines for resolving stasis and for replenishing Qi and restoring pulse can be used in combination with conventional treatment for hypertension complicated with arrhythmia, which can lower blood pressure while improving the outcome indicators such as the P-wave dispersion of arrhythmia, left atrial diameter, ejection fraction, heart rate, and recurrence time. Due to the heterogeneity, the efficacy evidence obtained by the umbrella review needs to be further verified through precise clinical studies and long-term follow-up.
Hypertension/physiopathology* ; Humans ; Drugs, Chinese Herbal/therapeutic use* ; Antihypertensive Agents/therapeutic use* ; Nonprescription Drugs/therapeutic use* ; Blood Pressure/drug effects*

Ultra performance liquid chromatography-quadrupole-time-of-flight-mass spectrometry/mass spectrometry(UPLC-Q-TOF-MS/MS), network pharmacology, and animal experiments were integrated o explore the blood glucose-lowering effects and mechanisms of Poria aqueous extract. Firstly, the active components of Poria aqueous extract were identified by UPLC-Q-TOF-MS/MS. Subsequently, network pharmacology was employed to predict the blood glucose-lowering components and mechanisms of Poria aqueous extract. Finally, a rat model of diabetes mellitus, 16S rDNA sequencing, and Western blot were employed to investigate the blood glucose-lowering effect and mechanism of Poria aqueous extract. A total of 39 triterpenoids were identified in the Poria aqueous extract, among them, 25-hydroxypachymic acid, 25α-hydroxytumulosic acid, 16α-hydroxytrametenolic acid, polyporenic acid C, and tumulosic acid may be the main active ingredients for treating diabetes. The Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis revealed that Poria might exert its therapeutic effects through multiple pathways such as NOD-like receptor signaling pathway, nuclear factor-kappa B(NF-κB) signaling pathway, and tumor necrosis factor(TNF) signaling pathway. The results of animal experiments demonstrated that Poria aqueous extract significantly reduced the levels of blood glucose and lipids and regulated the intestinal flora in diabetic rats. The main affected taxa included g＿Escherichia-Shigella, g＿Corynebacterium, g＿Prevotella＿9, g＿Prevotellaceae＿UCG-001, and g＿Bacteroidota＿unclassified. In addition, Poria aqueous extract lowered the levels of D-lactic acid and lipopolysaccharide, alleviated colonic mucosal damage, significantly down-regulated the protein levels of NOD-like receptor pyrin domain-containing protein 3(NLRP3), NF-κB, and TNF-α, and significantly up-regulated the protein levels of zonula occludens 1 and occludin in diabetic rates. Poria aqueous extract may play a role in treating diabetes mellitus by repairing the intestinal flora disturbance, protecting the intestinal barrier function, and inhibiting the NF-κB/NLRP3 signaling pathway. The results provide a scientific basis for clinical application and expansion of indications of Poria.
Animals ; Rats ; Network Pharmacology ; Tandem Mass Spectrometry ; Male ; Drugs, Chinese Herbal/pharmacology* ; Chromatography, High Pressure Liquid ; Blood Glucose/drug effects* ; Rats, Sprague-Dawley ; Hypoglycemic Agents/administration & dosage* ; Poria/chemistry* ; Diabetes Mellitus, Experimental/metabolism* ; NF-kappa B/genetics* ; Gastrointestinal Microbiome/drug effects* ; Humans

Cardiovascular diseases and psychological disorders represent two major threats to human physical and mental health. Research on electrocardiogram (ECG) signals offers valuable opportunities to address these issues. However, existing methods are constrained by limitations in understanding ECG features and transferring knowledge across tasks. To address these challenges, this study developed a multi-resolution feature encoding network based on residual networks, which effectively extracted local morphological features and global rhythm features of ECG signals, thereby enhancing feature representation. Furthermore, a model compression-based continual learning method was proposed, enabling the structured transfer of knowledge from simpler tasks to more complex ones, resulting in improved performance in downstream tasks. The multi-resolution learning model demonstrated superior or comparable performance to state-of-the-art algorithms across five datasets, including tasks such as ECG QRS complex detection, arrhythmia classification, and emotion classification. The continual learning method achieved significant improvements over conventional training approaches in cross-domain, cross-task, and incremental data scenarios. These results highlight the potential of the proposed method for effective cross-task knowledge transfer in ECG analysis and offer a new perspective for multi-task learning using ECG signals.
Humans ; Electrocardiography/methods* ; Mental Health ; Algorithms ; Signal Processing, Computer-Assisted ; Machine Learning ; Arrhythmias, Cardiac/diagnosis* ; Cardiovascular Diseases ; Neural Networks, Computer ; Mental Disorders

The risk prediction of paroxysmal atrial fibrillation (PAF) is a challenge in the field of biomedical engineering. This study integrated the advantages of machine learning feature engineering and end-to-end modeling of deep learning to propose a PAF risk prediction method based on multimodal feature fusion. Additionally, the study utilized four different feature selection methods and Pearson correlation analysis to determine the optimal multimodal feature set, and employed random forest for PAF risk assessment. The proposed method achieved accuracy of (92.3 ± 2.1)% and F1 score of (91.6 ± 2.9)% in a public dataset. In a clinical dataset, it achieved accuracy of (91.4 ± 2.0)% and F1 score of (90.8 ± 2.4)%. The method demonstrates generalization across multi-center datasets and holds promising clinical application prospects.
Humans ; Atrial Fibrillation/diagnosis* ; Machine Learning ; Deep Learning ; Risk Assessment/methods*

Deep learning method can be used to automatically analyze electrocardiogram (ECG) data and rapidly implement arrhythmia classification, which provides significant clinical value for the early screening of arrhythmias. How to select arrhythmia features effectively under limited abnormal sample supervision is an urgent issue to address. This paper proposed an arrhythmia classification algorithm based on an adaptive multi-feature fusion network. The algorithm extracted RR interval features from ECG signals, employed one-dimensional convolutional neural network (1D-CNN) to extract time-domain deep features, employed Mel frequency cepstral coefficients (MFCC) and two-dimensional convolutional neural network (2D-CNN) to extract frequency-domain deep features. The features were fused using adaptive weighting strategy for arrhythmia classification. The paper used the arrhythmia database jointly developed by the Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH) and evaluated the algorithm under the inter-patient paradigm. Experimental results demonstrated that the proposed algorithm achieved an average precision of 75.2%, an average recall of 70.1% and an average F ₁-score of 71.3%, demonstrating high classification accuracy and being able to provide algorithmic support for arrhythmia classification in wearable devices.
Humans ; Arrhythmias, Cardiac/diagnosis* ; Algorithms ; Electrocardiography/methods* ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Deep Learning ; Classification Algorithms

Cardiotocography (CTG) is a non-invasive and important tool for diagnosing fetal distress during pregnancy. To meet the needs of intelligent fetal heart monitoring based on deep learning, this paper proposes a TWD-MOAL deep active learning algorithm based on the three-way decision (TWD) theory and multi-objective optimization Active Learning (MOAL). During the training process of a convolutional neural network (CNN) classification model, the algorithm incorporates the TWD theory to select high-confidence samples as pseudo-labeled samples in a fine-grained batch processing mode, meanwhile low-confidence samples annotated by obstetrics experts were also considered. The TWD-MOAL algorithm proposed in this paper was validated on a dataset of 16 355 prenatal CTG records collected by our group. Experimental results showed that the algorithm proposed in this paper achieved an accuracy of 80.63% using only 40% of the labeled samples, and in terms of various indicators, it performed better than the existing active learning algorithms under other frameworks. The study has shown that the intelligent fetal heart monitoring model based on TWD-MOAL proposed in this paper is reasonable and feasible. The algorithm significantly reduces the time and cost of labeling by obstetric experts and effectively solves the problem of data imbalance in CTG signal data in clinic, which is of great significance for assisting obstetrician in interpretations CTG signals and realizing intelligence fetal monitoring.
Humans ; Pregnancy ; Female ; Cardiotocography/methods* ; Deep Learning ; Neural Networks, Computer ; Algorithms ; Fetal Monitoring/methods* ; Heart Rate, Fetal ; Fetal Distress/diagnosis* ; Fetal Heart/physiology*

Alzheimer's disease (AD) classification models usually segment the entire brain image into voxel blocks and assign them labels consistent with the entire image, but not every voxel block is closely related to the disease. To this end, an AD auxiliary diagnosis framework based on weakly supervised multi-instance learning (MIL) and multi-scale feature fusion is proposed, and the framework is designed from three aspects: within the voxel block, between voxel blocks, and high-confidence voxel blocks. First, a three-dimensional convolutional neural network was used to extract deep features within the voxel block; then the spatial correlation information between voxel blocks was captured through position encoding and attention mechanism; finally, high-confidence voxel blocks were selected and combined with multi-scale information fusion strategy to integrate key features for classification decision. The performance of the model was evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) datasets. Experimental results showed that the proposed framework improved ACC and AUC by 3% and 4% on average compared with other mainstream frameworks in the two tasks of AD classification and mild cognitive impairment conversion classification, and could find the key voxel blocks that trigger the disease, providing an effective basis for AD auxiliary diagnosis.
Alzheimer Disease/diagnosis* ; Humans ; Neuroimaging/methods* ; Neural Networks, Computer ; Brain/diagnostic imaging* ; Magnetic Resonance Imaging ; Deep Learning ; Machine Learning

Depression, a mental health disorder, has emerged as one of the significant challenges in the global public health domain. Investigating the pathogenesis of depression and accurately assessing the symptomatic changes are fundamental to formulating effective clinical diagnosis and treatment strategies. Utilizing non-invasive brain imaging technologies such as functional magnetic resonance imaging and scalp electroencephalography, existing studies have confirmed that the onset of depression is closely associated with abnormal neural activities and altered functional connectivity in multiple brain regions. Magnetoencephalography, unaffected by tissue conductivity and skull thickness, boasts high spatial resolution and signal-to-noise ratio, offering unique advantages and significant value in revealing the abnormal brain mechanisms and neural characteristics of depression. This review, starting from the rhythmic characteristics, nonlinear dynamic features, and connectivity characteristics of magnetoencephalography in depression patients, revisits the research progress on magnetoencephalography features related to depression, discusses current issues and future development trends, and provides insights for the study of pathophysiological mechanisms, as well as for clinical diagnosis and treatment of depression.
Humans ; Magnetoencephalography/methods* ; Brain/physiopathology* ; Depression/diagnosis* ; Electroencephalography ; Magnetic Resonance Imaging