Objective To clarify the risk factors of diaphragmatic dysfunction (DD) after cardiac surgery with extracorporeal circulation. Methods A retrospective analysis was conducted on the data of patients who underwent cardiac surgery with extracorporeal circulation in the Department of Cardiovascular Surgery of Peking University People's Hospital from January 2023 to March 2024. Patients were divided into two groups according to the results of bedside diaphragm ultrasound: a DD group and a control group. The preoperative, intraoperative, and postoperative indicators of the patients were compared and analyzed, and independent risk factors for DD were screened using multivariate logistic regression analysis. Results A total of 281 patients were included, with 32 patients in the DD group, including 23 males and 9 females, with an average age of (64.0±13.5) years. There were 249 patients in the control group, including 189 males and 60 females, with an average age of (58.0±11.2) years. The body mass index of the DD group was lower than that of the control group [(18.4±1.5) kg/m2 vs. (21.9±1.8) kg/m2, P=0.004], and the prevalence of hypertension, chronic obstructive pulmonary disease, heart failure, and renal insufficiency was higher in the DD group (P<0.05). There was no statistical difference in intraoperative indicators (operation method, extracorporeal circulation time, aortic clamping time, and intraoperative nasopharyngeal temperature) between the two groups (P>0.05). In terms of postoperative aspects, the peak postoperative blood glucose in the DD group was significantly higher than that in the control group (P=0.001), and the proportion of patients requiring continuous renal replacement therapy was significantly higher than that in the control group (P=0.001). The postoperative reintubation rate, tracheotomy rate, mechanical ventilation time, and intensive care unit stay time in the DD group were higher or longer than those in the control group (P<0.05). Multivariate logistic regression analysis showed that low body mass index [OR=0.72, 95%CI (0.41, 0.88), P=0.011], preoperative dialysis [OR=2.51, 95%CI (1.89, 4.14), P=0.027], low left ventricular ejection fraction [OR=0.88, 95%CI (0.71, 0.93), P=0.046], and postoperative hyperglycemia [OR=3.27, 95%CI (2.58, 5.32), P=0.009] were independent risk factors for DD. Conclusion The incidence of DD is relatively high after cardiac surgery, and low body mass index, preoperative renal insufficiency requiring dialysis, low left ventricular ejection fraction, and postoperative hyperglycemia are risk factors for DD.

INTRODUCTION: We aimed to understand the awareness and attitudes of elderly Southeast Asians towards telehealth services during the coronavirus disease 2019 (COVID-19) pandemic in this study. METHODS: In this qualitative study, 78 individuals from Singapore (51.3% female, mean age 73.0 ± 7.6 years) were interviewed via telephone between 13 May 2020 and 9 June 2020 during Singapore's first COVID-19 'circuit breaker'. Participants were asked to describe their understanding of telehealth, their experience of and willingness to utilise these services, and the barriers and facilitators underlying their decision. Transcripts were analysed using thematic analysis, guided by the United Theory of Acceptance Use of Technology framework. RESULTS: Of the 78 participants, 24 (30.8%) were able to describe the range of telehealth services available and 15 (19.2%) had previously utilised these services. Conversely, 14 (17.9%) participants thought that telehealth comprised solely home medication delivery and 50 (51.3%) participants did not know about telehealth. Despite the advantages offered by telehealth services, participants preferred in-person consultations due to a perceived lack of human interaction and accuracy of diagnoses, poor digital literacy and a lack of access to telehealth-capable devices. CONCLUSION Our results showed poor overall awareness of the range of telehealth services available among elderly Asian individuals, with many harbouring erroneous views regarding their use. These data suggest that public health education campaigns are needed to improve awareness of and correct negative perceptions towards telehealth services in elderly Asians.
Humans ; COVID-19/epidemiology* ; Female ; Telemedicine ; Aged ; Male ; Singapore/epidemiology* ; Qualitative Research ; Health Knowledge, Attitudes, Practice ; SARS-CoV-2 ; Aged, 80 and over ; Middle Aged ; Pandemics ; Awareness ; Asian People ; Southeast Asian People

OBJECTIVES: To investigate the value of weight growth velocity, calculated using the Patel exponential model and the Z-score change method, in predicting the neurological and physical development outcomes of preterm infants with a gestational age of <30 weeks in the long term. METHODS: A retrospective study was conducted involving preterm infants with a gestational age of <30 weeks who were hospitalized and treated in the Department of Neonatology at Tongji Hospital, Huazhong University of Science and Technology, from January 2017 to June 2022, and were followed up at the outpatient service more than 18 months of age. The preterm infants were divided into high and low rate groups based on the two calculation methods, and the two methods were compared regarding their predictive value for neurological and physical development outcomes in the long term. RESULTS: The average age of the last follow-up was (23.0±3.6) months. For neurological development, according to the Patel exponential model, the low rate group exhibited a significantly higher abnormal rate in the fine motor domain compared to the high rate group (P<0.05). Using the Z-score change method, the low rate group had significantly higher abnormal rates in both gross motor and fine motor domains, and significantly lower developmental quotients for gross motor, fine motor, and adaptive behavior domains compared to the high rate group (P<0.05). For physical development, there were no significant differences in body length, body weight, head circumference, or the incidence rate of growth restriction between the low rate and high rate groups identified by either method (P>0.05). CONCLUSIONS Weight growth velocity calculated using the Z-score change method is more effective in predicting long-term neurological outcomes in preterm infants, while weight growth velocity derived from both methods shows no significant association with long-term physical development outcomes.
Humans ; Infant, Premature/growth & development* ; Retrospective Studies ; Infant, Newborn ; Child Development ; Male ; Female ; Body Weight ; Infant ; Nervous System/growth & development*

General anesthesia, pivotal for surgical procedures, requires precise depth monitoring to mitigate risks ranging from intraoperative awareness to postoperative cognitive impairments. Traditional assessment methods, relying on physiological indicators or behavioral responses, fall short of accurately capturing the nuanced states of unconsciousness. This study introduces a machine learning-based approach to decode anesthesia depth, leveraging EEG data across different anesthesia states induced by propofol and esketamine in rats. Our findings demonstrate the model's robust predictive accuracy, underscored by a novel intra-subject dataset partitioning and a 5-fold cross-validation method. The research diverges from conventional monitoring by utilizing anesthetic infusion rates as objective indicators of anesthesia states, highlighting distinct EEG patterns and enhancing prediction accuracy. Moreover, the model's ability to generalize across individuals suggests its potential for broad clinical application, distinguishing between anesthetic agents and their depths. Despite relying on rat EEG data, which poses questions about real-world applicability, our approach marks a significant advance in anesthesia monitoring.
Animals ; Machine Learning ; Electroencephalography/methods* ; Ketamine/administration & dosage* ; Rats ; Male ; Propofol/administration & dosage* ; Rats, Sprague-Dawley ; Anesthesia, General/methods* ; Brain/physiology* ; Intraoperative Neurophysiological Monitoring/methods*

Abstract Alzheimer′s disease(AD) is a common neurodegenerative disease. It has been found that AD is related to various pathogenic factors such as genetics, cardiovascular and cerebrovascular disease, and excessive phosphorylation of tau protein. However, no definitive conclusions on its pathogenesis have been reached. In this paper, the research progress on the pathogenesis of AD inC.elegansmodel and the therapeutic effects of traditional Chinese medicine extracts on AD are reviewed, providing a basis for further research on the alleviating effects of Chinese medicine extracts on AD.

Objective: To evaluate the therapeutic effects of Epimedium brevicornu Maxim. (EBM, Yin Yang Huo) on breast cancer using network pharmacology and in vitro validation. It also aimed to explore the novel targets and mechanisms of EBM in the treatment of breast cancer to facilitate the discovery of new drugs and their clinical application. Methods: Network pharmacology was used to identify and screen the components and targets of EBM for breast cancer treatment. Molecular docking was further screened the effective components and targets of EBM. Wound-healing assays and flow cytometry analysis were used to detect the ability of two compounds to intervene in the migration and apoptosis of MDA-MB-231 cells, and their mechanism of action was further explored using western blotting experiments. Results: EBM contained 19 active components. Among them were β-anhydroicaritin (Anhy) and isoliquiritigenin (Iso), which were selected for in vitro experiments. Treatment resulted in a dose-dependent suppression of MDA-MB-231 cell viability, with an IC50 of 23.73 μmol/L for Iso and 21.28 μmol/L for Anhy. In the wound healing assay, cells in Anhy and Iso groups exhibited considerable inhibition of migration at 48 h. In flow cytometry analysis, treatment with Iso (20 μmol/L) for 96 h resulted in significantly higher levels of both early and late apoptosis in the Iso group than that in the control group (P = .004 and P = .014, respectively). Additionally, both Iso (20 μmol/L) and Anhy (10 and 20 μmol/L) induced cell necrosis at 96 h. Western blotting revealed that Anhy and Iso increased the expression of Bax and TBK1/NAK. Conclusion These findings suggested that Anhy and Iso, the two components of EBM, inhibit MDA-MB-231 cell proliferation and migration of and induce their apoptosis, providing substantial support for future studies on breast cancer.

Objective To observe the value of machine learning model for predicting the risk of dementia in patients with depression.Methods Totally 31 587 depression patients from UK Biobank database were retrospectively enrolled and divided into dementia group(n=896)or non-dementia group(n=30 691)based on follow-up data showed developed dementia or not,also divided into training set(n=18 952)or test set(n=12 635)at the ratio of 6:4.Based on interviews and questionnaire surveys,a total of 190 factors including demographic characteristics,lifestyle,health status,physical indicators and imaging data were included and screened to establish models with light gradient boosting machine(LightGBM),ridge regression(Ridge)and adaptive boosting(AdaBoost),and the value for predicting the risk of dementia in patients with depression was observed.Results A total of 10 factors were ultimately enrolled,including age,waist circumference,employment status,daytime rest,daytime doze or drowsiness,duration of mobile phone use,number of family members,duration of depression,guilt and seeking medical attention due to psychological issues.Based on the above factors,the models were established.In training set,the area under the curve(AUC)of LightGBM,Ridge and AdaBoost model for predicting dementia risk in patients with depression was 0.914,0.832 and 0.889,respectively,and the differences between each 2 models were significant(all P＜0.05);while in test set,the AUC was 0.866,0.842 and 0.859,respectively,except for LightGBM and AdaBoost,the other with significant differences between each two(both P＜0.05).The calibration curve showed that the LightGBM model had the best fit.Conclusion LightGBM machine learning model was helpful for predicting the risk of dementia in patients with depression.

A quartz crystal microbalance(QCM)-systematic evolution of ligands by the exponential en-richment(SELEX)technique was developed to screen out aptamers with high affinity for arsenic(Ⅲ).A random single strand DNA library was designed and fixed on the mercaptoethylamine-modified crystal plate with arsenic(Ⅲ)as the target,and the free aptamer was captured in the solution,and the QCM-SELEX screening method was constructed.After 6 rounds of screening,the secondary library was se-quenced with high throughput method,and the 6S1 dissociation coefficient Kd value was 0.36 μmol/L based on QCM resonance frequency.Using 6S1 as a probe,the QCM biosensor was constructed for the detection of arsenic(Ⅲ).The sensor has a good linear relationship in the range of 0.01 μmol/L～0.2μmol/L,and the detection limit of arsenic(Ⅲ)is 5.2 nmol/L(3σ),indicatinggood selectivity.