A prospective, single-center study to evaluate the clinical performance of Meril ABFind in individuals vaccinated against COVID-19

Background: Accurate rapid antibody detection kits requiring minimum infrastructure are beneficial in detecting post-vaccination antibodies in large populations. ChAdOx1-nCOV (COVISHIELD) and BBV-152 (Covaxin) vaccines are primarily used in India. Methods: In this single-centre prospective study, performance of Meril ABFind was investigated by comparing with Abbott SARS-CoV-2 IgG II Quant (Abbott Quant), GenScript cPass SARS-CoV-2 neutralization antibody detection kit (GenScript cPass), and COVID Kawach MERILISA (MERILISA) in 62 vaccinated health care workers (HCW) and 40 pre-pandemic samples. Results: In the vaccinated subjects, Meril ABFind kit displayed high sensitivity of 93.3% (CI, 89.83%-96.77%), 94.92% (CI, 91.88%-97.96%), and 90.3% (CI, 86.20%-94.4%) in comparison to Abbott Quant, MERILISA, and GenScript cPass respectively. The results of the Meril ABFind in the COVISHIELD-vaccinated group were excellent with 100% sensitivity in comparison to the other three kits. In the Covaxin-vaccinated group, Meril ABFind displayed sensitivity ranging from 80% to 88.9%. In control samples, there were no false positives detected by Meril ABFind, while Abbott Quant, MERILISA, and GenScript cPass reported 2.5%, 10.0%, and 12.5% false positives, respectively. In the pre-pandemic controls, specificity of Meril ABFind was 100%, Abbott Quant 97.5%, MERILISA 90%, and GenScript cPass 87.5%. Conclusion: The Meril ABFind kit demonstrated satisfactory performance when compared with the three commercially available kits and was the only kit without false positives in the pre-pandemic samples. This makes it a viable option for rapid diagnosis of post vaccination antibodies.

How much Fear? Exploring the Role of Integral Emotions on Stated Preferences for Wildlife Conservation

Scientific evidence suggests that emotions affect actual human decision-making, particularly in highly emotionally situations such as human-wildlife interactions. In this study we assess the role of fear on preferences for wildlife conservation, using a discrete choice experiment. The sample was split into two treatment groups and a control. In the treatment groups the emotion of fear towards wildlife was manipulated using two different pictures of a wolf, one fearful and one reassuring, which were presented to respondents during the experiment. Results were different for the two treatments. The assurance treatment lead to higher preferences and willingness to pay for the wolf, compared to the fear treatment and the control, for several population sizes. On the other hand, the impact of the fear treatment was lower than expected and only significant for large populations of wolves, in excess of 50 specimen. Overall, the study suggests that emotional choices may represent a source of concern for the assessment of stable preferences. The impact of emotional choices is likely to be greater in situations where a wildlife-related topic is highly emphasized, positively or negatively, by social networks, mass media, and opinion leaders. When stated preferences towards wildlife are affected by the emotional state of fear due to contextual external stimuli, welfare analysis does not reflect stable individual preferences and may lead to sub-optimal conservation policies. Therefore, while more research is recommended for a more accurate assessment, it is advised to control the decision context during surveys for potential emotional choices.

Ticks and Tick-Borne Pathogens in Domestic Animals, Wild Pigs, and Off-Host Environmental Sampling in Guam, USA

Background: Guam, a United States of America (USA) island territory in the Pacific Ocean, is known to have large populations of ticks; however, it is unclear what the risk is to wildlife and humans living on the island. Dog (Canis familiaris), cat (Felis catus), and wild pig (Sus scrofa) sentinels were examined for ticks, and environmental sampling was conducted to determine the ticks present in Guam and the prevalence of tick-borne pathogens in hosts.Methods and Results: From March 2019-November 2020, ticks were collected from environmental sampling, dogs, cats, and wild pigs. Blood samples were also taken from a subset of animals. A total of 99 ticks were collected from 27 environmental samples and all were Rhipicephalus sanguineus, the brown dog tick. Most ticks were collected during the dry season with an overall sampling success rate of 63% (95% CI: 42.4–80.6). 6,614 dogs were examined, and 12.6% (95% CI: 11.8–13.4) were infested with at least one tick. One thousand one hundred twelve cats were examined, and six (0.54%; 95% CI: 0.20–1.1) were found with ticks. Sixty-four wild pigs were examined and 17.2% (95% CI: 9.5–27.8) had ticks. In total, 1,956 ticks were collected and 97.4% of ticks were R. sanguineus. A subset of R. sanguineus were determined to be the tropical lineage. The other tick species found were Rhipicephalus microplus (0.77%), Amblyomma breviscutatum (0.77 %), and a Haemaphysalis sp. (0.51%). Blood samples from 136 dogs, four cats, and 64 wild pigs were tested using polymerase chain reaction (PCR) and DNA sequencing methods. Five different tick-borne pathogens with the following prevalences were found in dogs: Anaplasma phagocytophilum 5.9% (95% CI: 2.6–11.3); Anaplasma platys 19.1% (95% CI: 12.9–26.7); Babesia canis vogeli 8.8% (95% CI: 4.6–14.9); Ehrlichia canis 12.5% (95% CI: 7.5–19.3); Hepatozoon canis 14.7% (95% CI: 9.2–28.8). E. canis was detected in one cat, and no tick-borne pathogens were detected in wild pigs. Overall, 43.4% (95% CI: 34.9–52.1) of dogs had at least one tick-borne pathogen. Serological testing for antibodies against Ehrlichia spp. and Anaplasma spp. showed prevalences of 14.7% (95% CI: 9.2–28.8) and 31.6% (95% CI: 23.9–40), respectively.Conclusion: Four different tick species were found in Guam to include a Haemaphysalis sp., which is a previously unreported genus for Guam. Dogs with ticks have a high prevalence of tick-borne pathogens which makes them useful sentinels.

Adjustment of fecundity and sex ratio in response to social environments in a haplodiploid mite

Animals can adjust reproductive strategies in favour of corporation or competition in response to local population size and density, the two key factors of social environments. However, previous studies usually focus on either population size or density but ignore their interactions. Using a haplodiploid spider mite, Tetranychus ludeni Zacher, we carried out a factorial experiment in the laboratory to examine how ovipositing females adjust their fecundity and offspring sex ratio during their early reproductive life under various population size and density. We reveal that females laid significantly more eggs with increasing population size and significantly fewer eggs with increasing population density. This suggests that large populations favour cooperation between individuals and dense populations increase competition. We demonstrate a significant negative interaction of population size and density that resulted in significantly fewer eggs laid in the large and dense populations. Furthermore, we show that females significantly skewed the offspring sex ratio towards female-biased in small populations to reduce the local mate competition among their sons. However, population density incurred no significant impact on offspring sex ratio, while the significant positive interaction of population size and density significantly increased the proportion of female offspring in the large and dense populations, which will minimise food or space competition as females usually disperse after mating at crowded conditions. These results also suggest that population density affecting sex allocation in T. ludeni is intercorrelated with population size. This study provides evidence that animals can manipulate their reproductive output and adjust offspring sex ratio in response to various social environments, and the interactions of different socio-environmental factors may play significant roles.

Spread of new mutations through space

The terms population size and population density are often used interchangeably, when in fact they are quite different. When viewed in a spatial landscape, density is defined as the number of individuals within a square unit of distance, while population size is simply the total count of a population. In discrete population genetics models, the effective population size is known to influence the interaction between selection and random drift with selection playing a larger role in large populations while random drift has more influence in smaller populations. Using a spatially explicit simulation software we investigate how population density affects the flow of new mutations through a geographical space. Using population density, selectional advantage, and dispersal distributions, a model is developed to predict the speed at which the new allele will travel, obtaining more accurate results than current diffusion approximations provide. We note that the rate at which a neutral mutation spreads begins to decay over time while the rate of spread of an advantageous allele remains constant. We also show that new advantageous mutations spread faster in dense populations.

Meta-analysis of COVID-19 patients to understand the key predictors of mortality in the non-vaccinated groups in remote settings

Various studies have looked into the impact of the COVID-19 vaccine on large populations. However, very few studies have looked into the remote setting of hospitals where vaccination is challenging due to social structure, myths, and misconceptions. There is a consensus that elevated inflammatory markers such as CRP, ferritin, D-dimer correlate with increased severity of COVID-19 and are associated with worse outcomes. In the present study, through retrospective meta-analysis, we have looked into ~20 months of SARS-COV2 infected patients with known mortality status and identified predictors of mortality concerning their comorbidities, various clinical parameters, inflammatory markers, superimposed infections, length of hospitalization, length of mechanical ventilation and ICU stay. Studies with larger sample sizes have covered the outcomes through epidemiological, social, and survey-based analysis; however, most studies cover larger cohorts from tertiary medical centers. In the present study, we assessed the outcome of non-vaccinated COVID 19 patients in a remote setting for 20 months from January 1, 2020, to August 30, 2021, at CHI Mercy Health in Roseburg, Oregon. We also included two vaccinated patients from September 2021 to add to the power of our cohort. The study will provide a comprehensive methodology and deep insight into multi-dimensional data in the unvaccinated group, translational biomarkers of mortality, and state-of-art to conduct such studies in various remote hospitals.

Nanophotonics: An Emerging and Promising Approach for COVID-19 Diagnostic Technology

The public health crisis initiated by the emergence of the COVID-19 pandemic emphasizes the need for rapid and accurate diagnostic tests to monitor large populations through community mass testing. Many testing techniques have been implemented to prevent disease spread, critical to pandemic control. Polymerase chain reaction (PCR) tests for detecting viral RNA and immunoassay tests for detecting SARS-CoV-2 antibodies are currently used to diagnose COVID-19. PCR tests are time-consuming, with a 24–48 hours turnaround time. Samples undergoing PCR detection must also be sent to a laboratory to be processed by highly specialized workers, preventing a point-of-care diagnosis from being provided. Popular immunoassay tests have drawbacks as well. Enzyme-linked immunosorbent assays (ELISAs) are extremely labor-intensive and expensive, whereas lateral flow assays (LFAs) are primarily used for antigen detection. In this work, we propose a photonic SARS-CoV-2 detection method based on a ring resonator. We calculate the sensor performance using the finite-difference eigenmode (FDE) method. The sensor sensitivity in ring resonator resonance frequency is 29 nm/RIU, with an intrinsic detection level (iLOD) of 6.89 × 10-5 RIU. We envision ring resonator-based lab-on-chip devices being widely used for applications such as early diagnosis, with the added benefit of being ultra-compact and easily handled by non-specialists.

Comparing Pregnancy and Childbirth-related Hospital Visits in Arizona Before and During COVID-19 Using Network Analysis

The COVID-19 pandemic has had a severe effect on all facets of human society, including healthcare. One of the primary concerns in healthcare is understanding and mitigating the impact of the pandemic on pregnancy and childbirth. While several studies have looked at challenges such as contract tracing of positive cases, predicting confirmed cases and deaths in individuals and communities, few studies have examined differences in hospitalization and treatment of pregnant mothers and infant care in large populations. In this study, the prevalence and co-occurrence of pregnancy and childbirth-related diagnoses reported in Arizona State hospitals for three sixth-month periods - before COVID-19 (second half of 2019), COVID-19 onset (first half of 2020), and COVID-19 (second half of 2020) are analyzed using network analysis. The results show that there are considerable differences in ego networks of few diagnoses during these time periods warranting further investigation into the causality of such population changes.

SmartKC

Keratoconus is a severe eye disease affecting the cornea (the clear, dome-shaped outer surface of the eye), causing it to become thin and develop a conical bulge. The diagnosis of keratoconus requires sophisticated ophthalmic devices which are non-portable and very expensive. This makes early detection of keratoconus inaccessible to large populations in low-and middle-income countries, making it a leading cause for partial/complete blindness among such populations. We propose SmartKC, a low-cost, smartphone-based keratoconus diagnosis system comprising of a 3D-printed placido's disc attachment, an LED light strip, and an intelligent smartphone app to capture the reflection of the placido rings on the cornea. An image processing pipeline analyzes the corneal image and uses the smartphone's camera parameters, the placido rings' 3D location, the pixel location of the reflected placido rings and the setup's working distance to construct the corneal surface, via the Arc-Step method and Zernike polynomials based surface fitting. In a clinical study with 101 distinct eyes, we found that SmartKC achieves a sensitivity of 87.8% and a specificity of 80.4%. Moreover, the quantitative curvature estimates (sim-K) strongly correlate with a gold-standard medical device (Pearson correlation coefficient = 0.77). Our results indicate that SmartKC has the potential to be used as a keratoconus screening tool under real-world medical settings.