Thermal stability of diagnostic targets plasmodium falciparum histidine rich protein II and plasmodium lactate dehydrogenase in rapid detection.

During the COVID-19 epidemic, blood samples are usually processed at 56 to attenuate the virus before pathogen detection. 71 blood samples of malaria patients reported by Shanghai Center for Disease Control and Prevention in 2017-2019 were collected, including 38 with Plasmodium falciparum infection, 8 P. malariae, 11 P. ovale and 14 P. vivax. The effect of inactivation on the thermal stability of P. falciparum histidine rich protein II (PfHRPII) and Plasmodium lactate dehydrogenase (pLDH) in blood samples was assessed before and after incubation at 56 for 30 min using the rapid diagnostic test (RDT) kit. The results showed that among the 38 P. falciparum T1-positive (PfHRPII) blood samples before heat treatment, 35 samples remained to be T1-positive (92.11%, 35/38, chi2=3.123, P>0.05) after heat treatment;while 54 blood samples (26 P. falciparum, 6 P. vivax, 10 P. ovale and 12 P. vivax) that were T2-positive (pLDH) before heat treatment turned to be T2-negative (positive rate 0, 0/54, chi2=87.755, P<0.01) after heat treatment. It was demonstrated that PfHRPII is stable during incubation at 56 for 30 min, while pLDH is unstable and degraded or inactivated during the heating. Therefore, the detection results of P. falciparum will not be affected by RDT, but diagnosis of the parasites other than P. falciparum in blood samples may be missed.Copyright © 2021, National Institute of Parasitic Diseases. All rights reserved.

DeCoP: Deep Learning for COVID-19 Prediction of Survival

The current ongoing COVID-19 pandemic caused by the SARS-CoV-2 virus, has severely affected our daily life routines and behavior patterns. According to the World Health Organization, there have been 93 million confirmed cases with more than 1.99 million confirmed death around 235 Countries, areas or territories until 15 January 2021, 11:00 GMT+11. People who are affected with COVID-19 have different symptoms from people to people. When large amounts of patients are affected with COVID-19, it is important to quickly identify the health conditions of patients based on the basic information and symptoms of patients. Then the hospital can arrange reasonable medical resources for different patients. However, existing work has a low recall of 15.7% for survival predictions based on the basic information of patients (i.e., false positive rate (FPR) with 84.3%, FPR: actually survival but predicted as died). There is much room for improvement when using machine learning-based techniques for COVID-19 prediction. In this paper, we propose DeCoP to train a classifier to predict the survival of COVID-19 patients with high recall and F1 score. DeCoP is a deep learning (DL)-based scheme of Bidirectional Long Short-Term Memory (BiLSTM) along with Fuzzy-based Information Decomposition (FID) to predict the survival of patients. First of all, we apply FID oversampling to redistribute the training data of the Open COVID-19 Data Working Group. Then, we employ BiLSTM to learn the high-level feature representations from the redistributed dataset. After that, the high-level feature vector will be used to train the prediction model. Experimental results show that our proposed scheme achieves outstanding performances. Precisely, the improvement achieves about 19% and 18% in terms of recall and F1-measure.

Combined training prescriptions for improving cardiorespiratory fitness, physical fitness, body composition, and cardiometabolic risk factors in older adults: Systematic review and meta-analysis of controlled trials.

Background: Improved physical fitness is important for preventing COVID-19-related mortality. So, combined training can effectively increase peak oxygen consumption, physical fitness, body composition, blood pressure, and the healthrelated characteristics of adults; however, its impact in the elderly remains unclear. Methods: This systematic review and meta-analysis aimed to evaluate the effects of combined training on older adults. Four electronic databases (PubMed, Scopus, Medline, and Web of Science) were searched (until April 2021) for randomized trials comparing the effect of combined training on cardiorespiratory fitness, physical fitness, body composition, blood pressure, and cardiometabolic risk factors in older adults. Results: Combined training significantly improved peak oxygen consumption compared to no exercise (WMD = 3.10, 95% CI: 2.83 to 3.37). Combined resistance and aerobic training induced favorable changes in physical fitness (timed up-and-go = -1.06, 30-s chair stand = 3.85, sit and reach = 4.43, 6-minute walking test = 39.22, arm curl = 4.60, grip strength = 3.65, 10-m walk = -0.47, maximum walking speed = 0.15, one-leg balance = 2.71), body composition (fat mass = -2.91, body fat% = -2.31, body mass index = -0.87, waist circumference = -2.91), blood pressure (systolic blood pressure = -8.11, diastolic blood pressure = -4.55), and cardiometabolic risk factors (glucose = -0.53, HOMA-IR = -0.14, high-density lipoprotein = 2.32, total cholesterol = -5.32) in older individuals. Finally, the optimal exercise prescription was ≥ 30 min/session × 50-80% VO2peak, ≥ 3 times/week for ≥ 12 weeks and resistance intensity 70-75% one-repetition maximum, 8-12 repetitions × 3 sets. Conclusions: Combined training improved VO2peak and some cardiometabolic risk factors in older populations. The dose-effect relationship varied between different parameters. Exercise prescriptions must be formulated considering individual needs during exercise.

Contexte: L'amélioration de la condition physique est importante pour prévenir la mortalité liée au COVID-19. Ainsi, l'entraînement combiné peut augmenter efficacement la consommation maximale d'oxygène, la forme physique, la composition corporelle, la tension artérielle et les caractéristiques liées à la santé des adultes; cependant, son impact chez les personnes âgées reste incertain. Méthodes: Cette revue systématique et cette méta-analyse visaient à évaluer les effets de l'entraînement combiné chez les personnes âgées. Quatre bases de données électroniques (PubMed, Scopus, Medline et Web of Science) ont été consultées (jusqu'en avril 2021) pour trouver des essais randomisés comparant l'effet d'un entraînement combiné sur l'aptitude cardiorespiratoire, la forme physique, la composition corporelle, la tension artérielle et les facteurs de risque cardiométabolique chez les personnes âgées. Résultats: Au total, 37 publications ont été incluses dans cette étude. L'entraînement combiné a considérablement amélioré la consommation maximale d'oxygène par rapport à l'absence d'exercice (DMP = 3,10, IC95 % : 2,83 à 3,37). La combinaison résistance + entraînement aérobie a entraîné des changements favorables dans la forme physique (démarrage chronométré = −1,06, position assise pendant 30 s = 3,85, position assise et lever = 4,43, test de marche de 6 minutes = 39,22, flexion des bras = 4,60, adhérence force = 3,65, marche de 10 m = −0,47, vitesse de marche maximale = 0,15, équilibre sur une jambe = 2,71), composition corporelle (masse grasse = −2,91, pourcentage de graisse corporelle = −2,31, indice de masse corporelle = −0,87, taille circonférence = −2,91), tension artérielle (pression artérielle systolique = −8,11, pression artérielle diastolique = −4,55) et facteurs de risque cardiométabolique (glucose = −0,53, HOMA-IR = −0,14, lipoprotéines de haute densité = 2,32, cholestérol total = −5,32) chez les personnes âgées. Enfin, la prescription d'exercice optimale était ≥ 30 min/séance × 50­80 % VO2pic, ≥ 3 fois/semaine pendant ≥ 12 semaines et résistance à une intensité de 70­75 % une répétition maximale, 8­12 répétitions × 3 séries. Conclusions: L'entraînement combiné a amélioré la VO2pic et certains facteurs de risque cardiométabolique chez les populations âgées. La relation dose-effet variait entre les différents paramètres. Les prescriptions d'exercice doivent être formulées en tenant compte des besoins individuels pendant l'exercice.

Roles of Transmembrane Serine Protease TMPRSS2 in the Infection of SARS-CoV-2 to Human

Transmembrane serine protease 2 (TMPRSS2) is an androgen-dependent serine protease, and it had previously been reported that it had important pathological functions in tumor metastasis and invasion and virus infection. The entry of coronavirus into host cells is the prerequisite for its transmission and pathogenicity. TMPRSS2 can mediate the invasion of coronavirus into host cells by activating the spike glycoprotein of coronavirus, so it was considered as a potential target for the intervention of coronavirus infection.Current reported effective inhibitors of TMPRSS2 are broad-spectrum drugs targeting the serine protease family, suggesting urgency for exploring and developing novel TMPRSS2-specific inhibitory molecules. The biological characteristics and pathological functions of TMPRSS2 were summarized, with emphasis on the universal function of TMPRSS2 in human pathogenic coronavirus infection and the latest research trends of TMPRSS2 inhibitors in this paper, to highlight the potential of targeting TMPRSS2 as a novel strategy to prevent and limit early infection and transmission of novel coronavirus.

Multi-objective optimization of liner transportation under COVID-19 and dual carbon strategy background

Aiming at fleet deployment issue and cargo allocation issue under the background of COVID-19 epidemic and the "dual carbon" strategy, in order to meet the requirements of liner companies for the balanced development of fleet transportation efficiency, economic benefits, service quality and environmental benefits, a multi-objective fleet deployment and cargo allocation optimization model was established to achieve the goal of maximizing fleet average space utilization and operating profit, minimizing cargo time value loss and single container carbon emission. According to the internal relationship between subproblems, the model was decomposed into a two-level model. The upper level was mixed integer nonlinear programming to deal with route ship allocation and speed optimization, and the lower level was linear programming to deal with cargo allocation. The solution algorithm was designed based on NSGA-Ⅱ algorithm framework. Taking the fleet of a liner company as example, the results show that the model and the optimization solution method are feasible, and the liner company can adopt the mixed strategy of slightly increasing the speed and increasing the number of small and medium-sized ships to achieve the effect of carbon emission reduction while meeting more freight demand and coping with port congestion. © 2022, Editorial Office of Journal of Dalian Maritime University. All right reserved.

Masked Face Recognition Datasets and Validation

In order to effectively prevent the spread of COVID-19 virus, almost everyone wears a mask during coronavirus epidemic. This nearly makes conventional facial recognition technology ineffective in many scenarios, such as face authentication, security check, community visit check-in, etc. Therefore, it is very urgent to boost performance of existing face recognition systems on masked faces. Most current advanced face recognition approaches are based on deep learning, which heavily depends on a large number of training samples. However, there are presently no publicly available masked face recognition datasets. To this end, this work proposes three types of masked face datasets, including Masked Face Detection Dataset (MFDD), Real-world Masked Face Recognition Dataset (RMFRD) and Synthetic Masked Face Recognition Dataset (SMFRD). As far as we know, we are the first to publicly release large-scale masked face recognition datasets that can be downloaded for free at: https://github.com/X-zhangyany/Real-World-Masked-Face-Dataset.

Thermal stability of diagnostic targets plasmodium falciparum histidine rich protein Ⅱ and plasmodium lactate dehydrogenase in rapid detection

During the COVID-19 epidemic, blood samples are usually processed at 56 ℃ to attenuate the virus before pathogen detection. 71 blood samples of malaria patients reported by Shanghai Center for Disease Control and Prevention in 2017-2019 were collected, including 38 with Plasmodium falciparum infection, 8 P. malariae, 11 P. ovale and 14 P. vivax. The effect of inactivation on the thermal stability of P. falciparum histidine rich protein Ⅱ (PfHRPⅡ) and Plasmodium lactate dehydrogenase (pLDH) in blood samples was assessed before and after incubation at 56 ℃ for 30 min using the rapid diagnostic test (RDT) kit. The results showed that among the 38 P. falciparum T1-positive (PfHRPⅡ) blood samples before heat treatment, 35 samples remained to be T1-positive (92.11%, 35/38, χ2=3.123, P>0.05) after heat treatment;while 54 blood samples (26 P. falciparum, 6 P. vivax, 10 P. ovale and 12 P. vivax) that were T2-positive (pLDH) before heat treatment turned to be T2-negative (positive rate 0, 0/54, χ2=87.755, P<0.01) after heat treatment. It was demonstrated that PfHRPⅡ is stable during incubation at 56 ℃ for 30 min, while pLDH is unstable and degraded or inactivated during the heating. Therefore, the detection results of P. falciparum will not be affected by RDT, but diagnosis of the parasites other than P. falciparum in blood samples may be missed. © 2021, National Institute of Parasitic Diseases. All rights reserved.

WHEN FACE RECOGNITION MEETS OCCLUSION: A NEW BENCHMARK

The existing face recognition datasets usually lack occlusion samples, which hinders the development of face recognition. Especially during the COVID-19 coronavirus epidemic, wearing a mask has become an effective means of preventing the virus spread. Traditional CNN-based face recognition models trained on existing datasets are almost ineffective for heavy occlusion. To this end, we pioneer a simulated occlusion face recognition dataset. In particular, we first collect a variety of glasses and masks as occlusion, and randomly combine the occlusion attributes (occlusion objects, textures,and colors) to achieve a large number of more realistic occlusion types. We then cover them in the proper position of the face image with the normal occlusion habit. Furthermore, we reasonably combine original normal face images and occluded face images to form our final dataset, termed as Webface-OCC. It covers 804,704 face images of 10,575 subjects, with diverse occlusion types to ensure its diversity and stability. Extensive experiments on public datasets show that the ArcFace retrained by our dataset significantly outperforms the state-of-the-arts. Webface-OCC is available at https://github.com/Baojin-Huang/Webface-OCC.

Learning to Recognize Masked Faces by Data Synthesis

Face coverings have become the new normal for people living through the global COVID-19 pandemic crisis. While wearing a mask is a necessary public health measure, the social phenomenon raises new challenges to existing face recognition models. In this work, we evaluate deep neural network approaches for the masked face recognition task. We find that current deep networks can not generalize successfully to recognizing faces with masks. To address this issue, we investigate the use of images of faces with simulated masks to train a deep neural network model for face recognition. We train our modeling a collection of two face recognition datasets: the Labeled Faces in the Wild (LFW) dataset. the Real-world Masked Face Recognition (RMFR ) dataset and the Simulated Masked Face Recognition (SMFR) dataset. We find that the data sampling strategy during training plays a significant role when the number of simulated examples is much greater than that of available real instances. We show that the model trained using a combination of real and simulated data accurately classifies masked faces with an accuracy of 99%.

[Research progress on application of laboratory indicators detection of COVID-19].

The outbreak of corona virus disease-19 (corona virus disease-19, COVID-19) caused a huge human disaster from the end of 2019 which is caused by SARS-CoV-2. It will cause damage to multiple organs function in the disease occurrence and development, viral nucleic acid, antibody and serological biochemical immune indicators are mainly indicators of clinical laboratory. The results of these indicators can reflect the organs function of patients and further guide clinical treatment. In this paper, the detection and clinical application of COVID-19 laboratory indicators are reviewed.

Epidemiologic and clinical characteristics of 91 hospitalized patients with COVID-19 in Zhejiang, China: a retrospective, multi-centre case series.

BACKGROUND: Recent studies have focused on initial clinical and epidemiological characteristics of the coronavirus disease 2019 (COVID-19), which is the mainly revealing situation in Wuhan, Hubei. AIM: This study aims to reveal more data on the epidemiological and clinical characteristics of COVID-19 patients outside of Wuhan, Zhejiang, China. DESIGN: This study was a retrospective case series. METHODS: Eighty-eight cases of laboratory-confirmed and three cases of clinically confirmed COVID-19 were admitted to five hospitals in Zhejiang province, China. Data were collected from 20 January 2020 to 11 February 2020. RESULTS AND DISCUSSION: Of all 91 patients, 88 (96.70%) were laboratory-confirmed COVID-19 with throat swab samples that tested positive for SARS-Cov-2, three (3.30%) cases were clinically diagnosed. The median age of the patients was 50 (36.5-57) years, and female accounted for 59.34%. In this sample, 40 (43.96%) patients had contracted the disease from local cases, 31 (34.07%) patients had been to Wuhan/Hubei, eight (8.79%) patients had contacted with people from Wuhan, and 11 (12.09%) patients were diagnosed after having flown together in the same flight with no passenger that could later be identified as the source of infection. In particular within the city of Ningbo, 60.52% cases can be traced back to an event held in a temple. The most common symptoms were fever (71.43%), cough (60.44%) and fatigue (43.96%). The median of incubation period was 6 (interquartile range 3-8) days and the median time from the first visit to a doctor to the confirmed diagnosis was 1 (1-2) days. According to the chest computed tomography scans, 67.03% cases had bilateral pneumonia. CONCLUSIONS: Social activity cluster, family cluster and flying alongside with persons already infected with COVID-19 were how people got infected with COVID-19 in Zhejiang.