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Autism Spectrum Disorder (ASD) is a genetically complex neurodevelopmental condition that poses major challenges to mechanistic understanding and therapeutic development due to its etiological heterogeneity. It is marked by considerable variability in traits such as cognitive functioning and communication impairments, and frequent co-occurrence of other health or developmental challenges. Genetic changes play a central role in ASD, arising either through inheritance or as spontaneous (de novo) mutations. These alterations fall into two principal groups: common variants and rare variants. The rare variants occur in protein-coding sections and frequently disrupt genes involved in neuron connections and brain growth, leading to stronger effects. Emerging evidence indicates that many ASD-associated variants exert cell type–specific effects on transcription, particularly within neural progenitor cells (NPCs), gamma-aminobutyric acid-GABAergic and glutamatergic neurons, and glial populations such as astrocytes and microglia.In this thesis, we present an integrated genomic–transcriptomic framework to identify patterns for clustering and prediction of autism. We leveraged large-scale ASD cohort to aggregate rare protein-disrupting variants and coupled these data with single-cell expression data. We first applied dimensionality reduction (PCA) and unsupervised clustering (k-means) to the combined variant-expression matrix. These analyses revealed no clear separation between ASD and nonASD samples, highlighting the highly polygenic and heterogeneous nature of autism. Next, we trained and evaluated three supervised machine-learning classifiers support vector machine (SVM), gradient boosting machine (GBM), and XGBoost using demographic variables, aggregate variant-impact scores, cell-type burden, and gene-level burden metrics to predict ASD status. In our balanced training set, equal representation of ASD and non-ASD, all three classifiers demonstrated moderate predictive performance-the SVM achieved the highest AUC (0.648), the gradient boosting machine attained the best precision (0.713) and F1-score (0.711), and both GBM and SVM showed superior recall (0.709) compared to XGBoost. The feature-importance analysis of XGBoost revealed that aggregate variant-impact scores together with neuronal gene burdens contributed over 40% of total split-gain, whereas the single scRNA-seq-derived cell-type Oligodendrocyte precursor cells (OPC) feature had minimal influence. Altogether, the absence of distinct sample clustering in the dimensionality-reduced visualizations highlights the complex and heterogeneous genetic architecture of ASD. These findings underscore the importance of incorporating additional biological context, increasing sample sizes, and integrating richer multiomic data to develop more robust and generalizable predictive models for ASD.

Lung cancer is the most devastating cause of death among all cancers worldwide, and non-small cell lung cancer (NSCLC) accounts for 80% of all the lung cancer cases. Beyond common genetic research and epigenomic studies, the extraordinary investigations of non-coding RNAs have provided insights into the molecular basis of cancer. Existing evidence from various cancer models highlights that the regulation of non-coding RNAs is crucial and that their deregulation may be a common reason for the development and progression of cancer, and competition of cancer therapeutics. Non-coding RNAs, such as long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), are increasingly recognized as potential cancer biomarkers for early detection and application of therapeutic strategies. The miRNAs have gained importance as master regulators of target mRNAs by negatively regulating their expression. The lncRNAs function as both tumor suppressors and oncogenes, and also compete with miRNAs that influence the translational inhibition processes. This review addresses the role of lncRNAs in lung cancer development, highlights their mechanisms of action, and provides an overview of the impact of lncRNAs on lung cancer survival and progression via miRNA sponging. The improved understanding of lung cancer mechanisms has opened opportunities to analyze molecular markers and their potential therapeutics.

Monkeypox, a viral zoonotic disease resembling smallpox, has emerged as a significant national epidemic primarily in Africa. Nevertheless, the recent global dissemination of this pathogen has engendered apprehension regarding its capacity to metamorphose into a sweeping pandemic. To effectively combat this menace, a multi-epitope vaccine has been meticulously engineered with the specific aim of targeting the cell envelope protein of Monkeypox virus (MPXV), thereby stimulating a potent immunological response while mitigating untoward effects. This new vaccine uses T-cell and B-cell epitopes from a highly antigenic, non-allergenic, non-toxic, conserved, and non-homologous A30L protein to provide protection against the virus. In order to ascertain the vaccine design with the utmost efficacy, protein–protein docking methodologies were employed to anticipate the intricate interactions with Toll-like receptors (TLR) 2, 3, 4, 6, and 8. This meticulous approach led the researchers to discern an optimal vaccine architecture, bolstered by affirmative prognostications derived from both molecular dynamics (MD) simulations and immune simulations. The current research findings indicate that the peptides ATHAAFEYSK, FFIVVATAAV, and MNSLSIFFV exhibited antigenic properties and were determined to be non-allergenic and non-toxic. Through the utilization of codon optimization and in-silico cloning techniques, our investigation revealed that the prospective vaccine exhibited a remarkable expression level within Escherichia coli. Moreover, upon conducting immune simulations, we observed the induction of a robust immune response characterized by elevated levels of both B-cell and T-cell mediated immunity. Moreover, as the initial prediction with in-silico techniques has yielded promising results these epitope-based vaccines can be recommended to in vitro and in silico studies to validate their immunogenic properties.

Background: Transforaminal lumbar interbody fusion (TLIF) has emerged as one of the common procedures performed by spine surgeons. Back pain and radiculopathy due to degenerative disc disease, spondylolisthesis, or deformity are the usual indications. Minimally invasive surgery (MIS) techniques have proven to be effective in TLIF as they are associated with less blood loss, fewer wound complications and infections, faster recovery, and decreased hospital costs. The novel technique described in this study helps to achieve a circumferential lumbar fusion using a unilateral posterior approach, via a muscle-dilating exposure, thereby minimizing the approach-related morbidity. Objectives: An overview of the minimally invasive TLIF (MIS-TLIF) procedures including indications, techniques, and clinical experience along with a review of the medical literature is hereby presented. Methods: All patients who underwent MIS-TLIF for various indications at our institution from 2009 to 2014 were retrospectively reviewed. All patients in this series had low back pain as their predominant symptom, with varying degrees of radicular pain and neurologic symptoms. The data collected retrospectively for analysis were age, gender, previous diagnoses, revision diagnosis, duration of symptoms, levels of fusion, operating time, intraoperative blood loss, clinical and radiographic results after surgery, and complications. Back and leg pain quantified by visual analog scale scores preoperatively, postoperatively, and at the last follow-up were assessed for clinical outcomes. Conclusions: Our clinical experience along with a review of the medical literature indicates that TLIF can be effectively and safely performed in a minimally invasive fashion for a wide variety of indications.

The efficient and economical operation of a distribution power network (DPN) has been essential in recent times, considering the energy crisis and shortage of fossil fuels. A DPN is known to be efficient and economical if power losses are minimal, the voltage drop along the lines is less, and stability is maintained during different operating conditions. However, due to the crisis for primary fuel, all DPNs including radial power distribution networks (RPDN) are operated at threshold level. This has led to higher power losses, more voltage drops, and stability issues in RDPN. Hence, to reduce the power losses and voltage deviation and improve the stability of the power system network, distributed generation (DG) units are optimally allocated into radial DPN. In this study, an optimization technique using Jellyfish search optimizer (JSO) algorithm is proposed to optimize multiple DGs into RDPN to minimize a multi-objective function corresponds to real power loss (RPL) minimization, voltage stability (VS) enhancement, and total operating cost (TOC) minimization. The performance of the proposed technique is evaluated for multiple type I and type III DGs placement on an IEEE standard 33-bus RDPN. Besides, the effectiveness of the proposed technique is investigated considering a nominal and peak power demand. The efficacy of the research outcome of the suggested JSO approach has been compared with the outcome of other optimization algorithms presented in the literature. The comparison exemplifies that JSO gives more promising outcomes than other algorithms by delivering the least real power losses and better voltage profile enhancement at minimum operating cost.

The efficient and economical operation of a distribution power network (DPN) has been essential in recent times, considering the energy crisis and shortage of fossil fuels. A DPN is known to be efficient and economical if power losses are minimal, the voltage drop along the lines is less, and stability is maintained during different operating conditions. However, due to the crisis for primary fuel, all DPNs including radial power distribution networks (RPDN) are operated at threshold level. This has led to higher power losses, more voltage drops, and stability issues in RDPN. Hence, to reduce the power losses and voltage deviation and improve the stability of the power system network, distributed generation (DG) units are optimally allocated into radial DPN. In this study, an optimization technique using Jellyfish search optimizer (JSO) algorithm is proposed to optimize multiple DGs into RDPN to minimize a multi-objective function corresponds to real power loss (RPL) minimization, voltage stability (VS) enhancement, and total operating cost (TOC) minimization. The performance of the proposed technique is evaluated for multiple type I and type III DGs placement on an IEEE standard 33-bus RDPN. Besides, the effectiveness of the proposed technique is investigated considering a nominal and peak power demand. The efficacy of the research outcome of the suggested JSO approach has been compared with the outcome of other optimization algorithms presented in the literature. The comparison exemplifies that JSO gives more promising outcomes than other algorithms by delivering the least real power losses and better voltage profile enhancement at minimum operating cost.