AIM:To automatically identify and quantitatively assess myopia-related fundus structural changes by combining non-mydriatic color fundus photography with an artificial intelligence(AI)-powered quantitative fundus analysis system and to further analyze the correlations between these fundus parameters and spherical equivalent(SE), axial length(AL), and age, providing the objective basis for monitoring myopia progression and supporting the formulation of personalized myopia prevention and control strategies. METHODS:A cross-sectional study was conducted enrolling myopic patients aged 18-50 y who underwent myopia screening from March 2023 to December 2023. Patients were stratified into three groups based on SE: the -3.00 D

The study aims to address the issues of class imbalance in sleep EEG data and gradient vanishing or explosion phenomena that may occur when deep networks extract more features.An improved adaptive synthetic sampling technique is firstly employed to perform data augmentation on the minority classes of sleep EEG data.Subsequently,convolutional neural networks and residual networks are utilized to learn data features,while a 3-layer bidirectional gated recurrent network is applied to explore deep temporal information and establish correlations between different sleep stages,enabling automatic feature learning and sleep cycle extraction.Finally,a multi-head self-attention mechanism is adopted to enhance the model's focus on critical parts of the sequence,thereby completing the classification of various sleep stages.Experimental results show that according to the AASM sleep staging criteria,the automatic sleep staging model integrating CNN-BiGRU and multi-head self attention achieves an overall accuracy of 90.77%and a Kappa coefficient of 0.88 on the Sleep-EDF-20 dataset after data class balancing,with the precision of N1 stage reaching 87.1%.On the Sleep-EDFx dataset,the model attains an MF1 score of 0.84 while maintaining a precision of 77.2%for N1 stage classification.These metrics demonstrate significant improvements in performance as compared with CNN-BiGRU model tested on the original dataset.When benchmarked against other related studies,the proposed architecture exhibits superior sleep stage classification accuracy.These findings collectively validate the effectiveness and generalization capability of the proposed method.

Objective To analyze the relationships between color Doppler ultrasound parameters and insulin sensitivity index(ISI)and clinical efficacy in rats with polycystic ovary syndrome(PCOS)combined with hyperinsulinemia(HI).Methods A total of 140 3-week-old female SD SPF-grade rats were divided randomly into a PCOS without HI model(control group,n=70)and a PCOS combined with HI model(study group,n=70).After successful modeling,we used color Doppler ultrasound to detect the physical indicators,hemodynamic indicators,and ultrasound features of rat ovaries,and draw venous blood to evaluate ISI.Rats in the study group were treated with metformin by intragastric administration.The color Doppler ultrasound parameters of the good-effect and the poor-effect group were compared and a receiver operating characteristic curve(ROC)was drawn to analyze the value of the color Doppler ultrasound parameters for evaluating the curative effect of metformin in rats with PCOS combined with HI.Results The total ovarian area(TA),ovarian volume(OV),ovarian interstitial area(SA),vascularization index(VI),blood flow index(FI),fasting insulin(FINS),and fasting blood glucose(BFG)of the research group were all greater than those of the control group,while the resistance index,pulsatility index and ISI were observed significantly lower compared with contrast,there were obvious difference(P＜0.05).The color Doppler ultrasound parameters TA and SA were negatively correlated with ISI(r=-0.501,r=-0.492,respectively,P＜0.05),and ovarian RI and PI were positively correlated with ISI(r=0.504,r=0.485,respectively,P＜0.05).TA,OV,SA,VI,FI,VFI,PSV,PDV,low-density lipoprotein cholesterol,FPG,FINS,LH,and FSH were all significantly lower while RI and PI were significantly higher in the good-curative-effect group compared with the poor-curative-effect group(all P＜0.05).According to ROC curve analysis,the sensitivity and specificity of color Doppler ultrasound parameters combined with clinical efficacy were 90.9％and 90.2％,respectively,and the area under the curve(AUC)was 0.901.Conclusions Color Doppler ultrasound parameters are closely related to ISI and therapeutic efficacy in rats with PCOS combined with HI,and may thus predict clinical efficacy in patients with these conditions.

Objective To analyze the relationships between color Doppler ultrasound parameters and insulin sensitivity index(ISI)and clinical efficacy in rats with polycystic ovary syndrome(PCOS)combined with hyperinsulinemia(HI).Methods A total of 140 3-week-old female SD SPF-grade rats were divided randomly into a PCOS without HI model(control group,n=70)and a PCOS combined with HI model(study group,n=70).After successful modeling,we used color Doppler ultrasound to detect the physical indicators,hemodynamic indicators,and ultrasound features of rat ovaries,and draw venous blood to evaluate ISI.Rats in the study group were treated with metformin by intragastric administration.The color Doppler ultrasound parameters of the good-effect and the poor-effect group were compared and a receiver operating characteristic curve(ROC)was drawn to analyze the value of the color Doppler ultrasound parameters for evaluating the curative effect of metformin in rats with PCOS combined with HI.Results The total ovarian area(TA),ovarian volume(OV),ovarian interstitial area(SA),vascularization index(VI),blood flow index(FI),fasting insulin(FINS),and fasting blood glucose(BFG)of the research group were all greater than those of the control group,while the resistance index,pulsatility index and ISI were observed significantly lower compared with contrast,there were obvious difference(P＜0.05).The color Doppler ultrasound parameters TA and SA were negatively correlated with ISI(r=-0.501,r=-0.492,respectively,P＜0.05),and ovarian RI and PI were positively correlated with ISI(r=0.504,r=0.485,respectively,P＜0.05).TA,OV,SA,VI,FI,VFI,PSV,PDV,low-density lipoprotein cholesterol,FPG,FINS,LH,and FSH were all significantly lower while RI and PI were significantly higher in the good-curative-effect group compared with the poor-curative-effect group(all P＜0.05).According to ROC curve analysis,the sensitivity and specificity of color Doppler ultrasound parameters combined with clinical efficacy were 90.9％and 90.2％,respectively,and the area under the curve(AUC)was 0.901.Conclusions Color Doppler ultrasound parameters are closely related to ISI and therapeutic efficacy in rats with PCOS combined with HI,and may thus predict clinical efficacy in patients with these conditions.

Abstract Obesity is characterized by a body weight exceeding a certain range and a corpulent physique, often accompanied by symptoms such as dizziness and fatigue, lack of energy and motivation to speak, reduced physical activity, and shortness of breath. The core pathogenesis of obesity lies in the dysfunction of the spleen and stomach in transporting and transforming nutrients, leading to abnormal metabolism of water and grain essences and the subsequent accumulation of pathological substances such as phlegm-dampness and "Gaozhuo" (greasy-turbid substances). Traditional Chinese Medicine emphasizes that "spleen deficiency is the root cause, while phlegm-dampness and Gaozhuo are the manifestations", forming a dialectical system encompassing theories on spleen-stomach transportation and transformation, phlegm-dampness pathogenesis, Gaozhuo pathogenesis, and constitutional differentiation. Traditional Chinese Medicine plays a crucial role in preventing and treating obesity through interventions such as herbal medicine, external therapies, and dietary adjustments. This article systematically reviews classical Chinese medical texts, focusing on the etiology, pathogenesis, theoretical origins, and prevention and treatment methods of obesity, so as to provide references for modern Traditional Chinese Medicine approaches to obesity prevention and treatment.

Distinguishing pseudoprogression (PsP) from true progression (TP) in patients with high-grade gliomas after treatment remains a critical challenge, as their appearances on conventional MRI are often similar. Accurate differentiation between the two is of great clinical significance for guiding and adjusting therapeutic strategies. MRI functional imaging can reveal key tissue characteristics such as water molecule diffusion, microvascular perfusion, and metabolic changes within lesions after treatment, thereby providing valuable objective evidences for differentiating PsP from TP. Especially in recent years, the radiomics analysis method based on large-scale MRI image data has further improved the accuracy in distinguishing PsP from TP. This article reviews the research progress on applications of various MRI techniques and combination of radiomics with other artificial intelligence methods in differentiating PsP from TP in high-grade gliomas, aiming to enhance the clinicians' understanding of differential diagnosis of PsP and TP in high-grade gliomas, and offer ideas and directions for emerging MRI application in this field.

Objective:To compare the efficacy of body posture combined with pharmacotherapy and pharmacotherapy alone in the treatment of chronic subdural hematoma(CSDH).Methods:Firstly, retrospective case series study was conducted. Thirty cases of CSDH that had received body posture combined with pharmacotherapy at Department of Neurosurgery, Huashan Hospital Affiliated to Fudan University from December 2016 to October 2020 were studied retrospectively. Twenty-seven patients were male, and 3 patients were female. The age of patients ( M(IQR)) was 66(16) years (range:28 to 84). Nineteen patients had unilateral hematoma, and 11 patients had bilateral hematoma. All patients received pharmacotherapy and body posture therapy that was to raise their lower limbs 20 to 30 cm with leg lift pad and get abdominal compressed with customized abdominal belt in supine position. Patients were required to maintain the body posture as much as possible, with the maximum to 16 to 18 hours per day. Patients with unilateral hematoma should tilt the head to the affected side and avoid tilting it to the opposite side. For patients with bilateral hematoma, there was no need for head lateralization. Patient were treated with oral dexamethasone and atorvastatin simultaneously. The preliminary efficacy of body posture combined with pharmacotherapy was determined by hematoma improvement rate which was analyzed by Clopper-Pearson method. Then, the multi-center, prospective, randomized controlled trial had carried out in 9 medical centers from August 2020 to November 2021. The stratified block randomization method was adopted. Patients were randomized in a ratio of 1∶1 to either receive pharmacotherapy alone(the control group) or body posture combined with pharmacotherapy(the experiment group) for 3 months and followed up for 6 months. Effective treatment was defined as complete absorption of hematoma, or the hematoma volume decreased by more than 10 ml and Markwalder grading scale score had improved by more than 1 point compared to the baseline. The efficacy rate and surgery conversion rate at 3 months and recurrence at 6 months were observed. Comparison between groups was performed with paired sample t test, Mann-Whitney U test, χ2 test, corrected χ2 test, or Fisher exact probability method. Logistic regression was used to compare the effective rate and operation rate between the two groups. Results:In the respective study, 30 patients completed follow-up 13 to 353 days after treatment. At the last follow-up, the incidence of almost complete absorption or significantly absorption of hematoma (hematoma volume was significantly reduced accompanied by symptom improvement) was 93.3%. The 95% CI for the incidence that analyzed by the Clopper-Pearson method was 77.9% to 99.2%. One hundred and six patients were enrolled in the multicenter study. Fifty-five patients underwent body posture combined with pharmacotherapy. The age was 74(17) years (range:26 to 92). Thirty-nine patients were males and 16 were females. Fifty-one patients underwent pharmacotherapy alone. The age was 69(12) years (range:48 to 84). Thirty-seven patients were males and 14 were females. The length of body posture recorded in diary card was (15.7±2.3) hours(range:7.6 to 19.3 hours). The efficacy rate in the body posture combined with pharmacotherapy group and pharmacotherapy alone group were 83.6% (46/55) and 56.9% (29/51), respectively at 3 months. The result of the logistic regression analysis showed that the efficacy of body posture combined with pharmacotherapy group was better than that of pharmacotherapy alone group ( OR=3.88,95% CI:1.57 to 9.58, P=0.003). Surgery rate in the body posture combined with pharmacotherapy group and pharmacotherapy alone group were 5.5% (3/55) and 21.6% (11/51) respectively. The result of Logistic regression showed that the pharmacotherapy alone group was more likely to be converted to surgery ( OR=0.21,95% CI:0.05 to 0.80, P=0.023). At the 6 months, no recurrence of cases was found in the body posture combined with pharmacotherapy group. However, the recurrence rate of pharmacotherapy alone group was 6.3% (3/48), there was no significant difference between the two groups ( P>0.05). Conclusion:The effect of body posture combined with pharmacotherapy for chronic subdural hematoma is better than that of pharmacotherapy alone.

An epilepsy prediction model based on two-dimensional convolutional neural network and Cox-Stuart test for non-independent patients is proposed to address the problem of how to effectively predict whether epilepsy patients are going to have an attack or not. After EEG data normalization and EEG signal noise removal using a notch filter and a high-pass filter,the filtered signals are inputted into the two-dimensional convolutional neural network model for feature extraction and classification,and Cox-Stuart test is used to determine whether an early stopping is needed or not,thereby reducing the computational and time complexities of the model. The model is tested under the conditions with pre-seizure periods of 10,30 and 60 min,respectively,and the results show that the model performs best when the pre-seizure period is 10 min. The model has an average accuracy,sensitivity and specificity of 97.70％,97.36％and 98.04％on the test set,demonstrating its superior performance.

An epilepsy prediction model based on two-dimensional convolutional neural network and Cox-Stuart test for non-independent patients is proposed to address the problem of how to effectively predict whether epilepsy patients are going to have an attack or not. After EEG data normalization and EEG signal noise removal using a notch filter and a high-pass filter,the filtered signals are inputted into the two-dimensional convolutional neural network model for feature extraction and classification,and Cox-Stuart test is used to determine whether an early stopping is needed or not,thereby reducing the computational and time complexities of the model. The model is tested under the conditions with pre-seizure periods of 10,30 and 60 min,respectively,and the results show that the model performs best when the pre-seizure period is 10 min. The model has an average accuracy,sensitivity and specificity of 97.70％,97.36％and 98.04％on the test set,demonstrating its superior performance.

The study aims to address the issues of class imbalance in sleep EEG data and gradient vanishing or explosion phenomena that may occur when deep networks extract more features.An improved adaptive synthetic sampling technique is firstly employed to perform data augmentation on the minority classes of sleep EEG data.Subsequently,convolutional neural networks and residual networks are utilized to learn data features,while a 3-layer bidirectional gated recurrent network is applied to explore deep temporal information and establish correlations between different sleep stages,enabling automatic feature learning and sleep cycle extraction.Finally,a multi-head self-attention mechanism is adopted to enhance the model's focus on critical parts of the sequence,thereby completing the classification of various sleep stages.Experimental results show that according to the AASM sleep staging criteria,the automatic sleep staging model integrating CNN-BiGRU and multi-head self attention achieves an overall accuracy of 90.77%and a Kappa coefficient of 0.88 on the Sleep-EDF-20 dataset after data class balancing,with the precision of N1 stage reaching 87.1%.On the Sleep-EDFx dataset,the model attains an MF1 score of 0.84 while maintaining a precision of 77.2%for N1 stage classification.These metrics demonstrate significant improvements in performance as compared with CNN-BiGRU model tested on the original dataset.When benchmarked against other related studies,the proposed architecture exhibits superior sleep stage classification accuracy.These findings collectively validate the effectiveness and generalization capability of the proposed method.