Objective To explore the effectiveness of the peripheral arterial calcification scoring system(PACSS)based on computed tomography angiography(CTA)in assessing renal function in patients with peripheral arterial disease(PAD).Methods The clinical data,CTA imaging data,and laboratory results from PAD patients who underwent lower limb artery CTA examination were retrospectively collected.The PACSS was utilized to score the calcification in both lower limb arteries.Participants were categorized into three groups based on their estimated glomerular filtration rate(eGFR)(normal group:eGFR≥90 mL/min;mild renal dysfunction group:eGFR 60-89 mL/min,and moderate to severe renal dysfunction group:eGFR＜60 mL/min).The demographic data,clinical symptoms,and comorbidities among the three groups were compared by analysis of variance(ANOVA).The Spearman correlation coefficient was employed to evaluate the relationship between eGFR,cystatin C,and PACSS score.Results The age(P＜0.001)and PACSS score(P＜0.05)of patients with renal dysfunction were significantly higher than those of patients with normal renal function.However,there were no significant differences in gender,prevalence of diabetes,hypertension,or severe limb ischemia.Spearman correlation analysis showed that eGFR was negatively correlated with PACSS score(r=-0.18 in the right lower limb,P=0.037,r=-0.24 in the left lower limb,P=0.006).In contrast,cystatin C was positively correlated with PACSS score(r=0.26 in the right lower limb,P＜0.001,r=0.22 in the left lower limb,P=0.002).Conclusion The PACSS score of lower limb artery in PAD patients is corre-lated with the severity of renal dysfunction.This finding may facilitate early warning and clinical intervention for PAD patients with renal dysfunction.

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 explore the clinical value of energy-spectrum CT single-energy imaging in enhancing the image quality and stent display of stent placement CT angiography(CTA)in lower extremity atherosclerotic occlusive disease.Methods Twenty patients[mean(65.61±9.65)years;male/female,16/4]who underwent stenting for chronic occlusive disease of the lower extremity arteries by lower extremity arterial energetic spectral CTA were retrospectively recruited at our hospital.The original images were reconstructed into seven sets of single energy(40-100 keV),120 kVp,virtual unenhanced images(VUE)and metal artifact reduction(MAR)technique images.Images were debossed and then scaffolded for display with volumetric reconstruction(VR),maximum density projection(MIP)and curve planar reconstruction(CPR),and were objectively and subjectively assessed and compared using one-way analysis of variance(ANOVA).Results The 80 keV and MAR images had the highest scores compared to the other reconstruction group images(P＜0.01).Conclusion 80 keV single-energy imaging and de-metallization artifacts MAR improve the image quality of lower extremity arterial stent lumen and structure display;therefore,they have higher diagnostic value for clinicians.

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: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 To explore the effectiveness of the peripheral arterial calcification scoring system(PACSS)based on computed tomography angiography(CTA)in assessing renal function in patients with peripheral arterial disease(PAD).Methods The clinical data,CTA imaging data,and laboratory results from PAD patients who underwent lower limb artery CTA examination were retrospectively collected.The PACSS was utilized to score the calcification in both lower limb arteries.Participants were categorized into three groups based on their estimated glomerular filtration rate(eGFR)(normal group:eGFR≥90 mL/min;mild renal dysfunction group:eGFR 60-89 mL/min,and moderate to severe renal dysfunction group:eGFR＜60 mL/min).The demographic data,clinical symptoms,and comorbidities among the three groups were compared by analysis of variance(ANOVA).The Spearman correlation coefficient was employed to evaluate the relationship between eGFR,cystatin C,and PACSS score.Results The age(P＜0.001)and PACSS score(P＜0.05)of patients with renal dysfunction were significantly higher than those of patients with normal renal function.However,there were no significant differences in gender,prevalence of diabetes,hypertension,or severe limb ischemia.Spearman correlation analysis showed that eGFR was negatively correlated with PACSS score(r=-0.18 in the right lower limb,P=0.037,r=-0.24 in the left lower limb,P=0.006).In contrast,cystatin C was positively correlated with PACSS score(r=0.26 in the right lower limb,P＜0.001,r=0.22 in the left lower limb,P=0.002).Conclusion The PACSS score of lower limb artery in PAD patients is corre-lated with the severity of renal dysfunction.This finding may facilitate early warning and clinical intervention for PAD patients with renal dysfunction.

Objective To explore the clinical value of energy-spectrum CT single-energy imaging in enhancing the image quality and stent display of stent placement CT angiography(CTA)in lower extremity atherosclerotic occlusive disease.Methods Twenty patients[mean(65.61±9.65)years;male/female,16/4]who underwent stenting for chronic occlusive disease of the lower extremity arteries by lower extremity arterial energetic spectral CTA were retrospectively recruited at our hospital.The original images were reconstructed into seven sets of single energy(40-100 keV),120 kVp,virtual unenhanced images(VUE)and metal artifact reduction(MAR)technique images.Images were debossed and then scaffolded for display with volumetric reconstruction(VR),maximum density projection(MIP)and curve planar reconstruction(CPR),and were objectively and subjectively assessed and compared using one-way analysis of variance(ANOVA).Results The 80 keV and MAR images had the highest scores compared to the other reconstruction group images(P＜0.01).Conclusion 80 keV single-energy imaging and de-metallization artifacts MAR improve the image quality of lower extremity arterial stent lumen and structure display;therefore,they have higher diagnostic value for clinicians.

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 changes of abnormal spontaneous brain activity and whole-brain effector connectivity in patients with obsessive-compulsive disorder (OCD) by combining low frequency amplitude (ALFF) and Granger causality analysis (GCA), and explore their relations with clinical symptoms.Methods:Forty-nine patients with OCD admitted to Department of Psychiatry, Second Affiliated Hospital of Xinxiang Medical College from January 2020 to September 2023 were selected as OCD group; 50 healthy volunteers matched with gender, age and years of education were enrolled as healthy control (HC) group. Obsessive-compulsive symptoms and severities in the OCD group were assessed by Yale Brown obsessive-compulsive scale (Y-BOCS). All subjects underwent whole-brain resting-state functional magnetic resonance imaging scanning (rs-fMRI). ALFF differences between the 2 groups were compared. Brain regions with ALFF differences were used as seed points, and effector connectivity changes in seed points were compared with those in whole-brain by GCA. Correlations of ALFF and effector connectivity in brain regions with ALFF differences with total scores, obsession scores and compulsion scores of Y-BOCS were analyzed by partial correlation analysis.Results:(1) Compared with that in the HC group, ALFF was significantly enhanced in the right supplementary motor area, right hippocampus, left caudate nucleus, and right fusiform gyrus, and statistically attenuated in the left suboccipital gyrus in the OCD group ( P<0.05). (2) Compared with that in the HC group, effector connectivity from the right dorsolateral superior frontal gyrus to right supplementary motor area was significantly attenuated, and effector connectivity from the left superior occipital gyrus to right supplementary motor area was significantly enhanced in the OCD group ( P<0.05); compared with that in the HC group, effector connectivity from the right fusiform gyrus to right precentral gyrus was significantly attenuated, and effector connectivity from the right hippocampus to left mesial temporal gyrus was significantly enhanced in the OCD group ( P<0.05). (3) In OCD patients, altered ALFF in the left caudate nucleus was positively correlated with obsession scores ( r=0.357, P=0.027), and altered effector connectivity from the right dorsolateral superior frontal gyrus to right supplementary motor area was negatively correlated with obsession scores ( r=-0.312, P=0.029). Conclusion:Abnormalities in sensorimotor network function are closely related to clinical symptoms in patients with OCD.

Objective:To evaluate the interaction between remimazolam and propofol for sedation during hysteroscopy.Methods:American Society of Anesthesiologists Physical Status classification Ⅰ or Ⅱ patients, aged 20-45 yr, with body mass index of 18-28 kg/m 2, scheduled for elective hysteroscopy, were included. The test was conducted in two steps. Up-and-down sequential allocation was used to determine the median effective dose (ED 50) of remimazolam (group A) and propofol (group B). The ED 50 obtained in A and B groups were then used as the standard to determine the combination regimen in group C (0.25×ED 50 of remimazolam+ 0.75×ED 50 of propofol as the initial dose), in group D (0.5×ED 50 of remimazolam+ 0.5×ED 50 of propofol as the initial dose), and in group E (0.75×ED 50 of remimazolam+ 0.25×ED 50 of propofol as the initial dose). Up-and-down sequential allocation was used to determine the ED 50 of propofol when propofol and remimazolam were combined in C, D and E groups. The interaction between the sedative effects of two drugs was analyzed using the isobolographic analysis method, and the interaction coefficient and synergistic dose ratio of two drugs were calculated. Results:The ED 50 of remimazolam was 0.180 mg/kg in group A, and the ED 50 of propofol was 1.167 mg/kg in group B. The results of isobolographic analysis showed that remimazolam and propofol had a synergistic effect. When remimazolam 0.045, 0.090 and 0.135 mg/kg were combined with propofol 0.546, 0.288 and 0.160 mg/kg, the interaction coefficients were 1.393, 1.339 and 1.127 respectively. The synergistic dosage ratio of remimazolam and propofol was 1.0∶(3.2 to 12.0). Conclusions:Remimazolam and propofol have a synergistic effect on sedation when used for hysteroscopy, and the dose ratio is 1.0∶(3.2-12.0).