Alzheimer’s disease (AD) is the most frequent cause of neurodegenerative dementia and affects nearly 50 million people worldwide. Early stage diagnosis of AD is challenging, and there is presently no effective treatment for AD. The specific genetic alterations and pathological mechanisms of the development and progression of dementia remain poorly understood. Therefore, identifying essential genes and molecular pathways that are associated with this disease’s pathogenesis will help uncover potential treatments. In an attempt to achieve a more comprehensive understanding of the molecular pathogenesis of AD, we integrated the differentially expressed genes (DEGs) from six microarray datasets of AD patients and controls. We identified ATPase H+ transporting V1 subunit A (ATP6V1A), BCL2 interacting protein 3 (BNIP3), calmodulin-dependent protein kinase IV (CAMK4), TOR signaling pathway regulator-like (TIPRL), and the translocase of outer mitochondrial membrane 70 (TOMM70) as upregulated DEGs common to the five datasets. Our analyses revealed that these genes exhibited brain-specific gene co-expression clustering with OPA1, ITFG1, OXCT1, ATP2A2, MAPK1, CDK14, MAP2K4, YWHAB, PARK2, CMAS, HSPA12A, and RGS17. Taking the mean relative expression levels of this geneset in different brain regions into account, we found that the frontal cortex (BA9) exhibited significantly (p < 0.05) higher expression levels of these DEGs, while the hippocampus exhibited the lowest levels. These DEGs are associated with mitochondrial dysfunction, inflammation processes, and various pathways involved in the pathogenesis of AD. Finally, our blood–brain barrier (BBB) predictions using the support vector machine (SVM) and LiCABEDS algorithm and molecular docking analysis suggested that antrocin is permeable to the BBB and exhibits robust ligand–receptor interactions with high binding affinities to CAMK4, TOMM70, and T1PRL. Our results also revealed good predictions for ADMET properties, drug-likeness, adherence to Lipinskís rules, and no alerts for pan-assay interference compounds (PAINS) Conclusions: These results suggest a new molecular signature for AD parthenogenesis and antrocin as a potential therapeutic agent. Further investigation is warranted.

Transforaminal lumber interbody fusion (TLIF) is the last resort to address the lumber degenerative disorders such as spondylolisthesis, causing lower back pain. The current surgical intervention for these abnormalities includes open TLIF. However, in recent years, minimally invasive TLIF (MIS-TLIF) has gained a high momentum, as it could minimize the risk of infection, blood loss, and post-operative complications pertaining to fusion surgery. Further advancement in visualizing and guiding techniques along with grafting cage and materials are continuously improving the safety and efficacy of MIS-TLIF. These assistive techniques are also playing a crucial role to increase and improve the learning curve of surgeons. However, achieving an appropriate output through TLIF still remains a challenge, which might be synergized through 3D-printing and tissue engineering-based regenerative therapy. Owing to their differentiation potential, biomaterials such as stem/progenitor cells may contribute to restructuring lost or damaged tissues during MIS-TLIF, and this therapeutic efficacy could be further supplemented by platelet-derived biomaterials, leading to improved clinical outcomes. Thus, based on the above-mentioned strategies, we have comprehensively summarized recent developments in MIS-TLIF and its possible combinatorial regenerative therapies for rapid and long-term relief.

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.

Background: Accurate and non-invasive estimation of MGMT promoter methylation status in glioblastoma (GBM) patients is of paramount clinical importance, as it is a predictive biomarker associated with improved overall survival (OS). In response to the clinical need, recent studies have focused on the development of non-invasive artificial intelligence (AI)-based methods for MGMT estimation. In this systematic review, we not only delve into the technical aspects of these AI-driven MGMT estimation methods but also emphasize their profound clinical implications. Specifically, we explore the potential impact of accurate non-invasive MGMT estimation on GBM patient care and treatment decisions. Methods: Employing a PRISMA search strategy, we identified 33 relevant studies from reputable databases, including PubMed, ScienceDirect, Google Scholar, and IEEE Explore. These studies were comprehensively assessed using 21 diverse attributes, encompassing factors such as types of imaging modalities, machine learning (ML) methods, and cohort sizes, with clear rationales for attribute scoring. Subsequently, we ranked these studies and established a cutoff value to categorize them into low-bias and high-bias groups. Results: By analyzing the 'cumulative plot of mean score' and the 'frequency plot curve' of the studies, we determined a cutoff value of 6.00. A higher mean score indicated a lower risk of bias, with studies scoring above the cutoff mark categorized as low-bias (73%), while 27% fell into the high-bias category. Conclusion: Our findings underscore the immense potential of AI-based machine learning (ML) and deep learning (DL) methods in non-invasively determining MGMT promoter methylation status. Importantly, the clinical significance of these AI-driven advancements lies in their capacity to transform GBM patient care by providing accurate and timely information for treatment decisions. However, the translation of these technical advancements into clinical practice presents challenges, including the need for large multi-institutional cohorts and the integration of diverse data types. Addressing these challenges will be critical in realizing the full potential of AI in improving the reliability and accessibility of MGMT estimation while lowering the risk of bias in clinical decision-making.