his cross-sectional study employed convenience sampling to enroll 1 994 community-dwelling older adults (aged ≥65 years) undergoing health examinations at a Guangzhou community hospital between January and December 2024, aiming to investigate associations between blood lipid profiles and cognitive impairment. Cognitive function was assessed using the AD8 scale, with demographic characteristics (age, sex, education, occupation), health status (hypertension, diabetes mellitus), and lifestyle factors (smoking/alcohol use) collected via questionnaires. Fasting blood samples quantified triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C). Propensity score matching (PSM) balanced baseline characteristics between lipid-level groups. Pre-PSM analyses revealed significant intergroup differences: TG groups differed in sex, BMI, alcohol use, hypertension, and self-rated health ( P<0.05); TC groups in sex, age, occupation, diabetes, and hypertension; LDL-C groups in sex, age, occupation, diabetes, hypertension, and daily living ability; HDL-C groups in sex, age, education, occupation, BMI, smoking, diabetes, and hypertension. Post-PSM adjustment eliminated baseline differences ( P>0.05). Multivariable logistic regression adjusted for demographic, clinical, and lifestyle factors demonstrated that elevated TG levels conferred a 48% reduced risk of cognitive dysfunction [ OR (95% CI): 0.52 (0.29-0.94)], whereas TC, LDL-C, and HDL-C showed no significant associations (all P>0.05). These findings suggest an inverse association between higher triglyceride levels and cognitive dysfunction risk in older adults, highlighting TG′s potential protective role in cognitive health.

Objective:To explore the application value of radiomics, deep learning, and their combined models in predicting the efficacy of radioiodine adjuvant therapy in patients with papillary thyroid cancer (PTC).Methods:A retrospective analysis was conducted on the clinical and imaging data of 131 PTC patients (38 males, 93 females; age 41(33, 48) years) who received first 131I treatment at the Affiliated Hospital of Guilin Medical University from January 2018 to March 2023. Patients were randomly divided into a training set ( n=105) and a test set ( n=26) at the ratio of 8∶2. Multivariate logistic regression analysis was used to screen clinical features to determine independent predictors affecting the efficacy of 131I therapy. Radiomics and deep learning features were extracted from the enhanced CT scans and were combined by using the extremely randomized trees (ExtraTrees) algorithm to construct radiomics, deep learning, and combined models. The predictive abilities of the models were evaluated by AUC, and the Delong test was applied to compare the difference between AUCs. Results:Higher pre-ablation stimulated thyroglobulin (ps-Tg) levels (odds ratio( OR)=1.060, 95% CI: 1.025-1.095, P=0.004) and bilateral lesions ( OR=5.085, 95% CI: 1.452-17.814, P=0.033) were independent predictors of the efficacy of 131I therapy in intermediate to high-risk PTC patients. In the training set, the radiomics model (AUC=0.853) and combined model (AUC=0.880) significantly outperformed the deep learning model (AUC=0.711; Z values: 2.48, 3.09, P values: 0.013, 0.002), while there was no statistically significant difference between the radiomics and combined models ( Z=0.51, P=0.610). In the test set, AUCs of the radiomics, deep learning, and combined models were 0.746, 0.624, and 0.876, respectively, and the AUC of the combined model was higher than that of the radiomics model or deep learning model ( Z values: 2.05, 1.99, P values: 0.040, 0.047). Conclusion:The combined model demonstrates superior performance over the standalone radiomics model and deep learning model in predicting the efficacy of 131I treatment in PTC patients.

Objective To investigate the effect of 3D technology combined with smartphones in problem-based learning(PBL)for neuroendoscopy.Methods 82 trainees who were enrolled from January 2021 to January 2023 were selected as the research subjects.A randomized controlled trial was conducted,and the subjects were divided into a control group and an experimental group.PBL and 3D technology combined with smartphone-assisted PBL were implemented respectively for two groups of students.The data were analyzed using t-test.Teaching satisfac-tion is evaluated by 2 test.Results The results of the in-operation examination and theoretical examination of the ex-perimental group students were found to be higher than those of the control group students(t=8.630,6.087,P＜0.001),the satisfaction scores of students and teachers showing that the satisfaction of the experimental group was higher than that of the control group(x2=4.213,6.301,7.026,P＜0.01).Conclusion In the PBL of neuroendosco-py,the use of 3D technology combined with smart phones as an auxiliary teaching system can effectively improve students'sense of participation,reduce the difficulty of skull base anatomy learning,and improve students'theo-retical and surgical assessment scores and teaching satisfaction.

Objective In the clinical teaching of neurosurgery,the traditional Problem-Based Learning(PBL)teaching model faces systematic challenges such as the disconnection between the training cycle of specialized talents and technological iteration,the limitation of practical opportunities due to hospital infections,and the inefficient allocation of teaching resources.It provides a new path for teaching reform based on the deep integration of Artificial Intelligence and PBL,but it still needs to address issues such as differences in intern participation,insufficient technical adaptability of instructors,and fragmented resource allocation.Based on these problems,a collaborative mechanism of"technology development-talent cultivation"and a multi-dimensional optimization path of"intern participation-instructor training-hospital resource input"are proposed.On this basis,through collaborative strategies such as strengthening the incentive mechanism for autonomous learning,establishing a standardized instructor training system,and building a dynamic resource allocation platform,the management of neurosurgery clinical teaching is promoted towards intelligence,personalization,and systematization.

his cross-sectional study employed convenience sampling to enroll 1 994 community-dwelling older adults (aged ≥65 years) undergoing health examinations at a Guangzhou community hospital between January and December 2024, aiming to investigate associations between blood lipid profiles and cognitive impairment. Cognitive function was assessed using the AD8 scale, with demographic characteristics (age, sex, education, occupation), health status (hypertension, diabetes mellitus), and lifestyle factors (smoking/alcohol use) collected via questionnaires. Fasting blood samples quantified triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C). Propensity score matching (PSM) balanced baseline characteristics between lipid-level groups. Pre-PSM analyses revealed significant intergroup differences: TG groups differed in sex, BMI, alcohol use, hypertension, and self-rated health ( P<0.05); TC groups in sex, age, occupation, diabetes, and hypertension; LDL-C groups in sex, age, occupation, diabetes, hypertension, and daily living ability; HDL-C groups in sex, age, education, occupation, BMI, smoking, diabetes, and hypertension. Post-PSM adjustment eliminated baseline differences ( P>0.05). Multivariable logistic regression adjusted for demographic, clinical, and lifestyle factors demonstrated that elevated TG levels conferred a 48% reduced risk of cognitive dysfunction [ OR (95% CI): 0.52 (0.29-0.94)], whereas TC, LDL-C, and HDL-C showed no significant associations (all P>0.05). These findings suggest an inverse association between higher triglyceride levels and cognitive dysfunction risk in older adults, highlighting TG′s potential protective role in cognitive health.

Objective:To explore the application value of radiomics, deep learning, and their combined models in predicting the efficacy of radioiodine adjuvant therapy in patients with papillary thyroid cancer (PTC).Methods:A retrospective analysis was conducted on the clinical and imaging data of 131 PTC patients (38 males, 93 females; age 41(33, 48) years) who received first 131I treatment at the Affiliated Hospital of Guilin Medical University from January 2018 to March 2023. Patients were randomly divided into a training set ( n=105) and a test set ( n=26) at the ratio of 8∶2. Multivariate logistic regression analysis was used to screen clinical features to determine independent predictors affecting the efficacy of 131I therapy. Radiomics and deep learning features were extracted from the enhanced CT scans and were combined by using the extremely randomized trees (ExtraTrees) algorithm to construct radiomics, deep learning, and combined models. The predictive abilities of the models were evaluated by AUC, and the Delong test was applied to compare the difference between AUCs. Results:Higher pre-ablation stimulated thyroglobulin (ps-Tg) levels (odds ratio( OR)=1.060, 95% CI: 1.025-1.095, P=0.004) and bilateral lesions ( OR=5.085, 95% CI: 1.452-17.814, P=0.033) were independent predictors of the efficacy of 131I therapy in intermediate to high-risk PTC patients. In the training set, the radiomics model (AUC=0.853) and combined model (AUC=0.880) significantly outperformed the deep learning model (AUC=0.711; Z values: 2.48, 3.09, P values: 0.013, 0.002), while there was no statistically significant difference between the radiomics and combined models ( Z=0.51, P=0.610). In the test set, AUCs of the radiomics, deep learning, and combined models were 0.746, 0.624, and 0.876, respectively, and the AUC of the combined model was higher than that of the radiomics model or deep learning model ( Z values: 2.05, 1.99, P values: 0.040, 0.047). Conclusion:The combined model demonstrates superior performance over the standalone radiomics model and deep learning model in predicting the efficacy of 131I treatment in PTC patients.

Objective To investigate the effect of 3D technology combined with smartphones in problem-based learning(PBL)for neuroendoscopy.Methods 82 trainees who were enrolled from January 2021 to January 2023 were selected as the research subjects.A randomized controlled trial was conducted,and the subjects were divided into a control group and an experimental group.PBL and 3D technology combined with smartphone-assisted PBL were implemented respectively for two groups of students.The data were analyzed using t-test.Teaching satisfac-tion is evaluated by 2 test.Results The results of the in-operation examination and theoretical examination of the ex-perimental group students were found to be higher than those of the control group students(t=8.630,6.087,P＜0.001),the satisfaction scores of students and teachers showing that the satisfaction of the experimental group was higher than that of the control group(x2=4.213,6.301,7.026,P＜0.01).Conclusion In the PBL of neuroendosco-py,the use of 3D technology combined with smart phones as an auxiliary teaching system can effectively improve students'sense of participation,reduce the difficulty of skull base anatomy learning,and improve students'theo-retical and surgical assessment scores and teaching satisfaction.

Objective In the clinical teaching of neurosurgery,the traditional Problem-Based Learning(PBL)teaching model faces systematic challenges such as the disconnection between the training cycle of specialized talents and technological iteration,the limitation of practical opportunities due to hospital infections,and the inefficient allocation of teaching resources.It provides a new path for teaching reform based on the deep integration of Artificial Intelligence and PBL,but it still needs to address issues such as differences in intern participation,insufficient technical adaptability of instructors,and fragmented resource allocation.Based on these problems,a collaborative mechanism of"technology development-talent cultivation"and a multi-dimensional optimization path of"intern participation-instructor training-hospital resource input"are proposed.On this basis,through collaborative strategies such as strengthening the incentive mechanism for autonomous learning,establishing a standardized instructor training system,and building a dynamic resource allocation platform,the management of neurosurgery clinical teaching is promoted towards intelligence,personalization,and systematization.

Objective To explore the differences in efficacy and safety of drug selection based on pharmacogenomics and evidence-based medicine for treatment-resistant schizophrenia(TRS).Methods A total of 100 patients with TRS in our hospital from January 2023 to October 2023 were divided into observation group(gene-oriented antipsychotic drug selection group,22 males and 28 females)and control group(evidence-based medicine oriented antipsychotic drug selection group,23 males and 27 females).Oral mucosal epithelial cells of the observation group were noninvasive collected with a sampling brush and antipsychotic drug gene detection was performed.Antipsychotic drugs with normal metabolism,good response and little toxic side effects were selected according to the test results,and the drugs of the control group were selected by the designated physician on the basis of the Chinese Guidelines for the Prevention and Treatment of Schizophrenia,2015 revision.Antipsychotic efficacy was evaluated before treatment and 4 weeks,8 weeks after treatment with positive and negative syndrome scale(PANSS).Treatment emergent symptom scale(TESS)was used to assess adverse reactions at the 4th and 8th weekend after treatment.Results After 8 weeks of treatment,the incidence of adverse reactions in central nervous system,autonomic nervous system,endocrine system,circulatory system and digestive system in the control group was higher than that in the observation group.The difference was remarkable.The scores of positive symptoms,negative symptoms and general psychopathological symptoms between the observation group and the control group at baseline were basically the same(P>0.05).After 4 weeks of treatment,the scores of positive symptoms,negative symptoms and general psychopathological symptoms in the observation group were lower than those in the control group.The difference was remarkable.After 8 weeks of treatment,the scores of positive symptoms,negative symptoms and general psychopathological symptoms in the observation group were lower than those in the control group.The difference was remarkable.At the end of the 8th week after treatment,the effective rate of the observation group was higher than that of the control group,the difference was remarkable(44%vs.24%,P=0.035).Two-factor repeated measurement analysis of variance was used,indicating that PANSS scores of the two groups changed with time at baseline,4 weeks and 8 weeks after treatment,and the difference was remarkable(F-time=697.139,P<0.05);The difference of PANSS among groups was remarkable(F-groups=5.398,P<0.05);PANSS score was different with different treatment methods,and the difference was remarkable(F-interaction=3.008,P<0.05).Conclusion Gene-directed antipsychotic selection maybe is superior to evidence-based antipsychotic selection in improving effective rate and reducing adverse drug reactions.

Objective To understand the research hotspots and research trends about convolutional neural networks in the field of oncology imaging diagnosis by analyzing the characteristics of published literature at home and abroad over the past decade. Methods The SCI-E database was used as the data source to retrieve literature about convolutional neural networks in the field of oncology imaging diagnosis published from 2012 to 2022. The distribution characteristics of countries, institutions, journals, co-cited authors, and keywords of the studies were analyzed by CiteSpace software. Results A total of 1088 papers were eventually included, and they were mostly from China, the United States, and India. A total of 39 papers were published by Sun Yat-sen University, the research institution with the highest number of publications. Radiology Nuclear Medicine Medical Imaging was the journal with the highest number of publications. A total of 25 high-frequency keywords and 15 burst keywords were obtained. The formation of 12 author co-citation clusters such as image segmentation and lung nodule, as well as 11 keyword clusters such as automatic segmentation and breast cancer, was observed. Conclusion Current research on convolutional neural networks for oncology imaging diagnosis focuses on oncology segmentation, lung-nodule recognition, assisted diagnosis of breast cancer, and other high-frequency oncology.