Abstract Background The outbreak of coronavirus disease 2019 (COVID-19) has become a global pandemic acute infectious disease, especially with the features of possible asymptomatic carriers and high contagiousness. It causes acute respiratory distress syndrome and results in a high mortality rate if pneumonia is involved. Currently, it is difficult to quickly identify asymptomatic cases or COVID-19 patients with pneumonia due to limited access to reverse transcription-polymerase chain reaction (RT-PCR) nucleic acid tests and CT scans, which facilitates the spread of the disease at the community level, and contributes to the overwhelming of medical resources in intensive care units. Goal This study aimed to develop a scientific and rigorous clinical diagnostic tool for the rapid prediction of COVID-19 cases based on a COVID-19 clinical case database in China, and to assist global frontline doctors to efficiently and precisely diagnose asymptomatic COVID-19 patients and cases who had a false-negative RT-PCR test result. Methods With online consent, and the approval of the ethics committee of Zhongshan Hospital Fudan Unversity (approval number B2020-032R) to ensure that patient privacy is protected, clinical information has been uploaded in real-time through the New Coronavirus Intelligent Auto-diagnostic Assistant Application of cloud plus terminal (nCapp) by doctors from different cities (Wuhan, Shanghai, Harbin, Dalian, Wuxi, Qingdao, Rizhao, and Bengbu) during the COVID-19 outbreak in China. By quality control and data anonymization on the platform, a total of 3,249 cases from COVID-19 high-risk groups were collected. These patients had SARS-CoV-2 RT-PCR test results and chest CT scans, both of which were used as the gold standard for the diagnosis of COVID-19 and COVID-19 pneumonia. In particular, the dataset included 137 indeterminate cases who initially did not have RT-PCR tests and subsequently had positive RT-PCR results, 62 suspected cases who initially had false-negative RT-PCR test results and subsequently had positive RT-PCR results, and 122 asymptomatic cases who had positive RT-PCR test results, amongst whom 31 cases were diagnosed. We also integrated the function of a survey in nCapp to collect user feedback from frontline doctors. Findings We applied the statistical method of a multi-factor regression model to the training dataset (1,624 cases) and developed a prediction model for COVID-19 with 9 clinical indicators that are fast and accessible: ‘Residing or visiting history in epidemic regions’, ‘Exposure history to COVID-19 patient’, ‘Dry cough’, ‘Fatigue’, ‘Breathlessness’, ‘No body temperature decrease after antibiotic treatment’, ‘Fingertip blood oxygen saturation ≤93%’, ‘Lymphopenia’, and ‘C-reactive protein (CRP) increased’. The area under the receiver operating characteristic (ROC) curve (AUC) for the model was 0.88 (95% CI: 0.86, 0.89) in the training dataset and 0.84 (95% CI: 0.82, 0.86) in the validation dataset (1,625 cases). To ensure the sensitivity of the model, we used a cutoff value of 0.09. The sensitivity and specificity of the model were 98.0% (95% CI: 96.9%, 99.1%) and 17.3% (95% CI: 15.0%, 19.6%), respectively, in the training dataset, and 96.5% (95% CI: 95.1%, 98.0%) and 18.8% (95% CI: 16.4%, 21.2%), respectively, in the validation dataset. In the subset of the 137 indeterminate cases who initially did not have RT-PCR tests and subsequently had positive RT-PCR results, the model predicted 132 cases, accounting for 96.4% (95% CI: 91.7%, 98.8%) of the cases. In the subset of the 62 suspected cases who initially had false-negative RT-PCR test results and subsequently had positive RT-PCR results, the model predicted 59 cases, accounting for 95.2% (95% CI: 86.5%, 99.0%) of the cases. Considering the specificity of the model, we used a cutoff value of 0.32. The sensitivity and specificity of the model were 83.5% (95% CI: 80.5%, 86.4%) and 83.2% (95% CI: 80.9%, 85.5%), respectively, in the training dataset, and 79.6% (95% CI: 76.4%, 82.8%) and 81.3% (95% CI: 78.9%, 83.7%), respectively, in the validation dataset, which is very close to the published AI model. The results of the online survey ‘Questionnaire Star’ showed that 90.9% of nCapp users in WeChat mini programs were ‘satisfied’ or ‘very satisfied’ with the tool. The WeChat mini program received a significantly higher satisfaction rate than other platforms, especially for ‘availability and sharing convenience of the App’ and ‘fast speed of log-in and data entry’. Discussion With the assistance of nCapp, a mobile-based diagnostic tool developed from a large database that we collected from COVID-19 high-risk groups in China, frontline doctors can rapidly identify asymptomatic patients and avoid misdiagnoses of cases with false-negative RT-PCR results. These patients require timely isolation or close medical supervision. By applying the model, medical resources can be allocated more reasonably, and missed diagnoses can be reduced. In addition, further education and interaction among medical professionals can improve the diagnostic efficiency for COVID-19, thus avoiding the transmission of the disease from asymptomatic patients at the community level.
<b>Objective</b> We aimed to determine the individual and combined associations of lifestyle and metabolic factors with new-onset diabetes and major cardiovascular events among Chinese population aged 40 years or older. <p><b>Research design and methods </b>Baseline lifestyle information, waist circumference, blood pressure, lipid profiles and glycemic status were obtained in a nationwide, multicenter, prospective study of 170 240 participants. During the up to 5 years of follow-up, we detected 7 847 diabetes according to the American Diabetes Association 2010 criteria and 3 520 cardiovascular events including cardiovascular death, myocardial infarction, stroke, and hospitalized or treated heart failure.</p> <p><b>Results: </b>Based on 36.13% (population-attributable fraction, PAF) risk attributed to metabolic risk components collectively, physical inactivity (8.59%), sedentary behavior (6.35%), and unhealthy diet (4.47%) moderately contributed to incident diabetes. Physical inactivity (13.34%), unhealthy diet (8.70%), and current smoking (3.38%) significantly contributed to the risk of major cardiovascular events, on the basis of 37.42% PAF attributed to a cluster of metabolic risk factors. Significant associations of lifestyle health status with diabetes and cardiovascular events were found across all metabolic health categories. Risks of new-onset diabetes and major cardiovascular events increased simultaneously according to the worsening of lifestyle and metabolic health status.</p> <p><b>Conclusions: </b>We showed robust effects of lifestyle status on new-onset diabetes and major cardiovascular events regardless of metabolic status and a graded increment of risk according to the combination of lifestyle and metabolic health, highlighting the importance of lifestyle modification regardless of the present metabolic status.</p>
Failure mode of coal mill roller was comprehensively analyzed and current situation of mill roller repair was elaborated. Process characteristic and application effect of metal cerement and high chromium cast iron building-up materials were expounded in the article. Owing to excellent wear resistant and low cost, high chromium cast iron building-up materials have been applied widely. The current research situation of self-shielded flux-cored wire with high chromium cast iron was summarized and the existing problem and probable solution were pointed. Key word: Coal mill roller, Abrasive wear, Flux-cored wire, Resurfacing welding
Organic acids were investigated in the rain sequence. Samples were collected in Shanghai (East China) over a one-year period using an automatic volume-based sequential rain sampler designed by ourselves. Organic acids significantly contributed (17.8 ± 10.2%) to the acidity of rainfall events in Shanghai. We observed that the concentration of each water-soluble ion in the sequential volume-based rainwater samples did not change significantly after the cumulative rainfall reached ~1.2 mm, on average. The volume-weighted mean (VWM) concentrations of formic acid, acetic acid, and oxalic acid were 13.54 μeq L−1, 8.32 μeq L−1, and 5.85 μeq L−1, respectively. Organic acids might mostly come from fine particles, which was the reason for the differences in acid concentrations in rainfall events, cloud water, and early sequences of rainfall events. The VWM concentrations of organic acids in rainfall events, cloud water, and early sequences of rainfall events were highest in spring and lowest in winter. Further analysis, including positive matrix factorization (PMF), suggested that vehicle exhaust and secondary emission sources were dominant contributors of organic acids in rainfall events (40.5%), followed by biological emission sources (37.3%), and biomass combustion sources (18.6%). The overall results not only reveal the critical role of organic acids in cloud water and rainfall events but also indicate organic acids might pose an ecological threat to the local surface ecosystem.
Understanding various phenomena in non-equilibrium dynamics of closed quantum many-body systems, such as quantum thermalization, information scrambling, and nonergodic dynamics, is a crucial for modern physics. Using a ladder-type superconducting quantum processor, we perform analog quantum simulations of both the $XX$ ladder and one-dimensional (1D) $XX$ model. By measuring the dynamics of local observables, entanglement entropy and tripartite mutual information, we signal quantum thermalization and information scrambling in the $XX$ ladder. In contrast, we show that the $XX$ chain, as free fermions on a 1D lattice, fails to thermalize, and local information does not scramble in the integrable channel. Our experiments reveal ergodicity and scrambling in the controllable qubit ladder, and opens the door to further investigations on the thermodynamics and chaos in quantum many-body systems.
<p> </p> <p><strong>OBJECTIVE </strong></p> <p>To investigate the causal role of choline metabolites mediating sodium-glucose co-transporter 2 (SGLT2) inhibition on coronary artery disease (CAD) and type 2 diabetes (T2D) using Mendelian randomization (MR). </p> <p><strong>RESEARCH DESIGN AND METHODS </strong></p> <p>A two-sample two-step MR was used to determine: 1) causal effects of SGLT2 inhibition on CAD and T2D; 2) causal effects of three choline metabolites, total choline, phosphatidylcholine and glycine on CAD and T2D; and 3) mediation effects of these metabolites. Genetic proxies for SGLT2 inhibition were identified as variants in the <em>SLC5A2 </em>gene that were associated with both levels of gene expression and hemoglobin A1c. Summary statistics for metabolites were from UK Biobank, CAD from CARDIoGRAMplusC4D consortium, and T2D from DIAGRAM and FinnGen study. </p> <p><strong>RESULTS</strong> </p> <p>SGLT2 inhibition (per 1-SD, 6.75 mmol/mol [1.09%] lowering of HbA1c) was associated with lower risk of T2D and CAD (OR 0.25, 95% CI [0.12-0.54], and 0.51 [0.28-0.94], respectively), and positively with total choline (β 0.39, 95% CI [0.06-0.72]), phosphatidylcholine (β 0.40, 95% CI [0.13-0.67]) and glycine (β 0.34, 95% CI [0.05-0.63]). Total choline (OR 0.78, 95% CI [0.68-0.89]) and phosphatidylcholine (OR 0.81, 95% CI [0.72-0.91]) were associated with T2D, but not with CAD; while glycine was associated with CAD (OR 0.94, 95%CI [0.91-0.98]) but not with T2D. Mediation analysis showed evidence of indirect effect of SGLT2 inhibition on T2D through total choline (OR 0.91, 95% CI [0.83-0.99]) and phosphatidylcholine (OR 0.93, 95% CI [0.87, 0.99]) with a mediated proportion of 8% and 5% of the total effect, respectively; while through glycine (OR 0.98, [95% CI, 0.96-1.00] on CAD, with a mediated proportion of 2%. The results were well validated in at least one independent dataset.</p> <p><strong>CONCLUSIONS </strong></p> <p>Our study identified the causal roles of SGLT2 inhibition on choline metabolites. SGLT2 inhibition may influence T2D and CAD through different choline metabolites.</p>
A Study on Teaching English Reading through the Communicative Approach and Cooperative Learning in a Middle School of China Yu Xu (Qinghai Normanl University) Hwa-ja Lee (Sunchon National University) The subject of this dissertation is a research project in Qinghai Normal University No.1 Attached Middle School, to confirm the assumption that CA and CL will achieve a better result than traditional teaching in such a culturally, economically, academically deprived and multi-national area as Qinghai. During the half-year research project, the communicative instructional model was applied to reform traditional teaching, incorporating cooperative learning experiences. In the 2003 research project, the questionnaires, exams, performance records and the interviews of the students and the teachers indicate that teaching reading through the Communicative approach and Cooperative learning in a middle school in China can achieve a better result in improving students’ four language skills and developing their communicative competence. A follow-up study in 2009 was also conducted to trace the impact of the 2003 project, which was radical in English education in remote areas at that time. The result of the 2009 study was the same as the research project done in 2003, shown in data collected from questionnaires, paper interviews and classroom observation. So that the Communicative approach and Cooperative learning are still seen as crucial and important to English teaching, it must be pointed out that students will become more skilled in reading, listening, writing and speaking English if CA and CL continue to be used.
Nocardia are infrequent pathogens that disproportionately afflict organ transplant recipients. The present study aimed to summarize the clinical manifestations, diagnostic approaches, and treatment strategies of nocardiosis in lung transplant recipients. This retrospective study reviewed the clinical data of adult lung transplant recipients who were complicated with nocardiosis between January 2018 and December 2021 at the largest lung transplant center in South China. The incidence of nocardiosis was 4.2% (13/316), including 9 cases of pulmonary nocardiosis and 4 disseminated nocardiosis (blood, pulmonary and intracranial). The accuracy in diagnosing nocardiosis was 77.8% by culture and 100% by metagenomic next-generation sequencing (mNGS). Nocardia farcinica was the most common causative pathogen. Trimethoprim-sulfamethoxazole–based combination therapy was administered initially, followed by a single antibiotic as the maintained therapy, lasting for 4–8 months. mNGS is more accurate than culture in diagnosing nocardiosis. Most patients responded well to the antibiotic therapy with combined antibiotics at the initial stage followed by a single antibiotic treatment.
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