ObjectiveTo develop a community-family management model for older adults with mild cognitive impairment (MCI) and to formulate detailed application specifications, and to fully leverage the initiative of communities and families under limited resource conditions, for achieving community-based early detection and early intervention for older adults with MCI. MethodsA systematic literature review was conducted to identify pertinent publications. Corpus-based research methodologies were employed to extract, refine, integrate and synthesize management elements, thereby establishing the specific content and service processes for each stage of the management model. Utilizing the 5W2H analytical framework, essential elements such as management stakeholders, target populations, content and methods for each stage were delineated. The model and its application guidelines were finalized through expert consultation and demonstration. ResultsAn expert evaluation of the management model yielded mean scores of 4.84, 4.32 and 4.84 for acceptability, feasibility and systematicity, respectively. By integrating the identified core elements with expert ratings and feedback, the final iteration of the community-family management model for older adults with MCI was formulated. This model comprised of five stages: screening and identification, comprehensive assessment, intervention planning, monitoring and referral pathways to ensure implementation, and enhanced support for communities, family members and caregivers. Additionally, it included 18 specific application guidelines. ConclusionThe proposed management model may theoretically help delay cognitive decline, improve cognitive function and potentially promote reversal from MCI to normal cognition. It may also enhance the awareness and coping capacity of older adults and their families, strengthen community healthcare professionals' ability to early identify and manage MCI.

OBJECTIVE To explore the transformation of the dispensing and drug pickup process in traditional Chinese medicine pharmacy （TCM Pharmacy） in our hospital based on data-intelligence-driven， aiming to improve pharmacists’ work efficiency and patients’ drug pickup experience. METHODS Value stream mapping and journey mapping were used to systematically identify non-value-added links in pharmacists’ dispensing process and key pain points in patients’ drug pickup under the traditional process. An intelligent dispensing and drug pickup system for the TCM Pharmacy was developed based on the C# and Android television platforms， and a machine-learning model was adopted to predict patients’ drug pickup waiting time. A comprehensive evaluation was performed from three perspectives： system performance， prediction accuracy， and satisfaction of pharmacists and patients. RESULTS The system successfully streamlined non-value-added links such as “waiting for writing on the board” and “searching for drugs”， and realized multimodal dynamic prompts of dispensing status through auditory （number calling） and visual （television terminal） channels. The constructed model for predicting drug pickup waiting time exhibited good fitting degree and generalization ability （mean absolute error＝4.28 min， R 2 ＝0.882）. The comprehensive satisfaction scores of pharmacists and patients in the traditional mode were significantly increased from （70.99±1.74） and （73.58±1.98） to （90.02±1.30） and （88.61±2.08） in the new system， respectively （ P ＜0.01）. CONCLUSIONS The transformation of the intelligent drug dispensing and pickup system for TCM pharmacy based on data-intelligence-driven effectively improves the efficiency of pharmacists’ dispensing work， realizes process transparency and waiting time predictability， and significantly enhances patients’ drug pickup experience.

ObjectiveTo develop and validate a nomogram for predicting the risk of concurrent cognitive impairment in older adults with heart disease. MethodsSelf-reported data from 2 673 respondents in the 2020 China Health and Retirement Longitudinal Study were analyzed. The participants were randomly divided into a training set (n = 1 871) and a validation set (n = 802). Heart disease status was determined using a self-report questionnaire. Logistic regression analysis was performed to identify predictors, and a nomogram for predicting the risk of concurrent cognitive impairment in older adults with heart disease was developed using R software. Bootstrap resampling was used for internal validation, and model performance was further evaluated in the randomly split validation set. The predictive performance of the model was assessed using the area under the receiver operating characteristic curve (AUC) and calibration curves.. ResultsA total of 2 673 older adults with heart disease were included, out of whom 315 participants in the training set had cognitive impairment. Lasso regression analysis identified age, education level, place of residence, instrumental activities of daily living and depression as predictors of concurrent cognitive impairment. A prediction model incorporating these five variables was constructed. The AUC were 0.827 (95%CI 0.804 to 0.850) in the training set and 0.847 (95%CI 0.812 to 0.882) in the validation set. The Hosmer-Lemeshow test yielded P = 0.154 and P = 0.716 in the training and validation sets, respectively. Calibration curves showed good agreement between predicted and observed probabilities. Decision curve analysis indicated that the model provided favorable net benefit and predictive performance. ConclusionThe nomogram may provide a useful reference for risk screening and early intervention for cognitive impairment in older adults with heart disease.

OBJECTIVE To construct a multimodal similarity model for Chinese drug names and design optimized display names，compare the potential confusion risk of display names of the same drug catalogue in different scenarios，so as to provide a quantitative basis for the management of drug display names.METHODS A total of 1 221 drug entries were extracted from the drug dictionary of a tertiary grade A hospital，forming 744 810 drug pairs. “Five rules” were summarized based on the confusion characteristics of candidate confusable drug pairs under the currently used in-hospital display names （Scenario 1） and standard display names （Scenario 2），to generate optimized display names （Scenario 3）. A multimodal similarity model integrating Pinyin，glyph，character sequence and rendered visual features was constructed. The same threshold （total similarity S total ≥0.70） was adopted to screen candidate confusable drug pairs. Confusion risk grading and transition，confusion characteristic labeling，confusion network clustering and residual cluster analysis were carried out. The naming mechanism of optimized display names was investigated through display morphology indicators and rule ablation，and the robustness of the comparison results among the three scenarios was evaluated through similarity threshold and fusion weight sensitivity analysis.RESULTS The numbers of candidate confusable drug pairs in the three scenarios were 156，269 and 83 respectively，among which the number of pairs at high risk and above was 109， 215 and 64 respectively. Compared with Scenario 1，Scenario 3 reduced the number of candidate confusable drug pairs by 46.8%，and the number of those at high risk or above decreased by 41.3%. Display morphology analysis showed that Scenario 3 had the lowest average ink density （0.057 3） and the largest average number of structural slots （2.99）. Rule ablation analysis showed that after disabling the rule of manufacturer characteristic abbreviation prompts，the number of candidate confusable drug pairs increased the most （+74 pairs），while disabling structured naming alone resulted in a net decrease of 9 pairs. The residual clusters mainly involved drugs with the same active ingredient but different specifications or subtypes，as well as series products from the same manufacturer with similar main name stems. Under the threshold setting of 0.60 to 0.80 with a step size of 0.025 and 11 different weight configurations （including the baseline value），the number of candidate confusable drug pairs always maintained the ranking of Scenario 3＜Scenario 1＜Scenario 2.CONCLUSIONS The multimodal similarity model can be used for quantitative screening of potential confusion of Chinese drug display names，and the ranking of the number of candidate confusable drug pairs among scenarios remains stable within the set parameter range. The optimized display names based on the “five rules” can reduce the number of candidate confusable drug pairs， and the number of those at high risk or above in the hospital’s drug catalogue，but their impact on actual identification behavior and clinical medication safety still needs further verification.

Objective To investigate the effect of linezolid on platelets in patients with acute myeloid leukemia(AML)and analyze the safety profile of linezolid by comparing the platelet count and bleeding risk of linezolid during bone marrow suppression in patients after chemotherapy for AML.Methods A retrospective study was conducted on patients who underwent chemotherapy for AML in a tertiary hospital from January 2020 to November 2024.The patients treated with linezolid and those not receiving linezolid were matched in a 1∶2 ratio.The safety of linezolid during bone marrow suppression after chemotherapy for AML was analyzed in terms of platelet count＜20×109/L,＜50×109/L,minimum platelet count,total platelet transfusion volume,and clinical bleeding events.Results A total of 126 patients were enrolled,including 42 patients receiving linezolid and 84 patients not receiving linezolid.There was no significant difference between linezolid group and control group in the days for platelet count＜20×109/Land＜50×109/L.No life-threatening severe bleeding events were reported in either group.The time to platelet recovery and time to platelet count increase prolonged significantly in patients who received linezolid treatment for more than 7 days during bone marrow suppression.Albumin＜35 g/L may prolong the time to platelet count increase.Conclusions This study suggests that short-term use of linezolid for not more than 7 days is safe during bone marrow suppression in patients after chemotherapy for AML.When linezolid is used for more than 7 days,the time required for platelet recovery and platelet count increase will be significantly prolonged.In cases of albumin＜35 g/L,the time required for platelet count increase may be prolonged.These findings can inform clinical decision-making and help optimize infection management strategies for AML patients.

Objective To construct machine learning models to predict the incidence of linezolid-induced thrombocytopenia(LIT).Methods A total of 198 patients treated with linezolid in a hospital between January 2020 and March 2024 were retrospectively included.Firstly,the patients were divided into LIT and non-LIT groups,and the basic characteristics of the two groups were compared.Then,the variables with significant differences between the two groups were selected as potential risk factors to construct models for predicting LIT,including Logistic regression,decision tree and random forest models,and the prediction performance of the models was evaluated and compared.Results There were 52(26.3％)patients developed LIT during the treatment.The univariate analysis showed significant differences in linezolid trough concentration(Cmin),baseline platelet counts and creatinine clearance,the incidence of cerebrovascular disease,acute respiratory distress syndrome,and abdominal infection in patients with and without LIT.Among the three models built based on these variables,the random forest model has the best predictive performance.The results of variable importance analysis based on random forest model showed that Cmin,baseline platelet count and combined with acute respiratory distress syndrome had higher importance scores.Conclusions The random forest model has high accuracy in predicting the occurrence of LIT,and the risk of LIT is higher in patients with higher levels of linezolid exposure and lower baseline platelets.

Objective:To characterize the vaginal flora of pregnant women during the second trimester using full-length 16S rRNA sequencing.Methods:A total of 142 pregnant women were systematically sampled from a pregnancy cohort. Vaginal swabs were collected for full-length 16S rRNA gene sequencing，and bioinformatics analysis was performed to characterize the vaginal microbiota and identify associated influencing factors.Results:Among the 142 pregnant women，the most frequently detected species were Lactobacillus iners（83.10%，118/142）and Lactobacillus crispatus（49.30%，70/142）. The majority of samples（90.85%，129/142）were classified as Lactobacillus-dominant vagitypes，with the Lactobacillus iners vagitype accounting for 48.59%（69/142）and the Lactobacillus crispatus vagitype accounting for 38.73%（55/142）. The vaginal microbiota was clustered into five community state types（CSTs）：Ⅰa，Ⅰb，Ⅲa，Ⅲb，and Ⅳ. The most prevalent CSTs were Lactobacillus iners-dominated CST-Ⅲ（51.41%，73/142）and Lactobacillus crispatus-dominated CST-Ⅰ（24.65%，35/142）. No samples were classified as CST-Ⅱ or CST-Ⅴ. A significant negative correlation was observed between Lactobacill and vaginosis-associated bacteria. Age，alcohol consumption，smoking，and vaginal treatments showed significant associations or trends toward significance with various Alpha diversity indices. Vaginal douching was associated with CST clustering，while obstetric history（primiparity，previous miscarriage history）was associated with vagitype classification. However，no significant associations were identified between maternal baseline characteristics and Beta diversity indices. Conclusions:Full-length 16S rRNA gene sequencing reveals that the vaginal microbiota of pregnant women is dominated by Lactobacillus iners and Lactobacillus crispatus. Maternal age，lifestyle factors such as smoking and alcohol consumption，and obstetric history are significantly associated with variations in vaginal microbiota composition.

Objective:To investigate the prevalence and risk factors of overweight and obesity among Chinese children aged 3-18 years from 11 provinces, antonomous regions, or municipalities.Methods:This national cross-sectional community health survey utilized a multistage stratified cluster-random sampling method to recruit 193 997 nationally representative participants from 11 provinces, autonomous regions, or municipalities between January 2017 and December 2019. All participants underwent physical examinations, and their caregivers completed questionnaires assessing participants′ dietary, lifestyle, familial, and perinatal information. Multilevel multinomial logistic regression models were employed to identify the potential risk factors.Results:The cohort comprised 193 997 children (102 178 boys, 91 819 girls),aged (10±4) years. Overall prevalence rates were 30 574(15.8%)overweight children and 17 217(8.9%) obesity children. Boys exhibited higher overweight and obesity rates than girls (17.0% (17 368/102 178) vs. 14.4% (13 206/102 178), 11.3% (11 553/91 819) vs. 6.2% (5 664/91 819), χ2=249.12,1 578.69,both P<0.001). The detection rates of obesity in Tanner stage 2 and 3 were the highest in boys and girls, with 13.4%(2 231/16 665) and 8.6%(880/10 221) respectively. Risk factors for obesity included parental overweight (paternal OR=2.34 and maternal OR=2.29), annual household income of 100 000-200 000 yuan (compared with<100 000 yuan, OR=1.04), higher paternal education (compared with below high school,high school and a college education OR=1.09,1.14), birth weight >4.0 kg (≤5 and>5 years old OR=1.74, 1.44,respectively), and western food consumption≥1 time/month (compared with<1, 1-2, 3-4,>4 times/month OR=1.36, 1.30, 1.67(≤5 years), 1.19, 1.16, 1.15 (>5 years), respectively) (all P<0.05). Conversely, coarse grain intake≥1 times/week (compared with<1 times/week, every day, 3-4, 1-2 times/week OR=0.74, 0.80, 0.71 (≤5 years), 0.75, 0.87, 0.90(>5 years), respectively, all P<0.05) was associated with reduced obesity risk. Conclusions:Obesity epidemiology in children demonstrates significant heterogeneity across age, gender, geographic regions, and pubertal stages. It is necessary to establish a personalized prevention and control strategy.

Objective:To analyze the clinical features of children with infection-related eosinophilic lung diseases (ELD) caused by common respiratory pathogens.Methods:A retrospective cohort study was conducted. The clinical features, auxiliary examinations, treatments, and prognoses of 134 children with infection-related ELD caused by common respiratory pathogens at Yuying Children′s Hospital from January 2014 to June 2024 were collected. Participants were divided into the mild and severe groups based on whether the proportion of eosinophils in bronchoalveolar lavage fluid (BALF) exceeded 0.25. Chi-square test or Fisher exact test was used to analyze the differences between the two groups, and Logistic regression was performed to examine the correlation between BALF eosinophilia and the clinical outcomes.Results:Among the 134 children, 73 were males and 61 were females, with an age of 6.9 (4.6, 8.8) years on admission. A total of 154 pathogen detections were recorded, including 116 cases (75.3%) of Mycoplasma pneumoniae, 8 cases (5.2%) of influenza A virus, 6 cases (3.9%) of Streptococcus pneumoniae, and 6 cases (3.9%) of Chlamydia pneumoniae, among others. The percentage of eosinophils in the BALF of all children was 0.10 (0.07, 0.15). There were 117 cases in the mild group and 17 cases in the severe group. Compared with the mild group, significantly greater proportion of children in the severe group presented dyspnea (10/17 vs. 17.1% (20/117)), wheezing (7/17 vs. 8.5% (10/117)), respiratory failure (8/17 vs. 7.7% (9/117)), single lobe ≥2/3 consolidation (7/17 vs. 12.8% (15/117)), atelectasis (7/17 vs. 12.0% (14/117)), pleural effusion (7/17 vs. 16.2% (19/117)), plastic bronchiolitis (4/17 vs. 4.3% (5/117)), and systemic corticosteroid prescription (14/17 vs. 51.3% (60/117)) (all P<0.05). Univariate Logistic regression analysis revealed that severe eosinophilia in BALF was significantly associated with an elevated risk of respiratory failure ( OR=10.67, 95% CI 3.31-34.38, P<0.001), single lobe ≥2/3 consolidation ( OR=4.76, 95% CI 1.57-14.41, P=0.006), atelectasis ( OR=5.15, 95% CI 1.69-15.72, P=0.004), pleural effusion ( OR=3.61, 95% CI 1.22-10.67, P=0.020), and plastic bronchitis ( OR=6.89, 95% CI 1.64-28.94, P=0.008). Among the 126 children who were followed up, 106 cases (84.1%) were cured, 20 cases (15.9%) improved, and no relapses or deaths occurred. Conclusions:Mycoplasma pneumoniae and influenza A virus are common pathogens in children with infection-related ELD and the percentage of eosinophils in BALF is mildly increased, and the severe cases exhibit more severe clinical features and more significant pulmonary abnormalities, such as lobar consolidation and lung atelectasis. The prognosis is generally favorable.

Objective:To explore the differences in early clinical features between Kawasaki disease shock syndrome (KDSS) and septic shock (SS).Methods:A retrospective case-control study was conducted. Clinical data was collected from 64 children who were diagnosed with KDSS or SS and admitted to the Department of Critical Care Medicine of Capital Center for Children′s Health, Capital Medical University from January 2018 to February 2025. Mann-Whitney U test, χ2 test, or Fisher′s exact test were used to compare the differences in clinical features, treatment, and outcomes between children with KDSS and SS. Lasso regression was applied to screen predictive variables, and multivariable logistic regression analysis was performed to identify factors associated with KDSS. Receiver operating characteristic (ROC) curve was used to evaluate the predictive value of parameters for KDSS. Results:Among the 64 children (30 males and 34 females), the age was 3.6 (1.2, 6.5) years. There were 51 cases in the SS group and 13 cases in the KDSS group. Compared to children with SS, children with KDSS had a longer pre-shock fever duration, lower lactate levels and serum albumin levels, and higher soluble interleukin-2 receptor (sIL-2R) levels (all P<0.05). Additionally, they exhibited a higher incidence of coronary involvement, pericardial effusion, and ascites, a higher utilization rate of intravenous immunoglobulin, and a lower utilization rate of invasive mechanical ventilation (all P<0.05). There was no significant difference in in-hospital mortality between KDSS and SS ( P=0.574). Multivariate logistic regression analysis identified pre-shock fever duration and sIL-2R as independent factors associated with KDSS ( OR=1.52 and 1.54 per 1 000 U increase, 95% CI 1.12-2.05 and 1.06-2.24, respectively; both P<0.05). ROC curve analysis showed that the areas under the curve for pre-shock fever duration and sIL-2R in identifying KDSS were 0.83 (95% CI 0.73-0.94, P=0.001) and 0.70 (95% CI 0.53-0.87, P=0.042), respectively. The optimal cutoff values were 3.5 d and 3.8×10 6 U/L, with sensitivities of 0.91 and 0.82, and specificities of 0.71 and 0.62, respectively. Conclusions:Children with KDSS have higher incidences of coronary involvement, pericardial effusion, and ascites compared to those with SS. Pre-shock fever duration and sIL-2R may serve as potential early indicators for distinguishing KDSS from SS.