Analysis and contrast of Chadian-Senegalese Covid-19 outbreaks

As COVID-19 is an emerging pandemic, analysing its evolution is necessary to understand it in order to find appropriate answers. In this paper, we aim to observe and analyse it at the Chadian-Senegalese level. Thus, we collect public data in order to present via curves, histograms and tables the main characteristics of this pandemic. In this way, we implement a python program to construct these. We focus only on extracting long-term data without predictive models. We observed that there are mainly two waves (outbreak) per year with stable or even decreasing infection and death rates. We also identified moments of growth and relaxation of the disease. These results can be used to identify times when treatment or prevention should be intensified.

Establishment of differential diagnostic model for COVID-19 and influenza A based on laboratory indicators and its clinical significance. TT -.

Objective:To explore the differences in laboratory indicators test results of coronavirus disease 2019 (COVID-19) and influenza A and to establish a differential diagnosis model for the two diseases, and to clarify the clinical significance of the model for distinguishing the two diseases. Methods :A total of 56 common COVID-19 patients and 54 influenza A patients were enrolled , and 24 common COVID-19 patients and 30 influenza A patients were used for model validation. The average values of the laboratory indicators of the patients 5 d after admission were calculated,and the elastic network model and the stepwise Logistic regression model were used to screen the indicators for identifying COVID-19 and influenza A. Elastic network models were used for the first round of selection,in which the optimal cutoff of lambda was chosen by performing 10-fold cross validations. With different random seeds,the elastic net models were fit for 200 times to select the high-frequency indexes ( frequency>90% ). A Logistic regression model with AIC as the selection criterions was used in the second round of screening uses;a nomogram was used to represent the final model;an independent data were used as an external validation set,and the area under the curve (AUC) of the validation set were calculate to evaluate the predictive the performance of the model. Results:After the first round of screening, 16 laboratory indicators were selected as the high-frequency indicators. After the second round of screening,albumin/ globulin (A/G),total bilirubin (TBIL) and erythrocyte volume (HCT) were identified as the final indicators. The model had good predictive performance , and the AUC of the verification set was 0. 844 (95% CI:0. 747-0. 941). Conclusion:A differential diagnosis model for COVID-19 and influenza A based on laboratory indicators is successfully established,and it will help clinical and timely diagnosis of both diseases.Copyright © 2022 Jilin University Press. All rights reserved.

Advantages of Total Bilirubin for Predicting Malignant Obstructive Jaundice, A Combination of the Pandemic Era and Limited Resources Settings

BACKGROUND: Hepatopancreatobiliary (HPB) cancer incidence and mortality are increasing worldwide. An initial diagnostic predictor is needed for recommending further diagnostic modalities, referral, and curative or palliative decisions. There were no studies conducted in area with limited accessibility setting of the COVID-19 pandemic, coupled with limited human resources and facilities. AIM: We aimed to investigate the advantages of total bilirubin for predicting malignant obstructive jaundice, a combination of the pandemic era and limited resources settings. METHOD(S): Data from all cholestasis jaundice patients at M. Djamil Hospital in Pandemic COVID-19 period from July 2020 to May 2022 were retrospectively collected. The data included demographics, bilirubin fraction results, and final diagnosis. Bivariate analysis for obtain demographic risk factor, and Receiver Operating Characteristics (ROC) analysis for getting bilirubin value. RESULT(S): Of a total 132 patients included, 35.6% were malignant obstructive jaundice, and Pancreatic adeno ca was the most malignant etiology (34.4%). Bivariate analysis showed a significant correlation between age and malignant etiology (p = 0,024). Direct and total Bilirubin reach the same level of Area Under Curve (AUC). Total bilirubin at the cutoff point level of 10.7 mg/dl had the most optimal results on all elements of ROC output, AUC 0.88, sensitivity 76.6%, specificity 90.1%, +LR 8.14, and-LR 0.26. CONCLUSION(S): The bilirubin fraction is a good initial indicator for differentiating benign and malignant etiology (AUC 0.8-0.9) in pandemic era and resource-limited areas to improve diagnostic effectiveness and reduce referral duration.Copyright © 2023 Avit Suchitra, M. Iqbal Rivai, Juni Mitra, Irwan Abdul Rachman, Rini Suswita, Rizqy Tansa.

Liver biomarkers for prognosis in COVID-19

The novel coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2).Mortality attributable to COVID-19 remains considerably high, with case fatality rates as high as 8-11%. Early medical intervention in patients who are seriously and critically ill with COVID-19 reduces fatal outcomes. Thus, there is an urgent need to identify biomarkers that could help clinicians determine which patients with SARS-CoV-2 infection are at a higher risk of developing the most adverse outcomes, which include intensive care unit (ICU) admission, invasive ventilation, and death. In COVID-19 patients experiencing the most severe form of the disease, tests of liver function are frequently abnormal and liver enzymes are found to be elevated. For this reason, we examine the most promising liver biomarkers for COVID-19 prognosis in an effort to help clinicians predict the risk of ARDS, ICU admission, and death at hospital admission. In patients meeting hospitalization criteria for COVID-19, serum albumin < 36 g/L is an independent risk factor for ICU admission, with an AUC of 0.989, whereas lactate dehydrogenase (LDH) values > 365 U/L accurately predict death with an AUC of 0.943.The clinical scores COVID-GRAM and SOFA that include measures of liver function such as albumin, LDH, and total bilirubin are also good predictors of pneumonia development, ICU admission, and death, with AUC values ranging from 0.88 to 0.978.Thus, serum albumin and LDH, together with clinical risk scores such as COVID-GRAM and SOFA, are the most accurate biomarkers in the prognosis of COVID-19.Copyright © 2021 Sociedad Medica del Hospital General de Mexico. Published by Permanyer.

In silico screening of potential compounds from begonia genus as 3CL protease (3Cl pro) SARS-CoV-2 inhibitors

Background: The emergence of Coronavirus disease (COVID-19) has been declared a pandemic and made a medical emergency worldwide. Various attempts have been made, including optimizing effective treatments against the disease or developing a vaccine. Since the SARS-CoV-2 protease crystal structure has been discovered, searching for its inhibitors by in silico technique becomes possible. Objective(s): This study aims to virtually screen the potential of phytoconstituents from the Begonia genus as 3Cl pro-SARS-CoV- 2 inhibitors, based on its crucial role in viral replication, hence making these proteases "promising" for the anti-SARS-CoV-2 target. Method(s): In silico screening was carried out by molecular docking on the web-based program DockThor and validated by a retrospective method. Predictive binding affinity (Dock Score) was used for scoring the compounds. Further molecular dynamics on Desmond was performed to assess the complex stability. Result(s): Virtual screening protocol was valid with the area under curve value 0.913. Molecular docking revealed only beta-sitosterol-3-O-beta-D-glucopyranoside with a lower docking score of -9.712 kcal/mol than positive control of indinavir. The molecular dynamic study showed that the compound was stable for the first 30 ns simulations time with Root Mean Square Deviation <3 A, despite minor fluctuations observed at the end of simulation times. Root Mean Square Fluctuation of catalytic sites HIS41 and CYS145 was 0.756 A and 0.773 A, respectively. Conclusion(s): This result suggests that beta-sitosterol-3-O-beta-Dglucopyranoside might be a prospective metabolite compound that can be developed as anti-SARS-CoV-2.Copyright © 2023, Page Press Publications. All rights reserved.

Deep learning-based COVID-19 triage tool: An observational study on an X-ray dataset

Background: Easy availability, low cost, and low radiation exposure make chest radiography an ideal modality for coronavirus disease 2019 (COVID-19) detection. Objective(s): In this study, we propose the use of an artificial intelligence (AI) algorithm to automatically detect abnormalities associated with COVID-19 on chest radiographs. We aimed to evaluate the performance of the algorithm against the interpretation of radiologists to assess its utility as a COVID-19 triage tool. Material(s) and Method(s): The study was conducted in collaboration with Kaushalya Medical Trust Foundation Hospital, Thane, Maharashtra, between July and August 2020. We used a collection of public and private datasets to train our AI models. Specificity and sensitivity measures were used to assess the performance of the AI algorithm by comparing AI and radiology predictions using the result of the reverse transcriptase-polymerase chain reaction as reference. We also compared the existing open-source AI algorithms with our method using our private dataset to ascertain the reliability of our algorithm. Result(s): We evaluated 611 scans for semantic and non-semantic features. Our algorithm showed a sensitivity of 77.7% and a specificity of 75.4%. Our AI algorithm performed better than the radiologists who showed a sensitivity of 75.9% and specificity of 75.4%. The open-source model on the same dataset showed a large disparity in performance measures with a specificity of 46.5% and sensitivity of 91.8%, thus confirming the reliability of our approach. Conclusion(s): Our AI algorithm can aid radiologists in confirming the findings of COVID-19 pneumonia on chest radiography and identifying additional abnormalities and can be used as an assistive and complementary first-line COVID-19 triage tool.Copyright © Cancer Research, Statistics, and Treatment.

Usefulness of C-reactive protein to determine the severity of SARS-CoV-2 infection in Peru.

Background: Recently, many biomarkers have been studied to determine severe cases of COVID-19. C-reactive protein (CRP) has shown high sensitivity in identifying patients with severe disease and utility comparable to computed tomography. Aim(s): To determine the usefulness of CRP to predict the severity of SARS-CoV-2 infection in patients hospitalized at the Naval Medical Center of Peru during the period January-September in the year 2021. Method(s): A quantita-tive, observational, analytical, retrospective, and diagnostic test type design was used. A sample size of 503 patients was calculated, which were divided into two groups according to their severity. Result(s): An optimal cut-off point of 10.92 mg/L for CRP levels was determined for the diagnosis of severe COVID-19. An area under the curve (AUC) of 0.762 was calculated and sensitivity, specificity, positive and negative predictive values and diagnostic accuracy values of 78.88%, 66.4%;41.42%;87.01%;and 67.27%;respectively. Fagan's normogram showed a post-test probability of 41%. In the adjusted model, CRP (aOR = 4.853;95% CI 2.987-7.886;p = 0.001), ferritin (aOR = 1.001;95% CI: 1.001-1.002;p = 0.001) and hypothyroidism (adjusted OR = 4899;95% CI: 1272-18872;p = 0.021) showed significance. Conclusion(s): The present study showed an association between CRP and the severity of SARS-CoV-2 infection in an adjusted model, showing its potential utility and contributing to determine the cut-off point of CRP in the Peruvian population and its international comparison.Copyright © 2023, Sociedad Chilena de Infectologia. All rights reserved.

Flattening the Curve after the Initial Outbreak of Coronavirus Disease 2019: A Data-Driven Modeling Analysis for the Omicron Pandemic in China.

China is relaxing COVID-19 measures from the "dynamic zero tolerance" (DZT) level. The "flatten-the-curve" (FTC) strategy, which decreases and maintains the low rate of infection to avoid overwhelming the healthcare system by adopting relaxed nonpharmaceutical interventions (NPIs) after the outbreak, has been perceived as the most appropriate and effective method in preventing the spread of the Omicron variant. Hence, we established an improved data-driven model of Omicron transmission based on the age-structured stochastic compartmental susceptible-latent-infectious-removed-susceptible model constructed by Cai to deduce the overall prevention effect throughout China. At the current level of immunity without the application of any NPIs, more than 1.27 billion (including asymptomatic individuals) were infected within 90 days. Moreover, the Omicron outbreak would result in 1.49 million deaths within 180 days. The application of FTC could decrease the number of deaths by 36.91% within 360 days. The strict implementation of FTC policy combined with completed vaccination and drug use, which only resulted in 0.19 million deaths in an age-stratified model, will help end the pandemic within about 240 days. The pandemic would be successfully controlled within a shorter period of time without a high fatality rate; therefore, the FTC policy could be strictly implemented through enhancement of immunity and drug use.

A specific combination of laboratory data is associated with overweight lungs in patients with COVID-19 pneumonia at hospital admission: secondary cross-sectional analysis of a randomized clinical trial.

Background: Lung weight may be measured with quantitative chest computed tomography (CT) in patients with COVID-19 to characterize the severity of pulmonary edema and assess prognosis. However, this quantitative analysis is often not accessible, which led to the hypothesis that specific laboratory data may help identify overweight lungs. Methods: This cross-sectional study was a secondary analysis of data from SARITA2, a randomized clinical trial comparing nitazoxanide and placebo in patients with COVID-19 pneumonia. Adult patients (≥18 years) requiring supplemental oxygen due to COVID-19 pneumonia were enrolled between April 20 and October 15, 2020, in 19 hospitals in Brazil. The weight of the lungs as well as laboratory data [hemoglobin, leukocytes, neutrophils, lymphocytes, C-reactive protein, D-dimer, lactate dehydrogenase (LDH), and ferritin] and 47 additional specific blood biomarkers were assessed. Results: Ninety-three patients were included in the study: 46 patients presented with underweight lungs (defined by ≤0% of excess lung weight) and 47 patients presented with overweight lungs (>0% of excess lung weight). Leukocytes, neutrophils, D-dimer, and LDH were higher in patients with overweight lungs. Among the 47 blood biomarkers investigated, interferon alpha 2 protein was higher and leukocyte inhibitory factor was lower in patients with overweight lungs. According to CombiROC analysis, the combinations of D-dimer/LDH/leukocytes, D-dimer/LDH/neutrophils, and D-dimer/LDH/leukocytes/neutrophils achieved the highest area under the curve with the best accuracy to detect overweight lungs. Conclusion: The combinations of these specific laboratory data: D-dimer/LDH/leukocytes or D-dimer/LDH/neutrophils or D-dimer/LDH/leukocytes/neutrophils were the best predictors of overweight lungs in patients with COVID-19 pneumonia at hospital admission. Clinical trial registration: Brazilian Registry of Clinical Trials (REBEC) number RBR-88bs9x and ClinicalTrials.gov number NCT04561219.

Online Science Instruction Can Promote Adolescents' Autonomy Need Satisfaction: a Latent Growth Curve Analysis.

This research examined the differential motivational effects of a pre-college science enrichment program delivered in both online and in-person learning formats. Using self-determination theory as a guiding framework, we hypothesized that (a) students would exhibit growth in their perceived satisfaction of needs for autonomy, competence, and relatedness, (b) online learning would be associated with greater growth in autonomy, and (c) in-person learning would be associated with greater growth in both competence and relatedness. Using a sample of 598 adolescent participants, results of latent growth curve modeling indicated that satisfaction of the three needs grew unconditionally over the course of the program. However, format type was unrelated to growth in need satisfaction. Rather, this effect was found to be conditional upon the type of science project undertaken by students: astrophysics students exhibited significantly greater autonomy growth when receiving online instruction than did biochemistry students. Our findings suggest that online science learning can be just as effective in motivating students as in-person learning provided that the learning tasks are conducive to remote instruction.

Default risk transmission in the travel and leisure industry

Default risk in the Travel and Leisure (T&L) industry remains understudied despite its implications for the industry's health and stability. This paper investigates the transmission of default risk among US T&L firms over various credit horizons from July 22, 2008 to December 9, 2022, paying special attention to the impact of COVID-19. The short-, medium-, and long-term default risk factors are extracted from the Credit Default Spread (CDS) curve of the US T&L industry then used within a connectedness approach. The results reveal considerable default risk transmission, particularly in the long-term. Default risk transmission has spiked across all horizons since the pandemic, reflecting the deterioration in credit quality of T&L firms under the pandemic. Analysis of the drivers of default risk transmission shows that several macro-financial variables, especially news market sentiment and stock market volatility induced by the pandemic, have an important explanatory role.

Network connectedness of the term structure of yield curve and global Sukuks

This paper explores the connectedness between the returns and volatilities of the conventional and Islamic bond markets. We use the level, slope, and curvature of the US yield curve and estimate the connectedness of these factors with the Dow Jones Islamic indices (of 3 to 10 years of maturity) as well as the minimum connectedness portfolio. The static analysis shows that level and slope of the conventional yield curve are the net transmitters of shocks while the Islamic indices have been mostly at the receiving end. The dynamic connectedness analysis shows a varying degree of the connectedness over the full sample period characterized by distinctive trajectories of booms and busts. The pairwise connectedness analysis also confirms that level and slope are the net transmitters in the system with an exception in most recent times of Covid-19 pandemic. The findings have implications for the researchers, policy makers, regulators, shariah boards, investors, and fund managers.

Clinical significance of serum interleukin 6 in elderly patients infected with severe acute respiratory syndrome coronavirus 2 omicron variant and its correlation with underlying diseases

Objective To investigate the clinical significance of serum interleukin 6 (IL-6) in elderly patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) omicron variant and its correlation with underlying diseases. Methods A total of 22 elderly patients (>80 years old) infected with omicron variant, who were admitted to Department of Infectious Diseases, The First Affiliated Hospital of Naval Medical University (Second Military Medical University) from Apr. to Jun. 2022 and tested positive for SARS-CoV-2 RNA, were included. The level of serum IL-6 was measured by flow cytometry, and the level of serum C reactive protein (CRP) was measured by immunonephelometry. Patients were divided into pneumonia group (16 cases) and non-pneumonia group (6 cases) according to the imaging examination results, and were divided into severe group (severe and critical type, 5 cases) and non-severe group (mild and normal type, 17 cases) according to the condition. Binary logistic regression model and receiver operating characteristic (ROC) curve were used to analyze the correlation between serum IL-6 and CRP levels and the severity of the disease and whether it would progress to pneumonia. Meanwhile, the relationships between underlying diseases and serum IL-6 level were explored. Results Among the 22 patients, 6 were mild, 11 were normal, 3 were severe, and 2 were critical. The baseline serum IL-6 level in the pneumonia group was significantly higher than that in the non-pneumonia group ([20.16+/-12.36]pg/mL vs [5.42+/-1.57] pg/mL, P=0.009), and there was no significant difference in baseline serum CRP level between the 2 groups (P>0.05). There were no significant differences in baseline serum IL-6 or CRP levels between the severe group and the non-severe group (both P>0.05). Logistic regression analysis showed that the baseline serum IL-6 and CRP might be related to pneumonia after infection with omicron variant (odds ratio [OR]=2.407, 95% confidence interval [CI]0.915-6.328;OR=1.030, 95% CI 0.952-1.114). ROC curve analysis showed that the area under curve values of serum IL-6 and CRP in predicting the progression to pneumonia were 0.969 (95% CI 0.900-1.000) and 0.656 (95% CI 0.380-0.932), respectively, with statistical significance (Z=2.154, P=0.030). There were no significant differences in the baseline serum IL-6 level or proportions of severe patients or pneumonia patients among patients with or without hypertension, diabetes mellitus, coronary heart disease, chronic kidney disease or chronic obstructive pulmonary disease (all P>0.05). The baseline serum IL-6 levels of the omicron variant infected elderly patients with 1, 2, and 3 or more underlying diseases were 12.50 (9.15, 21.75), 23.55 (9.63, 50.10), and 10.90 (5.20, 18.88) pg/mL, respectively, with no statistical significance (P>0.05). Conclusion For omicron variant infected patients, serum IL-6 level is significantly increased in patients with pneumonia manifestations and is correlated with disease progression. Serum IL-6 level is of great guiding significance to judge disease progression and evaluate efficacy and prognosis of elderly coronavirus disease 2019 patients.Copyright © 2022, Second Military Medical University Press. All rights reserved.

Identification Symptoms and Underlying Diseases Related to COVID-19 And Prediction of Death Status Using Artificial Neural Network and Logistic Regression: A Data Mining Approach

Background and Objectives: Due to the high prevalence of COVID-19 disease and its high mortality rate, it is necessary to identify the symptoms, demographic information and underlying diseases that effectively predict COVID-19 death. Therefore, in this study, we aimed to predict the mortality behavior due to COVID-19 in Khorasan Razavi province. Method(s): This study collected data from 51, 460 patients admitted to the hospitals of Khorasan Razavi province from 25 March 2017 to 12 September 2014. Logistic regression and Neural network methods, including machine learning methods, were used to identify survivors and non-survivors caused by COVID-19. Result(s): Decreased consciousness, cough, PO2 level less than 93%, age, cancer, chronic kidney diseases, fever, headache, smoking status, and chronic blood diseases are the most important predictors of death. The accuracy of the artificial neural network model was 89.90% in the test phase. Also, the sensitivity, specificity and area under the rock curve in this model are equal to 76.14%, 91.99% and 77.65%, respectively. Conclusion(s): Our findings highlight the importance of some demographic information, underlying diseases, and clinical signs in predicting survivors and non-survivors of COVID-19. Also, the neural network model provided high accuracy in prediction. However, medical research in this field will lead to complementary results by using other methods of machine learning and their high power.Copyright © 2022 The Authors.

Utility of the Brixia CXR score, C-Reactive Protein and Absolute Neutrophil Count in Predicting the Need for Invasive Mechanical Ventilation, Length of Hospital Stay, and Mortality in Moderate to Severe COVID-19 Patients

Background: The Brixia Chest X-ray (CXR) score, C-reactive protein (CRP), and the absolute neutrophil count (ANC) have been useful to predict outcomes in Coronavirus disease 2019 (COVID-19 patients). We studied the utility of the Brixia CXR score, CRP, and ANC in predicting the outcomes in terms of the need for invasive mechanical ventilation, length of stay, and mortality in moderate-severe COVID-19 patients. Material(s) and Method(s): This was a single-centre, retrospective, study on 122 COVID-19 patients. Brixia CXR score, CRP, and ANC on admission to the hospital and the fifth day of hospital stay were noted along with the need for invasive mechanical ventilation (IMV), prolonged length of stay (LOS) >= 14 days, and mortality. Result(s): 122 patients were included for analysis. The median and interquartile range (IQR) for baseline CRP was 81.50 (39-151) mg/L and 11.0 (4-30) mg/L (p < 0.001) on the fifth day. The median and IQR for baseline Brixia score was 10.0 (7-13), and on the fifth day was 7 (4-11) (p <0.001). The receiver operating characteristic curve (ROC) showed that the baseline CRP >= 52.5mg/L predicted both the need for IMV, with an area under the curve (AUC) of 0.628, and prolonged LOS with an AUC of 0.608. The ROC curve depicted that the baseline ANC >8500/muL predicted IMV requirement with an AUC of 0.657. The fifth day CRP >= 32 mg/L, ANC >= 11,000/ muL and Brixia CXR score >= 7 predicted a higher mortality in hospitalized patients. Conclusion(s): Baseline CRP (> 52.5mg/L) predicts the need for IMV and a prolonged LOS, but not mortality. Baseline ANC (> 8500/muL) predicted the need for IMV. CRP, Brixia CXR score, and ANC on the fifth day were not useful to predict LOS or mortality, though there was a significant reduction in CRP and Brixia CXR score on the fifth day compared to baseline after treatment. The fifth day CRP >= 32 mg/L, ANC >= 11,000/ muL and Brixia CXR score >= 7 predicted a higher mortality.Copyright © 2023, College of Anaesthesiologists of Sri Lanka. All rights reserved.

A Shuffling Building Block and Augmentation Parameter Tuning Techniques to Handle Small Medical Dataset

The recent COVID-19 outbreak showed us the importance of faster disease diagnosis using medical image processing as it is considered the most reliable and accurate diagnostic tool. In a CNN architecture, performance improves with the increasing number of trainable parameters at the cost of processing time. We have proposed an innovative approach of combining efficient novel architectures like Inception, ResNet, and ResNet-Xt and created a new CNN architecture that benefits Extreme Cardinal dimensions. We have also created four variations of the same base architecture by varying the position of each building block and used X-Ray, Microscopic, MRI, and pathMNIST datasets to train our architecture. For learning curve optimization, we have applied learning rate changing techniques, tuned image augmentation parameters, and chose the best random states value. For a specific dataset, we reduced the validation loss from 0.22 to 0.18 by interchanging the architecture's building block position. Our results indicate that image augmentation parameters can help to decrease the validation loss. We have also shown rearrangement of the building blocks reduces the number of parameters, in our case, from 5,689,008 to 3,876,528. © 2023 IEEE.

Food-based bilateral trade balance performances between the United States and Canada under COVID-19.

The food industry has been greatly impacted by COVID-19, causing governments to restrict food exports to prevent shortages. A negative food trade balance reveals a country's dependence on imports and underscores the significance of a sound food policy. Hence, for the first time, this study examines the J-curve hypothesis for the U.S. with Canada at the state rather than country level and creates maps based on the findings. The approach of this study differs from all empirical studies using country-level J-curve analyses, because the U.S. may require a state level analysis since its states differ in terms of economic-population sizes, tax rates, and administrative structures. For this aim, this study employs the linear and nonlinear autoregressive distributed lag (ARDL) approaches. The results indicate that while only 8 out of 47 U.S. states support the food-based asymmetric J-curve hypothesis, 15 U.S. states support the asymmetric inverse J-curve hypothesis. Additionally, 9 U.S. states support the food-based symmetric J-curve hypothesis, and 2 U.S. states support the symmetric inverse J-curve hypothesis. Based on these results, policymakers of U.S. states where the J-curve hypothesis is not supported should review their food-based bilateral trade policies with Canada. Graphical abstract: These maps depict the U.S. states in green and red, indicating support for the J-curve and inverse J-curve hypotheses, respectively. The map on the left was generated using the linear model (symmetric approach), while the map on the right was generated using the nonlinear model (asymmetric approach). Supplementary Information: The online version contains supplementary material available at 10.1007/s00003-023-01436-x.

Stability and Change in Adolescents' Sense of Agency: Contributions of Sex, Multiple Risk, Pandemic Stress, and Attachment to Parents.

Although literature states that individual, relational, and contextual factors contribute to adolescents' sense of agency, more research is needed to clarify and understand how adolescents develop this belief over time. The current study examined the stability/change trajectories of the sense of agency during adolescence, specifically across high school, analyzing whether attachment to parents over time, adolescents' sex, cumulative risk in baseline, and pandemic-related stress explained these trajectories. The sample included 467 Portuguese adolescents (40.7% were males; Mage = 15.58 years, SD = 0.80), evaluated three times across 18 months. This work yielded three significant findings. First, adolescents' sense of agency significantly increased over time, with significant between-subject variance at the initial levels but not at the growth rate. Second, attachment to parents consistently links to adolescents' sense of agency across time, despite the differential contributions from attachment to mothers and fathers. Third, boys reported greater growth in the sense of agency than girls. Adolescents' cumulative risk at T1 predicted lower initial levels of sense of agency, whereas higher pandemic-related stress predicted less growth of the sense of agency. These findings emphasize the contributions of individual and family characteristics and the role of the broader social context in shaping the development of adolescents' sense of agency. The findings underline the need to consider further the differential influences of adolescents' relationships with mothers and fathers to understand changes in adolescents' sense of agency.

Rapid Single-Round Pool Testing of Infectious Disease Enabled by Multicolor Digital Melting PCR.

Pooled nucleic acid amplification test is a promising strategy to reduce cost and resources for screening large populations for infectious disease. However, the benefit of pooled testing is reversed when disease prevalence is high, because of the need to retest each sample to identify infected individual when a pool is positive. Split, Amplify, and Melt analysis of Pooled Assay (SAMPA) is presented, a multicolor digital melting PCR assay in nanoliter chambers that simultaneously identify infected individuals and quantify their viral loads in a single round of pooled testing. This is achieved by early sample tagging with unique barcodes and pooling, followed by single molecule barcode identification in a digital PCR platform using a highly multiplexed melt curve analysis strategy. The feasibility is demonstrated of SAMPA for quantitative unmixing and variant identification from pools of eight synthetic DNA and RNA samples corresponding to the N1 gene, as well as from heat-inactivated SARS-CoV-2 virus. Single round pooled testing of barcoded samples with SAMPA can be a valuable tool for rapid and scalable population testing of infectious disease.

The Benefits and Costs of Using Social Distancing to Flatten the Curve for COVID-19

We examine the net benefits of social distancing to slow the spread of COVID-19 in USA. Social distancing saves lives but imposes large costs on society due to reduced economic activity. We use epidemiological and economic forecasting to perform a rapid benefit–cost analysis of controlling the COVID-19 outbreak. Assuming that social distancing measures can substantially reduce contacts among individuals, we find net benefits of about $5.2 trillion in our benchmark case. We examine the magnitude of the critical parameters that might imply negative net benefits, including the value of statistical life and the discount rate. A key unknown factor is the speed of economic recovery with and without social distancing measures in place. A series of robustness checks also highlight the key role of the value of mortality risk reductions and discounting in the analysis and point to a need for effective economic stimulus when the outbreak has passed.