Human breast cancer (HBC) is a complex disease with several molecular subtypes, posing challenges in diagnosis and treatment. Among these subtypes, triple-negative breast cancer (TNBC) is particularly challenging due to the lack of targeted therapies and generally has a poorer prognosis than other molecular subtypes. Canine mammary carcinomas (CMCs) have been proposed as a spontaneous model of HBC and a suitable model for molecular subtypes. Notably, dogs exhibited a high prevalence of triple-negative subtypes compared to humans. This review explores the parallels between HBC and CMCs, with a special emphasis on triple-negative phenotype, through the lens of cancer hallmarks. Several similarities have been found between both species; however, challenges remain in understanding the full spectrum of cancer hallmarks in dogs and translating findings into effective therapies. The convergence of insights from the hallmarks of cancer and the unique attributes of CMCs model drives us toward future personalized medicine, offering new avenues for research in the field of comparative oncology.

Accurate identification of metastatic tumors is crucial for predicting cancer progression, designing effective treatment strategies, and enabling personalized medicine. However, current approaches for integrating heterogeneous multi-omics data and modeling gene-gene interactions often face challenges, limiting their ability to distinguish between primary and metastatic tumors. To overcome these limitations, we propose SEMO-GCN (Semantic Enhanced Multi-Omics Graph Representation Learning), a novel framework that combines Large Language Model (LLM)-derived gene embeddings with Graph Convolutional Networks (GCNs) for pan-cancer metastasis detection. SEMO-GCN integrates four types of omics data: mRNA expression, DNA methylation, somatic mutations, and copy number alterations (CNA). It leverages semantic gene representations from LLMs alongside the topology of a protein-protein interaction (PPI) network. The GCN architecture captures functional gene relationships using the PPI network, while LLM embeddings provide rich biological context derived from extensive biomedical literature. We applied SEMO-GCN to a cohort of 752 tumor samples, evenly split between primary and metastatic tumors, encompassing 12,174 genes. Ablation studies confirmed the critical contributions of both LLM-derived semantic embeddings and PPI network topology, as their removal led to decreased predictive performance. SEMO-GCN demonstrates robust capabilities in tumor classification, early metastasis detection, and personalized therapeutic guidance, representing a powerful tool for precision oncology. Keywords: Multi-omics integration, Graph Convolutional Network, Pan-cancer metastasis prediction, Biomedical language models, Semantic gene embedding.

Personalized Medicine (PM) also called as precision medicine, Personalized medicine has transformed cancer diagnosis and treatment by tailoring therapeutic strategies to individual genetic, molecular and phenotypic characteristics. Personalized Medicine revolutioned oncology management in high human development, Oncologist have been able to target on individual’s cancer, Pharmacogenomics, the study of how genetic variations influence drug response, plays a central role in optimizing anticancer therapy by improving efficacy, reducing toxicity, and minimizing trial-and-error prescribing. Advances in genomic technologies and bioinformatics have enabled precise tumor profiling, identification of actionable mutations, and development of targeted therapies. This review discusses the principles of pharmacogenomics in oncology, its role in cancer diagnosis, treatment selection, dose optimization, and future prospects in personalized cancer care.

From Balanced Blood Products to Personalized Medicine in Acute Resuscitation of Trauma Patients

Pharmacodynamic (PD) biomarkers provide crucial insights into a drug's mechanism of action (MoA) and efficacy by measuring its effects on biological targets within an organism. PD biomarkers can be proximal (e.g. receptor occupancy, enzyme inhibition) or distal (e.g. downstream pathway modulation) to the biological target. In drug development, PD biomarkers are essential for monitoring patient response, assessing therapeutic efficacy, optimizing dosage strategies, and streamlining the drug development process by informing go/no-go decisions. In personalized medicine, PD biomarkers enable tailored treatments based on individual responses, enhancing both effectiveness and safety. Sound bioanalytical strategies and rigorous assay validation practices are key for successful integration of PD biomarkers into clinical trials. This paper outlines the bioanalytical and assay considerations for developing and validating informative PD biomarker assays and their use in drug development.

Artificial Intelligence (AI) is rapidly transforming drug discovery and development by improving efficiency, accuracy, and speed. Traditional drug development is a lengthy and costly process that may take 10-15 years and require billions of dollars to bring a single drug to market. AI helps overcome these limitations by analysing large biological datasets, predicting outcomes, and supporting decision- making throughout different stages of drug development. Machine learning and generative AI models assist in target identification, de novo drug design, virtual screening, lead optimisation, and prediction of ADEMT properties, thereby reducing late- stage failures and improving overall success rates. AI also plays an important role in clinical trial optimisation through better patient selection, real-time monitoring, and outcome prediction. Growing investments and high adoption rates in the pharmaceutical industry highlight the expanding AI market and its economic impact. Despite its advantages, challenges such as data quality issues, high implementation costs, lack of interpretability, ethical concerns, and regulatory complexities remain significant. In India, AI-based drug discovery is guided by CDSCO regulations, ICMR ethical guidelines, NDCTR 2019, and the Digital Personal Data Protection Act, 2023 to ensure safety and compliance. Overall, AI has strong potential to revolutionize pharmaceutical research by accelerating innovation, reducing costs, and enabling personalized medicine. Keywords: Artificial Intelligence (AI), Drug Discovery, Molecular Design, validation.

Polydopamine-based multifunctional biomaterials: Strategies for controlling redox and photothermal properties for biomedical applications.

Background. NOTCH receptors play a pivotal role in carcinogenesis. Upon ligand binding, a cascade of proteolytic cleavages mediated by ADAM proteases and the γ-secretase complex activates the receptor, ultimately releasing the NOTCH intracellular domain (NICD). NICD translocates to the nucleus, where it regulates gene expression. This review mainly aims to evaluate γ-secretase inhibitors (GSIs) as anticancer agents in preclinical and clinical settings, with a focus on their ability to block tumor progression, target cancer stem cells, and overcome resistance to standard therapies. Methods. A systematic search was conducted in the ISI Web of Science, PubMed, and Scopus databases, following PRISMA guidelines. The review included preclinical in vitro and in vivo studies, as well as clinical trials, investigating GSIs, either as monotherapy or in combination with other treatments, in TNBC, metastatic melanoma, PDAC, gastric cancer, and NSCLC. Exclusion criteria included duplicates, non-English articles, studies published before 2010, studies on non-cancer conditions, research unrelated to NOTCH signaling, and studies outside the selected cancer types. Overall, 69 articles were included and categorized into the five types of cancer analyzed (20 on NSCLC, 22 on TNBC, 11 on metastatic melanoma, 7 on GC, and 9 on PDAC). Of these, 60 studies corresponded to preclinical research in the types of cancer, and 9 studies corresponded to clinical trials in the types of cancer except for GC. Two independent authors screened and extracted relevant data, with disagreements resolved by the corresponding author. Findings were synthesized qualitatively across cancer types under study. Results. This review summarizes therapeutic advances involving GSIs in cancers driven by oncogenic NOTCH signaling, based on the 69 articles included. Preclinical studies show that GSIs synergize with chemotherapy and radiotherapy, particularly in NSCLC, melanoma, and TNBC, and block EMT, overcome therapeutic resistance, and improve prognosis. Commonly used GSIs include DAPT and RO4929097, which enhance the efficacy of agents, such as gemcitabine (PDAC), paclitaxel, osimertinib, erlotinib, and crizotinib (NSCLC), and 5-FU (gastric cancer, TNBC). Promising strategies include combining GSIs with SAHA, ATRA, CB-103, and other NOTCH signaling targeting molecules, either alone or with chemo- and radiotherapy. Clinical trials with GSIs, however, remain limited. RO4929097 is the most extensively tested GSI in clinical settings. PDAC trials combining GSIs with gemcitabine showed no benefit; melanoma trials yielded modest outcomes; and TNBC trials demonstrated partial responses to GSIs but overall low efficacy and significant adverse events. Discussion and Conclusions. Despite encouraging preclinical evidence, clinical trials with GSIs have underperformed, largely due to tumor heterogeneity, dosing limitations, and the non-selective nature of γ-secretase inhibition. Other NOTCH inhibitors, such as DLL4 antibodies, also resulted in partial responses and secondary effects. Future strategies should prioritize receptor-specific NOTCH inhibitors, patient stratification based on NOTCH pathway activation, and optimized combination regimens. Emerging approaches include integrating immunotherapy with advanced technologies such as CRISPR, CAR-T cells, and bispecific antibodies, as well as targeted delivery systems to enhance efficacy and reduce toxicity. Additional research directions include addressing the tumor microenvironment and EMT-driven resistance, elucidating the mechanisms of immune evasion, and inhibiting tumor angiogenesis. Finally, leveraging artificial intelligence and big-data-driven personalized medicine, including sex-specific considerations, will be essential for improving patient outcomes.

School-based caries prevention programs are clinically and cost-effective public health approaches to increase access to essential oral healthcare for high-risk children. However, approximately 1 in 4 children participating in school caries prevention fail to respond to care, remaining at risk for dental caries and related sequela. The Building Adaptive School-based Interventions for Caries study (BASICS) will develop and test adaptive preventive interventions using a Sequential, Multiple Assignment, Randomized Trial (SMART) design, reducing treatment nonresponse by incorporating personalized medicine into school caries prevention. Children will receive a first-stage treatment of either silver diamine fluoride or glass ionomer dental sealants and atraumatic restorations. At subsequent observations, the primary outcome of reoccurrence or new presentation of dental caries will be used as a tailoring variable for treatment nonresponse. Nonresponsive participants in either first-stage pathway will subsequently receive either (1) reapplication of initial treatment plus fluoride varnish and receipt of an electronic toothbrush or (2) an intensified Silver Modified Atraumatic Restorative Technique. The targeted enrollment is 1200 children from primarily low-income rural families enrolled in kindergarten through third grades in public primary schools. Primary study objectives of BASICS include determining the most effective initial treatment for caries prevention and sequence of treatments to reduce nonresponse, identifying the optimal dynamic treatment regime given patient attributes, and estimating the most cost-efficient allocation of resources for adaptive school-based caries prevention. If successful, BASICS will result in a resource-efficient approach to school dental care that optimizes resources matched to patient needs. ClinicalTrials.gov #NCT07265830, Registered on 12/4/25. https://www. gov/study/NCT07265830.

Epilepsy remains one of the most prevalent neurological disorders, characterised by complex aetiology encompassing genetic, structural, metabolic, and inflammatory factors. Despite advances in neuroimaging and neurophysiological diagnostics, there is a persistent lack of sensitive and specific biomarkers to enable early diagnosis, risk stratification, and monitoring of therapeutic efficacy. Key epilepsy biomarkers include neurotransmitters, energy-related compounds, tryptophan pathway metabolites, and choline derivatives. Their determination employs liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS), high-performance liquid chromatography (HPLC) with electrochemical or fluorescence detection, gas chromatography with tandem mass spectrometry (GC-MS/MS), high-resolution mass spectrometry (HRMS), and proton nuclear magnetic resonance (1H-NMR) spectroscopy, revealing metabolic disturbances in neurotransmission, energy metabolism, and oxidative stress associated with epileptogenesis. Among these techniques, LC-MS/MS currently provides the highest analytical sensitivity and specificity for quantifying low-abundance epilepsy-related metabolites, while HPLC with conventional detection remains a simpler and more cost-effective alternative for routine clinical laboratories. This review presents the current state of knowledge regarding chromatographic techniques applied to the analysis of mentioned metabolites, as well as therapeutic drug monitoring of antiepileptic drugs. Key sample preparation stages are also discussed. Various biological matrices-plasma, serum, urine, cerebrospinal fluid (CSF), dried blood spots (DBSs), and brain tissue-are evaluated. Novel approaches are also presented, including hair samples, microsampling techniques, and headspace analysis of volatile metabolites. Chromatographic techniques constitute the foundation of contemporary metabolomic research in epileptology, enabling biomarker identification and supporting personalised medicine. Further standardisation and translational validation remain necessary, as current evidence is insufficient for routine clinical implementation.

The multiverse of zebrafish within the nanoworld.

Neurodegenerative disorders such as frontotemporal dementia (FTD) have strong hereditary links, yet these genes do not have full penetrance and environmental influences determine the lifetime risk of disease development. Better understanding of the environmental risk factors that determine age of onset, progression, and severity is needed. How these risk factors interact with genetic predisposition for these disorders will allow clinicians to provide better lifestyle recommendations for people with a familial history and deliver more personalized medicine. Here we examine the dose-dependent effects of the gene encoding progranulin (Grn), one of the most common mutations associated with familial FTD. We utilize both homozygous loss and heterozygous knockdown of Grn with the objective of assessing how a western diet consisting of high-fat and high-carbohydrate intake modulates the inflammatory and metabolic hallmarks in middle-aged mice. We found that while full Grn loss leads to heighted antigen presentation machinery and immune cell infiltration in the brain after obesogenic diet, a heterozygous gene primarily affects the periphery. Yet, further examination by RNA sequencing reveals that heterozygous mice have a disruption of MAPK signaling in the brain highlighting early disruption in the neuronal landscape. Our findings are consistent with reports that in individuals with genetic predisposition for FTD due to a GRN mutation, a western-style diet exacerbates the cellular stress in the peripheral immune system and affects the function of the prefrontal cortex. These data further support the use of heterozygous Grn knockout mice as a model for prodromal FTD in addition to the more common Grn full knockout which may not as accurately reflect disease onset biology.

Establishment of reference intervals for serum TNF-α, IFN-α, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12p70, IL-17 and IFN-γ in healthy Chinese adults using flow cytometry.

Premature ovarian insufficiency (POI) is a clinically heterogeneous disorder characterized by ovarian dysfunction occurring before the age of 40, leading to infertility and increased long-term health risks. Current management strategies alleviate symptoms but fail to restore ovarian function or fertility, thereby prompting an urgent demand for regenerative therapies for POI. Mesenchymal stromal cells (MSCs) have emerged as a promising regenerative strategy due to their multifunctional capabilities, including paracrine effects, immunomodulation, and cytoprotection effects. This review systematically synthesizes preclinical and clinical evidence supporting the efficacy of MSCs in mitigating ovarian damage, enhancing folliculogenesis, restoring hormonal balance, and promoting the restoration of fertility in both POI models and patients. In addition, this article examines ongoing challenges, such as cellular heterogeneity, the determination of optimal transplantation strategies, and ensuring long-term safety. It also highlights innovative approaches, including biomaterial-enhanced transplantation, genetic engineering, and exosome-based therapies. The integration of these advanced strategies within personalized medicine frameworks holds significant potential to transform the clinical management of POI.

Reinforcement learning (RL) has achieved significant success in complex, sequential decision-making tasks. However, it remains constrained by its dependence on predefined reward functions, limiting adaptability in dynamic environments. Inverse Reinforcement Learning (IRL) addresses this limitation by inferring reward structures from expert demonstrations, enabling more flexible and context-aware agents. The study explores the potential of IRL’s in enhancing the efficiency and adaptability of modern autonomous systems. The pivotal role of IRL in modelling human-like reasoning and imagination is examined across domains, including robotics, autonomous driving, personalised medicine, and cybersecurity, alongside discussion on current solutions, challenges, and emerging research directions. The findings underscore future improvements for human cognitive capabilities and machine autonomy.

Abstract - Orphan drugs are medicinal products specifically developed for the diagnosis, prevention, or treatment of rare diseases, which collectively affect 300 to 400 million individuals worldwide. Rare diseases are defined by low prevalence, such as less than 200,000 patients in the US or less than 5 out of 10,000 people in the EU. Due to their small patient populations and the high cost of intensive research and development (R&D), these drugs are often commercially unattractive to the pharmaceutical industry. Regulatory Frameworks and Incentives To overcome commercial barriers and address the significant unmet medical need, global regulatory bodies have established incentive- driven frameworks. Key examples include: United States: The Orphan Drug Act (ODA) of1983 provides incentives such as seven years of market exclusivity after approval, tax credits for clinical testing, and fee waivers. European Union: The EU Orphan Regulation (2000) offers ten years of market exclusivity, protocol assistance, and fee waivers. India: The New Drugs & Clinical Trial Regulations (2019) and the 2021 National Policy for Rare Diseases focus on expedited approval (within 90 days), possible local clinical trial waivers, and temporary price control exemptions. Advances in Drug Discovery Recent technological and scientific advancement are transforming the field Genomics and Personalized Medicine: Many rare diseases have a genetic origin (~80%). Advances in genomics enable the development of personalized treatments that provide "the right patient with the right drug at the right dose at the right time" Artificial Intelligence (AI): AI, Machine Learning (ML), and Deep Learning (DL) are vital tools for accelerating diagnosis, target identification, drug repurposing, and optimizing clinical trials. Challenges and Future Directions Despite the successes, several significant challenges persist: High Costs and Affordability: Orphan drugs often carry high prices, sometimes exceeding hundreds of thousands of dollars annually, straining health care systems Global Inequity: Access to approved orphan treatments remains limited in many low- and middle-income countries (LMICs) due to a lack of structured reimbursement and market incentives. Harmonization: Global harmonization is hindered by variations in rare disease definitions (e.g., US: <200,000; EU: <5/10,000; India: <500,000) and differing incentive packages. Key Words: orphan drug, Genomics, Rare Disease, Hormonization,

Abstract Homeopathy, built on the profound power of individualization, stands at a critical crossroads. Its personalized nature—its greatest strength—is also the very hurdle preventing scalability and full integration into evidence-based public health policy. In a global health landscape demanding multidisciplinary approaches and major knowledge shifts, we must evolve. Our answer is a ground breaking Artificial Intelligence (AI) framework designed to usher in nothing less than precision homeopathy, fundamentally revolutionizing protocols and measurably improving patient outcomes worldwide. This is a complete digital transformation of practice. Our core achievement is a robust, predictive model trained on a vast, anonymized, longitudinal patient dataset. This system is not reliant on simple statistics; it masters a rich tapestry of clinical symptoms, complex medical histories, and key demographics. Using advanced machine learning, the AI isolates subtle, non-linear patterns of treatment success that are impossible for human analysis to detect. This powerful system acts as a true clinical co-pilot, empowering practitioners to confidently and precisely select the optimal remedy and potency tailored to the unique profile of every single patient. The implications are immediate and profound, speaking directly to systemic global change. For Policy and research, this validated, evidence-based system provides the quantitative foundation needed to craft smarter, data-driven treatment guidelines and strengthen regulatory accountability. Furthermore, this tool serves as an essential educational and training hub for the next generation of practitioners, becoming a central driver of innovation. Ultimately, this framework provides a scalable blueprint for sustainable futures in personalized medicine, delivering crucial public health metrics for global governance and resource optimization. We assert that this work is not just a research contribution; it is a mandate for the digital transformation of global homeopathic healthcare through bold, multidisciplinary engagement. Keywords: Artificial Intelligence, Precision Homeopathy, Personalized Medicine, Digital Transformation Evidence-Based Policy, Machine Learning.

Cardiovascular diseases (CVDs) remain a leading cause of mortality worldwide. We explore the application of statistical shape modeling (SSM) as a powerful tool in cardiac anatomy assessment, facilitating innovative approaches to diagnosis and treatment. SSM uses advanced mathematical and statistical techniques to understand the geometric properties of anatomical structures across populations. By identifying significant shape parameters, it captures and quantifies subtle variations that may elude traditional approaches. We discuss its evolution, from landmark-based methods to point distribution models for establishing the point-to-point correspondence crucial for accurate shape analysis. We delve into the statistical techniques used to measure shape variability, with a focus on principal component analysis for dimensionality reduction. Key evaluation metrics in the assessment of model performance, such as compactness, generalization and specificity, are reviewed. The clinical utility of SSM across the spectrum of CVDs is examined, covering diagnosis, risk stratification, treatment optimization, follow-up and research applications. Future directions, including the development of multi-label models, integration of deep learning approaches, and spatio-temporal SSM to capture dynamic changes in cardiac geometry, are considered. Through this narrative review, we aim to underscore SSM's promise as a powerful tool in combating CVDs and advancing personalized medicine, ultimately improving patient outcomes.

The management of type 2 diabetes mellitus (T2DM) remains a complex clinical challenge, particularly for patients requiring multiple daily insulin injections (MDI). Advances in precision medicine and continuous glucose monitoring (CGM) have created opportunities to personalize treatment and potentially reduce the therapeutic burden on people with T2DM. Assessing β cell function and autoimmunity could help identify patients with T2DM eligible for simplified regimens without compromising glycemic control. The aim of this study is to test a simple personalized medicine protocol in routine clinical practice for people with T2DM treated with MDI. The intervention is based on evaluating C-peptide and glutamic acid decarboxylase autoantibody status with the goal of improving diagnostic accuracy and optimizing treatment. This is a pragmatic before-and-after intervention study involving people with T2DM currently receiving MDI across primary care centers and a referral hospital in the Lleida health care region in Catalonia (Spain). Eligible participants will undergo clinical and laboratory assessment, including C-peptide and glutamic acid decarboxylase autoantibody testing, and wear a CGM device. On the basis of a predefined algorithm, patients may either continue or discontinue prandial insulin. The primary outcome is the proportion of patients in whom prandial insulin is discontinued and remains discontinued over 6 months. Secondary outcomes include changes in hemoglobin A1c, CGM metric variables, quality of life, adherence, and treatment satisfaction. Recruitment was completed on March 31, 2025. The follow-up phase is ongoing and expected to conclude by September 30, 2025. Data analysis will begin thereafter. This study will evaluate the feasibility and impact of implementing a personalized therapeutic approach for persons with T2DM receiving MDI in real-world clinical settings. If effective, this strategy could contribute to safer, simpler, and more individualized diabetes care.

Despite advances in the treatment of cutaneous melanoma, there is still a high percentage of patients who fail to respond or develop resistance to treatment. Establishing robust in vitro melanoma models will enable mechanism-based drug screening while reducing animal testing. In this work, a three-dimensional (3D) melanoma skin model (3DMSM) was developed on a porous scaffold. The culture of three melanoma cell lines (SKMEL-1, A375, and G361) in co-culture with human fibroblasts, melanocytes, and keratinocytes allowed the formation of the dermis, and stratified epidermis. Tumors were established in this model using two methodologies: adding previously formed melanoma cell aggregates (CA) or seeding melanoma cells directly into the dermis (CD). In this model, melanoma cells remain in their original microenvironment and, after proliferation, invade the basal layer. The model recapitulates correct melanocyte localization, epidermal disruption, extracellular matrix (ECM) remodeling, including collagen deposition, and epithelial-to-mesenchymal transition (EMT). Additionally, the cytokine profiles studied indicate that the model could mirror the inflammatory and immune-evasive traits of melanoma. Overall, 3DMSM provides a useful tool for understanding the mechanisms of melanoma progression and invasion, and for developing personalized medicine strategies through the implementation of a patient-derived model.