Despite that sleep disturbance and poor neurocognitive performance are common complaints among coronavirus disease 2019 (COVID-19) survivors, few studies have focused on the effect of post-COVID-19 sleep disturbance (PCSD) on cognitive function. This study aimed to identify the impact of PCSD on neurocognitive function and explore the associated risk factors for the worsening of this condition. This cross-sectional study was conducted via the web-based assessment in Chinese mainland. Neurocognitive function was evaluated by the modified online Integrated Cognitive Assessment (ICA) and the Number Ordering Test (NOT). Propensity score matching (PSM) was utilized to match the confounding factors between individuals with and without PCSD. Univariate analyses were performed to evaluate the effect of PCSD on neurocognitive function. The risk factors associated with worsened neurocognitive performance in PCSD individuals were explored using binary logistic regression. A total of 8692 individuals with COVID-19 diagnosis were selected for this study. Nearly half (48.80%) of the COVID-19 survivors reported sleep disturbance. After matching by PSM, a total of 3977 pairs (7954 individuals in total) were obtained. Univariate analyses revealed that PCSD was related to worse ICA and NOT performance (P<0.05). Underlying disease, upper respiratory infection, loss of smell or taste, severe pneumonia, and self-reported cognitive complaints were associated with worsened neurocognitive performance among PCSD individuals (P<0.05). Furthermore, aging, ethnicity (minority), and lower education level were found to be independent risk factors for worsened neurocognitive performance in PCSD individuals (P<0.05). PCSD was related to impaired neurocognitive performance. Therefore, appropriate prevention and intervention measures should be taken to minimize or prevent PCSD and eliminate its potential adverse effect on neurocognitive function.
Humans ; COVID-19/epidemiology* ; Male ; Female ; Sleep Wake Disorders/epidemiology* ; Propensity Score ; Middle Aged ; Cross-Sectional Studies ; Adult ; SARS-CoV-2 ; Aged ; Risk Factors ; China/epidemiology* ; Cognition ; Cognitive Dysfunction/etiology* ; Neuropsychological Tests

Objective:To investigate the differences in cross-frequency coupling (CFC) characteristics of electroencephalography (EEG) between narcolepsy type 1 (NT1) and narcolepsy type 2 (NT2).Methods:A total of 23 NT1 and 31 NT2 patients were included from the Chinese Clinical Sleep Database (CCSD) between October 2022 and September 2023. All participants underwent overnight polysomnography and a multiple sleep latency test. CFC features were extracted from EEG signals during polysomnography, encompassing various combinations of sleep stages, electrode pairs, frequency bands, and coupling types. Feature selection was performed using elastic net regularization. The Spearman correlation between key CFC features and the Epworth Sleepiness Scale (ESS) scores was analyzed. Finally, a support vector classification (SVC) model was constructed to distinguish NT1 from NT2, and leave-one-out cross-validation was used to assess the generalization performance.Results:Among all coupling features during non-rapid eye movement sleep stage 1 (N1), the fronto-occipital θ-α1 and central δ-α1 couplings showed the highest absolute coefficients, reaching 1.13 and 1.10, respectively. In the NT1 group, the α1-β2 imaginary part of phase-locking value (iPLV) of the F3-C3 pair during N1 was significantly positively correlated with ESS scores ( r=0.52, P=0.012). In the machine learning classification task, the SVC model achieved an accuracy of 85% using leave-one-out cross-validation. Conclusion:The CFC features during the sleep-wake transition stage play an important role in distinguishing NT1 from NT2 and show a significant correlation with excessive daytime sleepiness (EDS) in NT1. CFC may serve as a potential biomarker for differentiating narcolepsy subtypes and provide new insights into the mechanisms and clinical evaluation of EDS.

Objective:To investigate the differences in cross-frequency coupling (CFC) characteristics of electroencephalography (EEG) between narcolepsy type 1 (NT1) and narcolepsy type 2 (NT2).Methods:A total of 23 NT1 and 31 NT2 patients were included from the Chinese Clinical Sleep Database (CCSD) between October 2022 and September 2023. All participants underwent overnight polysomnography and a multiple sleep latency test. CFC features were extracted from EEG signals during polysomnography, encompassing various combinations of sleep stages, electrode pairs, frequency bands, and coupling types. Feature selection was performed using elastic net regularization. The Spearman correlation between key CFC features and the Epworth Sleepiness Scale (ESS) scores was analyzed. Finally, a support vector classification (SVC) model was constructed to distinguish NT1 from NT2, and leave-one-out cross-validation was used to assess the generalization performance.Results:Among all coupling features during non-rapid eye movement sleep stage 1 (N1), the fronto-occipital θ-α1 and central δ-α1 couplings showed the highest absolute coefficients, reaching 1.13 and 1.10, respectively. In the NT1 group, the α1-β2 imaginary part of phase-locking value (iPLV) of the F3-C3 pair during N1 was significantly positively correlated with ESS scores ( r=0.52, P=0.012). In the machine learning classification task, the SVC model achieved an accuracy of 85% using leave-one-out cross-validation. Conclusion:The CFC features during the sleep-wake transition stage play an important role in distinguishing NT1 from NT2 and show a significant correlation with excessive daytime sleepiness (EDS) in NT1. CFC may serve as a potential biomarker for differentiating narcolepsy subtypes and provide new insights into the mechanisms and clinical evaluation of EDS.

Objective:To conduct visualization analysis,induction and summary for the study status,focus and trend in the dose measurement field of flash radiotherapy(Flash-RT)through bibliometric methods on the basis of the Web of Science Collection(WOS),and analyze the technical bottleneck of Flash-RT dose measurement.Methods:In the WOS database,the("FLASH radiotherapy"or"ultra high dose rate"or"FLASH irradiation")and TS=("dosimetric system"or"dosimeters"or"dosimetry")were used as the theme terms,and all relevant literatures about the study of Flash-RT dose measurement that were included in the Core Collection of WOS database were retrieved.The citation analysis function of WOS and Citespace software(Version 6.2)were used to conduct visualization analysis for the publication trends,sources and research focus of the included literature of Flash-RT dose measurement,and to draw corresponding visualization knowledge maps.Results:The literatures were mainly published during January 2015 and November 2023,and a total of 86 papers were included in the analysis by screening,which included 78 articles and 8 reviews.The top three countries in terms of publication volume were respectively the United States(38 papers),France(19 papers)and Italy(19 papers).The research focuses were respectively Cherenkov,radiotherapy,passive dosimeter,conventional radiation,laser particle acceleration,ionization chamber and proton therapy as cluster analysis.Currently,the study of Flash-RT dose measurement mainly focused on the relevant fields included Flash-RT radiation source,dose rate,and the equipment and method of dose measurement.Conclusion:Flash-RT is a new technology with a key breakthrough in the basic field of radiotherapy,and this technique faces many difficulties and challenges in the clinical translation process of this technique.With the continuous development of science and technique,and deepeningaccumulation of related researchclinical big data,the method and procedure of accurate and efficient dose measurement dosimetry methods and procedures will promote and realize the update and iteration of Flash-RT experiment research and device technique,which will successfully realize the clinical the broader application prospects of this technology.

The advancement of computers and data explosion have ushered in the third wave of artificial intelligence(AI). AI is an interdisciplinary field that encompasses new ideas, new theories, and new technologies, etc. AI has brought convenience to ophthalmology application and promoted its intelligent, precise, and minimally invasive development. At present, AI has been widely applied in various fields of ophthalmology, especially in oculoplastic surgery. AI has made rapid progress in image detection, facial recognition, etc., and its performance and accuracy have even surpassed humans in some aspects. This article reviews the relevant research and applications of AI in oculoplastic surgery, including ptosis, single eyelid, pouch, eyelid mass, and exophthalmos, and discusses the challenges and opportunities faced by AI in oculoplastic surgery, and provides prospects for its future development, aiming to provide new ideas for the development of AI in oculoplastic surgery.

Background Individuals may experience significant alterations in sleep hygiene during the major public health emergencies,consequently impacting their sleep quality.Objective To explore the relationship between sleep quality and sleep hygiene among adult residents during the major public health emergencies,so as to provide references for improving the sleep quality of residents during such a period.Methods A sample of 1 364 adult residents were enrolled as the research subjects from February 20 to 29,2020.All participants were asked to complete self-administered questionnaire to obtain basic-demographic information and sleep hygiene.Pittsburgh Sleep Quality Index(PSQI)was applied to assess sleep quality.Residents were classified into poor sleepers with PSQI score≥8 and good sleepers defined as PSQI score<8.Binary Logistic regression analysis was conducted to identify factors associated with sleep quality.Radar chart was used to visualize and compare the sleep hygiene between poor sleepers and good sleepers.Results According to PSQI score,891(65.32%)residents were good sleepers,while 473(34.68%)residents were poor sleepers.Comparison revealed that age(χ2=3.887),past medical history(χ2=27.938),awareness rate of importance of sleeping before major public health emergencies(χ2=4.337),impact of sleep quality on quality of life during the major public health emergencies(χ2=178.138),frequency of staying up late during the major public health emergencies(χ2=139.390),compensatory sleep behaviors(χ2=39.257),impact of sleep problems on daytime functioning(χ2=285.879),change of bedtime(χ2=63.031),sleep latency(χ2=168.672),wake-up time(χ2=59.221),changes in sleep duration(χ2=172.332),time spent in the bedroom(χ2=23.071),and sum of money spent on improving sleep environment(χ2=58.584)yielded statistical difference between poor sleepers and good sleepers(P<0.05 or 0.01).Logistic regression analysis denoted that past medical history(OR=1.680,95%CI:1.185～2.382),negative impact of sleep quality on quality life(OR=4.181,95%CI:2.722～6.422),staying up late 3 to 4 times per week(OR=3.145,95%CI:1.497～6.605),staying up late almost every day(OR=4.271,95%CI:1.970～9.260),negative impact of sleep problems on daytime functioning(OR=7.169,95%CI:5.188～9.907),prolonged sleep latency(OR=2.836,95%CI:2.019～3.982)and shortened sleep duration(OR=3.518,95%CI:2.144～5.772)were risk factors of poor sleep quality.The sum of money spent on improving sleep environment following the major public health emergencies≤500 RMB(OR=0.334,95%CI:0.134～0.830)was related to the incidence rate of poor sleep quality.Radar chart showed that poor sleepers were characterized by extravagant concerns,excessive cleanliness and poor sleep hygiene practices during the major public health emergencies,and poor sleepers were more likely to stay up late due to stress and emotional issues.Conclusion Some residents are facing poor sleep quality during the major public health emergencies,and poor sleep hygiene practice also contributes to poor sleep quality.

Objective:To evaluate artificial intelligence constructed by deep convolutional neural network (DCNN) for the site identification in upper gastrointestinal endoscopy.Methods:A total of 21 310 images of esophagogastroduodenoscopy from the Cancer Hospital of Chinese Academy of Medical Sciences from January 2019 to June 2021 were collected. A total of 19 191 images of them were used to construct site identification model, and the remaining 2 119 images were used for verification. The performance differences of two models constructed by DCCN in the identification of 30 sites of the upper digestive tract were compared. One model was the traditional ResNetV2 model constructed by Inception-ResNetV2 (ResNetV2), the other was a hybrid neural network RESENet model constructed by Inception-ResNetV2 and Squeeze-Excitation Networks (RESENet). The main indices were the accuracy, the sensitivity, the specificity, positive predictive value (PPV) and negative predictive value (NPV).Results:The accuracy, the sensitivity, the specificity, PPV and NPV of ResNetV2 model in the identification of 30 sites of the upper digestive tract were 94.62%-99.10%, 30.61%-100.00%, 96.07%-99.56%, 42.26%-86.44% and 97.13%-99.75%, respectively. The corresponding values of RESENet model were 98.08%-99.95%, 92.86%-100.00%, 98.51%-100.00%, 74.51%-100.00% and 98.85%-100.00%, respectively. The mean accuracy, mean sensitivity, mean specificity, mean PPV and mean NPV of ResNetV2 model were 97.60%, 75.58%, 98.75%, 63.44% and 98.76%, respectively. The corresponding values of RESENet model were 99.34% ( P<0.001), 99.57% ( P<0.001), 99.66% ( P<0.001), 90.20% ( P<0.001) and 99.66% ( P<0.001). Conclusion:Compared with the traditional ResNetV2 model, the artificial intelligence-assisted site identification model constructed by RESENNet, a hybrid neural network, shows significantly improved performance. This model can be used to monitor the integrity of the esophagogastroduodenoscopic procedures and is expected to become an important assistant for standardizing and improving quality of the procedures, as well as an significant tool for quality control of esophagogastroduodenoscopy.

This study aims to review the research progress on the effects of repetitive transcranial magnetic stimulation （rTMS） on cognitive function in generalized anxiety disorder （GAD） with the view of providing references for clinical application. As a common chronic mental disorder， GAD affects the cognitive function， the quality of life and social function of patients. Although first-line treatment is safe and effective for the majority of patients， its effect of improving cognitive function is unclear. As a non-invasive and safe physiotherapy technology， rTMS is expected to provide a new option for the clinical treatment of GAD patients. This review focuses on the characteristics of cognitive function of GAD， the current status of first-line therapy and the treatment of rTMS.

Objective:To explore the value of multi-parametric MRI for thyroid gland in differentiating benign and malignant thyroid nodules.Methods:From December 2018 to May 2020, 78 patients with 91 post-surgically pathologically confirmed thyroid nodules were enrolled in Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College. For each patient, the following MRI findings were obtained including the nodules′ location, size, shape, margin, signal intensity, cystic change, degree and pattern of contrast enhancement, involvement of surrounding structure, and ADC values. The time-intensity curve (TIC) were plotted and subtyped based on dynamic contrast enhancement MRI. The MRI findings between the benign and malignant thyroid nodules were compared using Mann-Whitney U test, χ 2 test or Fisher exact test. Multiple logistic regression analysis was used to select independent predictive variables and build a combined model, and the ROC curve was used to evaluate the diagnostic performance of each MRI finding and the combined model. Results:Between the benign and malignant thyroid nodules, the significant differences were found in size, shape, margin, presence of cystic changes, T 1WI signal intensity, ADC value, enhancement homogeneity, TIC subtypes and presence of thyroid capsule involvement ( P<0.05). Multivariate logistic analysis showed that ill-defined margin (OR=77.61), no presence of cystic changes (OR=36.11) and difference between TIC subtypes (OR=83.41) were independent predictive variables, and the area under the ROC curve (AUC) was 0.879, 0.788, and 0.751, respectively. The AUC, sensitivity and specificity of the combined model were 0.977, 0.986, and 0.904, respectively. Conclusions:Thyroid multi-parametric MRI derived findings can be used for the differential diagnosis of benign and malignant nodules. Combined with the independent risk factors with ill-defined margin, no presence of cystic changes, TIC of type plateau or washout, the diagnostic model has a higher diagnostic efficiency.

Objective: There is no effective therapy for patients with advanced medullary thyroid carcinoma (MTC). Vandetanib,a novel multitargeted receptor tyrosine kinase inhibitor, has previously shown antitumor activity in phase Ⅱ studies of patients with advanced MTC. This study was to evaluate the efficacy and the safety of vandetanib on advanced MTC. Methods: This study was an open, international multi-center phase Ⅲ clinical trial and the study number was NCT01298323. The single-center study was a sub-group analysis of the international study, which was conducted on 9 pathologically confirmed advanced MTC patients by Cancer Hospital Chinese Academy of Medical Sciences between March 2012 and October 2017. Vandetanib (300 mg) was orally administered daily till death or withdrawal. The efficacy was evaluated according to RECIST criteria and the adverse events were evaluated according to NCI criteria. Results: The objective response rate was 3/9,and the disease control rate was 4/9. The median progression-free survival was 44 months. All patients who had the elevated levels of calcitonin (CTN) and carcino-embryonic antigen (CEA) before treatment began to show the decreases in the level of CTN and CEA after 3 months and later showed again the increases in the levels of both tumor markers with tumor progression. By ROC curve analysis, CTN was of statistically significance(P<0.05, 95%CI 0.558-0.834), but CEA was not(P>0.05). Adverse events were generally mild (grade 1 or 2),including hypertension (9 cases),skin rash (9 cases), and diarrhea (6 cases). Two patients developed grade 3 elevation of serum glutamate pyruvate transaminase and one patient developed grade 3 elevation of drug-related bowel disease. No grade 4 drug-related adverse event occurred. Conclusions Vandetanib is effective and well tolerated for patients with locally advanced or metastatic MTC who have no chance for surgery. This indicates the increase of CTN is clinically relevant to disease progression, but the number of patients are extremely low, and, therefore further research is needed. Long-term use of vandetanib may cause resistance.