Postpartum depression is a psychological condition that deteriorates a mother’s cognitive health and overall family functioning. This survey-based cross-sectional study was done on postpartum mothers between 15-40 years of age to determine the epidemiology of postpartum depression in Pakistan. Edinburg postnatal depression scale (EPDS) and an adapted relationship assessment tool were used to determine the prevalence of postpartum depression. A total of n=103 responses were collected from different regions of Pakistan. Univariate and Bivariate analyses, Pearson Correlation Tests, and Binary Logistic Regression Analyses were applied to investigate the predictors. Postpartum depression was found in 67.96 % of the participating women and EPDS was found to be the most reliable tool to evaluate postpartum depression.

Rare neurodegenerative diseases are a diverse group of disorders characterized by progressive neuronal loss and considerable challenges in diagnosis and treatment. Recent advances in high-throughput omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, have generated large molecular datasets that provide deeper insights into disease-associated mechanisms. However, integrating heterogeneous omics datasets and extracting biologically meaningful patterns remains challenging. Specifically, the modernized machine learning architectures, such as deep neural networks, random forests, and graph-based computational models, are applied to decipher complex non-linear interactions across high-dimensional multi-omics datasets. By synthesizing multi-omics platforms with the layered biological information, AI-driven pipelines enhance the phenotypic profiling,early diagnostic classification, and streamline the discovery of targetable disease networks in rare neurodegenerative diseases. Furthermore, machine learning also supports the computational drug repurposing and candidate screening and accelerate the therapeutic development where the traditional clinical trials are are limited. Despite these technological strides, key bottleneck challenges persist, such as limited training sample sizes, data sparsity, missing multi-omics profiles, batch effects, and the need for complex model interpretation in clinical translations. These can be addressed through robust cross-validation, explainable AI, and international collaborative data training. Machine learning with multi-omics datasets with bioinformatics provides a transformative paradigm towards the precision medication and targeted disease-modifying therapies for patients. This chapter discusses the role of artificial intelligence (AI) and machine learning in supporting bioinformatics research on rare neurodegenerative disorders. It highlights how multiomics integration can aid in identifying disease-associated genes, molecular pathways, and potential biomarkers, while also addressing current challenges and future opportunities for precision medicine.

Background: Mucopolysaccharidoses (MPS) are rare lysosomal storage disorders caused by deficiencies in glycosaminoglycan (GAG)-degrading enzymes, leading to progressive multisystem involvement. Methods: We evaluated two unrelated consanguineous Pakistani families, each with three individuals showing features consistent with MPS. Affected individuals in Family 1 presented with developmental regression, severe cognitive impairment, behavioral abnormalities and facial dysmorphisms. The affected individuals in Family 2 showed classical skeletal dysplasia consistent with Morquio syndrome. Whole-exome sequencing (WES), segregation analysis, and in silico protein modeling were performed to identify and characterize pathogenic gene variants. Results: Analysis of WES data revealed a homozygous missense variant in the SGSH gene [c.548G>A (p.Cys183Tyr)] in the three cases of Family 1 and a homozygous splice-site variant in the GALNS gene (c.423-1G>A) in the cases of Family 2. The SGSH variant, located within the sulfatase catalytic domain and classified as likely pathogenic (ACMG), is consistent with the Sanfilippo A phenotype and represents the first clinical characterization of this allele. Regarding Family 2, we identified the GALNS mutation as a recurrent pathogenic founder allele previously reported in individuals of South Asian descent. Structural modeling of SGSH p.Cys183Tyr predicted disruption of a conserved cysteine residue and altered protein stability, likely supporting its deleterious effect. Conclusions: This study expands the spectrum of MPS-associated variants in Pakistan. The findings underscore the importance of genomic diagnostics for enabling early detection, accurate classification, and genetic counseling in populations with high consanguinity.

Nipah virus (NiV) is a highly lethal zoonotic paramyxovirus with no licensed vaccines or targeted antiviral therapies, posing a serious global health threat. Recurrent outbreaks in South and Southeast Asia highlight the critical need for efficacious and broadly protective vaccine strategies. In this research, an immunoinformatics-based approach was utilized to construct a multi-epitope vaccine (MEV) targeting the highly conserved NiV fusion protein (NCBI ID: AAY43915.1). The protein exhibited high antigenicity, non-allergenic potential, and favorable physicochemical properties. Cytotoxic T-lymphocyte (CTL), Helper T-lymphocyte (HTL), and B-cell epitopes were predicted and rigorously screened for immunogenicity, non-toxicity, and sequence conservancy, resulting in the selection of epitopes with over 90% identity across Bangladeshi and Malaysian NiV strains. Population coverage analysis confirmed the broad applicability of Human Leukocyte Antigen (HLA), particularly in endemic regions. The finalized MEV construct, incorporating appropriate linkers and a 50S ribosomal protein adjuvant, showed structural stability following modelling, refinement, and validation. Molecular docking revealed strong binding affinity with TLR3 and TLR4, Computational immune simulations predicted robust adaptive immune responses, and codon optimization, along with in silico cloning, confirmed favorable expression in E. coli. Although these findings are supported by computational analyses and should be validated experimentally, the proposed MEV demonstrates strong cross-protective and immunogenic potential, offering an encouraging platform for the design of a pan-strain NiV vaccine.