AIM: To investigate the efficacy of Yangyin Yiqi Decoction combined with polyethylene glycol eye drops on dry eye disease after cataract surgery and its effect on inflammatory factors in tears.METHODS:Prospective study. A total of 104 patients(104 eyes)with dry eye disease after cataract surgery admitted to our hospital from April 2022 to March 2024 were selected and randomly divided into two groups: 52 cases(52 eyes)in the control group were treated with polyethylene glycol eye drops; 52 cases(52 eyes)in the combined group were treated with Yangyin Yiqi Decoction combined with polyethylene glycol eye drops. The dry eye symptom score, tear film stability index, tear inflammatory factor level, ocular surface disease index score(OSDI)and clinical efficacy of the two groups were compared before and after treatment.RESULTS:There were 6 cases(6 eyes)lost to follow-up, and 3 cases(3 eyes)were lost to follow-up in each group, with a loss to follow-up rate of 5.8%. The total effective rate of treatment in the combination group was significantly higher than that in the control group(94% vs 80%, P=0.037), and the Schirmer I test(SIt)and tear film break-up time(BUT)were higher than those in the control group(all P<0.05), and the dry eye symptom score, corneal fluorescein staining(FL)score, IL-1β, TNF-α, hs-CRP levels in tears and OSDI score were lower than those in the control group(all P<0.05).CONCLUSION:The combination of Yangyin Yiqi Decoction and polyethylene glycol eye drops provides an effective treatment plan for patients with dry eye disease after cataract surgery. It can effectively alleviate dry eye symptoms, reduce eye discomfort, improve tear film stability and ocular surface status, and reduce inflammatory factors in tears.

The rapid development of artificial intelligence (AI), especially generative AI (GenAI), has already brought, and will continue to bring, revolutionary changes to our daily production and life, as well as create new opportunities and challenges for diagnostic and therapeutic practices in the medical field. Haihe Hospital of Tianjin University collaborates with the National Supercomputer Center in Tianjin, Tianjin University, and other institutions to carry out research in areas such as smart healthcare, smart services, and smart management. We have conducted research and development of a full-process disease diagnosis and treatment assistance system based on GenAI in the field of smart healthcare. The development of this project is of great significance. The first goal is to upgrade and transform the hospital's information center, organically integrate it with existing information systems, and provide the necessary computing power storage support for intelligent services within the hospital. We have implemented the localized deployment of three models: Tianhe "Tianyuan", WiNGPT, and DeepSeek. The second is to create a digital avatar of the chief physician/chief physician's voice and image by integrating multimodal intelligent interaction technology. With generative intelligence as the core, this solution provides patients with a visual medical interaction solution. The third is to achieve deep adaptation between generative intelligence and the entire process of patient medical treatment. In this project, we have developed assistant tools such as intelligent inquiry, intelligent diagnosis and recognition, intelligent treatment plan generation, and intelligent assisted medical record generation to improve the safety, quality, and efficiency of the diagnosis and treatment process. This study introduces the content of a full-process disease diagnosis and treatment assistance system, aiming to provide references and insights for the digital transformation of the healthcare industry.
Artificial Intelligence ; Humans ; Delivery of Health Care ; Generative Artificial Intelligence

OBJECTIVE: To develop a digital intelligent multimodal shift handover system for the intensive care unit (ICU) and evaluate its application effect in ICU shift handovers. METHODS: A research and development team was established, consisting of 1 department director, 1 head nurse, 3 information technology engineers, 3 nurses, and 2 doctors. Team members were assigned responsibilities including overall coordination and planning, platform design and maintenance, pre-application training, collection and organization of clinical feedback, and research investigation respectively. A digital intelligent multimodal shift handover system was developed for ICU based on the Shannon-Weaver linear transmission model. This innovative system integrated automated data collection, intelligent dynamic monitoring, multidimensional condition analysis and visual reporting functions. A cloud platform was used to gather data from multi-parameter vital signs monitors, infusion pumps, ventilators and other devices. Artificial intelligence algorithms were employed to standardize and analyze the data, providing personalized recommendations for healthcare professionals. A self-controlled before-after method was adopted. Before the application of the ICU digital intelligent multimodal shift handover system (from December 2023 to March 2024), the traditional verbal bedside handover was used; from June 2024 to March 2025, the ICU digital intelligent multimodal shift handover system was applied for shift handovers. Questionnaires before the application of the shift handover system were collected in April 2024, and those after the application were collected in April 2025. The shift handover time, handover quality (scored by the nursing handover evaluation scale), satisfaction with doctor-nurse communication (scored by the ICU doctor-nurse scale) before and after the application of the handover system were compared, and nurses' satisfaction with the shift handover system (scored by the clinical nursing information system effectiveness evaluation scale) was investigated. RESULTS: After the application of the ICU digital intelligent multimodal shift handover system, the shift handover time was significantly shorter than that before the application [minutes: 20 (15, 25) vs. 30 (22, 40)], the handover quality was significantly higher than that before the application [score: 84.0 (78.0, 88.5) vs. 71.0 (55.0, 79.0)], and the satisfaction with doctor-nurse communication was also significantly higher than that before the application (score: 84.58±6.79 vs. 74.50±11.30). All differences were statistically significant (all P < 0.05). In addition, the nurses' system effectiveness evaluation scale score was 102.30±10.56, which indicated that nurses had a very high level of satisfaction with the ICU digital intelligent multimodal shift handover system. CONCLUSIONS The application of the ICU digital intelligent multimodal shift handover system can shorten the shift handover time, improve the handover quality, and enhance the satisfaction with doctor-nurse communication. Nurses have a high level of satisfaction with this system.
Intensive Care Units ; Humans ; Patient Handoff ; Artificial Intelligence ; Algorithms

ObjectiveThis study aims to explore the potential mechanism of Danggui Niantongtang in treating knee osteoarthritis （KOA） by regulating the intestinal flora through 16S rDNA analysis. MethodThirty-six C57BL/6J mice were subjected to anterior cruciate ligament transection （ACLT） to establish a KOA model and were randomly divided into the sham surgery group， model group， low-dose Danggui Niantongtang group （0.819 g·kg^-1）， medium-dose Danggui Niantongtang group （1.638 g·kg^-1）， high-dose Danggui Niantongtang group （3.276 g·kg^-1）， and Meloxicam group （0.975 mg·kg^-1）， with 6 mice in each group. Except for the treatment groups， the sham surgery group and model group were given normal saline by gavage. After 4 weeks of continuous intervention， feces and intact knee joints of the mice were collected. Hematoxylin-eosin （HE） staining and Safranin O-Fast Green staining were performed to observe the pathological changes in knee joint tissue morphology. The 16S rDNA sequencing was used to analyze changes in the abundance and diversity of intestinal microorganisms before and after treatment， along with corresponding functional predictions. ResultHigh-dose Danggui Niantongtang and Meloxicam significantly relieved pain symptoms in KOA mice， improved the disorder of joint structure， maintained the integrity of knee articular cartilage， increased the expression of type Ⅱ collagen alpha 1 （Col2a1） in articular cartilage， and decreased the expression of matrix metalloproteinase-13 （MMP-13）. The results of 16S rDNA sequencing showed that high-dose Danggui Niantongtang could adjust the abundance and structure of intestinal microbial species. Compared with the sham surgery group， the abundance of Proteobacteria， Actinobacteria， Ruminococcus， and Bacteroides was significantly increased in the model group （P<0.05）， while in the Danggui Niantongtang group， the abundance of these four flora was significantly reduced compared with the model group. Compared with the sham surgery group， the abundance of Verrucomicrobia， Oscillospira， and Akkermansia was significantly decreased in the model group （P<0.05）， while in the Danggui Niantongtang groups， the abundance of these three flora was significantly increased compared with the model group （P<0.05）. Functional pathway prediction of differential genera revealed that species differences among groups mainly involved metabolic pathways with high abundance associated with biosynthesis and precursors， as well as energy production， including amino acid biosynthesis， nucleotide and nucleoside biosynthesis， cofactors， prosthetic groups， electron carriers， and vitamin biosynthesis. ConclusionDanggui Niantongtang can effectively protect articular cartilage and delay the progression of KOA， possibly by regulating the structure of the intestinal flora， promoting probiotics， and inhibiting the growth of harmful pathogenic bacteria.

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.

A middle-aged male came to Peking Union Medical College Hospital for treatment because of "pain for 10+ years, aggravated with emotional instability for 5 years". The patient's pain had a huge impact on life, with poor results even after repeated diagnosis and treatment in other hospitals. After multi-disciplinary discussion, it had been clarified that the pain was mainly caused by gout. The disease was heavily influenced by psychosocial factors. Therefore, the patient fits the diagnosis of "Psychological and Behavioral Factors Affecting Physical Diseases". The multi-disciplinary comprehensive management of the patient was carried out to identify and treat psychological factors affecting other medical conditions. After this mental treatment was performed, the patient's conditions significantly improved. The diagnosis and treatment of this patient demonstrates the importance of the multi-disciplinary treatment team for somatic symptoms (disorders).

Objective Cirrhotic cardiomyopathy(CCM)refers to cardiac dysfunction and electrophysiological disorder caused by liver cirrhosis and is closely associated with the prognosis of patients with liver cirrhosis.Endothelial cell-specific molecule 1(endocan)can be used as a diagnostic marker for cardiovascular diseases,and it remains unclear whether it is involved in the pathogenesis of CCM.The aim of this study is to investigate the expression of serum endocan in patients with CCM and its possible role in the development of CCM.Methods This cross-sectional study was conducted among the patients with liver cirrhosis who were consecutively admitted to Beijing YouAn Hospital,Capital Medical University,from January 2019 to January 2021,and according to the presence or absence of CCM,the patients were divided into CCM group with 19 patients and non-CCM group with 106 patients.ELISA was used to measure the serum level of endocan,and its correlation with liver function and cardiac function was analyzed.The independent-samples t test was used for comparison of normally distributed continuous data between two groups,and the Mann-Whitney U rank sum test was used for comparison of continuous data with skewed distribution between two groups;the chi-square test was used for comparison of categorical data between groups.A Pearson or Spearman correlation analysis was used to investigate the correlation between indicators,and the receiver operating characteristic(ROC)curve was used to assess the CCM predictive model.Results The CCM group had a significantly higher expression level of serum Endocan than the non-CCM group(2.69±0.43 ng/mL vs 2.23±0.52 ng/mL,t=2.247,P=0.034).The patients with compensated cirrhosis had a significantly lower expression level of serum endocan than those with decompensated cirrhosis(2.41±0.37 ng/mL vs 2.72±0.49 ng/mL,t=3.214,P=0.02).In the CCM group,the serum level of endocan was positively correlated with Child-Pugh score(r=0.509,P=0.026)and MELD-Na score(r=0.484,P=0.036)and was negatively correlated with mean arterial pressure(r=-0.591,P=0.013)and mitral ratio of peak early to late diastolic filling velocity(r=-0.515,P=0.042).The serum endocan had an area under the ROC curve of 0.658(95%CI:0.522～0.781)in predicting CCM,when the cut-off value was 2.61 ng/mL,the sensitivity was 67.1%and the specificity was 73.7%.Conclusion There is a certain association between serum endocan and CCM,and serum endocan may be involved in the pathogenesis of CCM.

As a new hypoglycemic drug,Dapagliflozin has attracted much attention because of its unique hypoglycemic mechanism. It has been used in many studies on type 2 diabetes mellitus,but the application of type 1 diabetes mellitus(T1DM)in the eastern population is rare. This article uses Dapagliflozin through a case of obese T1DM to provide new ideas for the treatment of T1DM.

Clinical pathway has great similarity with DRG,and plays an important role in standardizing diagnosis and treatment behavior and controlling medical expenses.Based on the analysis of the relationship between DRG payment method reform and clinical pathway,taking a public hospital in Wuhan City,Hubei Province as an example,the clinical pathway implementation strategy of large public hospitals under the DRG payment method reform was explored from five aspects:management system,suitable disease types,doctor's order setting,information system,training and assessment.

Objective:To explore the predictive factors for adverse clinical outcomes in critically ill adult patients with autoimmune encephalitis by analyzing their clinical characteristics and prognosis.Methods:Clinical data of patients diagnosed with " confirmed" or " possible" autoimmune encephalitis who were hospitalized in the intensive care unit (ICU) of the Department of Neurology at the Second Xiangya Hospital of Central South University from January 2015 to December 2023 were retrospectively collected. The neurological function of patients at 3, 6, and 12 months of onset was followed up, and the modified Rankin Scale (mRS) at 12 months was used as an evaluation index for clinical prognosis; Further analysis was conducted on the relationship between clinical features, auxiliary examinations, and prognosis.Results:The 12-month survival rate of critically ill adult patients with autoimmune encephalitis in our center was 90.7%(117/129), and the 6-month poor prognosis rate was 28.7%(37/129). Univariate logistic regression analysis found that age of onset ( P<0.01), presence of tumors ( P<0.01), mechanical ventilation ( P<0.01), Glasgow Coma Scale (GCS) at ICU admission ( P<0.01), Acute Physiology and Chronic Health Evaluation Ⅱ (APACHE Ⅱ) score ( P<0.01), cerebrospinal fluid glucose ( P<0.01), cerebrospinal fluid protein level ( P=0.02), epileptic waves in electroencephalography (EEG) ( P=0.03), use of glucocorticoids ( P=0.04), and time interval between initiation of intravenous immunoglobulin (IVIG) and onset ( P=0.04) were associated with prognosis. The results of multiple logistic regression analysis showed that mechanical ventilation [ P=0.01, area under the curve (AUC)=0.72)], APACHE Ⅱ score ( P=0.04, AUC=0.68), cerebrospinal fluid protein content ( P=0.04, AUC=0.65), and the time interval between initiation of IVIG and onset ( P=0.02, AUC=0.64) were independent predictive factors for the prognosis of adult critical autoimmune encephalitis. The prognostic prediction model for adult critical autoimmune encephalitis established by combining these four indicators has a higher AUC (0.85). Conclusions:Mechanical ventilation, APACHE Ⅱ score, cerebrospinal fluid protein level, and the time interval between initiation of IVIG and onset are predictive factors for poor clinical outcomes in critically ill autoimmune encephalitis in adults; The prognostic prediction model for adult critical autoimmune encephalitis established by combining these four indicators can identify patients with poor prognosis early, which is beneficial for early comprehensive management and intervention treatment to improve patient prognosis.