Type 2 diabetes is driven in part by metabolic inflammation, where activation of the Chrebp/Txnip axis promotes NLRP3 inflammasome assembly, leading to pancreatic β-cell dysfunction and pro-inflammatory cytokine release. Despite the therapeutic relevance of this pathway, the Chrebp-14-3-3 (regulatory-protein client) protein-protein interaction (PPI) remains largely underexplored, with only a limited number of small-molecule modulators reported to date. To address this gap, we developed an artificial intelligence-driven generative design framework for de novo discovery of selective PPI-targeting compounds. A conditional recurrent neural network (cRNN), implemented as a quantitative structure-property relationship-guided generative network (QSPR-GEN), was pretrained on a large, chemically diverse corpus to learn general SMILES syntax and structural priors, and subsequently fine-tuned on a curated, target-focused data set of approximately 5900 compounds, achieving high scaffold uniqueness (94.6%). Selectivity-oriented physicochemical descriptors were incorporated as conditional inputs to bias generation away from promiscuous chemotypes, while maintaining anchoring to a known active seed. Structure-based refinement was further applied by focusing on the noncanonical α-helical epitope unique to the Chrebp regulatory-protein interface, establishing a dual-layered strategy for selective PPI modulation. The integrated pipeline, combining virtual screening, molecular dynamics simulations, and MM/PBSA free-energy calculations, prioritized lead candidates with favorable binding energetics and pharmacokinetic profiles. In THP-1 macrophages under metabolic stress, the top candidate T7 markedly suppressed Txnip and NLRP3 expression, reduced IL-1β secretion, and attenuated pyroptotic cell death, outperforming a reference inhibitor. Collectively, this study presents a robust computational framework for the inverse design of challenging PPIs and demonstrates its utility through the identification and experimental validation of mechanistically precise lead compounds, exemplified by T2 and T7.

Toll-like receptor 7 (TLR7) is a key innate immune sensor implicated in autoimmune and inflammatory disorders. We report the discovery of novel small-molecule TLR7 antagonists, RTin7 and RTin11, using an AI-guided workflow combining a deep neural network (SMILES2ActNet), in silico screening, and medicinal chemistry optimization. The neural network accurately prioritized biologically active candidates from a large virtual chemical library. Both compounds exhibited low cytotoxicity and selectively inhibited imiquimod-induced proinflammatory cytokine release (TNF-α, IL-6, IL-8), with RTin11 showing superior potency in the low micromolar range compared to RTin7. Mechanistic studies demonstrated that RTin11 acts as a competitive antagonist at the TLR7-ligand interface, while RTin7 exhibits a distinct non-competitive inhibitory profile, with both compounds suppressing NF-κB and MAPK signaling. Molecular dynamics and MM/PBSA analyses revealed that RTin11 promotes receptor stabilization via cooperative structural adaptation, whereas RTin7 allows moderate flexibility, highlighting distinct binding behaviors. This study demonstrates the effectiveness of integrating deep learning with experimental validation for identifying selective, mechanistically validated TLR7 inhibitors as candidates for further therapeutic development. This integrative AI-to-experiment workflow may serve as a generalized model for identifying small-molecule modulators of pattern recognition receptors.

Aberrant activation of the nucleotide-binding oligomerization domain (NOD)-like receptor family pyrin domain-containing 3 (NLRP3) inflammasome drives neuroinflammation in multiple sclerosis (MS), experimental autoimmune encephalomyelitis (EAE), and Alzheimer's disease (AD). No clinically approved CNS-active NLRP3 inhibitor exists, highlighting the need for brain-penetrant modulators. We report the discovery and characterization of a novel chemical scaffold of NLRP3 inhibitory modulators (NIM5 series) that selectively suppress inflammasome activation. Lead analogs potently inhibited interleukin-1β (IL-1β), caspase-1, and gasdermin D (GSDMD) activation in THP-1 cells (IC50 = 0.75 μM) without affecting NF-κB, NLRC4, or AIM2 signaling, as shown by immunoblotting and biophysical analyses. Mechanistic studies demonstrated direct NLRP3 binding, consistent with selective inhibition over NLRC4 and AIM2. Permeability assays demonstrated robust blood-brain barrier penetration and CNS availability in vitro. In vivo, systemic administration attenuated neuronal injury, improved behavioral outcomes, and reduced neuroinflammatory markers in both EAE and Aβ-induced mouse models. These findings establish a brain-penetrant NLRP3 inhibitor chemotype for CNS-targeted therapeutic development.

Interleukin-23 (IL23) signaling, a critical driver of chronic inflammatory diseases like psoriasis, relies on the functional association of the IL23(p19) and its receptor (IL23R). Targeting this protein-protein interaction (PPI) with inhibitors presents a therapeutic advantage over biologics. Here, we report the discovery and mechanistic characterization of potent IL23/IL23R inhibitors. We employed a novel workflow starting with the Sequential Attachment-based Fragment Embedding (SAFE) deep generative model to design novel p19-targeting scaffolds, followed by virtual screening to identify commercially available compounds. Cellular screening of 31 candidates in HEK-Blue IL23 reporter cells identified Inh-31 as the optimal hit molecule. Analysis of all-atom molecular dynamics (MD) simulations (300 ns) revealed that Inh-31 binds to a deep pocket at the IL23/IL23R interface through multi-modal contacts, including crucial Pi-cation and H-bond interactions, which displace native binding hotspots. Critically, the MD data demonstrated that Inh-31 binding induces global rigidification and allosteric stabilization of the IL23R subunit (maintaining an RMSD <0.5 nm), locking the receptor complex in a non-signaling competent state to effectively halt the JAK-STAT3 cascade.

Tumor necrosis factor alpha (TNF-α) is a central mediator of inflammation and autoimmunity, where dysregulated activation of apoptotic and necroptotic pathways drives progressive tissue damage. Although monoclonal antibody therapies against TNF-α have provided clinical benefit, limitations related to parenteral delivery, immunogenicity, and incomplete suppression of downstream signaling underscore the need for alternative therapeutic approaches. Despite the pivotal role of TNF-α in chronic inflammation, no small-molecule inhibitor has yet reached clinical approval, with only a few candidates currently under clinical investigation. Here, we report the identification and mechanistic characterization of TI-16, a novel small-molecule inhibitor of TNF-α-TNFR1 interaction, discovered through structure-based virtual screening and validated by biophysical and cellular assays. TI-16 effectively protected fibroblasts from TNF-α-induced apoptosis and necroptosis and reduced the release of proinflammatory cytokines. Mechanistic analyses using immunoblotting, surface plasmon resonance, and molecular dynamics simulations demonstrated that TI-16 selectively disrupts tumor necrosis factor-alpha/tumor necrosis factor receptor 1 (TNF-α/TNFR1) interaction without altering TNF-α trimerization. By simultaneously suppressing apoptotic and necroptotic signaling, TI-16 overcomes key limitations of current biologics and represents a promising lead for the development of a novel class of orally available TNF-α-targeted therapeutics.

The PD-1/PD-L1 protein-protein interaction (PPI) is a critical immune checkpoint, and its inhibition represents a powerful strategy in oncology. Disrupting this macromolecular complex with small molecules remains a significant challenge. This study establishes a comprehensive pipeline for the discovery of novel in stock PD-1/PD-L1 inhibitors. We first developed a robust machine learning-based quantitative structure-activity relationship (ML-QSAR) model to screen chemical libraries virtually. Top-ranking hits were subjected to molecular docking against the PD-L1 dimer interface to evaluate potential binding modes. Subsequently, extensive molecular dynamics (MD) simulations provided critical insights into the structural stability and dynamic interactions at the macromolecular interface, revealing the compounds' mechanism of complex disruption. The most promising candidate, designated PDA13, was advanced to in vitro validation, demonstrating direct binding to the PD-L1 protein and effectively inhibiting the PD-1/PD-L1 interaction with an IC50 of hit 17.53 μM. Our work underscores the synergy of computational and experimental strategies in targeting PPI of PD-L1 dimer. The identified PDA13 scaffold provides a valuable starting point for the development of novel immunotherapeutic agents, and the detailed biophysical and structural insights into its mechanism of action form the core of this contribution.

Interleukin-36 (IL-36) is a pivotal driver of inflammatory responses in autoimmune disorders, including psoriasis, inflammatory bowel disease, and rheumatoid arthritis, and it also contributes to tumor progression. Upon activation, IL-36 triggers the release of pro-inflammatory cytokines such as C-X-C Motif Chemokine Ligand 1 (CXCL1), tumor necrosis factor-alpha (TNF-α), IL-6, and IL-8, exacerbating disease symptoms and fostering a tumor-supportive microenvironment. Despite its critical role, few IL-36 receptor (IL-36R) targeting molecules have been reported, and no specific small-molecule antagonist with confirmed receptor-level competition has been identified. Here, we present IRA10 and its potent derivative, IRA10L, as the first small molecules specifically designed to inhibit IL-36R. Molecular docking suggested strong binding of IRA10L to IL-36R, which molecular dynamics simulations further confirmed by demonstrating stable receptor-ligand interactions. Molecular mechanics poisson-boltzmann surface area (MMPBSA) binding free energy calculations revealed favorable energetics for IRA10L, correlating with its superior antagonistic activity compared with IRA10. Experimentally, IRA10L significantly reduced CXCL1 production across IL-36 isoforms alpha/beta/gamma (IL-36α/β/γ) and downregulated TNF-α, IL-6, and IL-8 expression in a dose-dependent manner. Western blot analyses showed inhibition of IL-36R. p65, Extracellular signal-regulated kinase (ERK), and p38 phosphorylation, blocking IL-36-driven downstream signaling. IRA10L also effectively suppressed IL-36-induced cancer cell metabolic activity, migration, spheroid growth, and colony formation. Biophysical assays, including surface plasmon resonance (SPR), Schild analysis, and competitive ELISA, confirmed direct competition of IRA10L with IL-36 ligands for receptor binding. These findings establish IRA10L as a specific, competitive IL-36R antagonist, highlighting its potential as a targeted therapeutic for IL-36-mediated inflammation and cancer and providing a foundation for future drug development.

Toll-like receptor 9 (TLR9) is a key sensor of CpG-rich DNA motifs, orchestrating host defense but also contributing to chronic inflammation, autoimmunity, and cancer progression when dysregulated. Selective small-molecule antagonists of TLR9 hold significant therapeutic promise; however, existing candidates exhibit off-target activity, suboptimal pharmacokinetics, and safety liabilities. Here, we employed an integrated computational-experimental strategy to discover and characterize novel TLR9 inhibitors. Machine learning-based QSAR classifiers were combined with molecular docking, pharmacophore modeling, and molecular dynamics simulations to predict active scaffolds and refine ligand candidates. This approach prioritized two compounds, TRin7 and TRin8, based on favorable binding free energies, stable receptor engagement, and key pharmacophoric features. In vitro, both compounds selectively suppressed CpG ODN2395-induced cytokine production (TNF-α, IL-6, MCP-1, and IL-8) in murine RAW264.7 macrophages and human Daudi cells, without affecting other TLR pathways, and did not cause significant toxicity even under extended treatment conditions. Mechanistic studies demonstrated that TRin7 and TRin8 directly disrupted TLR9-CpG DNA binding and inhibited downstream NF-κB and MAPK signaling, resulting in reduced COX2 and NOS2 expression. Comparative analyses indicated that TRin7 exhibited slightly greater potency, consistent with its lower binding free-energy profile in MM/PBSA calculations. Collectively, these findings establish TRin7 and TRin8 as promising small-molecule antagonists of TLR9 and highlight the utility of integrating machine learning with structural modeling and cellular validation in rational drug discovery.

Recent advances in machine learning have revolutionized molecular design; however, a gap remains in integrating generative models with physics-based simulations to develop functional modulators, such as stable peptides, for challenging targets like the interleukin-23 receptor (IL23R) and its associated cytokine, interleukin-23 (IL23). The IL23R/IL23 axis plays a critical role in autoimmune diseases, and current therapies have largely been limited to antibody-based approaches. To address this gap, we employed a hybrid computational approach that combines Long Short-Term Memory (LSTM) networks for peptide generation, a Gated Recurrent Unit (GRU)-based classifier for anti-inflammatory property prediction, and molecular dynamics (MD) simulations to assess structural dynamics, binding interactions, as well as key properties such as binding affinity and stability. Using this hybrid framework, we identified novel inhibitory peptides, particularly P4, with an IC50 of 2 μM. Systematic experimental validation established its inhibitory activity, elucidated its binding mechanism, confirmed its specificity toward the IL23R, and demonstrated its ability to disrupt IL23R/IL23 interaction. This integrated approach highlights the significant potential of combining deep learning and simulations to accelerate the identification of peptide-based therapeutics targeting key protein targets.

Endosomal Toll-like receptors (TLRs, including TLR3, TLR7, TLR8 and TLR9) play crucial roles in immune responses by recognizing pathogen-associated molecular patterns; however, their aberrant activation is implicated in inflammatory and autoimmune diseases. Developing endosomal TLR inhibitors against autoimmune diseases is clinically essential. Here we synthesized and optimized a series of compounds based on a candidate structure. The lead compounds, ETI41 and ETI60, potently inhibited endosomal TLR-mediated pro-inflammatory signaling with nanomolar activity in cellular, biophysical and in vivo assays. Both ETI41 and ETI60 selectively inhibited endosomal TLRs without affecting surface TLRs, as confirmed by immunoblotting and biophysical analyses. RNA sequencing revealed that these inhibitors modulated the expression of genes associated with inflammation. In vivo studies have shown that oral administration of ETI41 or ETI60 effectively ameliorates symptoms in mouse models of psoriasis, and systemic lupus erythematosus. These findings indicate that ETI41 and ETI60 hold significant potential as therapeutic agents for the treatment of autoimmune and inflammatory diseases through selective targeting of endosomal TLRs.

Haemonchus contortus poses a global challenge as a parasite affecting small ruminants, yet the problem of absence of an effective vaccine against H. contortus infection still exists. This investigation sought to appraise the immunological reaction induced by recombinant H. contortus excretory/secretory-24 (rHcES-24) in combination with complete Freund’s adjuvant (CFA) and bio-polymeric nanoparticles (NPs) within a murine model. In this study, rHcES-24 was encapsulated in poly(d, l-lactide-co-glycolide) (PLGA) and chitosan (CS) NPs, administered subcutaneously to mice. Researchers analyzed the NPs using scanning electron microscope (SEM) and assessed lymphocyte proliferation, specific antibodies, cytokines, T cell proliferation (CD3e+CD4+, CD3e+CD8a+), and phenotypic alteration in splenocytes (CD11c+CD83+, CD11c+CD86+) through flow cytometry to understand the immune response. The results demonstrated that the administration of nanovaccines (NVs) prompted immune responses towards Th1 pathway. This was indicated by notable enhancements in the production of specific antibodies, heightened cytokine levels, and a robust proliferation of lymphocytes observed in mice that received the NVs compared to control groups. Remarkably, mice vaccinated with the antigen-loaded NPs formulations exhibited considerably higher proportions of splenic dendritic cells (DCs) and T cells in comparison to those receiving the traditional adjuvant or the control groups. Incorporating HcES-24 protein into NPs effectively conferred immunity against H. contortus, paving the way for developing a targeted and commercial vaccine.

To audit the Management of Acute Exacerbation of Chronic Obstructive Pulmonary Disease (COPD) at leady reading hospital MTI Peshawar Enhancing Quality Care and Best Practices Methods: This was a clinical audit carried out at Lady Reading Hospital MTI Peshawar from February 2022 to October 2023. All patient charts with the diagnosis of COPD exacerbation were recovered. Our audit examined the factors that make COPD worse, such as smoking and work-related factors, spirometry to confirm COPM, predictive measures of COPA effects, and additional health issues for those with COPP. The study also examined the major complications that occur during a COPD onset, ways to recognize lung dysfunction, and the various drugs prescribed to individuals with this condition. Our findings were compared to those of the British Thoracic Society (BTS) in 2010. For data analysis SPSS software was used and percentage was calculated for various variables. Mean and standard deviation was calculated for BMI hospital stay and antibiotic description. The findings were displayed in the form of tables and graphs. Results: A total of 125 patients were recorded whose mean age was 63(±12 SD) years. 64%, 36% were female and male respectively. Smoking and Occupational history was recorded 38 and 26% correspondingly. Oxygen saturation was rec orded on all patients. Increase in the shortness of breath was recorded in 96% patients. Nebulized bronchodilators, intravenous steroids and intravenous antibiotics were prescribed to >90% of patients. Most of the patients were discharged on home treatment while in-hospital mortality was 13.68%. Conclusion The clinical notes for COPD did not include any information on smoking, occupational background, and the three key indicators of exacerbation. Most COPD exacerbation and respiratory failure were managed in a way that was consistent with BTS standards, but the diagnosis was not precise in almost two-thirds of cases.

The aberrant secretion of proinflammatory cytokines by immune cells is the principal cause of inflammatory diseases, such as systemic lupus erythematosus and rheumatoid arthritis. Toll-like receptor 7 (TLR7) and TLR9, sequestered to the endosomal compartment of dendritic cells and macrophages, are closely associated with the initiation and progression of these diseases. Therefore, the development of drugs targeting dysregulated endosomal TLRs is imperative to mitigate systemic inflammation. Here, we applied the principles of computer-aided drug discovery to identify a novel low-molecular-weight compound, TLR inhibitory compound 10 (TIC10), and its potent derivative (TIC10g), which demonstrated dual inhibition of TLR7 and TLR9 signaling pathways. Compared to TIC10, TIC10g exhibited a more pronounced inhibition of the TLR7- and TLR9-mediated secretion of the proinflammatory cytokine tumor necrosis factor-α in a mouse macrophage cell line and mouse bone marrow dendritic cells in a concentration-dependent manner. While TIC10g slightly prevented TLR3 and TLR8 activation, it had no impact on cell surface TLRs (TLR1/2, TLR2/6, TLR4, or TLR5), indicating its selectivity for TLR7 and TLR9. Additionally, mechanistic studies suggested that TIC10g interfered with TLR9 activation by CpG DNA and suppressed downstream pathways by directly binding to TLR9. Western blot analysis revealed that TIC10g downregulated the phosphorylation of the p65 subunit of nuclear factor κ-light-chain-enhancer of activated B cells (NF-κB) and mitogen-activated protein kinases (MAPKs), including extracellular-signal-regulated kinase, p38-MAPK, and c-Jun N-terminal kinase. These findings indicate that the novel ligand, TIC10g, is a specific dual inhibitor of endosomal TLRs (TLR7 and TLR9), disrupting MAPK- and NF-κB-mediated proinflammatory gene expression.

Background: Haemonchus contortus (H. contortus), a nematode with global prevalence, poses a major threat to the gastrointestinal health of sheep and goats. In an effort to combat this parasite, a nanovaccine was created using a recombinant ADP-ribosylation factor 1 (ARF1) antigen encapsulated within poly lactic-co-glycolic acid (PLGA). This study aimed to assess the effectiveness of this nanovaccine in providing protection against H. contortus infection. Methods: Fifteen goats were randomly divided into three groups. The experimental group received two doses of the PLGA encapsulated rHcARF1 (rHcARF1-PLGA) nanovaccine on days 0 and 14. Fourteen days after the second immunization, both the experimental and positive control groups were challenged with 8000 infective larvae (L3) of H. contortus, while the negative control group remained unvaccinated and unchallenged. At the end of the experiment on the 63rd day, all animals were humanly euthanized. Results: The results showed that the experimental group had significantly higher levels of sera IgG, IgA, and IgE antibodies, as well as increased concentrations of cytokines, such as IL-4, IL-9, IL-17, and TGF-β, compared to the negative control group after immunization. Following the L3 challenge, the experimental group exhibited a 47.5% reduction in mean eggs per gram of feces (EPG) and a 55.7% reduction in worm burden as compared to the positive control group. Conclusions: These findings indicate that the nanovaccine expressing rHcARF1 offers significant protective efficacy against H. contortus infection in goats. The results also suggest the need for more precise optimization of the antigen dose or a reassessment of the vaccination regimen. Additionally, the small sample size limits the statistical rigor and the broader applicability of the findings.

The creation of biofilms is heavily dependent on extracellular polymeric substances (EPSs), which are polymers made up of polysaccharides, proteins, and DNA. These can be produced organically using biosynthesizers made from a variety of microbe strains, and the environment strongly influences how these slimes come together. In many circumstances, the extracellular polymeric molecules' chemical and biological compositions are determined by their carbon source. Two types of EPS have been identified in flocs or biofilms: bound and soluble EPS. By creating a matrix surrounding the microbial cells, EPS creates a shield against substances that restrict microbial development and heavy metals. This is one of the EPS' most impressive activities. The use of bioremediation, leachate management, soil reclamation, and wastewater treatment also play a significant role in enhancing the natural environment. A variety of mechanisms contribute to the binding of closely spaced primary particles together in soil aggregates. These are the products of plant, animal, and microbial decomposition; the microorganisms themselves; and the microbial synthesis products. EPS formation is also involved in the maintenance of soil structure and aggregation. These are the byproducts of the breakdown of plants, animals, and microbial remnants, as well as the microorganisms themselves and the byproducts of microbial synthesis. The preservation of soil structure and aggregation is also aided by EPS production.

The human interleukin-1 receptor I (IL-1R1) is a cytokine receptor recognized by interleukin 1β (IL-1β), among other cytokines. Over activation of IL-1R1 has been implicated in various inflammatory conditions. This research aims to identify small-molecule inhibitors targeting the hIL1R1/IL1β interaction, employing a multi-task transfer learning approach for quantitative structure-activity relationship (QSAR) modelling. A comprehensive bioactivity dataset from functionally related proteins was utilised to build a robust ensemble machine learning model for predicting IC50 values against the target protein. Despite the availability of antibody-based therapies, the absence of orally available small-molecule inhibitors necessitates their development. By combining model predictions with docking and simulation approaches, the interleukin-1 receptor inhibitor (IRI-1) emerged as a lead compound. It potently inhibited human IL1-R1 with micromolar activity in THP-1 and Saos-2 cells and demonstrated good biocompatibility. Western blot analysis revealed that IRI-1 inhibits IL-1β-mediated phosphorylation of IL1-R1, JNK, IRAK-4, and ERK in THP-1 cells. Furthermore, molecular dynamics simulations confirmed the structural stability of the protein-ligand complexes. This study highlights the effectiveness of multi-task transfer learning approaches for building robust QSAR models against novel proteins or those with limited bioactivity data, such as hIL-1β/IL-1R1 protein.

Haemonchus contortus (H. contortus), a globally distributed nematode, is recognized as a significant pathogenic agent affecting the gastrointestinal health of both sheep and goats. In the context of addressing this parasitic threat, a nanovaccine was developed, comprising a recombinant ARF1 antigen encapsulated within the biopolymer PLGA (poly (lactic-co-glycolic acid) nanomaterial. The aim of this investigation was to evaluate the effectiveness of this nanovaccine in conferring defense against H. contortus infection, with fifteen goats evenly divided into three groups. The experimental group received two immunizations with the nanovaccine (rHcARF1-PLGA) on days 0 and 14. Subsequently, after a 14-day interval following the second immunization, both the experimental and positive control groups were subjected to a challenge with 8000 infective larvae (L3) of H. contortus. The negative control group remained unvaccinated and was not exposed to L3 challenge. The findings revealed a significant increase in sera IgG, IgA and IgE antibody concentrations, and heightened levels of cytokines, including IL-4, IL-9, IL-17, and TGF-β in the experimental group post-immunization compared to the negative control. Following the L3 challenge, the experimental group exhibited a remarkable reduction of 47.5% and 55.7% in mean eggs per gram of feces (EPG) and worm burdens, respectively. In conclusion, this investigation demonstrates the partial protective potential of the nanovaccine expressing rHcARF1 against H. contortus infection in goats.

Haemonchus contortus (H. contortus) is a gastrointestinal parasite affecting small ruminants, leading to a significant decline in animal productivity. In this study, we developed a nanovaccine by encapsulating the recombinant protein rHcES-15, derived from the excretory/secretory products of H. contortus, within biodegradable poly (D, L-lactide-co-glycolide) (PLGA) nanoparticles (NPs). To construct the nanovaccine, PLGA NPs were prepared using a modified double emulsion solvent evaporation technique. Scanning electron microscopy (SEM) illustrated successful encapsulation of rHcES-15 within PLGA NPs, with a size ranging between 350-400 nm. The encapsulation efficiency (EE) of the antigen in the nanovaccine was determined to be 72%. A total of forty experimental mice were divided into five groups, receiving the nanovaccine on day 0 and being humanely sacrificed at the end of the 14-day trial. The stimulation index (SI) from mice vaccinated with the nanovaccine indicated an amplified lymphocyte proliferation and a significant increase in anti-inflammatory cytokines (IL-4, IL-10, and IL-17). Furthermore, the percentages of T-cells (CD4+, CD8+) and dendritic cell phenotypes (CD83+, CD86+) were substantially upregulated in mice immunized with the nanovaccine compared to control groups and the rHcES-15 group. Similarly, higher levels of antigen-specific serum immunoglobulins (IgG1, IgG2a, IgM) were observed in response to the nanovaccine compared to both the antigenic (rHcES-15) and control groups. In conclusion, the data strongly supports the notion that encapsulation of rHcES-15 within PLGA NPs effectively stimulates immune cells in vivo, ultimately augmenting antigen-specific adaptive immune responses against H. contortus. This discovery highlights the promising potential of the nanovaccine, justifying additional investigations to ascertain its efficacy finally.

Coronaviruses belong to the group of RNA family of viruses that trigger diseases in birds, humans, and mammals, which can cause respiratory tract infections. The COVID-19 pandemic has badly affected every part of the world. Our study aimed to explore the genome of SARS-CoV-2, followed by in silico analysis of its proteins. Different nucleotide and protein variants of SARS-CoV-2 were retrieved from NCBI. Contigs and consensus sequences were developed to identify these variants using SnapGene. Data of the variants that significantly differed from each other was run through Predict Protein software to understand the changes produced in the protein structure. The SOPMA web server was used to predict the secondary structure of the proteins. Tertiary structure details of the selected proteins were analyzed using the web server SWISS-MODEL. Sequencing results showed numerous single nucleotide polymorphisms in the surface glycoprotein, nucleocapsid, ORF1a, and ORF1ab polyprotein while the envelope, membrane, ORF3a, ORF6, ORF7a, ORF8, and ORF10 genes had no or few SNPs. Contigs were used to identify variations in the Alpha and Delta variants of SARS-CoV-2 with the reference strain (Wuhan). Some of the secondary structures of the SARS-CoV-2 proteins were predicted by using Sopma software and were further compared with reference strains of SARS-CoV-2 (Wuhan) proteins. The tertiary structure details of only spike proteins were analyzed through the SWISS-MODEL and Ramachandran plots. Through the Swiss-model, a comparison of the tertiary structure model of the SARS-CoV-2 spike protein of the Alpha and Delta variants was made with the reference strain (Wuhan). Alpha and Delta variants of the SARS-CoV-2 isolates submitted in GISAID from Pakistan with changes in structural and nonstructural proteins were compared with the reference strain, and 3D structure mapping of the spike glycoprotein and mutations in the amino acids were seen. The surprisingly increased rate of SARS-CoV-2 transmission has forced numerous countries to impose a total lockdown due to an unusual occurrence. In this research, we employed in silico computational tools to analyze the SARS-CoV-2 genomes worldwide to detect vital variations in structural proteins and dynamic changes in all SARS-CoV-2 proteins, mainly spike proteins, produced due to many mutations. Our analysis revealed substantial differences in the functionality, immunological, physicochemical, and structural variations in the SARS-CoV-2 isolates. However, the real impact of these SNPs can only be determined further by experiments. Our results can aid in vivo and in vitro experiments in the future.

Intrinsic plagiarism detection is a critical domain in the field of text analysis that aims to identify plagiarized content within a document to check whether parts of the document are from the same author. As the development of Large Language Models (LLMs) based content generation tools such as, ChatGPT is publicly available, the challenge of intrinsic plagiarism has become increasingly significant in various domains. Consequently, there is a growing demand for robust and accurate detection methods to address this evolving landscape. In this context, the present study conducts a comprehensive Systematic Literature Review (SLR) to explore the landscape of intrinsic plagiarism detection. We systematically collected and rigorously analyzed 44 research papers that delve into various aspects of the domain, including common datasets, feature extraction techniques and intrinsic plagiarism detection methods. This SLR also highlights the evolution of detection approaches over time and the challenges faced in this context especially challenges associated with low-resource languages. To the best of our knowledge, there is no SLR exclusively based on the intrinsic plagiarism detection that bridge the gap in existing literature and offering valuable insights to researchers and practitioners. By consolidating the state-of-the-art findings, this SLR serves as a foundation for future research, enabling the development of more effective and efficient plagiarism detection solutions to combat the ever-evolving challenges posed by plagiarism in today’s digital age.