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.

ObjectiveTo investigate the clinical characteristics and risk factors of Mycoplasma pneumoniae pneumonia （MPP） in children based on a dual classification integrating imaging features and clinical severity. MethodsMedical records of 2 054 pediatric patients with MPP were retrospectively analyzed. The cohort was stratified into severe consolidation （n=253）， severe non-consolidation （n=118）， non-severe consolidation （n=393）， and non-severe non-consolidation groups （n=1 290） based on clinical and radiological findings. Inter group data and characteristics were compared and multiple regression analysis was conducted to construct a prediction model for severe consolidation group. ResultsSignificant differences were observed among the groups in terms of age， duration of fever， length of hospital stay， presence of pulmonary rales， inflammatory markers ［C-reactive protein （CRP） and lactate dehydrogenase （LDH）］， the use of hormones， and bronchoscopic treatment （all P < 0.05）. Compared with the severe non-consolidation group， non-severe consolidation group， and non-severe non-consolidation group， children in severe consolidation group exhibited the longest duration of fever ［8 （6， 11） days vs 6 （2， 9）， 7 （6， 9） and 6 （3， 8） days， respectively］ and the longest length of hospital stay ［7 （5， 8） days vs 6 （5， 8）， 6 （5， 8） and 6 （4， 7） days， respectively］. They also had the highest incidence of reduced breath sounds ［34 cases （13.4%） vs 2 cases （1.7%）， 29 cases （7.4%） and 13 cases （1.0%）， respectively］ and a substantially higher rate of coinfections， particularly viral infections ［63 cases （24.9%） vs 23 cases （19.5%）， 60 cases （15.3%） and 190 cases （14.7%）， respectively］. Multivariate analysis indicated that the independent risk factors for severe MPP （SMPP） were age > 4.5 years， length of hospital stay > 6.5 days， reduced breath sounds， neutrophil-to-lymphocyte ratio （NLR） > 1.66， LDH > 370.5 U/L， CRP > 9.5 mg/L， and coinfection with viruses. Reduced breath sounds （OR = 5.58， 95% CI： 2.45 - 12.69） and coinfection with bacteria （OR = 3.11， 95% CI： 1.43 - 6.75） were identified as the most significant risk factors for pulmonary consolidation in non-severe MPP children. Additionally， reduced breath sounds， coinfection with viruses， LDH > 365.5 U/L， and CRP > 32.1 mg/L were risk factors for severe pneumonia in children with pulmonary consolidation. For non-consolidation MPP children， the presence of pulmonary dry rales （OR = 2.28， 95% CI： 1.46 - 3.56） was the primary independent risk factor for the development of severe pneumonia. ConclusionThe chest imaging findings of MPP are associated with clinical severity， and the risk factor model constructed based on this imaging-clinical classification can assist in achieving precise hierarchical diagnosis and treatment in clinical practice.

Objective To elucidate the changes in the gut microbiota and molecular mechanism of huaier in enhancing the efficacy of oxaliplatin (OXA) chemotherapy in gastric cancer (GC). Methods The effects of huaier on the gut microbiota structure and intestinal barrier function in MKN45-bearing mice were analyzed by using 16S rRNA sequencing, RT-qPCR, and IHC. UPLC-MS and network pharmacology, as well as in vivo experimental approaches, were employed to investigate the key bioactive constituents of huaier systematically and elucidate the underlying mechanisms by which huaier exerts therapeutic effects against GC. The expression of the PI3K/AKT/SREBP pathway was detected in cancer cells and tissues via Western blot analysis. The safety profile of huaier combined with OXA was verified through serum biochemistry and HE-stained tissue section analyses. Results Huaier inhibited the PI3K/AKT/SREBP pathway by increasing the abundance of Akkermansia muciniphila, reduced lipid droplet deposition, and ultimately enhanced the sensitivity to OXA in the treatment of GC. Conclusion This study demonstrates the value of huaier in gastric cancer treatment by enhancing chemosensitivity and provides novel insights into its systemic therapeutic effects through molecular mechanisms and gut microbiota modulation.

ObjectiveTo investigate the risk factors for plastic bronchitis （PB） in children with Mycoplasma pneumoniae pneumonia （MPP） and to construct a predictive model. MethodsPropensity score matching （1∶4 nearest neighbor matching） was used to balance baseline data between the two groups. Univariate and multivariate Logistic regression analyses were performed to identify independent risk factors. Various machine learning algorithms were applied to build predictive models， and model performance was evaluated using receiver operating characteristic curves， decision curves， and calibration curves. SHapley Additive exPlanations （SHAP） method was used for model interpretation. ResultsAfter matching， baseline characteristics were comparable between the two groups. Compared with the non-PB group， the PB group had significantly longer fever duration， higher incidence of pleural effusion and right upper lobe involvement， and higher levels of procalcitonin （PCT）， D-dimer， lactate dehydrogenase （LDH）， alanine aminotransferase （ALT）， aspartate aminotransferase （AST）， immunoglobulin E （IgE）， and red blood cell count in bronchoalveolar lavage fluid （BALF） （all P<0.05）， while lymphocyte and eosinophil counts in blood were significantly lower （P<0.05）. Multivariate Logistic regression showed that PCT>0.207 μg/L， LDH>386.25 U/L， right upper lobe involvement， and red blood cell count in BALF>1 500×10⁶ were independent risk factors. Among machine learning models， the gradient boosting machine （GBM） model achieved an area under the curve of 0.786 in the validation set， demonstrating the best generalization ability. SHAP analysis validated the importance of the above variables. ConclusionThe four indicators above have independent predictive value for PB occurrence， and the GBM model shows certain clinical risk stratification capability.

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection，characterized by high mortality rate.The pathological mechanisms of sepsis-induced organ failure are highly complex，including multiple processes such as abnormal immune responses，hemodynamic alterations，and severe endothelial dysfunction.Chromogranin A（Cg A），an acidic glycoprotein belonging to the granin family，serves as a precursor for various bioactive peptides，and is involved in diverse physiological processes，including inflammatory regulation，tissue repair，vascular function modulation，and glucose/lipid metabolism.Recent studies have revealed that Cg A and its derived peptides not only participate in the pathological progression of sepsis，but also exhibit significant correlations with the severity of organ injury. Consequently，Cg A has garnered attention for its potential role in the clinical diagnosis and prognostic evaluation of sepsis.This article provides a comprehensive review of the structure and functions of Cg A，as well as current research advances and clinical applications in sepsis.

Exosomes are a kind of nanovesicles with a wide range of chemical components，which are enriched in a variety of bioactive molecules，such as lipids，proteins，nucleic acids，etc. Exosomes carry biologically active molecules that transmit different messages between cells，and play a role in human physiological and pathological processes. Compared with synthetic carriers such as liposomes and nanoparticles，by virtue of their natural endogenous attributes and cell-specific targeting capabilities，exosomes have broad application prospects in the fields of early disease diagnosis marker screening and targeted therapy drug carriers. This article reviews the role of exosomes in common children's diseases and their research progress，providing new ideas for pediatric clinicians to explore disease diagnosis and treatment.

Objective To analyze the situation of blood lipid and blood routine parameters in patients with pneumoconiosis,and construct a column chart diagnostic model to explore their diagnostic value for pneumo-coniosis.Methods A total of 456 patients with pneumoconiosis admitted to the First Affiliated Hospital of Chongqing Medical and Pharmaceutical College from January 2022 to January 2024 were selected as the pneu-moconiosis group,while 462 healthy subjects exposed to dust during the same period were chosen as the con-trol group.Serum lipids and blood routine parameters related to pneumoconiosis were measured and compared between two groups.Univariate and multivariate Logistic regression analyzes were conducted to examine ser-um lipids and blood routine parameters associated with pneumoconiosis.A risk prediction model was construc-ted using logistic regression in machine learning,and the diagnostic efficacy of the column chart diagnostic model was evaluated by calculating the C-index through receiver operating characteristic(ROC)curve and plotting the model calibration curve based on Hosmer Lemeshow goodness of fit.Decision curve analysis(DCA)was used to assess the clinical practicality of the column chart diagnostic model.Results The levels of serum high-density ester protein cholesterol(HDL-C),cholesterol(TC),red blood cell(RBC),hematocrit(HCT),hemoglobin concentration(HGB),lymphocyte number(LYM),and lymphocyte percentage(LYM％)in the pneumoconiosis group were lower than those in the control group(P＜0.05).The levels of neutrophil-lymphocyte ratio(NLR),platelet-to-lymphocyte ratio(PLR),and systemic immune inflammation index(SII)were higher than those in the control group(P＜0.05).Multivariate Logistic regression analysis showed that HDL-C,LYM％,PLR,and SII were independent influencing factors for pneumoconiosis(P＜0.05).A column chart diagnostic model for the occurrence of pneumoconiosis was constructed using HDL-C,TC,LYM％,PLR,and SII as diagnostic factors.The ROC curve C-index of the diagnostic model was 0.84(95％CI:0.81-0.86),with sensitivity for diagnosing pneumoconiosis of 75.29％,specificity of 77.51％,posi-tive predictive value of 83.25％,and negative predictive value of 67.88％.Internal validation was conducted on the constructed column chart diagnostic model,with a validation set ROC curve C-index of 0.84(95％CI:0.80-0.87),sensitivity of 80.91％,specificity of 72.62％,positive diagnostic value of 79.46％,and negative diagnostic value of 74.39％.The calibration positive curve slope of the diagnostic model was close to 1,and in the fit test P＞0.05.DCA analysis showed that the diagnostic model had clinical practical value for risk diag-nosis of pneumoconiosis.Conclusion HDL-C,TC,LYM％,PLR and SII are independent influencing factors for pneumoconiosis.A column chart diagnostic model for the occurrence of pneumoconiosis is successfully constructed based on machine learning principles,and it has been verified to have high diagnostic efficiency.

Objective To investigate the expression levels of microRNA-34a(miR-34a),β-catenin and pro-grammed death receptor-1(PD-L1)in cancer tissues of patients with cervical cancer(CC)and their correla-tion with patients' prognosis.Methods A total of 83 patients with CC admitted to Yuncheng Central Hospital Affiliated to Shanxi Medical University from June 2021 to January 2024 were selected as the research objects.General data,CC tissue and adjacent normal tissue samples of all patients were collected.The mRNA expres-sion levels of miR-34a,β-catenin and PD-L1 in CC tissues and adjacent normal tissues were detected by fluo-rescence quantitative PCR and compared.The expression levels of β-catenin and PD-L1 in CC tissues and adja-cent normal tissues were detected by immunohistochemistry.Multivariate Logistic regression analysis was used to analyze the influencing factors of prognosis in CC patients.Receiver operating characteristic(ROC)curve was used to analyze the predictive value of miR-34a,β-catenin and PD-L1 expression levels in CC pa-tients with poor prognosis.Results Compared with adjacent normal tissues,the expression level of miR-34a of the CC tissues was lower,while the expression levels of β-catenin and PD-L1 were higher(P＜0.05).The positive expression rates of β-catenin and PD-L1 protein in CC tissues were higher than those of adjacent nor-mal tissues(89.16％vs.10.84％,78.31％vs.20.48％,x2=101.807,55.526,P＜0.05).There were no sig-nificant differences in age and body mass index(BMI)between the good prognosis group and the poor progno-sis group(t=1.508,1.820,both P＞0.05),while there were significant differences in International Federa-tion of Gynecology and Obstetrics(FIGO)stage and differentiation degree between the two groups(x2=8.451,9.115,both P＜0.05).Compared with the good prognosis group,the expression level of miR-34a of the poor prognosis group was lower,while the expression levels of β-catenin and PD-L1 were higher(P＜0.05).Multivariate Logistic regression analysis showed that increased expression of miR-34a was an protective factor for poor prognosis in CC patients,while the increased expression levels of β-catenin and PD-L1,Ⅱ a and Ⅱ b FIGO staging and low differentiation were independent risk factors for poor prognosis in CC patients(P＜0.05).ROC curve analysis showed that miR-34a,β-catenin and PD-L1 all had certain predictive value for the poor prognosis of CC patient,and the area under the curve(AUC)of the single detection was 0.765,0.836 and 0.797,respectively.The AUC of the combined detection was 0.914(Z=2.631,2.331,2.571,P=0.009,0.020,0.010).Conclusion The expression levels of miR-34a,β-catenin and PD-L1 mRNA affect the prognosis of CC patients.Increased expression of miR-34a is an protective factor for poor prognosis in CC patients,in-creased β-catenin and PD-L1 expressions are independent risk factors for poor prognosis in CC patients,and all three factors can be used as biomarkers to predict poor prognosis in CC patients,and the combined detection of the three has the highest prognostic value.

Caregivers for chronic disease patients of caregiver activation emphasizes the subjective initiative and preparedness of caregivers in managing patients' health conditions and medical needs.Caregivers with high activation levels can participate more effectively in disease management,significantly improve the quality of care,and have a positive impact on their own mental health and quality of life.This article reviews the concept,assessment tools,current situation,influencing factors,and intervention strategies of caregiver activation,and proposes future research directions,aiming to provide references for conducting research and practical exploration related to caregiver activation.

Although a single nucleotide polymorphism for N-acetyltransferase 10 (NAT10) has been identified in patients with early-onset stroke, the role of NAT10 in ischemic injury and the related underlying mechanisms remains elusive. Here, we provide evidence that NAT10, the only known RNA N4-acetylcytidine (ac4C) modification "writer", is increased in the damaged cortex of patients with acute ischemic stroke and the peri-infarct cortex of mice subjected to photothrombotic (PT) stroke. Pharmacological inhibition of NAT10 with remodelin on Days 3-7 post-stroke or astrocytic depletion of NAT10 via targeted virus attenuates ischemia-induced infarction and improves functional recovery in PT mice. Mechanistically, NAT10 enhances ac4C acetylation of the inflammatory cytokine tissue inhibitor of metalloproteinase 1 (Timp1) mRNA transcript, which increases TIMP1 expression and results in the accumulation of microtubule-associated protein 1 light chain 3 (LC3) and progression of astrocyte autophagy. These findings demonstrate that NAT10 regulates astrocyte autophagy by targeting Timp1 ac4C after stroke. This study highlights the critical role of ac4C in the regulation of astrocyte autophagy and proposes a promising strategy to improve post-stroke outcomes via NAT10 inhibition.