Objective To explore the accuracy of machine learning algorithms based on SHOX2 and RASSF1A methylation levels in predicting early-stage lung adenocarcinoma pathological types. Methods A retrospective analysis was conducted on formalin-fixed paraffin-embedded (FFPE) specimens from patients who underwent lung tumor resection surgery at Affiliated Hospital of Nantong University from January 2021 to January 2023. Based on the pathological classification of the tumors, patients were divided into three groups: a benign tumor/adenocarcinoma in situ (BT/AIS) group, a minimally invasive adenocarcinoma (MIA) group, and an invasive adenocarcinoma (IA) group. The methylation levels of SHOX2 and RASSF1A in FFPE specimens were measured using the LungMe kit through methylation-specific PCR (MS-PCR). Using the methylation levels of SHOX2 and RASSF1A as predictive variables, various machine learning algorithms (including logistic regression, XGBoost, random forest, and naive Bayes) were employed to predict different lung adenocarcinoma pathological types. Results A total of 272 patients were included. The average ages of patients in the BT/AIS, MIA, and IA groups were 57.97, 61.31, and 63.84 years, respectively. The proportions of female patients were 55.38%, 61.11%, and 61.36%, respectively. In the early-stage lung adenocarcinoma prediction model established based on SHOX2 and RASSF1A methylation levels, the random forest and XGBoost models performed well in predicting each pathological type. The C-statistics of the random forest model for the BT/AIS, MIA, and IA groups were 0.71, 0.72, and 0.78, respectively. The C-statistics of the XGBoost model for the BT/AIS, MIA, and IA groups were 0.70, 0.75, and 0.77, respectively. The naive Bayes model only showed robust performance in the IA group, with a C-statistic of 0.73, indicating some predictive ability. The logistic regression model performed the worst among all groups, showing no predictive ability for any group. Through decision curve analysis, the random forest model demonstrated higher net benefit in predicting BT/AIS and MIA pathological types, indicating its potential value in clinical application. Conclusion Machine learning algorithms based on SHOX2 and RASSF1A methylation levels have high accuracy in predicting early-stage lung adenocarcinoma pathological types.

With the widespread use of antibiotics, drug-resistant bacterial infections have become a significant threat to human health. Finding new antibacterial strategies that can effectively control drug-resistant bacterial infections has become an urgent task. Unlike small molecule drugs that target bacterial proteins, antisense oligonucleotide (ASO) can target genes related to bacterial resistance, pathogenesis, growth, reproduction and biofilm formation. By regulating the expression of these genes, ASO can inhibit or kill bacteria, providing a novel approach for the development of antibacterial drugs. To overcome the challenge of delivering antisense oligonucleotide into bacterial cells, various drug delivery systems have been applied in this field, including cell-penetrating peptides, lipid nanoparticles and inorganic nanoparticles, which have injected new momentum into the development of antisense oligonucleotide in the antibacterial realm. This review summarizes the current development of small nucleic acid drugs, the antibacterial mechanisms, targets, sequences and delivery vectors of antisense oligonucleotide, providing a reference for the research and development of antisense oligonucleotide in the treatment of bacterial infections.

ObjectiveTo investigate the trends in diabetes mortality and potential years of life lost (PYLL) among residents of Huangpu District, Shanghai from 1993 to 2021, to analyze the long-term trends of diabetic patients with different characteristics and to provide a reference for scientific prevention and control of diabetes in aging urban areas. MethodsDiabetes mortality data were obtained from the Huangpu District cause of death registration records in the Shanghai death cause registration system. Indicators such as crude mortality rate, standardized mortality rate, potential years of life lost (PYLL), average years of life lost (AYLL), annual percentage change (APC), and average annual percentage change (AAPC) were used to analyze diabetes-related mortality and life loss. Statistical analyses were performed using software SPSS 21.0 and Joinpoint 5.0.2. ResultsFrom 1993 to 2021, the average annual crude mortality rate of diabetes in Huangpu District was 46.56/100 000, and the average annual standardized mortality rate was 20.44/100 000. The crude mortality rate and standardized mortality rate of diabetes for female residents were higher than those for males. The crude mortality rate showed an overall increasing trend [AAPC=2.81% (95%CI: 0.20%‒5.49%), P<0.05], while the increase in standardized mortality rate significantly slowed [AAPC=0.15% (95%CI: -2.27%‒2.63%)], P<0.05]. The mortality rate rose rapidly in the 70‒74 years age group and peaked in the 85‒ years age group (607.69/100 000). Diabetes accounted for a cumulative PYLL of22 741 person-years, with an average annual AYLL of 1.88 years and an average annual potential years of life lost rate (PYLLR) of 0.82‰. Male residents had higher PYLL, AYLL, and PYLLR than females. ConclusionDiabetes mortality rates in Huangpu District have increased year by year, resulting in significant life loss. However, the age-standardized mortality rate increase has markedly slowed. Efforts should focus on elderly diabetic patients aged ≥70 years, by leveraging platforms such as community-based chronic disease health support centers, efforts should be made to enhance diabetes screening service for middle-aged and elderly residents. Consequently, elderly diabetic patients’ awareness of diabetes and responce to related complications is improved, which would be conducive to controling the progression of complications and reducing the mortolity risk of diabetes.

Growth and development-related diseases result from the interplay of biological, psychological, and social factors. The collaboration between healthcare, sports, and education sectors integrates multidisciplinary resources and strengths to promote standardized diagnostic and therapeutic processes. This approach establishes a comprehensive closed-loop system encompassing early screening and referral, diagnosis and comprehensive evaluation, intervention and support plan formulation, as well as long-term management andoutcome assessment. It provides systematic scientific support for the healthy growth of children and adolescents, shifting disease intervention to the subclinical stage. Against the backdrop of societal informatization and intelligent development, this diagnostic and therapeutic model not only safeguards the holistic health of children and adolescents but also offers novel perspectives and feasible pathways for managing growth and development-related diseases. The implementation of this systematic diagnostic and therapeutic paradigm presents an innovative solution with Chinese characteristics for addressing such conditions, while injecting new vitality into the advancement of national health initiatives.

The growth and development of children is an important stage for health, and its monitoringand intervention are related to the long-term development of individuals. The construction of a standardized and multi-dimensional database of pediatric growth and development-related diseases is an important basis for realizing precise diagnosis and treatment and health management. Based on the needs of clinical practice, this study proposes to establish a specialized database of pediatric growth and development-related diseases that integrates facial phenotypes and clinical diagnosis and treatment information. This study elaborates on the construction process, including data sources, data collection content, and the operation and management of the database; and proposes key points for quality control, including the establishment of quality control nodes, database construction standards, and a full-process quality control framework. The above ensure the integrity, logic and effectiveness of the data, so that the database can provide an objective basis for the screening and diagnosis of pediatric growth and development-related diseases. On the basis of scientific data management and strict quality control, the database will help reveal the patterns of children's growth and development, and promote the level of children's health management.

As a new type of production factor that can empower the development of new quality productivity, the data element is an important engine to promote the high quality development of the industry. Traditional Chinese medicine(TCM) dispensing is the most basic work of TCM clinical pharmacy, and its quality directly affects the clinical efficacy of TCM. The integration of data elements and TCM dispensing can stimulate the innovation and vitality of the TCM dispensing industry and promote the high-quality and sustainable development of the industry. A large-scale, detailed, and systematic study on TCM dispensing was conducted. The innovative practice path of data fusion construction in the whole process of TCM dispensing was investigated by integrating the digital resources "nine full activities" of TCM dispensing, creating the digital dictionary of "TCM clinical information data elements", and exploring innovative applications of TCM dispensing driven by data and technology, so as to promote the standardized, digital, and intelligent development of TCM dispensing in medical health services. The research content of this project was successfully selected as the second batch of "Data element×" typical cases of National Data Administration in 2024, which is the only selected case in the field of TCM.
Medicine, Chinese Traditional/methods* ; Drugs, Chinese Herbal ; Humans

This study investigated the chemical constituents of Rhododendri Mollis Flos. The n-butanol fraction of 95% ethanol extract of Rhododendri Mollis Flos was separated and purified using chromatographic techniques, including normal-phase silica gel, ODS, and Sephadex LH-20 column chromatography. The structures of the isolated compounds were identified by spectroscopic techniques. Seven compounds were isolated and identified as rhodomollein LXⅦ(1), rhodomollein X(2), nivalenol(3),(3S,6S)-cis-linalool-3,7-oxide-β-D-glucopyranoside(4), thymidine(5), cyclo(Ala-Tyr)(6), and(S)-5-hydroxypyrrolidin-2-one(7). Among them, compound 1 was identified as a new compound, and compound 3 was isolated from this plant for the first time. The analgesic activity of compound 1 was evaluated using the acetic acid-induced writhing test. At a dose of 5.0 mg·kg~(-1), compound 1 showed a 75.4% inhibition rate in the writhing test, indicating significant analgesic activity.
Drugs, Chinese Herbal/isolation & purification* ; Rhododendron/chemistry* ; Animals ; Flowers/chemistry* ; Mice ; Male ; Analgesics/isolation & purification* ; Molecular Structure

Purpose: It was aimed to clarify the casual connection between prostate cancer (PCa) and arthritis by utilizing two-sample Mendelian randomization (MR) analysis and data from National Health and Nutrition Examination Survey (NHANES) database. Materials and Methods: This study utilized NHANES data. Through association analysis and risk stratification analysis, the association between arthritis and PCa were examined. MR analysis was performed to elucidate the causal relationship between arthritis and PCa. Sensitivity analysis and Steiger directionality test confirmed the reliability of the MR analysis results. Results: A total of 23,608 (PCa:controls=413:23,195) participants after a sample exclusion and variable definition process were screened in NHANES database. Adjustments across three diverse models consistently revealed a notable influence of arthritis on PCa progression. Arthritis was identified as a risk factor for PCa (odds ratio [OR] 1.88, 95% confidence interval [CI] 1.36–2.62, p<0.001). Subsequent analysis indicated that in the arthritis-adjusted model with multiple covariates, the area under the curve of the receiver operating characteristic curve was 0.94. The inverse variance weighting method of MR analysis showed a causal relationship between rheumatoid arthritis (RA) and PCa (OR 1.090, 95% CI 1.053–1.128, p<0.001) as well as osteoar-thritis and PCa (OR 1.002, 95% CI 1.001–1.004, p=0.002). This suggested that RA and osteoarthritis were risk factors for PCa. The heterogeneity (p>0.05), horizontal pleiotropy (p>0.05), leave-one-out and Steiger test confirmed reliability of MR results. Conclusions NHANES database and MR analyses identified arthritis as a risk factor for PCa, offering fresh avenues for preventive and therapeutic approaches.

Objective To explore the application of plasma 7 microRNA (miR7) testing in the differential diagnosis of very early-stage hepatocellular carcinoma (HCC). Methods This study is a multicenter real-world study. Patients with single hepatic lesion (maximum diameter≤2 cm) who underwent plasma miR7 testing at Zhongshan Hospital, Fudan University, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Anhui Provincial Hospital, and Peking University People’s Hospital between January 2019 and December 2024 were retrospectively enrolled. Patients were divided into very early-stage HCC group and non-HCC group, and the clinical pathological characteristics of the two groups were compared. The value of plasma miR7 levels, alpha-fetoprotein (AFP), and des-gamma-carboxy prothrombin (DCP) in the differential diagnosis of very early-stage HCC was evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC). In patients with both negative AFP and DCP (AFP＜20 ng/mL, DCP＜40 mAU/mL), the diagnostic value of plasma miR7 for very early-stage HCC was analyzed. Results A total of 64 528 patients from 4 hospitals underwent miR7 testing, and 1 682 were finally included, of which 1 073 were diagnosed with very early-stage HCC and 609 were diagnosed with non-HCC. The positive rate of miR7 in HCC patients was significantly higher than that in non-HCC patients (67.9% vs 24.3%, P＜0.001). ROC curves showed that the AUCs for miR7, AFP, and DCP in distinguishing HCC patients from the non-HCC individuals were 0.718, 0.682, and 0.642, respectively. The sensitivities were 67.85%, 43.71%, and 44.45%, and the specificities were 75.70%, 92.78%, and 83.91%, respectively. The pairwise comparison of AUCs showed that the diagnostic efficacy of plasma miR7 detection was significantly better than that of AFP or DCP (P＜0.05). Although its specificity was slightly lower than AFP and DCP, the sensitivity was significantly higher. Among patients negative for both AFP and DCP, miR7 maintained an AUC of 0.728 for diagnosing very early-stage HCC, with 67.82% sensitivity and 77.73% specificity. Conclusions Plasma miR7 testing is a potential molecular marker with high sensitivity and specificity for the differential diagnosis of small hepatic nodules. In patients with very early-stage HCC lacking effective molecular markers (negative for both AFP and DCP), miR7 can serve as a novel and effective molecular marker to assist diagnosis.

Urinary system tumors are very common nowadays，including prostate cancer，renal cancer，bladder cancer，and urothelial carcinoma.In recent years，the incidence of these tumors has been on the rise.This paper briefly summarizes the emerging technologies explored by West China Hospital in recent years for urinary system tumors，such as gene sequencing analysis，radiomics and big data，liquid chromatography-mass spectrometry，multi-modal intelligent fusion diagnostic technology，surgical decision-making tools built with artificial intelligence and big data，mRNA vaccines，combination of targeted and immune therapies，and irreversible electroporation technology.These technologies provide strong support and point out the ways for the prevention，early diagnosis，and individualized treatment of urinary system tumors.