Tuberculosis (TB) diagnostic monitoring is paramount to clinical decision-making and the host biomarkers appears to play a significant role. The currently available diagnostic technology for TB detection is inadequate. In the present study, we aimed to identify biomarkers for diagnosis of pulmonary tuberculosis (PTB) using urinary metabolomic and proteomic analysis. Methods: In the study, urine from 40 PTB, 40 lung cancer (LCA), 40 community-acquired pneumonia (CAP) patients and 40 healthy controls (HC) was collected. Biomarker panels were selected based on random forest (RF) analysis. Results: A total of 3,868 proteins and 1,272 annotated metabolic features were detected using pairwise comparisons. Using AUC ≥ 0.80 as a cutoff value, we picked up five protein biomarkers for PTB diagnosis. The five-protein panel yielded an AUC for PTB/HC, PTB/CAP and PTB/LCA of 0.9840, 0.9680 and 0.9310, respectively. Additionally, five metabolism biomarkers were selected for differential diagnosis purpose. By employment of the five-metabolism panel, we could differentiate PTB/HC at an AUC of 0.9940, PTB/CAP of 0.8920, and PTB/LCA of 0.8570. Conclusion: Our data demonstrate that metabolomic and proteomic analysis can identify a novel urine biomarker panel to diagnose PTB with high sensitivity and specificity. The receiver operating characteristic curve analysis showed that it is possible to perform non-invasive clinical diagnoses of PTB through these urine biomarkers.

The early stage of the COVID-19 epidemic happened to be the flu season Since some symptoms of influenza and COVID-19 are similar, symptomatic patients flocked to fever clinics and emergency departments Meanwhile, asymptomatic COVID-19 patients attending other departments in general hospitals made things worse Lack of knowledge of the pathogen, absence of awareness and short of personal protective equipment all posed threat to healthcare workers as well as other patients As SARS-CoV-2 can be spread via droplets, direct contacts and potentially aerosols, the indoor air environment of hospitals, especially fever clinics, must have strict measures to prevent hospital-acquired infection Thirty-two sensors were deployed in the Tsinghua University Affiliated Beijing Tsinghua Changgung Hospital (mentioned as Changgung Hospital hereinafter) from January 30, 2020, in order to monitor high-resolution real-time indoor environmental parameters at its fever clinic, isolation wards and other departments One sensor monitors and records CO2 concentration, PM2 5 mass concentration, relative humidity, temperature and illuminance every 5 minutes Six sensors were located at the fever clinic, where all patients with fever and/or other COVID-19 related symptoms firstly attended after arriving at the hospital The clinic has two parts, one for diagnosis and the other for quarantine Three sensors were placed in doctor's office, nursing station and waiting area in the diagnosis part, respectively Natural ventilation was chosen to dilute the environment, as the flowrate of outdoor airflow was abundant in Beijing's winter Atmospheric CO2 concentration surrounding Changgung Hospital was stable, and the rise of indoor CO2 concentration was caused by human exhalation During this pandemic, CO2 concentration can be regarded as an indicator of room ventilation condition and hospital congestion, if all the people in hospital were regarded as potential infector of SARS-CoV-2 According to the usage pattern of the fever clinic, the maximum number of patients in each functional area was set as 4 for doctor's office, 4 for nursing station and 11 for waiting area According to the ventilation regulation of infectious disease hospital, the air change rate at fever clinics should be at least 6 h-1 In addition, the outdoor CO2 concentration was assumed to be 400 ppm Based on these conditions, the upper limits of indoor CO2 concentration were 902, 864 and 867 ppm for doctor's office, nursing station and waiting area at the Changgung Hospital's fever clinic, respectively Indoor CO2 concentration exceeding these thresholds stands for poor ventilation or overcrowds Fortunately, this didn't happen during the monitoring period and indoor CO2 concentration didn't exceed 609-711 ppm In another word, natural ventilation was sufficient and effective in this specific case at the Changgung Hospital's fever clinic Moreover, together with environment disinfection and personal protective measures, good ventilation condition led to no COVID-19 hospital-acquired infection To conclude, this article introduced a real-time environmental monitoring campaign at the Changgung Hospital's fever clinic Similar methodology can help assess ventilation conditions and risk of hospital-acquired infection at the fever clinic during and after COVID-19 pandemic Once indoor CO2 concentration exceeds the set thresholds, areas with high infection risk can be identified rapidly and timely, so that prevention measures can be taken in time © 2021, Science Press All right reserved

Abstract Since the coronavirus disease 2019 (COVID‐19) outbreak, the nosocomial infection rate worldwide has been reported high. It is urgent to figure out an affordable way to monitor and alarm nosocomial infection. Carbon dioxide (CO2) concentration can reflect the ventilation performance and crowdedness, so CO2 sensors were placed in Beijing Tsinghua Changgung Hospital's fever clinic and emergency department where the nosocomial infection risk was high. Patients’ medical records were extracted to figure out their timelines and whereabouts. Based on these, site‐specific CO2 concentration thresholds were calculated by the dilution equation and sites’ risk ratios were determined to evaluate ventilation performance. CO2 concentration successfully revealed that the expiratory tracer was poorly diluted in the mechanically ventilated inner spaces, compared to naturally ventilated outer spaces, among all of the monitoring sites that COVID‐19 patients visited. Sufficient ventilation, personal protection, and disinfection measures led to no nosocomial infection in this hospital. The actual outdoor airflow rate per person (Q c) during the COVID‐19 patients’ presence was estimated for reference using equilibrium analysis. During the stay of single COVID‐19 patient wearing a mask, the minimum Q c value was 15–18 L/(s·person). When the patient was given throat swab sampling, the minimum Q c value was 21 L/(s·person). The Q c value reached 36–42 L/(s·person) thanks to window‐inducted natural ventilation, when two COVID‐19 patients wearing masks shared the same space with other patients or healthcare workers. The CO2 concentration monitoring system proved to be effective in assessing nosocomial infection risk by reflecting real‐time dilution of patients’ exhalation.

Background Coronovirus disease 2019 (COVID-19) has spread rapidly across the globe. People of all ages are susceptible to COVID-19. However, literature reports on pediatric patients are limited. Methods To improve the recognition of COVID-19 infection in children, we retrospectively reviewed two confirmed pediatric cases from two family clusters. Both clinical features and laboratory examination results of the children and their family members were described. Results The two confirmed children only presented with mild respiratory or gastrointestinal symptoms. Both of them had normal chest CT images. After general and symptomatic treatments, both children recovered quickly. Both families had travel histories to Hubei Province. Conclusions Pediatric patients with COVID-19 are mostly owing to family cluster or with a close contact history. Infected children have relatively milder clinical symptoms than infected adults. We should attach importance to early recognition, early diagnosis, and early treatment of infected children.

The sudden outbreak of novel coronavirus 2019 (COVID-19) increased the diagnostic burden of radiologists. In the time of an epidemic crisis, we hope artificial intelligence (AI) to reduce physician workload in regions with the outbreak, and improve the diagnosis accuracy for physicians before they could acquire enough experience with the new disease. In this paper, we present our experience in building and deploying an AI system that automatically analyzes CT images and provides the probability of infection to rapidly detect COVID-19 pneumonia. The proposed system which consists of classification and segmentation will save about 30%–40% of the detection time for physicians and promote the performance of COVID-19 detection. Specifically, working in an interdisciplinary team of over 30 people with medical and/or AI background, geographically distributed in Beijing and Wuhan, we are able to overcome a series of challenges (e.g. data discrepancy, testing time-effectiveness of model, data security, etc.) in this particular situation and deploy the system in four weeks. In addition, since the proposed AI system provides the priority of each CT image with probability of infection, the physicians can confirm and segregate the infected patients in time. Using 1,136 training cases (723 positives for COVID-19) from five hospitals, we are able to achieve a sensitivity of 0.974 and specificity of 0.922 on the test dataset, which included a variety of pulmonary diseases.

病史摘要 患者为青年女性，因"发热伴咳嗽3 d"就诊，就诊前14 d内曾于湖北孝感地区停留5 d，于就诊前1周返回北京，就诊后完善胸部CT不除外新型冠状病毒肺炎（COVID-19）。2020年2月1日予以隔离，连续两次鼻咽拭子新冠病毒核酸检测阴性，转至定点医院复检两次鼻咽拭子新冠病毒核酸阴性予以解除隔离，2020年2月3日其子（9岁）出现腹泻症状，查鼻咽拭子新冠病毒核酸检测阳性，遂将其转至地坛医院按疑似病例诊疗，期间连续复查咽拭子查新冠病毒核酸六次均为阴性。经治疗后2020年4月3日复查胸部CT肺部病变已经完全吸收，血新冠抗体IgG阳性、IgM阴性。 症状体征 就诊3 d前无明显诱因自感"低热" ，体温最高37.3℃，无畏寒寒战，无肌肉关节酸痛。咳嗽，伴干咳为主，少量白色泡沫样痰，2~3口/d，伴有鼻塞、流清涕，有咽痒、咽干，无咽痛，轻微乏力，无胸闷气短。体格检查：体温36.2℃，脉搏138次/min，呼吸25次/min，血压165/102 mmHg（1 mmHg=0.133 kPa），双侧呼吸动度、语颤正常，叩诊呈清音，双肺呼吸音粗，未闻及干湿啰音。 诊断方法 患者胸部CT提示双肺多发斑片影及磨玻璃影，主要位于双下肺，肺外带分布为主；先后10次鼻咽拭子核酸检测阴性，血新冠抗体IgG阳性、IgM阴性。 治疗方法 干扰素α雾化吸入联合阿比多尔、洛匹那韦/利托那韦及连花清瘟胶囊口服。 临床转归 患者经治疗后临床症状完全消失，复查胸部CT病变吸收。 适合阅读人群 呼吸与危重医学科；感染疾病科；感染控制科