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Diagnosing and then treating disease defines theranostics. The approach holds promise by facilitating targeted disease outcomes. The simultaneous analysis of finding the presence of disease pathophysiology while providing a parallel in treatment is a novel and effective strategy for seeking improved medical care. We discuss how theranostics improves disease outcomes is discussed. The chapter reviews the delivery of targeted therapies. Bioimaging techniques are highlighted as early detection and tracking systems for microbial infections, degenerative diseases, and cancers.

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ABSTRACT: Proton-pump inhibitors (PPIs) are widely prescribed to decrease stomach acid and treat various acid-related Gastrointestinal tract (GIT) diseases. However, genetic variations, particularly in the CYP2C19 gene, affect PPIs metabolism and efficacy. Variants in CYP2C19 can result in different rates of PPI metabolism, influencing their effectiveness. Personalized medicine strategies, such as genotyping for CYP2C19, have the potential to enhance the effectiveness of PPI therapy and patient safety. This review aims to describe the relevance of CYP2C19 genetic profiling in the indian population, including normal function (e.g. CYP2C19*1, *11, *13, *15, *18, *28, and 38), decreased function (e.g., CYP2C19*9, *10, *16, *19, *25, and 26), loss of function (e.g., CYP2C19*2, *3, *4, *5, *6, *7, *8, *22, *24, *35, *36, and *37), and increased function (e.g., CYP2C19*17) variants. This review also examines the clinical pharmacogenomics implementation consortium (CPIC)-CYP2C19-PPI guidelines to highlight the importance of pharmacogenomics (PGx)-informed personalized PPI therapy for gastroesophageal reflux disease and peptic ulcer disease treatment. On average, each person in India possesses eight pharmacogenetic (PGx) variants that can be clinically significant, underscoring the need for preemptive testing. Implementing CYP2C19 genetic testing in India requires expanding laboratory capacity, increasing accessibility in primary care, increasing public awareness, collaboration between pharmacovigilance and PGx programs, investing in advanced sequencing technologies, data management systems, and integration with electronic health records and clinical decision support systems. Addressing challenges such as genetic diversity, socioeconomic factors, health-care access issues, and shortage of trained professionals is essential for implementation. Due to the lack of definitive country-specific policies and PGx guidelines from Indian drug regulatory agencies, guidelines from international consortia such as the Clinical Pharmacogenetics Implementation Consortium and drug labeling offer crucial foundational evidence. This evidence can be used to enhance patient outcomes and ensure the safe and effective use of PPIs in India.

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Numerous studies are focused on nanoparticle penetration into the brain functionalizing them with ligands useful to cross the blood-brain barrier. However, cell targeting is also crucial, given that cerebral pathologies frequently affect specific brain cells or areas. Functionalize nanoparticles with the most appropriate targeting elements, tailor their physical parameters, and consider the brain's complex anatomy are essential aspects for precise therapy and diagnosis. In this review, we addressed the state of the art on targeted nanoparticles for drug delivery in diseased brain regions, outlining progress, limitations, and ongoing challenges. We also provide a summary and overview of general design principles that can be applied to nanotherapies, considering the areas and cell types affected by the most common brain disorders. We then emphasize lingering uncertainties that hinder the translational possibilities of nanotherapies for clinical use. Finally, we offer suggestions for continuing preclinical investigations to enhance the overall effectiveness of precision nanomedicine in addressing neurological conditions. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Neurological Disease Therapeutic Approaches and Drug Discovery > Emerging Technologies.

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This article is intended to introduce nuclear medicine technologists (NMTs) to the nuances of radiopharmaceutical therapy clinical trials. Here, we outline the potential roles and responsibilities of the NMT in clinical trials and provide context on different aspects of radionuclide therapy. The regulatory process involving investigational therapeutic radiopharmaceuticals is seldom taught to NMT students, nor is it included in the entry-level nuclear medicine certification examinations. Often, NMTs must spend significant time preparing for therapeutic clinical trials on their own, using multiple academic sources, seeking advice from various health care professionals, and reviewing numerous trial-specific manuals to recognize the detailed requirements. The emergence of theranostics has spurred an increase in the development of therapeutic radiopharmaceuticals. Investigators with a robust nuclear medicine background are required to help develop successful therapeutic clinical trials, and well-informed NMTs are crucial to the success of such trials. This article follows a series of previous publications from the Society of Nuclear Medicine and Molecular Imaging Clinical Trials Network research series for technologists and is intended to guide the investigational radiopharmaceutical landscape.

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INTRODUCTION: Trial recruitment is a crucial factor for precision oncology, potentially improving patient outcomes and generating new scientific evidence. To identify suitable, biomarker-based trials for patients' clinicians need to screen multiple clinical trial registries which lack support for modern trial designs and offer only limited options to filter for in- and exclusion criteria. Several registries provide trial information but are limited regarding factors like timeliness, quality of information and capability for semantic, terminology enhanced searching for aspects like specific inclusion criteria. METHODS: We specified a Fast Healthcare Interoperable Resources (FHIR) Implementation Guide (IG) to represent clinical trials and their meta data. We embedded it into a community driven approach to maintain clinical trial data, which is fed by openly available data sources and later annotated by platform users. A governance model was developed to manage community contributions and responsibilities. RESULTS: We implemented Community Annotated Trial Search (CATS), an interactive platform for clinical trials for the scientific community with an open and interoperable information model. It provides a base to collaboratively annotate clinical trials and serves as a comprehensive information source for community members. Its terminology driven annotations are coined towards precision oncology, but its principles can be transferred to other contexts. CONCLUSION: It is possible to use the FHIR standard and an open-source information model represented in our IG to build an open, interoperable clinical trial register. Advanced features like user suggestions and audit trails of individual resource fields could be represented by extending the FHIR standard. CATS is the first implementation of an open-for-collaboration clinical trial registry with modern oncological trial designs and machine-to-machine communication in mind and its methodology could be extended to other medical fields besides precision oncology. Due to its well-defined interfaces, it has the potential to provide automated patient recruitment decision support for precision oncology trials in digital applications.

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Sonogenetics is an emerging medical technology that uses acoustic waves to control cells through sonosensitive mediators (SSMs) that are genetically encoded, thus remotely and non-invasively modulating specific molecular events and/or biomolecular functions. Sonogenetics has opened new opportunities for targeted spatiotemporal manipulation in the field of gene and cell-based therapies due to its inherent advantages, such as its noninvasive nature, high level of safety, and deep tissue penetration. Sonogenetics holds impressive potential in a wide range of applications, from tumor immunotherapy and mitigation of Parkinsonian symptoms to the modulation of neural reward pathway, and restoration of vision. This review provides a detailed overview of the mechanisms and classifications of established sonogenetics systems and summarizes their applications in disease treatment and management. The review concludes by highlighting the challenges that hinder the further progress of sonogenetics, paving the way for future advances.

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BACKGROUND: Rifampicin resistant tuberculosis remains a global health problem with almost half a million new cases annually. In high-income countries patients empirically start a standardized treatment regimen, followed by an individualized regimen guided by drug susceptibility test (DST) results. In most settings, DST information is not available or is limited to isoniazid and fluoroquinolones. Whole genome sequencing could more accurately guide individualized treatment as the full drug resistance profile is obtained with a single test. Whole genome sequencing has not reached its full potential for patient care, in part due to the complexity of translating a resistance profile into the most effective individualized regimen. METHODS: We developed a treatment recommender clinical decision support system (CDSS) and an accompanying web application for user-friendly recommendation of the optimal individualized treatment regimen to a clinician. RESULTS: Following expert stakeholder meetings and literature review, nine drug features and 14 treatment regimen features were identified and quantified. Using machine learning, a model was developed to predict the optimal treatment regimen based on a training set of 3895 treatment regimen-expert feedback pairs. The acceptability of the treatment recommender CDSS was assessed as part of a clinical trial and in a routine care setting. Within the clinical trial setting, all patients received the CDSS recommended treatment. In 8 of 20 cases, the initial recommendation was recomputed because of stock out, clinical contra-indication or toxicity. In routine care setting, physicians rejected the treatment recommendation in 7 out of 15 cases because it deviated from the national TB treatment guidelines. A survey indicated that the treatment recommender CDSS is easy to use and useful in clinical practice but requires digital infrastructure support and training. CONCLUSIONS: Our findings suggest that global implementation of the novel treatment recommender CDSS holds the potential to improve treatment outcomes of patients with RR-TB, especially those with 'difficult-to-treat' forms of RR-TB.

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Objectives: This study aimed to investigate the dynamic patterns of perception and expectations among COPD patients. Methods: Conducted at the Heart-Respiratory Rehabilitation Unit, IRCCS Fondazione Don Carlo Gnocchi, in Milan, Italy, the research involved 28 participants (16 males; mean age 72.8 ± 9.9) in face-to-face interviews. Utilizing a Grounded Theory approach, complemented by clinical data, recorded, and transcribed interviews underwent enhancement through the integration of two pictorial tools. Results: The central theme that emerged was a profound sense of responsibility toward their condition, perceived as a significant threat to life. Key symptoms, such as shortness of breath, coupled with negative expectations about their condition, contributed to depressive mood and avoidance behaviors. A notable proportion (N = 17; 60.71%) of participants struggled to envision a positive future, expressing a pervasive sense of hopelessness, which significantly influenced their health behaviors and adherence to medical recommendations. Conversely, individuals who felt supported and optimistic about treatment efficacy exhibited more positive expectations and adopted proactive coping strategies. Discussion: Recognizing the dynamic nature of patients' perceptions and negative illness expectations is essential to create personalized therapeutic interventions and meet the specific needs of COPD patients, ultimately improving the overall effectiveness of their care journey.

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Suicidality remains a clear and present danger in society in general, and for mental health patients in particular. Lack of widespread use of objective and/or quantitative information has hampered treatment and prevention efforts. Suicidality is a spectrum of severity from vague thoughts that life is not worth living, to ideation, plans, attempts, and completion. Blood biomarkers that track suicidality risk provide a window into the biology of suicidality, as well as could help with assessment and treatment. Previous studies by us were positive. Here we describe new studies we conducted transdiagnostically in psychiatric patients, starting with the whole genome, to expand the identification, prioritization, validation and testing of blood gene expression biomarkers for suicidality, using a multiple independent cohorts design. We found new as well as previously known biomarkers that were predictive of high suicidality states, and of future psychiatric hospitalizations related to them, using cross-sectional and longitudinal approaches. The overall top increased in expression biomarker was SLC6A4, the serotonin transporter. The top decreased biomarker was TINF2, a gene whose mutations result in very short telomeres. The top biological pathways were related to apoptosis. The top upstream regulator was prednisolone. Taken together, our data supports the possibility that biologically, suicidality is an extreme stress-driven form of active aging/death. Consistent with that, the top subtypes of suicidality identified by us just based on clinical measures had high stress and high anxiety. Top therapeutic matches overall were lithium, clozapine and ketamine, with lithium stronger in females and clozapine stronger in males. Drug repurposing bioinformatic analyses identified the potential of renin-angiotensin system modulators and of cyclooxygenase inhibitors. Additionally, we show how patient reports for doctors would look based on blood biomarkers testing, personalized by gender. We also integrated with the blood biomarker testing social determinants and psychological measures (CFI-S, suicidal ideation), showing synergy. Lastly, we compared that to machine learning approaches, to optimize predictive ability and identify key features. We propose that our findings and comprehensive approach can have transformative clinical utility.

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In pre-clinical trials of anti-cancer drugs, cell lines are utilized as a model for patient tumor samples to understand the response of drugs. However, in vitro culture of cell lines, in general, alters the biology of the cell lines and likely gives rise to systematic differences from the tumor samples' genomic profiles; hence the drug response of cell lines may deviate from actual patients' drug response. In this study, we computed a similarity score for the selection of cell lines depicting the close and far resemblance to patient tumor samples in twenty-two different cancer types at genetic, genomic, and epigenetic levels integrating multi-omics datasets. We also considered the presence of immune cells in tumor samples and cancer-related biological pathways in this score which aids personalized medicine research in cancer. We showed that based on these similarity scores, cell lines were able to recapitulate the drug response of patient tumor samples for several FDA-approved cancer drugs in multiple cancer types. Based on these scores, several of the high-rank cell lines were shown to have a close likeness to the corresponding tumor type in previously reported in vitro experiments.

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Modern cancer research depends heavily on the identification and validation of biomarkers because they provide important information about the diagnosis, prognosis, and response to treatment of the cancer. This review will provide a comprehensive overview of cancer biomarkers, including their development phases and recent breakthroughs in transcriptomics and computational techniques for detecting these biomarkers. Blood-based biomarkers have great potential for non-invasive tumor dynamics and treatment response monitoring. These include circulating tumor DNA, exosomes, and microRNAs. Comprehensive molecular profiles are provided by multi-omic technologies, which combine proteomics, metabolomics, and genomes to support the identification of biomarkers and the targeting of therapeutic interventions. Genetic changes are detected by next-generation sequencing, and patterns of protein expression are found by protein arrays and mass spectrometry. Tumor heterogeneity and clonal evolution can be understood using metabolic profiling and single-cell studies. It is projected that the use of several biomarkers-genetic, protein, mRNA, microRNA, and DNA profiles, among others-will rise, enabling multi-biomarker analysis and improving individualised treatment plans. Biomarker identification and patient outcome prediction are further improved by developments in AI algorithms and imaging techniques. Robust biomarker validation and reproducibility require cooperation between industry, academia, and doctors. Biomarkers can provide individualized care, meet unmet clinical needs, and enhance patient outcomes despite some obstacles. Precision medicine will continue to take shape as scientific research advances and the integration of biomarkers with cutting-edge technologies continues to offer a more promising future for personalized cancer care.

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