ObjectiveTo investigate the distribution of traditional Chinese medicine （TCM） syndrome elements in type 2 diabetes mellitus （T2DM） patients based on maximum body mass index （maxBMI） and explore their association with complication risks. MethodsA retrospective cross-sectional study was used to collect clinical data from hospitalized T2DM patients， extracting age， gender， smoking history， alcohol consumption history， duration of disease， HbA1c level， complications， and TCM syndromes， and extracting the syndrome elements of disease location and disease nature based on their TCM syndromes. MaxBMI was calculated by telephone survey of patients' self-reported maximum body weight； patients with maxBMI ≥24 kg/m² were classified into spleen-heat syndrome group， and those with maxBMI ＜24 kg/m² were classified into consumptive-heat syndrome group. The distribution of TCM syndrome types and syndrome elements of patients in the two groups were analysed. Then the propensity score matching method was used to balance the baseline characteristics between the two groups and compare the differences in the distribution of syndrome types and syndrome elements and the risk of macrovascular and microvascular complications between the two groups. ResultsAmong the 1178 T2DM patients， syndrome elements in spleen-heat patients （1034 cases） were primarily located in the spleen （351 cases， 33.95%）， liver （240 cases， 23.21%）， and stomach （139 cases， 13.44%）， while in consumptive-heat patients （144 cases）， they were concentrated in the spleen （57 cases， 39.58%）， liver （34 cases， 23.61%）， and kidneys （17 cases， 11.81%）； regarding syndrome elements of disease nature， spleen-heat patients were predominantly characterized by qi deficiency （481 cases， 46.52%）， phlegm （353 cases， 22.73%）， and dampness （241 cases， 23.31%）， whereas consumptive-heat patients showed more qi deficiency （84 cases， 58.33%） and yin deficiency （44 cases， 30.56%）. After propensity score matching， 132 cases were included in each group， and no statistically significant differences were observed in the distribution of syndrome elements of disease location between the two groups （P＞0.05）， but the phlegm element was significantly more prevalent in spleen-heat patients than in consumptive-heat patients （P = 0.006）. Regarding the risk of complications， spleen-heat patients had a significantly higher risk of developing macrovascular complications compared to consumptive-heat patients （OR=2.04， P=0.010）， while no significant differences were found between groups in the occurrence of microvascular complications （P＞0.05）. ConclusionThe spleen-heat T2DM patients show a more frequent syndrome element of disease nature of phlegm， and a higher risk of developing macrovascular complications compared to consumptive-heat patients.

ObjectiveTo investigate the triglyceride-glucose (TyG) index and the level of serum homocysteine (Hcy) in association with the incidence of stroke in type 2 diabetes mellitus (T2DM) patients. MethodsBased on the chronic disease risk factor surveillance cohort in Pudong New Area, Shanghai, excluding those with stroke in baseline survey, T2DM patients who joined the cohort from January 2016 to October 2020 were selected as the research subjects. During the follow-up period, a total of 318 new-onset ischemic stroke patients were selected as the case group, and a total of 318 individuals matched by gender without stroke were selected as the control group. The Cox proportional hazards regression model was used to adjust for confounding factors and explore the serum TyG index and the Hcy biochemical indicator in association with the risk of stroke. ResultsThe Cox proportional hazards regression results showed that after adjusting for confounding factors, the risk of stroke in T2DM patients with 10 μmol·L⁻¹-¹ and Hcy>15 μmol·L⁻¹ was 1.532 times (95%CI: 1.017‒2.309 times, P=0.041) and 1.738 times (95%CI: 1.119‒2.699 times, P=0.014) higher respectively, compared to T2DM patients with Hcy≤10 μmol·L⁻¹. T2DM patients with TyG>9.074 had 1.396 times higher risk of stroke (95%CI: 1.091‒1.787, P=0.008) compared to those with TyG≤9.074. The study found that urea and α1-antitrypsin (α1-AT) were independent risk factors for the incidence of stroke in T2DM patients. For every unit increase in urea andα1-AT, the risk of stroke in T2DM patients increased by 233.6% (HR=3.336, 95%CI: 1.588‒7.009, P=0.001) and 11.3% (HR=1.113, 95%CI: 1.028‒1.205, P=0.008), respectively. Cumulative risk curves showed that Hcy>10 μmol·L⁻¹ and TyG>9.074 accelerated the time to stroke occurrence in T2DM patients. ConclusionElevated levels of Hcy>10 μmol·L⁻¹ and a higher TyG index are risk factors for stroke in T2DM patients. Effective interventions for Hcy and TyG should be conducted for diabetic patients to reduce the incidence of stroke.

Objective To analyze the relationship between neuroticism and gastroesophageal reflux disease (GERD) using the Mendelian randomization (MR) method. Methods Exposure and outcome data were downloaded from the Integrative Epidemiology Unit (IEU) database in August 2023, including summary statistics from genome-wide association studies (GWAS) for neuroticism (n=374 323) and GERD (n=602 604). MR was conducted using the weighted median method, MR-Egger method, inverse variance weighted method, weighted mode method, and simple mode method. The causal relationship between the two was assessed using odds ratio (OR), and sensitivity analyses were performed to ensure the accuracy of the results. Results Neuroticism was associated with an increased risk of GERD [OR=1.229, 95%CI (1.186, 1.274), P<0.001]. Similarly, GERD was associated with an increased risk of neuroticism [OR=1.786, 95%CI (1.623, 1.965), P<0.001]. Conclusion There is a bidirectional causal relationship between neuroticism and gastroesophageal reflux disease.

With the rapid advancement of artificial intelligence technology, chatbots have shown great potential in the healthcare sector. From personalized health advice to chronic disease management and psychological support, chatbots have demonstrated significant advantages in improving the efficiency and quality of healthcare services. As the scope of their applications expands, the relationship between technological complexity and practical application scenarios has become increasingly intertwined, necessitating a more comprehensive evaluation of both aspects. This paper, from the perspective of he althcare applications, systematically reviews the technological pathways and development of chatbots in the medical field, providing an in-depth analysis of their performance across various medical scenarios. It thoroughly examines the advantages and limitations of chatbots, aiming to offer theoretical support for future research and propose feasible recommendations for the broader adoption of chatbot technologies in healthcare.

Objective To identify the independent predictors of programmed death-1 (PD-1) inhibitor-related endocrine adverse events and construct a clinically usable risk prediction model. Methods A total of 302 patients with solid tumors treated with PD-1 inhibitors were retrospectively enrolled. According to the presence or absence of endocrine immune-related adverse events (irAEs), the patients were divided into case group and control group. The clinical and laboratory indexes were compared between the two groups. Multivariable logistic regression was used to confirm independent predictors of endocrine irAEs. The nomogram was constructed, while the receiver operating characteristic (ROC) curve was used to test the prediction performance of the model. Results The overall incidence of endocrine irAEs was 21.9% (66/302), and the incidence of hypothyroidism was 19.5% (59/302). The age, PD-1 inhibitors, free thyroxine, thyroid peroxidase antibody (TPOAb), thyroglobulin, amylase, lymphocyte subset CD3 expression were statistically different between the two groups (P＜0.05). Multivariable logistic regression showed that higher expression of lymphocyte subset CD3 was a protective factor to prevent endocrine irAEs occurrence (P＝0.004), while age＜60 years, higher TPOAb and use of pembrolizumab were independent risk factors of endocrine irAEs (P＜0.05). The nomogram model thus constructed, and when the threshold probability of the model exceeded 0.1, its net benefit was higher. ROC curve showed that the AUC of the model to predict endocrine irAEs was 0.760. The prediction result of the model was highly consistent with the actual result. Conclusions The age, type of PD-1 inhibitor, baseline TPOAb level, and baseline CD3 expression can independently predict endocrine irAEs occurrence or not. The nomogram model based on this model has good predictive efficiency, which can provide reference for early identification of high-risk patients and immunotherapy management.

Human taste is an important function of chemical perception. In recent years, brain taste evoked potentials have received more and more attention as a feasible tool for objective assessment of taste dysfunction. This paper reviews the main characteristics of gustatory evoked potential signals, the most widely used recording and processing techniques, and the scientific advances and relevance of gustatory evoked potentials in many important applications. In particular, taste evoked potentials are used to study the central effects of food intake and taste disorders, which may affect cognition and personality, or may be potential indicators of the onset or progression of neurological disorders. For these reasons, this paper presents and analyzes the latest scientific results and future challenges of using gustatory evoked potentials as an attractive solution to objective monitoring techniques for taste disorders. Human taste is an important function of chemical perception. In recent years, brain gustatory evoked potentials have received more and more attention as a feasible tool for objective assessment of taste dysfunction. This paper reviews the main characteristics of gustatory evoked potential signals, the most widely used recording and processing techniques, and the scientific advances and relevance of gustatory evoked potentials in many important applications. In particular, gustatory evoked potentials are used to study the central effects of food intake and taste disorders, which may affect cognition and personality, or may be potential indicators of the onset or progression of neurological disorders. For these reasons, this paper presents and analyzes the latest scientific results and future challenges of using gustatory evoked potentials as an attractive solution to objective monitoring techniques for taste disorders.
Humans ; Evoked Potentials ; Taste/physiology* ; Taste Perception/physiology*

Mitochondrial dysfunction in chondrocytes is a key pathogenic factor in osteoarthritis (OA), but directly modulating mitochondria in vivo remains a significant challenge. This study is the first to verify a correlation between mitochondrial dysfunction and the downregulation of the FOXO3 gene in the cartilage of OA patients, highlighting the potential for regulating mitophagy via FOXO3 gene modulation to alleviate OA. Consequently, we developed a chondrocyte-targeting CRISPR/Cas9-based FOXO3 gene-editing tool (FoxO3) and integrated it within a nanoengineered 'truck' (NETT, FoxO3-NETT). This was further encapsulated in injectable hydrogel microspheres (FoxO3-NETT@SMs) to harness the antioxidant properties of sodium alginate and the enhanced lubrication of hybrid exosomes. Collectively, these FoxO3-NETT@SMs successfully activate mitophagy and rebalance mitochondrial function in OA chondrocytes through the Foxo3 gene-modulated PINK1/Parkin pathway. As a result, FoxO3-NETT@SMs stimulate chondrocytes proliferation, migration, and ECM production in vitro, and effectively alleviate OA progression in vivo, demonstrating significant potential for clinical applications.

Accurate prediction of drug responses in cancer cell lines (CCLs) and transferable prediction of clinical drug responses using CCLs are two major tasks in personalized medicine. Despite the rapid advancements in existing computational methods for preclinical and clinical cancer drug response (CDR) prediction, challenges remain regarding the generalization of new drugs that are unseen in the training set. Herein, we propose a multimodal fusion deep learning (DL) model called drug-target and single-cell language based CDR (DTLCDR) to predict preclinical and clinical CDRs. The model integrates chemical descriptors, molecular graph representations, predicted protein target profiles of drugs, and cell line expression profiles with general knowledge from single cells. Among these features, a well-trained drug-target interaction (DTI) prediction model is used to generate target profiles of drugs, and a pretrained single-cell language model is integrated to provide general genomic knowledge. Comparison experiments on the cell line drug sensitivity dataset demonstrated that DTLCDR exhibited improved generalizability and robustness in predicting unseen drugs compared with previous state-of-the-art baseline methods. Further ablation studies verified the effectiveness of each component of our model, highlighting the significant contribution of target information to generalizability. Subsequently, the ability of DTLCDR to predict novel molecules was validated through in vitro cell experiments, demonstrating its potential for real-world applications. Moreover, DTLCDR was transferred to the clinical datasets, demonstrating satisfactory performance in the clinical data, regardless of whether the drugs were included in the cell line dataset. Overall, our results suggest that the DTLCDR is a promising tool for personalized drug discovery.

ObjectiveTo determine the optimal dose of Dachengqi Decoction （大承气汤， DCQD） for the treatment of acute intestinal obstruction （AIO） through a randomized， double-blind， dosage parallel controlled， multi-center clinical trial， and to providee evidence support for the reasonable dosage of DCQD in clinical practice. MethodsBased on the commonly used clinical dose of DCQD， three different groups were set up， including low-dose group which used Dahuang （Radix et Rhizoma Rhei） 12 g， Houpo （Cortex Magnoliae Officinalis） 9 g， Zhishi （Fructus Aurantii Immaturus） 9 g， and Mangxiao （Natrii Sulfas） 4.5 g， medium-dose group using Dahuang 36 g， Houpo 27 g， Zhishi 27 g， Mangxiao 13.5 g， and high-dose group using Dahuang 60 g， Houp0 45 g， Zhishi 45 g and Mangxiao 22.5 g. Initially， 149 AIO patients with Yangming （阳明） bowel excess syndrome were randomly assigned to three groups using a stratified randomization method， and both the patients and the doctors were blinded. In addition to conventional western medicine treatment， each group was given 12 bags of granules made from the raw herbs of DCQD at different doses， taken orally or injected through a gastric catheter once every 6 hours， 3 bags each time， for 3 consecutive days. After treatment， the indicators of the three groups of patients with primary AIO and non-primary AIO were evaluated respectively， and the full analysis set （FAS） and per-protocol set （PPS） were used for analysis. The primary outcomes were the time to recover voluntary bowel movements and voluntary flatulence. The secondary outcomes were the ideal rate of spontaneous defecation and the ideal rate of spontaneous flatus. The occurrence of adverse events during the study was recorded and analyzed using the safety analysis set （SS）. ResultsA total of 91 patients with primary AIO and 58 patients with non-primary AIO were included in the FAS and SS analysis， while 80 primary AIO patients and 56 non-primary AIO patients were included in the PPS analysis. Both FAS and PPS analysis showed significant differences in the time to recover voluntary bowel movements and voluntary flatulence among primary AIO patients in different dose groups of DCQD （P＜0.01）， and the high- and medium-dose groups assumed less time than the low-dose group （P＜0.05）. There was no statistically significant difference in the ideal rate of spontaneous defecation and spontaneous flatus among the three groups （P＞0.05）. And consistent results were seen in the non-primary AIO patients among the three groups. Five adverse events occurred in primary AIO patients （3 in the low-dose group， 1 in the medium-dose group， and 1 in the high-dose group）， mainly manifested as abdominal distension and abdominal pain， and there was no statistically significant difference in the incidence of adverse events （P＞0.05）. No adverse events occurred in patients with non-primary AIO. ConclusionDCQD， as an effective treatment for patients with AIO， is commonly used at a medium dose for patients with primary AIO and at a high dose for patients with non-primary AIO. The therapeutic advantage is mainly reflected in the shorter time to recover spontaneous defecation and spontaneous flatulence and the improvement of intestinal function.

Objective·To explore the predictive capability of retinal vascular features in preeclampsia(PE)based on artificial intelligence(AI)models.Methods·This retrospective study enrolled 789 pregnant women who registered from June 2020 to January 2021 at Shanghai First Maternity and Infant Hospital of Tongji University in the first 16 weeks of gestation.These women underwent regular prenatal examinations,had retinal fundus images captured,and delivered singleton live births at the hospital.According to whether they developed hypertensive disorders of pregnancy(HDP),they were divided into unaffected group(n=685)and HDP group(n=104).Within the HDP group,pregnancies were further categorized into gestational hypertension(GH)group(n=36)and PE group(n=68)based on the occurrence of PE.Based on the gestational age at onset,the PE group was further divided into early-onset PE group(gestational age<34 weeks)and late-onset PE group(gestational age≥34 weeks).Fundus images of the pregnant women were obtained,and an AI algorithm was utilized to diagnose retinal lesions and quantify retinal vascular features.Comparative analyses were conducted on fundus features and retinal vascular features.Univariate Logistic regression model was employed to analyze the influencing factors of PE occurrence,and multivariate Logistic regression model was further utilized to assess the correlation between retinal vascular features and the occurrence of PE.The predictive capability of retinal vascular features for PE(both early-and late-onset PE)was analyzed by using area under the curve(AUC)of receiver operator characteristic curve(ROC curve).Results·The comparative analysis of fundus features and retinal vascular features demonstrated statistically significant differences between the unaffected group and PE group in terms of central retinal artery equivalent(CRAE),central retinal vein equivalent(CRVE),arteriole-to-venular ratio(AVR),retinal artery tortuosity and retinal artery fractal dimension(all P<0.05).Univariate Logistic regression analysis indicated that second-trimester mean arterial pressure(MAP),second-trimester estimated fetal weight(EFW),CRAE,CRVE,AVR,retinal artery tortuosity and retinal artery fractal dimension were the influencing factors for PE occurrence(all P<0.05).Multivariate Logistic regression analysis revealed that second-trimester EFW,CRAE,CRVE,AVR,retinal artery tortuosity and retinal artery fractal dimension were the protective factors for the occurrence of PE,while second-trimester MAP was the risk factor for PE(all P<0.05).The analysis of ROC curves revealed that maternal risk factors along with second-trimester prenatal examination data(including MAP and EFW)and retinal vascular features model had good predictive ability for PE[AUC(95%CI)=0.784(0.725-0.843),and this model exhibited better predictive capability for early-onset PE,with an AUC(95%CI)of 0.840(0.756-0.924).Conclusion·The integration of quantified retinal vascular features based on AI models with maternal risk factors and second-trimester prenatal examination data(including MAP and EFW)enables a more effective prediction of PE occurrence,particularly early-onset PE.