BACKGROUND:Previous studies have suggested that gut microbiota and immune system regulation have potential links with type 2 diabetes (T2D). However, the causal association between gut microbiota and T2D and whether immune cells mediate this interaction is unclear. METHODS:A two-sample, two-step Mendelian randomization (MR) study utilizing an initial inverse-variance weighted (IVW) method was performed to explore the causal impact of gut microbiota on T2D and the intermediary role of immune cells. RESULTS:The MR analysis assigned 4 gut microbiota and metabolic pathways that increase the risk of T2D (G_Prevotella, g_Anaerotruncus, g_Streptococcus.s_ Streptococcus_parasanguinis, and the pathway of PANTO-PWY) and 4 other gut microbiota and metabolic pathways that have a protective effect against T2D (PWY- 5667, PWY-6892, PWY-7221, and the bacterial g_Paraprevotella.s_Paraprevotella_ clara). Furthermore, 17 immune cell traits have been identified as associated with T2D. The finding from mediation MR analysis revealed that PANTO-PWY increases T2D risk via CD3 on HLA DR+ CD4+, whereas PWY-7221 reduces T2D risk through CD4 on CD4 Treg. CONCLUSION:The research reveals a mediated causal link between the gut microbiota and T2D via immune cells.

Objective The ambitious research aimed to explore the protective impact and mechanism of the ethyl acetate extract of Taxus chinensis fruits (TCFE) on the hippocampi of rats subjected to chronic unpredictable mild stress (CUMS). Methods A total of 32 SD rats were randomly allocated into the control group, model group, TCFE group receiving 200 mg/(kg·d) of TCFE, fluoxetine group receiving 2 mg/(kg·d) of fluoxetine. The rats were induced with chronic unpredictable mild stress (CUMS) for 28 d, and concurrently administered the allocated drugs via gavage for the same duration. The body mass, performance in the open field test (OFT), and sugar-water preference rate of the rats in each group were assessed. The morphology of rat hippocampal tissues was examined using Hematoxylin-Eosin (H&E) staining. Nissl staining was employed to observe hippocampal pathological changes, while ELISA was utilized to measure the levels of IL-1β, TNF-α, IL-6, and IL-18 in the hippocampus. Immunofluorescence was employed to identify the expression of the microglia (MG) marker Iba1 in the rat hippocampus. Furthermore, immunohistochemistry was utilized to detect the expression of Bax and Bcl-2 in the hippocampus. Real-time fluorescence quantitative PCR (qRT-PCR) was utilized to assess the expression levels of two apoptosis-related genes in the rat hippocampus. TUNEL assay was performed to detect tissue cell apoptosis in the rat hippocampus. Finally, Western blotting analysis was conducted to determine the expression of Bax and Bcl-2 proteins in the hippocampus. Results The consumption of TCFE significantly enhanced body weight (P < 0.05), sucrose preference rate (P < 0.05), and total movement distance in the open field test (P < 0.05). It also improved hippocampal neuronal structure integrity and attenuated loss. Moreover, TCFE regulated the excessive activation of hippocampal microglia in rats, inhibited the expression of IL-1β and TNF-α (P < 0.05), and modulated the Bax and Bcl-2 expression levels, leading to a significant alteration in the Bax/Bcl-2 ratio (P < 0.05). Conclusion TCFE shows promise in attenuating cell apoptosis through the modulation of the Bax/Bcl-2 ratio, suggesting a prospective approach for combating depression.

Toll-like receptor 4 (TLR4) is a pivotal therapeutic target for inflammatory diseases and immune dysregulation. This study integrated artificial intelligence, molecular docking, dynamics simulations, and microscale thermophoresis (MST) to discover TLR4 inhibitors from medicinal food plants. A dataset of 890 TLR4 inhibitors from PubChem was utilized, with 445 molecular descriptors calculated using MOE 2022.02 software. Recursive feature elimination and four machine learning algorithms (linear discriminant analysis, support vector machines, logistic regression, and Lasso logistic regression) were employed for activity prediction. Furthermore, a model based on molecular fingerprints and graph fusion learning (FP&G) was developed for comparison. A chemical database of 6388 compounds was created enabling activity predictions. Traditional machine learning models demonstrated modest predictive accuracy of around 65 % with both sensitivity and specificity below 70 %, and Matthew's correlation coefficient (MCC) values under 0.5. In contrast, the FP&G learing model achieved predictive accuracies of 93.3 % on both the training and testing sets, respectively, with sensitivity and specificity of 94.0 % and 92.6 %, and a MCC value of 0.865. Our optimal model identified 283 compounds from 46 medicinal food plants as potential TLR4 inhibitors, with docking scores below -12.19 kcal/mol. Notably, goji berry contained the most number of TLR4 inhibitory components, which was consistent with its anti-inflammatory and antioxidant properties. Finally, molecular dynamics and MST revealed glyceryl trilinoleate stably binds TLR4's active site with strong affinity (Kd = 0.2 +/- 0.036 mu M) better than the classical TLR4 inhibitor C34's 33 +/- 11.7 mu M and high specificity. This comprehensive approach provided valuable tools and insights for future development of TLR4 inhibitors.

OBJECTIVES:To construct vocal recognition classification models using 6 machine learning algorithms and vocal emotional characteristics of individuals with subthreshold depression to facilitate early identification of subthreshold depression. METHODS:We collected voice data from both normal individuals and participants with subthreshold depression by asking them to read specifically chosen words and texts. From each voice sample, 384-dimensional vocal emotional feature variables were extracted, including energy feature, Meir frequency cepstrum coefficient, zero cross rate feature, sound probability feature, fundamental frequency feature, difference feature. The Recursive Feature Elimination (RFE) method was employed to select voice feature variables. Classification models were then built using the machine learning algorithms Adaptive Boosting (AdaBoost), Random Forest (RF), Linear Discriminant Analysis (LDA), Logistic Regression (LR), Lasso Regression (LRLasso), and Support Vector Machine (SVM), and the performance of these models was evaluated. To assess generalization capability of the models, we used real-world speech data to evaluate the best speech recognition classification model. RESULTS:The AdaBoost, RF, and LDA models achieved high prediction accuracies of 100%, 100%, and 93.3% on word-reading speech test set, respectively. In the text-reading speech test set, the accuracies of the AdaBoost, RF, and LDA models were 90%, 80%, and 90%, respectively, while the accuracies of the other 3 models were all below 80%. On real-world word-reading and text-reading speech data, the classification models using AdaBoost and Random Forest still achieved high predictive accuracies (91.7% and 80.6% for AdaBoost and 86.1% and 77.8% for Random, respectively). CONCLUSIONS:Analyzing vocal emotional characteristics allows effective identification of individuals with subthreshold depression. The AdaBoost and RF models show excellent performance for classifying subthreshold depression individuals, and may thus potentially offer valuable assistance in the clinical and research settings.

ETHNOPHARMACOLOGICAL RELEVANCE:As one of the important by-products of Taxus chinensis (Pilg.) Rehder, its fruit (TCF) has a sweet taste, which is commonly used in folklore to make health care wine reputed for enhancing immune function and promoting anti-aging effects, especially popular in the longevity villages of China for a long history. Evidences had showed that Taxus chinensis fruit contained polysaccharides, flavonoids, amino acids and terpenoids, which all were free of toxic compounds, but its medicinal value has not been fully recognized. Our previous studies have found that TCF extract may reverse many biological events, including oxidative stress, inflammatory response, neuronal apoptosis, etc. by in silico methods, suggesting potential avenues for future pharmaceutical exploration in aging and age-related diseases. AIM OF THE STUDY:Yet, the anti-aging properties of TCF have not been specifically studied, this study aims to fill this gap by investigating the effects of TCF extract (TCFE) in an aging mouse model, particularly focusing on its role in inhibiting microglial activation and elucidating its underlying anti-aging mechanisms. MATERIALS AND METHODS:An aging mouse model was induced using D-galactose, with interventions involving high, medium, and low doses of TCFE compared to a positive control (2 mg/kg rapamycin combined with 100 mg/kg metformin). The methodology involved evaluating behavioral changes, serum oxidative and antioxidative markers, hypothalamic β-galactosidase activity, expression of the aging-related protein P63, serum inflammatory factors, and the TLR4/NF-κB/NLRP3 inflammatory pathway in hypothalamic tissues. Additionally, to strengthen our in vivo findings, we conducted in vitro experiments on LPS-stimulated BV2 microglial cells. Finally, UPLC-MS/MS for precise component analysis using compound standards, coupled with molecular docking analyses, were employed to discern and elucidate the anti-inflammatory mechanisms of TCF. RESULTS:In vivo results revealed TCFE significantly ameliorated behavioral deficits, reduced oxidative stress markers (MDA) and pro-inflammatory cytokines (IL1-β, IL-6, IFNg, TNFα, IL-17), and increased in antioxidants (SOD, T-AOC) and anti-inflammatory factors (IL-10). TCFE also reduced hypothalamic senescence, improved cellular integrity, lowered p63, and inhibited microglia activation and inflammatory pathways (TLR4, NFKB, NLRP3). The overall effect of TCFE was better than that of the positive drug group (rapamycin combined with metformin). In vitro results further revealed that TCFE markedly decreased IL1-β, NFKB, and TLR4 levels in BV2 microglial cells, showing comparable efficacy to a TLR4 classic positive inhibitor C34, supporting its anti-inflammatory role. Through UPLC-MS/MS analysis coupled with compound standards, we identified ten bioactive compounds, including gallocatechin, epigallocatechin, catechin, procyanidin B2, kaempferol, quercetin, rutin, naringin, apigenin, ginkgetin. All these compounds showed strong binding affinity to TLR4, notably procyanidin B2 and rutin, potentially through hydrogen bonds, aromatic cation-π interactions, and hydrophobic interactions, suggesting a molecular basis for their anti-inflammatory action. CONCLUSION:TCFE showed strong anti-aging effects by inhibiting microglia activation and lessening oxidative stress and modulating inflammatory pathways. This research supports TCF's use in anti-aging and sets a base for future drug development in the realms of neuroinflammation and aging.

BACKGROUND:The taxus chinensis fruit (TCF) shows promises in treatment of aging-related diseases such as Alzheimer's disease (AD). However, its related constituents and targets against AD have not been deciphered. OBJECTIVE:This study was to uncover constituents and targets of TCF extracts against AD. METHODS:An integrated approach including ultrasound extractions and constituent identification of TCF by UPLC-QE-MS/MS, target identification of constituents and AD by R data-mining from Pubchem, Drugbank and GEO databases, network construction, molecular docking and the ROC curve analysis was carried out. RESULTS:We identified 250 compounds in TCF extracts, and obtained 3,231 known constituent targets and 5,326 differential expression genes of AD, and 988 intersection genes. Through the network construction and KEGG pathway analysis, 19 chemicals, 31 targets, and 11 biological pathways were obtained as core compounds, targets and pathways of TCF extracts against AD. Among these constituents, luteolin, oleic acid, gallic acid, baicalein, naringenin, lovastatin and rutin had obvious anti-AD effect. Molecular docking results further confirmed above results. The ROC AUC values of about 87% of these core targets of TCF extracts was greater than 0.5 in the two GEO chips of AD, especially 10 targets with ROC AUC values greater than 0.7, such as BCL2, CASP7, NFKBIA, HMOX1, CDK2, LDLR, RELA, and CCL2, which mainly referred to neuron apoptosis, response to oxidative stress and inflammation, fibroblast proliferation, etc.Conclusions:The TCF extracts have diverse active compounds that can act on the diagnostic genes of AD, which deserve further in-depth study.

Thinking of Traditional Chinese medicine is a kind of thinking of guiding the formation of traditional Chinese medicine theory and the establishment of TCM clinical syndrome differentiation under the special traditional cultural background of the Chinese nation, the unique social environmental conditions and the role of ancient philosophy. In the trend of thought of The Times, zhang Xichun germinated the idea of focusing on the Chinese and western medicine, combining the Chinese and western medicine, creating a precedent for the integration of Chinese and western medicine, and becoming a medical leader in modern Chinese medicine. This article expounds the meaning of TCM thinking and seeks for the TCM thinking reflected in zhang Xichun’s clinical cases in the treatment of diseases.

"阴阳自和"理论是阴阳学说的一项重要内容,对人体健康状态的调整具有重要指导意义.中医健康状态包含未病态、欲病态、已病态、病后态,当人体"阴阳自和"机制稳定发挥时,人体处于未病态,当"阴阳自和"机制发挥失常,则处于欲病态和已病态,在病后态时人体"阴阳自和"机制则处于极不稳定状态.从"阴阳自和"理论出发认识2型糖尿病患者中医健康状态,并在"阴阳自和"理论指导下对2型糖尿病患者健康状态进行调整,提出在未病态时需守"自和"以未病先防,在欲病态时需顺"自和"以防微杜渐,在已病态时需调"自和"以既病防变,在病后态时需助"自和"以防故疾再起,从而降低2型糖尿病发病率.

既往关于幽门螺杆菌感染的观点尚不能完全解释微生物与胃癌的相关性.口腔聚集菌群种类及数量众多,同时具有远处散播能力,部分研究报道显示口腔微生物与胃癌间存在潜在关系,如牙龈卟啉单胞菌、具核梭杆菌、放线菌、韦荣球菌等均与胃癌的发生发展相关,随着研究的深入,部分菌群诱发胃癌的作用机制逐步被阐明.口腔菌群聚集于舌面,在宏观层面上影响舌苔成像结果,研究报道舌苔与中医证素及菌群间有相互关系.基于现代科学技术,将中医舌苔与实验室检查相结合,旨在探讨舌苔、菌群、胃癌间的潜在联系,为胃癌早期筛查、预防、治疗提供新思路.

随着社会对中医诊疗设备智能化、便携化和产业化的要求不断提高,中医诊疗芯片研发逐渐成为现代中医诊疗装备研究中的前沿内容,在加快中医药现代化进程中发挥了关键作用.然而,一款芯片的研发并非易事,其中,数据要素模块是中医诊疗芯片研发的基点,主要包含中医规范化诊疗数据库的构建和中医现代化诊疗仪器设备的辅助采集两大方面内容.指令要素模块是中医诊疗芯片研发的支点,主要包含中医思维模型的构建和智能算法模型的构建两大方面内容.载体要素模块是中医诊疗芯片研发的落点,主要包含芯片封装技术的赋能和新型材料与技术的助力两大方面内容.本文结合中医诊疗原理与芯片制作原理从芯之基础、芯之内核以及芯之载体三个方面阐述中医诊疗芯片的研发路径.

精神分裂症是常见的精神类疾病之一,临床上对精神分裂症的诊治过程中常常受到多方因素的影响,且疾病症状复杂多变极易复发,仅靠西医诊治思维有各种局限,患者康复效果不佳.中医"五辨"思维可突破疾病诊治思维局限,将辨机、辨人、辨证、辨病、辨症相结合,综合把握精神分裂症的疾病变化规律,发挥中医特色优势,以期进一步丰富中医辨别诊治精神分裂症的理论体系.

目的:研究福建省冰毒成瘾者的流行病学特征及中医证素分布情况.方法:选取 2015 年 1 月—2020 年 2 月于福州市强制隔离戒毒所内戒毒的 2707 名冰毒成瘾者,分析冰毒成瘾者流行病学及中医证素分布情况.结果:①福建省冰毒成瘾者以男性、青年、未婚、无职业、初中及以下文化程度居多;②好奇心理和他人引诱是吸毒的主要原因;复吸原因排名前三为毒友影响、打发无聊时间和消除烦恼;③中医证素特征:病位证素以肾、肝、脾为主;虚性病性证素以气虚、阳虚、血虚、阴虚为主;实性证素以气滞、湿、痰为主;④聚类分析结果显示,脾肾阳气亏虚、肝郁气滞兼有阴血亏虚、痰浊蒙蔽心窍兼痰湿阻胃是其主要证型.结论:冰毒成瘾涉及多脏腑、气血功能失调,临床上呈现虚实夹杂并且以虚为主的复杂证候,相关戒毒工作者应充分结合流行病学特征开展戒毒,积极运用中医药方法进行戒毒、防复吸治疗.

避免误诊是医者应面对的重要问题,在中医原创整体思维指导下,从宏观"天时、地理",中观"生物、心理、社会",微观"理化、病理"三个角度,全面了解患者病情资料,综合考虑患者当前状态,从而对疾病进行正确的诊断和治疗,可避免中医误诊、误治的发生.

People judge the nature of human behaviors based on underlying intentions and possible outcomes. Recent studies have demonstrated a causal role of the right temporoparietal junction (rTPJ) in modulating both intention and intention-based outcome evaluations during social judgments. However, these studies mainly used hypothetical scenarios with socially undesirable contexts (bad/neutral intentions and bad/neutral outcomes), leaving the role of rTPJ in judging good intentions and good outcomes unclear. In the current study, participants were instructed to make goodness judgments as a third party toward the monetary allocations from one proposer to another responder. Critically, in some cases, the initial allocation by the proposer could be reversed by the computer, yielding combinations of good/bad intentions (of the proposer) with good/bad outcomes (for the responder). Anodal (n = 20), cathodal (n = 21), and sham (n = 21) transcranial direct current stimulation (tDCS) over the rTPJ were randomly assigned to 62 subjects to further examine the effects of stimulation over the rTPJ in modulating intention-based outcome evaluation. Compared to the anodal and sham stimulations, cathodal tDCS over the rTPJ reduced the goodness ratings of good/bad outcomes when the intentions were good, whereas it showed no significant effect on outcome ratings under unknown and bad intentions. Our results provide the first evidence that deactivating the rTPJ modulates outcome evaluation in an intention-dependent fashion, mainly by reducing the goodness rating towards both good/bad outcomes when the intentions are good. Our findings argue for a causal role of the rTPJ in modulating intention-based social judgments and point to nuanced effects of rTPJ modulation.

甲状腺结节与乳腺结节是育龄期女性的多发病,二者可独立出现,也常常合并发病.作者在跟师学习期间,发现患有甲状腺结节或乳腺结节者,多合并有睡眠问题,而肝郁气滞、痰凝血瘀为其关键病机,二者的发病与肝经关系密切,在治疗上常以疏肝理气、化痰散瘀为首要思路,现代医学认为,下丘脑-腺垂体轴功能失调可能成为甲状腺结节与乳腺结节发生的共同生理病理基础,并且能够对睡眠产生影响.此篇从睡眠角度出发,讨论两者中西医发病机制以及治疗的相关性,以期更好地指导临床,实现未病先防,既病防变.

精神分裂症是一种复发率和致残率都极高的常见精神疾病之一,严重影响患者的生理及心理健康.将中医健康管理引入到该病的诊治过程中,有助于早期发现、诊断、干预和治疗,提高患者的生活质量水平.通过结合精神分裂症的疾病特点,从整体上动态把握患者健康状态,构建精神分裂症的中医健康管理模式,以期为我国精神分裂症的防治提供新思路.

在梳理知识驱动及知识工程、数据驱动及大数据等人工智能技术要素的基础上,根据基于知识驱动与基于数据驱动的中医诊疗系统各自的优势与不足,提出基于知识与数据双驱动的人工智能来构建中医诊疗系统,以适用于中医临床.从中医学学科属性、基于逻辑演绎的中医药理论、基于归纳总结的实践经验角度分析构建双驱动诊疗系统的可行性,同时从中医药领域知识图谱、中医药医案大数据、知识图谱与大数据融合方面构建双驱动诊疗系统的实现路径,以期为中医诊疗系统的发展提供思路.

中医药治疗精神分裂症主要包括中药治疗、中西结合治疗和非药物治疗.目前,研究存在的不足之处有:由于精神分裂症的特殊性,单独应用中药治疗的研究较少;在临床研究中,由于某些试验的样本量较少,其结论尚需进一步验证;对方药作用机制研究的角度较为单一;中医药治疗精神分裂症仍然缺乏规范统一的模式,疗效评价也尚缺乏中医特色;非药物治疗研究有待进一步丰富补充等.今后,应融入中医状态辨识体系,对精神分裂症前驱期甚至前驱前期的状态进行早期识别干预,增加在"未病、欲病"阶段纯中药治疗的研究;还应深入研读经典古籍,多角度挖掘其他治疗神志疾病的方药并进行作用机制的探究.此外,需要尽可能开展多中心、大样本、随机盲法对照的临床试验,使研究结论更具有说服力;并且逐步建立统一规范的中医药诊疗方案,构建中医特色的疗效评价体系;加强温针灸、心理情志等中医特色疗法的深入研究与联合应用.

糖尿病足(DF)有高致残、致死及复发率等特点,是DM常见慢性并发症,严重影响患者生活质量.及时筛查、早期干预,是降低糖尿病足溃疡程度、提高患者生活质量的关键.近年来,随着物联网、人工智能技术发展,构建DF预警系统,实时监控DF高危因素成为DM领域研究热点.本文对DF预警系统研究进展进行综述.

目的 探讨精神分裂症患者的中医证素分布特征.方法 采用证素辨证方法提取111例精神分裂症患者病位、病性证素,运用频数统计、关联规则分析方法探索精神分裂症患者中医证素分布特征.结果 精神分裂症患者的病位证素分布频数从高到低主要为心神、肝、脾、肾、胃等.病性证素分布频数从高到低主要为阳虚、气虚、阴虚、气滞、血虚、湿、热、痰.对中医证素(积分≥70)进行关联性分析,设定置信度≥80％、支持度≥60％、提升度>1,最大前项数分别为1项和2项,后项均为1项.其中,2项证素关联规则分析结果显示"气虚-阳虚"是关联度最强的证素组合,表明精神分裂症患者多出现阳气亏虚证;3项证素关联规则分析结果显示"阴虚-阳虚、气虚"是关联度最高的证素组合,表明精神分裂症患者多出现气、阴、阳俱虚兼夹证.结论 精神分裂症是虚实夹杂之证,虚证多为肝脾肾亏虚、阳气不足,实证多为痰气郁结或痰火扰神,辨证时应注意辨虚实,治疗应重视补气、温阳、滋阴.