A hybrid method for discovering interferon-gamma inducing peptides in human and mouse.

Interferon-gamma (IFN-γ) is a versatile pleiotropic cytokine essential for both innate and adaptive immune responses. It exhibits both pro-inflammatory and anti-inflammatory properties, making it a promising therapeutic candidate for treating various infectious diseases and cancers. We present IFNepitope2, a host-specific technique to annotate IFN-γ inducing peptides, it is an updated version of IFNepitope introduced by Dhanda et al.

HLA-DR4Pred2: An improved method for predicting HLA-DRB1*04:01 binders.

HLA-DRB1*04:01 is associated with numerous diseases, including sclerosis, arthritis, diabetes, and COVID-19, emphasizing the need to scan for binders in the antigens to develop immunotherapies and vaccines. Current prediction methods are often limited by their reliance on the small datasets. This study presents HLA-DR4Pred2, developed on a large dataset containing 12,676 binders and an equal number of non-binders.

Identification of DLK1, a Notch ligand, as an immunotherapeutic target and regulator of tumor cell plasticity and chemoresistance in adrenocortical carcinoma.

Immunotherapeutic targeting of cell surface proteins is an increasingly effective cancer therapy. However, given the limited number of current targets, the identification of new surface proteins, particularly those with biological importance, is critical. Here, we uncover delta-like non-canonical Notch ligand 1 (DLK1) as a cell surface protein with limited normal tissue expression and high expression in multiple refractory adult metastatic cancers including small cell lung cancer (SCLC) and adrenocortical carcinoma (ACC), a rare cancer with few effective therapies.

Metastatic organotropism in small cell lung cancer.

Metastasis is the leading cause of cancer-related deaths, yet its regulatory mechanisms are not fully understood. Small-cell lung cancer (SCLC) is the most metastatic form of lung cancer, with most patients presenting with widespread disease, making it an ideal model for studying metastasis. However, the lack of suitable preclinical models has limited such studies.

A hybrid approach for predicting transcription factors.

Transcription factors are essential DNA-binding proteins that regulate the transcription rate of several genes and control the expression of genes inside a cell. The prediction of transcription factors with high precision is important for understanding biological processes such as cell differentiation, intracellular signaling, and cell-cycle control. In this study, we developed a hybrid method that combines alignment-based and alignment-free methods for predicting transcription factors with higher accuracy.

Acetalax (Oxyphenisatin Acetate, NSC 59687) and Bisacodyl Cause Oncosis in Triple-Negative Breast Cancer Cell Lines by Poisoning the Ion Exchange Membrane Protein TRPM4.

Unlabelled: Triple-negative breast cancer (TNBC) is clinically aggressive and relatively unresponsive to current therapies. Therefore, the development of new anticancer agents is needed to satisfy clinical needs. Oxyphenisatin acetate (Acetalax), which had been used as a laxative, has recently been reported to have anticancer activity in murine models.

Genome-wide Mapping of Topoisomerase Binding Sites Suggests Topoisomerase 3α (TOP3A) as a Reader of Transcription-Replication Conflicts (TRC).

Both transcription and replication can take place simultaneously on the same DNA template, potentially leading to transcription-replication conflicts (TRCs) and topological problems. Here we asked which topoisomerase(s) is/are the best candidate(s) for sensing TRC. Genome-wide topoisomerase binding sites were mapped in parallel for all the nuclear topoisomerases (TOP1, TOP2A, TOP2B, TOP3A and TOP3B).

A deep learning method for classification of HNSCC and HPV patients using single-cell transcriptomics.

Background: Head and Neck Squamous Cell Carcinoma (HNSCC) is the seventh most highly prevalent cancer type worldwide. Early detection of HNSCC is one of the important challenges in managing the treatment of the cancer patients. Existing techniques for detecting HNSCC are costly, expensive, and invasive in nature.

A ten-year experience of musculoskeletal tuberculosis at a tertiary care hospital in South India.

Background: The delayed identification and management of musculoskeletal tuberculosis (MSTB) poses substantial health challenges and leads to significant morbidity. This study aimed to collate ten years of hospital data and provide valuable insights into the clinical, diagnostics, and outcomes of the patients diagnosed with MSTB.

Methods: A retrospective study was undertaken to review clinic records from 2013 to 2022 for all individuals diagnosed with MSTB in a tertiary care hospital in South India.

AntiBP3: A Method for Predicting Antibacterial Peptides against Gram-Positive/Negative/Variable Bacteria.

Most of the existing methods developed for predicting antibacterial peptides (ABPs) are mostly designed to target either gram-positive or gram-negative bacteria. In this study, we describe a method that allows us to predict ABPs against gram-positive, gram-negative, and gram-variable bacteria. Firstly, we developed an alignment-based approach using BLAST to identify ABPs and achieved poor sensitivity.

Impact of early initiation of ezetimibe in patients with acute coronary syndrome: A systematic review and meta-analysis.

Objective: Scant data is available on the efficacy and safety of adding ezetimibe to high-intensity statin therapy for early and rapid reduction of low-density lipoprotein cholesterol (LDL-C) within 4-12 weeks of an acute-event in acute coronary syndrome (ACS). We undertook this meta-analysis to address this knowledge-gap.

Methods: Electronic databases were searched for RCTs involving patients with ACS receiving ezetimibe in intervention arm, and placebo/active comparator in control arm.

Automatic Gaze Analysis: A Survey of Deep Learning Based Approaches.

Eye gaze analysis is an important research problem in the field of Computer Vision and Human-Computer Interaction. Even with notable progress in the last 10 years, automatic gaze analysis still remains challenging due to the uniqueness of eye appearance, eye-head interplay, occlusion, image quality, and illumination conditions. There are several open questions, including what are the important cues to interpret gaze direction in an unconstrained environment without prior knowledge and how to encode them in real-time.

Risk assessment of cancer patients based on HLA-I alleles, neobinders and expression of cytokines.

Advancements in cancer immunotherapy have shown significant outcomes in treating cancers. To design effective immunotherapy, it's important to understand immune response of a patient based on its genomic profile. However, analyses to do that requires proficiency in the bioinformatic methods.

In Silico Tool for Identification, Designing, and Searching of IL13-Inducing Peptides in Antigens.

Interleukins are a distinctive class of molecules exhibiting various immune signaling functions. Immunoregulatory cytokine, Interleukin 13 (IL13), is primarily synthesized by activated T-helper 2 cells, mast cells, and basophils. IL13, is known to stimulate many allergic and autoimmune diseases, such as asthma, rheumatoid arthritis, systemic sclerosis, ulcerative colitis, airway hyperresponsiveness, glycoprotein hypersecretion, and goblet cell hyperplasia.

A Web-Based Method for the Identification of IL6-Based Immunotoxicity in Vaccine Candidates.

Interleukin 6 (IL6) is a major pro-inflammatory cytokine that plays a pivotal role in both innate and adaptive immune responses. In the past, a number of studies reported that high level of IL6 promotes the proliferation of cancer, autoimmune disorders, and cytokine storm in COVID-19 patients. Thus, it is extremely important to identify and remove the antigenic regions from a therapeutic protein or vaccine candidate that may induce IL6-associated immunotoxicity.

TNFepitope: A webserver for the prediction of TNF-α inducing epitopes.

Tumor Necrosis Factor alpha (TNF-α) is a pleiotropic pro-inflammatory cytokine that is crucial in controlling the signaling pathways within the immune cells. Recent studies reported that higher expression levels of TNF-α are associated with the progression of several diseases, including cancers, cytokine release syndrome in COVID-19, and autoimmune disorders. Thus, it is the need of the hour to develop immunotherapies or subunit vaccines to manage TNF-α progression in various disease conditions.

An ensemble method for prediction of phage-based therapy against bacterial infections.

Phage therapy is a viable alternative to antibiotics for treating microbial infections, particularly managing drug-resistant strains of bacteria. One of the major challenges in designing phage-based therapy is to identify the most appropriate potential phage candidate to treat bacterial infections. In this study, an attempt has been made to predict phage-host interactions with high accuracy to identify the potential bacteriophage that can be used for treating a bacterial infection.

Prediction of celiac disease associated epitopes and motifs in a protein.

Introduction: Celiac disease (CD) is an autoimmune gastrointestinal disorder causes immune-mediated enteropathy against gluten. Gluten immunogenic peptides have the potential to trigger immune responses which leads to damage the small intestine. HLA-DQ2/DQ8 are major alleles that bind to epitope/antigenic region of gluten and induce celiac disease.

Advances in the field of phage-based therapy with special emphasis on computational resources.

In the current era, one of the major challenges is to manage the treatment of drug/antibiotic-resistant strains of bacteria. Phage therapy, a century-old technique, may serve as an alternative to antibiotics in treating bacterial infections caused by drug-resistant strains of bacteria. In this review, a systematic attempt has been made to summarize phage-based therapy in depth.

DMPPred: a tool for identification of antigenic regions responsible for inducing type 1 diabetes mellitus.

There are a number of antigens that induce autoimmune response against β-cells, leading to type 1 diabetes mellitus (T1DM). Recently, several antigen-specific immunotherapies have been developed to treat T1DM. Thus, identification of T1DM associated peptides with antigenic regions or epitopes is important for peptide based-therapeutics (e.

Prediction of RNA-interacting residues in a protein using CNN and evolutionary profile.

This paper describes a method Pprint2, which is an improved version of Pprint developed for predicting RNA-interacting residues in a protein. Training and independent/validation datasets used in this study comprises of 545 and 161 non-redundant RNA-binding proteins, respectively. All models were trained on training dataset and evaluated on the validation dataset.