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.

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.

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.

Heterogeneous phenotype of Hereditary Xerocytosis in association with PIEZO1 variants.

Hereditary Xerocytosis (HX) is an autosomal dominantly inherited congenital hemolytic anemia associated with erythrocyte dehydration due to decreased intracellular potassium content resulting in increased mean corpuscular hemoglobin concentration. The affected members of HX families show compensated anemia with splenomegaly, hemosiderosis, and perinatal edema but are in large part transfusion independent. Functional studies show a link between mutations in mechanosensitive ion channel, encoded by PIEZO1 gene and the HX. We identified new PIEZO1 variants that are likely pathogenic in three phenotypically characterized multi-generational HX Brazilian families. Interestingly, one missense variant of the PIEZO1 gene identified, p.E2494V was associated in trans with the previously reported most frequent pathogenic duplication p.E2496ELE. The three-dimensional structure of the human protein modeled using structural coordinates of the mouse Piezo1 solved by cryo-electron microscopy (Cryo-ME) showed that the two identified variants, p.M2007L and p.T2014I, are localized to an important mechanosensitive transmembrane domain suggesting a conformational mechanism for altered channel's gating. The p.E2496ELE variant identified alters the extension of helix α1 bringing it much closer to the beam affecting the position of it structure at the end of the pore.

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.

Molecular diagnosis of Strongyloides stercoralis among transplant candidates.

Strongyloidiasis can occur without any symptoms or as a potentially fatal hyperinfection or disseminated infection, principally in immunosuppressed patients. Our study aimed to evaluate the application of conventional polymerase chain reaction (cPCR) and real-time PCR (qPCR). Polymerase chain reaction (PCR) and real-time PCR (qPCR) targeting the 18S rRNA gene for detection of Strongyloides stercoralis infection among transplant candidates were applied in stool samples obtained from 150 transplant candidates, preliminarily analyzed by parasitological methods. S. stercoralis larvae were visualized in 15/150 (10.0%) transplant candidates by parasitological methods. DNA from S. stercoralis was amplified in 26/150 (17.3%) and 49/150 (32.7%) stool samples of transplant candidates, using cPCR and qPCR, respectively. The results suggest that molecular methods, especially qPCR, should be used as an additional tool for diagnostic of S. stercoralis infection among transplant candidates.

Point-of-Care Testing: General Aspects.

Point-of-Care Testing (POCT) has been highlighted in the health care sector in recent decades. On the other hand, due to its low demand, POCT is at a disadvantage compared to conventional equipment, since its cost is inversely proportional to the volume of use. In addition, for the implementation of POCT to succeed, it is essential to rely on the work of a multidisciplinary team. The awareness of health professionals of the importance of each step is perhaps the critical success factor. The trend towards the continuous advancement of the use of POCT and the great potential of its contributions reinforce the need to implement quality management tools, including performance indicators, to ensure their results. This review presents some advantages and disadvantages concerning POCT and the real need to use it. A worldwide call for the availability of easy-to-use health technologies that are increasingly closer to the final user is one of the main reasons for this focus.

Clinical Applications of Point-of-Care Testing in Different Conditions.

BACKGROUND: The use of point-of-care testing (POCT) in different clinical applications is justified by the fact that the time to release the result is shortened, allowing the physician to define the diagnosis and most appropriate therapy in a shorter time. However, the negative aspects must also be highlighted and studied so that we can move forward with the use of these devices. These negative aspects include greater analytical imprecision compared to laboratory automation, the variability between different equipment from different manufacturers, the risk of inappropriate use, a low level of global regulation, higher costs compared with laboratory testing and cost ineffectiveness in terms of health care. Methods and. RESULTS: This review presents some clinical applications of POCT in different scenarios, such as for diabetes mellitus, infectious diseases, pediatrics, and chronic kidney disease, among others. CONCLUSIONS: We hope to see a global consensus on an acceptable quality standard for performing POCT that is adaptable, practical, and cost effective in primary care settings, ensuring patient safety, and minimizing the risk of harm.

Persistence of Zika Virus in Breast Milk after Infection in Late Stage of Pregnancy.

We detected Zika virus in breast milk of a woman in Brazil infected with the virus during the 36th week of pregnancy. Virus was detected 33 days after onset of signs and symptoms and 9 days after delivery. No abnormalities were found during fetal assessment or after birth of the infant.

Characterization of hepatitis B virus infection in illicit drug users in the Marajó Archipelago, northern Brazil.

South America is considered to have a low prevalence of hepatitis B virus (HBV) infection, although areas with a relatively high prevalence have been identified in northern Brazil. Few epidemiological studies of populations at risk of HBV infection are available for this region. Given this, in the present study, we investigated the prevalence of HBV and the factors associated with infection among illicit drug users (DUs) in the Marajó Archipelago, northern Brazil. In this cross-sectional study, we collected samples and epidemiological information from DUs in 11 municipalities of the Marajó Archipelago. The diagnosis was established by ELISA and real-time PCR; and genotyping was done by multiplex real-time PCR. Statistical modeling was based on simple and multiple logistical regressions with the Hosmer-Lemeshow test. The mean age of the 466 DUs was 28.4 years, and most were male. The most-consumed illicit drugs were crack cocaine and marijuana. In all, 171 DUs were exposed to HBV, with genotypes A, D and F being identified. The factors associated with higher frequencies of HBV infection were (i) male gender, (ii) age above 35 years, (iii) anti-HIV positivity, (iv) tattoos, (v) the use of injected drugs, (vi) the use of illicit drugs for more than 3 years, (vii) sexual relations without protection, (viii) sexual relations with another DU, and (ix) more than 10 sexual partners in the past 24 months. In summary, this study provides important insights into the dynamics of HBV infection among DUs in the Marajó Archipelago. We hope that these findings will contribute to the development of strategies, actions and public health policies aimed at preventing and controlling this viral infection more effectively.

Increased hepatic expression of miRNA-122 in patients infected with HCV genotype 3.

Hepatitis C virus (HCV) infection affects approximately 3 % of the world population. HCV targets hepatic tissue, and most infected patients develop a chronic infection. Currently, studies have demonstrated an association between HCV-RNA replication and miR-122, the most abundant microRNA in the liver. Our aim was to evaluate liver and serum expression of miR-122 in patients infected with HCV genotypes 1 and 3, and to identify possible associations between miR-122 expression and lipid profiles, HCV viral load, apolipoproteins and liver enzymes. MicroRNAs were isolated from blood and liver tissue, and miR-122 expression was quantified by real-time PCR. HCV viral load was quantified by real-time PCR and HCV genotype, and serum biomarkers were obtained from medical report. The levels of miR-122 were higher in liver than those in blood from individuals infected with HCV genotypes 1 and 3 (p < 0.0001). The tissue levels of miR-122 were higher in subjects infected with HCV genotype 3 (6.22-fold, p < 0.001). A positive correlation was observed between the blood and hepatic levels of miR-122 in patients infected with HCV genotype 1 (r = 0.302, p = 0.026); in these patients, an inverse correlation was observed between serum apolipoprotein A-II (ApoA-II) levels and the blood (r = -0.330; p = 0.014) and hepatic (r = -0.311; p = 0.020) levels of miR-122. In patients infected with HCV genotype 3, there was a positive correlation between the hepatic miR-122 and the high-density lipoprotein-HDL (r = 0.412, p = 0.036) and insulin (r = 0.478, p = 0.044). Lipid metabolism proteins and miR-122 expression levels have different relations in HCV-3- and HCV-1-infected patients.

Predicting respiration rate in unrestrained dairy cows using image analysis and fast Fourier transform.

Respiratory rate (RR) is commonly employed for identifying animals experiencing heat-stress conditions and respiratory diseases. Recent advancements in computer vision algorithms have enabled the estimation of the RR in dairy cows through image-based approaches, with a primary focus on standing positions, thermal imaging, and deep learning techniques. In this study, our objective was to develop a system capable of accurately predicting the RR of lying Holstein cows under unrestrained conditions using red, green, and blue (RGB) and infrared (IR) night vision images. Thirty lactating cows were continuously recorded for 12 h per day over a 3-d period, capturing at least one 30-s video segment of each cow during lying time. A total of 95 videos were manually annotated with rectangular bounding boxes encompassing the flank area (region of interest; ROI) of the lying cows. For future applications, we trained a model for ROI identification using YOLOv8 to avoid manual annotations. The observed RR was determined by visual counting of breaths in each video. To predict the RR, we devised an image processing pipeline involving (1) capturing the ROI for the entire video, (2) reshaping the pixel intensity of each image channel into a 2-dimensional object and calculating its per-frame mean, (3) applying fast Fourier transform (FFT) to the average pixel intensity vector, (4) filtering frequencies specifically associated with respiratory movements, and (5) executing inverse FFT on the denoized data and identifying peaks on the resulting plot, with the count of peaks serving as the predicted RR per minute. The evaluation metrics, root mean squared error of prediction (RMSEP) and R2, yielded values of 8.3 breaths/min (17.1% of the mean RR) and 0.77, respectively. To further validate the method, an additional dataset comprising preweaning dairy calves was used, consisting of 42 observations from 25 calves. The RMSEP and R2 values for this dataset were 13.0 breaths/min and 0.73, respectively. The model trained to identify the ROI exhibited a precision of 100%, a recall of 71.8%, and an F 1 score of 83.6% for bounding box detection. These are promising results for the implementation of this pipeline in future studies. The application of FFT to signals acquired from both RGB and IR images proved to be an effective and accurate method for computing the RR of cows in unrestrained conditions.

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.

Analysis of HCV quasispecies dynamic under selective pressure of combined therapy.

BACKGROUND: The quasispecies composition of Hepatitis C virus (HCV) could have important implications with regard to viral persistence and response to interferon-based therapy. The complete NS5A was analyzed to evaluate whether the composition of NS5A quasispecies of HCV 1a/1b is related to responsiveness to combined interferon pegylated (PEG-IFN) and ribavirin therapy. METHODS: Viral RNA was isolated from serum samples collected before, during and after treatment from virological sustained responder (SVR), non-responder (NR) and the end-of-treatment responder patients (ETR). NS5A region was amplified, cloned and sequenced. Six hundred and ninety full-length NS5A sequences were analyzed. RESULTS: This study provides evidence that lower nucleotide diversity of the NS5A region pre-therapy is associated with viral clearance. Analysis of samples of NRs and the ETRs time points showed that genetic diversity of populations tend to decrease over time. Post-therapy population of ETRs presented higher genetic distance from baseline probably due to the bottleneck phenomenon observed for those patients in the end of treatment. The viral effective population of those patients also showed a strong decrease after therapy. Otherwise, NRs demonstrated a continuous variation or stability of effective populations and genetic diversity over time that did not seem to be related to therapy. Phylogenetic relationships concerning complete NS5A sequences obtained from patients did not demonstrate clustering associated with specific response patterns. However, distinctive clustering of pre/post-therapy sequences was observed. In addition, the evolution of quasispecies over time was subjected to purifying or relaxed purifying selection. Codons 157 (P03), 182 and 440 (P42), 62 and 404 (P44) were found to be under positive selective pressure but it failed to be related to the therapy. CONCLUSION: These results confirm the hypothesis that a relationship exists between NS5A heterogeneity and response to therapy in patients infected with chronic hepatitis C.

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.

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.