Cancer is one of the major causes of mortality worldwide, therefore it is considered a major health concern. Breast cancer is the most frequent type of cancer which affects women on a global scale. Various current treatment strategies have been implicated for breast cancer therapy that includes surgical removal, radiation therapy, hormonal therapy, chemotherapy, and targeted biological therapy. However, constant effort is being made to introduce novel therapies with minimal toxicity. Gene therapy is one of the promising tools, to rectify defective genes and cure various cancers. In recent years, a novel genome engineering technology, namely the clustered regularly interspaced short palindromic repeat (CRISPR)-associated protein-9 (Cas9) has emerged as a gene-editing tool and transformed genome-editing techniques in a wide range of biological domains including human cancer research and gene therapy. This could be attributed to its versatile characteristics such as high specificity, precision, time-saving and cost-effective methodologies with minimal risk. In the present review, we highlight the role of CRISPR/Cas9 as a targeted therapy to tackle drug resistance, improve immunotherapy for breast cancer.

Alzheimer’s disease (AD) is a neurodegenerative disorder that is characterized by a progressive loss of cognitive functions at a higher level than normal aging. Although the apolipoprotein (APOE) gene is a major risk factor in developing AD, other genes have also been reported to be linked with complex phenotypes. Therefore, this genome-wide expression study explored differentially expressed genes as possible novel biomarkers involved in AD. The mRNA expression dataset, GSE28146, containing 15 sample data composed of 7 AD cases from the hippocampus region with age-matched control (n = 8, >80 years), was analyzed. Using “affy” R-package, mRNA expression was calculated, while pathway enrichment analysis was performed to determine related biological processes. Of 58 differentially expressed genes, 44 downregulated and 14 upregulated genes were found to be significantly (p < 0.001) altered. The pathway enrichment analysis revealed two altered genes, i.e., dynein light chain 1 (DYNLL1) and kalirin (KLRN), associated with AD in the elderly population. The majority of genes were associated with retrograde endocannabinoid as well as vascular endothelial growth factors affecting the complex phenotypes. The DYNLL1 and KLRN genes may be involved with AD and Huntington’s disease (HD) phenotypes and represent a common genetic basis of these diseases. However, the hallmark of AD is dementia, while the classic motor sign of HD includes chorea. Our data warrant further investigation to identify the role of these genes in disease pathogenesis.

Increasing incidences of insomnia in adults, as well as the aging population, have been reported for their negative impact on the quality of life. Insomnia episodes may be associated with neurocognitive, musculoskeletal, cardiovascular, gastrointestinal, renal, hepatic, and metabolic disorders. Epidemiological evidence also revealed the association of insomnia with oncologic and asthmatic complications, which has been indicated as bidirectional. Two therapeutic approaches including cognitive behavioral therapy (CBT) and drugs-based therapies are being practiced for a long time. However, the adverse events associated with drugs limit their wide and long-term application. Further, Traditional Chinese medicine, acupressure, and pulsed magnetic field therapy may also provide therapeutic relief. Notably, the recently introduced cryotherapy has been demonstrated as a potential candidate for insomnia which could reduce pain, by suppressing oxidative stress and inflammation. It seems that the synergistic therapeutic approach of cryotherapy and the above-mentioned approaches might offer promising prospects to further improve efficacy and safety. Considering these facts, this perspective presents a comprehensive summary of recent advances in pathological aetiologies of insomnia including COVID-19, and its therapeutic management with a greater emphasis on cryotherapy.

Objective : Glioblastoma multiforme (GBM), particularly the isocitrate dehydrogenase (IDH)-wildtype type, represents a significant clinical challenge due to its aggressive nature and poor prognosis. Despite advancements in medical imaging and its modalities, survival rates have not improved significantly, demanding innovative treatment planning and outcome prediction approaches.Methods : This study utilizes a support vector machine (SVM) classifier using radiomics features to predict the overall survival (OS) of GBM, IDH-wildtype patients to short (<12 months) and long (≥12 months) survivors. A dataset comprising multi-parametric magnetic resonance imaging scans from 574 patients was analyzed. Radiomic features were extracted from T1, T2, fluid-attenuated inversion recovery, and T1 with gadolinium (T1GD) sequences. Low variance features were removed, and recursive feature elimination was used to select the most informative features. The SVM model was trained using a k-fold cross-validation approach. Furthermore, clinical parameters such as age, gender, and MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation status were integrated to enhance prediction accuracy.Results : The model showed reasonable results in terms of cross-validated area under the curve of 0.84 (95% confidence interval, 0.80–0.90) with (p<0.001) effectively categorizing patients into short and long survivors. Log-rank test (chi-square statistics) analysis for the developed model was 0.00029 along with the 1.20 Cohen’s d effect size. Most importantly, clinical data integration further refined the survival estimates, providing a more fitted prediction that considers individual patient characteristics by Kaplan-Meier curve with p-value <0.0001.Conclusion : The proposed method significantly enhances the predictive accuracy of OS outcomes in GBM, IDH-wildtype patients. By integrating detailed imaging features with key clinical indicators, this model offers a robust tool for personalized treatment planning, potentially improving OS.

Recent regenerative studies imply conflicting results on knee osteoarthritic (OA) chondrocytes and mesenchymal stem cells (MSC)-mediated cartilage constructs in terms of compressive properties and tensile strength. This could be attributed to different gene expression patterns between MSC and OA chondrocytes during chondrogenic differentiation. Therefore, we analyzed differentially expressed genes (DEGs) between OA and MSC-derived chondrocytes using bioinformatics tools. We downloaded and analyzed the GSE19664 dataset from the Gene Expression Omnibus to identify DEGs. DAVID was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, while a protein-protein interaction network of DEGs was constructed through the Search Tool for the Retrieval of Interacting Genes (STRING) and identified hub genes by CytoHubba. A total of 43 DEGs identified (15 downregulated and 28 upregulated) were found to be deregulated between OA and MSC-derived chondrocytes. KEGG analysis revealed the enrichment of complement and coagulation cascades and other pathways among the studied chondrocytes. The pathway enrichment identified top KEGG, gene ontology biological process, molecular function, and cellular component. The hub networks identified the top 5 hub genes involved in chondrogenesis, including CLU, PLAT, CP, TIMP3, and SERPINA1. Our results identified significant genes involved in chondrogenesis. These findings provide new avenues for exploring the genetic mechanism underlying cartilage synthesis and novel targets for preclinical intervention and clinical treatment.