Counterfeit Detection with Multispectral Imaging

Multispectral imaging is becoming more practical for a variety of applications due to its ability to provide hyper specific information through a non-destructive analysis. Multispectral imaging cameras can detect light reflectance from different spectral bands of visible and nonvisible wavelengths. Based on the different amount of band reflectance, information can be deduced on the subject. Counterfeit detection applications of multispectral imaging will be decomposed and analyzed in this thesis. Relations between light reflectance and objects’ features will be addressed. The process of the analysis will be broken down to show how this information can be used to provide more insight on the object. This technology provides desired and viable information that can greatly improve multiple fields. For this paper, the multispectral imaging research process of element solution concentrations and counterfeit detection applications of multispectral imaging will be discussed. BaySpec’s OCI-M Ultra Compact Multispectral Imager is used for data collection. This camera is capable of capturing light reflectance from wavelengths of 400 – 1000 nm. Further research opportunities of developing self-automated unmanned aerial vehicles for precision agriculture and extending counterfeit detection applications will also be explored

Safety and effectiveness of RBD-specific polyclonal equine F(ab´)2 fragments for the treatment of hospitalized patients with severe Covid-19 disease: A retrospective cohort study

Background Passive immunotherapy has been evaluated as a therapeutic alternative for patients with COVID-19 disease. Equine polyclonal immunotherapy for COVID-19 (EPIC) showed adequate safety and potential efficacy in a clinical trial setting and obtained emergency use authorization in Argentina. We studied its utility in a real world setting with a larger population. Methods We conducted a retrospective cohort study at “Hospital de Campaña Escuela-Hogar" (HCEH) in Corrientes, Argentina, to assess safety and effectiveness of EPIC in hospitalized adults with severe COVID-19 pneumonia. Primary endpoints were 28-days all-cause mortality and safety. Mortality and improvement in modified WHO clinical scale at 14 and 21 days were secondary endpoints. Potential confounder adjustment was made by logistic regression weighted by the inverse of the probability of receiving the treatment (IPTW) and doubly robust approach. Findings Subsequent clinical records of 446 non-exposed (Controls) and 395 exposed (EPIC) patients admitted between November 2020 and April 2021 were analyzed. Median age was 58 years and 56.8% were males. Mortality at 28 days was 15.7% (EPIC) vs. 21.5% (Control). After IPTW adjustment the OR was 0.66 (95% CI: 0.46–0.96) P = 0.03. The effect was more evident in the subgroup who received two EPIC doses (complete treatment, n = 379), OR 0.58 (95% CI 0.39 to 0.85) P = 0.005. Overall and serious adverse events were not significantly different between groups. Conclusions In this retrospective cohort study, EPIC showed adequate safety and effectiveness in the treatment of hospitalized patients with severe SARS-CoV-2 disease.Fil: Farizano Salazar, Diego H.. Hospital de Campaña Escuela Hogar; ArgentinaFil: Achinelli, Fernando. Hospital de Campaña Escuela Hogar; ArgentinaFil: Colonna, Mariana. Inmunova; ArgentinaFil: Pérez, Lucía. Hospital Italiano; ArgentinaFil: Giménez, Analía A.. Hospital de Campaña Escuela Hogar; ArgentinaFil: Ojeda, Maria Alejandra. Hospital de Campaña Escuela Hogar; ArgentinaFil: Miranda Puente, Susana N.. Hospital de Campaña Escuela Hogar; ArgentinaFil: Sánchez Negrette, Lía. Hospital de Campaña Escuela Hogar; ArgentinaFil: Cañete, Florencia. Hospital de Campaña Escuela Hogar; ArgentinaFil: Martelotte Ibarra, Ornela I.. Hospital de Campaña Escuela Hogar; ArgentinaFil: Sanguineti, Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Spatz, Linus. Inmunova; ArgentinaFil: Goldbaum, Fernando Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Massa, Carolina. Inmunova; ArgentinaFil: Rivas, Marta. Inmunova; ArgentinaFil: Pichel, Mariana. Inmunova; ArgentinaFil: Hiriart, Yanina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Estudios Inmunológicos y Fisiopatológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Estudios Inmunológicos y Fisiopatológicos; ArgentinaFil: Zylberman, Vanesa. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Gallego, Sandra Veronica. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Córdoba. Facultad de Medicina. Instituto de Virología Dr. J. M. Vanella; ArgentinaFil: Konigheim, Brenda Salome. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Córdoba. Facultad de Medicina. Instituto de Virología Dr. J. M. Vanella; ArgentinaFil: Fernández, Francisco. No especifíca;Fil: Deprati, Matías. No especifíca;Fil: Roubicek, Ian. Inmunova; ArgentinaFil: Giunta, Diego Hernan. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Hospital Italiano; ArgentinaFil: Nannini, Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Inmunología Clinica y Experimental de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Médicas. Instituto de Inmunología Clinica y Experimental de Rosario; ArgentinaFil: Lopardo, Gustavo. No especifíca;Fil: Belloso, Waldo Horacio. Hospital Italiano; Argentin

Long COVID Clinical Phenotypes up to 6 Months After Infection Identified by Latent Class Analysis of Self-Reported Symptoms

BACKGROUND: The prevalence, incidence, and interrelationships of persistent symptoms after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection vary. There are limited data on specific phenotypes of persistent symptoms. Using latent class analysis (LCA) modeling, we sought to identify whether specific phenotypes of COVID-19 were present 3 months and 6 months post-infection. METHODS: This was a multicenter study of symptomatic adults tested for SARS-CoV-2 with prospectively collected data on general symptoms and fatigue-related symptoms up to 6 months postdiagnosis. Using LCA, we identified symptomatically homogenous groups among COVID-positive and COVID-negative participants at each time period for both general and fatigue-related symptoms. RESULTS: Among 5963 baseline participants (4504 COVID-positive and 1459 COVID-negative), 4056 had 3-month and 2856 had 6-month data at the time of analysis. We identified 4 distinct phenotypes of post-COVID conditions (PCCs) at 3 and 6 months for both general and fatigue-related symptoms; minimal-symptom groups represented 70% of participants at 3 and 6 months. When compared with the COVID-negative cohort, COVID-positive participants had higher occurrence of loss of taste/smell and cognition problems. There was substantial class-switching over time; those in 1 symptom class at 3 months were equally likely to remain or enter a new phenotype at 6 months. CONCLUSIONS: We identified distinct classes of PCC phenotypes for general and fatigue-related symptoms. Most participants had minimal or no symptoms at 3 and 6 months of follow-up. Significant proportions of participants changed symptom groups over time, suggesting that symptoms present during the acute illness may differ from prolonged symptoms and that PCCs may have a more dynamic nature than previously recognized

Study protocol for the Innovative Support for Patients with SARS-COV-2 Infections Registry (INSPIRE): A longitudinal study of the medium and long-term sequelae of SARS-CoV-2 infection

Background: Reports on medium and long-term sequelae of SARS-CoV-2 infections largely lack quantification of incidence and relative risk. We describe the rationale and methods of the Innovative Support for Patients with SARS-CoV-2 Registry (INSPIRE) that combines patient-reported outcomes with data from digital health records to understand predictors and impacts of SARS-CoV-2 infection. Methods: INSPIRE is a prospective, multicenter, longitudinal study of individuals with symptoms of SARS-CoV-2 infection in eight regions across the US. Adults are eligible for enrollment if they are fluent in English or Spanish, reported symptoms suggestive of acute SARS-CoV-2 infection, and if they are within 42 days of having a SARS-CoV-2 viral test (i.e., nucleic acid amplification test or antigen test), regardless of test results. Recruitment occurs in-person, by phone or email, and through online advertisement. A secure online platform is used to facilitate the collation of consent-related materials, digital health records, and responses to self-administered surveys. Participants are followed for up to 18 months, with patient-reported outcomes collected every three months via survey and linked to concurrent digital health data; follow-up includes no in-person involvement. Our planned enrollment is 4,800 participants, including 2,400 SARS-CoV-2 positive and 2,400 SARS-CoV-2 negative participants (as a concurrent comparison group). These data will allow assessment of longitudinal outcomes from SARS-CoV-2 infection and comparison of the relative risk of outcomes in individuals with and without infection. Patient-reported outcomes include self-reported health function and status, as well as clinical outcomes including health system encounters and new diagnoses. Results: Participating sites obtained institutional review board approval. Enrollment and follow-up are ongoing. Conclusions: This study will characterize medium and long-term sequelae of SARS-CoV-2 infection among a diverse population, predictors of sequelae, and their relative risk compared to persons with similar symptomatology but without SARS-CoV-2 infection. These data may inform clinical interventions for individuals with sequelae of SARS-CoV-2 infection

Association Between SARS-CoV-2 Variants and Frequency of Acute Symptoms: Analysis of a Multi-institutional Prospective Cohort Study-December 20, 2020-June 20, 2022.

Background: While prior work examining severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern focused on hospitalization and death, less is known about differences in clinical presentation. We compared the prevalence of acute symptoms across pre-Delta, Delta, and Omicron. Methods: We conducted an analysis of the Innovative Support for Patients with SARS-CoV-2 Infections Registry (INSPIRE), a cohort study enrolling symptomatic SARS-CoV-2-positive participants. We determined the association between the pre-Delta, Delta, and Omicron time periods and the prevalence of 21 coronavirus disease 2019 (COVID-19) acute symptoms. Results: We enrolled 4113 participants from December 2020 to June 2022. Pre-Delta vs Delta vs Omicron participants had increasing sore throat (40.9%, 54.6%, 70.6%; Conclusions: Participants infected during Omicron were more likely to report symptoms of common respiratory viruses, such as sore throat, and less likely to report loss of smell and taste. Trial Registration: NCT04610515

Association of Initial SARS-CoV-2 Test Positivity With Patient-Reported Well-being 3 Months After a Symptomatic Illness.

IMPORTANCE: Long-term sequelae after symptomatic SARS-CoV-2 infection may impact well-being, yet existing data primarily focus on discrete symptoms and/or health care use. OBJECTIVE: To compare patient-reported outcomes of physical, mental, and social well-being among adults with symptomatic illness who received a positive vs negative test result for SARS-CoV-2 infection. DESIGN, SETTING, AND PARTICIPANTS: This cohort study was a planned interim analysis of an ongoing multicenter prospective longitudinal registry study (the Innovative Support for Patients With SARS-CoV-2 Infections Registry [INSPIRE]). Participants were enrolled from December 11, 2020, to September 10, 2021, and comprised adults (aged ≥18 years) with acute symptoms suggestive of SARS-CoV-2 infection at the time of receipt of a SARS-CoV-2 test approved by the US Food and Drug Administration. The analysis included the first 1000 participants who completed baseline and 3-month follow-up surveys consisting of questions from the 29-item Patient-Reported Outcomes Measurement Information System (PROMIS-29; 7 subscales, including physical function, anxiety, depression, fatigue, social participation, sleep disturbance, and pain interference) and the PROMIS Short Form-Cognitive Function 8a scale, for which population-normed T scores were reported. EXPOSURES: SARS-CoV-2 status (positive or negative test result) at enrollment. MAIN OUTCOMES AND MEASURES: Mean PROMIS scores for participants with positive COVID-19 tests vs negative COVID-19 tests were compared descriptively and using multivariable regression analysis. RESULTS: Among 1000 participants, 722 (72.2%) received a positive COVID-19 result and 278 (27.8%) received a negative result; 406 of 998 participants (40.7%) were aged 18 to 34 years, 644 of 972 (66.3%) were female, 833 of 984 (84.7%) were non-Hispanic, and 685 of 974 (70.3%) were White. A total of 282 of 712 participants (39.6%) in the COVID-19-positive group and 147 of 275 participants (53.5%) in the COVID-19-negative group reported persistently poor physical, mental, or social well-being at 3-month follow-up. After adjustment, improvements in well-being were statistically and clinically greater for participants in the COVID-19-positive group vs the COVID-19-negative group only for social participation (β = 3.32; 95% CI, 1.84-4.80; P \u3c .001); changes in other well-being domains were not clinically different between groups. Improvements in well-being in the COVID-19-positive group were concentrated among participants aged 18 to 34 years (eg, social participation: β = 3.90; 95% CI, 1.75-6.05; P \u3c .001) and those who presented for COVID-19 testing in an ambulatory setting (eg, social participation: β = 4.16; 95% CI, 2.12-6.20; P \u3c .001). CONCLUSIONS AND RELEVANCE: In this study, participants in both the COVID-19-positive and COVID-19-negative groups reported persistently poor physical, mental, or social well-being at 3-month follow-up. Although some individuals had clinically meaningful improvements over time, many reported moderate to severe impairments in well-being 3 months later. These results highlight the importance of including a control group of participants with negative COVID-19 results for comparison when examining the sequelae of COVID-19

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages