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 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.

Aim To explore the mechanism of hispidu-lin in the treatment of cervical cancer by using network pharmacology and molecular docking methods and veri-fy it by in vitro experiments.Methods Cervical canc-er HeLa and SiHa cells were cultivated in vitro,and CCK-8 assay,cloning assay,scratch assay,transwell as-say,and flow cytometry were used to detect the effects of hispidulin on cell proliferation,migration,invasion,and apoptosis.SwissTarget Prediction was used to ob-tain predicted targets for hispidulin.Potential targets for cervical cancer were screened in GeneCards disease database.R software Venn package was used to obtain the intersection target genes of hispidulin and cervical cancer,STRING website and Cytoscape software were used to obtain protein-protein interaction(PPI)net-work,and the core targets were screened.The GEIPA data analysis platform was employed to analyze the dif-ferential gene expression levels of core targets in cervi-cal cancer.Gene Ontology(GO)and Kyoto Encyclo-pedia of Genes and Genomes(KEGG)enrichment a-nalysis were performed,and molecular docking was car-ried out on key targets.Western blot was used to detect the regulatory effects of hispidulin on the expression of key proteins PI3K,p-Akt,as well as core target pro-teins MMP9 and RARP1 in the PI3K/Akt signaling pathway.Results Cell experiments showed that after treatment with hispidulin,the proliferation and colony formation abilities of HeLa and SiHa cells significantly decreased in a concentration-and time-dependent man-ner.At the same time,the lateral and longitudinal mi-gration and invasion abilities of HeLa cells decreased,and the level of apoptosis significantly increased.A to-tal of 87 intersection targets between hispidulin and cervical cancer were obtained,and eight core targets,namely,Akt1,EGFR,SRC,ESR1,PTGS2,GSK3β,MMP9,and PARP1,were selected based on the degree values in network topology analysis.KEGG enrichment screening identified PI3K/Akt signaling pathway,canc-er pathway,and other signaling pathways.The molecu-lar docking results showed that hispidulin had strong affinity activity with AktⅠ,P13K,MMP9,and RARP1.Western blot results showed downregulation of PI3K,p-Akt expression,as well as MMP9 and RARP1 expres-sion.Conclusions Hispidulin can inhibit the prolif-eration,migration,invasion,and promote apoptosis of cervical cancer cells by downregulating the PI3K/Akt signaling pathway and the expression of MMP9 and RARP1.

This study was aimed at exploring novel auxiliary diagnostic biomarkers for brucellosis and their potential miR-NA-mRNA regulatory networks.High-throughput sequencing was used to compare miRNA expression differences in serum ex-osomes between patients with brucellosis and healthy controls.Subsequently,RT-qPCR was used to validate the expression of significantly upregulated exosomal miRNAs.The diagnostic value of these miRNAs was assessed with ROC curves,and bioin-formatics analyses were performed to investigate the potential roles of the miRNAs in brucellosis infection.The ROC curve a-nalysis indicated that the area under the curve for exosomal hsa-miR-11400(P＜0.05),hsa-miR-199a-5p(P＜0.05),and hsa-miR-148a-5p(P＜0.05)was 0.79,0.81,and 0.74,respectively.A total of 465 differentially expressed miRNAs and their tar-get genes were predicted,including 25 immune-related target genes,most of which were closely associated with cancer-related proteoglycans,NF-kappa B signaling pathways,and IL-17 signaling pathways.The constructed differentially expressed gene network indicated that the immune genes PLXNA2,IL17RA,PRKCA,CD22,ACVR1B,and CBL might be regulated by hsa-miR-199a-5p and hsa-miR-148a-5p.These findings suggest that exosomal miRNAs might serve as auxiliary diagnostic indicators for brucellosis.Our exosomal miRNA-mRNA regulatory network provides new insights into the pathogenesis and treatment of brucellosis.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Dry eye disease (DED) is a prevalent and intractable ocular disease induced by a variety of causes. Elevated sphingomyelin (SM) levels and pro-inflammatory cytokines were detected on the ocular surface of DED patients, particularly in the meibomian glands. Sphingomyelin synthase 2 (SMS2), one of the proteins involved in SM synthesis, would light a novel way of developing a DED therapy strategy. Herein, we report the design and optimization of a series of novel thiophene carboxamide derivatives to afford 14l with an improved highly potent inhibitory activity on SM synthesis (IC_{50, SMS2} = 28 nmol/L). Moreover, 14l exhibited a notable protective effect of anti-inflammation and anti-apoptosis on human corneal epithelial cells (HCEC) under TNF-α-hyperosmotic stress conditions in vitro, with an acceptable ocular specific distribution (corneas and meibomian glands) and pharmacokinetics (PK) profiles (t _{1/2, cornea} = 1.11 h; t _{1/2, meibomian glands} = 4.32 h) in rats. Furthermore, 14l alleviated the dry eye symptoms including corneal fluorescein staining scores and tear secretion in a dose-dependent manner in mice. Mechanically, 14l reduced the mRNA expression of Tnf-α, Il-1β and Mmp-9 in corneas, as well as the proportion of very long chain SM in meibomian glands. Our findings provide a new strategy for DED therapy based on selective SMS2 inhibitors.