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Patient-derived organoid models hold promise for advancing clinical cancer research, including diagnosis and personalized and precision medicine approaches, and cytology, in particular, plays a pivotal role in this process. These three-dimensional multicellular structures are heterogeneous, potentially maintain the cancer phenotype, and conserve the genomic, transcriptomic, and epigenomic patterns of the parental tumors. To ensure that only tumor tissue is used for organoid development, cytologic validation is necessary before initiating the process of organoid generation. Here, we explore the technology of tumor organoids and discuss the fundamental application of cytology as a simple and cost-effective approach toward organoid development. We also underscore the potential application of organoid development in drug efficacy studies for lung cancer and head and neck tumors. Additionally, we stress the importance of using fine-needle aspiration to generate tumoroids.

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Circulating tumor cells (CTCs) hold unique biological characteristics that directly involve them in hematogenous dissemination. Studying CTCs systematically is technically challenging due to their extreme rarity and heterogeneity and the lack of specific markers to specify metastasis-initiating CTCs. With cutting-edge technology, single-cell RNA sequencing (scRNA-seq) provides insights into the biology of metastatic processes driven by CTCs. Transcriptomics analysis of single CTCs can decipher tumor heterogeneity and phenotypic plasticity for exploring promising novel therapeutic targets. The integrated approach provides a perspective on the mechanisms underlying tumor development and interrogates CTCs interactions with other blood cell types, particularly those of the immune system. This review aims to comprehensively describe the current study on CTC transcriptomic analysis through scRNA-seq technology. We emphasize the workflow for scRNA-seq analysis of CTCs, including enrichment, single cell isolation, and bioinformatic tools applied for this purpose. Furthermore, we elucidated the translational knowledge from the transcriptomic profile of individual CTCs and the biology of cancer metastasis for developing effective therapeutics through targeting key pathways in CTCs.

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Sensory processing sensitivity (SPS) is a biological/temperament trait that is associated with greater awareness of and reactivity to the environment, which results in amplified responses to various stimuli, and possibly medications. We investigated the relationship between SPS and medication sensitivity in three studies. Participants (ages 18-81) were recruited from university (Study 1: N = 125; Study 2: N = 214) and online (Study 3: N = 351) samples. In each study, participants completed a medication sensitivity scale, the standard highly sensitive person (HSP) scale to assess SPS, and a negative affectivity (NA) scale as a control variable. All three studies found moderate, significant correlations between SPS and medication sensitivity (r = 0.34, p < 0.001: r = 0.21, p = 0.003; r = 0.36, p < 0.001, respectively). Correlations remained significant, and similar, when controlling for NA and gender; and there were no significant interactions with gender. In sum, our results suggest that SPS is associated with medication sensitivity, even when considering NA and gender. Thus, future work might consider SPS when investigating recommended medication, medication dosage, effectiveness, and adverse drug reactions.

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Medical abzymology has made a great contribution to the development of general autoimmunity theory: it has put the autoantibodies (Ab) as the key brick of the theory to the level of physiological functionality by providing such Ab with the ability to catalyze and mediate direct and independent cytotoxic effect on cellular and molecular targets. Natural catalytic autoantibodies (abzymes) while being a pool of canonical Abs and possessing catalytic activity belong to the new group of physiologically active substances whose features and properties are evolutionary consolidated in one functionally active biomolecule. Therefore, further studies on Ab-mediated autoAg degradation and other targeted Ab-mediated proteolysis may provide biomarkers of newer generations and thus a supplementary tool for assessing the disease progression and predicting disability of the patients and persons at risks. This chapter is a summary of current knowledge and prognostic perspectives toward catalytic Abs in autoimmunity and thus some autoimmune clinical cases, their role in pathogenesis, and the exploitation of both whole molecules and their constituent parts in developing highly effective targeted drugs of the future to come, and thus the therapeutic protocols being individualized.

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Alcohol Use Disorder (AUD) is a chronic psychiatric disorder marked by impaired control over drinking behavior that poses a significant challenge to the individual, their community, the healthcare system and economy. While the negative consequences of chronic excessive alcohol consumption are well-documented, effective treatment for AUD and alcohol-associated diseases remains challenging. Cognitive and behavioral treatment, with or without pharmaceutical interventions, remain the most commonly used methods; however, their efficacy is limited. The development of new treatment protocols for AUD is challenged by difficulty in accurately measuring patterns of alcohol consumption in AUD patients, a lack of a clear understanding of the neuropsychological basis of the disorder, the high likelihood of AUD patients relapsing after receiving treatment, and the numerous end-organ comorbidities associated with excessive alcohol use. Identification and prediction of patients who may respond well to a certain treatment mechanism as well as clinical measurement of a patient's alcohol exposure are bottlenecks in AUD research which should be further addressed. In addition, greater focus must be placed on the development of novel strategies of drug design aimed at targeting the integrated neural pathways implicated in AUD pathogenesis, so that next-generation AUD treatment protocols can address the broad and systemic effects of AUD and its comorbid conditions.

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Breast cancer susceptibility gene 1 (BRCA1) is a tumor suppressor gene, which is mainly involved in the repair of DNA damage, cell cycle regulation, maintenance of genome stability, and other important physiological processes. Mutations or defects in the BRCA1 gene significantly increase the risk of breast, ovarian, prostate, and other cancers in carriers. In this review, we summarized the molecular functions and regulation of BRCA1 and discussed recent insights into the detection and treatment of BRCA1 mutated breast cancer.

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Machine learning is increasingly integrated into clinical practice, with applications ranging from pre-clinical data processing, bedside diagnosis assistance, patient stratification, treatment decision making, and early warning as part of primary and secondary prevention. However, a multitude of technological, medical, and ethical considerations are critical in machine-learning utilization, including the necessity for careful validation of machine-learning-based technologies in real-life contexts, unbiased evaluation of benefits and risks, and avoidance of technological over-dependence and associated loss of clinical, ethical, and social-related decision-making capacities. Other challenges include the need for careful benchmarking and external validations, dissemination of end-user knowledge from computational experts to field users, and responsible code and data sharing, enabling transparent assessment of pipelines. In this review, we highlight key promises and achievements in integration of machine-learning platforms into clinical medicine while highlighting limitations, pitfalls, and challenges toward enhanced integration of learning systems into the medical realm.

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INTRODUCTION: Exogenous progesterone is prescribed for a variety of conditions with endogenous progesterone deficiency, e.g. menstrual alterations, primary or secondary infertility or premenopause. To the best of our knowledge, no pharmacogenetic studies have been published in relation to exogenous progesterone pharmacokinetic safety or progesterone metabolites so far. METHODS: Candidate-gene study where we evaluated whether five single-nucleotide polymorphisms (CYP2C9*2, *3, CYP2C19*2, *3 and *17) were related to the pharmacokinetics, safety and metabolism of progesterone in 24 healthy volunteers who received a 200-mg progesterone formulation either orally or vaginally. RESULTS: The vaginal formulation had an average AUCt value approximately 18 times greater than the oral formulation. CYP2C19 intermediate metabolizers (IM) consistently showed higher adjusted AUCt and adjusted Cmax than extensive metabolizers (EM) (P < 0.05); CYP2C9 EM incongruently exhibited higher adjusted Cmax and longer half-life than IM (p < 0.05). CONCLUSION: This is the first study that reports variability in progesterone disposition according to the CYP2C19 and CYP2C9 phenotype. We suggest that CYP2C19 may condition progesterone disposition and that it may be more relevant than CYP2C9. This study lays the foundations for further in-depth research to evaluate the pharmacogenetics of progesterone. TRIAL REGISTRATION: EudraCT numbers are 2012-005105-43 and 2012-005011-10.

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In-stent restenosis corresponds to the diameter reduction of coronary vessels following percutaneous coronary intervention (PCI), an invasive procedure in which a stent is deployed into the coronary arteries, producing profuse neointimal hyperplasia. The reasons for this process to occur still lack a clear answer, which is partly why it remains as a clinically significant problem. As a consequence, there is a vigorous need to identify useful non-invasive biomarkers to differentiate and follow-up subjects at risk of developing restenosis, and due to their extraordinary stability in several bodily fluids, microRNA research has received extensive attention to accomplish this task. This review depicts the current understanding, diagnostic potential and clinical challenges of microRNA molecules as possible blood-based restenosis biomarkers.

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Modeling and simulation in computational neuroscience is currently a research enterprise to better understand neural systems. It is not yet directly applicable to the problems of patients with brain disease. To be used for clinical applications, there must not only be considerable progress in the field but also a concerted effort to use best practices in order to demonstrate model credibility to regulatory bodies, to clinics and hospitals, to doctors, and to patients. In doing this for neuroscience, we can learn lessons from long-standing practices in other areas of simulation (aircraft, computer chips), from software engineering, and from other biomedical disciplines. In this manuscript, we introduce some basic concepts that will be important in the development of credible clinical neuroscience models: reproducibility and replicability; verification and validation; model configuration; and procedures and processes for credible mechanistic multiscale modeling. We also discuss how garnering strong community involvement can promote model credibility. Finally, in addition to direct usage with patients, we note the potential for simulation usage in the area of Simulation-Based Medical Education, an area which to date has been primarily reliant on physical models (mannequins) and scenario-based simulations rather than on numerical simulations.

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Predictive biomarkers are developed for treatment selection to identify patients who are likely to benefit from a particular therapy. This review describes statistical methods and discusses issues in the development of predictive biomarkers to enhance study efficiency for detection of treatment effect on the selected responder patients in clinical studies. The statistical procedure for treatment selection consists of three components: biomarker identification, subgroup selection and clinical utility assessment. Major statistical issues discussed include biomarker designs, procedures to identify predictive biomarkers, classification models for subgroup selection, subgroup analysis and multiple testing for clinical utility assessment and evaluation.

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