A method that integrates the features of image patches from corneal lesions and conjunctival congestion-like complications is proposed to address the limitations of manual keratitis diagnosis(i.e.,time-consuming,laborious,high subjectivity)and the generally low accuracy of automatic keratitis diagnosis based on original slit-lamp images.Specifically,samples are acquired from the corneal and conjunctival regions.A cost-sensitive convolutional neural network is then used to extract and concatenate the high-level features of these image patches.After dimensionality reduction through principal component analysis,the processed features are input into the fully connected layer for classification.Trained and evaluated on 6414 slit-lamp images collected from Ningbo Eye Hospital,the proposed method achieves accuracies of 97.8%,98.6%,and 97.0%for keratitis,normal cornea,and other abnormal corneas,respectively.This method effectively integrates relevant features and provides a feasible solution for high-accuracy keratitis diagnosis.

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.

Objective:Using resting-state functional magnetic resonance imaging (rs-fMRI), we explored the changes in dynamic functional network connections (dFNC) in the brains of patients with first-episode schizophrenia (SZ) and evaluated the potential clinical value of dFNC changes in combination with a machine learning model.Methods:Clinical data of 50 patients with schizophrenia (schizophrenia group), 29 males and 21 females, aged 18-47 (28.3±7.2) years, who attended the psychiatric department of the Second Affiliated Hospital of Xinxiang Medical College from January 2022 to August 2023, were retrospectively included. In the same period, 50 healthy controls matched for age and education (healthy control group) were recruited, of which 24 were male and 26 were female, aged 18-48 (28.0±6.9) years. The rs-fMRI imaging data were acquired for each subject. The dFNC cluster analysis was performed based on independent component analysis, and the differences between groups with different state FNC matrices were statistically analyzed. The dataset samples were divided into a training set (35 SZ patients and 35 healthy controls) and a validation set (15 SZ patients and 15 healthy controls) in a 7∶3 ratio. A machine learning classification model was constructed based on the dFNC matri. The performance of the model for distinguishing between schizophrenia and healthy controls was assessed by five-fold cross-validation using accuracy (ACC), recall (REC), F1 score, and area under curve (AUC) metrics of the working characteristics of the subjects.Results:Five network functional connectivity states were obtained by dFNC cluster analysis. Patients with first SZ showed a wide range of high connectivity and low connectivity changes on the neural dynamic functional networks, as shown by increased dynamic connectivity within the visual network (VIS) in state 1 (weak connectivity); The dynamic connectivity between executive control network (ECN) and VIS, frontal parietal network (FPN) and VIS decreases at state 3 (strong connectivity); The dynamic connectivity between default mode network (DMN) and FPN, DMN and ventral attention network (VAN) decreases at state 4 (weak connectivity). The machine learning results show that the classification model constructed by the dFNC matrix combined with SVM in state 3 (strongly connected) in the validation set obtains the best classification results (ACC=0.938; REC=0.938; F1=0.937; AUC=0.984), and the overall average classification ACC of the five states reaches 0.751, and AUC reaches 0.784.Conclusion:Patients with first-episode SZ have some brain functional network connectivity abnormalities, and a machine learning model based on dFNC features has high classification performance in distinguishing first-episode SZ from HC.

Objective To investigate the relationship between sleep disorders and coronary heart disease through big data combined with Mendelian randomization analysis.Methods Data from 2005 to 2018 National Health and Nutrition Examination Survey in the United States were utilized.Logistic regression analysis was employed to evaluate the association between sleep disorders and coronary heart disease,while analyzing relevant influencing factors.A two-sample Mendelian randomization approach was implemented using Genome-Wide Association Studies to establish causal relationships.Results Logistic regression analysis demonstrated a significant association between sleep disorders and coronary heart disease(P＜0.001),with the neutrophil-to-lymphocyte ratio serving as a mediating factor in this relationship(P＜0.001).Mendelian randomization analysis revealed a positive correlation between sleep disorders and coronary heart disease(OR=1.030,95％CI:1.01-1.04).Conclusion Sleep disorders can increase the risk of coronary heart disease by activating inflammatory factors.

Objective To explore the effect of dexmedetomidine nasal spray(DNS)on perioperative sleep quality,anxiety stress in patients undergoing hysterectomy.Methods A total of 75 patients who underwent laparoscopic hysterectomy at Taizhou First People's Hospital from May to December 2024 were selected and divided into control group(37 cases)and experimental group(38 cases)according to the random number table method.Patients in experimental group were respectively sprayed with 50μg or 75pg of DNS at 20:30 on the night before surgery and the night after surgery,patients in control group were respectively given an equal volume of normal saline nasal spray at 20:30 on the night before surgery and the night after surgery.Athens insomnia scale(AIS)scores and sleep quality on the first night of admission(T0),the first night before surgery(T1),the first night after surgery(T2),and the second night after surgery(T3),the scores of hospital anxiety and depression scale(HADS),salivary cortisol andα-amylase on the first day of admission,before entering the operating room,and on the first and second days after surgery,and incidence of adverse reactions 60 minutes after administration between two groups of patients were observed and compared.Results Compared with control group,the AIS scores in experimental group at T1,T2 and T3 were significantly lower,the latency to sleep was significantly shortened,the total sleep time and non-rapid eye movement sleep time were significantly prolonged,and rapid eye movement sleep time was significantly shortened(P＜0.05),number of awakenings at T1 and T2 decreased significantly(P＜0.05),and HADS score,salivary cortisol and α-amylase levels before entering the operating room,and on the first and second days after surgery were all significantly decreased(P＜0.05).There was no statistically significant difference in the incidence of adverse reactions within 60 minutes after administration between two groups of patients(P＞0.05).Conclusion Perioperative administration of DNS can significantly improve the sleep quality of patients undergoing laparoscopic hysterectomy,relieve anxiety,reduce stress levels,and has high safety.

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.

A method that integrates the features of image patches from corneal lesions and conjunctival congestion-like complications is proposed to address the limitations of manual keratitis diagnosis(i.e.,time-consuming,laborious,high subjectivity)and the generally low accuracy of automatic keratitis diagnosis based on original slit-lamp images.Specifically,samples are acquired from the corneal and conjunctival regions.A cost-sensitive convolutional neural network is then used to extract and concatenate the high-level features of these image patches.After dimensionality reduction through principal component analysis,the processed features are input into the fully connected layer for classification.Trained and evaluated on 6414 slit-lamp images collected from Ningbo Eye Hospital,the proposed method achieves accuracies of 97.8%,98.6%,and 97.0%for keratitis,normal cornea,and other abnormal corneas,respectively.This method effectively integrates relevant features and provides a feasible solution for high-accuracy keratitis diagnosis.

Objective To investigate the relationship between sleep disorders and coronary heart disease through big data combined with Mendelian randomization analysis.Methods Data from 2005 to 2018 National Health and Nutrition Examination Survey in the United States were utilized.Logistic regression analysis was employed to evaluate the association between sleep disorders and coronary heart disease,while analyzing relevant influencing factors.A two-sample Mendelian randomization approach was implemented using Genome-Wide Association Studies to establish causal relationships.Results Logistic regression analysis demonstrated a significant association between sleep disorders and coronary heart disease(P＜0.001),with the neutrophil-to-lymphocyte ratio serving as a mediating factor in this relationship(P＜0.001).Mendelian randomization analysis revealed a positive correlation between sleep disorders and coronary heart disease(OR=1.030,95％CI:1.01-1.04).Conclusion Sleep disorders can increase the risk of coronary heart disease by activating inflammatory factors.

Objective To explore the effect of dexmedetomidine nasal spray(DNS)on perioperative sleep quality,anxiety stress in patients undergoing hysterectomy.Methods A total of 75 patients who underwent laparoscopic hysterectomy at Taizhou First People's Hospital from May to December 2024 were selected and divided into control group(37 cases)and experimental group(38 cases)according to the random number table method.Patients in experimental group were respectively sprayed with 50μg or 75pg of DNS at 20:30 on the night before surgery and the night after surgery,patients in control group were respectively given an equal volume of normal saline nasal spray at 20:30 on the night before surgery and the night after surgery.Athens insomnia scale(AIS)scores and sleep quality on the first night of admission(T0),the first night before surgery(T1),the first night after surgery(T2),and the second night after surgery(T3),the scores of hospital anxiety and depression scale(HADS),salivary cortisol andα-amylase on the first day of admission,before entering the operating room,and on the first and second days after surgery,and incidence of adverse reactions 60 minutes after administration between two groups of patients were observed and compared.Results Compared with control group,the AIS scores in experimental group at T1,T2 and T3 were significantly lower,the latency to sleep was significantly shortened,the total sleep time and non-rapid eye movement sleep time were significantly prolonged,and rapid eye movement sleep time was significantly shortened(P＜0.05),number of awakenings at T1 and T2 decreased significantly(P＜0.05),and HADS score,salivary cortisol and α-amylase levels before entering the operating room,and on the first and second days after surgery were all significantly decreased(P＜0.05).There was no statistically significant difference in the incidence of adverse reactions within 60 minutes after administration between two groups of patients(P＞0.05).Conclusion Perioperative administration of DNS can significantly improve the sleep quality of patients undergoing laparoscopic hysterectomy,relieve anxiety,reduce stress levels,and has high safety.

Objective:Using resting-state functional magnetic resonance imaging (rs-fMRI), we explored the changes in dynamic functional network connections (dFNC) in the brains of patients with first-episode schizophrenia (SZ) and evaluated the potential clinical value of dFNC changes in combination with a machine learning model.Methods:Clinical data of 50 patients with schizophrenia (schizophrenia group), 29 males and 21 females, aged 18-47 (28.3±7.2) years, who attended the psychiatric department of the Second Affiliated Hospital of Xinxiang Medical College from January 2022 to August 2023, were retrospectively included. In the same period, 50 healthy controls matched for age and education (healthy control group) were recruited, of which 24 were male and 26 were female, aged 18-48 (28.0±6.9) years. The rs-fMRI imaging data were acquired for each subject. The dFNC cluster analysis was performed based on independent component analysis, and the differences between groups with different state FNC matrices were statistically analyzed. The dataset samples were divided into a training set (35 SZ patients and 35 healthy controls) and a validation set (15 SZ patients and 15 healthy controls) in a 7∶3 ratio. A machine learning classification model was constructed based on the dFNC matri. The performance of the model for distinguishing between schizophrenia and healthy controls was assessed by five-fold cross-validation using accuracy (ACC), recall (REC), F1 score, and area under curve (AUC) metrics of the working characteristics of the subjects.Results:Five network functional connectivity states were obtained by dFNC cluster analysis. Patients with first SZ showed a wide range of high connectivity and low connectivity changes on the neural dynamic functional networks, as shown by increased dynamic connectivity within the visual network (VIS) in state 1 (weak connectivity); The dynamic connectivity between executive control network (ECN) and VIS, frontal parietal network (FPN) and VIS decreases at state 3 (strong connectivity); The dynamic connectivity between default mode network (DMN) and FPN, DMN and ventral attention network (VAN) decreases at state 4 (weak connectivity). The machine learning results show that the classification model constructed by the dFNC matrix combined with SVM in state 3 (strongly connected) in the validation set obtains the best classification results (ACC=0.938; REC=0.938; F1=0.937; AUC=0.984), and the overall average classification ACC of the five states reaches 0.751, and AUC reaches 0.784.Conclusion:Patients with first-episode SZ have some brain functional network connectivity abnormalities, and a machine learning model based on dFNC features has high classification performance in distinguishing first-episode SZ from HC.