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May 2026 DOI 10.14302/issn.2572-3030.jcgb-26-6307
The development of tumor biomarkers derived from blood, or its components, has become pivotal in advancing early cancer diagnosis. Malignant transformations induce cancer-specific alterations in the transcriptome, proteome, and secretome of tumor cells. Recent studies highlighted similar alterations in peripheral blood mononuclear cells (PBMCs) in cancer patients, which appear to mirror the state of transformation in tumor cells. These findings suggest an intercellular communication–driven mechanism rather than a systemic inflammatory response and, in addition to current ctDNA-based liquid biopsy biomarkers, point to a novel, simple, and highly robust approach for the early detection of cancer. Using this phenomenon to advance PBMC-based biomarker development, it will be essential to achieve 3D in vitro tumor models that reproduce a highly physiological tumor microenvironment (TME). Likewise, more enhanced 3D ex vivo models are required to enable the replication of cell-to-cell and organ-to-organ communication. These systems will guide the self-organization of mixed microenvironments derived from different tissues and enable them to accurately reproduce the molecular connections underlying these alterations. In this study, an innovative new modular 3D co-culturing approach was used to expose PBMCs to lung tumoroids, under physiologically relevant conditions. Changes in DNA fragmentation of PBMCs in the presence of lung cancer were quantified and used as a biomarker. To validate the predictiveness of this biomarker, our results were compared with clinical data from a clinical evaluation study. Similar to the clinical trial observations, PBMCs, when exposed to lung tumoroids, showed a significantly lower level of DNA fragmentation (37%). This modular 3D co-culturing model showed a predictiveness of the clinical data of > 90%, demonstrating its power to monitoring cell-to-cell communication effects and support the development of blood-based biomarkers.
May 2018 DOI 10.14302/issn.2578-8590.ipj-18-2113
Isatin (1H-indole-2,3-dione ) and derivatives demonstrate a diverse array of biological activities. Isatin and 5-halo derivatives has reacted to form the schiff’s bases , mannich bases and friedal craft alkylation’s to form C-C, C-N, C=N bonds. From the spectral studies, isatin has undergoes reaction at C-3 and N-1 position and synthesized lead in present schme and seen the similarity of structure and analgesic-anti-inflammatory activity.
Sep 2025 DOI 10.14302/issn.2768-0207.jbr-25-5706
Artificial Intelligence (AI) is emerging as a transformative force across many sectors, with healthcare representing both one of the most promising and most challenging areas of application. This review summarizes current and future applications of AI in healthcare, focusing on its potential to improve diagnosis, therapy, chronic disease management, and overall patient care, while also alleviating physicians’ workload. Recent literature demonstrates that AI systems can reduce diagnostic errors/delays by mitigating cognitive biases, support imaging and pathology through improved accuracy and speed, and prevent prescribing errors by integrating pharmacogenomic and clinical data into decision-support systems. In chronic disease management, AI-powered wearable devices enable continuous monitoring and early detection of conditions such as atrial fibrillation, thereby reducing the risk of stroke and long-term disability, particularly in elderly people. Therapeutic applications include AI-driven drug discovery, personalized oncology, and tailored medicine that integrates multi-omics and lifestyle data. Beyond direct medical intervention, AI contributes by automating routine tasks, optimizing workflows, and facilitating greater patient–clinician interaction. Despite these benefits, significant challenges remain, including issues of data quality, privacy, security, equity, and the need for transparency and trust in “black box” systems. Looking ahead, the integration of multimodal data, digital twins, and robotics is expected to advance more comprehensive, equitable, and human-centered care. We conclude that, when applied ethically and responsibly, AI should not replace clinicians but rather serve as a powerful partner that enhances medicine by restoring empathy and humanity.
Jun 2022 DOI 10.14302/issn.2328-0182.japst-22-4193
The strategy for safe drug discovery and development has limited clinical success as compared to wasted time and resources annually. This is due to the fact that the results of multiphase preclinical trials are less likely to make an accurate early prediction on the safety of test compounds to progress into the clinic as a valuable therapeutic agent. A lot of time and resources has been wasted in the multistage processes of drug discovery and development that does not work at the end of the procedure every year. During pre-marketing stage, for instance, the number of unsuccessful clinical trials are greater than the successful one because of safety issues. A toxicity study at different stages of preclinical and clinical trials is a routine procedure to investigate the undesirable side effects of test compounds being manifested on the natural processes of living things. It deals with the effect and mechanism of toxicity of test compounds that triggers different biological responses on different organ systems. The biological responses that would be manifested as a result of interaction between the receptors and active molecules of a test compound could be desirable pharmacological effect or undesirable side effect or both responses are manifested simultaneously depending on the selectivity or specificity of the molecule of a test compound for its receptor subtype which makes safe drug discovery and development very challenging. The response efficiency of the body (the net outcome of the body’s biological reaction against the side effect) would determine the potency of a test compound to manifest undesirable pharmacologic effect. In other words, the amount of a drug required to cause a biological harm or injury depends on the magnitude of the body’s biological reaction in which the immune response plays a great pharmacological role by neutralizing and harmonizing xenobiotics with the biological molecules. The dose of a test compound at 100 mg/kg body weight, for instance, could be lethal to some of the study animals while it is still non-lethal to some other study animals depending on the response efficiency of the body. The immune system is well connected to each and every biological systems of the body which allows it to detect undesirable side effects being manifested through immunoglobulins signalling and activation mechanisms. This complex communication network helps to localize the diverse side effects of a test compound being manifested on different organ systems into the immune system which makes a toxicity study relatively simple to monitor. The cellular immune system becomes active following the molecule-receptor interaction and start producing antibodies which is also known as immunoglobulins to protect bodily harm and destruction. Under normal biological circumstances, the amount of immunoglobulins produced by the cellular immune system following exposure to a test compound is proportional to the number of harmful molecules interacted with its receptor subtype. Thus, with the reference to the changes in the immune response against the administered dose, it would be able to deal with the diverse undesirable side effects of a test compound being manifested on treated study animals using computational systemic biology.
Aug 2018
Drug design, referred to the fields of pharmacology, biotechnology and medicine, is in silico, in vitro and in vivo assay processes of finding new candidate medications based on the biological targets. The in silicoexperiments of drug discovery are involved in the macromolecular structure databases, small molecule databases, molecular docking, de novo drug design and molecular dynamics simulations. The in vitro experiments of drug discovery need evaluate the direct interaction information between ligands and targets as well as the function of ligands on signaling pathway in the cell. The in vivo experiments of drug discovery give the convincing evidence for preclinical trial at the physiological level. In this review, we outline the drug design components of databases, virtual screening tools, biochemical assays, cell-based system and animal models.
Apr 2018 DOI 10.14302/issn.2768-0207.jbr-17-1925
Big data research has become popular and exciting studies in almost all scientific fields such as biology, chemistry, epidemiology, medicine and drug discovery. The various systems and platforms produce large amounts of data every day. It will be very helpful for the researchers and workers to deal with big data if the practical database and useful software are introduced in time. The Journal of Big Data Research (JBR) supplies an efficient and open access publishing platform for big data research. The first issue of JBR aims to foster the dissemination of high-quality big data studies in the biological, medical and chemical database as well as the new algorithm and software for big data processing. The database and computing framework are selected to introduce the development of big data in the biological, medicine and drug discovery. The mature and functional database can be serviced in big data research of scientific fields. It promotes the scientists to extract the useful and essential dataset from the massive data. The grid computing and cloud computing supplies a new paradigm that offers an effective framework of computing and services. The research papers are welcomed from the scopes of the practical database, new algorithm and software for big data studies. All these kinds of papers not only provide the effective application methods and platforms, but also give a good promising future for big data research.