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.

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.

Diaphragmatic dysfunction often occurs in ICU patients with prolonged mechanical ventilation, which seriously affects patients′prognosis. This article reviewed the progress of rehabilitation treatment of diaphragmatic dysfunction in ICU patients with mechanical ventilation, focused on the concept and assessment of diaphragm rehabilitation, the current status, evaluation indexes, emphasized the importance of nurses, analyzed the dilemmas and countermeasures in the application, which aimed to provide a reference for the promotion of diaphragm rehabilitation in clinical practice.

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 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 investigate the changes in thyroid hormone levels and inflammatory markers in critically ill children，analyze their correlation with disease severity，and explore their potential impact on prognosis,providing references for clinical management and prognosis assessment in critical illness.Methods:A retrospective cohort study was conducted involving 394 pediatric patients admitted to the ICU of the Capital Pediatric Institute Affiliated Children's Hospital from 2019 to 2023.Based on the pediatric critical illness score,patients were divided into three groups:the extremely critical group (score ≤ 70, n=81),the critical group (score 71–80, n=150),and the non-critical group (score>80, n=163).Data collected included thyroid function indicators，inflammatory markers[C-reactive protein（CRP)，procalcitonin(PCT),tumor necrosis factor(TNF)-α，interleukin (IL)，etc.]，clinical information，and outcomes.The correlation between thyroid function indicators and inflammatory markers were analyzed.The predictive value of thyroid function indicators and inflammatory markers for prognosis in critically ill pediatric patients was assessed. Results:Of the 394 children,non-thyroidal disease syndrome occurred in 321 cases,with an overall incidence of 81.5%,which increased with disease severity.Thyroid hormone [total triiodothyronine (TT3),free triiodothyronine (FT3),and total tetraiodothyronine (TT4)] levels were significantly lower in the extremely critical group than in the other groups ( P<0.05).Inflammatory markers such as CRP,PCT,TNF-α,IL-6,IL-8,and IL-10 were significantly higher in the extremely critical group than in the other groups ( P<0.05).Thyroid hormones were negatively correlated with inflammatory markers,and the receivor operating characteristic curves analysis indicated that TT3,FT3,IL-6 and IL-8 levels,could effectively differentiate disease prognosis.Univariate regression model showed significant associations between TT3,FT3,TT4,PCT,IL-8,and IL-10 and disease prognosis.The multivariate Logistic regression model showed IL-6 and IL-8 were independent predictors of disease prognosis. Conclusion:Significant reductions in thyroid hormone levels are closely related to disease severity and poor prognosis.Changes in inflammatory markers reflect the inflammatory state and severity of the disease and impact prognosis.Monitoring thyroid function and inflammatory status is important in clinical management，which provids new insights into prognosis assessment and treatment strategies for critically ill children.

Objective To explore how digital and intelligent technologies can optimize the storage allocation and picking paths for Traditional Chinese medicine decoction pieces.Methods Based on prescription information and medicine data from the hospital information system(HIS),this study employs MATLAB programming to use an integer linear programming(ILP)algorithm for optimizing the storage allocation of Traditional Chinese medicine decoction pieces.Additionally,a greedy algorithm is applied to optimize the picking paths to reduce the picking distance for pharmacists.Finally,an independent samples paired t-test is used to analyze the experimental data to verify the significance of the optimizations.Results A total of 7 734 prescriptions were collected from the HIS.The results of storage optimization showed that the total distance before optimization was(87.58±0.29)m,which was reduced to(85.35±0.28)m after optimization(P﹤0.000 1).The picking path optimization results showed that the picking path was(85.35±0.28)m before optimization and(40.06±0.11)m after optimization(P﹤0.000 1).The results of the independent samples paired t-test indicate that the path was significantly reduced after both storage and picking path optimizations,with statistical significance.Conclusion By using digital and intelligent methods,informatics pharmacists can effectively shorten the picking paths for Traditional Chinese medicine decoction pieces,improve dispensing efficiency,and reduce patient waiting times.

Crohn's disease (CD) is a refractory and recurrent chronic inflammatory bowel disease with an unclear etiology. In addition to intestinal symptoms, the disease is often accompanied by various extraintestinal manifestations. Studies have found that abnormalities in central nervous system activity in CD patients are closely related to the brain-gut axis, which may be one of the reasons for the high incidence of adverse emotions such as anxiety and depression. Functional magnetic resonance imaging (fMRI) has been widely used in the field of neurogastroenterology due to its non-invasiveness, high spatial resolution, and whole-brain coverage. This article reviews the application and progress of fMRI in CD brain functional study, aiming to comprehensively analyze and integrate the impact of the disease on brain function and provide a reference for clinical diagnosis, treatment, and future research.

Objective:To explore the relationship between urinary cadmium levels and thyroid hormone levels in people aged 40-89 years old in selected areas of China.Methods:Based on the "Investigation of the Impact of Soil Quality of Agricultural Land on Human Health in Typical Areas" project from October 2019 to August 2020, a multi-stage stratified random sampling method was used to include 6 588 middle-aged and older adults aged 40-89. Demographic characteristics, dietary frequency and disease status were collected through the questionnaire and physical examination. Urinary cadmium and urinary creatinine were detected by random midstream urine. Fasting venous blood was collected for the detection of Triiodothyronine (T3) and Thyroxine (T4). The linear mixed effects model was used to explore the association of urine cadmium levels with thyroid hormone levels. Its dose-response relationship was explored by using the restricted cubic spline.Results:The age of the subjects was (63.48±12.18) years, with males accounting for 51.28%. The M ( Q 1,Q 3) of urinary cadmium level, T3 and T4 was 2.48 (1.36, 4.42) μg/g·creatinine, (1.96±0.51) nmol/L and (113.75±29.11) nmol/L, respectively. The linear mixed effects model showed that the changes of T3 and T4 were 0.027 (0.009, 0.044) nmol/L and 2.019 (1.084, 2.953) nmol/L for each one-unit increase (natural logarithm transformed) of urinary cadmium. The restricted cubic spline showed that there was a positive nonlinear association between urinary cadmium and T3 as well as T4 (all Pnonlinear<0.05). Conclusion:In selected areas of China, the urinary cadmium level of middle-aged and older adults aged 40-89 years is positively associated with T3 and T4.