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To construct the prediction model of death risk of Stanford type A aortic dissection (AAD) based on Cox proportional risk regression model.AAD patients who were diagnosed and received surgical treatment admitted to the department of cardiothoracic surgery of Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology from January 1st, 2019 to April 30th, 2020 were enrolled. The general situation, clinical manifestations, pre-hospital data, laboratory examination and imaging examination results of the patients were collected. The observation period was up to the death of the patients or ended on April 30th, 2021. They were divided into the model group and the verification group according to the ratio of 7:3. Lasso method was used to screen prognostic variables from the data of the modeling group, and multivariate Cox regression analysis was included to construct the AAD death risk prediction model, which was displayed by nomogram. The receiver operator characteristic curve (ROC curve) was used to evaluate the discrimination of the model, the calibration curve to evaluate the accuracy of the model, and the clinical decision curve (DCA) to evaluate the effectiveness of the model.A total of 454 patients with AAD were finally included, and the mortality was 19.4% (88/454). Lasso regression analysis was used to screen out 10 variables from the data of 317 patients in the model group, and the prediction model of death risk was constructed: 0.511×abdominal pain+1.061×syncope+0.428×lower limb pain/numbness-0.365×emergency admission-1.933×direct admission-1.493×diagnosis before referral+0.662×preoperative systolic blood pressure (SBP)100 mmHg (1 mmHg = 0.133 kPa)+0.632×hypersensitivity cardiac troponin I (hs-cTnI)34.2 ng/L+1.402×De Bakey type+0.641× pulmonary infection+1.472×postoperative delirium. The area under the ROC curve (AUC) and 95% confidence interval (95%CI) of the AAD death risk prediction model were 0.873 (0.817-0.928), and that of the verification group was 0.828 (0.740-0.916). DCA showed that the net benefit value of the model was higher. The calibration curve showed that there was a good correlation between the actual observation results and the model prediction results.The AAD death risk prediction model based on abdominal pain, syncope, lower limb pain/numbness, mode of admission, diagnosis before referral, preoperative SBP100 mmHg, hs-cTnI34.2 ng/L, De Bakey type , pulmonary infection, and postoperative delirium can effectively help clinicians identify patients at high risk for AAD, evaluate their postoperative survival and timely adjust treatment strategies.

Flavonoids can influence the bioaccessibility of carotenoids. In this study, the effect of citrus flavanones on β-carotene (Bc) bioaccessibility, lipid lipolysis and Bc micellarization were investigated. The results show that citrus flavanones significantly increased the bioaccessibility of Bc, made it from 15.85% to 19.67% (p

Coronavirus disease 2019 (COVID-19) has become a worldwide pandemic. Hospitalized patients of COVID-19 suffer from a high mortality rate, motivating the development of convenient and practical methods that allow clinicians to promptly identify high-risk patients. Here, we have developed a risk score using clinical data from 1479 inpatients admitted to Tongji Hospital, Wuhan, China (development cohort) and externally validated with data from two other centers: 141 inpatients from Jinyintan Hospital, Wuhan, China (validation cohort 1) and 432 inpatients from The Third People’s Hospital of Shenzhen, Shenzhen, China (validation cohort 2). The risk score is based on three biomarkers that are readily available in routine blood samples and can easily be translated into a probability of death. The risk score can predict the mortality of individual patients more than 12 d in advance with more than 90% accuracy across all cohorts. Moreover, the Kaplan–Meier score shows that patients can be clearly differentiated upon admission as low, intermediate, or high risk, with an area under the curve (AUC) score of 0.9551. In summary, a simple risk score has been validated to predict death in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); it has also been validated in independent cohorts.

Highlights • There were 9.87% of COVID-19 patients presenting with diarrhea. • The initial symptom was diarrhea observed in 9% of 90 cases. • The most common fecal characteristics on admission were watery stool (64%). • Clinicians should be careful to promptly identify the patients with initial diarrhea symptom., Background The novel coronavirus disease (COVID-19) has spread worldwide. Herein, we aimed to clarify the epidemiological and clinical characteristics of patients presenting with diarrhea. Methods A descriptive design was adopted from Jan 10 to Feb 17, 2020. All the cases included were diagnosed with COVID-19 under the interim guidance of the WHO. Results 912 patients with COVID-19 were admitted to hospital, in which, 90 cases (9.87%) presented with diarrhea. Among the 90 cases, 8 cases (9%) presented with diarrhea as the initial symptom, and 24%, 17%, and 24% of the patients complained of nausea, vomiting, and poor appetite, respectively. The most common fecal characteristics on admission were watery stool (64%) and mushy stool (28%). For the defecation frequency, 37% of the cases defecated over three times a day. The median time from illness onset to diarrhea was 3.0 days (IQR 0.0–5.0) and the median duration of diarrhea was 5.0 days (IQR 2.0–9.3). Conclusion Clinicians are required to promptly identify the patients with initial diarrhea symptoms and pay adequate attention to the nutrient requirements of the patients with diarrhea during hospitalization. Standardized management is also recommended for the discharge of the patients to avoid potential fecal-oral transmission.

Coronavirus Disease 2019 (COVID-19) has become a world-wide pandemic. Hospitalized patients of COVID-19 suffer from a high mortality rate, motivating the development of convenient and practical methods for clinicians to promptly identify high-risk patients. Here we developed a risk score using clinical data from 1,479 inpatients admitted to Tongji Hospital, Wuhan, China (development cohort) and externally validated with data from two other centers: 141 inpatients from Jinyintan Hospital in Wuhan (validation cohort 1) and 432 inpatients from the Third People’s Hospital Shenzhen (validation cohort 2). The risk score is based on three biomarkers readily available in routine blood samples and can be easily translated into a probability of death. The risk score can predict the mortality of individual patients more than 12 days in advance with more than 90% accuracy across all cohorts. Moreover, the Kaplan-Meier score shows that patients upon admission can clearly be differenciated into low, medium or high risk, with an AUC score of 0.9551. In summary, a simple risk score was validated to predict death in patients infected with COVID-19 and was validated in independent cohorts.

The sudden increase in COVID-19 cases is putting high pressure on healthcare services worldwide. At this stage, fast, accurate and early clinical assessment of the disease severity is vital. To support decision making and logistical planning in healthcare systems, this study leverages a database of blood samples from 485 infected patients in the region of Wuhan, China, to identify crucial predictive biomarkers of disease mortality. For this purpose, machine learning tools selected three biomarkers that predict the mortality of individual patients more than 10 days in advance with more than 90% accuracy: lactic dehydrogenase (LDH), lymphocyte and high-sensitivity C-reactive protein (hs-CRP). In particular, relatively high levels of LDH alone seem to play a crucial role in distinguishing the vast majority of cases that require immediate medical attention. This finding is consistent with current medical knowledge that high LDH levels are associated with tissue breakdown occurring in various diseases, including pulmonary disorders such as pneumonia. Overall, this Article suggests a simple and operable decision rule to quickly predict patients at the highest risk, allowing them to be prioritized and potentially reducing the mortality rate. Early and accurate clinical assessment of disease severity in COVID-19 patients is essential for planning the allocation of scarce hospital resources. An explainable machine learning tool trained on blood sample data from 485 patients from Wuhan selected three biomarkers for predicting mortality of individual patients with high accuracy.

Objective: To observe and analyze the application effect of the combined mode of Massive Open Online Course (MOOC) micro-video during the COVID-19 epidemic period in the distance teaching practice of interns in the emergency department. Materials and Methods: The subjects of this study were 60 trainee nurses who conducted emergency nursing practice in Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology from January 1 to February 29, 2020. At the time of the COVID-19 outbreak in Wuhan, they were divided into two groups: (1) the experimental group (combined mode of MOOC micro-video) and (2) the control group (traditional theory teaching combined with clinical practice teaching). The differences of theoretical and practical examination scores and teaching satisfaction between the two groups were compared. Results: There was no significant difference in theoretical, practical, and total examination scores between the two groups, but in terms of teaching satisfaction, the overall satisfaction, the degree of easy understanding, the evaluation of teachers and learning results in the experimental group were higher than those in the control group, with statistical difference (p < 0.05). Conclusion: Compared with the traditional teaching methods, the effect of combined mode of MOOC micro-video in emergency nursing practice is the same as that of traditional teaching methods, but the satisfaction is higher, so it is more suitable to be used in nursing practice during the COVID-19 epidemic period, so as to effectively reduce the cross-infection between doctors, nurses, and teaching staff.

BACKGROUND Most patients with coronavirus disease (COVID-19) who show mild symptoms are sent home by physicians to recover. However, the condition of some of these patients becomes severe or critical as the disease progresses. OBJECTIVE The aim of this study was to evaluate a telemedicine model that was developed to address the challenges of treating patients with progressive COVID-19 who are home-quarantined and shortages in the medical workforce. METHODS A telemedicine system was developed to continuously monitor the progression of home-quarantined patients with COVID-19. The system was built based on a popular social media smartphone app called WeChat; the app was used to establish two-way communication between a multidisciplinary team consisting of 7 medical workers and 188 home-quarantined individuals (including 74 confirmed patients with COVID-19). The system helped patients self-assess their conditions and update the multidisciplinary team through a telemedicine form stored on a cloud service, based on which the multidisciplinary team made treatment decisions. We evaluated this telemedicine system via a single-center retrospective study conducted at Tongji Hospital in Wuhan, China, in January 2020. RESULTS Among 188 individuals using the telemedicine system, 114 (60.6%) were not infected with COVID-19 and were dismissed. Of the 74 confirmed patients with COVID-19, 26 (35%) recovered during the study period and voluntarily stopped using the system. The remaining 48/76 confirmed patients with COVID-19 (63%) used the system until the end of the study, including 6 patients whose conditions progressed to severe or critical. These 6 patients were admitted to hospital and were stabilized (one received extracorporeal membrane oxygenation support for 17 days). All 74 patients with COVID-19 eventually recovered. Through a comparison of the monitored symptoms between hospitalized and nonhospitalized patients, we found prolonged persistence and deterioration of fever, dyspnea, lack of strength, and muscle soreness to be diagnostic of need for hospitalization. CONCLUSIONS By continuously monitoring the changes in several key symptoms, the telemedicine system reduces the risks of delayed hospitalization due to disease progression for patients with COVID-19 quarantined at home. The system uses a set of scales for quarantine management assessment that enables patients to self-assess their conditions. The results are useful for medical staff to identify disease progression and, hence, make appropriate and timely treatment decisions. The system requires few staff to manage a large cohort of patients. In addition, the system can solicit help from recovered but self-quarantined medical workers to alleviate shortages in the medical workforce and free healthy medical workers to fight COVID-19 on the front line. Thus, it optimizes the usage of local medical resources and prevents cross-infections among medical workers and patients.

Satisfactory outcome was observed in one mild case and one severe case of COVID-19 pneumonia after the use of the online/offline multidisciplinary quarantine observation form, online monitoring, an...

At present, new coronavirus pneumonia (COVID-19) is circulating worldwide. The pathogen of this coronavirus pneumonia is named SARS-CoV-2. The virus has a long incubation period and is highly contagious. There is currently no specific targeted drug treatment. The focus of anti-epidemic work should be more on prevention and control while cutting off the virus transmission route while treating infected patients, and protecting healthy people. In order to protect the safety and health of the Chinese citizens and to maintain the safety of world public health, the Chinese government and people have made unprecedented efforts to control the epidemic. Many people in the international community have joined in limiting the spread of COVID-19. This article combines the development of COVID-19 epidemic situation in Wuhan, the relevant prevention and control measures of the Wuhan government and local health authorities to share Wuhan’s experience on control the cluster epidemic and provide new suggestions and ideas for epidemic prevention and control.

BackgroundFalse negative results of SARS-CoV-2 nucleic acid detection pose threats to COVID-19 patients and medical workers alike.ObjectiveTo develop multivariate models to determine clinical characteristics that contribute to false negative results of SARS-CoV-2 nucleic acid detection, and use them to predict false negative results as well as time windows for testing positive.DesignRetrospective Cohort Study (Ethics number of Tongji Hospital: No. IRBID: TJ-20200320)SettingA database of outpatients in Tongji Hospital (University Hospital) from 15 January 2020 to 19 February 2020.Patients1,324 outpatients with COVID-19MeasurementsClinical information on CT imaging reports, blood routine tests, and clinic symptoms were collected. A multivariate logistic regression was used to explain and predict false negative testing results of SARS-CoV-2 detection. A multivariate accelerated failure model was used to analyze and predict delayed time windows for testing positive.ResultsOf the 1,324 outpatients who diagnosed of COVID-19, 633 patients tested positive in their first SARS-CoV-2 nucleic acid test (47.8%), with a mean age of 51 years (SD=14.9); the rest, which had a mean age of 47 years (SD=15.4), tested negative in the first test. “Ground glass opacity” in a CT imaging report was associated with a lower chance of false negatives (aOR, 0.56), and reduced the length of time window for testing positive by 26%. “Consolidation” was associated with a higher chance of false negatives (aOR, 1.57), and extended the length of time window for testing positive by 44%. In blood routine tests, basophils (aOR, 1.28) and eosinophils (aOR, 1.29) were associated with a higher chance of false negatives, and were found to extend the time window for testing positive by 23% and 41%, respectively. Age and gender also affected the significantly.LimitationData were generated in a large single-center study.ConclusionTesting outcome and positive window of SARS-CoV-2 detection for COVID-19 patients were associated with CT imaging results, blood routine tests, and clinical symptoms. Taking into account relevant information in CT imaging reports, blood routine tests, and clinical symptoms helped reduce a false negative testing outcome. The predictive AFT model, what we believe to be one of the first statistical models for predicting time window of SARS-CoV-2 detection, could help clinicians improve the accuracy and efficiency of the diagnosis, and hence, optimizes the timing of nucleic acid detection and alleviates the shortage of nucleic acid detection kits around the world.Primary Funding SourceNone.

The sudden increase of COVID-19 cases is putting a high pressure on healthcare services worldwide. At the current stage, fast, accurate and early clinical assessment of the disease severity is vital. To support decision making and logistical planning in healthcare systems, this study leverages a database of blood samples from 404 infected patients in the region of Wuhan, China to identify crucial predictive biomarkers of disease severity. For this purpose, machine learning tools selected three biomarkers that predict the survival of individual patients with more than 90% accuracy: lactic dehydrogenase (LDH), lymphocyte and high-sensitivity C-reactive protein (hs-CRP). In particular, relatively high levels of LDH alone seem to play a crucial role in distinguishing the vast majority of cases that require immediate medical attention. This finding is consistent with current medical knowledge that high LDH levels are associated with tissue breakdown occurring in various diseases, including pulmonary disorders such as pneumonia. Overall, this paper suggests a simple and operable formula to quickly predict patients at the highest risk, allowing them to be prioritised and potentially reducing the mortality rate.FundingNone.

SummaryBackgroundThe recent outbreak of the novel coronavirus in December 2019 (COVID-19) has activated top-level response nationwide. We developed a new treatment model based on the online-to-offline (O2O) model for the home isolated patients, because in the early stages the medical staff were insufficient to cope with so many patients.MethodsIn this single-centered, retrospective study, we enrolled 48 confirmed/suspected COVID-19 patients who underwent home isolation in Wuhan between January 6 and January 31, 2020. By WeChat and online document editing all patients were treated with medical observation scale. The clinical indications such as Fever, Muscle soreness, Dyspnea and Lack of strength were collected with this system led by medical staff in management, medicine, nursing, rehabilitation and psychology.FindingsThe mean age of 48 patients was 39·08±13·88 years, 35(72·9%) were women. Compared with non-hospitalized patients, inpatients were older(≥70years, 2·4% vs 33·3%, PInterpretationMonitoring the trends of symptoms is more important for identifying severe cases. Excessive laboratory data and physical examination are not necessary for the evaluation of patients with mild symptoms. The system we developed is the first to convert the subjective symptoms of patients into objective scores. This type of O2O, subjective-to-objective strategy may be used in regions with similar highly infectious diseases to minimize the possibility of infection among medical staff.

Background: Coronavirus disease 2019 (COVID-19) has spread globally. However, the association between COVID-19 and disseminated intravascular coagulation (DIC) has been scarcely addressed. We aimed to systematically characterize the clinical features and examine risk factors for DIC development in COVID-19 patients. Methods: In this single-centered, retrospective, and observational study, all patients with DIC (N=59) and 270 patients without DIC were matched by propensity score matching based on age, sex, and comorbidities. Demographic data, symptoms, radiological, laboratory examinations, and clinical outcomes were compared between patients with and without DIC. Furthermore, univariable and multivariable logistic regression were used to explore the risk factors associated with DIC development in COVID-19 patients. Findings: Higher proportion of patients with DIC and COVID-19 (54 of 59 [91·53%]) developed into death than non DIC patients (58 of 270 [21·48%]). Patients with DIC presented aggravated inflammation responses, liver damage, and especially coagulation dysfunction. Moreover, in addition to previously reported coagulation-related markers, such as FDP, D-dimer, and platelet, we also identified several novel risk factors associated with DIC development, including decreased fibrinogen (OR=0·476, 95%CI=0·380-0·596, P

The effects of ultrasound (US) pretreatment on natural structure modification from broccoli were investigated, and the influences of the modification on the bioaccessibility and cellular uptake efficiency of bioactive compounds were analyzed. US treated broccoli showed significant cell wall and cellular matrix degradation, which could be mainly responsible for the release of bioactive compounds from broccoli cells. Data from digested broccoli fractions showed that US pretreatment resulted in decrease of viscosity of the digesta, which could be partially responsible for the release of bioactive compounds from the digesta into the supernatant. High bioaccessibility of bioactive compounds appeared to be somewhat a similar high cellular uptake. The reduction of the particle size upon US pretreatment might be contributed to the increased carotenoid cellular uptake. These data indicated that US pretreatment may be considered as a potential processing method for improving the bioavailability of bioactive compounds.