OBJECTIVE To analyze the influencing factors for achieving target plasma drug concentration （trough） （abbreviated as “PDC”） of vancomycin in patients from high-altitude regions and establish a predictive model for PDC using single- center data， providing references for rational clinical drug use. METHODS Inpatients with vancomycin （1 g， q12 h） administered intravenously in our hospital from January 2021 to June 2024 were retrospectively included. Demographic data， liver and kidney function and hematological indexes were collected. Spearman correlation analysis was used to evaluate the correlation between vancomycin PDC and each detection index. Univariate analysis was used to evaluate the differences of each index in patients with different PDC， and the effects of different gender， body mass index， age and underlying diseases （hypertension/diabetes） on vancomycin PDC. Based on the results of correlation analysis and univariate analysis， multiple linear stepwise regression analysis was used to obtain the independent predictors of vancomycin PDC and construct the prediction model. RESULTS A total of 141 patients were included， with an overall attainment rate of 46.81% for the target PDC of vancomycin. Correlation analysis showed that the vancomycin PDC was positively correlated with age， blood urea nitrogen， uric acid （UA）， serum creatinine （CRE） and β2- microglobulin （β2-MG）， and negatively correlated with height， weight， creatinine clearance rate （CCR）， glomerular filtration rate （GFR）， alanine transaminase （ALT）， hemoglobin （HGB）， white blood cell count and neutrophils （P＜0.05）. There were significant differences in age， CRE and other 14 indexes among different PDC groups （P＜0.05 or P＜0.01）. Age and underlying diseases had significant effects on vancomycin PDC （P＜0.05 or P＜0.01）. CCR， direct bilirubin （DBil）， β2-MG， UA， HGB and height （standardized coefficients were －0.371， 0.367， 0.169， 0.232， －0.140， －0.132； P＜0.05） were independent predictors of vancomycin PDC. The F value of the regression equation was 34.858 （P＜0.05）， the R2 was 0.610， and the adjusted R2 was 0.592. CONCLUSIONS The vancomycin PDC of patients in high-altitude regions is affected by multiple factors such as renal function， liver function and hematological indexes. CCR， HGB and height could be used to predict vancomycin PDC negatively， while DBil， β2-MG and UA could be used to predict vancomycin PDC positively. The variables of the established prediction model could explain 59.2% of the variation of vancomycin PDC.

ObjectiveTo explore the clinical and phenomics characteristics of patients with phlegm-stasis binding syndrome and its accompanied patterns in stable angina pectoris （SAP） of coronary heart disease （CHD）. MethodsA multicenter cross-sectional study design was adopted. A total of 300 patients with SAP of CHD were enrolled and classified into 120 cases of phlegm-stasis binding syndrome， 125 cases of qi deficiency-accompanied syndrome， 38 cases of qi stagnation-accompanied syndrome， and 17 cases of toxin accumulation-accompanied syndrome according to traditional Chinese medicine （TCM） patterns. Data of patients with different TCM patterns were collected， including general condition， TCM symptoms， blood lipids， coagulation function， immune indicators， and serum metabolomics. Metabolic pathway enrichment analysis was used to compare differences in phenomics characteristics among groups. Metabolites with variable importance in projection （VIP） ≥1 and fold change （FC） ≥2 were considered as representative differential metabolites. ResultsPatients in each TCM pattern type were most commonly in the 60-75 years age group， with a relatively high proportion of males. Regarding TCM symptoms， patients with phlegm-stasis binding syndrome most commonly presented with wiry-choppy or wiry-slippery pulse， chest pain， and chest tightness； in patients with qi deficiency-accompanied syndrome， fatigue， chest pain， and weak pulse were most common； in patients with qi stagnation-accompanied sydnrome， wiry-choppy or wiry-slippery pulse， chest pain， and symptoms that increase or decrease with emotional changes， belching， or flatulence were most common； in patients with toxin accumulation-accompanied syndrome， bitter taste in the mouth， chest tightness， irritability， restlessness or manic delirium， and dry and hard stools or foul-smelling diarrhea were most common. Comparisons among the different TCM patterns showed statistically significant differences in coagulation parameters （P＜0.05）， whereas no statistically significant difference was found in blood lipid levels and immune indicators （P＞0.05）.Metabolomics analysis suggested that disorders of glycerophosphate metabolism and valine， leucine， and isoleucine metabolism are characteristic features of phlegm-stasis binding syndrome and its accompanied patterns. The representative differential metabolites between phlegm-stasis binding syndrome and qi deficiency-accompanied syndrome were 7,8-dihydrobiopterin （FC = 3.58） and oxypurinol （FC = 124.50）. Those between phlegm-stasis binding syndrome and qi stagnation-accompanied syndrome were cytidine 5′-diphosphocholine （FC = 2.62） and D-mannosamine （FC = 2.99）. Those between phlegm-stasis binding syndrome and toxin accumulation-accompanied syndrome were anserine （FC = 7.83） and canrenone （FC = 8.94）. ConclusionThere are certain differences in the clinical characteristics and phenomics characteristics among patients with SAP due to CHD exhibiting phlegm-stasis binding syndrome and its accompanied patterns. The phenomics characteristics mainly involve biological alterations such as lipid metabolism disorders， amino acid metabolism abnormalities， and multiple immune indicators activation， which may provide a reference for precise differentiation and treatment of SAP of CHD in TCM clinical practice.

ObjectiveTo explore the clinical and phenomics characteristics of patients with phlegm-stasis binding syndrome and its accompanied patterns in stable angina pectoris （SAP） of coronary heart disease （CHD）. MethodsA multicenter cross-sectional study design was adopted. A total of 300 patients with SAP of CHD were enrolled and classified into 120 cases of phlegm-stasis binding syndrome， 125 cases of qi deficiency-accompanied syndrome， 38 cases of qi stagnation-accompanied syndrome， and 17 cases of toxin accumulation-accompanied syndrome according to traditional Chinese medicine （TCM） patterns. Data of patients with different TCM patterns were collected， including general condition， TCM symptoms， blood lipids， coagulation function， immune indicators， and serum metabolomics. Metabolic pathway enrichment analysis was used to compare differences in phenomics characteristics among groups. Metabolites with variable importance in projection （VIP） ≥1 and fold change （FC） ≥2 were considered as representative differential metabolites. ResultsPatients in each TCM pattern type were most commonly in the 60-75 years age group， with a relatively high proportion of males. Regarding TCM symptoms， patients with phlegm-stasis binding syndrome most commonly presented with wiry-choppy or wiry-slippery pulse， chest pain， and chest tightness； in patients with qi deficiency-accompanied syndrome， fatigue， chest pain， and weak pulse were most common； in patients with qi stagnation-accompanied sydnrome， wiry-choppy or wiry-slippery pulse， chest pain， and symptoms that increase or decrease with emotional changes， belching， or flatulence were most common； in patients with toxin accumulation-accompanied syndrome， bitter taste in the mouth， chest tightness， irritability， restlessness or manic delirium， and dry and hard stools or foul-smelling diarrhea were most common. Comparisons among the different TCM patterns showed statistically significant differences in coagulation parameters （P＜0.05）， whereas no statistically significant difference was found in blood lipid levels and immune indicators （P＞0.05）.Metabolomics analysis suggested that disorders of glycerophosphate metabolism and valine， leucine， and isoleucine metabolism are characteristic features of phlegm-stasis binding syndrome and its accompanied patterns. The representative differential metabolites between phlegm-stasis binding syndrome and qi deficiency-accompanied syndrome were 7,8-dihydrobiopterin （FC = 3.58） and oxypurinol （FC = 124.50）. Those between phlegm-stasis binding syndrome and qi stagnation-accompanied syndrome were cytidine 5′-diphosphocholine （FC = 2.62） and D-mannosamine （FC = 2.99）. Those between phlegm-stasis binding syndrome and toxin accumulation-accompanied syndrome were anserine （FC = 7.83） and canrenone （FC = 8.94）. ConclusionThere are certain differences in the clinical characteristics and phenomics characteristics among patients with SAP due to CHD exhibiting phlegm-stasis binding syndrome and its accompanied patterns. The phenomics characteristics mainly involve biological alterations such as lipid metabolism disorders， amino acid metabolism abnormalities， and multiple immune indicators activation， which may provide a reference for precise differentiation and treatment of SAP of CHD in TCM clinical practice.

ObjectiveTo compare the performance of different automatic evaluation methods for detecting pattern hallucinations generated by large language models （LLMs） in traditional Chinese medicine （TCM） syndrome differentiation and to identify a strategy with higher overall discriminative performance against expert manual judgment. MethodsA standardized TCM syndrome-differentiation dataset containing 598 cases was constructed from case reports published in Chinese core journals indexed by Peking University Core Journals or Chinese Science Citation Database. Each of the 598 cases was submitted to five Chinese LLMs， with each model generating one syndrome-pattern output per case， yielding 2990 outputs in total. The 598 outputs generated by Qwen3-Next-80B-A3B-Thinking were independently annotated for syndrome-pattern hallucinations by two licensed TCM physicians with intermediate or higher professional titles. Disagreements were adjudicated by a third licensed TCM physician with a senior associate professional title and more than 10 years of clinical experience. The resulting consensus annotations served as the reference labels for training and validating the automated evaluation method. Based on these reference labels， three categories of automated evaluation methods were developed and compared following a progressive strategy from text-level semantic similarity， to syndrome-element structural consistency， and finally multi-feature fusion. Method A employed sentence-embedding models based on semantic similarity between pattern texts. Method B adopted a rule-based threshold method using weighted Jaccard coefficients of disease-location and disease-nature syndrome elements. Method C utilized a machine-learning classifier integrating syndrome-element matching scores， missing and redundant syndrome-element counts， and multiple semantic-similarity features. Five-fold cross-validation was used to evaluate the discriminative performance of each method against manual judgment. The optimal method was then applied to uniformly assess the pattern hallucination rates of five LLMs. ResultsIn method A， text2vec-large-chinese showed best overall performance， with an area under the receiver operating characteristic curve （AUROC）， area under the precision-recall curve （PR-AUC）， F1-score， sensitivity， and specificity of 0.849， 0.884， 0.794， 0.801， and 0.691， respectively. In method B， the corresponding metrics of the rule-based syndrome-element Jaccard threshold method were 0.848， 0.851， 0.817， 0.845， and 0.684， respectively； those of the LLM-based syndrome-element Jaccard method were 0.872， 0.906， 0.795， 0.764， and 0.780， respectively. In method C， the multilayer perceptron （MLP） classifier showed the best overall performance， with AUROC， PR-AUC， F1-score， sensitivity， and specificity of 0.933， 0.951， 0.871， 0.857， and 0.846， respectively. Under the unified evaluation framework combining DeepSeek-R1 syndrome-element extraction and MLP classifier， the estimated pattern hallucination rates of the five LLMs ranged from 56.0% to 77.4%. ConclusionAmong the three automatic evaluation methods， Method C integrating syndrome-element discrepancy features and text semantic features using an MLP classifier showed the highest overall discriminative performance against manual judgment， and is more suitable for evaluating syndrome hallucinations in LLM-based TCM syndrome differentiation tasks.

Objective: To reveal the molecular biological mechanism of a rare Dc-variant individual using PacBio third-generation sequencing technology. Methods: ABO and Rh blood type identification, DAT, unexpected antibody screening and D antigen enhancement test were conducted by serological testing. The absorption-elution test was used to detect the e antigen. RHCE gene typing was performed by PCR-SSP, and the 1-10 exons of RHCE were sequenced by Sanger sequencing. The full-length sequences of RHCE, RHD and RHAG were detected by PacBio third-generation sequencing technology. Results: Serological findings: Blood type O, Dc-phenotype, DAT negative, unexpected antibody screening negative; enhanced D antigen expression; no detection of e antigen in the absorption-elution test. PCR-SSP genotyping indicated the presence of only the RHCE c allele. Sanger sequencing results: Exons 5-9 of RHCE were deleted, exon 1 had a heterozygous mutation at c. 48G/C, and exon 2 had five heterozygous mutations at c. 150C/T, c. 178C/A, c. 201A/G, c. 203A/G and c. 307C/T. Third-generation sequencing results: RHCE genotype was RHCE 02N. 08/RHCE-D(5-9)-CE; RHD genotype was RHD 01/RHD 01; RHAG genotype was RHAG 01/RHAG 01 (c. 808G>A and c. 861G>A). Conclusion: This Dc-individual carries the allele RHCE 02N. 08 and the novel allele RHCE-D(5-9)-CE. The findings of this study provide data support and a theoretical basis for elucidating the molecular mechanisms underlying RhCE deficiency phenotypes.

Objective To investigate the mid- to long-term follow-up results of ascending aorta (AAO)-descending thoracic aorta (DTA) bypass grafting via median sternotomy incision for the treatment of complex aortic arch coarctation. Methods A retrospective analysis was conducted on the clinical data of patients with complex aortic arch coarctation who underwent AAO-DTA bypass grafting via median sternotomy incision at the First Hospital of Tsinghua University from August 2004 to May 2017. Results A total of 7 patients were enrolled, including 4 males and 3 females, aged (13.3±4.6) years, and weighted (40.2±12.2) kg. Six (85.7%) patients had concomitant upper limb hypertension. Four patients were aortic arch coarctation combined with intracardiac malformations, two were post-operative restenosis, and 1 was post-operative restenosis combined with intracardiac malformation. All patients underwent surgery under cardiopulmonary bypass. There were no perioperative deaths or major complications. The pre-operative upper-lower limb pressure difference was (39.3±19.2) mm Hg, which decreased to (2.9±2.7) mm Hg post-operatively (P<0.01). The follow-up period was (14.9±5.9) years. There were no long-term deaths or artificial graft-related complications. Except for one patient who still had mild hypertension, the blood pressure of the remaining patients returned to normal. Conclusion AAO-DTA bypass grafting via median sternotomy incision for the treatment of complex aortic arch coarctation can effectively reduce upper limb blood pressure and the upper-lower limb arterial pressure difference, has fewer complications, and demonstrates satisfactory mid- to long-term efficacy.

Objective: To investigate the current status and influencing factors for cognitive frailty among the elderly in community, so as to provide the evidence for early identification and prevention of cognitive frailty among the elderly. Methods: Residents aged 60 years and above with local household registration from 11 counties (cities, districts) in Zhejiang Province from 2021 to 2023 were selected as study participants using a multistage random sampling method. Demographic information, lifestyle, and health status were collected through questionnaire surveys. Depressive symptoms were assessed using the Patient Health Questionnaire. Cognitive frailty was evaluated using the FRAIL Scale and the Mini-Mental State Examination. Factors affecting cognitive frailty among the elderly in community were identified using a multivariable logistic regression model. Results: A total of 16 613 individuals were surveyed, including 7 465 males (44.93%) and 9 148 females (55.07%). The average age was (70.97±7.29) years. A total of 784 individuals were detected with depressive symptoms, with a detection rate of 4.72%. A total of 724 individuals were detected with cognitive frailty, with a detection rate of 4.36%. Multivariable logistic regression analysis showed that females (OR=1.419, 95%CI: 1.179-1.708), aged ≥70 years (70-<80 years old, OR=1.869, 95%CI: 1.490-2.345; ≥80 years old, OR=5.017, 95%CI: 3.935-6.398), without a spouse (OR=1.495, 95%CI: 1.234-1.810), sedentary (OR=2.420, 95%CI: 1.829-3.202), chronic diseases (1 type, OR=1.456, 95%CI: 1.175-1.804; ≥2 types, OR=1.639, 95%CI: 1.314-2.045), and depressive symptoms (OR=4.191, 95%CI: 3.361-5.225) were associated with a higher risk of cognitive frailty among the elderly in community. Conversely, a lower risk of cognitive frailty was seen among the elderly in community who had primary school or above (primary school, OR=0.512, 95%CI: 0.389-0.676; junior high school or above, OR=0.464, 95%CI: 0.354-0.608), engaged in physical exercise (OR=0.396, 95%CI: 0.291-0.539), and were reported average or good self-rated health status (average, OR=0.641, 95%CI: 0.475-0.866; good, OR=0.150, 95%CI: 0.109-0.208). Conclusions The detection rate of cognitive frailty among the elderly in community is relatively low and is influenced by demographic factors such as gender, age, education level, as well as lifestyle like sedentary and physical exercise, and health status. It is recommended to reduce the risk of cognitive frailty among the elderly through multidimensional interventions, including health education, promotion of healthy lifestyles, and enhanced mental health support.

Objective: To establish a nomogram prediction model for Alzheimer's disease (AD) among the elderly in community, so as to provide the evidence for early screening and prevention of AD. Methods: Based on the Zhejiang Healthy Aging Cohort Study, the elderly aged 60-90 years who completed the baseline survey were selected as the study subjects. Follow-up surveys were conducted from 2015 to 2016 and from 2019 to 2021. Sociodemographic characteristics, lifestyle factors, medical history, and waist circumference were collected through questionnaire surveys and physical examinations. Cognitive function was assessed using the Mini-Mental State Examination (MMSE), and a diagnosis of AD was made based on the Alzheimer's Disease Assessment Scale-Cognitive Subscale and medical history. The participants were randomly divided into training and validation sets at 8∶2 ratio. LASSO regression was used to screen for predictive factors. Multivariable logistic regression model was used to analyze predictive factors and construct a nomogram. The model was analyzed and evaluated using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Results: A total of 6 988 elderly were included at baseline, with a mean age of (68.19±6.63) years. There were 3 438 males (49.20%), and 3 550 females (50.80%). The median follow-up duration was 4.90 (interquartile range, 3.80) years, with 817 new cases of AD were identified, yielding an incidence of 11.69%. LASSO regression and multivariable logistic regression showed that age (OR=1.017, 95%CI: 1.005-1.030), gender (female, OR=1.820, 95%CI: 1.533-2.165), educational level (primary school, OR=0.813, 95%CI: 0.673-0.980), physical exercise (not active, OR=1.572, 95%CI: 1.260-1.980), dining companions (spouse and children, OR=0.771, 95%CI: 0.598-0.995), baseline MMSE score (OR=0.843, 95%CI: 0.821-0.866), and waist circumference (OR=0.981, 95%CI: 0.973-0.989) were risk predictors for AD among the elderly in community. The prediction model demonstrated an area under the ROC curve of 0.740 (95%CI: 0.698-0.783) in the validation set, with a sensitivity of 0.731 and a specificity of 0.667. DCA indicated that when the probability threshold was 0.060 to 0.325, the clinical net benefit was relatively high. Conclusion The AD risk prediction model constructed in this study has good discrimination and clinical practicability, can be used for early screening of AD among the elderly in the community.

Meropenem （MEM） is one of the important drugs for the treatment of severe infections， but the standard dose is often difficult to achieve an effective therapeutic concentration target. This article reviews the related studies on the population pharmacokinetics of MEM in patients with severe infection. It is found that the apparent volume of distribution （Vd） and clearance rate are the most important factors affecting the dose adjustment， and the factors affecting Vd include serum albumin， age， overall weight， shock status， and chest/abdomen/cerebrospinal fluid drainage. The main factors affecting the clearance rate were renal function， renal replacement therapy treatment mode and combination therapy. For adult patients with severe infections in China， MEM is recommended to be administered in an individualized manner based on glomerular filtration rate， with a dosage range of 500 to 1 500 mg given every 4 to 6 hours， and prolonged infusion is preferred. When the minimum inhibitory concentration （MIC） of the pathogenic bacteria reaches 64 mg/L， therapeutic drug monitoring is required. For therapeutic efficacy， it is essential to ensure that the trough concentration remains above the MIC； to prevent drug resistance， it should be maintained above 4×MIC. Regarding safety， it is recommended that the upper limit of the trough concentration be 32 mg/L， and blood sampling for monitoring can be conducted as early as after 1 to 2 doses of administration.