Pancreatic cancer is a kind of highly malignant tumor with a low survival rate and poor prognosis. The effectiveness of gemcitabine as a first-line chemotherapy drug is limited; however, it can activate dendritic cells and improve antigen presentation which increase the sensitivity of tumor cell to immunotherapy. Although immunotherapy has made some advancements in cancer treatment, the therapeutic benefit of programmed cell death receptor 1/programmed death receptor-ligand 1 (PD-1/PD-L1) blockade therapy remains relatively low. The chemokine C-X-C chemokine ligand 12 (CXCL12) contributes to an immunosuppressive tumor microenvironment by recruiting immunosuppressive cells. The receptor C-X-C motif chemokine receptor 4 (CXCR4), highly expressed in various tumors including pancreatic cancer, plays a crucial role in tumor development and progression. In this study, the anti-tumor immune response of human peripheral blood mononuclear cell (hPBMC) was enhanced using the combination of BsNb PX4 (anti-PD-L1&CXCR4 bispecific nanobody) and gemcitabine. In a co-culture system of gemcitabine-pretreated hPBMCs with tumor cells, the BsNb PX4 synergized gemcitabine to improve the cytotoxic activity of hPBMCs against tumor cells. Flow cytometry analysis confirmed increased ratio of CD8⁺ to CD4⁺ T cells in combination treatment. In NOD/SCID mice bearing pancreatic cancer, the combination treatment exhibited more infiltration of CD8⁺ T cells into tumor tissues, contributing to an effective anti-tumor response. This study presents potential new therapies for the treatment of pancreatic cancer. Ethical approval was obtained for collection of hPBMC samples from the Local Ethics Committee of Shanghai Jiao Tong University. All animal experiments were approved by the Animal Ethic Committee of Shanghai Jiao Tong University (authorizing number: A2024246).

Based on ultra-performance liquid chromatography-quadrupole-electrostatic field Orbitrap high-resolution mass spectrometry(UPLC-Q-Orbitrap HRMS) technology and molecular docking, the bitter-tasting substances(hereafter referred to as "bitter substances") in Cistanche deserticola extract were investigated, and the bitter taste and efficacy relationship was explored to lay the foundation for future research on de-bittering and taste correction. Firstly, UPLC-Q-Orbitrap HRMS was used for the qualitative analysis of the constituents of C. deserticola, and 69 chemical components were identified. These chemical components were then subjected to molecular docking with the bitter taste receptor, leading to the screening of 20 bitter substances, including 6 phenylethanol glycosides, 5 flavonoids, 3 phenolic acids, 2 cycloalkenyl ether terpenes, 2 alkaloids, and 2 other components. Nine batches of fresh C. deserticola samples were collected from the same origin but harvested at different months. These samples were divided into groups based on harvest month and plant part. The bitterness was quantified using an electronic tongue, and the content of six potential bitter-active compounds(pineconotyloside, trichothecene glycoside, tubulin A, iso-trichothecene glycoside, jinshihuaoside, and jingnipinoside) was determined by high-performance liquid chromatography(HPLC). The total content of phenylethanol glycosides, polysaccharides, alkaloids, flavonoids, and phenolic acids was determined using UV-visible spectrophotometry. Chemometric analyses were then conducted, including Pearson's correlation analysis, gray correlation analysis, and orthogonal partial least squares discriminant analysis(OPLS-DA), to identify the bitter components in C. deserticola. The results were consistent with the molecular docking findings, and the two methods mutually supported each other. Finally, network pharmacological predictions and analyses were performed to explore the relationship between the targets of bitter substances and their efficacy. The results indicated that key targets of the bitter substances included EGFR, PIK3CB, and PTK2. These substances may exert their bitter effects by acting on relevant disease targets, confirming that the bitter substances in C. deserticola are the material basis of its bitter taste efficacy. In conclusion, this study suggests that the phenylethanol glycosides, primarily pineconotyloside, mauritiana glycoside, and gibberellin, are the material basis for the "bitter taste" of C. deserticola. The molecular docking technique plays a guiding role in the screening of bitter substances in traditional Chinese medicine(TCM). The bitter substances in C. deserticola not only contribute to its bitter taste but also support the concept of the "taste-efficacy" relationship in TCM, providing valuable insights and references for future research in this area.
Molecular Docking Simulation ; Taste ; Chromatography, High Pressure Liquid ; Cistanche/chemistry* ; Drugs, Chinese Herbal/chemistry* ; Humans ; Mass Spectrometry

OBJECTIVE: As an age-related neurodegenerative disease, the prevalence of mild cognitive impairment (MCI) increases with age. Within the framework of traditional Chinese medicine, spleen-kidney deficiency syndrome (SKDS) is recognized as the most frequent MCI subtype. Due to the covert and gradual onset of MCI, in community settings it poses a significant challenge for patients and their families to discern between typical aging and pathological changes. There exists an urgent need to devise a preliminary diagnostic tool designed for community-residing older adults with MCI attributed to SKDS (MCI-SKDS). METHODS: This investigation enrolled 312 elderly individuals diagnosed with MCI, who were randomly distributed into training and test datasets at a 3:1 ratio. Five machine learning methods, including logistic regression (LR), decision tree (DT), naive Bayes (NB), support vector machine (SVM), and gradient boosting (GB), were used to build a diagnostic prediction model for MCI-SKDS. Accuracy, sensitivity, specificity, precision, F1 score, and area under the curve were used to evaluate model performance. Furthermore, the clinical applicability of the model was evaluated through decision curve analysis (DCA). RESULTS: The accuracy, precision, specificity and F1 score of the DT model performed best in the training set (test set), with scores of 0.904 (0.845), 0.875 (0.795), 0.973 (0.875) and 0.973 (0.875). The sensitivity of the training set (test set) of the SVM model performed best among the five models with a score of 0.865 (0.821). The area under the curve of all five models was greater than 0.9 for the training dataset and greater than 0.8 for the test dataset. The DCA of all models showed good clinical application value. The study identified ten indicators that were significant predictors of MCI-SKDS. CONCLUSION The risk prediction index derived from machine learning for the MCI-SKDS prediction model is simple and practical; the model demonstrates good predictive value and clinical applicability, and the DT model had the best performance. Please cite this article as: Ai YT, Zhou S, Wang M, Zheng TY, Hu H, Wang YC, Li YC, Wang XT, Zhou PJ. Development of a machine learning-based risk prediction model for mild cognitive impairment with spleen-kidney deficiency syndrome in the elderly. J Integr Med. 2025; 23(4): 390-397.
Humans ; Cognitive Dysfunction/diagnosis* ; Aged ; Male ; Female ; Machine Learning ; Spleen ; Aged, 80 and over ; Kidney ; Medicine, Chinese Traditional

OBJECTIVE: This study aimed to identify high-risk areas for type 2 diabetes mellitus (T2DM) mortality to provide relevant evidence for interventions in emerging economies. METHODS: Empirical Bayesian Kriging and a discrete Poisson space-time scan statistic were applied to identify the spatiotemporal clusters of T2DM mortality. The relationships between economic factors, air pollutants, and the mortality risk of T2DM were assessed using regression analysis and the Poisson Log-linear Model. RESULTS: A coastal district in East Guangdong, China, had the highest risk (Relative Risk [RR] = 4.58, P < 0.01), followed by the 10 coastal districts/counties in West Guangdong, China (RR = 2.88, P < 0.01). The coastal county in the Pearl River Delta, China (RR = 2.24, P < 0.01), had the third-highest risk. The remaining risk areas were two coastal counties in East Guangdong, 16 districts/counties in the Pearl River Delta, and two counties in North Guangdong, China. Mortality due to T2DM was associated with gross domestic product per capita (GDP per capita). In pilot assessments, T2DM mortality was significantly associated with carbon monoxide. CONCLUSION High mortality from T2DM occurred in the coastal areas of East and West Guangdong, especially where the economy was progressing towards the upper middle-income level.
Diabetes Mellitus, Type 2/epidemiology* ; China/epidemiology* ; Humans ; Risk Factors ; Spatio-Temporal Analysis ; Air Pollutants/analysis* ; Socioeconomic Factors ; Bayes Theorem ; Female ; Male ; Middle Aged

Objective:The epidemiological characteristics of tuberculosis deaths among Chinese residents from 2006 to 2021 were analyzed, and the tuberculosis mortality rate from 2022 to 2027 was predicted to provide a reference for tuberculosis prevention and control in China.Methods:The data set of tuberculosis deaths from 2006 to 2021 was published regularly by the China CDC, and the crude mortality rate (CMR) and age-standardized mortality rates (ASMR) were calculated according to the population structure of China in 2000. The distribution characteristics of age, sex, region, and time of tuberculosis deaths were analyzed, the Joinpoint regression analysis model was used to analyze the changing trend, and the grey model was applied to predict CMR and ASMR from 2022 to 2027.Results:From 2006 to 2021, the CMR and ASMR of tuberculosis showed a downward trend among males and females, urban and rural areas, and all age groups, in a word, all the Chinese residents. Except for the age group ≥85 years old, the mortality trend was insignificant. In the eastern, central, or western regions. CMR and ASMR were significantly higher in males than in females.CMR and ASMR were significantly lower in urban areas than in rural areas. In general, active tuberculosis patients present a higher mortality rate. The CMR and ASMR in the western region were higher than those in the eastern and central regions and lower in the eastern region than in the central region, but the differences were less obvious. The ASMR of the eastern cities was lower than that of the central and western regions, and the ASMR of the central cities was higher than that of the western region from 2006 to 2009 and 2012 and lower than that of the western region in other years. The ASMR in the western countryside was higher than that in the eastern and central regions and lower in the eastern part than in the central region, but the difference was not obvious. The grey model prediction results show that the CMR (/100 000) of Chinese residents from 2022 to 2027 is 1.585, 1.471, 1.360, 1.250, 1.143, and 1.038, and the ASMR (/100 000) is 0.779, 0.653, 0.531, 0.411, 0.295 and 0.181, respectively.Conclusions:The CMR and ASMR of tuberculosis will continue to decline, and extraordinary achievements have been made in tuberculosis prevention and control in Chinese residents from 2006 to 2021 and, presumably, from 2022 to 2027. However, tuberculosis screening and treatment programs in the western region, men, the elderly population, and rural areas should be further strengthened, and targeted prevention and control measures should be formulated to reduce mortality.

Transcatheter aortic valve replacement(TAVR)has become one of the effective methods for treating patients with aortic valve disease.With the continuous maturity of technology,innovation of instruments and increasing experience,the indications for TAVR has been expanded.Following international trends,the number of TAVR in China has steadily increased with each passing year.In 2023,the long-term follow-up results of TAVR in low-risk AS patients further confirm the long-term benefits of TAVR.The relevant research on TAVR for patients with aortic regurgitation and patients with bicuspid aortic stenosis provide evidence support for the expansion of TAVR indications.At the same time,the development of valve devices and new technological innovations are emerging in an endless stream,and the new concept of full life cycle management is increasingly being valued.Especially in China,the development of local devices is progressing rapidly,and multiple devices have entered the clinical research stage.The clinical manifestations and research results are worth pursuing.

[摘 要] 目的：探究桃叶珊瑚苷（AU）调控RhoA/ROCK信号通路对胃癌MGC803细胞上皮间质转化（EMT）进程和血管生成拟态（VM）形成的影响。方法：常规培养人胃癌MGC803细胞，将其随机分为对照组、AU-L组（20 μmol/L AU）、AU-M组（40 μmol/L AU）、AU-H组（80 μmol/L AU）、AU-H+RhoA激活剂水仙环素（Nar）组（AU-H+Nar组，80 μmol/L AU+30 μmol/L Nar）。采用CCK-8法、Transwell实验、细胞划痕实验分别检测不同浓度AU对细胞增殖、迁移和侵袭的影响，三维细胞培养法观察不同浓度AU对细胞体外VM管腔结构形成的影响，WB法检测AU对各组细胞RhoA、ROCK、VM与EMT相关蛋白表达的影响。结果：与对照组相比，AU-M组、AU-H组MGC803细胞增殖率（48、72 h时）、细胞迁移率、细胞侵袭数目、VM管腔结构数，以及RhoA、ROCK1、N-cadherin、vimentin、VE-cadherin的蛋白表达均显著降低（均P<0.05），E-cadherin表达显著升高（P<0.05）；同时，使用Nar处理显著减弱了AU对MGC803细胞EMT和VM形成的抑制作用（均P<0.05）。结论：AU通过下调RhoA/ROCK信号通路抑制胃癌MGC803细胞的增殖、迁移、侵袭、EMT和VM形成过程。

In the field of dental aesthetics,digital aesthetic design plays a crucial role in helping dentists to predict treatment outcomes vis-ually,as well as in enhancing the consistency of knowledge and understanding of aesthetic goals between dentists and patients.It serves as the foundation for achieving ideal aesthetic effects.However,there is no clear standard for this digital process currently in China and abroad.Many dentists lack of systematic understanding of how to carry out digital aesthetic design for treatment.To establish standardized processes for dental aesthetic design and to improve the homogeneity of treatment outcomes,Chinese Society of Digital Dental Industry(CSD-DI)convened domestic experts in related field to compile this consensus.This article elaborates on the key aspects of digital aesthetic data collection,integration steps,and the digital aesthetic design process.It also formulates a decision tree for dental aesthetics at macro level and outlines corresponding workflows for various clinical scenarios,serving as a reference for clinicians.

Objective:To study the current status of longitudinal extrauterine growth restriction (EUGR) in extremely preterm infants (EPIs) and to develop a prediction model based on clinical data from multiple NICUs.Methods:From January 2017 to December 2018, EPIs admitted to 32 NICUs in North China were retrospectively studied. Their general conditions, nutritional support, complications during hospitalization and weight changes were reviewed. Weight loss between birth and discharge > 1SD was defined as longitudinal EUGR. The EPIs were assigned into longitudinal EUGR group and non-EUGR group and their nutritional support and weight changes were compared. The EPIs were randomly assigned into the training dataset and the validation dataset with a ratio of 7∶3. Univariate Cox regression analysis and multiple regression analysis were used in the training dataset to select the independent predictive factors. The best-fitting Nomogram model predicting longitudinal EUGR was established based on Akaike Information Criterion. The model was evaluated for discrimination efficacy, calibration and clinical decision curve analysis.Results:A total of 436 EPIs were included in this study, with a mean gestational age of (26.9±0.9) weeks and a birth weight of (989±171) g. The incidence of longitudinal EUGR was 82.3%(359/436). Seven variables (birth weight Z-score, weight loss, weight growth velocity, the proportion of breast milk ≥75% within 3 d before discharge, invasive mechanical ventilation ≥7 d, maternal antenatal corticosteroids use and bronchopulmonary dysplasia) were selected to establish the prediction model. The area under the receiver operating characteristic curve of the training dataset and the validation dataset were 0.870 (95% CI 0.820-0.920) and 0.879 (95% CI 0.815-0.942), suggesting good discrimination efficacy. The calibration curve indicated a good fit of the model ( P>0.05). The decision curve analysis showed positive net benefits at all thresholds. Conclusions:Currently, EPIs have a high incidence of longitudinal EUGR. The prediction model is helpful for early identification and intervention for EPIs with higher risks of longitudinal EUGR. It is necessary to expand the sample size and conduct prospective studies to optimize and validate the prediction model in the future.