AIM: To explore the factors influencing the efficacy of diabetic macular edema(DME)with conbercept and to construct a nomogram prediction model. METHODS:Retrospective analysis. Patients with DME admitted from January 2021 to October 2025 were selected. All patients received intravitreal conbercept injection once monthly for three consecutive months. Subjects were divided into a good response group and a poor response group according to the reduction in central macular thickness(CMT)at 3 mo after treatment. The clinical data of the patients were collected to analyze factors associated with poor response to conbercept therapy in patients with DME, and to construct a nomogram prediction model for identifying poor responders to conbercept treatment among DME patients.RESULTS:A total of 400 DME patients(400 eyes)were included in this study. The response group consisted of 312 patients(312 eyes), with 200 males and 112 females, with an average age of 64.85±5.13 y. The non-response group consisted of 88 patients(88 eyes), with 54 males and 34 females, with an average age of 65.14±4.93 y. There were no differences between the two groups in terms of gender, age, alcohol consumption, smoking status, duration of diabetes, edema classification, disease classification, low-density lipoprotein, HRF, ELM and/or EZ continuity interruption, and SRF(all P>0.05). However, there were differences in DR disease duration, pre-treatment BCVA, high-density lipoprotein, glycated hemoglobin, DRIL, and VMIA(all P<0.05). Multivariate Logistic regression analysis showed that DR disease duration ≥1 y(OR=2.351, 95%CI: 1.360-4.065), poor pre-treatment BCVA(OR=3.432, 95%CI: 1.581-6.988), high glycated hemoglobin(OR=1.656, 95%CI: 1.346-2.038), presence of DRIL(OR=3.115, 95%CI: 1.532-6.333), and presence of VMIA(OR=2.526, 95%CI: 1.445-4.415)were all risk factors for poor response to conbercept treatment in DME patients, while elevated high-density lipoprotein(OR=0.025, 95%CI: 0.004-0.163)was a protective factor for poor response to conbercept treatment in DME patients(P<0.05). ROC analysis showed that the area under the curve(AUC)of the nomogram model for predicting poor response to conbercept treatment in DME patients was 0.817(95%CI: 0.776-0.854), with a sensitivity of 73.86% and a specificity of 78.21%. When the threshold probability was >3%, the nomogram model for predicting poor response to conbercept treatment in DME patients was more favorable for all patient treatment plans. CONCLUSION:The nomogram prediction model constructed in this study is helpful for the clinical early identification of patients with DME who have a high risk of poor response to conbercept treatment, and can provide a reference for the early prevention of poor response to conbercept treatment.

Alzheimer's disease(AD), a neurodegenerative disorder associated with aging, is the most prevalent form of dementia.As the aging population continues to expand, AD presents significant health and caregiving challenges for families and society, making it a pressing international public health concern.In recent years, numerous countries have implemented dementia prevention and treatment strategies that emphasize community-based comprehensive approaches.Currently, the community-based AD prevention and treatment model in China is still in the exploratory phase, with community efforts lacking organization.In alignment with China's action plan for advancing dementia prevention and treatment, and to achieve the strategic objective of "healthy aging, " this consensus is based on the principle of three-level prevention and is tailored to the characteristics of AD disease progression.It aims to develop a comprehensive prevention and treatment strategy for AD that is suitable for communities in China, providing technical guidance and support to establish a scientific basis for formulating community AD prevention and treatment models.

Alzheimer's disease(AD), a neurodegenerative disorder associated with aging, is the most prevalent form of dementia.As the aging population continues to expand, AD presents significant health and caregiving challenges for families and society, making it a pressing international public health concern.In recent years, numerous countries have implemented dementia prevention and treatment strategies that emphasize community-based comprehensive approaches.Currently, the community-based AD prevention and treatment model in China is still in the exploratory phase, with community efforts lacking organization.In alignment with China's action plan for advancing dementia prevention and treatment, and to achieve the strategic objective of "healthy aging, " this consensus is based on the principle of three-level prevention and is tailored to the characteristics of AD disease progression.It aims to develop a comprehensive prevention and treatment strategy for AD that is suitable for communities in China, providing technical guidance and support to establish a scientific basis for formulating community AD prevention and treatment models.

Objective:To explore the speech features of female patients with anhedonic depression and their recognition of pleasure deficient symptoms.Methods:A total of 102 female depression patients who were hospitalized at Nanjing Brain Hospital from September 2020 to October 2021 were selected, including 62 anhedonic depression patients (anhedonic group) and 40 non-anhedonic depression patients (non-anhedonic group). A total of 50 female healthy controls were recruited during the same period.All participants were evaluated by the 17-item Hamilton depression scale (HAMD-17), Snaith-Hamilton pleasure scale (SHAPS), and the temporal experience of pleasure scale (TEPS), as well as voice acquisition.SPSS 23.0 software was used for data processing.Statistical analysis was conducted using one-way ANOVA, non-parametric tests, Logistic regression, and receiver operating characteristic curve.Results:Compared with the non-anhedonic group, the anhedonic group showed significant changes in 15 voice features(all P<0.05), including Mel-frequency cepstral coefficients, formant frequencies, intensity, and energy features.Among these features, Mel-frequency cepstral coefficients exhibited the highest accuracy in identifying anhedonic depression, with sensitivity of 47.5%, specificity of 91.9%, area under curve (AUC) of 0.751, 95% CI=0.686-0.866.Formant frequencies could identify female anhedonic depression, with a sensitivity of 90.0%, a specificity of 40.3%, an AUC of 0.647, and 95% CI=0.605-0.824.Energy features could identify anhedonic deficient depression, with a sensitivity of 60.0%, a specificity of 74.2%, an AUC of 0.679, and 95% CI=0.587-0.804.Intensity features could identify female anhedonic depression, with a sensitivity of 70.0%, a specificity of 58.1%, an AUC of 0.640, and 95% CI=0.554-0.769. Conclusion:Mel-frequency cepstral coefficients, formant frequencies, intensity features, and energy features may have specific changes in female patients with anhedonic depression.The Mel-frequency cepstral coefficients has the highest recognition accuracy for anhedonic symptoms in female depression patients, and is expected to become an objective evaluation index for female anhedonic depression.

Objective:Construction of deep learning classification models based on functional magnetic resonance imaging (fMRI) data assists the clinicians to achieve better diagnosis of bipolar disorder (BD), which can improve the recognition rate of BD by identifying the critical imaging features.Methods:A total of 146 patients who met the diagnosis criteria of BD according to DSM-Ⅳ and 234 healthy control (HC) were recruited for fMRI scans. Regional homogeneity (ReHo) and amplitude of low frequency fluctuation (ALFF) were used to analyze fMRI data. Based on ReHo and ALFF, the classification models were constructed by deeping neural network (DNN) and dual-channel convolution neural network (DCNN) respectively, and the best classification model was developed by comparing the accuracy and area under curve (AUC) of the two models. Based on each brain region divided by anatomical automatic labeling (AAL), the support vector machine (SVM) classification model was constructed using imaging index with a better performance, and the critical imaging features were identified by comparing the accuracy of each brain region.Results:The performances of the DCNN classification model (accuracy = 75.3%, and 72.6%, respectively, based on ReHo and ALFF) were significantly better than the DNN classification model (accuracy = 67.1%, and 65.1%, respectively). Meanwhile, the accuracy of classification model constructed using ReHo was higher than ALFF. Based on the SVM classification model, critical brain regions were identified above the accuracy of 65.0%, including the occipital lobe (middle occipital gyrus, superior occipital gyrus and lingual gyrus), hippocampus, and thalamus.Conclusion:The computational model based on DCNN using ReHo can help the clinicians to achieve better diagnosis of BD. Furthermore, occipital lobe, hippocampus and thalamus may be the critical imaging features for the auxiliary recognition of BD.

Objective:Construction of deep learning classification models based on functional magnetic resonance imaging (fMRI) data assists the clinicians to achieve better diagnosis of bipolar disorder (BD), which can improve the recognition rate of BD by identifying the critical imaging features.Methods:A total of 146 patients who met the diagnosis criteria of BD according to DSM-Ⅳ and 234 healthy control (HC) were recruited for fMRI scans. Regional homogeneity (ReHo) and amplitude of low frequency fluctuation (ALFF) were used to analyze fMRI data. Based on ReHo and ALFF, the classification models were constructed by deeping neural network (DNN) and dual-channel convolution neural network (DCNN) respectively, and the best classification model was developed by comparing the accuracy and area under curve (AUC) of the two models. Based on each brain region divided by anatomical automatic labeling (AAL), the support vector machine (SVM) classification model was constructed using imaging index with a better performance, and the critical imaging features were identified by comparing the accuracy of each brain region.Results:The performances of the DCNN classification model (accuracy = 75.3%, and 72.6%, respectively, based on ReHo and ALFF) were significantly better than the DNN classification model (accuracy = 67.1%, and 65.1%, respectively). Meanwhile, the accuracy of classification model constructed using ReHo was higher than ALFF. Based on the SVM classification model, critical brain regions were identified above the accuracy of 65.0%, including the occipital lobe (middle occipital gyrus, superior occipital gyrus and lingual gyrus), hippocampus, and thalamus.Conclusion:The computational model based on DCNN using ReHo can help the clinicians to achieve better diagnosis of BD. Furthermore, occipital lobe, hippocampus and thalamus may be the critical imaging features for the auxiliary recognition of BD.

Objective: To analyze the correlations between intraoperative ultrasound and MRI metrics of the spinal cord in degenerative cervical myelopathy and identify novel potential predictive ultrasonic indicators of neurological recovery for degenerative cervical myelopathy. Materials and Methods: Twenty-two patients who underwent French-door laminoplasty for multilevel degenerative cervical myelopathy were followed up for 12 months. The Japanese Orthopedic Association (JOA) scores were assessed preoperatively and 12 months postoperatively. Maximum spinal cord compression and compression rates were measured and calculated using both intraoperative ultrasound imaging and preoperative T2-weight (T2W) MRI. Signal change rates of the spinal cord on preoperative T2W MRI and gray value ratios of dorsal and ventral spinal cord hyperechogenicity on intraoperative ultrasound imaging were measured and calculated. Correlations between intraoperative ultrasound metrics, MRI metrics, and the recovery rate JOA scores were analyzed using Spearman correlation analysis. Results: The postoperative JOA scores improved significantly, with a mean recovery rate of 65.0 ± 20.3% (p < 0.001). No significant correlations were found between the operative ultrasound metrics and MRI metrics. The gray value ratios of the spinal cord hyperechogenicity was negatively correlated with the recovery rate of JOA scores (ρ = -0.638, p = 0.001), while the ventral and dorsal gray value ratios of spinal cord hyperechogenicity were negatively correlated with the recovery rate of JOA-motor scores (ρ = -0.582, p = 0.004) and JOA-sensory scores (ρ = -0.452, p = 0.035), respectively. The dorsal gray value ratio was significantly higher than the ventral gray value ratio (p < 0.001), while the recovery rate of JOA-motor scores was better than that of JOA-sensory scores at 12 months post-surgery (p = 0.028). Conclusion For degenerative cervical myelopathy, the correlations between intraoperative ultrasound and preoperative T2W MRI metrics were not significant. Gray value ratios of the spinal cord hyperechogenicity and dorsal and ventral spinal cord hyperechogenicity were significantly correlated with neurological recovery at 12 months postoperatively.

Objective: To analyze the correlations between intraoperative ultrasound and MRI metrics of the spinal cord in degenerative cervical myelopathy and identify novel potential predictive ultrasonic indicators of neurological recovery for degenerative cervical myelopathy. Materials and Methods: Twenty-two patients who underwent French-door laminoplasty for multilevel degenerative cervical myelopathy were followed up for 12 months. The Japanese Orthopedic Association (JOA) scores were assessed preoperatively and 12 months postoperatively. Maximum spinal cord compression and compression rates were measured and calculated using both intraoperative ultrasound imaging and preoperative T2-weight (T2W) MRI. Signal change rates of the spinal cord on preoperative T2W MRI and gray value ratios of dorsal and ventral spinal cord hyperechogenicity on intraoperative ultrasound imaging were measured and calculated. Correlations between intraoperative ultrasound metrics, MRI metrics, and the recovery rate JOA scores were analyzed using Spearman correlation analysis. Results: The postoperative JOA scores improved significantly, with a mean recovery rate of 65.0 ± 20.3% (p < 0.001). No significant correlations were found between the operative ultrasound metrics and MRI metrics. The gray value ratios of the spinal cord hyperechogenicity was negatively correlated with the recovery rate of JOA scores (ρ = -0.638, p = 0.001), while the ventral and dorsal gray value ratios of spinal cord hyperechogenicity were negatively correlated with the recovery rate of JOA-motor scores (ρ = -0.582, p = 0.004) and JOA-sensory scores (ρ = -0.452, p = 0.035), respectively. The dorsal gray value ratio was significantly higher than the ventral gray value ratio (p < 0.001), while the recovery rate of JOA-motor scores was better than that of JOA-sensory scores at 12 months post-surgery (p = 0.028). Conclusion For degenerative cervical myelopathy, the correlations between intraoperative ultrasound and preoperative T2W MRI metrics were not significant. Gray value ratios of the spinal cord hyperechogenicity and dorsal and ventral spinal cord hyperechogenicity were significantly correlated with neurological recovery at 12 months postoperatively.

Objective To analyze the etiologies of severe vitreous hemorrhage in venerable age patients.Methods A retrospective series case study was adopted.Medical records of 30 cases 30 eyes severe vitreous hemorrhage venerable age patients were analyzed in Xuanwu Hospital Capital Medical University,Peking University Third Hospital and Beijing Tsinghua Chang Gung Hospital from July in 1999 to June in 2015.Visual acuity,intraocular pressure,anterior segment slit lamp microscopy,mydriasis funduscopy and ophthalmic B type ultrasound examination were used to evaluate 30 cases 30 eyes.Thirty eyes were all treated with pars plans vitrectomy (PPV).Results Mydriasis funduscopy examination showed that vitreous hemorrhage was so dense in 30 eyes that the fundus cannot be observed.The initial visual acuity examination showed that 8 eyes were count finger,5 eyes were hand move,16 eyes were light perception,and 1 eye was no light perception.There were dense light spots or clusters of high echoes in vitreous cavities,complete/incomplete posterior vitreous detachment (PVD) of all eyes;retinal detachment with different degrees in 5 eyes;proliferating cord adhesion to the wall of eyeball in 3 eyes.The etiologies included retinal vein occlusion (RVO) in 10 eyes (33.33％),retinal tear or retinal detachment in 7 eyes (23.33％),polypoidal choroidal vasculopathy (PCV) in 7 eyes (23.33％),proliferative diabetic retinopathy (PDR) in 4 eyes (13.33％).The above 4 etiologies were in 28 eyes,accounting for 93.33％.The others were ocular trauma in 2 eyes (6.67％).Conclusions The main cause to severe vitreous hemorrhage venerable age patients is RVO,followed by retinal tear or retinal detachment,PCV and PDR.Ocular trauma is rare.Vitrectomy is a diagnostic treatment,which can remove vitreous hemorrhage,improve the visual acuity of the patients,and determine the etiologies of the vitreous hemorrhage.

Objective To investigate the predictive value of plasma homocysteine (Hcy) on contrast-induced nephropathy after percutaneous coronary intervention (PCI) in patients with acute myocardial infarction (AMI) .Methods Totally 156 patients with AMI receiving PCI in Beijing Electric Power Hospital from January 2014 to December 2015 were enrolled for the study and divided into contrast-induced nephropathy group and non-contrast-induced nephropathy group .Baseline data ,perioperative data and auxilia-ry examination results were compared between two groups .The single factor analysis and multivariate Logistic regression were used to analyze the influencing factors .Results In all 156 patients ,32 cases occurred contrast-induced nephropathy after PCI ,and the in-cidence was 20 .5% .The plasma Hcy level in contrast-induced nephropathy group was (21 .3 ± 8 .7)μmol/L ,significantly higher than (13 .3 ± 6 .1) μmol/L in non-contrast-induced nephropathy group (P< 0 .05) .Multiple Logistic regression analysis results showed that plasma Hcy levels was the independent risk factors of contrast-induced nephropathy (OR=2 .254 ,95% CI:1 .359 -3 .737 ,P=0 .002) .Conclusion About 1/5 of patients with AMI occur contrast-induced nephropathy after PCI ,and preoperative plasma Hcy level can well predict the risk of contrast-induced nephropathy ,which is worthy of clinical application .