Time to Resolution of Infection in COVID-19 Patients: the Experience of a Tertiary Medical Center in Israel.

With the spread of coronavirus disease, reports have indicated that young patients are usually asymptomatic with a short convalescence period. The current study compared the time to resolution of infection in symptomatic and asymptomatic patients. Seventy-six patients aged 44.4 ± 23.3 years were admitted to the coronavirus disease 2019 (COVID-19) unit during the study period. Data were collected from patient records. Throat and nasal swabs for COVID-19 were collected for reverse transcriptase-polymerase chain reaction (RT-PCR). Time to resolution of infection was defined as the number of days from the date of the first COVID-19 positive outcome to the second consecutive negative PCR results. Most patients showed COVID-19 signs and symptoms (71.1%) between 1 and 6 days, and the rest were asymptomatic. No association was found between the time to resolution of infection and the presence of COVID-19 signs and symptoms (symptomatic: median [Md] 10.0, 95% confidence interval [CI] 8.4-11.6; asymptomatic: Md 15.0, 95% CI 10.5-15.5; P = 0.54). Age was not correlated with the number of COVID-19 signs and symptoms (r = 0.13, P = 0.37) or with the time to resolution of infection (r = 0.06, P = 0.58). In patients with mild to moderate symptoms, the time to resolution of infection from COVID-19 is not different from that in asymptomatic patients.

Clinicoepidemiological and laboratory findings of COVID positive patients presenting to a tertiary care centre in South India: A retrospective analysis.

BACKGROUND: SARS CoV2 continues to pose a threat to human race even after one year of its outset in China. Observational studies from across the world have shown huge disparity in the clinicoepidemiological and laboratory features of this disease. In this study we attempt to assess the clinical, epidemiological and laboratory parameters of COVID 19 positive patients in this geographic location. METHODS: This is a descriptive retrospective study of patients who were tested positive for SARS-CoV-2 at a tertiary care centre in central Kerala, India between July 16, 2020 and November 30, 2020. The clinicoepidemiological and laboratory parameters of the confirmed patients were collected from the laboratory and hospital records and analysed. RESULTS: A total of 1051 patients were tested positive during the study period. The mean age of the patients was 45.7 years ± Standard Deviation (SD): 8.68; 51.76% were male. Among them 658 (62.61%) were symptomatic and 393(37.39%) were asymptomatic; males (54.7%) were more symptomatic than females (45.3%). The common presenting symptoms were fever (43.58%), cough (21.50%), myalgia or fatigue (10.28%). Lymphocytopenia was more in males than females. Laboratory parameters such as Serum Ferritin, Alanine Transferase, Aspartate Transferase, Sodium level were elevated in males compared to female. CONCLUSION: The common symptoms in our study could emphasize on identifying potential patients in this geographic area. Asymptomatic patients should be monitored and investigated for effective control of the disease. A knowledge about the disease presentation in each geographic area is important in planning the effective management strategies since the features are varied from place to place.

Descriptive epidemiology of SARS-CoV-2 infection in Karnataka state, South India: Transmission dynamics of symptomatic vs. asymptomatic infections.

BACKGROUND: The huge surge in COVID-19 cases in Karnataka state, India, during early phase of the pandemic especially following return of residents from other states and countries required investigation with respect to transmission dynamics, clinical status, demographics, comorbidities and mortality. Knowledge on the role of symptomatic and asymptomatic cases in transmission of SARS-CoV-2 was not available. METHODS: The study included all the cases reported from March 8 - May 31, 2020. Individuals with a history of international or domestic travel from high burden states, Influenza-like Illness or Severe Acute Respiratory Illness and high-risk contacts of COVID-19 cases were included. Detailed analysis based on contact tracing data available from the line-list of state surveillance unit was performed using cluster network analysis software. FINDINGS: Amongst the 3404 COVID-19 positive cases, 3096 (91%) were asymptomatic while 308 (9%) were symptomatic. Majority of asymptomatic cases were in the age range of 16 and 45 years while symptomatic cases were between 31 and 65 years. Mortality rate was especially higher among middle-aged and elderly cases with co-morbidities, 34/38 (89·4%). Cluster network analysis of 822 cases indicated that the secondary attack rate, size of the cluster and superspreading events were higher when the source case was symptomatic as compared to an asymptomatic. INTERPRETATION: Our findings indicate that both asymptomatic and symptomatic SARS-CoV-2 cases transmit the infection, although symptomatic cases were the main driving force within the state during the beginning of the pandemic. Considering the large proportion of asymptomatic cases, their ability to spread infection cannot be overlooked. Notwithstanding the limitations and bias in identifying asymptomatic cases, the findings have major implications for testing policies. Active search, early testing and treatment of symptomatic elderly patients with comorbidities should be prioritized for containing the spread of COVID-19 and reducing mortality. FUNDING: Intermediate Fellowship, Wellcome Trust-DBT India Alliance to Giridhara R Babu, Grant number: IA/CPHI/14/1/501499.

Ten Fast Transfer Learning Models for Carotid Ultrasound Plaque Tissue Characterization in Augmentation Framework Embedded with Heatmaps for Stroke Risk Stratification.

Background and Purpose: Only 1-2% of the internal carotid artery asymptomatic plaques are unstable as a result of >80% stenosis. Thus, unnecessary efforts can be saved if these plaques can be characterized and classified into symptomatic and asymptomatic using non-invasive B-mode ultrasound. Earlier plaque tissue characterization (PTC) methods were machine learning (ML)-based, which used hand-crafted features that yielded lower accuracy and unreliability. The proposed study shows the role of transfer learning (TL)-based deep learning models for PTC. Methods: As pertained weights were used in the supercomputer framework, we hypothesize that transfer learning (TL) provides improved performance compared with deep learning. We applied 11 kinds of artificial intelligence (AI) models, 10 of them were augmented and optimized using TL approaches-a class of Atheromatic™ 2.0 TL (AtheroPoint™, Roseville, CA, USA) that consisted of (i-ii) Visual Geometric Group-16, 19 (VGG16, 19); (iii) Inception V3 (IV3); (iv-v) DenseNet121, 169; (vi) XceptionNet; (vii) ResNet50; (viii) MobileNet; (ix) AlexNet; (x) SqueezeNet; and one DL-based (xi) SuriNet-derived from UNet. We benchmark 11 AI models against our earlier deep convolutional neural network (DCNN) model. Results: The best performing TL was MobileNet, with accuracy and area-under-the-curve (AUC) pairs of 96.10 ± 3% and 0.961 (p < 0.0001), respectively. In DL, DCNN was comparable to SuriNet, with an accuracy of 95.66% and 92.7 ± 5.66%, and an AUC of 0.956 (p < 0.0001) and 0.927 (p < 0.0001), respectively. We validated the performance of the AI architectures with established biomarkers such as greyscale median (GSM), fractal dimension (FD), higher-order spectra (HOS), and visual heatmaps. We benchmarked against previously developed Atheromatic™ 1.0 ML and showed an improvement of 12.9%. Conclusions: TL is a powerful AI tool for PTC into symptomatic and asymptomatic plaques.

Human to mosquito transmission of dengue viruses.

The successful transmission of dengue virus from a human host to a mosquito vector requires a complex set of factors to align. It is becoming increasingly important to improve our understanding of the parameters that shape the human to mosquito component of the transmission cycle so that vaccines and therapeutic antivirals can be fully evaluated and epidemiological models refined. Here we describe these factors, and discuss the biological and environmental impacts and demographic changes that are influencing these dynamics. Specifically, we examine features of the human infection required for the mosquito to acquire the virus via natural blood feeding, as well as the biological and environmental factors that influence a mosquito's susceptibility to infection, up to the point that they are capable of transmitting the virus to a new host.