Objective To systematically evaluate the impact of pulmonary hypertension (PH) on the prognosis of patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR). Methods A computerized search was conducted in CNKI, Wanfang Data, VIP, CBM, PubMed, The Cochrane Library, EMbase, and Web of Science databases from inception to June 2023 for cohort studies on the prognostic impact of PH in severe AS patients undergoing TAVR. Two researchers independently screened the literature, extracted data, and assessed the quality of included studies. Stata 17.0 software was used for meta-analysis. Results A total of 16 cohort studies were included, all with Newcastle-Ottawa Scale scores≥7. Meta-analysis results showed that, compared with AS patients without PH, those with PH had significantly higher 1-year all-cause mortality after TAVR [OR=2.10, 95%CI (1.60, 2.75), P<0.01], 30-day all-cause mortality [OR=2.09, 95%CI (1.54, 2.83), P<0.01], and cardiovascular mortality [OR=1.49, 95%CI (1.18, 1.90), P<0.01]. The differences between the two groups in major bleeding events, stroke, myocardial infarction, pacemaker implantation, and postoperative renal failure were not statistically significant. For outcome indicators with significant heterogeneity, subgroup analyses were performed based on PH measurement methods, diagnostic criteria, and different types of PH. The results showed that most subgroup combined results were consistent with the overall findings and that heterogeneity was significantly reduced. Conclusion PH significantly increases the 30-day all-cause mortality, 1-year all-cause mortality, and cardiovascular mortality in patients with severe AS undergoing TAVR.

Objective Explore a non-contact physiological and psychological detection model based on facial video in simulations of weightlessness effects,research new methods for non-contact heart rate and negative mood state detection in long-term simulations of weightlessness effect analysis.Methods Construct a non-contact physiological and psychological data collection system for fusion analysis of visible light and thermal infrared videos.Collect physiological and psychological data of volunteers in the"Earth Star-Ⅱ"90-day head-down bed rest experiment.A non-contact heart rate detection model based on GCN facial multi-region feature fusion and a non-contact negative mood state detection model considering data reliability were constructed,and the effectiveness of the models were validated with finger clip heart rate and POMS-SF scale as labels.Results The experimental results show that the average difference in the Bland-Altman plot of the non-contact heart rate detection model is-1.26 bpm,and 96.3%of value error detection data falls within the 95%confidence interval,indicating a high consistency between the model detected heart rate and the finger clip heart rate.The non-contact negative mood state detection model achieves an accuracy of>0.85 for detecting tension,depression,anger,and fatigue.Features such as heart rate,AU06,eye gaze,and head pose were observed to be important to mood state detection.Conclusion Non-contact physiological and psychological detection methods not only can be utilized for long-term physiological analysis in simulations of weightlessness effects,but also provide a novel technical approach for on-orbit astronauts health assurance during long-term space flight in the future.

Enzyme-driven micro/nanomotors consuming in situ chemical fuels have attracted lots of attention for biomedical applications. However, motor systems composed by organism-derived organics that maximize the therapeutic efficacy of enzymatic products remain challenging. Herein, swimming proteomotors based on biocompatible urease and human serum albumin are constructed for enhanced antitumor therapy via active motion and ammonia amplification. By decomposing urea into carbon dioxide and ammonia, the designed proteomotors are endowed with self-propulsive capability, which leads to improved internalization and enhanced penetration in vitro. As a glutamine synthetase inhibitor, the loaded l-methionine sulfoximine further prevents the conversion of toxic ammonia into non-toxic glutamine in both tumor and stromal cells, resulting in local ammonia amplification. After intravesical instillation, the proteomotors achieve longer bladder retention and thus significantly inhibit the growth of orthotopic bladder tumor in vivo without adverse effects. We envision that the as-developed swimming proteomotors with amplification of the product toxicity may be a potential platform for active cancer treatment.

The coronavirus disease 2019 (COVID-19) pandemic is caused by infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is spread primary via respiratory droplets and infects the lungs. Currently widely used cell lines and animals are unable to accurately mimic human physiological conditions because of the abnormal status of cell lines (transformed or cancer cells) and species differences between animals and humans. Organoids are stem cell-derived self-organized three-dimensional culture in vitro and model the physiological conditions of natural organs. Here we showed that SARS-CoV-2 infected and extensively replicated in human embryonic stem cells (hESCs)-derived lung organoids, including airway and alveolar organoids which covered the complete infection and spread route for SARS-CoV-2 within lungs. The infected cells were ciliated, club, and alveolar type 2 (AT2) cells, which were sequentially located from the proximal to the distal airway and terminal alveoli, respectively. Additionally, RNA-seq revealed early cell response to virus infection including an unexpected downregulation of the metabolic processes, especially lipid metabolism, in addition to the well-known upregulation of immune response. Further, Remdesivir and a human neutralizing antibody potently inhibited SARS-CoV-2 replication in lung organoids. Therefore, human lung organoids can serve as a pathophysiological model to investigate the underlying mechanism of SARS-CoV-2 infection and to discover and test therapeutic drugs for COVID-19.
Adenosine Monophosphate/therapeutic use* ; Alanine/therapeutic use* ; Alveolar Epithelial Cells/virology* ; Antibodies, Neutralizing/therapeutic use* ; COVID-19/virology* ; Down-Regulation ; Drug Discovery ; Human Embryonic Stem Cells/metabolism* ; Humans ; Immunity ; Lipid Metabolism ; Lung/virology* ; RNA, Viral/metabolism* ; SARS-CoV-2/physiology* ; Virus Replication/drug effects*

Objective:To evaluate the effectiveness of mentorship-Leichester assessment package (LAP) model in general practice teaching.Methods:Sixty five-year Traditional Chinese Medicine (TCM) students undergoing general medicine internship from July 2020 to December 2020 were divided into study group and control group with 30 students in each group. Students in study group undertook general practice internship in Fangzhuang Community Health Service Center with the mentorship-LAP model, including group-based teaching, weekly LAP case discussion and mentor-intern individualized LAP clinical practice teaching; students in control group undertook general practice internship in other community teaching bases with the traditional clinical practice training methods. After one month, students were evaluated with LAP score sheet and the evaluation results were compared between two groups.Results:After one month of training, the scores of medical history collection, physical examination, problem solving, patient management, doctor-patient relationship, expected care and medical records in both groups increased compared with pre-training (the control group: t=4.23, 5.06, 8.94, 4.85, 3.99, 1.04, 5.13, study group: t=1.86, 2.85, 12.42, 13.61, 6.29, 4.83, 5.63, all P<0.05). The scores of medical history collection, physical examination, problem solving, patient management, doctor-patient relationship, and expected care after training in the study group were higher than those in the control group ( t=5.66, 2.83, 5.67, 8.13, 2.76, 2.52, all P<0.05). After the training, in the medical history collection items, the scores of patient-centered communication, RICE recognition, and physical-psychological-social factors in study group were higher than those in the control group ( t=5.66, 7.35 and 2.66, all P<0.05); in the problem-solving items, the scores of clinical data application, comprehensive analysis ability and personal ability limitation in study group were higher than those in the control group ( t=4.32, 3.38 and 2.26, all P<0.05); in the patient management items, the scores of overall management plans, doctor-patient collaboration, dispelling doubts, subsequent visits/follow-up, and the degree of patient understanding the management plan in the study group were higher than those in the control group ( t=3.03, 2.07, 2.43, 3.15 and 2.17, all P<0.05). Conclusion:The mentorship-LAP teaching model is helpful to improve the effectiveness of general practice internship for medicine students.

The community is the first line of endemic prevention and control. Facing the challenge of epidemic outbreak, Fangzhuang Community Health Service Center applied the intelligent family doctor model to carry out joint community prevention and control of coronavirus disease 2019 (COVID-19) epidemic. The intelligent family doctor model is a people-centered, information-based approach, which integrates community resources to carry out health education, counseling and public opinion guidance. With this model the community health service center can effectively screen and follow up the key groups to strengthen the joint prevention and control of COVID-19; at the same time it also can manage the chronic disease and the elderly care. This article introduces the intelligent family doctor model, and analyzes its advantages and existing problems, to provide references for community epidemic prevention and control.

During the prevention and control of new coronavirus pneumonia, Fangzhuang community health service center gave full play the advantages of internet technology, developed and implemented the Intelligent Family Doctors Optimized Coordination Model for epidemic prevention in the community. An epidemic prevention and control team was established in the center, the team carried out the epidemic prevention and control with the opening of health records and by using "doctors around" APP. The center also opened the intelligent voice return visits, the 24-hour service hotline, and WeChat public column to provide consultation for the community residents; it also strentherned pre-check triage, realized the medical insurance sharing and the other measures. All these measures enabled the residents to have a correct understanding of the epidemic situation, eased their psychological panic, enhanced their sense of gain, and made residents more convenient to seek medical treatment nearby reducing interpersonal contact and aggregation, reduced the risk of cross-infection, and achieved accurate health management among community residents during COVID-19 epidemic.

Objective:To compare the accuracy of influenza-like illness (ILI) surveillance by automatic computer analysis based on electronic medical records and by doctor’s report.Methods:A total of 3 542 patients who presented to Yichang Central Hospital fever clinic, respiratory department or emergency department between April to October 2019 with an ICD-10 code for acute respiratory illness (J00-J22) and complete electronic medical information of ILI related syndromes were drawn as the study sample. Taking the classification of the study sample according to the ILI case definition by influenza surveillance professionals as the gold standard, draw the receiver operating characteristic (ROC) curve and calculate sensitivity, specificity, diagnostic consistency to compared the accuracy of ILI surveillance by automatic computer analysis and by doctor's report.Results:Median age of 3 542 cases was 30 (24, 38) years old; 1 179 cases (33.29%) compliance with the case definition, ILI reported by doctors was 1 306 cases (36.87%), and computer automatic identification ILI were 1 150 cases (32.47%); 1 391 (39.27%) cases were men. The results of automatic computer analysis and doctor report consistency of kappa values with gold standard judgment were 0.97 and 0.66 respectively; area under the ROC curve was 0.98 and 0.84, respectively. And the sensitivity and specificity of automatic computer analysis were higher than that of doctor's report (all P values were <0.001), the sensitivity was 96.95% and 82.27%, and the specificity was 99.70% and 85.78%, respectively. Conclusion:The automatic computer analysis based on electronic medical records can identified ILI cases with good sensitivity and specificity in ILI case surveillance.

Objective:To compare the accuracy of influenza-like illness (ILI) surveillance by automatic computer analysis based on electronic medical records and by doctor’s report.Methods:A total of 3 542 patients who presented to Yichang Central Hospital fever clinic, respiratory department or emergency department between April to October 2019 with an ICD-10 code for acute respiratory illness (J00-J22) and complete electronic medical information of ILI related syndromes were drawn as the study sample. Taking the classification of the study sample according to the ILI case definition by influenza surveillance professionals as the gold standard, draw the receiver operating characteristic (ROC) curve and calculate sensitivity, specificity, diagnostic consistency to compared the accuracy of ILI surveillance by automatic computer analysis and by doctor's report.Results:Median age of 3 542 cases was 30 (24, 38) years old; 1 179 cases (33.29%) compliance with the case definition, ILI reported by doctors was 1 306 cases (36.87%), and computer automatic identification ILI were 1 150 cases (32.47%); 1 391 (39.27%) cases were men. The results of automatic computer analysis and doctor report consistency of kappa values with gold standard judgment were 0.97 and 0.66 respectively; area under the ROC curve was 0.98 and 0.84, respectively. And the sensitivity and specificity of automatic computer analysis were higher than that of doctor's report (all P values were <0.001), the sensitivity was 96.95% and 82.27%, and the specificity was 99.70% and 85.78%, respectively. Conclusion:The automatic computer analysis based on electronic medical records can identified ILI cases with good sensitivity and specificity in ILI case surveillance.

In the original publication the Supplementary Material and Fig. 2 are incorrect. The correct version is provided in this correction article. The text HBG2 appearing in the article should be read as HBG1.