Objective:To identify competency evaluation indicators for psychological hotline professionals,provi-ding scientific reference for their training and assessment.Methods:Through literature review,initial competency evalua-tion indicators were selected,followed by Delphi consultation with sixteen experts across China.The analytic hierarchy process(AHP)was used to assign weights to each indicators.Results:The response rate of expert consultation question-naire was 100％,and the expert authority coefficients for the two rounds of consultation were 0.92 and 0.90 respective-ly.After two rounds of expert consultation,a competency evaluation index system for professional counselors of psycho-logical aids hotline was constructed,including four first-level indexes,nine second-level indexes,and 34 third-level inde-xes.The weight of each index was determined using the AHP.The Kendall coefficient of concordance of all level's inde-xes in the two rounds of consultation were 0.16-0.18 and 0.16-0.25,respectively.Conclusion:The expert consensus developed in this study serves as an reference for evaluating the competency of psychological hotline professionals,sup-porting their training and assessment.

Objective:To develop and validate a combined model integrating radiomics, dosiomics, and clinical parameters based on CT simulation and dosimetric images in order to predict the occurrence of radiation pneumonitis (RP) in patients with non-small cell lung cancer (NSCLC).Methods:A retrospective study was conducted on the clinic data of 143 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022. Patients were randomly stratified into a training group ( n = 100) and an internal validation group ( n = 43) at a 7∶3 ratio. Moreover, clinic data were collected from 34 NSCLC patients who received radiotherapy at the Jining Cancer Hospital between January 2019 and December 2022 as an external validation group. All three groups (the training group, internal validation, and external validation groups) were further categorized into two groups based on the RP severity (i.e., RP ≥ grade 2 and RP < grade 2). Their radiotherapy dose, CT simulation, and 3D dose distribution images were collected. Then, the total lung minus planning target volume (TL-PTV) was defined as the region of interest (ROI) for radiomics and dosiomic feature extraction, followed by feature dimensionality reduction. Consequently, key features associated with RP were determined. Four predictive models were developed using machine learning approaches (especially multilayer perceptron, MLP): a clinical model (CM), a radiomics model (RM), a dosiomics model (DM), and a radiomics and dosiomics nomogram (RDN), with a nomogram subsequently constructed. Ultimately, the performance and clinical feasibility of these models were assessed using receiver operating characteristic (ROC), area under the curve (AUC), and decision curve analysis (DCA). Results:A total of 1 834 radiomic features and 1 834 dosiomic features were extracted. Using the occurrence of RP ≥ grade 2 as the marker variable, 14 radiomic features, 15 dosiomic features, and three clinical features were selected from the training group to construct the prediction models (CM, RM, DM, and RDN). The performance and generalizability of these models were subsequently validated in both the internal validation and external validation groups. Specifically, the RDN exhibited AUCs of 0.915 (95% CI: 0.852-0.978), 0.879 (95% CI: 0.777-0.982), and 0.838 (95% CI: 0.701-0.975) in the three groups, respectively. A nomogram was established for RDN by integrating the radiomics score (R-score), dosiomics score (D-score), mean lung dose (MLD), V20, and V30. This nomogram allowed for individualized risk estimation of RP and facilitated personalized radiotherapy planning. Conclusions:The RDN model that is developed based on CT simulation and 3D dose distribution images and integrates radiomics, dosiomics, and clinical features can effectively predict the RP risk of NSCLC patients. The integration of multidimensional data contributes to the formation of the optimal predictive model, offering guidance for clinicians.

In recent years,organs-on-chips(OOCs)technology has gradually matured and been recognized by regulatory agencies,playing a key role in the construction of pathophysiological models,pharmacodynamic screening,toxicological evaluation and other fields,providing guidance for the research and development of innovative drugs of Chinese materia medica and the clinical application of classic prescriptions.This article reviewed the application progress of OOCs technology in the research of Chinese materia medica from four dimensions:pharmacodynamic and toxicological evaluation,quality control,active component analysis and innovative drug research and development,in order to provide a reference for the precise application of this technology in the field of Chinese materia medica.

The innate immune sensor NLRP3 inflammasome overactivation is involved in the pathogenesis of ulcerative colitis. PGAM5 is a mitochondrial phosphatase involved in NLRP3 inflammasome activation in macrophages. However, the role of PGAM5 in ulcerative colitis and the mechanisms underlying PGAM5 regulating NLRP3 activity remain unknown. Here, we show that PGAM5 deficiency ameliorates dextran sodium sulfate (DSS)-induced colitis in mice via suppressing NLRP3 inflammasome activation. By combining APEX2-based proximity labeling focused on PGAM5 with quantitative proteomics, we identify NEK7 as the new binding partner of PGAM5 to promote NLRP3 inflammasome assembly and activation in a PGAM5 phosphatase activity-independent manner upon inflammasome induction. Interfering with PGAM5-NEK7 interaction by punicalagin inhibits the activation of the NLRP3 inflammasome in macrophages and ameliorates DSS-induced colitis in mice. Altogether, our data demonstrate the PGAM5-NEK7 interaction in macrophages for NLRP3 inflammasome activation and further provide a promising therapeutic strategy for ulcerative colitis by blocking the PGAM5-NEK7 interaction.

Objective To understand the influencing factors of chronic dyslipidemia in T2DM patients who signed a contract for diabetes point system management in Qingpu District, and to provide a basis for comprehensive intervention and prevention and control of dyslipidemia in T2DM patients and to optimize the management strategy of Qingpu District diabetes point system. Methods Among the T2DM patients who signed the diabetes point system from 2017 to 2023, patients with chronic dyslipidemia and normal blood lipids were selected and included in the case group and the control group, respectively. A case-control study was conducted with 1:1 matching by age and gender to analyze the factors influencing dyslipidemia. Results Multifactorial paired logistic regression analysis showed that overweight/obesity and central obesity and smoking in T2DM patients increased the risk of dyslipidemia by 1.93, 2.27, and 2.16 times, respectively. Long-term use of lipid-lowering drugs, duration of diabetes for 5 years or more, regular physical exercise, knowledge of blood lipid status, and married status could reduce the risk of dyslipidemia in T2DM patients (OR values were 0.547, 0.452, 0.685, 0.386 and 0.354, respectively). Current complications (history of stroke, coronary heart disease, and renal insufficiency) were also associated with dyslipidemia (OR=1.802, 95% CI:1.125-2.888). Conclusion The management of diabetes point system in Qingpu District should strengthen the feedback and interpretation of blood lipid monitoring results, improve patients’ health awareness of blood lipid management, and actively take comprehensive management of lifestyle intervention and drug treatment to effectively control blood lipid and reduce the occurrence of related complications.

Objective:To develop and validate a combined model integrating radiomics, dosiomics, and clinical parameters based on CT simulation and dosimetric images in order to predict the occurrence of radiation pneumonitis (RP) in patients with non-small cell lung cancer (NSCLC).Methods:A retrospective study was conducted on the clinic data of 143 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022. Patients were randomly stratified into a training group ( n = 100) and an internal validation group ( n = 43) at a 7∶3 ratio. Moreover, clinic data were collected from 34 NSCLC patients who received radiotherapy at the Jining Cancer Hospital between January 2019 and December 2022 as an external validation group. All three groups (the training group, internal validation, and external validation groups) were further categorized into two groups based on the RP severity (i.e., RP ≥ grade 2 and RP < grade 2). Their radiotherapy dose, CT simulation, and 3D dose distribution images were collected. Then, the total lung minus planning target volume (TL-PTV) was defined as the region of interest (ROI) for radiomics and dosiomic feature extraction, followed by feature dimensionality reduction. Consequently, key features associated with RP were determined. Four predictive models were developed using machine learning approaches (especially multilayer perceptron, MLP): a clinical model (CM), a radiomics model (RM), a dosiomics model (DM), and a radiomics and dosiomics nomogram (RDN), with a nomogram subsequently constructed. Ultimately, the performance and clinical feasibility of these models were assessed using receiver operating characteristic (ROC), area under the curve (AUC), and decision curve analysis (DCA). Results:A total of 1 834 radiomic features and 1 834 dosiomic features were extracted. Using the occurrence of RP ≥ grade 2 as the marker variable, 14 radiomic features, 15 dosiomic features, and three clinical features were selected from the training group to construct the prediction models (CM, RM, DM, and RDN). The performance and generalizability of these models were subsequently validated in both the internal validation and external validation groups. Specifically, the RDN exhibited AUCs of 0.915 (95% CI: 0.852-0.978), 0.879 (95% CI: 0.777-0.982), and 0.838 (95% CI: 0.701-0.975) in the three groups, respectively. A nomogram was established for RDN by integrating the radiomics score (R-score), dosiomics score (D-score), mean lung dose (MLD), V20, and V30. This nomogram allowed for individualized risk estimation of RP and facilitated personalized radiotherapy planning. Conclusions:The RDN model that is developed based on CT simulation and 3D dose distribution images and integrates radiomics, dosiomics, and clinical features can effectively predict the RP risk of NSCLC patients. The integration of multidimensional data contributes to the formation of the optimal predictive model, offering guidance for clinicians.

Objective:To explore the latent class characteristics of fear of childbirth among expectant fathers of third trimester based on latent profile analysis, and to analyze the differences in characteristics among different classes and influencing factors.Methods:Convenience sampling was used to select expectant fathers accompanying their partners for prenatal check-ups at Obstetrics and Gynecology Hospital Affiliated to Zhejiang University School of Medicine from March to September 2024 as the subjects of the investigation. A General Information Questionnaire, Fathers' Fear of Childbirth Scale, Perceived Social Support Scale, Connor-Davidson Resilience Scale, and Edinburgh Postnatal Depression Scale were used for the survey; latent profile analysis was employed to explore the latent typing of fear of childbirth among expectant fathers of third trimester, and univariate analysis and multiple logistic regression analysis were used to investigate related influencing factors.Results:A total of 269 expectant fathers were included in the final analysis, aged 31 (29, 34) years old. The score of Fathers' Fear of Childbirth Scale was (49.58 ± 13.28); the scores of Perceived Social Support Scale was 64.00 (51.00, 71.50); the scores of Connor-Davidson Resilience Scale was 29.00 (26.00, 33.00); the score of Edinburgh Postnatal Depression Scale was 7.00 (5.00, 9.00). Latent profile analysis showed that the level of fear of childbirth among expectant fathers of third trimester could be divided into four latent classes: "low fear of childbirth-pain fear group" (19.0%, 51/269), "moderate fear of childbirth-maternal and infant safety concern group" (24.5%, 66/269), "moderate fear of childbirth-medical trust group" (32.0%, 86/269), and "high fear of childbirth-comprehensive group" (24.5%, 66/269). Multiple logistic regression analysis showed that expectant fathers with high psychological resilience were more likely to be classified into the low fear of childbirth-pain fear group ( OR values ranged from 0.863 to 0.909, all P<0.05); expectant fathers with higher levels of prenatal depression were more likely to be classified into the high fear of childbirth-comprehensive group ( OR values ranged from 1.286 to 1.366, all P<0.05); compared with the low fear of childbirth-pain fear group, younger expectant fathers were more likely to be classified into the moderate fear of childbirth-medical trust group ( OR=0.871, P<0.05); compared with the moderate fear of childbirth-medical trust group, expectant fathers with lower family monthly income were more likely to be classified into the high fear of childbirth-comprehensive group ( OR=3.093, P<0.05). Conclusions:There are significant class characteristics in the level of fear of childbirth among expectant fathers of third trimester, and it is recommended that medical staff implement targeted personalized intervention measures based on the characteristics of each class to reduce the level of fear of childbirth among expectant fathers.

Objective:To investigate anxiety symptoms and associated factors in relocated elderly residents of new townships,and to provide evidence for prevention interventions.Methods:A cross-sectional study was conduc-ted in relocated elderly residents in new townships of three urban areas in Shandong Province from 2021 to 2023.The study instruments included Ascertain Dementia-8,Generalized Anxiety Disorder-7,self-administered de-mographic characteristics information questionnaire.Multivariate analysis of factors associated with anxiety symp-toms in elderly residents was performed using multivariate logistic regression.Results:The prevalence rate of mild anxiety symptoms was 5.8％,and the rate of moderate-to-severe anxiety symptoms was 1.3％in 3 313 resi-dents.Multivariate analysis found that self-assessed general psychological condition(OR=0.52),good family envi-ronment(OR=0.34),no self-perceived cognitive impairment(OR=0.31),no chronic diseases(OR=0.42),and only one chronic disease(OR=0.61)were protective factors for mild anxiety symptoms,and very good dietary structure(OR=2.15)and fair dietary structure(OR=2.39)were risk factors for those.Very good family environ-ment(OR=0.11)and average family environment(OR=0.16),and no self-perceived cognitive impairment(OR=0.14)were protective factors for moderate-to-severe anxiety symptoms,and 0-3 years(OR=3.24)and 4-6 years(OR=3.28)of relocation were risk factors for those.Conclusion:Family environment,dietary structure,and duration since relocation are key factors associated with anxiety symptoms among relocated elderly residents in new townships.Targeted interventions should be implemented to address their mental health needs.

Objective To investigate changes in the pro-inflammatory mediator S100A8/9 and NLRP3/Caspase-1/IL-1β pathway in a rat kidney-aging model induced by D-galactose.Methods Twelve SD rats were divided into control and D-galactose groups,and injected subcutaneously in the back of the neck with D-galactose(150 mg/kg)to establish a rat model of kidney aging.Kidney samples were collected under anesthesia after 8 weeks.Kidneys were stained for senescence-associated beta-galactosidase(SA-β-Gal),mRNA expression levels of the aging-related genes p21,p16,and p53 were detected by quantitative reverse transcription-polymerase chain reaction(qRT-PCR),and histopathological changes were observed by hematoxylin-eosin(HE)and Masson staining.Serum urea nitrogen and creatinine,and catalase(CAT),glutathione peroxidase(GSH-PX),superoxide dismutase(SOD),and malondialdehyde(MDA)levels in the kidney tissues were detected.Reactive oxygen species(ROS)were detected by dihydroethdium staining and protein expression levels of collagen Ⅲ,α-smooth muscle actin(α-SMA),Protein expression of S100A8/9 was detected by immunofluorescence,and transforming growth factor(TGF)-β1 levels in kidney tissues and key factors in the NLRP3/Caspase-1/IL-1β inflammatory pathway were detected by Western Blot.A renal senescence model using HK-2 cells was constructed using H2O2 in vitro,and expression levels of the senescence proteins p21 and p16 and mRNA expression levels of the inflammatory factors IL-18 and tumor necrosis factor-α(TNF-α)were detected.Cell senescence was observed by SA-β-Gal staining.The effects of the S100A8/9 inhibitor paquinimod on expression levels of S100A8/9 and NLRP3/Caspase-1/IL-1β pathway-related proteins in the aging model were also detected.Results mRNA levels of the aging genes p21,p16,and p53 in kidney tissues were significantly increased in rats in the D-galactose group compared with the control group(P＜0.01),and SA-β-Gal staining showed a significant increase in senescent cells(P＜0.01).Serum blood urea nitrogen and creatinine levels increased(P＜0.05),CAT,GSH-PX,and SOD activities decreased(P＜0.01),while MDA activity increased in the D-galactose group(P＜0.01).Collagen Ⅲ,α-SMA,and TGFβ1 expression and the ROS content in tissues increased(P＜0.05).Glomeruli were atrophied or absent in the D-galactose group,the lumens of the renal sacs and renal tubules were enlarged,the nuclei were deeply stained and constricted,and numerous collagen fibers were deposited.Levels of S100A8 and S100A9 protein(P＜0.01),as well as NLRP3,Caspase-1,and IL-1β increased(P＜0.05).Paquinimod alleviated HK-2 cell senescence and decreased expression levels of the senescence proteins p21 and p16,and mRNA levels of the inflammatory factors IL-18 and TNF-α(P＜0.05,P＜0.01).The number of senile cells was also decreased,shown by SA-β-Gal staining(P＜0.01).Paquinimod also inhibited the protein expression of S100A8 and S100A9(P＜0.01)and NLRP3,Caspase-1,and IL-1β(P＜0.05 or P＜0.01).Conclusions S100A8/9 participates in the chronic inflammatory response by activating the NLRP3/Caspase-1/IL-1β pathway,thereby promoting D-galactose-induced renal aging.

Objective:To investigate the self-perceived cognitive decline status in the community population,and to explore the association between different dietary habits and self-perceived cognitive decline.Methods:A cross-sectional study was carried out in 11 879 community residents in the three regions of Weifang,Jining,and Zoucheng in Shandong Province.The Ascertain Dementia-8 and dietary habits information questionnaire were used to assess self-perceived cognitive decline and dietary habits,and their association were analyzed using single factor and multivariate logistic regression.Results:The detection rate of self-perceived cognitive decline was 21.4％.Lo-gistic regression showed that smoking in the past was positively associated with self-perceived cognitive decline(OR=1.40,95％CI:1.14-1.73).However,intake of fruits(often,OR=0.70,95％CI:0.52-0.94;everyday,OR=0.60,95％CI:0.44-0.81),nuts(daily,OR=0.62,95％CI:0.44-0.88),mushrooms(often,OR=0.74,95％CI:0.57-0.92)and high tryptophan foods(sometimes,OR=0.79,95％CI:0.68-0.91;everyday,OR=0.54,95％CI:0.34-0.87)were negatively associated with self-perceived cognitive decline.Conclusion:Smoking history might be a risk factor for self-perceived cognitive decline,and high frequency intake of fruits,nuts,mush-rooms,and high tryptophan foods might protective factors for it.