DentalSegmentator: Robust open source deep learning-based CT and CBCT image segmentation.

OBJECTIVES: Segmentation of anatomical structures on dento-maxillo-facial (DMF) computed tomography (CT) or cone beam computed tomography (CBCT) scans is increasingly needed in digital dentistry. The main aim of this research was to propose and evaluate a novel open source tool called DentalSegmentator for fully automatic segmentation of five anatomical structures on DMF CT and CBCT scans: maxilla/upper skull, mandible, upper teeth, lower teeth, and the mandibular canal. METHODS: A retrospective sample of 470 CT and CBCT scans was used as a training/validation set. The performance and generalizability of the tool was evaluated by comparing segmentations provided by experts and automatic segmentations in two hold-out test datasets: an internal dataset of 133 CT and CBCT scans acquired before orthognathic surgery and an external dataset of 123 CBCT scans randomly sampled from routine examinations in 5 institutions. RESULTS: The mean overall results in the internal test dataset (n = 133) were a Dice similarity coefficient (DSC) of 92.2 ± 6.3 % and a normalised surface distance (NSD) of 98.2 ± 2.2 %. The mean overall results on the external test dataset (n = 123) were a DSC of 94.2 ± 7.4 % and a NSD of 98.4 ± 3.6 %. CONCLUSIONS: The results obtained from this highly diverse dataset demonstrate that this tool can provide fully automatic and robust multiclass segmentation for DMF CT and CBCT scans. To encourage the clinical deployment of DentalSegmentator, the pre-trained nnU-Net model has been made publicly available along with an extension for the 3D Slicer software. CLINICAL SIGNIFICANCE: DentalSegmentator open source 3D Slicer extension provides a free, robust, and easy-to-use approach to obtaining patient-specific three-dimensional models from CT and CBCT scans. These models serve various purposes in a digital dentistry workflow, such as visualization, treatment planning, intervention, and follow-up.

Superior Visible Photoelectric Response with Au/Cu2NiSnS4 Core-Shell Nanocrystals.

The incorporation of plasmonic metal nanostructures into semiconducting chalcogenides in the form of core-shell structures provides a promising approach to enhancing the performance of photodetectors. In this study, we combined Au nanoparticles with newly developed copper-based chalcogenides Cu2NiSnS4 (Au/CNTS) to achieve an ultrahigh optoelectronic response in the visible regime. The high-quality Au/CNTS core-shell nanocrystals (NCs) were synthesized by developing a unique colloidal hot-injection method, which allowed for excellent control over sizes, shapes, and elemental compositions. The as-synthesized Au/CNTS hybrid core-shell NCs exhibited enhanced optical absorption, carrier extraction efficiency, and improved photosensing performance owing to the plasmonic-induced resonance energy transfer effect of the Au core. This effect led to a significant increase in the carrier density of the Au/CNTS NCs, resulting in a measured responsivity of 1.2 × 103 AW-1, a specific detectivity of 6.2 × 1011 Jones, and an external quantum efficiency of 3.8 × 105 % at an incident power density of 318.5 µW cm-2. These results enlighten a new era in the development of plasmonic core-shell nanostructure-based visible photodetectors.

Aspergillus infections of lateral skull base: a case series.

PURPOSE: While extensive research with accurate classification has been done in mycoses of the paranasal sinuses and anterior skull base, a similar understanding of lateral skull base fungal pathologies is lacking due to relative rarity and diagnostic difficulties. We introduce a series of eleven cases and two different invasive entities of Aspergillus temporal bone diseases-fungal skull base osteomyelitis (SBO)/malignant otitis externa (MOE) and chronic invasive granulomatous fungal disease (CIGFD). METHODOLOGY: A retrospective observational study was conducted at the neuro-otology unit of a tertiary care referral center between July 2017 and November 2022. Diagnosed cases of lateral skull base osteomyelitis with atypical symptoms and lack of response to culture-directed antibiotics were evaluated for fungal origin. Patient data, including history, laboratory findings, serum galactomannan assay, CT and MRI imaging findings, clinical examination findings, and co-morbidities, were analyzed. The treatment course and response were assessed. RESULTS: A total of 11 cases were included in the study. Of these, 9 were cases of Aspergillus-induced skull base osteomyelitis (SBO) and 2 of Aspergillus-induced chronic invasive granulomatous fungal disease (CIGFD). CIGFD presented with persistent ear discharge and slowly progressive post-aural swelling, while all patients of fungal SBO had lower cranial nerve palsies. CIGFD responded to excision and antifungals, while SBO responded well to conservative anti-fungal treatment. CONCLUSION: In cases of lateral SBO not responding to antibiotic therapy, the possibility of fungal etiology should be considered. Aspergillus spp. seems to be the major fungal pathogen.

Bile acid fitness determinants of a Bacteroides fragilis isolate from a human pouchitis patient.

IMPORTANCE: The Gram-negative bacterium Bacteroides fragilis is a common member of the human gut microbiota that colonizes multiple host niches and can influence human physiology through a variety of mechanisms. Identification of genes that enable B. fragilis to grow across a range of host environments has been impeded in part by the relatively limited genetic tractability of this species. We have developed a high-throughput genetic resource for a B. fragilis strain isolated from a UC pouchitis patient. Bile acids limit microbial growth and are altered in abundance in UC pouches, where B. fragilis often blooms. Using this resource, we uncovered pathways and processes that impact B. fragilis fitness in bile and that may contribute to population expansions during bouts of gut inflammation.

Drought and salinity stresses induced physio-biochemical changes in sugarcane: an overview of tolerance mechanism and mitigating approaches.

Sugarcane productivity is being hampered globally under changing environmental scenarios like drought and salinity. The highly complex nature of the plant responses against these stresses is determined by a variety of factors such as genotype, developmental phase of the plant, progression rate and stress, intensity, and duration. These factors influence plant responses and can determine whether mitigation approaches associated with acclimation are implemented. In this review, we attempt to summarize the effects of drought and salinity on sugarcane growth, specifically on the plant's responses at various levels, viz., physiological, biochemical, and metabolic responses, to these stresses. Furthermore, mitigation strategies for dealing with these stresses have been discussed. Despite sugarcane's complex genomes, conventional breeding approaches can be utilized in conjunction with molecular breeding and omics technologies to develop drought- and salinity-tolerant cultivars. The significant role of plant growth-promoting bacteria in sustaining sugarcane productivity under drought and salinity cannot be overlooked.

Effect of image resolution on automated classification of chest X-rays.

Purpose: Deep learning (DL) models have received much attention lately for their ability to achieve expert-level performance on the accurate automated analysis of chest X-rays (CXRs). Recently available public CXR datasets include high resolution images, but state-of-the-art models are trained on reduced size images due to limitations on graphics processing unit memory and training time. As computing hardware continues to advance, it has become feasible to train deep convolutional neural networks on high-resolution images without sacrificing detail by downscaling. This study examines the effect of increased resolution on CXR classification performance. Approach: We used the publicly available MIMIC-CXR-JPG dataset, comprising 377,110 high resolution CXR images for this study. We applied image downscaling from native resolution to 2048×2048 pixels, 1024×1024 pixels, 512×512 pixels, and 256×256 pixels and then we used the DenseNet121 and EfficientNet-B4 DL models to evaluate clinical task performance using these four downscaled image resolutions. Results: We find that while some clinical findings are more reliably labeled using high resolutions, many other findings are actually labeled better using downscaled inputs. We qualitatively verify that tasks requiring a large receptive field are better suited to downscaled low resolution input images, by inspecting effective receptive fields and class activation maps of trained models. Finally, we show that stacking an ensemble across resolutions outperforms each individual learner at all input resolutions while providing interpretable scale weights, indicating that diverse information is extracted across resolutions. Conclusions: This study suggests that instead of focusing solely on the finest image resolutions, multi-scale features should be emphasized for information extraction from high-resolution CXRs.

Comparison of traditional essay questions versus case based modified essay questions in biochemistry.

Adult learning involves the analysis and synthesis of knowledge to become competent, which cannot be assessed only by traditional assessment tool and didactic learning methods. Stimulation of higher domains of cognitive learning needs to be inculcated to reach a better understanding of the subject rather than traditional assessment tools that relies primarily on rote learning. So, there is need for an alternative assessment tool. Hence, we conducted a study where we used case-based examination methodology. This study was conducted on 226 Ist year MBBS students in Maulana Azad Medical College, New Delhi (India). Based on their compiled internal assessment marks according to monthly formative assessment, students were categorized into 3 groups (I: 0-7; II: 8-14; III: 15-20) marks out of 20 marks respectively. Two sets of question papers were set by three examiners, on the same topics carrying 50 marks each. The first set was based on traditional assessment tool (Paper-A) with recall questions and second set on case-based assessment method (Paper-B). Out of 226 students, 146 were males and 80 were females. For all groups, marks (mean ± SD) in Paper B were found to be higher (18.40 ± 4.29, 30.01 ± 4.12, and 40.33 ± 1.15) as compared to paper A (10.88 ± 4.34, 21.96 ± 7.34, and 31.50 ± 6.94) respectively. However, we found that there was significant (p < 0.001) difference in group I & II, whereas with group III, difference was found to be insignificant. Hence, we concluded that students performed better in case-based assessment rather than traditional method due to their direct involvement. Thus, for better memory and deeper learning the subjects can be assessed by case-based assessment method.

PP2Ac Deficiency Enhances Tumor Immunogenicity by Activating STING-Type I Interferon Signaling in Glioblastoma.

Glioblastoma (GBM) is an immunologically "cold" tumor that does not respond to current immunotherapy. Here, we demonstrate a fundamental role for the α-isoform of the catalytic subunit of protein phosphatase-2A (PP2Ac) in regulating glioma immunogenicity. Genetic ablation of PP2Ac in glioma cells enhanced double-stranded DNA (dsDNA) production and cGAS-type I IFN signaling, MHC-I expression, and tumor mutational burden. In coculture experiments, PP2Ac deficiency in glioma cells promoted dendritic cell (DC) cross-presentation and clonal expansion of CD8+ T cells. In vivo, PP2Ac depletion sensitized tumors to immune-checkpoint blockade and radiotherapy treatment. Single-cell analysis demonstrated that PP2Ac deficiency increased CD8+ T-cell, natural killer cell, and DC accumulation and reduced immunosuppressive tumor-associated macrophages. Furthermore, loss of PP2Ac increased IFN signaling in myeloid and tumor cells and reduced expression of a tumor gene signature associated with worse patient survival in The Cancer Genome Atlas. Collectively, this study establishes a novel role for PP2Ac in inhibiting dsDNA-cGAS-STING signaling to suppress antitumor immunity in glioma. SIGNIFICANCE: PP2Ac deficiency promotes cGAS-STING signaling in glioma to induce a tumor-suppressive immune microenvironment, highlighting PP2Ac as a potential therapeutic target to enhance tumor immunogenicity and improve response to immunotherapy.

Bile acid fitness determinants of a Bacteroides fragilis isolate from a human pouchitis patient.

Bacteroides fragilis comprises 1-5% of the gut microbiota in healthy humans but can expand to >50% of the population in ulcerative colitis (UC) patients experiencing inflammation. The mechanisms underlying such microbial blooms are poorly understood, but the gut of UC patients has physicochemical features that differ from healthy patients and likely impact microbial physiology. For example, levels of the secondary bile acid deoxycholate (DC) are highly reduced in the ileoanal J-pouch of UC colectomy patients. We isolated a B. fragilis strain from a UC patient with pouch inflammation (i.e. pouchitis) and developed it as a genetic model system to identify genes and pathways that are regulated by DC and that impact B. fragilis fitness in DC and crude bile. Treatment of B. fragilis with a physiologically relevant concentration of DC reduced cell growth and remodeled transcription of one-quarter of the genome. DC strongly induced expression of chaperones and select transcriptional regulators and efflux systems and downregulated protein synthesis genes. Using a barcoded collection of ≈50,000 unique insertional mutants, we further defined B. fragilis genes that contribute to fitness in media containing DC or crude bile. Genes impacting cell envelope functions including cardiolipin synthesis, cell surface glycosylation, and systems implicated in sodium-dependent bioenergetics were major bile acid fitness factors. As expected, there was limited overlap between transcriptionally regulated genes and genes that impacted fitness in bile when disrupted. Our study provides a genome-scale view of a B. fragilis bile response and genetic determinants of its fitness in DC and crude bile.

Prevalence of dyslipidaemia in OSA patients at a tertiary care center.

Background Obstructive sleep apnea (OSA) is associated with many diseases, but evidence indicating that OSA is a risk factor for dyslipidemia is lacking. Aim This cross-sectional study investigated the prevalence of lipid abnormalities in patients with OSA and its association with OSA severity. MATERIAL AND METHODS: In this cross-sectional study, 102 patients with suspected OSA underwent standard polysomnography. All patients with an apnea-hypopnea index (AHI) of ≥5 with symptoms were diagnosed as having OSA. A fasting blood sample was collected from all patients. Blood levels of triglycerides (TGs), total cholesterol (TC), high-density lipoprotein cholesterol (HDL), and low-density lipoprotein cholesterol (LDL) were measured. The relationship between the AHI and lipid profiles was analyzed, and linear regression analysis was performed to evaluate the effect of dyslipidemia on OSA. RESULTS: The patients with OSA had a significantly higher TG level and a significantly lower HDL level than did those without OSA. The lipid abnormalities increased with OSA severity. The mean serum TG level was higher in the severe OSA group (175±46.5 vs. 153±42.45, mg/dl P = 0.048), and the mean serum HDL level was lower in the severe OSA group (38.43 ± 5.19 vs. 45.73 ± 4.98, mg/dl P = 0.004). Serum TG, cholesterol, and LDL levels were correlated with a BMI of <30 and a BMI of >30 in the OSA group. Linear regression analysis indicated that only age (ß = 0.301, P = 0.000), BMI (ß = 0.455, P = 0.000), serum HDL level (ß = -0.297, P = 0.012), and serum LDL level (ß = 0.429, P = 0.001) were the independent predictors of OSA. CONCLUSION: OSA and obesity are potential risk factors for dyslipidemia. The diagnosis of hyperlipidemia was linked to OSA, and the association was more significant with OSA severity. Hyperlipidemia was well recognized in patients with OSA. LDL and HDL are the independent predictors of OSA.

Ms6244 is a novel Mycobacterium smegmatis TetR family transcriptional repressor that regulates cell growth and morphophysiology.

Transcriptional factors such as the TetR family of transcriptional regulators (TFTRs) are widely found amongst bacteria, including mycobacteria, and are accountable for their survival. Here, we characterized a novel TFTR, Ms6244, from Mycobacterium smegmatis that negatively autoregulates its expression and represses its neighbouring gene, Ms6243. We also report the binding of Ms6244 to the inverted repeats in the intergenic region of Ms6244 and Ms6243. Further, an Ms6244-deleted strain shows various morpho-physiological differences compared to the wild type. We further confirmed that the deletion of Ms6244 itself and not the resultant Ms6243 overexpression is the cause of the altered physiology. Our data thus suggest that Ms6244 is an essential regulator, having far-reaching effects on M. smegmatis physiology.

A novel de novo FEM1C variant is linked to neurodevelopmental disorder with absent speech, pyramidal signs and limb ataxia.

The principal component of the protein homeostasis network is the ubiquitin-proteasome system. Ubiquitination is mediated by an enzymatic cascade involving, i.e. E3 ubiquitin ligases, many of which belong to the cullin-RING ligases family. Genetic defects in the ubiquitin-proteasome system components, including cullin-RING ligases, are known causes of neurodevelopmental disorders. Using exome sequencing to diagnose a pediatric patient with developmental delay, pyramidal signs and limb ataxia, we identified a de novo missense variant c.376G>C; p.(Asp126His) in the FEM1C gene encoding a cullin-RING ligase substrate receptor. This variant alters a conserved amino acid located within a highly constrained coding region and is predicted as pathogenic by most in silico tools. In addition, a de novo FEM1C mutation of the same residue p.(Asp126Val) was associated with an undiagnosed developmental disorder, and the relevant variant (FEM1CAsp126Ala) was found to be functionally compromised in vitro. Our computational analysis showed that FEM1CAsp126His hampers protein substrate binding. To further assess its pathogenicity, we used the nematode Caenorhabditis elegans. We found that the FEM-1Asp133His animals (expressing variant homologous to the FEM1C p.(Asp126Val)) had normal muscle architecture yet impaired mobility. Mutant worms were sensitive to the acetylcholinesterase inhibitor aldicarb but not levamisole (acetylcholine receptor agonist), showing that their disabled locomotion is caused by synaptic abnormalities and not muscle dysfunction. In conclusion, we provide the first evidence from an animal model suggesting that a mutation in the evolutionarily conserved FEM1C Asp126 position causes a neurodevelopmental disorder in humans.

Evaluation of lablab bean [Lablab purpureus (L.) sweet] genotypes: unveiling superior pod yield, nutritional quality, and collar rot resistance.

Introduction: Malnutrition continues to be a significant concern at unacceptably high levels globally. There is significant potential for addressing malnutrition of human population through the biofortification of climate-resilient vegetables using strategic breeding strategies. Lablab bean [Lablab purpureus (L.) Sweet], a underutilized nutrient-dense crop holds great potential in this aspect. Despite its advantageous nutritional profile, the production, research, and consumption of lablab bean are currently limited. Addressing these limitations and unlock the nutritional benefits of lablab beans needs to prioritized for fighting malnutrition in local inhabitants on a global scale. Materials and methods: Twenty five genotypes of lablab bean collected through exploration survey in Eastern India and were evaluated in 2020-2021. Among them, the nine highly diverse well adapted genotypes were again evaluated at the experimental farm of ICAR-Research Complex for Eastern Region, Patna, Bihar, India in 2021-2022. Horticultural important traits of lablab bean were recorded by using the minimum descriptors developed by ICAR-NBPGR in New Delhi and biochemical analysis was done by using standard protocols. Genotypic and phenotypic correlation and path coefficient analysis was done used understand relationships, interdependencies, and causal pathways between different traits. The outcome was revalidated by using principal component analysis (PCA). Results: Descriptive statistics revealed substantial heterogeneity across the traits of lablab bean evaluated. Vitamin A content showed nearly a five-fold variation, Fe ranged from 5.97 to 10.5 mg/100 g, and Vitamin C varied from 4.61 to 9.45 mg/100 g. Earliness and dwarf growth was observed in RCPD-1 (60 cm) and early flowering (41 days). RCPD-3 and RCPD-12 had high pod yield due to their high number of pods and pod weight. Pod yield was significantly correlated with number of pod per plant (NPP) (rg = 0.995) and with average pod weight (APW) (rg = 0.882). A significant positive correlation was also found between protein and Zn content (rg = 0.769). Path coefficient analysis revealed that average pod weight had the most direct positive effect on pod yield, followed by NPP and protein content. The reaction of lablab bean genotypes to collar rot disease was also evaluated and significant differences in disease intensity were observed among the genotypes, with the resistant check RCPD-15 exhibiting the lowest disease intensity. Discussion: The study highlights the substantial heterogeneity in lablab bean traits, particularly in nutritional components such as vitamin A, iron, and vitamin C concentrations. Early flowering and dwarf growth habit are desirable qualities for lablab bean, and certain genotypes were found to exhibit these traits. Positive correlations, both phenotypic and genotypic, existed among different traits, suggesting the potential for simultaneous improvement. Path coefficient and PCA revealed genotypes with high yield and nutritional traits. Finally, resistant and moderately resistant lablab bean genotypes to collar rot disease were identified. These findings contribute to the selection and breeding strategies for improving lablab bean production and nutritional value.

Impact of COVID-19 on Public Transit Accessibility and Ridership.

COVID-19 has radically transformed urban travel behavior throughout the world. Agencies have had to provide adequate service while navigating a rapidly changing environment with reduced revenue. As COVID-19-related restrictions are lifted, transit agencies are concerned about their ability to adapt to changes in ridership behavior and public transit usage. To aid their becoming more adaptive to sudden or persistent shifts in ridership, we addressed three questions: To what degree has COVID-19 affected fixed-line public transit ridership and what is the relationship between reduced demand and -vehicle trips? How has COVID-19 changed ridership patterns and are they expected to persist after restrictions are lifted? Are there disparities in ridership changes across socioeconomic groups and mobility-impaired riders? Focusing on Nashville and Chattanooga, TN, ridership demand and vehicle trips were compared with anonymized mobile location data to study the relationship between mobility patterns and transit usage. Correlation analysis and multiple linear regression were used to investigate the relationship between socioeconomic indicators and changes in transit ridership, and an analysis of changes in paratransit demand before and during COVID-19. Ridership initially dropped by 66% and 65% over the first month of the pandemic for Nashville and Chattanooga, respectively. Cellular mobility patterns in Chattanooga indicated that foot traffic recovered to a greater degree than transit ridership between mid-April and the last week in June, 2020. Education-level had a statistically significant impact on changes in fixed-line bus transit, and the distribution of changes in demand for paratransit services were similar to those of fixed-line bus transit.

Attention-Based UNet Deep Learning Model for Plaque Segmentation in Carotid Ultrasound for Stroke Risk Stratification: An Artificial Intelligence Paradigm.

Stroke and cardiovascular diseases (CVD) significantly affect the world population. The early detection of such events may prevent the burden of death and costly surgery. Conventional methods are neither automated nor clinically accurate. Artificial Intelligence-based methods of automatically detecting and predicting the severity of CVD and stroke in their early stages are of prime importance. This study proposes an attention-channel-based UNet deep learning (DL) model that identifies the carotid plaques in the internal carotid artery (ICA) and common carotid artery (CCA) images. Our experiments consist of 970 ICA images from the UK, 379 CCA images from diabetic Japanese patients, and 300 CCA images from post-menopausal women from Hong Kong. We combined both CCA images to form an integrated database of 679 images. A rotation transformation technique was applied to 679 CCA images, doubling the database for the experiments. The cross-validation K5 (80% training: 20% testing) protocol was applied for accuracy determination. The results of the Attention-UNet model are benchmarked against UNet, UNet++, and UNet3P models. Visual plaque segmentation showed improvement in the Attention-UNet results compared to the other three models. The correlation coefficient (CC) value for Attention-UNet is 0.96, compared to 0.93, 0.96, and 0.92 for UNet, UNet++, and UNet3P models. Similarly, the AUC value for Attention-UNet is 0.97, compared to 0.964, 0.966, and 0.965 for other models. Conclusively, the Attention-UNet model is beneficial in segmenting very bright and fuzzy plaque images that are hard to diagnose using other methods. Further, we present a multi-ethnic, multi-center, racial bias-free study of stroke risk assessment.

Conventional and molecular breeding for disease resistance in chickpea: status and strategies.

Chickpea (Cicer arietinum L.) is an important grain legume at the global level. Among different biotic stresses, diseases are the most important factor limiting its production, causing yield losses up to 100% in severe condition. The major diseases that adversely affect yield of chickpea include Fusarium wilt, Ascochyta blight and Botrytis gray mold. However, dry root rot, collar rot, Sclerotinia stem rot, rust, stunt disease and phyllody have been noted as emerging biotic threats to chickpea production in many production regions. Identification and incorporation of different morphological and biochemical traits are required through breeding to enhance genetic gain for disease resistance. In recent years, remarkable progress has been made in the development of trait-specific breeding lines, genetic and genomic resources in chickpea. Advances in genomics technologies have opened up new avenues to introgress genes from secondary and tertiary gene pools for improving disease resistance in chickpea. In this review, we have discussed important diseases, constraints and improvement strategies for enhancing disease resistance in chickpea.

Ferritin-mediated iron detoxification promotes hypothermia survival in Caenorhabditis elegans and murine neurons.

How animals rewire cellular programs to survive cold is a fascinating problem with potential biomedical implications, ranging from emergency medicine to space travel. Studying a hibernation-like response in the free-living nematode Caenorhabditis elegans, we uncovered a regulatory axis that enhances the natural resistance of nematodes to severe cold. This axis involves conserved transcription factors, DAF-16/FoxO and PQM-1, which jointly promote cold survival by upregulating FTN-1, a protein related to mammalian ferritin heavy chain (FTH1). Moreover, we show that inducing expression of FTH1 also promotes cold survival of mammalian neurons, a cell type particularly sensitive to deterioration in hypothermia. Our findings in both animals and cells suggest that FTN-1/FTH1 facilitates cold survival by detoxifying ROS-generating iron species. We finally show that mimicking the effects of FTN-1/FTH1 with drugs protects neurons from cold-induced degeneration, opening a potential avenue to improved treatments of hypothermia.

Trypanosoma evansi RoTat 1.2 variant surface antigen mimotopes selected by panning of the random peptide phage-display library against monoclonal antibodies.

Mimotope peptides of native antigens are valuable for diverse applications such as diagnostics, therapeutics and modern vaccine design. Here, we report for the first time the selection and identification of peptide mimotopes of Trypanosoma evansi RoTat 1.2 variant surface glycoprotein (VSG) for their potential uses in surra diagnostics and multi-epitope vaccine research. First, we produced the mouse monoclonal antibodies (mAbs), designated as 2E11 (IgG1) and 1C2 (IgG1), against the antigens in T. evansi RoTat 1.2 lysates. We then used 2E11 mAb to immunoprecipitate the target antigen. The immunoprecipitated antigen was then identified to be the VSG by mass spectrometry. Both 2E11 and 1C2 mAbs reacted with the VSG in immunoblots. The surface plasmon resonance immunosensors developed with both the mAbs detected VSG in the parasite lysates as well as in the rodent sera. Further, the mAbs were biotinylated and used in three rounds of panning to select peptide mimotopes from the random peptide phage display library (PhD-12; New England Biolabs, USA). The phage clones selected against each mAb were amplified and tested by phage capture ELISA for specificity. The peptide coding regions of the selected phages were sequenced and the protein blast search of the deduced amino acid sequences was performed by accessing the non-redundant protein database at https://blast.ncbi.nlm.nih.gov/. The conformational B epitope prediction of the selected mimotope sequences was done by using 3D Pepitope algorithms accessed at: http://pepitope.tau.ac.il/. The potential applications of the selected mimotopes in surra diagnostics and research are being explored.

Robust anti-SARS-CoV2 single domain antibodies cross neutralize multiple viruses.

We report robust SARS-CoV2 neutralizing sdAbs targeting the viral peptides encompassing the polybasic cleavage site (CSP) and in the receptor binding domain (RBD) of the spike (S) protein. Both the sdAbs inhibited infectivity of the CoV2 S protein expressing pseudoviruses (LV-CoV2S). Both anti-CSP and RBD intrabodies (IB) inhibited the output of LV(CoV2 S). Anti-CSP IB altered the proteolytic processing and targeted the viral S protein for degradation. Because of cross-reactive CSPs in the entry mediators, the anti-CSP sdAb neutralized in vitro and in vivo the infectivity of SARS-CoV2 unrelated viruses such as herpes simplex virus 1 (HSV1) and pestes des petits ruminants virus (PPRV). Conversely, anti-HSV1 and anti-PPRV sera neutralized LV(CoV2 S) owing to the presence of CSP reactive antibodies indicating that a prior infection with such pathogens could impact on the pattern of COVID-19.