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Aim:To study the effect of supplementation of different levels of selenium as nanoparticles/sodium selenite on blood biochemical profile and humoral immunity in male Wistar rats. Materials and Methods:The experimental research was conducted at Division of Animal Nutrition, Indian Veterinary Research Institute, Izatnagar. 63 male Wistar rats were divided into 9 equal groups on the basis of their mean body weight (BW) (124.3±3.1 g BW) following completely randomized design. Experimental feeding was similar in all the groups except for the source and level of selenium (Se) in the diet. While Group 1 (control) was fed a basal diet with no Se supplementation, in Groups 2 and 3, 150 ppb Se was supplemented either as sodium selenite or Se nanoparticles, respectively. In Groups 4, 5, 6 and 7, Se was supplemented as its nanoparticles at 50%, 25%, 12.5% and 6.25% levels respectively i.e. at 75 ppb, 37.5 ppb, 18.75 ppb and 9.375 ppb levels respectively. In Groups 8 and 9, 300 ppb Se was supplemented either as Se nanoparticles or sodium selenite, respectively. Experimental feeding was conducted for a period of 91 days. At the end of the experimental trial, blood samples were collected to analyze the blood serum biochemical profile (serum glucose, serum total protein (TP), serum albumin, serum globulin, serum albumin: globulin ratio [A:G ratio], serum total cholesterol) and humoral immunity. Results:The levels of serum glucose, serum TP and serum albumin were comparable (p>0.05) among the nine groups of male Wistar rats. The mean serum total cholesterol was significantly (p<0.001) lowered in all the Se supplemented Wistar rats compared to the control group. The mean serum globulin level was significantly (p<0.05) higher and A:G ratio was significantly (p<0.05) lowered in Group 3 (supplemented with 150 ppb selenium nanoparticles) followed by Groups 2, 4, 5, 6, 8, and 9 as compared to the control group. The mean serum antibody titer was significantly (p<0.001) higher in all the Se supplemented groups with the highest value in Group 3 (supplemented with 150 ppb selenium nanoparticles) followed by Groups 4, 5, 8 and 9 compared to the control group. Conclusion:Supplementation of selenium nanoparticles at the level of 150 ppb gave the best performance in terms of increased serum globulin level, reduced A:G ratio, and improved humoral immune status in male Wistar rats.

The overuse of antibiotics has led to emergence of antimicrobial resistance, and as a result, antibacterial peptides (ABPs) are receiving significant attention as an alternative. Identification of effective ABPs in lab from natural sources is a cost-intensive and time-consuming process. Therefore, there is a need for the development of in silico models, which can identify novel ABPs in protein sequences for chemical synthesis and testing. In this study, we propose a deep learning classifier named Deep-ABPpred that can identify ABPs in protein sequences. We developed Deep-ABPpred using bidirectional long short-term memory algorithm with amino acid level features from word2vec. The results show that Deep-ABPpred outperforms other state-of-the-art ABP classifiers on both test and independent datasets. Our proposed model achieved the precision of approximately 97 and 94% on test dataset and independent dataset, respectively. The high precision suggests applicability of Deep-ABPpred in proposing novel ABPs for synthesis and experimentation. By utilizing Deep-ABPpred, we identified ABPs in the tail protein sequences of Streptococcus bacteriophages, chemically synthesized identified peptides in lab and tested their activity in vitro. These ABPs showed potent antibacterial activity against selected Gram-positive and Gram-negative bacteria, which confirms the capability of Deep-ABPpred in identifying novel ABPs in protein sequences. Based on the proposed approach, an online prediction server is also developed, which is freely accessible at https://abppred.anvil.app/. This web server takes the protein sequence as input and provides ABPs with high probability (&gt;0.95) as output.

We report detection of Baculoviral inhibitor of apoptosis repeat containing-5 (BIRC5) protein biomarker in dog serum by label-free surface plasmon resonance (SPR) immunosensor. Initially, overexpression of BIRC5 in canine mammary tumour (CMT) tissues was confirmed by real-time PCR. Recombinant BIRC5 was produced and protein specific antibodies developed in guinea pig specifically reacted with native protein in immunohistochemistry and immunocytochemistry. SPR immunosensor was developed by fabricating anti-BIRC5 antibodies on gold sensor disc. The equilibrium dissociation constant, (KD = kd/ka) was 12.1 × 10−12 M; which indicates that antibodies are of high affinity with sensitivity in picomolar range. The SPR assay could detect as low as 6.25 pg/ml of BIRC5 protein in a calibration experiment (r2 = 0.9964). On testing real clinical samples, 95% specificity and 73.33% sensitivity were recorded. The average amount of serum BIRC5 in dogs with CMT was 110.02 ± 9.77 pg/ml; whereas, in non-cancerous disease conditions, 44.79 ± 4.28 pg/ml and in healthy dog sera 30.28 ± 2.99 pg/ml protein was detected. The SPR immunosensor for detection of BIRC5 in dog sera is reported for the first time and this may find prognostic and diagnostic applications in management of CMT. In future, ‘on-site’ sensors can be developed using this technique for near-patient testing.

Brucellosis is a bacterial disease, which, although affecting cattle primarily, has been associated with human infections, making its detection an important challenge. The existing gold standard diagnosis relies on the culture of bacteria which is a lengthy and costly process, taking up to 45 days. New technologies based on molecular diagnosis have been proposed, either through dip-stick, immunological assays, which have limited specificity, or using nucleic acid tests, which enable to identify the pathogen, but are impractical for use in the field, where most of the reservoir cases are located. Here we demonstrate a new test based on hybridization assays with metal nanoparticles, which, upon detection of a specific pathogen-derived DNA sequence, yield a visual colour change. We characterise the components used in the assay with a range of analytical techniques and show sensitivities down to 1000 cfu/ml for the detection of Brucella. Finally, we demonstrate that the assay works in a range of bovine samples including semen, milk and urine, opening up the potential for its use in the field, in low-resource settings.

With advancements in genomics, there has been substantial reduction in the cost and time of genome sequencing and has resulted in lot of data in genome databases. Antimicrobial host defense proteins provide protection against invading microbes. But confirming the antimicrobial function of host proteins by wet-lab experiments is expensive and time consuming. Therefore, there is a need to develop an in silico tool to identify the antimicrobial function of proteins. In the current study, we developed a model AniAMPpred by considering all the available antimicrobial peptides (AMPs) of length $\in $[10 200] from the animal kingdom. The model utilizes a support vector machine algorithm with deep learning-based features and identifies probable antimicrobial proteins (PAPs) in the genome of animals. The results show that our proposed model outperforms other state-of-the-art classifiers, has very high confidence in its predictions, is not biased and can classify both AMPs and non-AMPs for a diverse peptide length with high accuracy. By utilizing AniAMPpred, we identified 436 PAPs in the genome of Helobdella robusta. To further confirm the functional activity of PAPs, we performed BLAST analysis against known AMPs. On detailed analysis of five selected PAPs, we could observe their similarity with antimicrobial proteins of several animal species. Thus, our proposed model can help the researchers identify PAPs in the genome of animals and provide insight into the functional identity of different proteins. An online prediction server is also developed based on the proposed approach, which is freely accessible at https://aniamppred.anvil.app/.

Fungal infections or mycosis cause a wide range of diseases in humans and animals. The incidences of community acquired; nosocomial fungal infections have increased dramatically after the emergence of COVID-19 pandemic. The increase in number of patients with immunodeficiency / immunosuppression related diseases, resistance to existing antifungal compounds and availability of limited therapeutic options has triggered the search for alternative antifungal molecules. In this direction, antifungal peptides (AFPs) have received a lot of interest as an alternative to currently available antifungal drugs. Although the AFPs are produced by diverse population of living organisms, identifying effective AFPs from natural sources is time-consuming and expensive. Therefore, there is a need to develop a robust in silico model capable of identifying novel AFPs in protein sequences. In this paper, we propose Deep-AFPpred, a deep learning classifier that can identify AFPs in protein sequences. We developed Deep-AFPpred using the concept of transfer learning with 1DCNN-BiLSTM deep learning algorithm. The findings reveal that Deep-AFPpred beats other state-of-the-art AFP classifiers by a wide margin and achieved approximately 96% and 94% precision on validation and test data, respectively. Based on the proposed approach, an online prediction server is created and made publicly available at https://afppred.anvil.app/. Using this server, one can identify novel AFPs in protein sequences and the results are provided as a report that includes predicted peptides, their physicochemical properties and motifs. By utilizing this model, we identified AFPs in different proteins, which can be chemically synthesized in lab and experimentally validated for their antifungal activity.

SummaryWe report a rare case of a cardiac hydatid cyst that was incidentally found during routine work up for a redo-CABG and was picked up on echocardiography and confirmed by magnetic resonance imaging and, after successful removal, further confirmed by histopathology. The report emphasizes the importance of early and urgent surgery for such cardiac hydatid cysts whenever discovered to prevent fatal and unexpected death. Cardiac hydatidosis is a most infrequent type, in comparison with hydatidosis of the liver (65%) and lung (25%).Learning points Hydatidosis or cystic echinococcosis is caused by infection with the metacestode stage of the tapeworm Echinococcus (family Taeniidae). The adult tapeworm is usually found in dogs or other canines; the tapeworm eggs are expelled in the animal's feces and humans become infected after ingestion of the eggs. The initial phase of primary infection is asymptomatic.Cardiac hydatidosis is extremely rare, more commonly the liver and lungs are affected.Morbidity from heart echinococcosis in men is three times higher than that in women. Solitary cysts occur in almost 60% of the cases; the most frequent location is the ventricular myocardium and they are usually subepicardially located, hence they rarely rupture in the pericardial space. The left ventricle is damaged twofold to threefold more frequently than the right one.The diagnosis of echinococcosis in heart can be divided into two steps: detection of the cyst and its identification as echinococcus. It is based on serological reactions, echocardiography, X-ray, computerized tomography, and/or magnetic resonance imaging.The most dangerous complication of cardiac echinococcosis is cyst perforation. After cyst perforation three quarters of the patients die from septic shock or embolic complications.It is very important to understand that chemotherapy may lead to cyst death, and destruction of its wall and result in cyst rupture. Therefore, no germicide must be administered before surgical removal.