Big Science
Publishers
Serving Towards Science
Big Science
Publishers
Serving Towards Science
Big Science
Publishers
Serving Towards Science
ISSN: 3139-4191
Big Science Publishers LLP (registered under section 12(1) of the LLP act 2008) is a fully open access non-annual journal, publish high quality science-based articles. Big Science Publishers also guide the young budding researchers, UG/PG students and scholars and other academic professional who wants to learn the manuscript writing.
Big Science Publishers LLP (registered under section 12(1) of the LLP act 2008) is a fully open access non-annual journal, publish high quality science-based articles. Big Science Publishers also guide the young budding researchers, UG/PG students and scholars and other academic professional who wants to learn the manuscript writing.
2
Volumes Published
4
Issues Per Year
3
Indexing Services
50+
Global Authors
With a median overall survival of around 15–20 months after full surgical resection, radiation, and temozolomide chemotherapy, glioblastoma (GBM) continues to be the most aggressive and treatment-resistant primary brain tumor in humans. The effectiveness of traditional and targeted therapy has been hampered by the intrinsic heterogeneity of GBM, its highly infiltrative nature, and the severely immunosuppressive tumor microenvironment. A potential treatment approach that combines direct tumor cell lysis with the induction of anticancer immune responses is oncolytic virotherapy. Herpes simplex virus, adenovirus, poliovirus, vaccinia virus, and parvovirus are among the genetically modified viruses that have shown promising preclinical and early clinical action against glioblastoma. The therapeutic potential of oncolytic viruses has been significantly increased by recent developments in viral engineering, tumor-specific targeting, immune modulation, and combination therapy. This editorial explores the state of oncolytic virotherapy for glioblastoma, emphasizes new developments and persistent difficulties, and offers insights into potential future paths that might revolutionize the treatment of this terrible illness.
The burden of fetal diseases is an ever-increasing problem, particularly in infectious diseases conditions, neurodegenerative disorders and in cases of cancer [1,2]. Even though, the advancement in research has contributed to the drug discovery significantly; the researchers are still not able to find a cure for such fatal diseases. This creates a fundamental paradox in drug discovery domain. The understanding for epidemiology of disease and its molecular mechanism drug-protein interaction has expanded to a large scale; but translating this information into medicine development is still a slow process. Now, the question arises: do we need to begin the drug discovery process from the beginning to find out new treatment?. Drug repurposing also known as drug screening methods has been utilized by the researchers over decades to generate information about existing drug’s pharmacology and safety [3]. But only drug repurposing, utilizing AI tools, does not establish efficacy or safety for new indications. Necessary, pre-clinicals and clinicals are still required [4]. The upcoming artificial intelligence (AI), machine learning and large-scale biomedical database further adds-on to the potential of drug repurposing process against various disease [5]. Instead of relying only on chance-based observations, researchers can now systematically analyze relationships between molecules, targets, pathways and diseases. Thus, the future of new drug development is not only depended on discovering new molecules but also on new therapeutic possibilities within the medicines that already exist.
Burnout among healthcare workers is a serious and under-recognised threat to India’s health system. Indian studies report burnout in a substantial proportion of resident doctors, alongside high rates of depression and suicidal ideation among medical professionals, while the nursing workforce operates well below World Health Organization (WHO) staffing norms. Institutional responses have largely gone the other way, favouring individual-level interventions such as mindfulness applications and resilience workshops. We argue that these measures, whatever their merits, cannot succeed in a system where burnout is produced by structural conditions: chronic understaffing, excessive working hours, and workforce migration. We set out a framework for reform resting on three pillars workforce restructuring, institutional accountability, and policy-level action and argue that in the Indian context, tackling burnout at its roots is as much a patient-safety question as an ethical one.
For many years, the Paxinos and Watson stereotaxic atlas has been essential to accuracy in rodent neurosurgery. However, inter-individual anatomical variability in Wistar rats, which is impacted by age, weight, and strain substructures, commonly leads to electrode or cannula misplacement. This narrative research explores the paradigm shift from atlas-based “blind” surgery to tailored, image-guided planning using clinical-grade MRI and Micro-CT. We look at how 3D-segmentation helps identify target nuclei (such the Subthalamic Nucleus), how this enhances surgical results, and how DICOM data is transformed into stereotaxic coordinates. We show that pre-operative imaging lowers animal attrition and improves the translational validity of neurological models by integrating clinical radiography and preclinical surgery.
Despite maximal safe surgical resection, radiation, and temozolomide-based chemotherapy, glioblastoma (GBM) is still the most aggressive primary malignant brain tumor in adults, with a median survival of about 15–20 months. The immunosuppressive tumor microenvironment, fast treatment resistance, infiltrative growth pattern, and amazing genetic heterogeneity continue to pose challenges to researchers and clinicians throughout the world. Artificial intelligence (AI) has become a game-changing technology in recent years. It has the potential to completely change every aspect of managing glioblastoma, from early diagnosis and molecular classification to treatment planning, drug discovery, prognosis prediction, and individualized therapeutic decision-making. Precision neuro-oncology is becoming more and more possible thanks to deep learning, machine learning, radiomics, genomics, and multi-omics integration, which reveal clinically significant patterns that are frequently missed by traditional analytical techniques. Additionally, AI-driven advancements are speeding up the identification of new therapeutic targets, refining immunotherapeutic approaches, enhancing surgical navigation, and enabling adaptive therapy monitoring. AI is one of the most promising paths for precision medicine in glioblastoma, despite the fact that there are still many obstacles to overcome, such as data standardization, algorithm openness, regulatory approval, and ethical issues. This editorial addresses the latest developments in AI technologies that are changing the way glioblastoma is treated, outlines the successes and drawbacks of the current state of the field, and offers suggestions for future lines of inquiry.
Designed and Developed By Kryoni