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 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.

Periodontal disease is a risk factor for many systemic diseases such as Alzheimer's disease and adverse pregnancy outcomes. Cleft palate (CP), the most common congenital craniofacial defect, has a multifaceted etiology influenced by complex genetic and environmental risk factors such as maternal bacterial or virus infection. A prior case-control study revealed a surprisingly strong association between maternal periodontal disease and CP in offspring. However, the precise relationship remains unclear. In this study, the relationship between maternal oral pathogen and CP in offspring was studied by sonicated P. gingivalis injected intravenously and orally into pregnant mice. We investigated an obvious increasing CP (12.5%) in sonicated P. gingivalis group which had inhibited osteogenesis in mesenchyme and blocked efferocytosis in epithelium. Then glycolysis and H4K12 lactylation (H4K12la) were detected to elevate in both mouse embryonic palatal mesenchyme (MEPM) cells and macrophages under P. gingivalis exposure which further promoted the transcription of metallopeptidase domain17 (ADAM17), subsequently mediated the shedding of transforming growth factor-beta receptor 1 (TGFBR1) in MEPM cells and mer tyrosine kinase (MerTK) in macrophages and resulted in the suppression of efferocytosis and osteogenesis in palate, eventually caused abnormalities in palate fusion and ossification. The abnormal efferocytosis also led to a predominance of M1 macrophages, which indirectly inhibited palatal osteogenesis via extracellular vesicles. Furthermore, pharmacological ADAM17 inhibition could ameliorate the abnormality of P. gingivalis-induced abnormal palate development. Therefore, our study extends the knowledge of how maternal oral pathogen affects fetal palate development and provides a novel perspective to understand the pathogenesis of CP.
Animals ; Female ; Porphyromonas gingivalis ; Pregnancy ; Mice ; Cleft Palate/etiology* ; Glycolysis

ObjectiveTo explore the clinical safety of Feilike Mixture （FLK） in the real world. MethodsThe safety of all children who received FLK from 29 institutions in 12 provinces between January 21，2021 and December 25，2021 was evaluated through prospective centralized surveillance and a nested case control study. ResultsA total of 3 035 juveniles were included. There were 29 research centers involved，which are distributed across 12 provinces，including one traditional Chinese medicine （TCM） hospital and 28 general hospitals. The average age among the juveniles was （4.77±3.56） years old，and the average weight was （21.81±12.97） kg. Among them，119 cases （3.92%） of juveniles had a history of allergies. Acute bronchitis was the main diagnosis for juveniles，with 1 656 cases （54.46%）. FLK was first used in 2 016 cases （66.43%），and 142 juvenile patients had special dosages，accounting for 4.68%. Among them，92 adverse drug reactions （ADRs） occurred，including 73 cases of gastrointestinal system disorders，10 cases of metabolic and nutritional disorders，eight cases of skin and subcutaneous tissue diseases，two cases of vascular and lymphatic disorders，and one case of systemic diseases and various reactions at the administration site. The manifestations of ADRs were mainly diarrhea，stool discoloration，and vomiting，and no serious ADRs occurred. The results of multi-factor analysis indicated that special dosages （the use of FLK）［odds ratio （OR） of 2.642， 95% confidence interval （CI） of 1.105-6.323］，combined administration： spleen aminopeptide （OR of 4.978， 95%CI of 1.200-20.655），and reason for combined administration： anti-infection （OR of 1.814， 95%CI of 1.071-3.075） were the risk factors for ADRs caused by FLK. Conclusion92 ADRs occurred among 3 035 juveniles using FLK. The incidence of ADRs caused by FLK was 3.03%，and the severity was mainly mild or moderate. Generally，the prognosis was favorable after symptomatic treatment such as drug withdrawal or dosage reduction，suggesting that FLK has good clinical safety.

Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.

Objective:To develop a deep learning model based on 3D super-resolution reconstruction technology and to analyze its feasibility and effectiveness in predicting paraglottic space invasion in hypopharyngeal cancer.Methods:A retrospective study was conducted involving 382 patients with hypopharyngeal squamous cell carcinoma treated at Qilu Hospital of Shandong University between January 2014 and December 2020. The cohort included 364 males and 18 females, with a mean age of 62±7 years. Patients were divided into a training set ( n=300) and a test set ( n=82) based on enrollment time. A generative adversarial network was used to perform 3D super-resolution reconstruction on contrast-enhanced CT images, improving spatial resolution by 16 times. A 2.5D deep learning strategy was employed to construct Resnet-NR and Resnet-SR models based on conventional and super-resolution images, respectively, to predict whether the paraglottic space was invaded. Model performance was evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC). A multi-reader multi-case study was conducted to assess the impact of the artificial intelligence (AI) model on clinicians′ diagnostic capabilities. Results:The super-resolution model Resnet-SR achieved the highest accuracy in both the training set (AUC=0.87, 95% CI: 0.84-0.90) and the test set (AUC=0.88, 95% CI: 0.81-0.96), significantly outperforming traditional clinical indicators (T stage, N stage, tumor diameter, and pathological differentiation degree) (AUC range: 0.55-0.70, all P<0.05). In comparison, the conventional-resolution model Resnet-NR achieved AUCs of 0.81 (95% CI: 0.77-0.84, P=0.005) and 0.80 (95% CI: 0.71-0.89, P=0.184) in the training and test sets, respectively. Using Resnet-SR to assist clinical decision-making improved the diagnostic accuracy of junior physicians (AUC=0.793 without AI assistance vs. AUC=0.871 with AI assistance, P=0.012) and significantly reduced diagnosis time for clinicians of all experience levels (86.5 s without AI assistance vs. 82.5 s with AI assistance, t=2.01, P=0.032). Conclusion:This study successfully develops a deep learning model based on 3D super-resolution reconstruction technology, which can assist in preoperative prediction of paraglottic space invasion in hypopharyngeal cancer. The AI-assisted tool improves diagnostic accuracy for junior physicians and enhances diagnostic efficiency for clinicians across all experience levels.

Paying for TCM according to the value of efficacy is an intrinsic requirement for establishing an effective and efficient diversified medical insurance payment method, and is also an important measure to support the inheritance, innovation and development of TCM. This study used text mining and PMC index model to quantitatively analyze and evaluate the texts of 20 special policies on payment based on efficacy value in TCM. It was found that there were 6 excellent policies, 14 acceptable policies, and there were no undesirable policies or perfect policies; overall, the average policy PMC index was 6.94, and the average provincial policy was 7.06, which was higher than the prefecture-level average of 6.27; the overall quality of special policies of TCM based on efficacy value was relatively high, but there was still much room for improvement in terms of policy timeliness, policy evaluation, policy measures, policy tools, and balance of policy design between provinces and cities.

OBJECTIVE To provide a reference for revising the safety content of traditional Chinese medicine（TCM） instructions. METHODS A systematic analysis was conducted on the relevant announcements on the revision of TCM instruction from 2013 to 2024， including the year of publication， drug name， drug formulation， announcement release time， and the average number of revisions made to three safety contents （contraindication， adverse drug reaction， and precaution）. According to the report data of national adverse drug reaction monitoring from 2013 to 2024， analyze the proportion of TCM in all adverse drug reactions， the composition ratio of adverse drug reaction of different dosage forms of drugs [TCM injection， oral TCM， other （topical） TCM]. RESULTS From 2013 to 2024， the National Medical Products Administration issued a total of 101 notices on the revision of TCM instruction， involving 241 types of TCM. Among them， there were 163 types of oral TCM， 25 types of TCM injection， and 53 types of other （topical） TCM. There were 3， 10 and 0 types of TCM injection， oral TCM， and other （topical） TCM with missing safety content， respectively. The main source of adverse drug reaction data for TCM injections was post- marketing monitoring data （accounting for 48.00%）； the main source of adverse drug reaction data for oral TCM was monitoring data （accounting for 71.17%）； 73.58% of other （topical） TCM did not mention the source of adverse drug reaction data. The report on national adverse drug reaction monitoring showed that the proportion of all adverse drug reactions of TCM had decreased from 17.3% in 2013 to 12.1% in 2024. Among them， the proportion of adverse drug reaction of TCM injection in the three dosage forms of TCM had decreased from 61.3% in 2015 to 24.6% in 2024， while the proportion of adverse drug reaction of oral TCM in the three dosage forms of TCM had increased from 34.7% in 2015 to 64.0% in 2024. CONCLUSIONS The risk management of TCM safety content has achieved results in China， but still faces three major challenges： strongdependence on passive monitoring， insufficient data traceability， and missing key content. It is urgent to improve safety content by dosage form， optimize data sources， and fully utilize real-world evidence to supplement safety content.

Objective To investigate whether Dauricine(Dau)can ameliorate acute kidney injury induced by renal ischemia-reperfusion(IR)in mice.Methods C57BL/6 mice were randomly assigned to three experimental groups:sham operation,ischemia-reperfusion injury(IRI),and IRI treated with daunorubicin(IRI+Dau),with 12 animals in each group.Following oral administration of Dau(15 mg/kg),renal ischemia-reperfusion was induced,and blood and kidney tissue samples were collected 24 hours post-surgery.Histopathological changes were assessed using hematoxylin and eosin(HE)staining.Renal function was evaluated by measuring serum creatinine and blood urea nitrogen(BUN)levels.Protein expression related to lipid peroxidation was analyzed using western blotting and immunofluorescence.Inflammatory gene expression was determined via quantitative polymerase chain reaction(qPCR).Nuclear translocation of nuclear factor κB(NF-κB),a key inflammatory marker,was assessed using immu-nofluorescence.Statistical comparisons between groups were performed using t-tests.Results The administration of Dau significantly ameliorated IR-induced acute kidney injury compared to the Sham group.Serum creatinine(P<0.001)and urea nitrogen(P<0.000 1)levels were markedly decreased in Dau-treated mice relative to those in the IRI group.Furthermore,Dau significantly suppressed lipid peroxide production in renal tissues(P<0.001),without significantly affecting the expression levels of Gpx4(P=0.919)and Acsl4(P=0.086),two key proteins involved in lipid peroxidation.In addition,Dau effectively inhibited IR-induced nuclear translocation of NF-κB(P<0.001)and reduced apoptosis in kidney cells(P=0.004).Conclusion Dau mitigates IR-induced kidney damage by reducing the accumulation of lipid peroxides and inhibiting the nuclear translocation of NF-κB,thereby attenuating inflammation and renal cell apoptosis.

Objective:To develop a deep learning model based on 3D super-resolution reconstruction technology and to analyze its feasibility and effectiveness in predicting paraglottic space invasion in hypopharyngeal cancer.Methods:A retrospective study was conducted involving 382 patients with hypopharyngeal squamous cell carcinoma treated at Qilu Hospital of Shandong University between January 2014 and December 2020. The cohort included 364 males and 18 females, with a mean age of 62±7 years. Patients were divided into a training set ( n=300) and a test set ( n=82) based on enrollment time. A generative adversarial network was used to perform 3D super-resolution reconstruction on contrast-enhanced CT images, improving spatial resolution by 16 times. A 2.5D deep learning strategy was employed to construct Resnet-NR and Resnet-SR models based on conventional and super-resolution images, respectively, to predict whether the paraglottic space was invaded. Model performance was evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC). A multi-reader multi-case study was conducted to assess the impact of the artificial intelligence (AI) model on clinicians′ diagnostic capabilities. Results:The super-resolution model Resnet-SR achieved the highest accuracy in both the training set (AUC=0.87, 95% CI: 0.84-0.90) and the test set (AUC=0.88, 95% CI: 0.81-0.96), significantly outperforming traditional clinical indicators (T stage, N stage, tumor diameter, and pathological differentiation degree) (AUC range: 0.55-0.70, all P<0.05). In comparison, the conventional-resolution model Resnet-NR achieved AUCs of 0.81 (95% CI: 0.77-0.84, P=0.005) and 0.80 (95% CI: 0.71-0.89, P=0.184) in the training and test sets, respectively. Using Resnet-SR to assist clinical decision-making improved the diagnostic accuracy of junior physicians (AUC=0.793 without AI assistance vs. AUC=0.871 with AI assistance, P=0.012) and significantly reduced diagnosis time for clinicians of all experience levels (86.5 s without AI assistance vs. 82.5 s with AI assistance, t=2.01, P=0.032). Conclusion:This study successfully develops a deep learning model based on 3D super-resolution reconstruction technology, which can assist in preoperative prediction of paraglottic space invasion in hypopharyngeal cancer. The AI-assisted tool improves diagnostic accuracy for junior physicians and enhances diagnostic efficiency for clinicians across all experience levels.