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Current evidence indicates that dual antiplatelet therapy with aspirin plus a P2Y 12 inhibitor is essential for the prevention of thrombotic events after percutaneous coronary interventions. However, dual antiplatelet therapy is associated with increased bleeding which may outweigh the benefits. This has set the foundations for customizing antiplatelet treatments to the individual patient. However, bleeding and ischemic risks are often present in the same patient, making it difficult to achieve this balance. The fact that oral P2Y 12 inhibitors (clopidogrel, prasugrel, and ticagrelor) have diverse pharmacodynamic profiles that affect clinical outcomes supports the rationale for using platelet function and genetic testing to individualize antiplatelet treatment regimens. Indeed, up to one-third of patients treated with clopidogrel, but a minority of those treated with prasugrel or ticagrelor, exhibit high residual platelet reactivity resulting in an increased thrombotic risk. On the other hand, prasugrel and ticagrelor are frequently associated with low platelet reactivity and increased bleeding risk compared with clopidogrel without providing any additional reduction in ischemic events compared with patients who adequately respond to clopidogrel. The use of platelet function and genetic testing may allow for a guided selection of oral P2Y 12 inhibitors. However, the nonuniform results of randomized controlled trials have led guidelines to provide limited recommendations on the implementation of these tests in patients undergoing percutaneous coronary intervention. In light of recent advancements in the field, this consensus document by a panel of international experts fills in the guideline gap by providing updates on the latest evidence in the field as well as recommendations for clinical practice. [Display omitted] [ABSTRACT FROM AUTHOR]

ABSTRACT The study of precision medicine involves dynamic treatment regimes (DTRs), which are sequences of treatment decision rules recommended based on patient‐level information. The primary goal of the DTR study is to identify an optimal DTR, a sequence of treatment decision rules that optimizes the clinical outcome across multiple decision points. Statistical methods have been developed in recent years to estimate an optimal DTR, including Q‐learning, a regression‐based method in the DTR literature. Although there are many studies concerning Q‐learning, little attention has been paid in the presence of noisy data, such as misclassified outcomes. In this article, we investigate the effect of outcome misclassification on identifying optimal DTRs using Q‐learning and propose a correction method to accommodate the misclassification effect on DTR. Simulation studies are conducted to demonstrate the satisfactory performance of the proposed method. We illustrate the proposed method using two examples from the National Health and Nutrition Examination Survey Data I Epidemiologic Follow‐up Study and the Population Assessment of Tobacco and Health Study. [ABSTRACT FROM AUTHOR]

Brain surgery is a widely practised and effective treatment for brain tumours, but accurately identifying and classifying tumour boundaries is crucial to maximise resection and avoid neurological complications. This precision in classification is essential for guiding surgical decisions and subsequent treatment planning. Hyperspectral (HS) imaging (HSI) is an emerging multidimensional optical imaging method that captures detailed spectral information across multiple wavelengths, allowing for the identification of nuanced differences in tissue composition, with the potential to enhance intraoperative tissue classification. However, current frameworks often require retraining models for each HSI to extract meaningful features, resulting in long processing times and high computational costs. Additionally, most methods utilise the deep semantic features at the end of the network for classification, ignoring the spatial details contained in the shallow features. To overcome these challenges, we propose a novel approach called MedDiffHSI, which combines diffusion and transformer techniques. Our method involves training an unsupervised learning framework based on the diffusion model to extract high‐level and low‐level spectral–spatial features from HSI. This approach eliminates the need for retraining of spectral–spatial feature learning model, thereby reducing time complexity. We then extract intermediate multistage features from different timestamps for classification using a pretrained denoising U‐Net. To fully explore and exploit the rich contextual semantics and textual information hidden in the extracted diffusion feature, we utilise a spectral–spatial attention module. This module not only learns multistage information about features at different depths, but also extracts and enhances effective information from them. Finally, we employ a supervised transformer‐based classifier with weighted majority voting (WMV) to perform the HSI classification. To validate our approach, we conduct comprehensive experiments on in vivo brain database data sets and also extend the analysis to include additional HSI data sets for breast cancer to evaluate the framework performance across different types of tissue. The results demonstrate that our framework outperforms existing approaches by using minimal training samples (5%) while achieving state‐of‐the‐art performance. [ABSTRACT FROM AUTHOR]

Background: The incidence and mortality of colorectal cancer (CRC) have been rising steadily. Early diagnosis and precise treatment are essential for improving patient survival outcomes. Over the past decade, the integration of artificial intelligence (AI) and medical imaging technologies has positioned radiomics as a critical area of research in the diagnosis, treatment, and prognosis of CRC. Methods: We conducted a comprehensive review of CRC-related radiomics literature published between 1 January 2013 and 31 December 2023 using the Web of Science Core Collection database. Bibliometric tools such as Bibliometrix, VOSviewer, and CiteSpace were employed to perform an in-depth bibliometric analysis. Results: Our search yielded 1,226 publications, revealing a consistent annual growth in CRC radiomics research, with a significant rise after 2019. China led in publication volume (406 papers), followed by the United States (263 papers), whereas the United States dominated in citation numbers. Notable institutions included General Electric, Harvard University, University of London, Maastricht University, and the Chinese Academy of Sciences. Prominent researchers in this field are Tian J from the Chinese Academy of Sciences, with the highest publication count, and Ganeshan B from the University of London, with the most citations. Journals leading in publication and citation counts are Frontiers in Oncology and Radiology. Keyword and citation analysis identified deep learning, texture analysis, rectal cancer, image analysis, and management as prevailing research themes. Additionally, recent trends indicate the growing importance of AI and multi-omics integration, with a focus on improving precision medicine applications in CRC. Emerging keywords such as deep learning and AI have shown rapid growth in citation bursts over the past 3 years, reflecting a shift toward more advanced technological applications. Conclusions: Radiomics plays a crucial role in the clinical management of CRC, providing valuable insights for precision medicine. It significantly contributes to predicting molecular biomarkers, assessing tumor aggressiveness, and monitoring treatment efficacy. Future research should prioritize advancing AI algorithms, enhancing multi-omics data integration, and further expanding radiomics applications in CRC precision medicine. [ABSTRACT FROM AUTHOR]

ABSTRACT Precision medicine is the future of drug development, and subgroup identification plays a critical role in achieving the goal. In this paper, we propose a powerful end‐to‐end solution squant (available on CRAN) that explores a sequence of quantitative objectives. The method converts the original study to an artificial 1:1 randomized trial, and features a flexible objective function, a stable signature with good interpretability, and an embedded false discovery rate (FDR) control. We demonstrate its performance through simulation and provide a real data example. [ABSTRACT FROM AUTHOR]

Background: Multiple first-line treatment options have been developed for advanced non-small cell lung cancer (NSCLC) in each subgroup determined by predictive biomarkers, specifically driver oncogene and programmed cell death ligand-1 (PD-L1) status. However, the methodology for optimal treatment selection in individual patients is not established. This study aimed to develop artificial intelligence (AI)-based personalized survival prediction model according to treatment selection. Methods: The prediction model was built based on random survival forest (RSF) algorithm using patient characteristics, anticancer treatment histories, and radiomics features of the primary tumor. The predictive accuracy was validated with external test data and compared with that of cox proportional hazard (CPH) model. Results: A total of 459 patients (training, n = 299; test, n = 160) with advanced NSCLC were enrolled. The algorithm identified following features as significant factors associated with survival: age, sex, performance status, Brinkman index, comorbidity of chronic obstructive pulmonary disease, histology, stage, driver oncogene status, tumor PD-L1 expression, administered anticancer agent, six markers of blood test (sodium, lactate dehydrogenase, etc.), and three radiomics features associated with tumor texture, volume, and shape. The C-index of RSF model for test data was 0.841, which was higher than that of CPH model (0.775, P < 0.001). Furthermore, the RSF model enabled to identify poor survivor treated with pembrolizumab because of tumor PD-L1 high expression and those treated with driver oncogene targeted therapy according to driver oncogene status. Conclusions: The proposed AI-based algorithm accurately predicted the survival of each patient with advanced NSCLC. The AI-based methodology will contribute to personalized medicine. Trial registration: The trial design was retrospectively registered study performed in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Nagoya University Graduate School of Medicine (approval: 2020 − 0287). [ABSTRACT FROM AUTHOR]

The discovery of cancer-specific biomarkers has resulted in major advancements in the field of cancer diagnostics and therapeutics, therefore significantly lowering cancer-related morbidity and mortality. Cancer biomarkers can be generally classified as prognostic biomarkers that predict specific disease outcomes and predictive biomarkers that predict disease response to targeted therapeutic interventions. As research in the area of predictive biomarkers continues to grow, precision medicine becomes far more integrated in cancer treatment. This article presents a general overview on the most recent advancements in the area of cancer biomarkers, immunotherapy, artificial intelligence, and pharmacogenomics of the Middle East. [ABSTRACT FROM AUTHOR]

Pediatric obesity is a growing health concern that has many secondary adverse health implications. Personalized medicine is a tool that can be used to optimize diagnosis and treatments of many diseases. In this review, we will focus on three areas related to the genetics of pediatric obesity: (i) genetic causes predisposing to pediatric obesity, (ii) pharmacogenomics that may predict weight gain associated with pharmacotherapy, and (iii) pharmacogenomics of anti‐obesity pharmacotherapy. This narrative review evaluates genetic cause of pediatric obesity and how genetic findings can be used to optimize pharmacotherapy to minimize weight gain and optimize obesity treatment in pediatric patients. Pediatric obesity has many genetic causes including genomic obesity syndromes and monogenic obesity disorders. Several genetic etiologies of obesity have current or emerging targeted genetic therapies. Pharmacogenomic (PGx) targets associated with pharmacotherapy‐induced weight gain have been identified for antipsychotic, antiepileptic, antidepressant therapies, and steroids, yet to date no clinical guidelines exist for application use of PGx to tailor pharmacotherapy to avoid weight gain. As legislation evolves for genetic testing coverage and technology advances, this will decrease cost and expand access to genetic testing. This will result in identification of potential genetic causes of obesity and genes that predispose to pharmacotherapy‐induced weight gain. Advances in precision medicine can ultimately lead to development of clinical practice guidelines on how to apply genetic findings to optimize pharmacotherapy to treat genetic targets of obesity and avoid weight gain as an adverse event associated with pharmacotherapy. [ABSTRACT FROM AUTHOR]

Precise regulation of gene expression is crucial to development. Enhancers, the core of gene regulation, determine the spatiotemporal pattern of gene transcription. Since many disease-associated mutations are characterized in enhancers, the research on enhancer will provide clues to precise medicine. Rapid advances in high-throughput sequencing technology facilitate the characterization of enhancers at genome wide, but understanding the functional mechanisms of enhancers remains challenging. Herein, we provide a panorama of enhancer characteristics, including epigenetic modifications, enhancer transcripts, and enhancer-promoter interaction patterns. Furthermore, we outline the applications of high-throughput sequencing technology and functional genomics methods in enhancer research. Finally, we discuss the role of enhancers in human disease and their potential as targets for disease prevention and treatment strategies. [ABSTRACT FROM AUTHOR]

Phospholamban (PLN) plays a crucial role in regulating sarcoplasmic reticulum (SR) Ca2+ cycling and cardiac contractility. Mutations within the PLN gene have been detected in patients with cardiomyopathy, with the heterozygous variant c.40_42delAGA (p.R14del) of PLN being the most prevalent. Investigations into the mechanisms underlying the pathology of PLN-R14del have revealed that cardiac cells from affected patients exhibit pathological aggregates containing PLN. Herein, we performed comprehensive molecular and cellular analyses to delineate the molecular aberrations associated with the formation of these aggregates. We determined that PLN aggregates contain autophagic proteins, indicating inefficient degradation via the autophagy pathway. Our findings demonstrate that the expression of PLN-R14del results in diminished autophagic flux due to impaired fusion between autophagosomes and lysosomes. Mechanistically, this defect is linked to aberrant recruitment of key membrane fusion proteins to autophagosomes, which is mediated in part by changes in Ca2+ homeostasis. Collectively, these results highlight a novel function of PLN-R14del in regulating autophagy, that may contribute to the formation of pathogenic aggregates in patients with cardiomyopathy. Prospective strategies tailored to ameliorate impaired autophagy may hold promise against PLN-R14del disease. [ABSTRACT FROM AUTHOR]

ABSTRACT Introduction Materials and Methods Results Conclusion The early detection of high‐risk individuals is crucial to delay and reduce the incidence of type 2 diabetes. In this study, we aimed to explore the performance of a novel subgroup‐specific biomarker strategy in the prediction of incident diabetes.In the Taiwan Lifestyle Cohort Study, adult subjects without diabetes were included and followed for the incidence of diabetes in 2006–2019. The biomarkers measured included blood secretogranin III (SCG3), vascular adhesion protein‐1 (VAP‐1), fibrinogen‐like protein 1 (FGL1), angiopoietin‐like protein 6 (ANGPTL6), and angiopoietin‐like protein 4 (ANGPTL4).Among the 1,287 subjects, 12.2% developed diabetes during a 6 year follow‐up. Blood VAP‐1 was significantly associated with incident diabetes in the overall population (HR = 0.724, P < 0.05), participants under 65 years old (HR = 0.685, P < 0.05), those with a BMI of ≥24 kg/m2 (HR = 0.673, P < 0.05), and females (HR = 0.635, P < 0.05). Blood ANGPTL6 was significantly correlated with incident diabetes in participants aged 65 and older (HR = 0.314, P < 0.05), and blood SCG3 was associated with incident diabetes in those with a BMI of <24 kg/m2 (HR = 1.296, P < 0.05). Two subgroup‐specific biomarker strategies were developed. The gender and BMI‐specific biomarker strategy, using traditional risk factors and blood SCG3 or VAP‐1 in different subgroups, could improve prediction performance, especially the specificity and positive prediction value, compared with the whole‐population strategy using only traditional risk factors or traditional risk factors plus blood VAP‐1.Gender‐ and BMI‐specific biomarker strategy can improve the prediction of incident diabetes. A subgroup‐specific biomarker strategy is a novel approach in the prediction of incident diabetes. [ABSTRACT FROM AUTHOR]

Background and Aims Cardiogenic shock (CS) remains the primary cause of in-hospital death after acute coronary syndromes (ACS), with its plateauing mortality rates approaching 50%. To test novel interventions, personalized risk prediction is essential. The ORBI (Observatoire Régional Breton sur l'Infarctus) score represents the first-of-its-kind risk score to predict in-hospital CS in ACS patients undergoing percutaneous coronary intervention (PCI). However, its sex-specific performance remains unknown, and refined risk prediction strategies are warranted. Methods This multinational study included a total of 53 537 ACS patients without CS on admission undergoing PCI. Following sex-specific evaluation of ORBI, regression and machine-learning models were used for variable selection and risk prediction. By combining best-performing models with highest-ranked predictors, SEX-SHOCK was developed, and internally and externally validated. Results The ORBI score showed lower discriminative performance for the prediction of CS in females than males in Swiss (area under the receiver operating characteristic curve [95% confidence interval]: 0.78 [0.76–0.81] vs. 0.81 [0.79–0.83]; P =.048) and French ACS patients (0.77 [0.74–0.81] vs. 0.84 [0.81–0.86]; P =.002). The newly developed SEX-SHOCK score, now incorporating ST-segment elevation, creatinine, C-reactive protein, and left ventricular ejection fraction, outperformed ORBI in both sexes (females: 0.81 [0.78–0.83]; males: 0.83 [0.82–0.85]; P <.001), which prevailed following internal and external validation in RICO (females: 0.82 [0.79–0.85]; males: 0.88 [0.86–0.89]; P <.001) and SPUM-ACS (females: 0.83 [0.77–0.90], P =.004; males: 0.83 [0.80–0.87], P =.001). Conclusions The ORBI score showed modest sex-specific performance. The novel SEX-SHOCK score provides superior performance in females and males across the entire spectrum of ACS, thus providing a basis for future interventional trials and contemporary ACS management. [ABSTRACT FROM AUTHOR]

Background and Aims Somatic mutations in the TET2 gene that lead to clonal haematopoiesis (CH) are associated with accelerated atherosclerosis development in mice and a higher risk of atherosclerotic disease in humans. Mechanistically, these observations have been linked to exacerbated vascular inflammation. This study aimed to evaluate whether colchicine, a widely available and inexpensive anti-inflammatory drug, prevents the accelerated atherosclerosis associated with TET2-mutant CH. Methods In mice, TET2 -mutant CH was modelled using bone marrow transplantations in atherosclerosis-prone Ldlr −/− mice. Haematopoietic chimeras carrying initially 10% Tet2 −/− haematopoietic cells were fed a high-cholesterol diet and treated with colchicine or placebo. In humans, whole-exome sequencing data and clinical data from 37 181 participants in the Mass General Brigham Biobank and 437 236 participants in the UK Biobank were analysed to examine the potential modifying effect of colchicine prescription on the relationship between CH and myocardial infarction. Results Colchicine prevented accelerated atherosclerosis development in the mouse model of TET2 -mutant CH, in parallel with suppression of interleukin-1β overproduction in conditions of TET2 loss of function. In humans, patients who were prescribed colchicine had attenuated associations between TET2 mutations and myocardial infarction. This interaction was not observed for other mutated genes. Conclusions These results highlight the potential value of colchicine to mitigate the higher cardiovascular risk of carriers of somatic TET2 mutations in blood cells. These observations set the basis for the development of clinical trials that evaluate the efficacy of precision medicine approaches tailored to the effects of specific mutations linked to CH. [ABSTRACT FROM AUTHOR]

Nanotheranostics, an amalgamation of therapeutic and diagnostic capabilities at the nanoscale, is revolutionizing personalized medicine. Polymer-drug conjugates (PDCs) stand at the forefront of this arena, offering a multifaceted approach to treat complex diseases such as cancer. This review explores the recent advancements in PDCs, highlighting their design principles, working mechanisms, and the therapeutic applications. We discuss the incorporation of imaging agents into PDCs that allow for real-time monitoring of drug delivery and treatment efficacy. With the aim of improving patient care, the review examines how PDCs enable targeted drug delivery, minimize side effects, and provide valuable diagnostic data, hence enhancing the precision of medical interventions. We also address the challenges facing the clinical translation of PDCs, such as scalability, regulatory hurdles, and cost-effectiveness, providing a comprehensive outlook on the future of nanotheranostics in patient management. [ABSTRACT FROM AUTHOR]

The complexity of tumors and the challenges associated with treatment often stem from the limitations of existing models in accurately replicating authentic tumors. Recently, organoid technology has emerged as an innovative platform for tumor research. This bioengineering approach enables researchers to simulate, in vitro, the interactions between tumors and their microenvironment, thereby enhancing the intricate interplay between tumor cells and their surroundings. Organoids also integrate multidimensional data, providing a novel paradigm for understanding tumor development and progression while facilitating precision therapy. Furthermore, advancements in imaging and genetic editing techniques have significantly augmented the potential of organoids in tumor research. This review explores the application of organoid technology for more precise tumor simulations and its specific contributions to cancer research advancements. Additionally, we discuss the challenges and evolving trends in developing comprehensive tumor models utilizing organoid technology. [ABSTRACT FROM AUTHOR]

BACKGROUND: Lung cancer is one of the most common malignant tumors with the worst prognosis worldwide. Its incidence rate and mortality rate have long been among the top of malignant tumors. The heterogeneity and drug resistance are among the reasons contributing to its poor prognosis. Lung cancer organoid, which is a 3D in vitro model cultured from patient-derived tumor cells recapitulating the biological characteristics of the primary tumor, can be used for various researches of lung cancer. OBJECTIVE: To review the application and research progress of lung cancer organoids in chemotherapy, targeted therapy, and immunotherapy drug sensitivity screening, analyze its limitations, aiming to provide new strategies for personalized and precision medicine of lung cancer. METHODS: The first author searched relevant articles published from 2013 to 2023 in CNKI and PubMed in July 2023. The search terms were “organoid, lung cancer organoid, lung cancer experimental model, precision medicine, drug sensitivity test, chemotherapy, targeted therapy, immunotherapy” in Chinese and English. Finally, a total of 84 articles were incorporated. RESULTS AND CONCLUSION: (1) Compared with traditional lung cancer research models, which can only demonstrate two-dimensional cell activities, lack cell-to-cell interactions, and suffer from species differences, lung cancer organoids offer a diverse cell source and continuously optimized culture conditions. They can simulate cellular interactions in a three-dimensional context while retaining the biological characteristics of the original tumor. These organoids represent a promising research model with significant potential, providing a solid foundation for their use in cancer drug screening. (2) Lung cancer organoids have shown preliminary significance in guiding anticancer drug selection. Their predictive outcomes align closely with actual clinical outcomes, marking a pivotal step towards precision in lung cancer treatment. By assessing the efficacy of common chemotherapy, targeted therapy, and immunotherapy drugs, these organoids enable the customization of individualized treatment strategies, reducing unnecessary drug trials and toxic and side reaction while exploring possible alternative drugs or combination regimens for drug resistance issues so as to guide the precision treatment of rare mutated lung cancer and fill the clinical gap. A more comprehensive drug evaluation is provided by comparing the activity of different drugs. Additionally, it is essential to consider the internal heterogeneity of organoids, emphasizing the importance of multiple sampling to enhance result accuracy. (3) Lung cancer organoids have limitations in practical applications such as inconsistent success rates and purity in cultivation and the lack of vascular tissue. To address these shortcomings, improvements are needed in cultivation conditions, expedited testing processes, and the development of multi-organ systems to simulate the overall effects of drugs in multiple organs. These enhancements will contribute to a more accurate assessment of drug efficacy and toxicity, thereby enhancing the precision of lung cancer treatment. [ABSTRACT FROM AUTHOR]

Backgrounds: Gastric cancer (GC) remains a global health threat due to frequent treatment failures caused by primary or acquired resistance. Although cancer-associated fibroblasts (CAFs) have been implicated in this process, it is still unclear which specific subtype(s) of CAFs hinder T-cell infiltration and promote resistance to immunotherapy. Methods: We analyzed the GC fibroblast atlas in detail by combining 63,955 single cells from 14 scRNA-seq datasets. We also performed RNA-seq data in a local GC cohort and examined 13 bulk RNA-seq datasets to understand the biological and clinical roles of different CAF subsets. Additionally, we conducted in vitro experiments to study the role of specific proteins in GC development. Results: We identified a total of 17 fibroblast subsets in gastric cancer, nine of which did not fit into the existing CAFs classification. These subsets exhibited significant heterogeneity in distribution and biological characteristics (metabolism, cell-cell interactions, differentiation state), as well as clinical functions such as prognosis and response to immunotherapy. In particular, cluster 6 stood out for its high expression of MFAP5, CFD, and PI16; it was found to be negatively associated with both overall survival and response to immunotherapy in GC. This association was linked to an immunosuppressive microenvironment characterized by an increase in M2 macrophages but higher levels of T cell dysfunction and exclusion—a feature shared by tumors expressing MFAP5. Furthermore, the addition of human recombinant MFAP5 promoted proliferation and migration of HGC-27 cells by inducing the MFAP5/NOTCH1/HEY1 signaling pathway. Conclusion: We introduce a high-resolution GC fibroblast atlas. The 17 identified fibroblast clusters provide valuable opportunities for gaining deeper biological insights into the relationship between fibroblasts and GC development. Particularly, cluster 6 and its specific marker MFAP5 could serve as prognostic factors in GC and form a foundation for personalized therapeutic combinations to address primary resistance to ICIs. [ABSTRACT FROM AUTHOR]

Background: Migraine is a widespread, recurrent primary headache disorder primarily characterized by severe pulsatile headache, typically on one or both sides. It is often accompanied by nausea, vomiting, and hypersensitivity to sound and light. Despite the availability of multiple drugs for migraine management, the condition often becomes chronic due to untimely or irrational drug use, significantly distressing patients and increasing the burden on families and society. Over the past two decades, numerous clinical studies on migraine have been published. This study aimed to provide a comprehensive summary of the current status and trends of migraine clinical trials through bibliometric analysis. Methods: We used visual network tools such as CiteSpace and VOSviewer to perform a knowledge graph analysis of publications related to migraine clinical trials extracted from the WoSCC. Results: This study analyzed 1,129 articles published in 389 journals from 61 countries. The number of publications on migraine clinical trials has steadily increased from 2004 to 2023. The United States and Albert Einstein College of Medicine are the leading countries and institutions in this field, respectively. Richard B. Lipton is the most prolific author, making significant contributions to the research. The journal Headache has the highest number of publications and citations in this area. Keywords such as "efficacy," "RCT," "CGRP," "prophylaxis," "disability," "depression," "questionnaire," and "real-world effectiveness" received significant attention. Conclusion: This study identified reliable research hotspots and provided directions for clinicians. The treatment of migraine continues to be challenging. Future trends may include continued growth in migraine classification, risk factor analysis, and comorbidity studies. Research on CGRP and epigenetics will advance the progress of precision medicine in the migraine field. [ABSTRACT FROM AUTHOR]

Introduction: The Cuban population is genetically diverse, and information on the prevalence of genetic variants is still limited. As complex admixture processes have occurred, we hypothesized that the frequency of pharmacogenetic variants and drug responses may vary within the country. The aims of the study were to describe the frequency distribution of 43 single-nucleotide variants (SNVs) from 25 genes of pharmacogenetic interest within the Cuba population and in relation to other populations, while taking into consideration some descriptive variables such as place of birth and skin color. Materials and Methods: SNVs were analyzed in 357 unrelated healthy Cuban volunteers. Genotype, allele frequencies, and ancestry proportions were determined, and the pairwise fixation index (FST) was evaluated. Results: Hardy–Weinberg equilibrium (HWE) deviations in six loci (rs11572103, rs2740574, rs776746, rs3025039, rs861539, and rs1762429) were identified. Minor allele frequencies (MAFs) ranged from 0.00 to 0.15 for variants in genes encoding xenobiotic metabolizing enzymes. They also ranged from 0.01 to 0.21 for variants in DNA repair, growth factors, methyltransferase, and methyl-binding proteins, while they ranged from 0.04 to 0.27 for variants in the O-6-methylguanine-DNA methyltransferase enzyme. Moderate genetic divergence was observed upon comparison to Africans (FST = 0.071 and SD 0.079), with 19 markers exhibiting moderate-to-large genetic differentiation. The average European, African, and Amerindian ancestry proportions were 67.8%, 27.2%, and 5.3%, respectively. Ancestry proportions differed by skin color and birthplace for both African and European components, with the exception of the European component, which showed no significant difference between individuals from Western and Eastern regions. Meanwhile, the statistical significance varied in comparisons by skin color and birthplace within the Amerindian component. Low genetic divergence was observed across geographical regions. We identified 12 variants showing moderate-to-large differentiation between White/Black individuals. Conclusion: Altogether, our results may support national strategies for the introduction of pharmacogenetic tools in clinical practice, contributing to the development of precision medicine in Cuba. [ABSTRACT FROM AUTHOR]

Introduction: In collaboration with the Minnesota Hmong community, we have previously discovered significant differences in allele frequencies for key Single Nucleotide Variations (SNVs) within Very Important Pharmacogenes (VIPs) between Hmong and East Asians. Recognizing the potential clinical implications of these observed differences, we sought to validate these observations in a Hmong cohort residing in California, the state with the largest Hmong population in the US. Robust validation of these differences would affect motivation for clinicians treating individuals who identify as Hmong to consider pharmacogenomic (PGx) testing as a means to improve clinical decision making when using therapeutic agents in this unique population. Method: Guided by California Hmong community leaders and utilizing the basic approach of community-based participatory research, demographic, clinical information and a buccal swab was obtained from Hmong adults residing in California. A commercial PGx testing panel was performed on these samples and specific allele frequencies of interest were compared between California and Minnesota Hmong. Allele frequency differences between California Hmong, East Asians and Europeans, were also compared. Return-of-PGx-results and presentations of group data were made to members of the Hmong along with PGx educational sessions to help interpret the observations. Results: In 118 California Hmong who completed the study, the allele frequencies for SNV's were similar to previous Minnesota Hmong results. Furthermore, out of the 18 SNVs that were not previously reported in Hmong, allele frequencies were statistically different in 38% (7/18) of SNVs comparing California Hmong to East Asians, and in 77.8% (14/18) SNVs comparing California Hmong to Europeans. Conclusion: These results validate the original study's findings that Hmong people living in different US locations have similar allele frequencies for key PGx genes. Further, for many of these PGx genes, their allele frequencies are significantly different compared to either East Asians or Europeans. Clinicians should consider these important differences when prescribing medications for people who identify as Hmong. [ABSTRACT FROM AUTHOR]

Patients with metastatic triple-negative breast cancer (TNBC) show variable responses to PD-1 inhibition. Efficient patient selection by predictive biomarkers would be desirable but is hindered by the limited performance of existing biomarkers. Here, we leveraged in silico patient cohorts generated using a quantitative systems pharmacology model of metastatic TNBC, informed by transcriptomic and clinical data, to explore potential ways to improve patient selection. We evaluated and quantified the performance of 90 biomarker candidates, including various cellular and molecular species, at different cutoffs by a cutoff-based biomarker testing algorithm combined with machine learning-based feature selection. Combinations of pretreatment biomarkers improved the specificity compared to single biomarkers at the cost of reduced sensitivity. On the other hand, early on-treatment biomarkers, such as the relative change in tumor diameter from baseline measured at two weeks after treatment initiation, achieved remarkably higher sensitivity and specificity. Further, blood-based biomarkers had a comparable ability to tumor-or lymph node-based biomarkers in identifying a subset of responders, potentially suggesting a less invasive way for patient selection. [ABSTRACT FROM AUTHOR]

The field of drug delivery has significantly advanced tumor therapy, yet challenges such as the predictability of treatment outcomes and the real‐time monitoring of tumor progression, such as recurrence or metastasis. Luminescent metal–organic frameworks (L‐MOFs) are a promising solution to overcome these issues. This review offers a comprehensive exploration of the design and synthesis of L‐MOFs, presenting their crucial optical properties. The review highlights the pivotal role of L‐MOFs in enabling precision medicine for tumor treatment driven by their performance. Through strategically harnessing light‐manipulated metal–organic frameworks (MOFs), real‐time therapeutic monitoring and precise spatiotemporal control in tumor treatment can be achieved. Moreover, this review meticulously examines the influence of photomanipulating MOFs on in situ drug synthesis, introducing the catalytic role in light‐manipulated gas therapy. Additionally, the role of L‐MOFs as gene therapy vectors that utilize light to trigger gene expression responses is also comprehensively explored in the article, aiming to highlight their role in enabling tumor therapy through gene therapy. The review concludes by proposing an artificial intelligence (AI)‐mediated blueprint for tumor treatment with L‐MOFs, aiming to pave the way for innovative approaches through light manipulation. [ABSTRACT FROM AUTHOR]

Hypospadias occurs sporadically in male livestock and is characterized by a non‐fused urethra during fetal development. In this study, perineal hypospadias, a bifid scrotum, penile hypoplasia, and bilateral abdominal cryptorchidism were diagnosed in a neonatal Holstein male calf. Septicemia was also suspected due to hypothermia, blurred conjunctivae, and loss of sucking and swallowing reflexes. Gross pathology revealed that both testicles were located intraabdominally caudally to the kidneys. Histopathological examination of the hypospadias showed a urothelium‐lined mucosal fold and parts of the corpus cavernosum penis and prepuce in the subcutis. Whole genome sequencing was performed on the affected calf. Analysis of short‐read coverage depth along the chromosomes identified an entire extra copy of chromosome 26. Based on the comparison of available variant calling data from the sire, the identified trisomy 26 is due to non‐disjunction of homologous chromosomes during the generation of paternal gametes. We have shown for the first time an association between bovine hypospadias and trisomy 26, which adds to the understanding of variation in fetal male sexual development. [ABSTRACT FROM AUTHOR]

Objective: Defined as prospective single‐patient crossover studies with repeated paired cycles of active and control intervention, N‐of‐1 trials have gained attention as an option to obtain high‐quality evidence of efficacy, particularly for patients with rare epilepsies in whom conduction of well‐powered randomized controlled trials can be challenging. The objective of this systematic review is to provide an appraisal of the literature on N‐of‐1 trials in individuals with epilepsy. Methods: We searched PubMed and Embase on January 12, 2024, for studies meeting the following criteria: prospectively planned, within‐patient, multiple‐crossover design in individuals with epilepsy and outcomes related to comorbidities. Information on design, outcome measurements, intervention, and analyses was retrieved. Risk of bias assessment was performed using the Risk of Bias in N‐of‐1 Trials (RoBiNT) scale. We highlighted methodological aspects of the N‐of‐1 trials identified and discuss future recommendations. Results: Five studies met our inclusion criteria. An additional multiple‐crossover trial that evaluated treatment effects exclusively at group level was also included because of its relevance to N‐of‐1 study methodology. The studies enrolled individuals with focal seizures, absences or cognitive impairement and electrographic discharges. Treatments included established or investigational antiseizure medications, off‐label medications, neurostimulation or lifestyle intervention. Three of the five N‐of‐1 trials reported on individual cases. The studies' strengths were the use of individualized treatment dosages and symptom‐specific patient‐reported outcomes. Limitations were related to minimal reporting of baseline characteristics and seizure burden. Significance: The trials identified by our search exemplify how the N‐of‐1 design can be applied to assess interventions in individuals with epilepsy‐related disorders. Future N‐of‐1 trials of antiseizure interventions should take into account baseline seizure frequency, should apply statistical models suited to capture seizure frequency changes reliably and make predefined interim assessments. Non‐seizure outcome measures evaluable over short periods should be considered. Tailored N‐of‐1 methodology could pave the way to evidence‐based, treatment selection for patients with rare epilepsies. [ABSTRACT FROM AUTHOR]

The lungs serve as a site of interchange between the bodily and environmental, an interface between the internal and external world, enacted through breath. We draw on the primacy of this exchange to explore the complexities of living with lung cancer amidst the enduring social challenge of stigma and the advent of 'targeted therapies' at the cutting edge of precision medicine. Lung cancer's association with smoking and resulting stigmatisation of those with lung cancer has been well documented – positioning those with lung cancer as failed subjects of a biopolitics of lifestyle. However, recent developments in 'precision medicine' have drawn attention to alternate, genetic causes of lung cancer, disrupting easy equivalences between deviant cells (malignancy) and deviant conduct (smoking). Despite this, and drawing on interviews with 32 people receiving targeted therapy for lung cancer in Australia, we identify enduring resonances of deviance and stigma which still foreground individual lifestyle and blame, even for those – like many participants in this study – who have never smoked. Even in the context of uncertain causal origins and genetic mutations, the stigma of lung cancer and the figure of 'the smoker' as an object of abjection and disavowal persists. We find Alaimo's concept of transcorporeality instructive for moving away from this biopolitics of lifestyle, toward greater recognition of the collective, though unequal, conditions of 'carcinogenic capitalism' in which we all must live and breathe. In turn, our analysis of the specificities of lung cancer may inform broader sociological approaches to tackle stigma at a structural level. [ABSTRACT FROM AUTHOR]

Introduction: Matrix Metalloproteinase 3 (MMP3) is a vital member of the MMP family, known for its wide range of substrate specificity and proteolytic activity against Extracellular Matrix (ECM) components. The role of functional polymorphisms in the MMP genes has been previously investigated in relation to cancer susceptibility, particularly breast cancer. Several Single- Nucleotide Polymorphisms (SNPs) in the MMP3 gene have been linked to a number of clinical illnesses, such as Coronary Artery Disease (CAD); however, the results were not entirely conclusive. Aim: To identify pathogenic missense SNPs in the human MMP3 gene and analyse their effects on structure and function. Materials and Methods: This was a record-based cross-sectional study performed using data retrieved from online resources. The analysis was conducted using a series of different bioinformatic tools, for which ethical clearance was obtained from the institution. The online tools used included Sorting Intolerant from Tolerant (SIFT), PolyPhen-2, PhD-SNP, PANTHER, PROVEAN, and SNPs and GO to predict harmful non synonymous SNPs (nsSNPs). Further analysis was performed using I-Mutant 2.0, MutPred2, Consurf, and HOPE software. These tools were able to filter out damaging SNPs and predict the impact of deleterious SNPs on the structure and function of the MMP3 protein. Results: This study predicted two potentially pathogenic SNPs (D175Y and Y116C) out of 443 missense SNPs from dbSNP, which is a database of SNPs available on the National Centre for Biotechnology Information website. Further analysis revealed that these SNPs were located in highly conserved regions and were predicted to decrease protein stability. Conclusion: In this study, two potentially pathogenic SNPs (D175Y and Y116C) were identified. Characterisation of these SNPs can help us gain a better understanding of the molecular basis of clinical conditions. The results of this study can be further validated by designing population-based studies and wet lab experiments. This will help in augmenting research and personalised medicine. [ABSTRACT FROM AUTHOR]

Objectives: The role of inflammation in the pathophysiology of atherosclerosis is extensive. Our study aims to assess the predictive role of inflammatory indexes regarding in-hospital mortality in major vascular surgery of Leriche syndrome as a convenient, low-cost, and noninvasive prognostic marker to optimize the patient's perioperative course. Methods: Our retrospective single-center study enrolled consecutive patients diagnosed with aortoiliac occlusive disease, Leriche syndrome, who underwent elective major vascular surgery between 2017 and 2023 in a tertiary cardiovascular center. Preoperative, postoperative, and day-one after-surgery data, including systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), aggregate index of systemic inflammation (AISI), neutrophil–lymphocyte ratio (NLR), platelet–lymphocyte ratio, and monocyte–lymphocyte ratio, were studied to the endpoint, in-hospital death. We also tested the delta values of the indexes to the endpoint. The indexes were compared to the Revised Cardiac Risk Index (RCRI) and Vascular Surgery Group Cardiac Risk Index (VSG-CRI) for outcome prediction. Results: The tested inflammatory indexes significantly increased from the preoperative to postoperative and, further, to the day-one settings. Preoperative AISI (p = 0.040) emerged as the only independent risk factor regarding in-hospital death occurrence in Leriche patients who underwent major revascularization surgery. While RCRI did not significantly predict the endpoint (AUC = 0.698, p = 0.057), VSG-CRI (AUC = 0.864, p = 0.001) presented the best result in ROC analysis. Postoperative NLR (AUC = 0.758, p = 0.006) was next, followed by NLR postoperative–preoperative (_Preop-_Postop) delta value (AUC = 0.725, p = 0.004), postoperative SIRI (AUC = 0.716, p = 0.016), SIRI_Preop-_Postop delta value (AUC = 0.712, p = 0.016), postoperative SII (AUC = 0.692, p = 0.032), and SII_Preop-_Postop delta value (AUC = 0.631, p = 0.030). Conclusions: Inflammatory indexes are valuable tools for assessing perioperative risk in major vascular surgery, enhancing the value of the already validated risk scores. [ABSTRACT FROM AUTHOR]

Epigenetic dysregulation has long been recognized as a significant contributor to tumorigenesis and tumor maintenance, impacting all recognized cancer hallmarks. Although some epigenetic drugs have received regulatory approval for certain hematological malignancies, their efficacy in treating solid tumors has so far been largely disappointing. However, recent advancements in developing new compounds and a deeper understanding of cancer biology have led to success in specific solid tumor subtypes through precision medicine approaches. Moreover, epigenetic drugs may play a crucial role in synergizing with other anticancer treatments, enhancing the sensitivity of cancer cells to various anticancer therapies, including chemotherapy, radiation therapy, hormone therapy, targeted therapy, and immunotherapy. In this review, we critically evaluate the evolution of epigenetic drugs, tracing their development from initial use as monotherapies to their current application in combination therapies. We explore the preclinical rationale, completed clinical studies, and ongoing clinical trials. Finally, we discuss trial design strategies and drug scheduling to optimize the development of possible combination therapies. [ABSTRACT FROM AUTHOR]

Biochemical phenotyping has been the milestone for diagnosing and managing patients affected by inborn errors of intermediary metabolism (IEiM); however, identifying the genotype responsible for these monogenic disorders greatly contributes to achieving these goals. Herein, whole-exome sequencing (WES) was used to determine the genotypes of 95 unrelated Mexican pediatric patients suspected of having IEiM. They were classified into those bearing specific biochemical abnormalities (Group 1), and those presenting unspecific biochemical profiles (Group 2). The overall concordance between the initial biochemical diagnosis and final genotypic diagnoses was 72.6% (N = 69/95 patients), with the highest concordance achieved in Group 1 (91.3%, N = 63/69), whereas the concordance was limited in Group 2 (23.07%). This finding suggests that previous biochemical phenotyping correlated with the high WES diagnostic success. Concordance was high for urea cycle disorders (94.1%) and organic acid disorders (77.4%). The identified mutational spectrum comprised 83 IEiM-relevant variants (pathogenic, likely pathogenic, and variants of uncertain significance or VUS), including three novel ones, distributed among 29 different genes responsible for amino acid, organic acid, urea cycle, carbohydrate, and lipid disorders. Inconclusive WES results (7.3%, N = 7/95) relied on monoallelic pathogenic genotypes or those involving two VUS for autosomal-recessive IEiMs. A second monogenic disease was observed in 10.5% (N = 10/95) of the patients. According to the WES results, modifications in treatment had to be made in 33.6% (N = 32/95) of patients, mainly attributed to the presence of a second monogenic disease, or to an actionable trait. This study includes the largest cohort of Mexican patients to date with biochemically suspected IEiM who were genetically diagnosed through WES, underscoring its importance in medical management. [ABSTRACT FROM AUTHOR]

One of the most critical goals in healthcare is safe and effective drug therapy, which is directly related to an individual's response to treatment. Precision medicine can improve drug safety in many scenarios, including polypharmacy, and it requires the development of new genetic characterization methods. In this report, we use real-time PCR, microarray techniques, and mass spectrometry (MALDI-TOF), which allows us to compare them and identify the potential benefits of technological improvements, leading to better quality medical care. These comparative studies, as part of our pharmacogenetic Five-Step Precision Medicine (5SPM) approach, reveal the superiority of mass spectrometry over the other methods analyzed and highlight the importance of updating the laboratory's pharmacogenetic methodology to identify new variants with clinical impact. [ABSTRACT FROM AUTHOR]

Amelanotic melanoma (AM) is a subtype of hypomelanotic or completely amelanotic melanoma. AM is a rare subtype of melanoma that exhibits a higher recurrence rate and aggressiveness as well as worse surveillance than typical melanoma. AM shows a dysregulation of melanin production, cell cycle control, and apoptosis pathways. Knowing these pathways has an application in medicine due to targeted therapies based on the inhibiting elements of the abovementioned pathways. Therefore, we summarized and discussed AM biochemical and molecular induction pathways and personalized medicine approaches, clinical management, and future directions due to the fact that AM is relatively rare. AM is commonly misdiagnosed. Hence, the role of biomarkers is becoming significant. Nonetheless, there is a shortage of biomarkers specific to AM. BRAF, NRAS, and c-KIT genes are the main targets of therapy. However, the role of BRAF and KIT in AM varied among studies. BRAF inhibitors combined with MAK inhibitors demonstrate better results. Immune checkpoint inhibitors targeting CTLA-4 combined with a programmed death receptor 1 (PD-1) show better outcomes than separately. Fecal microbiota transplantation may overcome resistance to immune checkpoint therapy of AM. Immune-modulatory vaccines against indoleamine 2,3-dioxygenase (IDO) and PD ligand (PD-L1) combined with nivolumab may be efficient in melanoma treatment. [ABSTRACT FROM AUTHOR]

Non-small cell lung cancer (NSCLC) has established predictive biomarkers that enable decisions on treatment regimens for many patients. However, resistance to therapy is widespread. It is therefore essential to have a panel of molecular biomarkers that may help overcome therapy resistance and prevent adverse effects of treatment. We performed in silico analysis of NSCLC prognostic indicators, separately for adenocarcinomas and squamous carcinomas, by using The Cancer Genome Atlas (TCGA) and non-TCGA data sources in cBioPortal as well as UALCAN. This review describes lung cancer biology, elaborating on the key genetic alterations and specific genes responsible for resistance to conventional treatments. Importantly, we examined the mechanisms associated with resistance to immune checkpoint inhibitors. Our analysis indicated that a robust prognostic biomarker was lacking for NSCLC, especially for squamous cell carcinomas. In this work, our screening uncovered previously unidentified prognostic gene expression indicators, namely, MYO1E, FAM83 homologs, and DKK1 for adenocarcinoma, and FGA and TRIB1 for squamous cell carcinoma. It was further observed that overexpression of these genes was associated with poor prognosis. Additionally, FAM83 homolog and TRIB1 unexpectedly harbored copy number amplifications. In conclusion, this study elucidated novel prognostic indicators for NSCLC that may serve as targets to overcome therapy resistance toward improved patient outcomes. [ABSTRACT FROM AUTHOR]

Simple Summary: Head and neck squamous cell carcinoma (HNSCC) is a deadly form of cancer, affecting areas like the mouth, throat, and larynx. This review explores the complex molecular pathways involved in HNSCC development and progression, focusing on the role of microRNAs (miRNAs)—small molecules that regulate gene expression. We aim to provide a comprehensive overview of how miRNAs influence HNSCC, their potential as diagnostic and prognostic markers, and their use in developing new targeted therapies. We also discuss promising nanotechnology-based approaches for delivering miRNA therapies more effectively. By synthesizing the current knowledge on miRNAs in HNSCC, this research may help identify new biomarkers for early detection and prognosis, as well as novel therapeutic targets. Ultimately, these insights could lead to improved personalized treatments and better outcomes for HNSCC patients. This paper presents a comprehensive comparative analysis of biomarkers for head and neck cancer (HNC), a prevalent but molecularly diverse malignancy. We detail the roles of key proteins and genes in tumourigenesis and progression, emphasizing their diagnostic, prognostic, and therapeutic relevance. Our bioinformatic validation reveals crucial genes such as AURKA, HMGA2, MMP1, PLAU, and SERPINE1, along with microRNAs (miRNA), linked to HNC progression. OncomiRs, including hsa-miR-21-5p, hsa-miR-31-5p, hsa-miR-221-3p, hsa-miR-222-3p, hsa-miR-196a-5p, and hsa-miR-200c-3p, drive tumourigenesis, while tumour-suppressive miRNAs like hsa-miR-375 and hsa-miR-145-5p inhibit it. Notably, hsa-miR-155-3p correlates with survival outcomes in addition to the genes RAI14, S1PR5, OSBPL10, and METTL6, highlighting its prognostic potential. Future directions should focus on leveraging precision medicine, novel therapeutics, and AI integration to advance personalized treatment strategies to optimize patient outcomes in HNC care. [ABSTRACT FROM AUTHOR]

Treatments targeting the immune system only benefit a subset of patients with bladder cancer (BC). Biomarkers predictive of BC progression and response to specific therapeutic interventions are required. We evaluated whether peripheral blood immune subsets and expression of clinically relevant immune checkpoint markers are associated with clinicopathologic features of BC. Peripheral blood mononuclear cells isolated from blood collected from 23 patients with BC and 9 age‐matched unaffected‐by‐cancer control donors were assessed using a 21‐parameter flow cytometry panel composed of markers of T, B, natural killer and myeloid populations and immune checkpoint markers. Patients with BC had significantly lower numbers of circulating CD19+ B cells and elevated circulating CD4+CD8+ T cells compared with the control cohort. Immune checkpoint markers programmed cell death protein 1 (PD‐1) and T‐cell immunoglobulin and mucin‐domain containing‐3 (TIM‐3) were elevated in the total peripheral immune cell population in patients with BC. Within the BC cohort, PD‐1 expression in T and myeloid cells was elevated in muscle‐invasive compared with non–muscle‐invasive disease. In addition, elevated T, B and myeloid PD‐1 cell surface expression was significantly associated with tumor stage, suggesting that measures of peripheral immune cell exhaustion may be a predictor of tumor progression in BC. Finally, positive correlations between expression levels of the various immune checkpoints both overall and within key peripheral blood immune subsets collected from patients with BC were observed, highlighting likely coregulation of peripheral immune checkpoint expression. The peripheral blood immunophenotype in patients with BC is altered compared with cancer‐free individuals. Understanding this dysregulated immune profile will contribute to the identification of diagnostic and prognostic indicators to guide effective immune‐targeted, personalized treatments. [ABSTRACT FROM AUTHOR]

Among chronic diseases, diabetes mellitus carries one of the most life-threatening and disabling complications. It has its cost complications in addition to reducing life expectancy. Diabetes management aims to find each patient's best course of action at the ideal time. Using precision medicine for patients with diabetes mellitus can improve the quality of therapy. For this, genetic screening becomes an essential component. The pharmacogenetics of antidiabetic medications can be a hallmark component that can be incorporated into genetic screening. For the management of diabetes, precision medicine is gaining momentum. Long-lasting response to oral sulphonylureas in insulin-dependent infants because of neonatal diabetes (abnormal beta cells) is considered one of the best examples of the significance of precision medicine. The present review aims to pinpoint the new subgroups of diabetes based on glutamic acid decarboxylase antibodies, age, body mass index, A1C, insulin resistance and glycemic response to metformin. Incorporating genetic screening into therapeutic therapy and achieving precision medicine for type 2 diabetes patients depends on the pharmacogenetics of anti-diabetic drugs. The importance of PM in the treatment of diabetes is expanding and its use can revolutionize the lives of diabetic individuals. [ABSTRACT FROM AUTHOR]

Metabolomics enables the analysis of metabolites within an organism, which offers the closest direct measurement of the physiological activity of the organism, and has advanced efforts to characterize metabolic states, identify biomarkers, and investigate metabolic pathways. A high degree of innovation in analytical techniques has promoted the application of metabolomics, especially in the study of clinical surgery. Metabolomics can be employed as a clinical testing method to maximize therapeutic outcomes, and has been applied in rapid diagnosis of diseases, timely postoperative monitoring, prognostic assessment, and personalized medicine. This review focuses on the use of mass spectrometry and nuclear magnetic resonance-based metabolomics in clinical surgery, including identifying metabolic changes before and after surgery, finding disease-associated biomarkers, and exploring the potential of personalized therapy. Challenges and opportunities of metabolomics in organ transplantation are also discussed, with a particular emphasis on metabolomics in donor organ evaluation and protection, prognostic outcome prediction, as well as postoperative adverse reaction monitoring. In the end, current limitations of metabolomics in clinical surgery and future research directions are presented. [ABSTRACT FROM AUTHOR]

Tacrolimus, a calcineurin inhibitor, is a highly effective immunosuppressant used in solid organ transplantation (SOT). However, it is characterized by a narrow therapeutic range and high inter‐patient variability in pharmacokinetics. Standard weight‐based dosing followed by empiric dose titration is suboptimal in controlling drug concentrations, increasing risk of rejection or toxicity, particularly in the initial months post transplantation. This review explores the potential of combined pre‐transplant genotyping and pharmacokinetic (PK) modelling to improve tacrolimus dosing in paediatric SOT recipients. A systematic search of Medline, Embase and Cochrane databases identified studies published between March 2013 and March 2023 that investigated genotype‐ and PK model‐informed tacrolimus dosing in children post‐SOT. The Newcastle‐Ottawa Scale assessed study quality. Seven studies encompassing paediatric kidney, heart, liver and lung transplants reported using genotype and model‐informed dosing. A combination of clinical and genetic factors significantly impacts tacrolimus clearance and thus initial dose recommendation. Body size, transplant organ and co‐medications were consistently important, while either time post‐transplant or haematocrit emerged in some studies. Several models were identified, however, with limitations evident in some and with absence of evidence for their effectiveness in optimizing initial and subsequent dosing. This review highlights the development of PK models in paediatric SOT that integrate genotype and clinical covariates to personalize early tacrolimus dosing. While promising, prospective studies are needed to validate and confirm their effectiveness in improving time to therapeutic concentrations and reducing under‐ or overexposure. This approach has the potential to optimize tacrolimus therapy in paediatric SOT, thereby improving outcomes. [ABSTRACT FROM AUTHOR]

This is an invited commentary article for the special issue. The main thesis is that an effective strategy for computational psychiatry to handle the (possibly intrinsic) heterogeneity of psychiatric disorders is to focus on developing clinical principles rather than solely precision medicine. General Scientific Summary: The field of computational psychiatry has overwhelmingly focused on the goal of precision medicine (a "fine-scalpels" approach). I argue for reallocating some effort toward deriving potent clinical principles to improve psychological treatments (a "strong-swords" approach)—a worthwhile goal in its own right with an established track record. I draw on an example from my research program to illustrate the strong-swords approach. [ABSTRACT FROM AUTHOR]

The initial management of patients with lung cancer is growing more complex in the context of an expanding number of precision medicine treatments. These challenges are accompanied by opportunities to deliver more efficacious and less toxic treatments to patients. Indications for these treatments are also expanding, and patients with lung cancer across multiple stages now require biomarker testing. Given their role in the initial management of patients being diagnosed with lung cancer, pulmonologists must have fundamental knowledge regarding the importance, indications, and implications of biomarker testing across the spectrum of histology and stage. The purpose of this review is to provide fundamental knowledge regarding biomarker testing, its incorporation into the initial diagnostic and staging evaluation, and guidance for working within a multidisciplinary team to achieve timely and comprehensive biomarker testing to direct the use of precision medicine treatments. [ABSTRACT FROM AUTHOR]

The varied treatment response to inhaled corticosteroids (ICS) in patients with COPD and the associated increased risk of pneumonia necessitate a personalized ICS therapeutic approach. This is informed by blood eosinophil count (BEC), which predicts ICS treatment response. However, BEC appears to change in response to ICS treatment. Does (1) BEC measured on ICS treatment (2) BEC measured off ICS treatment, or (3) the change in BEC during ICS treatment best predict treatment response to ICS in COPD? The Fluticasone Salmeterol on COPD Exacerbations Trial (FLAME), a 52-week, double-blind randomized controlled trial compared long-acting beta-2 agonists (LABAs)/long-acting muscarinic antagonists (LAMAs) with LABA/ICS. Corticosteroids were prohibited during a 4-week run-in period. We chose patients previously on ICS, thereby allowing BEC before and after the run-in period to represent BEC on and off ICS, respectively. In this post hoc analysis, we revisited outcome data, exploring how the three BEC biomarkers interacted with treatment response to the ICS-containing regimen. Our study showed that LABA/LAMA combination was superior, or at least noninferior, to LABA/ICS in curbing exacerbations for most FLAME participants. However, higher BEC off ICS, higher BEC on ICS, and significant BEC suppression during ICS treatment corresponded to superior response to LABA/ICS in terms of exacerbation rate, time to first exacerbation, and time to first pneumonia. In a subgroup, including 9% of participants, BEC changed significantly during ICS treatment (≥ 200 cells/μL), and higher BEC on ICS did not predict ICS treatment response. For these patients, BEC off ICS and BEC change proved more predictive. Excess pneumonia risk associated with ICS appeared to be confined to patients who do not benefit from this treatment. BEC was not predictive of treatment effects on lung function and health status. This exploratory analysis advocates preferentially using BEC off ICS or BEC change during ICS treatment for guiding ICS treatment decisions. BEC measured on ICS is less predictive of treatment response. ClinicalTrials.gov; No.: NCT01782326 ; URL: www.clinicaltrials.gov [ABSTRACT FROM AUTHOR]

Shortened telomere length (TL) is a genomic risk factor for fibrotic interstitial lung disease (ILD), but its role in clinical management is unknown. What is the clinical impact of TL testing on the management of ILD? Patients were evaluated in the Columbia University ILD clinic and underwent Clinical Laboratory Improvement Amendments-certified TL testing by flow cytometry and fluorescence in situ hybridization (FlowFISH) as part of clinical treatment. Short TL was defined as below the 10th age-adjusted percentile for either granulocytes or lymphocytes by FlowFISH. Patients were offered genetic counseling and testing if they had short TL or a family history of ILD. FlowFISH TL was compared with research quantitative polymerase chain reaction (qPCR) TL measurement. A total of 108 patients underwent TL testing, including those with clinical features of short telomere syndrome such as familial pulmonary fibrosis (50%) or extrapulmonary manifestations in the patient (25%) or a relative (41%). The overall prevalence of short TL was 46% and was similar across clinical ILD diagnoses. The number of short telomere clinical features was independently associated with detecting short TL (OR, 2.00; 95% CI, 1.27-3.32). TL testing led to clinical treatment changes for 35 patients (32%), most commonly resulting in reduction or avoidance of immunosuppression. Of the patients who underwent genetic testing (n = 34), a positive or candidate diagnostic finding in telomere-related genes was identified in 10 patients (29%). Inclusion of TL testing below the 1st percentile helped reclassify eight of nine variants of uncertain significance into actionable findings. The quantitative polymerase chain reaction test correlated with FlowFISH, but age-adjusted percentile cutoffs may not be equivalent between the two assays. Incorporating TL testing in ILD impacted clinical management and led to the discovery of new actionable genetic variants. [ABSTRACT FROM AUTHOR]

Genomic tumour profiling has a crucial role in the management of patients with solid cancers, as it helps selecting and prioritising therapeutic interventions based on prognostic and predictive biomarkers, as well as identifying markers of hereditary cancers. Harmonised approaches to interpret the results of genomic testing are needed to support physicians in their decision making, prevent inequalities in precision medicine and maximise patient benefit from available cancer management options. The European Society for Medical Oncology (ESMO) Translational Research and Precision Medicine Working Group assembled a group of international experts to propose recommendations for preparing clinical genomic reports for solid cancers. These recommendations aim to foster best practices in integrating genomic testing within clinical settings. After review of available evidence, several rounds of surveys and focused discussions were conducted to reach consensus on the recommendation statements. Only consensus recommendations were reported. Recommendation statements were graded in two tiers based on their clinical importance: level A (required to maintain common standards in reporting) and level B (optional but necessary to achieve ideal practice). Genomics reports should present key information in a front page(s) followed by supplementary information in one or more appendices. Reports should be structured into sections: (i) patient and sample details; (ii) assay and data analysis characteristics; (iii) sample-specific assay performance and quality control; (iv) genomic alterations and their functional annotation; (v) clinical actionability assessment and matching to potential therapy indications; and (vi) summary of the main findings. Specific recommendations to prepare each of these sections are made. We present a set of recommendations aimed at structuring genomics reports to enhance physician comprehension of genomic profiling results for solid cancers. Communication between ordering physicians and professionals reporting genomic data is key to minimise uncertainties and to optimise the impact of genomic tests in patient care. • These Recommendations cover the preparation of genomic reports to inform clinical decisions for patients with solid cancers. • Recommendations are based on consensus from a multidisciplinary group of experts after reviewing available evidence. • The manuscript provides guidance on structuring genomic reports, and the optimal presentation of content for each section. • These recommendations are relevant to most next-generation sequencing assays utilised in clinical practice and research. • Recommendations are categorised into priority levels (A, B) to adapt to diverse clinical and laboratory contexts. [ABSTRACT FROM AUTHOR]

To assess the impact of genetic risk estimation for primary open-angle glaucoma (POAG) in Japanese individuals. Cross-sectional analysis. Genetic risk scores (GRSs) were constructed based on a genome-wide association study (GWAS) of POAG in Japanese people. A total of 3625 Japanese individuals, including 1191 patients and 2434 controls (Japanese Tohoku), were used for the model selection. We also evaluated the discriminative accuracy of constructed GRSs in a dataset comprising 1034 patients and 1147 controls (the Japan Glaucoma Society Omics Group [JGS-OG] and the Genomic Research Committee of the Japanese Ophthalmological Society [GRC-JOS]) and 1900 participants from a population-based study (Hisayama Study). We evaluated 2 types of GRSs: polygenic risk scores using the pruning and thresholding procedure and a GRS using variants associated with POAG in the GWAS of the International Glaucoma Genetics Consortium (IGGC). We selected the model with the highest areas under the receiver operating characteristic curve (AUC). In the population-based study, we evaluated the correlations between GRS and ocular measurements. Proportion of patients with POAG after stratification according to the GRS. We found that a GRS using 98 variants, which showed genome-wide significance in the IGGC, showed the best discriminative accuracy (AUC, 0.65). In the Japanese Tohoku, the proportion of patients with POAG in the top 10% individuals was significantly higher than that in the lowest 10% (odds ratio [OR], 6.15; 95% confidence interval [CI], 4.35–8.71). In the JGS-OG and GRC-JOS, we confirmed similar impact of POAG GRS (AUC, 0.64; OR [top vs. bottom decile], 5.81; 95% CI, 3.79–9.01). In the population-based study, POAG prevalence was significantly higher in the top 20% individuals of the GRS compared with the bottom 20% (9.2% vs. 5.0%). However, the discriminative accuracy was low (AUC, 0.56). The POAG GRS was correlated positively with intraocular pressure (r = 0.08: P = 4.0 × 10–4) and vertical cup-to-disc ratio (r = 0.11; P = 4.0 × 10–6). The GRS showed moderate discriminative accuracy for POAG in the Japanese population. However, risk stratification in the general population showed relatively weak discriminative performance. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article. [ABSTRACT FROM AUTHOR]

[Display omitted] Microbial therapies have promising applications in the treatment of a broad range of diseases. However, effective colonization of the target region by therapeutic microorganisms remains a significant challenge owing to the complexity of the intestinal system. Here, we developed surface nanocoating-based universal platform (SNUP), which enabled the manipulation of controlled release and targeted colonization of therapeutic microbes in the digestive tract without the utilization of any targeting molecules. The system controlled the decomposition time of SNUP in the gut by regulating different modification layers and modification sequences on the microorganism's surface, so that the microorganism was released at a predetermined time and space. With the SNUP nanomodification technology, we could effectively deliver therapeutic microorganisms to specific complex intestinal regions such as the small intestine and colon, and protect the bioactivity of therapeutic microorganisms from destruction by both strong acids and digestive enzymes. In this study, we found that two layers SNUP-encapsulated Liiliilactobacillus salivarius (LS@CCMC) could efficiently colonize the small intestine and significantly improve the symptoms of a mouse model of Parkinson's disease through sustained secretion of γ-aminobutyric acid (GABA). This surface nanocoating-based universal platform system does not require the design of specific targeting molecules, providing a simple and universal method for colonized microbial therapy, target theranostics, precision medicine, and personalized medicine. [ABSTRACT FROM AUTHOR]

The emergence of targeted therapies has transformed ovarian cancer treatment. However, biomarker profiling for precision medicine is limited by access to quality, tumour‐enriched tissue samples. The use of cell‐free DNA (cfDNA) in ascites presents a potential solution to this challenge. In this study, next‐generation sequencing was performed on ascites‐derived cfDNA samples (26 samples from 15 human participants with ovarian cancer), with matched DNA from ascites‐derived tumour cells (n = 5) and archived formalin‐fixed paraffin‐embedded (FFPE) tissue (n = 5). Similar tumour purity and variant detection were achieved with cfDNA compared to FFPE and ascites cell DNA. Analysis of large‐scale genomic alterations, loss of heterozygosity and tumour mutation burden identified six cases of high genomic instability (including four with pathogenic BRCA1 and BRCA2 mutations). Copy number profiles and subclone prevalence changed between sequential ascites samples, particularly in a case where deletions and chromothripsis in Chr17p13.1 and Chr8q resulted in changes in clinically relevant TP53 and MYC variants over time. Ascites cfDNA identified clinically actionable information, concordant to tissue biopsies, enabling opportunistic molecular profiling. This advocates for analysis of ascites cfDNA in lieu of accessing tumour tissue via biopsy. [ABSTRACT FROM AUTHOR]

Next-generation sequencing (NGS) technologies have become an indispensable tool within the research field driving genomic discoveries and furthering our understanding of the genomic changes that lead to human disease. NGS technologies have great potential to provide invaluable genomic data that can be used to improve clinical diagnosis and the delivery of precision medicine. Over the past decade NGS has translated into the clinical setting for both hereditary and somatic indications. Whilst it has great potential in the clinic, the application of this technology for cancer faces a number of challenges, both technical and logistic. Consideration must be given to the role of this technology and how it is best used in the patient pathway. In this review we describe the current technologies routinely used in the clinical laboratory and provide insights into their application in solid tumour testing. [ABSTRACT FROM AUTHOR]

Objective: We used machine learning to develop and validate a multivariable algorithm allowing the accurate and early prediction of postoperative hypocalcemia risk. Background: Postoperative hypocalcemia is frequent after total thyroidectomy. An early and accurate individualized prediction of the risk of hypocalcemia could guide the selective prescription of calcium supplementation only to patients most likely to present with hypocalcemia after total thyroidectomy. Methods: This retrospective study enrolled all patients undergoing total thyroidectomy in a single referral center between November 2019 and March 2022 (derivation cohort) and April 2022 and September 2022 (validation cohort). The primary study outcome was postoperative hypocalcemia (serum calcium under 80 mg/L). Exposures were multiple clinical and biological variables prospectively collected and analyzed with various machine learning methods to develop and validate a multivariable prediction algorithm. Results: Among 610/118 participants in the derivation/validation cohorts, 100 (16.4%)/26 (22%) presented postoperative hypocalcemia. The most accurate prediction algorithm was obtained with random forest and combined intraoperative parathyroid hormone measurements with 3 clinical variables (age, sex, and body mass index) to calculate a postoperative hypocalcemia risk for each patient. After multiple cross-validation, the area under the receiver operative characteristic curve was 0.902 (0.829--0.970) in the derivation cohort, and 0.928 (95% CI: 0.86; 0.97) in the validation cohort. Postoperative hypocalcemia risk values of 7% (low threshold) and 20% (high threshold) had, respectively, a sensitivity of 92%, a negative likelihood ratio of 0.11, a specificity of 90%, and a positive of 7.6 for the prediction of postoperative hypocalcemia. Conclusions: Using machine learning, we developed and validated a simple multivariable model that allowed the accurate prediction of postoperative hypocalcemia. The resulting algorithm could be used at the point of care to guide clinical management after total thyroidectomy. [ABSTRACT FROM AUTHOR]

• Identification of protein biomarkers for predicting Tocilizumab response in Rheumatoid arthritis. • High-precision proteomics approach using Data-independent acquisition mass spectrometry in RA patient serum. • Developed a robust multi-biomarker panel with 86 % discriminative power and 0.84 AUC. • Potential diagnostic tool for TCZ non-responder prediction in RA patients. Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease characterized by inflammation in the synovial lining of the joints. Key inflammatory cytokines such as interleukin-6 (IL-6), TNF-α, and others play a critical role in the activation of local synovial leukocytes and the induction of chronic inflammation. Tocilizumab (TCZ), a humanized anti-IL-6 receptor monoclonal antibody, has demonstrated significant clinical efficacy in treating RA patients. However, similar to other inflammatory cytokine blockers, such as TNF-alpha inhibitors, Interleukin-1 inhibitors, or CD20 inhibitors, some patients do not respond to treatment. To address this challenge, our study employed a high-precision proteomics approach to identify protein biomarkers capable of predicting clinical responses to Tocilizumab in RA patients. Through the use of data-independent acquisition (DIA) mass spectrometry, we analyzed serum samples from both TCZ responders and non-responders to discover potential biomarker candidates. These candidates were subsequently validated using individual serum samples from two independent cohorts: a training set (N = 70) and a test set (N = 18), allowing for the development of a robust multi-biomarker panel. The constructed multi-biomarker panel demonstrated an average discriminative power of 86 % between response and non-response groups, with a high area under the curve (AUC) value of 0.84. Additionally, the panel exhibited 100 % sensitivity and 60 % specificity. Collectively, our multi-biomarker panel holds promise as a diagnostic tool to predict non-responders to TCZ treatment in RA patients. [ABSTRACT FROM AUTHOR]

The obscure drug response continues to be a limiting factor for accurate cures for cancer. Next generation sequencing technologies have propelled the pharmacogenomic studies with characterized large panels of cancer cell line at multi‐omics level. However, the sufficient integration of the multi‐omics data and the efficient prediction for drug response and synergy still remain a challenge. To address these problems, ECFD is designed, an ensemble cascade forest‐based framework that predicts drug response and synergy using five types of omics data. Experimental results show the significant advantages of the ECFD model over existing models. The best integration of feature extraction is determined and the superiorities of robust stability in the face of new and small samples are highlighted. In addition, the methodological framework highlights the explainability of the model, the mechanisms of drug resistance and drug combination treatment strategies based on explainable analyses and biological networks. In sum, ECFD may facilitate the evaluation of drug response and speculation of potential synergy therapies in personalized and precision treatment. [ABSTRACT FROM AUTHOR]