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We report a patient with severe spontaneous pneumomediastinum (SPM), pneumothorax and widespread subcutaneous emphysema with acute epiglottitis after inhaling pepper spray. The effects of pepper spray, which is a lachrymatory agent, on the respiratory system have not been reported. Upper airway obstruction is not a well-described cause of SPM, with which subcutaneous emphysema and pneumothorax might coexist; thus, mechanical ventilation might be detrimental.

Recurrence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive detection in infected but recovered individuals has been reported. Patients who have recovered from coronavirus disease 2019 (COVID-19) could profoundly impact the health care system. We sought to define the kinetics and relevance of PCR-positive recurrence during recovery from acute COVID-19 to better understand risks for prolonged infectivity and reinfection. A series of 414 patients with confirmed SARS-Cov-2 infection, at The Second Affiliated Hospital of Southern University of Science and Technology in Shenzhen, China from January 11 to April 23, 2020. Statistical analyses were performed of the clinical, laboratory, radiologic image, medical treatment, and clinical course of admission/quarantine/readmission data, and a recurrence predictive algorithm was developed. 16.7% recovered patients with PCR positive recurring one to three times, despite being in strict quarantine. Younger patients with mild pulmonary respiratory syndrome had higher risk of PCR positivity recurrence. The recurrence prediction model had an area under the ROC curve of 0.786. This case series provides characteristics of patients with recurrent SARS-CoV-2 positivity. Use of a prediction algorithm may identify patients at high risk of recurrent SARS-CoV-2 positivity and help to establish protocols for health policy.

BACKGROUND Cardiac dysrhythmia is currently an extremely common disease. Severe arrhythmias often cause a series of complications, including congestive heart failure, fainting or syncope, stroke, and sudden death. OBJECTIVE The aim of this study was to predict incident arrhythmia prospectively within a 1-year period to provide early warning of impending arrhythmia. METHODS Retrospective (1,033,856 individuals enrolled between October 1, 2016, and October 1, 2017) and prospective (1,040,767 individuals enrolled between October 1, 2017, and October 1, 2018) cohorts were constructed from integrated electronic health records in Maine, United States. An ensemble learning workflow was built through multiple machine learning algorithms. Differentiating features, including acute and chronic diseases, procedures, health status, laboratory tests, prescriptions, clinical utilization indicators, and socioeconomic determinants, were compiled for incident arrhythmia assessment. The predictive model was retrospectively trained and calibrated using an isotonic regression method and was prospectively validated. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). RESULTS The cardiac dysrhythmia case-finding algorithm (retrospective: AUROC 0.854; prospective: AUROC 0.827) stratified the population into 5 risk groups: 53.35% (555,233/1,040,767), 44.83% (466,594/1,040,767), 1.76% (18,290/1,040,767), 0.06% (623/1,040,767), and 0.003% (27/1,040,767) were in the very low-risk, low-risk, medium-risk, high-risk, and very high-risk groups, respectively; 51.85% (14/27) patients in the very high-risk subgroup were confirmed to have incident cardiac dysrhythmia within the subsequent 1 year. CONCLUSIONS Our case-finding algorithm is promising for prospectively predicting 1-year incident cardiac dysrhythmias in a general population, and we believe that our case-finding algorithm can serve as an early warning system to allow statewide population-level screening and surveillance to improve cardiac dysrhythmia care.

Various medical treatments for COVID-19 are attempted. After patients are discharged, SARS-CoV-2 recurring cases are reported and the recurrence could profoundly impact patient healthcare and social economics. To date, no data on the effects of medical treatments on recurrence has been published. We analyzed the treatment data of combinations of ten different drugs for the recurring cases in a single medical center, Shenzhen, China. A total of 417 patients were considered and 414 of them were included in this study (3 deaths) with mild-to-critical COVID-19. Patients were treated by 10 different drug combinations and followed up for recurrence for 28 days quarantine after being discharged from the medical center between February and May, 2020. We applied the Synthetic Minority Oversampling Technique (SMOTE) to overcome the rare recurring events in certain age groups and performed Virtual Twins (VT) analysis facilitated by random forest regression for medical treatment-recurrence classification. Among those drug combinations, Methylprednisolone/Interferon/Lopinavir/Ritonavir/Arbidol led to the lowest recurring rate (0.133) as compared to the average recurring rate (0.203). For the younger group (age 20-27) or the older group (age 60-70), the optimal drug combinations are different, but the above combination is still the second best. For obese patients, the combination of Ribavirin/Interferon/Lopinavir/Ritonavir/Arbidol led to the lowest recurring rate for age group of 20-50, whereas the combination of Interferon/Lopinavir/Ritonavir/Arbidol led to lowest recurring rate for age group of 50-70. The insights into combinatorial therapy we provided here shed lights on the use of a combination of (biological and chemical) anti-virus therapy and/or anti-cytokine storm as a potentially effective therapeutic treatment for COVID-19.

IMPORTANCEHow to appropriately care for patients who become PCR-negative for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is still not known. Patients who have recovered from coronavirus disease 2019 (COVID-19) could profoundly impact the health care system if a subset were to be PCR-positive again with reactivated SARS-CoV-2.OBJECTIVETo characterize a single center COVID-19 cohort with and without recurrence of PCR positivity, and develop an algorithm to identify patients at high risk of retest positivity after discharge to inform health care policy and case management decision-making.DESIGN, SETTING, AND PARTICIPANTSA cohort of 414 patients with confirmed SARS-CoV-2 infection, at The Second Affiliated Hospital of Southern University of Science and Technology in Shenzhen, China from January 11 to April 23, 2020.EXPOSURESPolymerase chain reaction (PCR) and IgM-IgG antibody confirmed SARS-CoV-2 infection.MAIN OUTCOMES AND MEASURESUnivariable and multivariable statistical analysis of the clinical, laboratory, radiologic image, medical treatment, and clinical course of admission/quarantine/readmission data to develop an algorithm to predict patients at risk of recurrence of PCR positivity.RESULTS16.7% (95CI: 13.0%-20.3%) patients retest PCR positive 1 to 3 times after discharge, despite being in strict quarantine. The driving factors in the recurrence prediction model included: age, BMI; lowest levels of the blood laboratory tests during hospitalization for cholinesterase, fibrinogen, albumin, prealbumin, calcium, eGFR, creatinine; highest levels of the blood laboratory tests during hospitalization for total bilirubin, lactate dehydrogenase, alkaline phosphatase; the first test results during hospitalization for partial pressure of oxygen, white blood cell and lymphocyte counts, blood procalcitonin; and the first test episodic Ct value and the lowest Ct value of the nasopharyngeal swab RT PCR results. Area under the ROC curve is 0.786.CONCLUSIONS AND RELEVANCEThis case series provides clinical characteristics of COVID-19 patients with recurrent PCR positivity, despite strict quarantine, at a 16.7% rate. Use of a recurrence prediction algorithm may identify patients at high risk of PCR retest positivity of SARS-CoV-2 and help modify COVID-19 case management and health policy approaches.Key PointsQuestionWhat are the characteristics, clinical presentations, and outcomes of COVID-19 patients with PCR retest positivity after resolution of the initial infection and consecutive negative tests? Can we identify recovered patients, prior to discharge, at risk of the recurrence of SARS-CoV-2 PCR positivity?FindingsIn this series of 414 COVID-19 inpatients discharged to a designated quarantine center, 69 retest positive (13 with 2 readmissions, and 3 with 3 readmissions). A multivariable model was developed to predict the risk of the recurrence of SARS-CoV-2 PCR positivity.MeaningRate and timing of the recurrence of PCR positivity following strict quarantine were characterized. Our prediction algorithm may have implications for COVID-19 clinical treatment, patient management, and health policy.

BACKGROUND: Recurrence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive detection in infected but recovered individuals has been reported. Patients who have recovered from coronavirus disease 2019 (COVID-19) could profoundly impact the health care system if a subset were to be polymerase chain reaction (PCR)-positive again with reactivated SARS-CoV-2. We sought to define the kinetics and relevance of PCR-positive recurrence during recovery from acute COVID-19 to better understand risks for prolonged infectivity and reinfection. METHODS: A series of COVID-19 414 patients, at The Second Affiliated Hospital of Southern University of Science and Technology in Shenzhen, China from January 11 to April 23, 2020. Univariable and multivariable statistical analysis of inpatient data were performed to develop an algorithm to predict patients at risk of recurrence of PCR positivity. FINDINGS: 16·7% recovered patients with PCR positive recurring one to three times, despite being in strict quarantine. Younger patients with mild pulmonary respiratory syndrome had higher risk of PCR positivity recurrence. The recurrence prediction model had an area under the ROC curve of 0·786. INTERPRETATION: This case series provides clinical characteristics of recovered COVID-19 patients with recurrent SARS-CoV-2 positivity, despite strict quarantine, at a 16·7% rate. Use of a recurrence prediction algorithm may identify patients at high risk of recurrent SARS-CoV-2 positivity and help understand reactivation and reinfection possibilities to establish protocols for health policy. FUNDING STATEMENT: This work was supported by grants from Sanming Project of Medicine in Shenzhen (Jia Huang, No. SZSM201812065); Bill & Melinda Gates Foundations (Lei Liu); and from National Natural Science Foundation of China (Jia Huang, No. 81501651) DECLARATION OF INTERESTS: The authors declare no competing interests. ETHICS APPROVAL STATEMENT: This study was approved by the Ethics Committee of the Second Affiliated Hospital of Southern University of Science and Technology. Written informed consent was obtained from all patients.

Background Cardiac dysrhythmia is currently an extremely common disease. Severe arrhythmias often cause a series of complications, including congestive heart failure, fainting or syncope, stroke, and sudden death. Objective The aim of this study was to predict incident arrhythmia prospectively within a 1-year period to provide early warning of impending arrhythmia. Methods Retrospective (1,033,856 individuals enrolled between October 1, 2016, and October 1, 2017) and prospective (1,040,767 individuals enrolled between October 1, 2017, and October 1, 2018) cohorts were constructed from integrated electronic health records in Maine, United States. An ensemble learning workflow was built through multiple machine learning algorithms. Differentiating features, including acute and chronic diseases, procedures, health status, laboratory tests, prescriptions, clinical utilization indicators, and socioeconomic determinants, were compiled for incident arrhythmia assessment. The predictive model was retrospectively trained and calibrated using an isotonic regression method and was prospectively validated. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). Results The cardiac dysrhythmia case-finding algorithm (retrospective: AUROC 0.854; prospective: AUROC 0.827) stratified the population into 5 risk groups: 53.35% (555,233/1,040,767), 44.83% (466,594/1,040,767), 1.76% (18,290/1,040,767), 0.06% (623/1,040,767), and 0.003% (27/1,040,767) were in the very low-risk, low-risk, medium-risk, high-risk, and very high-risk groups, respectively; 51.85% (14/27) patients in the very high-risk subgroup were confirmed to have incident cardiac dysrhythmia within the subsequent 1 year. Conclusions Our case-finding algorithm is promising for prospectively predicting 1-year incident cardiac dysrhythmias in a general population, and we believe that our case-finding algorithm can serve as an early warning system to allow statewide population-level screening and surveillance to improve cardiac dysrhythmia care.

1provide stillbirth rates retrieved from China’s National Maternal Near Miss Surveillance System between 2012 and 2014. The authors found the stillbirth rate was particularly high in young women (aged

Baicalein, a flavonoid compound, is one of the active constituents of the root of Scutellariae Radix. Its antitumor effects have attracted widespread attention worldwide. One of its major functions is to induce the apoptosis of tumor cells, but the antitumor mechanism is currently unclear. In the present study, we found that baicalein increased MG-63 cell mortality in a dose-dependent manner. Meanwhile, baicalein activated apoptosis through induced intracellular reactive oxygen species (ROS) generation, and that ROS scavenger N-acetyl-cysteine (NAC), glutathione (GSH), and superoxide dismutase (SOD) apparently inhibited intracellular ROS production, consequently attenuating the baicalein-induced apoptosis. Baicalein also induce the mitochondrial fragmentation which precedes the cell apoptosis. This morphological alteration is accompanied by an increase in the expression of the protein BNIP3 as well as Mul1 and Drp1. Furthermore, we show that the inhibition of BNIP3 expression can inhibit cell apoptosis by baicalein treatment. Taken together, our results bring the evidence of a mechanism that links apoptosis and ROS-induced BNIP3 expression in MG-63 cells with bacalein treatment and suggest that baicalein has a good potential as an anti-osteosarcoma drug.

Ren et al. [1] report that lumbosacral and thoracolumbar junction have a higher recurrence rate of spinal tuberculosis (TB) via a retrospective follow-up study of 223 patients with spinal TB. This is not a random control trial. Therefore, patients status such as the presence or absence of chronic medical disease, age, drug-resistant TB, which could affect the final findings, should be considered fully. Drug-resistant TB, especially multidrug-resistant and extensively drug-resistant TB is a major threat to the control of TB worldwide [2]. National survey of drug-resistant TB in China demonstrated that China has the world’s largest number of patients with multidrug-resistant TB, and primary transmission is responsible for most cases [3]. Drug resistance complicates efforts to control TB. Patients infected with organisms resistant to rifampin receive a high rate of therapy failure. Patients infected with organisms resistant to both isoniazid and rifampin need at least 18 months of treatment [4]. Thus, the authors should not ignore the effect of drug-resistant TB contributed to the spinal tuberculosis recurrence or relapse. We think it is necessary for the authors to supplement the mycobacterial drug sensitivity test or to give molecular detection of drug resistant tuberculosis, which could provide more evidence for spinal tuberculosis recurrence or relapse in this study.