Hepatitis E virus (HEV) is recognized as one of the leading causes of acute viral hepatitis (AVH) in developing countries, where it is primarily transmitted through the consumption of contaminated food and water. Although often self-limiting, HEV infection poses a significant public health concern, particularly among pregnant women, due to its potential complications. The present study aimed to determine the seroprevalence of HEV infection in asymptomatic antenatal women attending a tertiary care center in South Punjab, Pakistan.

A total of 100 asymptomatic pregnant women were screened for anti-HEV antibodies (IgM and IgG) using an ELISA kit (DIA PRO, Italy). The overall seropositivity rate was found to be 12%, indicating prior exposure to HEV infection in this cohort. Specifically, IgG antibodies were detected in 6% of women and IgM antibodies in 5%, while two women showed evidence of both IgG and IgM positivity, suggestive of recent or ongoing infection. Notably, the majority of participants reported reliance on untreated water sources irrespective of educational background, highlighting environmental risk factors.

Although HEV is generally self-limiting, these findings underscore the importance of routine serological screening in antenatal populations to prevent adverse pregnancy outcomes. In addition, increased community awareness regarding transmission routes and preventive measures is essential. Given the scarcity of regional data, this study emphasizes the need for larger-scale epidemiological investigations to better understand the burden of HEV in South Punjab, Pakistan.

Keywords: Hepatitis E Virus, Antenatal Women, Seroprevalence, Immunoglobulins, South Punjab

Antibody–drug conjugates (ADCs) represent a novel and rapidly evolving class of targeted therapeutics that combine the high specificity of monoclonal antibodies (mAbs) with the potent cytotoxic effects of small-molecule drugs. These engineered molecules are designed to selectively deliver cytotoxic agents to specific cells, thereby reducing off-target toxicity and enhancing therapeutic efficacy. In oncology, ADCs have already demonstrated significant clinical success, particularly in the treatment of hematological malignancies and solid tumors. Agents such as trastuzumab emtansine and brentuximab vedotin exemplify how ADCs can effectively target cancer cells while limiting damage to healthy tissues. Over the past decade, the development of new linkers, payloads, and antibody engineering technologies has further refined the safety and effectiveness of ADCs, leading to an expanded pipeline of approved and investigational compounds. Following their success in oncology, interest has grown in repurposing ADCs for the treatment of non-malignant diseases, including autoimmune disorders. These conditions are characterised by inappropriate immune responses in which autoreactive cells and inflammatory mediators attack host tissues. ADCs offer a promising solution by enabling the selective depletion or modulation of these pathogenic immune cell subsets without broadly suppressing the entire immune system. Preclinical and early clinical studies have shown encouraging results. For instance, ADCs targeting CD19 or CD22 are being explored for systemic lupus erythematosus and other B-cell-driven diseases, while anti-CD3 ADCs have shown potential in type 1 diabetes. As knowledge of disease-specific immune targets increases, ADCs may provide a new avenue for achieving durable remission in autoimmune diseases with improved safety profiles compared to conventional immunosuppressants. This presentation comprehensively explores the evolving landscape of ADCs in autoimmune therapeutics.

Salmonella infections in poultry represent a significant public health and economic burden, particularly in regions where poultry serves as a major source of animal protein. Traditional control methods often rely on antibiotics, which contribute to antimicrobial resistance and raise consumer health concerns. This study developed a novel, sustainable strategy by integrating indigenous plant-based bacterial attenuation with immunotherapy using IgY antibodies. Specifically, garlic (Allium sativum) and onion (Allium cepa) extracts were employed to attenuate wild-type Salmonella serovars. These plant-derived agents exhibited strong antimicrobial properties, effectively inhibiting bacterial growth in vitro. The attenuated strains were then used to vaccinate chickens, which led to the induction of high levels of anti-Salmonella IgY antibodies, as confirmed by an enzyme-linked immunosorbent assay (ELISA). Functional assays revealed that the harvested IgY antibodies possessed robust agglutination activity, highlighting their potential role in passive immunisation strategies. Importantly, the vaccine was found to be both safe and immunogenic, with no adverse effects observed in the immunised birds. This dual approach—combining natural antimicrobial agents with immune-based protection—offers a cost-effective and environmentally friendly alternative to conventional methods. It holds particular promise for improving poultry health and food safety in the Caribbean, where locally available resources and affordable interventions are crucial. This strategy may also be applicable to broader global contexts, especially in low-resource agricultural settings.

In the last decade, immuno-oncology has revolutionized cancer therapy by harnessing the body's immune system to target and eliminate tumor cells, offering durable responses particularly in malignancies previously considered treatment-resistant. This approach exploits immune checkpoints, monoclonal antibodies, Chimeric Antigen Receptor (CAR) T-cells, and innovative cancer vaccines to activate, promote, and enhance immune responses specific to tumor-associated antigens. However, the complexity of tumor–immune interactions necessitates advanced tumor imaging techniques to accurately diagnose, monitor, and tailor effective immunotherapy techniques. Tumor imaging plays a pivotal role in visualizing immune cell infiltration, tracking immune responses in real-time, and identifying immune-related adverse events. Recent innovations such as hybrid Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Positron Emission Tomography (PET) (PET/CT and MRI) combined with novel tracers—like radiolabeled antibodies targeting Programmed Death-Ligand 1 PD-L1—allow precise quantification of immune activation within the tumor microenvironment. These modalities facilitate early assessment of therapeutic efficacy, guiding personalized treatment adjustments. Furthermore, multimodal imaging approaches integrating molecular and anatomical data improve the delineation of tumor boundaries, detect metastases, and evaluate emerging resistance mechanisms. As immuno-oncology moves toward personalized medicine, imaging biomarkers will be essential in stratifying patients most likely to benefit from immune-based therapies, predicting outcomes, and minimizing toxicity. Continued research in tumor imaging will enhance our understanding of immune dynamics, ultimately improving treatment precision and patient survival in cancer care. The main aim of this was to advance and develop the in-depth understanding and application of innovative tumor imaging techniques that accurately visualize and quantify immune responses within the tumor microenvironment. By enabling personalized immunotherapy strategies, improving personalized treatment and monitoring will significantly enhance patient therapeutic outcomes, as a result of appropriate treatment corresponding to effective and modern cancer care.

Novel and developed computational approaches in the field of antibody engineering have revolutionized and leveraged advanced algorithms through machine learning and detailed structural modeling in order to deeply facilitate the de novo design of new, effective therapeutic agents based on antibodies, affinity maturation, and stability optimization, significantly enhancing and accelerating the drug development processes. The main purpose of this paper is to clearly illustrate how advanced computational antibody design techniques, such as de novo engineering, affinity maturation, and personalized modeling, can deeply affect modern and precision therapeutics by enabling the rapid development of highly specific and potent monoclonal antibodies tailored to specific critical diseases. We observed the rapid development of highly specific and potent monoclonal antibodies tailored for some specific critical diseases—particularly cancers; for example, Human Epidermal Growth Factor Receptor 2 (HER2) or Receptor Tyrosine-Protein Kinase erbB-2- positive breast cancer or colorectal cancer. For autoimmune disorders such as Rheumatoid Arthritis (RA), Antikeratin, anticitrullinated peptides, anti-RA33, anti-Sa, and anti-p68 autoantibodies have been shown to have >90% specificity for RA. Regarding infectious diseases, the immunoglobulins lg M, lg A, and lg G are the key players in the response and the fight against COVID-19. The ultimate essential goal consists is to improve therapeutic efficacy, reduce off-target effects, and facilitate specific personalized treatment strategies that effectively address individual patients' molecular profiles. Machine learning algorithms like DeepMind’s AlphaFold have dramatically improved the accuracy of antibody–antigen structure prediction, facilitating the rapid identification of high-affinity binders. In one instance, a computational redesign of an anti-PD-1 (Immune checkpoint inhibitor) antibody enhanced its binding affinity and stability, leading to a more potent immune checkpoint inhibitor for cancer therapy. Moreover, the de novo design of bispecific antibodies has enabled simultaneous targeting of multiple tumor antigens, such as Cluster of Differentiation 3 (CD3) and Epidermal Growth Factor Receptor (EGFR), boosting immune activation in resistant cancers.

Advances in immuno-oncology have significantly transformed the therapeutic landscape of cancer treatment. However, evaluating the spatial and temporal dynamics of therapeutic antibodies within the tumor microenvironment (TME) remains a major challenge. Molecular imaging using radiolabeled antibodies has emerged as a powerful strategy to visualize and quantify antibody distribution, target engagement, and immune activation in real time. In this study, we describe the design and application of radiolabeled monoclonal antibodies that selectively bind to immune checkpoints and tumor-associated antigens. Employing imaging modalities such as positron emission tomography (PET) and single-photon emission computed tomography (SPECT), we demonstrate high specificity in detecting tumors, immune cell infiltration, and antigen heterogeneity in preclinical models. Radiolabeled antibody imaging enables longitudinal monitoring of therapeutic responses, allowing real-time assessment of treatment efficacy and adaptive changes in the TME. Importantly, this approach provides a non-invasive method for guiding patient selection, optimizing therapeutic dosing, and identifying mechanisms of resistance. By integrating diagnostic imaging with therapeutic antibodies, our work contributes to the advancement of antibody-based theranostics and precision oncology. The convergence of immunotherapy and imaging technologies paves the way for a more personalized and effective approach to cancer care, improving both clinical decision-making and patient outcomes.
This innovative strategy holds significant potential to enhance the design and clinical translation of next-generation antibody therapeutics.

Vectored Immunoprophylaxis (VIP) is a gene-based approach to vaccination that delivers genetic instructions for an antibody, enabling the host to produce an immune response without engaging the traditional humoral pathway. Adeno-associated virus (AAV) vectors are well-suited for in vivo antibody gene delivery due to their low pathogenicity, minimal genome integration, sustained transgene expression, and liver/muscle tropism—optimal sites for monoclonal antibody (mAb) production. AAV has been used in non-human primates (NHPs) and clinical trials, but studies report transient antibody expression due to anti-drug antibodies (ADAs) and anti-capsid immune responses, limiting repeat dosing.

A study by the Wootton lab demonstrated AAV6.2FF-mediated expression of the anti-Marburg virus mAb MR191 in sheep for over 1100 days with low ADA and anti-capsid responses, contradicting findings in NHPs. This discrepancy may be due to differences in AAV serotype, host age, or the sequence divergence from the germline of the expressed mAb. Notably, NHP studies primarily tested highly somatically hypermutated broadly neutralizing HIV mAbs (bNAbs), whereas MR191 was derived from a patient recovering from acute Marburg infection and would therefore have undergone less hypermutation. The higher divergence from the germline in bNAbs may contribute to increased immunogenicity.

This study will assess these factors in a sheep model to better understand their role in ADA development. Determining how mAb sequence divergence, host age, and AAV serotype influence immune responses to VIP will hopefully help decrease the incidence of ADAs and improve the durability of AAV-mediated antibody expression.

Introduction
Nanobodies—compact, single-domain antibody fragments—are seeing increasing use in therapeutics and diagnostics due to their high specificity and stability. However, optimizing multiple properties such as expression yield and binding affinity remains experimentally costly. While machine learning can accelerate candidate selection, its effectiveness depends on the quality and diversity of labeled data. Standard active learning (AL) approaches address this by prioritizing informative samples, but typically ignore the practical constraints critical to nanobody development.

Methods
We present a multi-objective active learning (MOAL) framework tailored to nanobody discovery. This framework integrates predictive models for binding affinity and expression yield with uncertainty estimation from ensemble learning. Candidate selection is guided by three objectives: informativeness (model improvement), feasibility (predicted expression), and performance (binding affinity). To balance trade-offs among these objectives, we apply evolutionary multi-objective optimization algorithms, specifically NSGA-II and IBEA. This enables exploration of diverse, high-potential regions of nanobody sequence space.

Results
We evaluate our framework on a curated dataset of characterized nanobody sequences and a large-scale nanobody repertoire comprising over 10 million candidates. The curated data enable supervised learning, while the repertoire supports broad exploration. Our approach identifies nanobody candidates that are both experimentally viable and model-informative, improving generalization while reducing experimental costs. By avoiding redundant queries and favoring biologically diverse selections, this method supports efficient discovery.

Conclusions
Our domain-aware MOAL approach provides an effective strategy for guiding nanobody selection under multiple constraints. It enables iterative refinement of predictive models while maintaining experimental feasibility. Though it was developed for nanobody engineering, the framework generalizes to other biological domains requiring data-efficient, multi-objective decision-making.

Introduction: Predicting accurate binding poses in antibody–antigen complexes is challenging because some biological molecules might have more than one epitope. This study presents a computational workflow that integrates epitope/paratope prediction and protein–protein docking to predict accurate epitope and paratope residues, as well as antibody–antigen binding poses.

Methods: The computational workflow was applied to three different antibody–antigen systems: TNF-alpha, PD-L1, and IL-1 beta. First, Essential Site Scanning Analysis (ESSA), a fast and effective elastic network model-based method, was used to determine essential residues for binding in the studied antigens and antibodies. ESSA-detected essential residues in antigens were then clustered to determine epitopes as well as central epitope residues and epitope-forming residues. Each epitope and ESSA-detected essential residues in antibodies were used to guide antibody–antigen docking calculations. The LightDock was used to perform docking calculations with/without the antibody mode option, and the top three poses with the highest docking score for each case were used for further analysis. Additionally, for a fair comparison, two blind docking runs were also performed on the studied systems.

Results: Our results showed that ESSA can successfully detect different epitope regions in the antigens, and these residues significantly improve the accuracy of antibody–antigen pose prediction compared to those of blind docking. Interestingly, we observe that the antibodies tend to bind to the incorrect epitope with high docking scores, despite the dockings being performed in antibody mode. On the other hand, without the antibody mode, ESSA-guided dockings alone generate more accurate binding poses with the highest docking scores compared to those of blind dockings.

Conclusions: These findings show that ESSA-detected essential residues improve antibody–antigen docking calculations and accurately pinpoint the correct epitope region for a specific antibody. This integrative workflow offers a powerful tool for computational epitope mapping, a critical step in rational antibody design.

Introduction: Alzheimer’s disease is the most common type of neurodegenerative disorder, and amyloid-β (Aβ) plaques are associated with this disorder. According to the amyloid cascade hypothesis, the accumulation of neurotoxic Aβ42 generally plays a crucial role in the disease's progression. Emerging evidence shows that soluble oligomeric forms, especially tetramers and larger assemblies, are more neurotoxic than mature fibrils. Although several anti-Aβ antibodies target different states of aggregation (from monomers to plaques), nanobody-based therapies do not exist yet. Here, we aim to identify nanobodies targeting the Aβ42 octamer computationally.

Materials and Methods: A total of 40 nanobodies were modeled based on three enzymes known to interact with Aβ42. Using three Aβ42-interacting enzymes as templates, we designed 40 nanobody sequences through Essential Site Scanning Analysis, Peptide Atlas, and AbNatiV, with structural modeling performed via SWISS-MODEL and AlphaFold3. Then, a site-specific docking approach using ClusPro was performed to evaluate nanobody–Aβ42 binding, producing 120 nanobody–Aβ42 octamer complexes. They were ranked based on docking scores, salt bridge formation, stable interface interactions, and solvent-accessible surface area, narrowing the candidates down to seven promising complexes. This pipeline prioritized seven high-affinity candidates, which were further evaluated by conventional molecular dynamics (MD) simulations to assess complex stability.

Results: Among all screened complexes, a single nanobody exhibited persistent binding to the Aβ42 octamer, marked by prolonged interfacial contacts and minimal structural deviation. This candidate emerges as a potential diagnostic or therapeutic lead.

Conclusion: This study establishes an integrative computational framework for the systematic identification and evaluation of nanobodies targeting pathogenic amyloid aggregates, additionally offering a scalable strategy to prioritize high-affinity binders against disease targets.