In the era of evidence-based medicine， the progress of medical science and technology has enriched medical diagnostic tools and treatment methods， but it has also led to the loss of medical warmth and the alienation of the doctor-patient relationships. William Osler emphasized that while medical technology advances， attention should also be paid to the practice of narrative medicine and the development of physician’s narrative literacy. The view of the physician he advocated reminds us that the core of medicine still lies in the narrative connection between doctors and patients， as well as a deep understanding of human nature. By exploring the relationship between Osler’s view of the physician and narrative medicine as well as physician’s narrative literacy， this paper analyzed the methods of cultivating physician’s narrative literacy， providing references for modern medical education and practice， and assisting in the harmony and unity of science and technology and humanity.

ObjectiveTo investigate the role and mechanism of ribosomal DNA （rDNA） transcription and ribosome biogenesis in muscle atrophy induced by acute kidney injury （AKI）. MethodsEight male C57BL/6 mice were randomly divided into a control group （Ctrl） and a model group （AKI）. An AKI model was established via intraperitoneal injection of cisplatin. Muscle atrophy was phenotypically assessed by measuring muscle mass， myofiber cross-sectional area （HE staining）， and mRNA expression levels of atrophy-related genes （Murf-1， Atrogin-1， Igf-1） using qRT-PCR. In vivo protein synthesis rates were determined via the SUnSET assay （puromycin incorporation）. Ribosome biogenesis was evaluated by assessing rRNA content and 47S pre-rRNA expression levels. Myotubes differentiated from mouse skeletal muscle cell lines （C2C12 myotubes） were treated with serum from AKI mice， and the effects on rDNA transcription， ribosome biogenesis， and protein metabolism were analyzed using chromatin immunoprecipitation followed by quantitative polymerase chain reaction （ChIP-qPCR） and Western blot. ResultsAKI successfully induced muscle atrophy， as evidenced by a significant reduction in skeletal muscle mass. The most pronounced decrease occurred in the extensor digitorum longus muscle （21.0%， P 0.01）， along with a trend toward reduced myofiber cross-sectional area. At the molecular level， AKI inhibited muscle protein synthesis （83.14% reduction in puromycin incorporation， P 0.000 1） and impaired ribosome biogenesis， manifested by suppressed rDNA transcription elongation （52.62% decrease in 47S pre-rRNA ITS-1 levels， P 0.01） and reduced total rRNA content （65.29%， P 0.000 1）. In contrast， serum from AKI mice promoted rDNA transcription initiation and protein synthesis in C2C12 myotubes in vitro. ConclusionAKI induces muscle atrophy by suppressing rDNA transcription and ribosome biogenesis in skeletal muscle， leading to impaired protein synthesis. Dysregulated ribosome biogenesis may play a critical role in AKI-induced muscle atrophy.

Infection is an important trigger for the initiation of pancreatic islet autoimmunity in type 1 diabetes mellitus （T1DM）. From the perspective of latent pathogen and dryness disorder， this study analyzes the core pathogenesis and dynamic evolution of T1DM in the subclinical and clinical stages. Combined with the T1DM "constraint， heat， deficiency， damage" state and target differentiation and treatment theory， the course of infection-induced T1DM is divided into three stages， including latent dryness， dry-heat and collateral damage. The "latent dryness" stage corresponds to the subclinical phase of T1DM， while the "dry-heat" stage corresponds to the clinical phase， and the "collateral damage" stage corresponds to the phase in which chronic complications develop. Treatment principles include supplementing deficiency and dispelling pathogen during the "latent dryness" stage， clearing heat and moistening dryness in the "dry-heat" stage， and dissolving stasis and unblocking collaterals in the "collateral damage" stage. Furthermore， syndrome-targeted and target-directed therapeutic modifications were made according to T1DM-related autoimmune activity， metabolic comorbidities， and vascular comorbidities， providing a reference for clinical management of T1DM.

BACKGROUND:Osteoporotic fracture is the most serious complication of osteoporosis.Previous studies have demonstrated that gut microbiota has a regulatory effect on skeletal tissue and that gut microbiota has an important relationship with osteoporotic fracture,but the causal relationship between the two is unclear. OBJECTIVE:To explore the causal relationship between gut microbiota and osteoporotic fractures using Mendelian randomization method. METHODS:The genome-wide association study(GWAS)datasets of gut microbiota and osteoporotic fracture were obtained from the IEU Open GWAS database and the Finnish database R9,respectively.Using gut microbiota as the exposure factor and osteoporotic fracture as the outcome variable,Mendelian randomization analyses with random-effects inverse variance weighted,MR-Egger regression,weighted median,simple model,and weighted model methods were performed to assess whether there is a causal relationship between gut microbiota and osteoporotic fracture.Sensitivity analyses were performed to test the reliability and robustness of the results.Reverse Mendelian randomization analyses were performed to further validate the causal relationship identified in the forward Mendelian randomization analyses. RESULTS AND CONCLUSION:The results of this Mendelian randomization analysis indicated a causal relationship between gut microbiota and osteoporotic fracture.Elevated abundance of Actinomycetales[odds ratio(OR)=1.562,95%confidence interval(CI):1.027-2.375,P=0.037),Actinomycetaceae(OR=1.561,95%CI:1.027-2.374,P=0.037),Actinomyces(OR=1.544,95%CI:1.130-2.110,P=0.006),Butyricicoccus(OR=1.781,95%CI:1.194-2.657,P=0.005),Coprococcus 2(OR=1.550,95%CI:1.068-2.251,P=0.021),Family ⅩⅢ UCG-001(OR=1.473,95%CI:1.001-2.168,P=0.049),Methanobrevibacter(OR=1.274,95%CI:1.001-1.621,P=0.049),and Roseburia(OR=1.429,95%CI:1.015-2.013,P=0.041)would increase the risk of osteoporotic fractures in patients.Elevated abundance of Bacteroidia(OR=0.660,95%CI:0.455-0.959,P=0.029),Bacteroidales(OR=0.660,95%CI:0.455-0.959,P=0.029),Christensenellacea(OR=0.725,95%CI:0.529-0.995,P=0.047),Ruminococcaceae(OR=0.643,95%CI:0.443-0.933,P=0.020),Enterorhabdus(OR=0.558,95%CI:0.395-0.788,P=0.001),Eubacterium rectale group(OR=0.631,95%CI:0.435-0.916,P=0.016),Lachnospiraceae UCG008(OR=0.738,95%CI:0.546-0.998,P=0.048),and Ruminiclostridium 9(OR=0.492,95%CI:0.324-0.746,P=0.001)would reduce the risk of osteoporotic fractures in patients.We identified 16 gut microbiota associated with osteoporotic fracture by the Mendelian randomization method.That is,using gut microbiota as the exposure factor and osteoporotic fracture as the outcome variable,eight gut microbiota showed positive causal associations with osteoporotic fracture and another eight gut microbiota showed negative causal associations with osteoporotic fracture.The results of this study not only identify new biomarkers for the early prediction of osteoporotic fracture and potential therapeutic targets in clinical practice,but also provide an experimental basis and theoretical basis for the study of improving the occurrence and prognosis of osteoporotic fracture through gut microbiota in bone tissue engineering.

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.

Traditional Chinese medicine(TCM)has rich experience in the diagnosis and treatment of epidemics,but the basic con-cepts and diagnosis and treatment strategies of TCM for epidemics have not yet formed a common understanding.This paper preliminar-ily constructs a diagnosis and treatment system of TCM epidemics from the perspective of"type differentiation-stage differentiation-syndrome differentiation"by combing the basic concepts of the etiology,pathogenesis,and identification of epidemics,taking state-target differentiation and treatment as the general principle.First,the epidemics are divided into five types:cold epidemics,warm epi-demics,cold-damp epidemics,damp epidemics,and miscellaneous epidemics;then,according to the overall course of the epidemics,they are divided into four stages:"initial stage-peak stage-extreme stage-relapse stage",the initial stage is the stage of exterior syn-dromes,the peak stage is the stage of heat peak,the extreme stage is the stage of internal closure and external loss,and the relapse stage is the stage of recovery of the healthy qi and retreat of the evil qi;finally,on the basis of staging,combined with the syndrome char-acteristics of various epidemics(five types of background diseases)at different stages,the syndromes are divided demonstratively.Through the construction of a modern diagnosis and treatment system of TCM epidemics,the basic theoretical concepts and basic diag-nosis and treatment strategies of epidemics can be clarified,various new and emerging infectious diseases can be actively responded to,and a diagnosis and treatment plan for a new and emerging infectious disease can be quickly constructed.

Objective To explore and identify the nutritional/inflammatory indicator with the highest predictive potential for overall survival(OS)in postoperative tumor patients so as to provide guidance for postoperative rehabilitation of tumor patients.Methods Data from 3 191 surgical patients were collected,including 15 nutritional/inflammatory indicators.The maximum selection rank statistic method was used to calculate the optimal cut-off values for continuous indicators.The Kaplan-Meier method was used to assess OS,and Cox proportional hazards models were used to analyze the association between the aforementioned 15 indicators and survival.The predictive value of these 15 indicators was evaluated with receiver operating characteristic(ROC)curves and C-index.Results Multivariate analysis showed that all 15 indicators were significantly associated with poorer OS in surgical patients(P＜0.05 for all).Time-dependent area under the curve(AUC)and C-index analysis indicated that 3 indicators with the highest predictive potential in OS in postoperative tumor patients were the nutritional risk index(NRI)(C-index:0.597),C-reactive protein-to-albumin ratio(CAR)(C-index:0.587),and C-reactive protein-to-lymphocyte ratio(CLR)(C-index:0.587).The optimal cut-off value for NRI was determined to be 104.31(i.e.,NRI＜104.31 suggests malnutrition)with the maximum selection rank statistic method,the optimal cut-off value for CAR to be 0.05(i.e.,CAR≥0.05 suggests a strong inflammatory response,often accompanied by malnutrition),and the optimal cut-off value for CLR to be 1.18(i.e.,CLR≥1.18 suggests a strong inflammatory response).Subgroup analysis indicated that NRI,CAR,and CLR had good correlation with tumor staging,and there were significant differences between tumor node metastasis(TNM)Ⅲ/Ⅳ stage patients and TNM Ⅰ/Ⅱ stage patients when there was a strong inflammatory response or malnutrition.Conclusion In postoperative tumor patients,NRI,CLR,and CAR have high prognostic value.Combining these with the patient's clinical stage,it enables more precise guidance for clinical diagnosis and treatment strategies.

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.

With the accelerated progress of population aging and the shift in the disease spectrum, the prevention and management of chronic diseases and multimorbidity are facing great challenges. Multimorbidity measurement is a process that assesses and quantifies the complexity and severity of multiple disease states in a population, which is of great significance for understanding the epidemiology and disease burden of multimorbidity, as well as for optimizing medical services and policy. This article reviews existing multimorbidity measurement indicators, tools, and applications from two dimensions, static and dynamic attributes, aiming to improve the accuracy and comprehensiveness of multimorbidity epidemiology, diagnosis, and treatment.