Inferences for multiple interval type-I censoring scheme.

In this paper, we have introduced a new type of censoring scheme named the multiple interval type-I censoring scheme. Further, We have assumed that the test units are drawn from the Weibull population. We have also proposed the maximum product of spacing estimators for unknown parameters under the multiple interval type-I censoring scheme and compare them with the existing maximum likelihood estimators. In addition to this, the Bayes estimators for shape and scale parameters are also obtained under the squared error loss function. Their corresponding asymptotic confidence/credible intervals are also discussed. A real data set containing the breakdown time of insulating fluids are used to demonstrate the appropriateness of the proposed methodology.

Positive Microbiological Stains of Corneal Scrapings among Patients with Keratitis in a Tertiary Care Centre: A Descriptive Cross-sectional Study.

Introduction: Keratitis, an ocular emergency, requires rapid and accurate treatment to prevent vision impairment. Wet mount direct microscopy examination of corneal scraping smear using gram and 10% potassium hydroxide stain helps in early diagnosis and treatment. The main objective of this study was to find out the prevalence of positive microbiological stains of corneal scrapings among patients with keratitis in a tertiary care centre. Methods: A descriptive cross-sectional study was conducted in the Department of Ophthalmic Pathology and Laboratory Medicine from January, 2018 to December, 2019. Data collection was done after taking ethical approval from the Institutional Review Committee of the hospital (Reference number: BEH-IRC-35/A). All corneal smear samples received in this department were included in this study. Case records with incomplete data were excluded. Whole sampling was done. The data were analyzed using Statistical Package for the Social Sciences version 22.0. Frequency and percentage was calculated for binary data. Results: Among 4631 corneal scrapings, microbiological stains were positive in 3538 (76.40%) patients. Conclusions: The prevalence of positive microbiological stains of corneal scrapings in our study was higher in comparison to other studies done in similar settings. This technique could be used where culture facilities are unavailable or unaffordable. Keywords: keratitis; microscopy; Nepal.

Accelerating the discovery of antifungal peptides using deep temporal convolutional networks.

The application of machine intelligence in biological sciences has led to the development of several automated tools, thus enabling rapid drug discovery. Adding to this development is the ongoing COVID-19 pandemic, due to which researchers working in the field of artificial intelligence have acquired an active interest in finding machine learning-guided solutions for diseases like mucormycosis, which has emerged as an important post-COVID-19 fungal complication, especially in immunocompromised patients. On these lines, we have proposed a temporal convolutional network-based binary classification approach to discover new antifungal molecules in the proteome of plants and animals to accelerate the development of antifungal medications. Although these biomolecules, known as antifungal peptides (AFPs), are part of an organism's intrinsic host defense mechanism, their identification and discovery by traditional biochemical procedures is arduous. Also, the absence of a large dataset on AFPs is also a considerable impediment in building a robust automated classifier. To this end, we have employed the transfer learning technique to pre-train our model on antibacterial peptides. Subsequently, we have built a classifier that predicts AFPs with accuracy and precision of 94%. Our classifier outperforms several state-of-the-art models by a considerable margin. The results of its performance were proven as statistically significant using the Kruskal-Wallis H test, followed by a post hoc analysis performed using the Tukey honestly significant difference (HSD) test. Furthermore, we identified potent AFPs in representative animal (Histatin) and plant (Snakin) proteins using our model. We also built and deployed a web app that is freely available at https://tcn-afppred.anvil.app/ for the identification of AFPs in protein sequences.

Deep-AFPpred: identifying novel antifungal peptides using pretrained embeddings from seq2vec with 1DCNN-BiLSTM.

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.

StaBle-ABPpred: a stacked ensemble predictor based on biLSTM and attention mechanism for accelerated discovery of antibacterial peptides.

Due to the rapid emergence of multi-drug resistant (MDR) bacteria, existing antibiotics are becoming ineffective. So, researchers are looking for alternatives in the form of antibacterial peptides (ABPs) based medicines. The discovery of novel ABPs using wet-lab experiments is time-consuming and expensive. Many machine learning models have been proposed to search for new ABPs, but there is still scope to develop a robust model that has high accuracy and precision. In this work, we present StaBle-ABPpred, a stacked ensemble technique-based deep learning classifier that uses bidirectional long-short term memory (biLSTM) and attention mechanism at base-level and an ensemble of random forest, gradient boosting and logistic regression at meta-level to classify peptides as antibacterial or otherwise. The performance of our model has been compared with several state-of-the-art classifiers, and results were subjected to analysis of variance (ANOVA) test and its post hoc analysis, which proves that our model performs better than existing classifiers. Furthermore, a web app has been developed and deployed at https://stable-abppred.anvil.app to identify novel ABPs in protein sequences. Using this app, we identified novel ABPs in all the proteins of the Streptococcus phage T12 genome. These ABPs have shown amino acid similarities with experimentally tested antimicrobial peptides (AMPs) of other organisms. Hence, they could be chemically synthesized and experimentally validated for their activity against different bacteria. The model and app developed in this work can be further utilized to explore the protein diversity for identifying novel ABPs with broad-spectrum activity, especially against MDR bacterial pathogens.

AniAMPpred: artificial intelligence guided discovery of novel antimicrobial peptides in animal kingdom.

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/.

Deep-ABPpred: identifying antibacterial peptides in protein sequences using bidirectional LSTM with word2vec.

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 (>0.95) as output.

p53 mutation regulates PKD genes and results in co-occurrence of PKD and tumorigenesis.

OBJECTIVE: Polycystic kidney disease (PKD) is the major cause of kidney failure and mortality in humans. It has always been suspected that the development of cystic kidney disease shares features with tumorigenesis, although the evidence is unclear. METHODS: We crossed p53 mutant mice (p53N236S, p53S) with Werner syndrome mice and analyzed the pathological phenotypes. The RNA-seq, ssGSEA analysis, and real-time PCR were performed to dissect the gene signatures involved in the development of disease phenotypes. RESULTS: We found enlarged kidneys with fluid-filled cysts in offspring mice with a genotype of G3mTerc -/- WRN -/- p53 S/S (G3TM). Pathology analysis confirmed the occurrence of PKD, and it was highly correlated with the incidence of tumorigenesis. RNA-seq data revealed the gene signatures involved in PKD development, and demonstrated that PKD and tumorigenesis shared common pathways, including complement pathways, lipid metabolism, mitochondria energy homeostasis and others. Interestingly, this G3TM PKD and the classical PKD1/2 deficient PKD shared common pathways, possibly because the mutant p53S could regulate the expression levels of PKD1/2, Pkhd1, and Hnf1b. CONCLUSIONS: We established a dual mouse model for PKD and tumorigenesis derived from abnormal cellular proliferation and telomere dysfunction. The innovative point of our study is to report PKD occurring in conjunction with tumorigenesis. The gene signatures revealed might shed new light on the pathogenesis of PKD, and provide new molecular biomarkers for clinical diagnosis and prognosis.

Immunomodulatory effect of curcumin on lipopolysaccharide- and/or flagellin-induced production of prostaglandin E2 and relative expression of proinflammatory cytokines in the primary bubaline endometrial stromal cells.

Developing alternate therapies for bovine endometritis is important in the context of drug residues in the milk and emergence of antimicrobial resistant bacteria. In this regard, we studied the immunomodulatory effect of curcumin 30 µM, on lipopolysaccharide- (LPS) and/or flagellin (100 ng/ml each)-induced prostaglandin E2 (PGE2 ) and proinflammatory cytokines (PIC) using primary bubaline endometrial stromal cells. After 24 hr treatment, the supernatant was assayed for PGE2 while cells were used for relative quantification of cytokines like IL-1ß, IL-6, IL-8 and TNF α transcripts using a control group as calibrator. LPS was found to possess potent stimulatory effect on PGE2 production, whereas the flagellin was not as potent as LPS in stimulating the PGE2 production either per se or in combination with LPS. LPS markedly up-regulated the transcripts of IL-8 and IL-6 as compared to IL-1ß and TNF α in the bubaline endometrial stromal cells. Except for IL-8, flagellin did not up-regulate other PICs. There was no additive effect between LPS and flagellin on the up-regulation of inflammatory cytokines. Curcumin inhibited the LPS-induced up-regulation of PIC with strong down-regulation of IL-8. The inhibitory effects of curcumin on the inflammatory mediators suggest a potential in the treatment of bovine endometritis.