Chinese Medical Sciences Journal最新文献

Objectives: Recent advancements in single-cell RNA sequencing (scRNA-seq) have revolutionized the study of cellular heterogeneity, particularly within the hematological system. However, accurately annotating cell types remains challenging due to the complexity of immune cells. To address this challenge, we develop a PAN-blood single-cell Data Annotator (scPANDA), which leverages a comprehensive 10-million-cell atlas to provide precise cell type annotation.

Methods: The atlas, constructed from data collected in 16 studies, incorporated rigorous quality control, preprocessing, and integration steps to ensure a high-quality reference for annotation. scPANDA utilizes a three-layer inference approach, progressively refining cell types from broad compartments to specific clusters. Iterative clustering and harmonization processes were employed to maintain cell type purity throughout the analysis. Furthermore, the performance of scPANDA was evaluated in three external datasets.

Results: The atlas was structured hierarchically, consisting of 16 compartments, 54 classes, 4,‍460 low-level clusters (pd_cc_cl_tfs), and 611 high-level clusters (pmid_cts). Robust performance of the tool was demonstrated in annotating diverse immune scRNA-seq datasets, analyzing immune-tumor coexisting clusters in renal cell carcinoma, and identifying conserved cell clusters across species.

Conclusions: scPANDA exemplifies effective reference mapping with a large-scale atlas, enhancing the accuracy and reliability of blood cell type identification.

Viral infectious diseases, characterized by their intricate nature and wide-ranging diversity, pose substantial challenges in the domain of data management. The vast volume of data generated by these diseases, spanning from the molecular mechanisms within cells to large-scale epidemiological patterns, has surpassed the capabilities of traditional analytical methods. In the era of artificial intelligence (AI) and big data, there is an urgent necessity for the optimization of these analytical methods to more effectively handle and utilize the information. Despite the rapid accumulation of data associated with viral infections, the lack of a comprehensive framework for integrating, selecting, and analyzing these datasets has left numerous researchers uncertain about which data to select, how to access it, and how to utilize it most effectively in their research.This review endeavors to fill these gaps by exploring the multifaceted nature of viral infectious diseases and summarizing relevant data across multiple levels, from the molecular details of pathogens to broad epidemiological trends. The scope extends from the micro-scale to the macro-scale, encompassing pathogens, hosts, and vectors. In addition to data summarization, this review thoroughly investigates various dataset sources. It also traces the historical evolution of data collection in the field of viral infectious diseases, highlighting the progress achieved over time. Simultaneously, it evaluates the current limitations that impede data utilization.Furthermore, we propose strategies to surmount these challenges, focusing on the development and application of advanced computational techniques, AI-driven models, and enhanced data integration practices. By providing a comprehensive synthesis of existing knowledge, this review is designed to guide future research and contribute to more informed approaches in the surveillance, prevention, and control of viral infectious diseases, particularly within the context of the expanding big-data landscape.

Objectives: The study of medicine formulas is a core component of traditional Chinese medicine (TCM), yet traditional learning methods often lack interactivity and contextual understanding, making it challenging for beginners to grasp the intricate composition rules of formulas. To address this gap, we introduce Formula-S, a situated visualization method for TCM formula learning in augmented reality (AR) and evaluate its performance. This study aims to evaluate the effectiveness of Formula-S in enhancing TCM formula learning for beginners by comparing it with traditional text-based formula learning and web-based visualization.

Methods: Formula-S is an interactive AR tool designed for TCM formula learning, featuring three modes (3D, Web, and Table). The dataset included TCM formulas and herb properties extracted from authoritative references, including textbook and the SymMap database. In Formula-S, the hierarchical visualization of the formulas as herbal medicine compositions, is linked to the multidimensional herb attribute visualization and embedded in the real world, where real herb samples are presented. To evaluate its effectiveness, a controlled study (n=30) was conducted.Participants who had no formal TCM knowledge were tasked with herbal medicine identification, formula composition, and recognition. In the study, participants interacted with the AR tool through HoloLens 2. Data were collected on both task performance (accuracy and response time) and user experience, with a focus on task efficiency, accuracy, and user preference across the different learning modes. Results The situated visualization method of Formula-S had comparable accuracy to other methods but shorter response time for herbal formula learning tasks. Regarding user experience, our new approach demonstrated the highest system usability and lowest task load, effectively reducing cognitive load and allowing users to complete tasks with greater ease and efficiency. Participants reported that Formula-S enhanced their learning experience through its intuitive interface and immersive AR environment, suggesting this approach offers usability advantages for TCM education.

Conclusions: The situated visualization method in Formula-S offers more efficient and accurate searching capabilities compared to traditional and web-based methods. Additionally, it provides superior contextual understanding of TCM formulas, making it a promising new solution for TCM learning.

Amid the global wave of digital economy, China's medical artificial intelligence applications are rapidly advancing through technological innovation and policy support, while facing multifaceted evaluation and regulatory challenges. The dynamic algorithm evolution undermines assessment criteria consistency, multimodal systems lack unified evaluation metrics, and conflicts persist between data sharing and privacy protection. To address these issues, the China National Health Development Research Center has established a value assessment framework for artificial intelligence medical technologies, formulated the country's first technical guidelines for clinical evaluation, and validated their practicality through scenario-based pilot studies. Furthermore, this paper proposes introducing a "regulatory sandbox" model to test technical compliance in controlled environments, thereby balancing innovation incentives with risk governance.

Biomedical big data, characterized by its massive scale, multi-dimensionality, and heterogeneity, offers novel perspectives for disease research, elucidates biological principles, and simultaneously prompts changes in related research methodologies. Biomedical ontology, as a shared formal conceptual system, not only offers standardized terms for multi-source biomedical data but also provides a solid data foundation and framework for biomedical research. In this review, we summarize enrichment analysis and deep learning for biomedical ontology based on its structure and semantic annotation properties, highlighting how technological advancements are enabling the more comprehensive use of ontology information. Enrichment analysis represents an important application of ontology to elucidate the potential biological significance for a particular molecular list. Deep learning, on the other hand, represents an increasingly powerful analytical tool that can be more widely combined with ontology for analysis and prediction. With the continuous evolution of big data technologies, the integration of these technologies with biomedical ontologies is opening up exciting new possibilities for advancing biomedical research.

Data space, as an innovative data management and sharing model, is emerging in the medical and health sectors. This study expounds on the conceptual connotation of data space and delineates its key technologies, including distributed data storage, standardization and interoperability of data sharing, data security and privacy protection, data analysis and mining, and data space assessment. By analyzing the real-world cases of data spaces within medicine and health, this study compares the similarities and differences across various dimensions such as purpose, architecture, data interoperability, and privacy protection. Meanwhile, data spaces in these fields are challenged by the limited computing resources, the complexities of data integration, and the need for optimized algorithms. Additionally, legal and ethical issues such as unclear data ownership, undefined usage rights, risks associated with privacy protection need to be addressed. The study notes organizational and management difficulties, calling for enhancements in governance framework, data sharing mechanisms, and value assessment systems. In the future, technological innovation, sound regulations, and optimized management will help the development of the medical and health data space. These developments will enable the secure and efficient utilization of data, propelling the medical industry into an era characterized by precision, intelligence, and personalization.

Biomedical data are surging due to technological innovations and integration of multidisciplinary data, posing challenges in data management. This article summarizes the policies, data collection efforts, platform construction, and applications of biomedical data in China, aiming to identify key issues and needs, enhance the capacity-building of platform construction, unleash the value of data, and leverage the advantages of China's vast amount of data.

We report a case involving an 85-year-old man who underwent laparoscopic radical prostatectomy for prostate cancer in 2011. During follow-up, he required long-term use of a penile clamp to manage urination due to permanent severe stress incontinence. In February 2023, he presented with a painless cystic mass in the scrotum. Upon pressing the mass with hand, fluid drained from the external urethral orifice, causing the mass to shrink in size, although it returned to its original size a few hours later. Urography and cystoscopy showed a globular urethral diverticulum located anteriorly. The patient underwent surgical excision of the diverticulum along with urethroplasty. Postoperatively, the urinary stress incontinence persisted, but he declined further surgical intervention. An artificial urinary sphincter is currently the first-line treatment for male urinary incontinence. However, devices such as penile clamps can serve as an alternative when considering surgical suitability or cost. It is important to note that these devices can lead to serious complications such as urethral erosion, stricture, or diverticulum. Therefore, caution is advised when using such devices, and they should be removed periodically at short intervals.