ObjectiveAutism spectrum disorder (ASD) is a neurodevelopmental condition characterized by difficulties with communication and social interaction, restricted and repetitive behaviors. Previous studies have indicated that individuals with ASD exhibit early and lifelong attention deficits, which are closely related to the core symptoms of ASD. Basic visual attention processes may provide a critical foundation for their social communication and interaction abilities. Therefore, this study explores the behavior of children with ASD in capturing attention to changes in topological properties. MethodsOur study recruited twenty-seven ASD children diagnosed by professional clinicians according to DSM-5 and twenty-eight typically developing (TD) age-matched controls. In an attention capture task, we recorded the saccadic behaviors of children with ASD and TD in response to topological change (TC) and non-topological change (nTC) stimuli. Saccadic reaction time (SRT), visual search time (VS), and first fixation dwell time (FFDT) were used as indicators of attentional bias. Pearson correlation tests between the clinical assessment scales and attentional bias were conducted. ResultsThis study found that TD children had significantly faster SRT (P<0.05) and VS (P<0.05) for the TC stimuli compared to the nTC stimuli, while the children with ASD did not exhibit significant differences in either measure (P>0.05). Additionally, ASD children demonstrated significantly less attention towards the TC targets (measured by FFDT), in comparison to TD children (P<0.05). Furthermore, ASD children exhibited a significant negative linear correlation between their attentional bias (measured by VS) and their scores on the compulsive subscale (P<0.05). ConclusionThe results suggest that children with ASD have difficulty shifting their attention to objects with topological changes during change detection. This atypical attention may affect the child’s cognitive and behavioral development, thereby impacting their social communication and interaction. In sum, our findings indicate that difficulties in attentional capture by TC may be a key feature of ASD.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

ObjectiveTo improve the quality standard of Yuanhu Zhitong oral liquid in order to strengthen the quality control of this oral liquid. MethodThin layer chromatography（TLC） was used for the qualitative identification of Corydalis Rhizoma and Angelicae Dahuricae Radix in Yuanhu Zhitong oral liquid by taking tetrahydropalmatine， corydaline reference substances and Corydalis Rhizoma reference medicinal materials as reference， and cyclohexane-trichloromethane-methanol（5∶3∶0.5） as developing solvent， Corydalis Rhizoma was identified using GF254 glass thin layer plate under ultraviolet light（365 nm）. And taking petroleum ether（60-90 ℃） -ether-formic acid（10∶10∶1） as developing solvent， Angelicae Dahuricae Radix was identified using a silica gel G TLC plate under ultraviolet light（305 nm）. High performance liquid chromatography（HPLC） was performed on a Waters XSelect HSS T3 column（4.6 mm×250 mm， 5 μm） with acetonitrile（A）-0.1% glacial acetic acid solution（adjusted pH to 6.1 by triethylamine）（B） as the mobile phase for gradient elution（0-10 min， 20%-30%A； 10-25 min， 30%-40%A； 25-40 min， 40%-50%A； 40-60 min， 50%-60%A）， the detection wavelength was set at 280 nm， then the fingerprint of Yuanhu Zhitong oral liquid was established， and the contents of tetrahydropalmatine and corydaline were determined. ResultIn the thin layer chromatograms， the corresponding spots of Yuanhu Zhitong oral liquid， the reference substances and reference medicinal materials were clear， with good separation and strong specificity. A total of 12 common peaks were identified in 10 batches of Yuanhu Zhitong oral liquid samples， and the peaks of berberine hydrochloride， dehydrocorydaline， glaucine， tetrahydropalmatine and corydaline. The similarities between the 10 batches of samples and the control fingerprint were all >0.90. The results of determination showed that the concentrations of corydaline and tetrahydropalmatine had good linearity with paek area in the range of 0.038 6-0.193 0， 0.034 0-0.170 0 g·L^-1， respectively. The methodological investigation was qualified， and the contents of corydaline and tetrahydropalmatine in 10 batches of Yuanhu Zhitong oral liquid samples were 0.077 5-0.142 9、0.126 1-0.178 2 g·L^-1， respectively. ConclusionThe established TLC， fingerprint and determination are simple， specific and reproducible， which can be used to improve the quality control standard of Yuanhu Zhitong oral liquid.

Ovarian cancer has become the most lethal gynecological tumor due to the difficulty in early diagnosis,the late stage when diagnosed and the high recurrence rate.Resistance to platinum-based anti-tumor chemotherapy drugs is an important reason for the poor prognosis of ovarian cancer.circular RNA(circRNA)is more stable than mRNA in cells due to its special structure,and it is involved in the regulation of the occurrence,development and chemotherapy resistance of a variety of tumors.circRNA can be used as a competing endogenous RNA(ceRNA)of miRNA to bind to proteins,and regulates the phenotypic polarization of macrophages,it can also be used as an exosomal circRNA to regulate the sensitivity of platinum resistance in ovarian cancer.circRNA is expected to be a new marker of platinum resistance and a new target for the treatment of platinum resistance.In order to further explore the relationship between circRNA and platinum resistance in ovarian cancer,this article summarizes the recent literature related to circRNA and platinum resistance in ovarian cancer.

Objective:To investigate the effect of early pulmonary rehabilitation (PR) training on the improvement of respiratory function in patients with acute respiratory distress syndrome (ARDS) after weaning of invasive mechanical ventilation in the intensive care unit (ICU).Methods:The retrospective cohort research method was used. The clinical information of adult patients with ARDS receiving invasive mechanical ventilation admitted to the ICU of Qingdao Municipal Hospital from January 2019 to March 2023 was collected. The patients were divided into a control group and an observation group according to off-line training program. The control group received traditional training after weaning, and the observation group received the early PR training after weaning. Other treatments and nursing were implemented according to the routine of the ICU. The scores of the short physical performance battery (SPPB) on day 3-day 6 of the weaning training, respiratory muscle strength, level of interleukin-6 (IL-6), number of aspirations of sputum after weaning, length of stay after weaning, rehospitalization rate within 6 months after discharge, and pulmonary function indicators at discharge and 3 months after discharge [peak expiratory flow (PEF), forced expiratory volume in one second/forced vital capacity ratio (FEV1/FVC), and vital capacity (VC)] of the two groups of patients were compared. The Kaplan-Meier survival curve was drawn to analyze the cumulative survival rate of patients 6 months after discharge.Results:A total of 50 of which 25 cases received the traditional training after weaning, 25 cases received the early PR training after weaning. There was no significant difference in gender, age, acute physiology and chronic health evaluationⅡ (APACHEⅡ), oxygenation index upon admission, etiological diagnosis of ARDS upon admission, time of invasive ventilation, mode of invasive mechanical ventilation, pulmonary function indicators at discharge, and other baseline data of the two groups. The SPPB questionnaire scores and respiratory muscle strength in both groups were increased gradually with the extended offline training time, the serum level of IL-6 in both groups were descend gradually with the extended offline training time, especially in the observation group [SPPB questionnaire score in the observation group were 7.81±0.33, 8.72±0.53, 9.44±0.31, 10.57±0.50, while in the control group were 7.74±0.68, 8.73±0.37, 8.72±0.40, 9.33±0.26, effect of time: F = 192.532, P = 0.000, effect of intervention: F = 88.561, P = 0.000, interaction effect between intervention and time: F = 24.724, P = 0.000; respiratory muscle strength (mmHg, 1 mmHg≈0.133 kPa) in the observation group were 123.20±24.84, 137.00±26.47, 149.00±24.70, 155.40±29.37, while in the control group were 129.00±20.34, 126.00±24.01, 132.20±25.15, 138.60±36.67, effect of time: F = 5.926, P = 0.001, effect of intervention: F = 5.248, P = 0.031, interaction effect between intervention and time: F = 3.033, P = 0.043; serum level of IL-6 in the observation group were 80.05±6.81, 74.76±9.33, 63.66±10.19, 56.95±4.72, while in the control group were 80.18±7.21, 77.23±9.78, 71.79±10.40, 66.51±6.49, effect of time: F = 53.485, P = 0.000, effect of intervention: F = 22.942, P = 0.000, interaction effect between intervention and time: F = 3.266, P = 0.026]. Compared with the control group, the number of aspirations of sputum after weaning of patients in the observation group significantly decreased (number: 22.46±1.76 vs. 27.31±0.90), the length of ICU stay after weaning significantly became shorter (days: 6.93±0.95 vs. 8.52±2.21), and the rehospitalization rate within 6 months after discharge significantly decreased [20.00% (5/25) vs. 48.00% (12/25)]. There were significant differences. The pulmonary function indicators 3 months after discharge of two groups of patients significantly increased compared with those at discharge and those of the observation group were significantly higher than those of the control group [PEF (L/min): 430.20±95.18 vs. 370.00±108.44, FEV1/FVC ratio: 0.88±0.04 vs. 0.82±0.05, VC (L): 3.22±0.72 vs. 2.74±0.37, all P < 0.05]. The Kaplan-Meier survival curve showed that the cumulative survival rate of patients 6 months after discharge of patients in the observation group was significantly higher than that of patients in the control group [76.9% vs. 45.5%, hazard ratio ( HR) = 0.344, P = 0.017]. Conclusion:Early PR training can significantly improve the respiratory function of patients with ARDS after weaning of invasive mechanical ventilation. Continuous active respiratory training after discharge can improve the respiratory function of patients and effectively decrease mortality.

Objective To explore the molecular mechanism of chronic osteomyelitis and to clarify the role of MAPK signal pathway in the pathogenesis of chronic osteomyelitis,by collecting and analyzing the transcriptional information of bone tissue in patients with chronic osteomyelitis.Methods Four cases of traumatic osteomyelitis in limbs from June 2019 to June 2020 were selected,and the samples of necrotic osteonecrosis from chronic osteomyelitis(necrotic group),and normal bone tissue(control group)were collected.Transcriptome information was collected by Illumina Hiseq Xten high throughput sequencing platform,and the gene expression in bone tissue was calculated by FPKM.The differentially expressed genes were screened by comparing the transcripts of the Necrotic group and control group.Genes were enriched by GO and KEGG.MAP3K7 and NFATC1 were selected as differential targets in the verification experiments,by using rat osteomyelitis animal model and im-munohistochemical analysis.Results A total of 5548 differentially expressed genes were obtained by high throughput sequenc-ing by comparing the necrotic group and control group,including 2701 up-regulated and 2847 down-regulated genes.The genes enriched in MAPK pathway and osteoclast differentiation pathway were screened,the common genes expressed in both MAPK and osteoclast differentiation pathway were(inhibitor of nuclear factor κ subunit Beta,IκBKβ),(mitogen-activated protein ki-nase 7,MAP3K7),(nuclear factor of activated t cells 1,NFATC1)and(nuclear factor Kappa B subunit 2,NFκB2).In rat os-teomyelitis model,MAP3K7 and NFATC1 were highly expressed in bone marrow and injured bone tissue.Conclusion Based on the transcriptome analysis,the MAPK signaling and osteoclast differentiation pathways were closely related to chronic os-teomyelitis,and the key genes IκBKβ,MAP3K7,NFATC1,NFκB2 might be new targets for clinical diagnosis and therapy of chronic osteomyelitis.