Background: The World Health Organization published the 2019 Integrated Care for Older People (ICOPE) framework to guide, assess, and promote the intrinsic capacity (IC) of older adults, referring to their physical and mental health. This study aims to investigate the relationship between IC and frailty among older adults. Methods: This cross-sectional study was conducted in a medical center in Taiwan in 2021. Two hundred ten patients over 65 admitted to the geriatric ward were invited to participate. The questionnaire included an IC measure, Fried Frailty Scale, and demographic items. The IC measure was ascertained using the six domains of ICOPE (cognition, mobility, nutrition, visual, hearing, and depressive symptoms). The Fried Frailty Scale was used to categorize participants as robust (Fried Frailty Scale=0), prefrail (Fried Frailty Scale=1-2), or frail (Fried Frailty Scale ≥3). Multinomial logistic regression was used to analyze the association between individual ICOPE domains and frailty stages, while adjusting for confounders. Results: Among the participants, 39.0% were prefrail, and 28.6% were frail. Limited mobility and depressive symptoms were significantly associated with prefrail (adjusted odds ratio [aOR]=4.44, 95% confidence interval [CI] 1.82–10.82; aOR=8.41, 95% CI 1.75–40.37) and frail (aOR=11.57, 95% CI 3.63–36.93; aOR=13.77, 95% CI 2.62–72.49) individuals, respectively. Malnutrition (aOR=4.01, 95% CI 1.18–13.62) and hearing loss (aOR=4.37, 95% CI 1.09–19.66) were significantly associated with frail older adults. Conclusion Limited mobility and depressive symptoms occurring at the prefrail stage could be used as assessment items for early detection of prefrail.

Objective:To create nodal integrated topological attributes (NITA) index and explore its application value in obsessive-compulsive disorder (OCD) identification by combining with machine learning model.Methods:Sixty-nine patients with OCD and 69 healthy volunteers matched with gender, age and years of education from the Second Affiliated Hospital of Xinxiang Medical University who met the enrollment criteria from January 2022 to September 2023 were included in the study.Their whole-brain functional magnetic resonance imaging (MRI) data were collected and preprocessed to construct the brain functional network, and the global and nodal topological attributes were extracted as the two sets of training features for the support vector machine (SVM), random forest and gradient boosting tree, and the better features were selected by comparing the classification results of the three machine learning models. The selected features were downgraded using principal component analysis algorithm, and the above models were trained again to filter out the models that were compatible with the new dimensional features. Finally, the new dimensional features with statistically significant differences in brain regions were screened and used to train the adapted model. SPSS 20.0 software was used to process relevant data, and independent sample t-test was used for inter group comparison. Results:Each machine learning model trained based on node topological attribute metrics was higher than the global metrics in terms of accuracy, recall, F1 value and AUC, and the average accuracy of the former was higher than that of the latter by about 10.00%. The node topology attribute metrics were downscaled and named NITA, which can synthesize about 95.00% of the feature information of node topology attribute metrics on average. SVM was finally chosen as the fitness model for NITA (accuracy of 86.00%, recall of 87.00%, F1 value of 0.86, AUC of 0.92). Compared with healthy controls, the differences in NITA in the medial superior frontal gyrus, middle frontal gyrus, ventral inferotemporal gyrus, caudal inferior parietal lobule, medial precuneus, insula hypergranular cellular area, caudal cuneus gyrus, inferior occipital gyrus, caudal hippocampus, dorsal caudate nucleus, and several subregions of the superior temporal gyrus and the thalamus were statistically significant in the OCD group (all P<0.05, FDR-corrected). Training the NITA of the above brain regions as features yielded the optimal model FDR-NITA-SVM, which had an accuracy of 91.38% in the training group and 90.00% in the test group. Conclusion:NITA can be used as a potential imaging marker for recognizing OCD.NITA abnormal brain regions are key nodes for information exchange and integration among brain networks in OCD patients.

The incidence of age-related macular degeneration(AMD)is rising with the intensifying aging trend,be-coming a critical challenge that demands urgent solutions.Dry AMD(dAMD)accounts for approximately 80％of all AMD cases,and there is currently a lack of highly effective treatment options.In recent years,traditional Chinese medicine(TCM)has been proven to effectively treat dAMD through actions such as antioxidation,anti-apoptosis,anti-inflammation,and lipid-lowering.By reviewing domestic and international literature,this article discusses TCM monomers,extracts,and compound formulations for dAMD,analyzing their various mechanisms.Based on traditional TCM efficacy theories and in-tegrated with modern mechanisms of action,it targets the active components of TCM to elucidate the connection between effective medicinal targets of TCM and dAMD,thereby clarifying the efficacy and scientific basis of TCM monomers,com-pounds,and extracts in treating dAMD.The aim is to provide new perspectives for the prevention and clinical treatment of dAMD.

Objective To investigate the value of the combined detection of neutrophil CD64(nCD64),C-reactive protein(CRP)and lactate dehydrogenase(LDH)in the diagnosis of refractory Mycoplasma pneumoniae pneumonia(RMPP)in children.Methods A total of 147 children with Mycoplasma pneumoniae infection were enrolled and divided into the RMPP group(n=70)and the general Mycoplasma pneumoniae pneumonia group(GMPP,n=77)based on disease severity and treatment response.The age,gender,white blood cell count(WBC)within 24 hours of admission,serum procalcitonin(PCT),C-reactive protein(CRP)and lactate dehydrogenase(LDH)levels were collected in the study participants.The expression level of nCD64 in peripheral blood was measured using flow cytometry.Multivariate Logistic regression analysis was conducted to identify the independent risk factors associated with RMPP in children.Additionally,a receiver operating characteristic(ROC)curve was constructed to assess the diagnostic performance of the combined detection of nCD64,CRP and LDH for RMPP in children.Results The RMPP group had a longer hospital stay than the GMPP group(P＜0.05).Levels of nCD64,CRP and LDH were significantly higher in the RMPP group compared to those of the GMPP group(P＜0.05),and there were no significant differences in WBC and PCT levels between the two groups.Multivariate Logistic regression analysis showed that elevated nCD64,CRP and LDH were risk factors for RMPP in children(P＜0.05).ROC curve analysis revealed that the areas under the curve(AUC)for nCD64,CRP and LDH in diagnosing RMPP were 0.817,0.863 and 0.805,respectively.The combined detection of three indicators for AUC was 0.948.Conclusion The levels of nCD64,CRP and LDH in blood of children with RMPP are higher than those of children with GMPP.The combined detection of the three indicators has a high diagnostic value for RMPP in children with Mycoplasma pneumoniae pneumonia.

Objective:To create nodal integrated topological attributes (NITA) index and explore its application value in obsessive-compulsive disorder (OCD) identification by combining with machine learning model.Methods:Sixty-nine patients with OCD and 69 healthy volunteers matched with gender, age and years of education from the Second Affiliated Hospital of Xinxiang Medical University who met the enrollment criteria from January 2022 to September 2023 were included in the study.Their whole-brain functional magnetic resonance imaging (MRI) data were collected and preprocessed to construct the brain functional network, and the global and nodal topological attributes were extracted as the two sets of training features for the support vector machine (SVM), random forest and gradient boosting tree, and the better features were selected by comparing the classification results of the three machine learning models. The selected features were downgraded using principal component analysis algorithm, and the above models were trained again to filter out the models that were compatible with the new dimensional features. Finally, the new dimensional features with statistically significant differences in brain regions were screened and used to train the adapted model. SPSS 20.0 software was used to process relevant data, and independent sample t-test was used for inter group comparison. Results:Each machine learning model trained based on node topological attribute metrics was higher than the global metrics in terms of accuracy, recall, F1 value and AUC, and the average accuracy of the former was higher than that of the latter by about 10.00%. The node topology attribute metrics were downscaled and named NITA, which can synthesize about 95.00% of the feature information of node topology attribute metrics on average. SVM was finally chosen as the fitness model for NITA (accuracy of 86.00%, recall of 87.00%, F1 value of 0.86, AUC of 0.92). Compared with healthy controls, the differences in NITA in the medial superior frontal gyrus, middle frontal gyrus, ventral inferotemporal gyrus, caudal inferior parietal lobule, medial precuneus, insula hypergranular cellular area, caudal cuneus gyrus, inferior occipital gyrus, caudal hippocampus, dorsal caudate nucleus, and several subregions of the superior temporal gyrus and the thalamus were statistically significant in the OCD group (all P<0.05, FDR-corrected). Training the NITA of the above brain regions as features yielded the optimal model FDR-NITA-SVM, which had an accuracy of 91.38% in the training group and 90.00% in the test group. Conclusion:NITA can be used as a potential imaging marker for recognizing OCD.NITA abnormal brain regions are key nodes for information exchange and integration among brain networks in OCD patients.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

OBJECTIVE To explore the intervention mechanism of Shangke Lengtongtie on cold hyperalgesia in KOA mice based on the HMGB1/CXCL12/CXCR4 signaling axis.METHODS Monosodium iodoacetate(MIA)was used for the intra-articular injec-tion into the knee joint to establish mice model of knee osteoarthritis(KOA).Peripheral blood monocytes were extracted from mice,cultured,and then reinfused into the tail vein of the mice.Subsequently,in vivo animal imaging was used to observe the recruitment sites of these monocytes.The cold hyperalgesia threshold was measured at various time points in each group of mice.Hematoxylin and eosin(HE)staining was used to evaluate the level of synovial pathological changes.ELISA was employed to detect the expression of in-flammatory factors IL-1β,TNF-α,and pain mediators CGRP and Substance P in mouse serum.Western blot and qPCR methods were used to detect the protein and gene expression of cold hyperalgesia-related indicators such as TRPA1,TRPM8,HMGB1,CXCL12,CXCR4,Collagen Ⅰ,and Netrin-1 in synovial tissue,as well as DCC in dorsal root ganglia(DRG)tissue.RESULTS In vivo ima-ging showed that after the monocytes were reinfused into KOA mice,they were recruited to the knee joint area,with the HMGB1 group exhibiting a greater recruitment of circulating monocytes at the knee joint.Additionally,compared to the control group,the KOA group and HMGB1 group showed inflammatory pathological changes in the synovium,increased expression of serum inflammatory factors and pain mediators,reduced cold hyperalgesia threshold,and upregulated protein and gene expression of cold hyperalgesia-related indica-tors in synovial and DRG tissues.The changes were more significant in the HMGB1 group compared to the KOA group(P<0.05).Af-ter treatment with Shangke Lengtongtie or GL intervention,synovial inflammation was alleviated,serum inflammatory factors and pain mediators decreased,cold hyperalgesia threshold increased,and the upregulation of cold hyperalgesia-related indicator protein and gene expression levels was significantly reversed(P<0.05).CONCLUSION Shangke Lengtongtie exerts a beneficial effect on the mitigation of synovitis and cold hyperalgesia in KOA mice,a therapeutic mechanism that possibly mediated through the inhibition of the HMGB1/CXCL12/CXCR4 signaling axis.

The treatment of multidrug-resistant gram-negative bacterial infections has become a challenge in today's healthcare field,and time-to-event data analysis has shown significant value in clinical prognostic studies of critically ill patients.This study comprehensively reviewed the results of the application of time-to-event analysis in clinical studies of anti-multidrug-resistant gram-negative bacterial drugs,aiming to provide a scientific basis for the optimization of therapeutic strategies and prognostic assessment of multidrug-resistant gram-negative bacterial infections,and to promote the widespread application of time-to-event analysis methods in similar studies.

Objective To investigate the expression and clinical significance of S100 cabinding protein A7(S100A7),insulin-like growth factor-1(IGF-1),and receptor for advanced glycation end products(RAGE)in type Ⅰ endometrial carcinoma(EC).Methods 60 patients with type 1 EC and 30 normal endometrial tissues were selected and analyzed by real-time fluorescent quantita-tive polymerase chain reaction(RT-qPCR).RT-qPCR was used to detect the expression of S100A7,IGF-1 and RAGE mRNA in dif-ferent endometrial tissues.The protein expression of S100A7,IGF-1 and RAGE in 107 type Ⅰ EC tissues,24 cases endometrial tissues with atypical hyperplasia and 30 normal endometrial tissues were detected by immunohistochemical method.The correlation between the three and the clinicopathological characteristics of the patients was analyzed.Kaplan-Meier method was used to draw the survival curve,and COX regression analysis was performed to analyze the factors affecting the survival and prognosis of patients.Results The expres-sions of S100A7,IGF-1 and RAGE mRNA in EC tissues were increased compared with normal endometrial tissues(P＜0.05).The positive rates of S100A7,IGF-1 and RAGE proteins increased with the progression of endometrial lesions,and there was a significant positive correlation among them(P＜0.05).For different International Federation of Gynecology and Obstetrics(FIGO)staging,pres-ence or absence of lymph node metastasis,and degree of tissue differentiation in type Ⅰ EC,The positive rates of S100A7,IGF-1 and RAGE were different(P＜0.05).Kaplan-Meier survival analysis showed that the average survival time of patients with positive expres-sion of S100A7,IGF-1 and RAGE was lower.COX regression analysis showed that lymph node metastasis,high expression of S100A7,IGF-1 and RAGE were independent risk factors for prognosis of patients with type Ⅰ EC.Conclusion The increased expression levels of S100A7,IGF-1 and RAGE in type Ⅰ EC are closely associated with the emergence,progress and bleak prognosis of EC.