The Risk of Reinfection or Primary Hepatitis E Virus Infection at a Liver Transplant Center in Brazil: An Observational Cohort Study.

The hepatitis E virus is a major etiological agent of chronic hepatitis in immunosuppressed individuals. Seroprevalence in the liver transplantation setting varies according to the seroprevalence of the general population in different countries. This was a prospective cohort study of liver transplant recipients in southeastern Brazil. Recipients were systematically followed for one year, with the objective of determining the prevalence, incidence, and natural history of HEV infection in this population. We included 107 liver transplant recipients and 83 deceased donors. Positivity for anti-HEV IgG was detected in 10.2% of the recipients and in 9.7% of the donors. None of the patients tested positive for HEV RNA at baseline or during follow-up. There were no episodes of reactivation or seroconversion, even in cases of serological donor-recipient mismatch or in recipients with acute hepatitis. Acute and chronic HEV infections seem to be rare events in the region studied. That could be attributable to social, economic, and environmental factors. Our data indicate that, among liver transplant recipients, hepatitis E should be investigated only when there are elevated levels of transaminases with no defined cause, as part of the differential diagnosis of seronegative hepatitis after transplantation.

Current challenges and future of agricultural genomes to phenomes in the USA.

Dramatic improvements in measuring genetic variation across agriculturally relevant populations (genomics) must be matched by improvements in identifying and measuring relevant trait variation in such populations across many environments (phenomics). Identifying the most critical opportunities and challenges in genome to phenome (G2P) research is the focus of this paper. Previously (Genome Biol, 23(1):1-11, 2022), we laid out how Agricultural Genome to Phenome Initiative (AG2PI) will coordinate activities with USA federal government agencies expand public-private partnerships, and engage with external stakeholders to achieve a shared vision of future the AG2PI. Acting on this latter step, AG2PI organized the "Thinking Big: Visualizing the Future of AG2PI" two-day workshop held September 9-10, 2022, in Ames, Iowa, co-hosted with the United State Department of Agriculture's National Institute of Food and Agriculture (USDA NIFA). During the meeting, attendees were asked to use their experience and curiosity to review the current status of agricultural genome to phenome (AG2P) work and envision the future of the AG2P field. The topic summaries composing this paper are distilled from two 1.5-h small group discussions. Challenges and solutions identified across multiple topics at the workshop were explored. We end our discussion with a vision for the future of agricultural progress, identifying two areas of innovation needed: (1) innovate in genetic improvement methods development and evaluation and (2) innovate in agricultural research processes to solve societal problems. To address these needs, we then provide six specific goals that we recommend be implemented immediately in support of advancing AG2P research.

Reduction of SARS-CoV-2 viral load in saliva after rinsing with mouthwashes containing cetylpyridinium chloride: a randomized clinical study.

Background: Symptomatic patients with COVID-19 typically have a high SARS-CoV-2 viral load in their saliva. Procedures to reduce the viral load in their oral cavity are important for mitigating the viral transmission. Methods: This randomized clinical trial investigated the impact of two mouthwashes (0.075% cetylpyridinium chloride plus 0.28% zinc lactate (CPC+Zn) (n = 32), and 0.075% cetylpyridinium chloride (CPC) (n = 31)) on the viral load of SARS-CoV-2 in saliva when compared to the distilled water negative control (n = 32). Saliva was collected before (T0) and after (5 min, T1; 30 min, T2; and 60 min, T3) the intervention. Viral load in saliva was measured by qRT-PCR assays. The data in both groups was normalized for T0 and Negative Control, resulting in fold change values. Results: CPC+Zn oral solution reduced the viral load in saliva by 6.34-fold at T1, 3.6-fold at T2 and 1.9-fold at T3. Rinsing with the CPC mouthwash reduced the viral load in saliva by 2.5-fold at T1, 1.9-fold at T2 and 2.0-fold at T3. Conclusion: CPC+Zn mouthwash or with the CPC mouthwash reduced the viral load in saliva of COVID-19 patients immediately after rinsing. These reductions extended up to 60 min.

Image analysis to automatically classify anemia based on Famacha score in sheep using ocular conjunctiva images.

Haemonchus contortus is the most pathogenic blood-feeding parasitic in sheep, causing anemia and consequently changes in the color of the ocular conjunctiva, from the deep red of healthy sheep to shades of pink to practically white of non-healthy sheep. In this context, the Famacha method has been created for detecting sheep unable to cope with the infection by H. contortus, through visual assessment of ocular conjunctiva coloration. Thus, the objectives of this study were (1) to extract ocular conjunctiva image features to automatically classify Famacha score and compare two classification models (multinomial logistic regression-MLR and random forest-RF) and (2) to evaluate the applicability of the best classification model on three sheep farms. The dataset consisted of 1,156 ocular conjunctiva images from 422 animals. RF model was used to segment the images, i.e., to select the pixels that belong to the ocular conjunctiva. After segmentation, the quantiles (1%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 99%) of color intensity in each image channel (red, blue, and green) were determined and used as explanatory variables in the classification models, and the Famacha scores 1 (non-anemic) to 5 (severely anemic) were the target classes to be predicted (scores 1 to 5, with 162, 255, 443, 266, and 30 images, respectively). For objective 1, the performance metrics (precision and sensitivity) were obtained using MLR and RF models considering data from all farms randomly split. For objective 2, a leave-one-farm-out cross-validation technique was used to assess prediction quality across three farms (farms A, B, and C, with 726, 205, and 225 images, respectively). The RF provided the best performances in predicting anemic animals, as indicated by the high values of sensitivity for Famacha score 3 (80.9%), 4 (46.2%), and 5 (60%) compared to the MLR model. The precision of the RF was 72.7% for Famacha score 1 and 62.5% for Famacha score 2. These results indicate that is possible to successfully predict Famacha score, especially for scores 2 to 4, in sheep via image analysis and RF model using ocular conjunctiva images collected in farm conditions. As expected, model validation excluding entire farms in cross-validation presented a lower prediction quality. Nonetheless, this setup is closer to reality because the developed models are supposed to be used across farms, including new ones, and with different environments and management conditions.

Exploring associations among morphometric measurements, genetic group of sire, and performance of beef on dairy calves.

Sire selection for beef on dairy crosses plays an important role in livestock systems as it may affect future performance and carcass traits of growing and finishing crossbred cattle. The phenotypic variation found in beef on dairy crosses has raised concerns from meat packers due to animals with dairy-type carcass characteristics. The use of morphometric measurements may help to understand the phenotypic structures of sire progeny for selecting animals with greater performance. In addition, due to the relationship with growth, these measurements could be used to early predict the performance until the transition from dairy farms to sales. The objectives of this study were 1) to evaluate the effect of different beef sires and breeds on the morphometric measurements of crossbred calves including cannon bone (CB), forearm (FA), hip height (HH), face length (FL), face width (FW) and growth performance; and (2) to predict the weight gain from birth to transition from dairy farms to sale (WG) and the body weight at sale (BW) using such morphometric measurements obtained at first days of animals' life. CB, FA, HH, FL, FW, and weight at 7 ±â€…5 d (BW7) (Table 1) were measured on 206 calves, from four different sire breeds [Angus (AN), SimAngus (SA), Simmental (SI), and Limousin (LI)], from five farms. To evaluate the morphometric measurements at the transition from dairy farms to sale and animal performance 91 out of 206 calves sourced from four farms, and offspring of two different sires (AN and SA) were used. To predict the WG and BW, 97 calves, and offspring of three different sires (AN, SA, and LI) were used. The data were analyzed using a mixed model, considering farm and sire as random effects. To predict WG and BW, two linear models (including or not the morphometric measurements) were used, and a leave-one-out cross-validation strategy was used to evaluate their predictive quality. The HH and BW7 were 7.67% and 10.7% higher (P < 0.05) in SA crossbred calves compared to AN, respectively. However, the ADG and adjusted body weight to 120 d were 14.3% and 9.46% greater (P < 0.05) in AN compared to SA. The morphometric measurements improved the model's predictive performance for WG and BW. In conclusion, morphometric measurements at the first days of calves' life can be used to predict animals' performance in beef on dairy. Such a strategy could lead to optimized management decisions and greater profitability in dairy farms.

Using pseudo-labeling to improve performance of deep neural networks for animal identification.

Contemporary approaches for animal identification use deep learning techniques to recognize coat color patterns and identify individual animals in a herd. However, deep learning algorithms usually require a large number of labeled images to achieve satisfactory performance, which creates the need to manually label all images when automated methods are not available. In this study, we evaluated the potential of a semi-supervised learning technique called pseudo-labeling to improve the predictive performance of deep neural networks trained to identify Holstein cows using labeled training sets of varied sizes and a larger unlabeled dataset. By using such technique to automatically label previously unlabeled images, we observed an increase in accuracy of up to 20.4 percentage points compared to using only manually labeled images for training. Our final best model achieved an accuracy of 92.7% on an independent testing set to correctly identify individuals in a herd of 59 cows. These results indicate that it is possible to achieve better performing deep neural networks by using images that are automatically labeled based on a small dataset of manually labeled images using a relatively simple technique. Such strategy can save time and resources that would otherwise be used for labeling, and leverage well annotated small datasets.

Longitudinal analysis of bovine mammary gland development.

Many studies on bovine mammary glands focus on one stage of development. Often missing in those studies are repeated measures of development from the same animals. As milk production is directly affected by amount of parenchymal tissue within the udder, understanding mammary gland growth along with visualization of its structures during development is essential. Therefore, analysis of ultrasound and histology data from the same animals would result in better understanding of mammary development over time. Thus, this research aimed to describe mammary gland development using non-invasive and invasive tools to delineate growth rate of glandular tissue responsible for potential future milk production. Mammary gland ultrasound images, biopsy samples, and blood samples were collected from 36 heifer dairy calves beginning at 10 weeks of age, and evaluated at 26, 39, and 52 weeks. Parenchyma was quantified at 10 weeks of age using ultrasound imaging and histological evaluation, and average echogenicity was utilized to quantify parenchyma at later stages of development. A significant negative correlation was detected between average echogenicity of parenchyma at 10 weeks and total adipose as a percent of histological whole tissue at 52 weeks. Additionally, a negative correlation between average daily gain at 10 and 26 weeks and maximum echogenicity at 52 weeks was present. These results suggest average daily gain and mammary gland development prior to 39 weeks of age is associated with development of the mammary gland after 39 weeks. These findings could be predictors of future milk production, however this must be further explored.

Detection of Hepatitis E Virus Genotype 3 in Feces of Capybaras (Hydrochoeris hydrochaeris) in Brazil.

Hepatitis E virus (HEV) is an emerging zoonotic pathogen associated with relevant public health issues. The aim of this study was to investigate HEV presence in free-living capybaras inhabiting urban parks in São Paulo state, Brazil. Molecular characterization of HEV positive samples was undertaken to elucidate the genetic diversity of the virus in these animals. A total of 337 fecal samples were screened for HEV using RT-qPCR and further confirmed by conventional nested RT-PCR. HEV genotype and subtype were determined using Sanger and next-generation sequencing. HEV was detected in one specimen (0.3%) and assigned as HEV-3f. The IAL-HEV_921 HEV-3f strain showed a close relationship to European swine, wild boar and human strains (90.7-93.2% nt), suggesting an interspecies transmission. Molecular epidemiology of HEV is poorly investigated in Brazil; subtype 3f has been reported in swine. This is the first report of HEV detected in capybara stool samples worldwide.

Predicting ribeye area and circularity in live calves through 3D image analyses of body surface.

The use of sexed semen at dairy farms has improved heifer replacement over the last decade by allowing greater control over the number of retained females and enabling the selection of dams with superior genetics. Alternatively, beef semen can be used in genetically inferior dairy cows to produce crossbred (beef x dairy) animals that can be sold at a higher price. Although crossbreeding became profitable for dairy farmers, meat cuts from beef x dairy crosses often lack quality and shape uniformity. Technologies for quickly predicting carcass traits for animal grouping before harvest may improve meat cut uniformity in crossbred cattle. Our objective was to develop a deep learning approach for predicting ribeye area and circularity of live animals through 3D body surface images using two neural networks: 1) nested Pyramid Scene Parsing Network (nPSPNet) for extracting features and 2) Convolutional Neural Network (CNN) for estimating ribeye area and circularity from these features. A group of 56 calves were imaged using an Intel RealSense D435 camera. A total of 327 depth images were captured from 30 calves and labeled with masks outlining the calf body to train the nPSPNet for feature extraction. Additional 42,536 depth images were taken from the remaining 26 calves along with three ultrasound images collected for each calf from the 12/13th ribs. The ultrasound images (three by calf) were manually segmented to calculate the average ribeye area and circularity and then paired with the depth images for CNN training. We implemented a nested cross-validation approach, in which all images for one calf were removed (leave-one-out, LOO), and the remaining calves were further divided into training (70%) and validation (30%) sets within each LOO iteration. The proposed model predicted ribeye area with an average coefficient of determination (R2) of 0.74% and 7.3% mean absolute error of prediction (MAEP) and the ribeye circularity with an average R2 of 0.87% and 2.4% MAEP. Our results indicate that computer vision systems could be used to predict ribeye area and circularity in live animals, allowing optimal management decisions toward smart animal grouping in beef x dairy crosses and purebred.

This work proposes a method for predicting ribeye specific carcass traits of beef x dairy crossbred calves from 3D images using deep learning. This method completely automates the measurement of carcass traits, providing an efficient way of grouping calves for breeding programs or meat quality control.

Testicular pathology in fatal COVID-19: A descriptive autopsy study.

BACKGROUND: Multi-organ damage is a common feature of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, going beyond the initially observed severe pneumonia. Evidence that the testis is also compromised is growing. OBJECTIVE: To describe the pathological findings in testes from fatal cases of COVID-19, including the detection of viral particles and antigens, and inflammatory cell subsets. MATERIALS AND METHODS: Postmortem testicular samples were obtained by percutaneous puncture from 11 deceased men and examined by reverse-transcription polymerase chain reaction (RT-PCR) for RNA detection and by light and electron microscopy (EM) for SARS-CoV-2. Immunohistochemistry (IHC) for the SARS-CoV-2 N-protein and lymphocytic and histiocytic markers was also performed. RESULTS: Eight patients had mild interstitial orchitis, composed mainly of CD68+ and TCD8+ cells. Fibrin thrombi were detected in five cases. All cases presented congestion, interstitial edema, thickening of the tubular basal membrane, decreased Leydig and Sertoli cells with reduced spermatogenesis, and strong expression of vascular cell adhesion molecule (VCAM) in vessels. IHC detected SARS-Cov-2 antigen in Leydig cells, Sertoli cells, spermatogonia, and fibroblasts in all cases. EM detected viral particles in the cytoplasm of fibroblasts, endothelium, Sertoli and Leydig cells, spermatids, and epithelial cells of the rete testis in four cases, while RT-PCR detected SARS-CoV-2 RNA in three cases. DISCUSSION AND CONCLUSION: The COVID-19-associated testicular lesion revealed a combination of orchitis, vascular changes, basal membrane thickening, Leydig and Sertoli cell scarcity, and reduced spermatogenesis associated with SARS-CoV-2 local infection that may impair hormonal function and fertility in men.

COVID-19 convalescent plasma cohort study: Evaluation of the association between both donor and recipient neutralizing antibody titers and patient outcomes.

BACKGROUND: Current evidence regarding COVID-19 convalescent plasma (CCP) transfusion practices is limited and heterogeneous. We aimed to determine the impact of the use of CCP transfusion in patients with previous circulating neutralizing antibodies (nAbs) in COVID-19. METHODS: Prospective cohort including 102 patients with COVID-19 transfused with ABO compatible CCP on days 0-2 after enrollment. Clinical status of patients was assessed using the adapted World Health Organization (WHO) ordinal scale on days 0, 5, and 14. The nAbs titration was performed using the cytopathic effect-based virus neutralization test with SARS-CoV-2 (GenBank MT126808.1). The primary outcome was clinical improvement on day 14, defined as a reduction of at least two points on the adapted WHO ordinal scale. Secondary outcomes were the number of intensive care unit (ICU)-free days and the number of invasive mechanical ventilation-free days. RESULTS: Both nAbs of CCP units transfused (p < 0.001) and nAbs of patients before CCP transfusions (p = 0.028) were associated with clinical improvements by day 14. No significant associations between nAbs of patients or CCP units transfused were observed in the number of ICU or mechanical ventilation-free days. Administration of CCP units after 10 days of symptom onset resulted in a decrease in ICU-free days (p < 0.001) and mechanical ventilation-free days (p < 0.001). CONCLUSION: Transfusion of high titer nAbs CCP units may be a determinant in clinical strategies against COVID-19. We consider these data as useful parameters to guide future CCP transfusion practices.

Anti-A and SARS-CoV-2: an intriguing association.

BACKGROUND: Blood groups and anti-A isohemagglutinin may be involved in susceptibility to SARS-CoV-2 infection. MATERIALS AND METHODS: We retrospectively studied 268 COVID-19 convalescent plasma donors and 162 COVID-19 inpatients (total 430 subjects, confirmed by RT-PCR) and 2,212 healthy volunteer first-time blood donors as a control group. These were further divided into two groups: those with anti-A (blood types O and B) and those without it (types A and AB). Titres of nucleoproteins, and neutralizing SARS-CoV-2 antibody were measured in the convalescent plasma donors and inpatients. Multivariate logistic regression and non-parametric tests were applied. RESULTS: Persons having types O or B showed less infection prevalence than those of types A or AB (OR = 0·62, 95% CI 0·50-0·78; P < 0·001), but there was no difference when COVID-19 inpatients were analysed. Immunoglobulins M, G and A were lower in COVID-19 subjects of types O or B group than those of A or AB (0·16 vs. 0·19; P = 0·03, 2·11 vs. 2·55; P = 0·02, 0·23 vs. 0·32; P = 0·03, respectively). CONCLUSION: In this retrospective cohort, COVID-19 individuals were less likely to belong to blood types O and B, and also had lower SARS-CoV-2 antibody titres than A and AB individuals. COVID-19 severity did not associate with the blood groups.

Human T-cell leukemia virus type 1 infection among Japanese immigrants and their descendants living in Southeast Brazil: A call for preventive and control responses.

Human T-cell leukemia virus type 1 (HTLV-1) has worldwide distribution and is considered endemic in southwestern Japan. HTLV-1 infection has been associated with adult T-cell leukemia/lymphoma (ATL) and HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) besides other diseases. This cross-sectional study aimed to investigate the prevalence, risk factors and molecular characterization of HTLV-1, among the world's largest population of Japanese immigrants and their descendants outside of Japan, in São Paulo, Southeast Brazil, as well as to analyze the phylogenetic relationship among isolates of HTLV-1. From July to December 2017, 2,139 individuals from five Japanese associations were interviewed and submitted to blood collection. All serum samples were first tested for the presence of anti-HTLV-1/2 antibodies by ELISA and then peripheral blood from individuals with positive serological results were analyzed for the presence of HTLV-1 5'LTR proviral DNA. Partial sequencing of the 5'LTR region of HTLV-1 proviral DNA was performed by Sanger. The prevalence of HTLV-1 infection was 5.1% (CI 95%: 4.2-6.0). In the multiple logistic regression model, HTLV-1 infection was associated with age ≥ 45 years, female sex, being first and second-generation Japanese immigrants, and having sexual partners with history of blood transfusion. The phylogenetic analysis revealed that all HTLV-1 were classified as Cosmopolitan (1a) subtype. Of them, 47.8% were classified as Transcontinental (A) subgroup and 52.2% as belonging to the Japanese (B) subgroup. Although most HTLV-1-infected patients were asymptomatic (97.3%), blurred vision was associated with HTLV-1 infection. The high prevalence of HTLV-1 infection found in this studied population and especially the intra- and interfamily HTLV-1 transmission presents an urgent call for preventive and control responses of this infection in Brazil.

The Molecular Characterization of Hepatitis A Virus Strains Circulating during Hepatitis A Outbreaks in São Paulo, Brazil, from September 2017 to May 2019.

Outbreaks of hepatitis A may occur in countries of medium and high socioeconomic levels in which the population generally exhibits an increased susceptibility in young adults to this infection if they are not vaccinated against the hepatitis A virus (HAV). In Europe, an outbreak involved approximately 22 European countries with 4475 cases reported from 2016 to 2018; most of them were men who have sex with men (MSM). This outbreak expanded to North and South America, including Brazil, particularly in São Paulo city with 1547 reported cases from 2016 to 2019. In the present study, we characterized the HAV strains involved in the acute hepatitis A cases identified in the reference centers of São Paulo city during this outbreak. A total of 51 cases with positive anti-HAV IgM were included, 80.4% male, 68.6% of them between 20 and 40 years old and 41.7% MSM. HAV RNA was detected in 92% (47/51) of the cases. Subgenotype IA of HAV was identified and most of the strains were closely related to that isolated in outbreaks that occurred in different European countries in 2016. These results showed the epidemiological relation between these outbreaks and reinforce the need to implement vaccination against hepatitis A for the adult population, particularly for a population with a high-risk behavior.

Would large dataset sample size unveil the potential of deep neural networks for improved genome-enabled prediction of complex traits? The case for body weight in broilers.

BACKGROUND: Deep neural networks (DNN) are a particular case of artificial neural networks (ANN) composed by multiple hidden layers, and have recently gained attention in genome-enabled prediction of complex traits. Yet, few studies in genome-enabled prediction have assessed the performance of DNN compared to traditional regression models. Strikingly, no clear superiority of DNN has been reported so far, and results seem highly dependent on the species and traits of application. Nevertheless, the relatively small datasets used in previous studies, most with fewer than 5000 observations may have precluded the full potential of DNN. Therefore, the objective of this study was to investigate the impact of the dataset sample size on the performance of DNN compared to Bayesian regression models for genome-enable prediction of body weight in broilers by sub-sampling 63,526 observations of the training set. RESULTS: Predictive performance of DNN improved as sample size increased, reaching a plateau at about 0.32 of prediction correlation when 60% of the entire training set size was used (i.e., 39,510 observations). Interestingly, DNN showed superior prediction correlation using up to 3% of training set, but poorer prediction correlation after that compared to Bayesian Ridge Regression (BRR) and Bayes Cπ. Regardless of the amount of data used to train the predictive machines, DNN displayed the lowest mean square error of prediction compared to all other approaches. The predictive bias was lower for DNN compared to Bayesian models, across all dataset sizes, with estimates close to one with larger sample sizes. CONCLUSIONS: DNN had worse prediction correlation compared to BRR and Bayes Cπ, but improved mean square error of prediction and bias relative to both Bayesian models for genome-enabled prediction of body weight in broilers. Such findings, highlights advantages and disadvantages between predictive approaches depending on the criterion used for comparison. Furthermore, the inclusion of more data per se is not a guarantee for the DNN to outperform the Bayesian regression methods commonly used for genome-enabled prediction. Nonetheless, further analysis is necessary to detect scenarios where DNN can clearly outperform Bayesian benchmark models.

Infrared Spectrometry as a High-Throughput Phenotyping Technology to Predict Complex Traits in Livestock Systems.

High-throughput phenotyping technologies are growing in importance in livestock systems due to their ability to generate real-time, non-invasive, and accurate animal-level information. Collecting such individual-level information can generate novel traits and potentially improve animal selection and management decisions in livestock operations. One of the most relevant tools used in the dairy and beef industry to predict complex traits is infrared spectrometry, which is based on the analysis of the interaction between electromagnetic radiation and matter. The infrared electromagnetic radiation spans an enormous range of wavelengths and frequencies known as the electromagnetic spectrum. The spectrum is divided into different regions, with near- and mid-infrared regions being the main spectral regions used in livestock applications. The advantage of using infrared spectrometry includes speed, non-destructive measurement, and great potential for on-line analysis. This paper aims to review the use of mid- and near-infrared spectrometry techniques as tools to predict complex dairy and beef phenotypes, such as milk composition, feed efficiency, methane emission, fertility, energy balance, health status, and meat quality traits. Although several research studies have used these technologies to predict a wide range of phenotypes, most of them are based on Partial Least Squares (PLS) and did not considered other machine learning (ML) techniques to improve prediction quality. Therefore, we will discuss the role of analytical methods employed on spectral data to improve the predictive ability for complex traits in livestock operations. Furthermore, we will discuss different approaches to reduce data dimensionality and the impact of validation strategies on predictive quality.

Comparison of data analytics strategies in computer vision systems to predict pig body composition traits from 3D images.

Computer vision systems (CVS) have been shown to be a powerful tool for the measurement of live pig body weight (BW) with no animal stress. With advances in precision farming, it is now possible to evaluate the growth performance of individual pigs more accurately. However, important traits such as muscle and fat deposition can still be evaluated only via ultrasound, computed tomography, or dual-energy x-ray absorptiometry. Therefore, the objectives of this study were: 1) to develop a CVS for prediction of live BW, muscle depth (MD), and back fat (BF) from top view 3D images of finishing pigs and 2) to compare the predictive ability of different approaches, such as traditional multiple linear regression, partial least squares, and machine learning techniques, including elastic networks, artificial neural networks, and deep learning (DL). A dataset containing over 12,000 images from 557 finishing pigs (average BW of 120 ± 12 kg) was split into training and testing sets using a 5-fold cross-validation (CV) technique so that 80% and 20% of the dataset were used for training and testing in each fold. Several image features, such as volume, area, length, widths, heights, polar image descriptors, and polar Fourier transforms, were extracted from the images and used as predictor variables in the different approaches evaluated. In addition, DL image encoders that take raw 3D images as input were also tested. This latter method achieved the best overall performance, with the lowest mean absolute scaled error (MASE) and root mean square error for all traits, and the highest predictive squared correlation (R2). The median predicted MASE achieved by this method was 2.69, 5.02, and 13.56, and R2 of 0.86, 0.50, and 0.45, for BW, MD, and BF, respectively. In conclusion, it was demonstrated that it is possible to successfully predict BW, MD, and BF via CVS on a fully automated setting using 3D images collected in farm conditions. Moreover, DL algorithms simplified and optimized the data analytics workflow, with raw 3D images used as direct inputs, without requiring prior image processing.

Pulmonary and systemic involvement in COVID-19 patients assessed with ultrasound-guided minimally invasive autopsy.

AIMS: Brazil ranks high in the number of coronavirus disease 19 (COVID-19) cases and the COVID-19 mortality rate. In this context, autopsies are important to confirm the disease, determine associated conditions, and study the pathophysiology of this novel disease. The aim of this study was to assess the systemic involvement of COVID-19. In order to follow biosafety recommendations, we used ultrasound-guided minimally invasive autopsy (MIA-US), and we present the results of 10 initial autopsies. METHODS AND RESULTS: We used MIA-US for tissue sampling of the lungs, liver, heart, kidneys, spleen, brain, skin, skeletal muscle and testis for histology, and reverse transcription polymerase chain reaction to detect severe acute respiratory syndrome coronavirus 2 RNA. All patients showed exudative/proliferative diffuse alveolar damage. There were intense pleomorphic cytopathic effects on the respiratory epithelium, including airway and alveolar cells. Fibrinous thrombi in alveolar arterioles were present in eight patients, and all patients showed a high density of alveolar megakaryocytes. Small thrombi were less frequently observed in the glomeruli, spleen, heart, dermis, testis, and liver sinusoids. The main systemic findings were associated with comorbidities, age, and sepsis, in addition to possible tissue damage due to the viral infection, such as myositis, dermatitis, myocarditis, and orchitis. CONCLUSIONS: MIA-US is safe and effective for the study of severe COVID-19. Our findings show that COVID-19 is a systemic disease causing major events in the lungs and with involvement of various organs and tissues. Pulmonary changes result from severe epithelial injury and microthrombotic vascular phenomena. These findings indicate that both epithelial and vascular injury should be addressed in therapeutic approaches.