Stroke often results in varying degrees of functional impairment, significantly affecting patients' quality of daily life. In recent years, brain-computer interface-controlled functional electrical stimulation has offered new therapeutic approaches for post-stroke rehabilitation. This paper reviews the application of BCI-FES in the recovery of upper limb function after stroke and explores its underlying mechanisms. By analyzing relevant studies, the aim is to provide a theoretical basis for rehabilitating upper limb function post-stroke, promote BCI-FES, and offer guidance for future clinical practice.

Chronic obstructive pulmonary disease (COPD), characterized by high incidence and mortality rates, is a chronic respiratory disorder that places a substantial burden on healthcare systems. Artificial Intelligence (AI), with its deep integration into the medical field, particularly through its core branches—Machine Learning (ML) and Deep Learning (DL)—has demonstrated significant potential in the intervention and management of COPD. From early risk prediction based on multimodal data to the enhancement of precise diagnosis and treatment through radiomics and clinical decision support systems, and further to the dynamic assessment of acute exacerbation and comorbidity risks via machine learning models, AI has, in combination with bioinformatics and multi-omics analysis, established a novel intelligent management framework that spans the entire disease continuum. This framework offers innovative, individualized solutions aimed at alleviating the burden on healthcare systems. This article reviews the technical applications and clinical value of AI in the diagnosis, prevention, treatment, and prognosis of COPD, discusses current challenges, and outlines future development directions to provide insights for clinical practice and research.

Early screening of lung nodules is mainly done manually by reading the patient’s lung CT. This approach is time-consuming laborious and prone to leakage and misdiagnosis. Current methods for lung nodule detection face limitations such as the high cost of obtaining large-scale, high-quality annotated datasets and poor robustness when dealing with data of varying quality. The challenges include accurately detecting small and irregular nodules, as well as ensuring model generalization across different data sources. Therefore, this paper proposes a lung nodule detection model based on semi-supervised learning and knowledge distillation (SSLKD-UNet). In this paper, a feature encoder with a hybrid architecture of CNN and Transformer is designed to fully extract the features of lung nodule images, and at the same time, a distillation training strategy is designed in this paper, which uses the teacher model to instruct the student model to learn the more relevant features to nodule regions in the CT images and, and finally, this paper applies the rough annotation of the lung nodules to the LUNA16 and LC183 dataset with the help of semi-supervised learning idea, and completes the model with the accurate annotation of lung nodules. Combined with the accurate lung nodule annotation to complete the model training process. Further experiments show that the model proposed in this paper can utilize a small amount of inexpensive and easy-to-obtain coarse-grained annotations of pulmonary nodules for training under the guidance of semi-supervised learning and knowledge distillation training strategies, which means inaccurate annotations or incomplete information annotations, e.g., using nodule coordinates instead of pixel-level segmentation masks, and realize the early recognition of lung nodules. The segmentation results further corroborates the model’s efficacy, with SSLKD-UNet demonstrating superior delineation of lung nodules, even in cases with complex anatomical structures and varying nodule sizes.

Microbial communities play a vital role in the human defense system, existing symbiotically with us, contributing to metabolic processes, and strengthening immune defenses against pathogens. A diverse bacterial population in the vagina contributes to maintaining dynamic homeostasis, with their interactions playing a critical role in determining health or disease status. The balanced vaginal microbiota, dominated by Lactobacilli , helps maintain vaginal pH, converts glycogen to lactic acid, and produces bacteriocins and hydrogen peroxide (H ₂ O ₂ ), all contributing to its protective functions. On the other hand, an abnormal vaginal microbial composition, characterized by a decrease in beneficial microorganisms, heightens the risk of gynecological diseases such as bacterial vaginosis (BV), sexually transmitted infections, human papillomavirus (HPV) infections, and cervical cancer due to persistent infections. Variation in microbial composition is influenced by factors such as racial background, ethnicity, pregnancy, hormonal fluctuations, sexual behavior, personal hygiene practices, and various physiological conditions. This review aims to offer a detailed overview of the existing literature, focusing on the complex interplay between vaginal microbiota and gynecological conditions such as HPV infection. Our goal is to provide valuable insights that can inform future clinical strategies and interventions.

COPD affects approximately 391 million people globally. While spirometry is recognized as the gold standard for diagnosing COPD according to the GOLD guidelines, its availability is limited in primary healthcare settings, particularly in low- and middle-income countries. Furthermore, spirometry requires patient cooperation, which may be challenging for individuals with physical limitations or comorbidities, potentially impacting its accuracy. As a result, there is a need for alternative diagnostic methods, particularly those suited for resource-constrained environments. This study proposes a novel multimodal deep learning framework, COPD-MMDDxNet, which integrates structured pulmonary CT reports, blood gas analysis, and hematological analysis from electronic medical records (EMRs) to overcome the limitations of existing diagnostic methods. This framework develops the first multimodal diagnostic tool for COPD that does not rely on spirometry. It innovatively fuses cross-modal data, incorporating four key components: parametric numerical embedding, hierarchical interaction mechanisms, contrastive regularization strategies, and dynamic fusion coefficients. These innovations significantly enhance the model's ability to capture complex cross-modal relationships, thereby improving diagnostic accuracy.The dataset used in this study comprises 800 COPD patients, with a balanced age and sex distribution, and data were collected over a 24-month period. Experimental results demonstrate that COPD-MMDDxNet outperforms traditional single-modality models and other state-of-the-art multimodal models in terms of accuracy (81.76%), precision (78.87%), recall (77.78%), and F1 score (78.32%). Ablation studies confirm the critical importance of each model component, particularly the contrastive learning module and cross-modal attention mechanism, in enhancing model performance.This framework offers a robust solution for more accurate and accessible COPD diagnosis, particularly in resource-constrained environments, without the need for spirometry.

Soft tissue sarcomas are sarcomas that often occur in the soft tissues such as striated muscle and adipose tissue with a high mortality rate. To effectively devise an optimized surgical strategy, it is crucial to comprehensively assess the sarcoma region, leveraging the patient’s MRI images as a critical resource. However, inconsistencies in MRI imaging machine, image parameter settings, etc. often result in differences in the quality of soft tissue sarcoma images, which not only increases the time and effort of manual diagnosis, but also easily leads to misdiagnosis. To investigate ways of developing improved automatic segmentation methods for soft tissue sarcoma and utilizing multi-modal MRI images to delineate the sarcoma region, we have gathered 21,433 horizontal MRI images and 11,741 coronal MRI images from 50 patients diagnosed with soft tissue sarcoma. After conducting a series of preprocessing steps, multiple clinicians annotate the soft tissue sarcoma areas. Two multi-modal image datasets of soft tissue sarcomas are thus obtained. Thereafter, a multi-encoder and single-decoder network is developed to adapt to different modalities of input to segment the sarcoma region. At the same time, a feature fusion strategy is implemented at the bottleneck, and an attention mechanism is integrated into the skip connection to enable the network to capture key semantic features across different modalities. Moreover, the effects of different methods of corrupting input images on network performance for weakly-supervised training are explored. Further experiments demonstrate the availability of our datasets, and our network outperforms the current state-of-the-art methods and produces robust results when one or more modalities are missing.