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 develop a toolkit suitable for assisting community health institutions in the early identification and inter-vention of mild cognitive impairment(MCI)among older adults.Methods A literature review was conducted to construct a draft of the identification and intervention toolkit.Tools with an expert approval rate above 70%were included after expert consultation.The final version of the toolkit was developed by integrating these tools with officially recommended tools in China.Results The expert consultation yielded an authority coefficient of 0.84.The finalized toolkit included the assessment tools of Mini-Mental State Examination,Montreal Cognitive Assessment,General Practitioner Assessment of Cognition,Cognitive Abilities Screening Instrument and Clock Drawing Test,and 18 intervention measures in-cluding pharmacological treatment,cognitive training and psychological interventions,etc.Conclusion The MCI Identification-Intervention Toolkit may serve as a reference for guiding the identification and inter-vention of MCI among older adults for community health institutions.

Objective:To construct a machine learning model based on localization CT-based radiomics, dosiomics and clinical features for predicting radiotherapy response in non-small cell lung cancer (NSCLC) and validate its application value.Methods:A retrospective case series study was conducted. A total of 138 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022 were selected. The efficacy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, and the patients were stratified according to the objective remission (complete remission+partial remission). Random stratified sampling was used to divide the 138 patients into a training group (96 cases) and an internal validation group (42 cases) at a ratio of 7∶3. Additionally, 33 patients who received radiotherapy at Jining Cancer Hospital from January 2019 to December 2022 were included as the external validation group. Based on the pre-radiotherapy data of the radiotherapy planning system, PyRadiomics software package was used to extract 107 radiomics features and 107 dosiomics features for each patient. Pearson correlation analysis and LASSO regression analysis were used for dimensionality reduction screening; the final selected features were weighted and integrated to generate radiomics-dosiomics scores (RDS), which were then input into logistic regression (LR), support vector machine (SVM), extremely randomized forest (Extra Trees), K-nearest neighbor algorithm (KNN), lightweight gradient boosting machine (Light GBM), and multi-layer perceptron (MLP) machine learning algorithms to construct 6 radiomics-dosiomics models (RDM) for predicting the objective remission. RECIST 1.1 standard was used to evaluate objective remission as the gold standard, receiver operating characteristic (ROC) curve of 6 RDM for predicting objective remission was plotted, and the optimal algorithm for RDM was selected. Univariate and multivariate logistic regression were performed on demographic characteristics, hematological indicators and radiotherapy parameters of the training group to screen independent risk factors for NSCLC patients who received radiotherapy but did not achieve objective remission. These factors were input into the optimal machine learning algorithm to construct a clinical model (CM). Combined with features from RDS and CM, the clinical feature-radiomics-dosiomics combined model (CRDM) was established, and the nomogram of the model for predicting objective remission in NSCLC patients with radiotherapy was drawn. ROC curves were used to evaluate the efficacy of CM, RDM and CRDM in predicting the objective remission in NSCLC patients with radiotherapy in the training group, internal validation group and external validation group.Results:Four radiomics features (including grayscale variance, low grayscale long-range operation emphasis, low grayscale area emphasis, and small area low grayscale area emphasis, all of which were texture features) and 6 dosiomics features [including 1 first-order feature (robust mean absolute deviation), 4 texture features (grayscale non-uniformity, large area emphasis, large area high grayscale emphasis, contrast) and 1 shape feature (shortest axis length)] were selected. ROC curve analysis showed that the area under the curve (AUC) of the RDM constructed using SVM algorithm for judging the objective remission in the training group and the internal validation group was 0.907 (95% CI: 0.836-0.977) and 0.822 (95% CI: 0.685-0.959), which were higher than RDM constructed using other algorithms, and the sensitivity (96.2% and 91.7%), specificity (78.6% and 76.7%) and accuracy (83.3% and 81.0%) at the optimal cut-off values were all higher. Considering the stability and generalization ability of the model, SVM algorithm was ultimately used to construct RDM, CM and CRDM uniformly. Based on training group data, univariate and multivariate logistic regression analysis showed that elevated platelet-to-lymphocyte ratio (PLR) ( OR = 1.001, 95% CI: 1.000-1.003, P = 0.035) and increased target volume of radiotherapy plan ( OR = 1.001, 95% CI: 1.000-1.001, P = 0.008) were independent risk factors for failure to achieve objective remission. ROC curve analysis showed that in the training group and the internal validation group, the AUC of CRDM predicting objective remission were 0.914 (95% CI: 0.856-0.972) and 0.864 (95% CI: 0.754-0.974), respectively, which were better than CM [AUC were 0.735 (95% CI: 0.612-0.857) and 0.697 (95% CI: 0.507-0.888)] and RDM, respectively. In the external validation group, the AUC of CRDM, CM and RDM were 0.778 (95% CI: 0.500-1.000), 0.667 (95% CI: 0.434-0.899) and 0.741 (95% CI: 0.463-1.000), respectively. Conclusions:The CRDM constructed by combining radiomics, dosiomics and clinical features can comprehensively and accurately evaluate the radiotherapy response of NSCLC patients, and may have important clinical application value in achieving precision medicine and optimizing treatment strategies.

In the past decade, real-world data (RWD) research has undergone significant transformations due to data aggregation and processing technologies. However, there is still a lack of consensus regarding the scope of RWD applications and the value of real-world evidence (RWE). This study briefly outlined the origins of the concept of RWD study and its early research scope to promote further development in this area. We also reviewed the understanding of RWD applications and research models from the five perspectives of healthcare professionals, medical institutions, decision-making departments, cross-regional cooperation model, and the practice of the One-Health model. Finally, we systematically summarized the renewed understanding of the value of RWE while looking ahead to the challenges and future developments in this field.

Objective To investigate the effect of midazolam on neuronal damage in ischemic stroke （IS） rats and its regulatory effect on PTEN-induced putative kinase 1 （PINK1）/E3 ubiquitin ligase （PARKIN） signaling pathway. Methods An IS rat model was established using arterial occlusion method. The rats with successful model were randomly divided into IS group, drug-low, medium, high-dose （drug-L, M, H, 30, 60, 90 mg/kg midazolam） groups, drug-H+autophagy inhibitor 3-MA group （90 mg/kg midazolam+30 mg/kg 3-MA）, and rats with only isolated blood vessels were used as sham surgery groups. Each group received corresponding doses of drugs or physiological saline intervention, and the neurological function scoring, brain histopathology, neuronal apoptosis, ultrastructure, and expression of PINK1, PARKIN, microtubule-associated protein 1 light chain 3 （LC3）, and P62 protein in mitochondria were detected. Results Compared with the IS group, the pathological damage of the drug-L group, drug-M group, and drug-H group was improved, and autophagosomes showed an increasing trend, the expression of PINK1, PARKIN, and LC3 proteins increased, the neurological function score, neuronal apoptosis rate, and P62 protein obviously decreased in a dose-dependent manner （P<0.01 or P<0.001）; compared with the drug-H group, the pathological damage in the drug-H+3-MA group increased and autophagosomes decreased, the expression of PINK1, PARKIN, and LC3 proteins decreased, the neurological function score, neuronal apoptosis rate, and P62 protein obviously increased （P<0.001）. Conclusion Midazolam induced mitochondrial autophagy in IS rats by activating the PINK1/PARKIN signaling pathway, neuronal apoptosis was reduced and neuronal damage were improved in IS rats.

Objective:To construct a machine learning model based on localization CT-based radiomics, dosiomics and clinical features for predicting radiotherapy response in non-small cell lung cancer (NSCLC) and validate its application value.Methods:A retrospective case series study was conducted. A total of 138 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022 were selected. The efficacy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, and the patients were stratified according to the objective remission (complete remission+partial remission). Random stratified sampling was used to divide the 138 patients into a training group (96 cases) and an internal validation group (42 cases) at a ratio of 7∶3. Additionally, 33 patients who received radiotherapy at Jining Cancer Hospital from January 2019 to December 2022 were included as the external validation group. Based on the pre-radiotherapy data of the radiotherapy planning system, PyRadiomics software package was used to extract 107 radiomics features and 107 dosiomics features for each patient. Pearson correlation analysis and LASSO regression analysis were used for dimensionality reduction screening; the final selected features were weighted and integrated to generate radiomics-dosiomics scores (RDS), which were then input into logistic regression (LR), support vector machine (SVM), extremely randomized forest (Extra Trees), K-nearest neighbor algorithm (KNN), lightweight gradient boosting machine (Light GBM), and multi-layer perceptron (MLP) machine learning algorithms to construct 6 radiomics-dosiomics models (RDM) for predicting the objective remission. RECIST 1.1 standard was used to evaluate objective remission as the gold standard, receiver operating characteristic (ROC) curve of 6 RDM for predicting objective remission was plotted, and the optimal algorithm for RDM was selected. Univariate and multivariate logistic regression were performed on demographic characteristics, hematological indicators and radiotherapy parameters of the training group to screen independent risk factors for NSCLC patients who received radiotherapy but did not achieve objective remission. These factors were input into the optimal machine learning algorithm to construct a clinical model (CM). Combined with features from RDS and CM, the clinical feature-radiomics-dosiomics combined model (CRDM) was established, and the nomogram of the model for predicting objective remission in NSCLC patients with radiotherapy was drawn. ROC curves were used to evaluate the efficacy of CM, RDM and CRDM in predicting the objective remission in NSCLC patients with radiotherapy in the training group, internal validation group and external validation group.Results:Four radiomics features (including grayscale variance, low grayscale long-range operation emphasis, low grayscale area emphasis, and small area low grayscale area emphasis, all of which were texture features) and 6 dosiomics features [including 1 first-order feature (robust mean absolute deviation), 4 texture features (grayscale non-uniformity, large area emphasis, large area high grayscale emphasis, contrast) and 1 shape feature (shortest axis length)] were selected. ROC curve analysis showed that the area under the curve (AUC) of the RDM constructed using SVM algorithm for judging the objective remission in the training group and the internal validation group was 0.907 (95% CI: 0.836-0.977) and 0.822 (95% CI: 0.685-0.959), which were higher than RDM constructed using other algorithms, and the sensitivity (96.2% and 91.7%), specificity (78.6% and 76.7%) and accuracy (83.3% and 81.0%) at the optimal cut-off values were all higher. Considering the stability and generalization ability of the model, SVM algorithm was ultimately used to construct RDM, CM and CRDM uniformly. Based on training group data, univariate and multivariate logistic regression analysis showed that elevated platelet-to-lymphocyte ratio (PLR) ( OR = 1.001, 95% CI: 1.000-1.003, P = 0.035) and increased target volume of radiotherapy plan ( OR = 1.001, 95% CI: 1.000-1.001, P = 0.008) were independent risk factors for failure to achieve objective remission. ROC curve analysis showed that in the training group and the internal validation group, the AUC of CRDM predicting objective remission were 0.914 (95% CI: 0.856-0.972) and 0.864 (95% CI: 0.754-0.974), respectively, which were better than CM [AUC were 0.735 (95% CI: 0.612-0.857) and 0.697 (95% CI: 0.507-0.888)] and RDM, respectively. In the external validation group, the AUC of CRDM, CM and RDM were 0.778 (95% CI: 0.500-1.000), 0.667 (95% CI: 0.434-0.899) and 0.741 (95% CI: 0.463-1.000), respectively. Conclusions:The CRDM constructed by combining radiomics, dosiomics and clinical features can comprehensively and accurately evaluate the radiotherapy response of NSCLC patients, and may have important clinical application value in achieving precision medicine and optimizing treatment strategies.

Objective·To investigate the role of HEN methyltransferase 1(HENMT1)in the proliferation and migration of gastric cancer(GC)and its potential molecular mechanisms.Methods·The expression of HENMT1 in GC was examined using bioinformatics databases,Western blotting and quantitative real-time PCR(qPCR).Kaplan-Meier Plotter and BEST online tools were used to analyze the correlations between HENMT1 expression and overall survival,perineural invasion,subtypes,tumor location and Lauren classification in clinical GC patients.GC cells were cultured in vitro and treated with small interfering RNA(siRNA)targeting HENMT1 and HENMT1 overexpression vectors,in combination with a PI3K activator(740 Y-P)or PI3K inhibitor(3-MA).The roles of HENMT1 in GC cell proliferation and migration were assessed using cell counting kit-8(CCK-8)assay,colony formation assay,wound healing assay and Transwell migration assay.Results·HENMT1 was significantly upregulated in GC and positively associated with perineural invasion.Its expression was closely related to GC subtypes,being most pronounced in the proliferative subtype,and was higher in intestinal-type GC according to the Lauren classification.However,HENMT1 expression showed no significant correlation with overall survival or tumor location(including gastric body,cardia,antrum and whole stomach).Functional experiments demonstrated that silencing HENMT1 inhibited GC cell proliferation and migration,whereas overexpression of HENMT1 enhanced these capabilities.Mechanistically,silencing HENMT1 reduced the levels of phosphorylated PI3K,AKT and mTOR,as well as their total protein expression.Conversely,HENMT1 overexpression upregulated these proteins.Moreover,siHENMT1 combined with the PI3K activator 740 Y-P effectively reversed the proliferation and migration effects induced by 740 Y-P,while overexpressed HENMT1 combined with the PI3K inhibitor 3-MA reversed the suppressive effects of 3-MA on GC cell proliferation and migration.Conclusion·HENMT1 is highly expressed in GC and positively regulates the proliferation and migration of gastric cancer cells by activating the PI3K-AKT-mTOR signaling pathway.

Objective·To investigate the role of HEN methyltransferase 1(HENMT1)in the proliferation and migration of gastric cancer(GC)and its potential molecular mechanisms.Methods·The expression of HENMT1 in GC was examined using bioinformatics databases,Western blotting and quantitative real-time PCR(qPCR).Kaplan-Meier Plotter and BEST online tools were used to analyze the correlations between HENMT1 expression and overall survival,perineural invasion,subtypes,tumor location and Lauren classification in clinical GC patients.GC cells were cultured in vitro and treated with small interfering RNA(siRNA)targeting HENMT1 and HENMT1 overexpression vectors,in combination with a PI3K activator(740 Y-P)or PI3K inhibitor(3-MA).The roles of HENMT1 in GC cell proliferation and migration were assessed using cell counting kit-8(CCK-8)assay,colony formation assay,wound healing assay and Transwell migration assay.Results·HENMT1 was significantly upregulated in GC and positively associated with perineural invasion.Its expression was closely related to GC subtypes,being most pronounced in the proliferative subtype,and was higher in intestinal-type GC according to the Lauren classification.However,HENMT1 expression showed no significant correlation with overall survival or tumor location(including gastric body,cardia,antrum and whole stomach).Functional experiments demonstrated that silencing HENMT1 inhibited GC cell proliferation and migration,whereas overexpression of HENMT1 enhanced these capabilities.Mechanistically,silencing HENMT1 reduced the levels of phosphorylated PI3K,AKT and mTOR,as well as their total protein expression.Conversely,HENMT1 overexpression upregulated these proteins.Moreover,siHENMT1 combined with the PI3K activator 740 Y-P effectively reversed the proliferation and migration effects induced by 740 Y-P,while overexpressed HENMT1 combined with the PI3K inhibitor 3-MA reversed the suppressive effects of 3-MA on GC cell proliferation and migration.Conclusion·HENMT1 is highly expressed in GC and positively regulates the proliferation and migration of gastric cancer cells by activating the PI3K-AKT-mTOR signaling pathway.

In the past decade, real-world data (RWD) research has undergone significant transformations due to data aggregation and processing technologies. However, there is still a lack of consensus regarding the scope of RWD applications and the value of real-world evidence (RWE). This study briefly outlined the origins of the concept of RWD study and its early research scope to promote further development in this area. We also reviewed the understanding of RWD applications and research models from the five perspectives of healthcare professionals, medical institutions, decision-making departments, cross-regional cooperation model, and the practice of the One-Health model. Finally, we systematically summarized the renewed understanding of the value of RWE while looking ahead to the challenges and future developments in this field.

Objective To develop a toolkit suitable for assisting community health institutions in the early identification and inter-vention of mild cognitive impairment(MCI)among older adults.Methods A literature review was conducted to construct a draft of the identification and intervention toolkit.Tools with an expert approval rate above 70%were included after expert consultation.The final version of the toolkit was developed by integrating these tools with officially recommended tools in China.Results The expert consultation yielded an authority coefficient of 0.84.The finalized toolkit included the assessment tools of Mini-Mental State Examination,Montreal Cognitive Assessment,General Practitioner Assessment of Cognition,Cognitive Abilities Screening Instrument and Clock Drawing Test,and 18 intervention measures in-cluding pharmacological treatment,cognitive training and psychological interventions,etc.Conclusion The MCI Identification-Intervention Toolkit may serve as a reference for guiding the identification and inter-vention of MCI among older adults for community health institutions.