This paper introduces a real-world cohort research model for community-acquired pneumonia （CAP） based on the Jiangsu Traditional Chinese Medicine （TCM） Dominant Diseases Diagnosis and Treatment Data Platform. Firstly， data cleaning is performed by standardizing diagnosis， symptoms， treatment and imaging， intelligently extracting unstructured information， and cleaning and constructing a standardized database. Secondly， for cohort establishment， CAP patients across the province are screened in accordance with CAP diagnostic criteria to build a high-quality disease-specific cohort. Lastly， in terms of protocol design， the characteristics of TCM research and the CAP disease profile are considered to determine appropriate inclusion and exclusion criteria， estimate sample size， define interventions， outcomes and economic evaluations， providing a reference for real-world TCM research on CAP.

This paper introduces a real-world cohort research model for community-acquired pneumonia （CAP） based on the Jiangsu Traditional Chinese Medicine （TCM） Dominant Diseases Diagnosis and Treatment Data Platform. Firstly， data cleaning is performed by standardizing diagnosis， symptoms， treatment and imaging， intelligently extracting unstructured information， and cleaning and constructing a standardized database. Secondly， for cohort establishment， CAP patients across the province are screened in accordance with CAP diagnostic criteria to build a high-quality disease-specific cohort. Lastly， in terms of protocol design， the characteristics of TCM research and the CAP disease profile are considered to determine appropriate inclusion and exclusion criteria， estimate sample size， define interventions， outcomes and economic evaluations， providing a reference for real-world TCM research on CAP.

Objective:This current study aims to explore the approaches for constructing a professional clinical research system within the context of research ward development, with the ultimate objective of providing valuable guidance for the establishment and development of proficient clinical research teams.Methods:Through a comprehensive case analysis, integrating the practical experiences from clinical trials conducted in the research ward of a Class-A tertiary hospital in Beijing, along with an extensive review of relevant literature and policy studies, this paper examined the current state of domestic clinical research implementation teams. Subsequently, a series of strategies were devised to build and foster professional clinical research teams and to explore corrective measures for cultivating a dynamic professional clinical research talent ecosystem.Results:The development of full-time clinical research teams in China was rather slow, and there was a lack of mature clinical trial teams training blueprints. Drawing on the practical experience accumulated during the establishment of a professional clinical research team in a leading hospital in Beijing, it was crucial to attach utmost importance to the optimal allocation of human and material resources. This required the systematic training of principal investigators, coordinating researchers, and research assistants, as well as the setting up of a comprehensive support system, an advanced scientific research team, and a quality control unit. Moreover, the standardization of operational models of both domestic and foreign research institutions, along with the implementation of corresponding support and incentive mechanisms, and the strengthening of training and continuing education frameworks were equally significant.Conclusions:During the process of assembling a full-time clinical research team, it is of utmost significance to cultivate professional principal investigators, coordinating researchers, and research assistants. Complemented by the establishment of a comprehensive support team, a scientific research team, and a quality control team, along with corresponding support and incentive mechanisms, this is crucial for constructing a professional clinical research execution team and a sustainable talent ecosystem in the research ward. Eventually, this will drive the efficient and high-quality progress of China's pharmaceutical industry.

Liposomes serve as critical carriers for drugs and vaccines,with their biological effects influenced by their size.The microfluidic method,renowned for its precise control,reproducibility,and scalability,has been widely employed for liposome preparation.Although some studies have explored factors affecting liposomal size in microfluidic processes,most focus on small-sized liposomes,predominantly through experimental data analysis.However,the production of larger liposomes,which are equally significant,remains underexplored.In this work,we thoroughly investigate multiple variables influencing liposome size during microfluidic preparation and develop a machine learning(ML)model capable of accurately predicting liposomal size.Experimental validation was conducted using a staggered herringbone micromixer(SHM)chip.Our findings reveal that most investigated variables significantly influence liposomal size,often interrelating in complex ways.We evaluated the predictive performance of several widely-used ML algorithms,including ensemble methods,through cross-validation(CV)for both lipo-some size and polydispersity index(PDI).A standalone dataset was experimentally validated to assess the accuracy of the ML predictions,with results indicating that ensemble algorithms provided the most reliable predictions.Specifically,gradient boosting was selected for size prediction,while random forest was employed for PDI prediction.We successfully produced uniform large(600 nm)and small(100 nm)liposomes using the optimised experimental conditions derived from the ML models.In conclusion,this study presents a robust methodology that enables precise control over liposome size distribution,of-fering valuable insights for medicinal research applications.

Biomolecular condensates are formed through phase separation of biomacromolecules such as proteins and RNAs. These condensates exhibit liquid-like properties that can futher transition into more stable material states. They form complex internal structures via multivalent weak interactions, enabling precise spatiotemporal regulations. However, the use of inconsistent and non-standardized terminology has become increasingly problematic, hindering academic exchange and the dissemination of scientific knowledge. Therefore, it is necessary to discuss the terminology related to biomolecular condensates in order to clarify concepts, promote interdisciplinary cooperation, enhance research efficiency, and support the healthy development of this field.

Objective:To integrate and analyze large-scale proteomic datasets from clinical hepatocellular carcinoma (HCC) samples to identify key oncogenic proteins and investigate their underlying mechanisms.Methods:Based on two publicly reported large-scale proteomic datasets of hepatocellular carcinoma, we identified lysosomal acid glucosylceramidase (GBA) as a differentially expressed protein between tumor and adjacent non-tumor tissues. The Cancer Genome Atlas (TCGA) database was utilized to analyze survival differences between patients with high ( n=118) and low ( n=118) GBA expression. Tumor and adjacent non-tumor tissue samples were collected from eight HCC patients who underwent surgical treatment at Henan Cancer Hospital between March 2021 and May 2021. Among them, six were male and two were female, ranging from 41 to 66 years old, with a median age of 56 years. The human HCC cell line HCC-LM3 was transfected with siRNAs targeting GBA (siGBA-1 and siGBA-2) or a negative control (siNC). Western blot analysis was performed to assess the expression of relevant proteins in HCC tissues and cells. Quantitative proteomic analysis was conducted to identify differentially expressed proteins in HCC cells, and bioinformatics tools were used to construct an interaction network of downregulated proteins. Cell proliferation and colony formation assays were conducted to evaluate the impact of GBA expression on HCC cell growth. Results:GBA was identified as a significantly upregulated protein in HCC tissues across two clinical proteomic datasets. TCGA analysis further revealed that high GBA expression was associated with poor prognosis in HCC patients. Western blot analysis confirmed that the relative expression level of GBA protein in clinical HCC tissues was (0.60±0.35), which was significantly higher than that in adjacent non-tumor tissues (0.26±0.20) ( t=2.84, P=0.025). Quantitative proteomic analysis interaction analysis of GBA knockdown and control cells identified epidermal growth factor receptor (EGFR) as a potential downstream regulatory target of GBA. Western blot validation demonstrated that EGFR protein expression was significantly reduced following GBA knockdown [siNC: (0.92±0.08), siGBA-1: (0.64±0.07), siGBA-2: (0.51±0.07)]. Cell proliferation and colony formation assays showed that GBA knockdown significantly inhibited cell growth and colony-forming ability. The OD450 values on day 4 in control group and GBA knockdown group (siGBA-1 and siGBA-2) were (1.91±0.17), (1.24±0.11), and (1.21±0.04), respectively. The colony numbers were (674.33±6.51), (388.33±7.51), and (360.00±29.00), respectively. The knockown groups were both lower than negative control group and the differences were statistically significant (both P<0.05). Conclusion:GBA is significantly overexpressed in HCC tissues and may promote HCC cell proliferation by regulating EGFR protein expression.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.