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A significant challenge in the treatment of central nervous system (CNS) disorders is represented by the presence of the blood--brain barrier (BBB), a highly selective membrane that regulates molecular transport and restricts the passage of pathogens and therapeutic compounds. Traditional in vivo models are constrained by high costs, lengthy experimental timelines, ethical concerns, and interspecies variations. In vitro models, particularly microfluidic BBB-on-a-chip devices, have been developed to address these limitations. These advanced models aim to more accurately replicate human BBB conditions by incorporating human cells and physiological flow dynamics. In this framework, here we developed an innovative microfluidic system that integrates thin-film electrodes for non-invasive, real-time monitoring of BBB integrity using electrochemical impedance spectroscopy (EIS). EIS measurements showed frequencydependent impedance changes, indicating BBB integrity and distinguishing well-formed from non-mature barriers. The data from EIS monitoring was confirmed by permeability assays performed with a fluorescence tracer. The model incorporates human endothelial cells in a vessel-like arrangement to mimic the vascular component and three-dimensional cell distribution of human astrocytes and microglia to simulate the parenchymal compartment. By modeling the BBB-on-a-chip with an equivalent circuit, a more accurate trans-endothelial electrical resistance (TEER) value was extracted. The device demonstrated successful BBB formation and maturation, confirmed through live/dead assays, immunofluorescence and permeability assays. Computational fluid dynamics (CFD) simulations confirmed that the device mimics in vivo shear stress conditions. Drug crossing assessment was performed with two chemotherapy drugs: doxorubicin, with a known poor BBB penetration, and temozolomide, conversely a specific drug for CNS disorders and able to cross the BBB, to validate the model predictive capability for drug crossing behavior. The proposed sensorized microfluidic device represents a significant advancement in BBB modeling, offering a versatile platform for CNS drug development, disease modeling, and personalized medicine. [ABSTRACT FROM AUTHOR]

Purpose: To map and summarise available literature on the effectiveness or other benefits of group- and individual-based interventions provided for adults living with stroke or ischaemic heart disease (IHD) in the community. Material and Methods: The review was conducted based on JBI methodology and reported using Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews. Articles were retrieved from: Medline, PsychInfo, Embase, Scopus, and CINAHL from 2002–2022. Extracted data from eligible studies included type of health outcomes (e.g., impairments), retention and adherence, social connectedness, and the costs associated with group- and individual-based interventions. Results: After screening, five articles (representing 4 unique studies) comparing a group- and individual-based intervention were included (total sample size n = 87). Three types of interventions were assessed: exercise (3/5), communication (1/5), and occupational therapy (1/5). Effectiveness of group- and individual-based interventions at improving health outcomes (i.e. physical ability, communication, motivation, and quality of life) is unclear. Currently there is insufficient evidence to guide clinical practice. Conclusions: There is limited evidence comparing interventions delivered in a group and individual modality for adults living with stroke or IHD. Adequately powered studies are needed to determine if mode of delivery is equivalent or more cost effective. IMPLICATIONS FOR REHABILITATION: Rehabilitation interventions can be offered individually or in group settings with clinicians choosing the most appropriate modality. Both group- and individual-based interventions have advantages and disadvantages, with clinical, practical, and economic factors as important considerations when deciding between the two modalities. Based on this scoping review, the authors conclude that there is currently insufficient evidence to guide clinical practice in deciding which mode of delivery (group or individual) is optimal. There is insufficient research evidence to guide clinicians in their choice between offering rehabilitation interventions for stroke or IHD in groups or individually. [ABSTRACT FROM AUTHOR]

Neoantigen immunogenicity prediction is a highly challenging problem in the development of personalised medicines. Low reactivity rates in called neoantigens result in a difficult prediction scenario with limited training datasets. Here we describe ImmugenX, a modular protein language modelling approach to immunogenicity prediction for CD8+ reactive epitopes. ImmugenX comprises of a pMHC encoding module trained on three pMHC prediction tasks, an optional TCR encoding module and a set of context specific immunogenicity prediction head modules. Compared with state-of-the-art models for each task, ImmugenX's encoding module performs comparably or better on pMHC binding affinity, eluted ligand prediction and stability tasks. ImmugenX outperforms all compared models on pMHC immunogenicity prediction (Area under the receiver operating characteristic curve = 0.619, average precision: 0.514), with a 7% increase in average precision compared to the next best model. ImmugenX shows further improved performance on immunogenicity prediction with the integration of TCR context information. ImmugenX performance is further analysed for interpretability, which locates areas of weakness found across existing immunogenicity models and highlight possible biases in public datasets. Author summary: Accurate prediction of neoantigen immunogenicity has the potential to greatly improve the effectiveness of targeted therapies for cancer. While there are a number of associated tasks, such as peptide-HLA elution prediction, which can be now predicted with high accuracy by a number of published models, direct prediction of which epitopes will produce an immune response has proven more difficult. In this paper we demonstrate a modular protein language model approach which is trained iteratively to include data from related sub-tasks and can be extended to include information such as candidate TCRs of interest when available. There is a relatively small amount of immunogenicity data available, with even less data available with paired TCRs. This makes directly training a model to predict immunogenicity challenging. Our approach has the advantage of utilising data from sub-tasks and masked language modelling to allow for training a highly performant model with a small dataset. Using a cancer-specific benchmarking dataset we show this approach improves on existing state-of-the-art models and can be improved further with the addition of TCR context. This provides a framework that can serve as the basis for utilising additional information sources and datasets as they become available. [ABSTRACT FROM AUTHOR]

Background and purpose: Glioblastoma is a highly aggressive brain tumor with limited survival that poses challenges in predicting patient outcomes. The Karnofsky Performance Status (KPS) score is a valuable tool for assessing patient functionality and contributes to the stratification of patients with poor prognoses. This study aimed to develop a 6-month postoperative KPS prediction model by combining clinical data with deep learning-based image features from pre- and postoperative MRI scans, offering enhanced personalized care for glioblastoma patients. Materials and methods: Using 1,476 MRI datasets from the Brain Tumor Segmentation Challenge 2020 public database, we pretrained two variational autoencoders (VAEs). Imaging features from the latent spaces of the VAEs were used for KPS prediction. Neural network-based KPS prediction models were developed to predict scores below 70 at 6 months postoperatively. In this retrospective single-center analysis, we incorporated clinical parameters and pre- and postoperative MRI images from 150 newly diagnosed IDH wild-type glioblastoma, divided into training (100 patients) and test (50 patients) sets. In training set, the performance of these models was evaluated using the area under the curve (AUC), calculated through fivefold cross-validation repeated 10 times. The final evaluation of the developed models assessed in the test set. Results: Among the 150 patients, 61 had 6-month postoperative KPS scores below 70 and 89 scored 70 or higher. We developed three models: a clinical-based model, an MRI-based model, and a multimodal model that incorporated both clinical parameters and MRI features. In the training set, the mean AUC was 0.785±0.051 for the multimodal model, which was significantly higher than the AUCs of the clinical-based model (0.716±0.059, P = 0.038) using only clinical parameters and the MRI-based model (0.651±0.028, P<0.001) using only MRI features. In the test set, the multimodal model achieved an AUC of 0.810, outperforming the clinical-based (0.670) and MRI-based (0.650) models. Conclusion: The integration of MRI features extracted from VAEs with clinical parameters in the multimodal model substantially enhanced KPS prediction performance. This approach has the potential to improve prognostic prediction, paving the way for more personalized and effective treatments for patients with glioblastoma. [ABSTRACT FROM AUTHOR]

Developing nanophotosensitizer (nanoPS) for synergistic phototherapy by leveraging the aggregation of small‐molecule dyes is compelling frontier in precision medicine, but challenges remain due to the limited understanding of the interactions between molecular structure, assembly behavior, and theranostic function. Here, the concept of “spin‐orbit charge‐transfer intersystem crossing (SOCT‐ISC) in crystalline aggregates” is introduced, which significantly enhances photodynamic (PD) and photothermal (PT) properties of small‐molecule‐based nanoPS. Specifically, a cationic pyridine (Py) group is incorporated at the meso‐position of a tricyanofuran (TCF)‐containing anionic heptamethine cyanine (TCF‐Cy7‐TCF) dye to construct an orthogonal charge‐transfer dyad (TCF‐Cy7(Py)‐TCF), boosting triplet state formation through SOCT‐ISC mechanism to enhance the PD properties. Remarkably, the zwitterion moieties impart TCF‐Cy7(Py)‐TCF with high crystallinity even at ultralow concentrations, dictating its stacking behavior within aggregates and enhancing both PT and PD properties. The TCF‐Cy7(Py)‐TCF nanoaggregates exhibit superior singlet oxygen quantum yield (0.8% vs 0.2%) and photothermal conversion efficiency (55.06% vs 7.78%) compared to TCF‐Cy7‐TCF. Impressively, TCF‐Cy7(Py)‐TCF preferentially accumulates at tumor sites, yielding high signal‐to‐background ratios for tumor imaging in NIR‐I and NIR‐II windows, with minimal nonspecific binding. Finally, in vivo experiments confirm TCF‐Cy7(Py)‐TCF nanoaggregates function as nanoPS for synergistic phototherapy, offering a simple yet effective strategy to design single‐component PS for clinical translation. [ABSTRACT FROM AUTHOR]

Contrast-enhanced ultrasonography (CEUS) has revolutionized diagnostic imaging by significantly improving the accuracy and capabilities of ultrasound technology. By utilizing contrast agents, CEUS offers enhanced visualization of blood flow, superior detection of vascular diseases, and delineation of pathological from healthy tissues. Real-time imaging capabilities afforded by CEUS have been shown to be instrumental in guiding interventions and assessing treatment efficacy, thereby contributing to personalized patient care and improved clinical outcomes. This document provides a comprehensive review of the current benefits, limitations, and future directions of contrast-enhanced ultrasonography. It acknowledges the potential for allergic reactions, the necessity for specialized training, high costs, and the occurrence of image artifacts as drawbacks, while also anticipating advancements in targeted agents and imaging techniques. Moreover, the document emphasizes the need for ongoing research to evaluate the long-term safety and efficacy of contrast agents and their impact across various medical disciplines. Despite the challenges, the integration of CEUS into clinical practice holds enormous potential to further refine diagnostic processes and enhance patient management. [ABSTRACT FROM AUTHOR]

Background: In the current study, we evaluated whether atopic dermatitis (AD) affects the entire body rather than being limited to skin barrier damage and inflammation. We hypothesized that medium-term exposure of distant organs to systemic inflammatory cytokines in sub-chronic inflammatory skin diseases has detrimental effects on distant tissues. Results: Our findings demonstrated the dysregulation of genes and pathways associated with inflammation and the skin barrier, as well as genes and pathways involved in muscle development that respond to chemicals or stress in muscle tissues, all of which were reversed by hydrocortisone (Hc) administration. The expression of Ces1d showed significant differences during disease onset and after treatment in both skin and skeletal muscle, suggesting that Ces1d is likely responsible for the alleviation of subchronic AD. Conclusions: Using NC/Nga mice with AD-like symptoms, we compared the transcriptomes of the skeletal muscle (a tissue that is relatively distant from the skin) with those of the skin (the lesion induction site) before and after disease induction, after which Hc was administered. Although further study is needed to better understand the effects of Ces1d on AD, skeletal muscle was associated with AD pathogenesis, and AD-like symptoms appeared to affect the body in a systemic manner. Given the importance of evidence-based medicine and the development of precision medicine, our findings provide insights into the mechanisms of AD onset and progression. [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: Previous studies have been limited by their inability to differentiate between the effects of insulin sensitivity and β-cell function on the risk of kidney function decline, cardiovascular disease (CVD), and all-cause mortality. To address this knowledge gap, we aimed to investigate whether the physiological subtypes based on homeostasis model assessment-2 (HOMA2) indices of β-cell function (HOMA2-B) and insulin sensitivity (HOMA2-S) could be used to identify individuals with subsequently high or low of clinical outcome risk. Methods: This retrospective cohort study included 7,317 participants with a follow-up of up to 5 years. Based on HOMA2 indices, participants were categorized into four physiologic subtypes: the normal phenotype (high insulin sensitivity and high β-cell function), the insulinopenic phenotype (high insulin sensitivity and low β-cell function), the hyperinsulinaemic phenotype (low insulin sensitivity and high β-cell function), and the classical phenotype (low insulin sensitivity and low β-cell function). The outcomes included kidney function decline, CVD events (fatal and nonfatal), and all-cause mortality. Cox regression models were used to calculate hazard ratios (HRs) for outcomes, and spline models were used to examine the dose-dependent associations of HOMA2-B and HOMA2-S with outcomes. Results: A total of 1,488 (20.3%) were classified as normal, 2,179 (29.8%) as insulinopenic, 2,173 (29.7%) as hyperinsulinemic, and 1,477 (20.2%) as classical subtypes. Compared with other physiological subtypes, the classical subtype presented the highest risk of kidney function decline (classical vs. normal HR 11.50, 95% CI 4.31–30.67). The hyperinsulinemic subtype had the highest risk of CVD and all-cause mortality (hyperinsulinemic vs. normal: fatal CVD, HR 6.56, 95% CI 3.09–13.92; all-cause mortality, HR 4.56, 95% CI 2.97–7.00). Spline analyses indicated the dose-dependent associations of HOMA2-B and HOMA2-S with outcomes. Conclusions: The classical subtype had the strongest correlation with the risk of kidney function decline, and the hyperinsulinemic subtype had the highest risk of CVD and all-cause mortality, which should be considered for interventions with precision medicine. [ABSTRACT FROM AUTHOR]

Background: Orthopedic surgeries often involve the insertion of bone screws for various fixation systems. The risk of postoperative screw loosening is usually assessed through experimental or finite element (FE) evaluations of the screw pull-out strength. FE simulations are based on either personalized complex but accurate heterogeneous modeling or non-personalized simple but relatively less accurate homogeneous modeling. This study aimed to develop and validate a novel personalized computed tomography (CT)-based homogeneous FE simulation approach to predict the pull-out force of cancellous bone screws. Methods: Twenty FE simulations of L1-L5 vertebral screw pull-out tests were conducted, i.e., 10 heterogeneous and 10 homogenous models. Screws were inserted into the lower-middle region of vertebrae. In our novel homogeneous model, the region around approximately twice the diameter of the screw was used as a bone material reference volume. Subsequently, the overall material property of this region was homogeneously attributed to the entire vertebra, and pull-out simulations were conducted. Results: The mean error of the predicted pull-out forces by our novel homogenous simulations was ~ 7.9% with respect to our heterogeneous model. When solely the cancellous bone was involved during the pull-out process (i.e., for L1, L2, and L3 vertebral bodies whose cortical bone in the inferior region is thin), the novel homogenous model yielded small mean error of < 6.0%. This error, however, increased to ~ 11% when the screw got involved to the cortical bone (for L4 and L5 vertebrae whose cortical bone in the inferior region is thick). Conclusion: The proposed personalized CT-based homogenous model was highly accurate in estimating the pull-out force especially when only the cancellous bone was involved with the screw. [ABSTRACT FROM AUTHOR]

Several decades have passed since the anterograde corticomotoneuronal hypothesis for amyotrophic lateral sclerosis (ALS) was proposed. The intervening years have witnessed its emergent support based on anatomical, pathological, physiological, neuroimaging, and molecular biological studies. The evolution of an extensive corticomotoneuronal system appears restricted to the human species, with ALS representing a uniquely human disease. While some, very select non‐human primates have limited corticomotoneuronal projections, these tend to be absent in all other animals. From a general perspective, the early clinical features of ALS may be considered to reflect failure of the corticomotoneuronal system. The characteristic loss of skilled motor dexterity involving the limbs, and speech impairment through progressive bulbar dysfunction specifically involve those motor units having the strongest corticomotoneuronal projections. A similar explanation likely underlies the unique “split phenotypes” that have now been well characterized in ALS. Large Betz cells and other pyramidal corticomotoneuronal projecting neurons, with their extensive dendritic arborization, are particularly vulnerable to the elements of the ALS exposome such as aging, environmental stress and lifestyle changes. Progressive failure of the proteosome impairs nucleocytoplasmic shuffling and induces toxic but soluble TDP‐43 to aggregate in corticomotoneurons. Betz cell failure is further accentuated through dysfunction of its profuse dendritic arborizations. Clarification of system specific genomes and neural networks will likely promote the initiation of precision medicine approaches directed to support the key structure that underlies the neurological manifestations of ALS, the corticomotoneuronal system. [ABSTRACT FROM AUTHOR]

The rising aging population underscores the need for advances in tissue engineering and regenerative medicine. Alterations in cellular response in microgravity might be pivotal in unraveling the intricate cellular mechanisms governing tissue and organ regeneration. Microgravity could improve multicellular spheroid, tissue, and organ formation. This review summarizes microgravity-induced cellular alterations and highlights the potential of tissue engineering in microgravity for future breakthroughs in space travel, transplantation, drug testing, and personalized medicine. [ABSTRACT FROM AUTHOR]

Introduction: Respiratory diseases encompass a diverse range of conditions that significantly impact global morbidity and mortality. While common diseases like asthma and COPD exhibit moderate symptoms, less prevalent conditions such as pulmonary hypertension and cystic fibrosis profoundly affect quality of life and mortality. The prevalence of these diseases has surged by approximately 40% over the past 3 decades. Despite advancements in pharmacotherapy, challenges in drug administration, adherence, and adverse effects persist. This study aimed to develop and perform an interim validation of a Capacity-Motivation-Opportunity (CMO) model tailored for respiratory outpatients to enhance pharmaceutical care, which is the direct, responsible provision of medication-related care for the purpose of achieving definite outcomes that improve a patient's quality of life, and overall wellbeing. Methodology: This cross-sectional, multicenter study was conducted from March 2022 to March 2023. It comprised four phases: 1) forming an expert panel of 15 hospital pharmacists, 2) selecting respiratory pathologies based on prevalence and severity, 3) developing the CMO model's pillars, and 4) integrating and conducting an interim validation of the model. The Capacity pillar focused on patient stratification and personalized care; the Motivation pillar aligned therapeutic goals through motivational interviewing; and the Opportunity pillar promoted the use of information and communication technologies (ICTs) for telemedicine. Results: The model included eight respiratory diseases based on expert assessment. For the Capacity pillar, 22 variables were defined for patient stratification, leading to three priority levels for personalized pharmaceutical care. In a preliminary test involving 201 patients across six hospitals, the stratification tool effectively classified patients according to their needs. The Motivation pillar adapted motivational interviewing techniques to support patient adherence and behavior change. The Opportunity pillar established teleconsultation protocols and ICT tools to enhance patient monitoring and care coordination. Conclusion: The CMO model, tailored for respiratory patients, provides a comprehensive framework for improving pharmaceutical care. By focusing on patient-centered care, aligning therapeutic goals, and leveraging technology, this model addresses the multifaceted needs of individuals with respiratory conditions. Future studies are necessary to validate this model in other healthcare systems and ensure its broad applicability. [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]

Genome-wide association studies (GWAS) provide valuable insights into the genetic architecture of complex traits, yet interpreting their results remains challenging due to the polygenic nature of most traits. Gene set analysis offers a solution by aggregating genetic variants into biologically relevant pathways, enhancing the detection of coordinated effects across multiple genes. In this study, we present and evaluate a gene set prioritization approach utilizing Bayesian Linear Regression (BLR) models to uncover shared genetic components among different phenotypes and facilitate biological interpretation. Through extensive simulations and analyses of real traits, we demonstrate the efficacy of the BLR model in prioritizing pathways for complex traits. Simulation studies reveal insights into the model's performance under various scenarios, highlighting the impact of factors such as the number of causal genes, proportions of causal variants, heritability, and disease prevalence. Comparative analyses with MAGMA (Multi-marker Analysis of GenoMic Annotation) demonstrate BLR's superior performance, especially in highly overlapped gene sets. Application of both single-trait and multi-trait BLR models to real data, specifically GWAS summary data for type 2 diabetes (T2D) and related phenotypes, identifies significant associations with T2D-related pathways. Furthermore, comparison between single- and multi-trait BLR analyses highlights the superior performance of the multi-trait approach in identifying associated pathways, showcasing increased statistical power when analyzing multiple traits jointly. Additionally, enrichment analysis with integrated data from various public resources supports our results, confirming significant enrichment of diabetes-related genes within the top T2D pathways resulting from the multi-trait analysis. The BLR model's ability to handle diverse genomic features, perform regularization, conduct variable selection, and integrate information from multiple traits, genders, and ancestries demonstrates its utility in understanding the genetic architecture of complex traits. Our study provides insights into the potential of the BLR model to prioritize gene sets, offering a flexible framework applicable to various datasets. This model presents opportunities for advancing personalized medicine by exploring the genetic underpinnings of multifactorial traits. Author summary: Our study introduces a new method for prioritizing biological pathways in complex diseases using Bayesian Linear Regression (BLR) models. Genome-wide association studies (GWAS) have significantly advanced our understanding of complex traits, but interpreting their results remains challenging due to their complexity. Our method addresses this challenge by integrating genetic variants into biologically relevant pathways, aiding in identifying coordinated effects across multiple genes. Through simulations and analyses of real traits, including type 2 diabetes (T2D) and related phenotypes, we demonstrate the effectiveness of the BLR model in prioritizing pathways associated with complex traits. Our study provides insights into the model's performance under various scenarios and highlights its ability to identify significant associations with T2D-related pathways. Furthermore, our findings suggest that our approach could inspire future research in genetics by offering a robust framework for gene set prioritization. This could potentially lead to advancements in personalized medicine by enhancing our understanding of the genetic basis of complex diseases. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusion A patient with an extensively drug‐resistant (XDR) New Delhi metallo‐β‐lactamase (NDM) and oxacillinase (OXA‐48) producing Escherichia coli (E. coli) infection was awaiting orthotopic liver transplant. There is no standardized antibiotic prophylaxis regimen; however, in line with the Infectious Diseases Society of America guidance, an antibiotic prophylactic regimen of ceftazidime‐avibactam 2.5 g TDS with aztreonam 2 g three times a day (TDS) IV was proposed.The hollow fiber system (HFS) was applied to inform the individualized pharmacodynamic outcome likelihood prior to prophylaxis.A 4‐log reduction in CFU/mL in the first 10 h of the regimen exposure was observed; however, the killing dynamics were slow and six 8‐hourly infusions were required to reduce bacterial cells to below the limit of quantification. Thus, the HFS supported the use of the regimen for infection clearance; however, it highlighted the need for several infusions. Standard local practice is to administer prophylaxis antibiotics at induction of orthotopic liver transplantation (OLT); however, the HFS provided data to rationalize earlier dosing. Therefore, the patient was dosed at 24 h prior to their OLT induction and subsequently discharged 8 days after surgery.The HFS provides a dynamic culture solution for informing individualized medicine by testing antibiotic combinations and exposures against the bacterial isolates cultured from the patient's infection. . [ABSTRACT FROM AUTHOR]

Precision medicine is revolutionizing the world in combating different disease modalities, including cancer. The concept of personalized treatments is not new, but modeling it into a reality has faced various limitations. The last decade has seen significant improvements in incorporating several novel tools, scientific innovations and governmental support in precision oncology. However, the socio-economic factors and risk-benefit analyses are important considerations. This mini review includes a summary of some commendable milestones, which are not just a series of successes, but also a cautious outlook to the challenges and practical implications of the advancing techno-medical era. [ABSTRACT FROM AUTHOR]

Background: Arterial dissection, a condition marked by the tearing of the carotid artery's inner layers, can result in varied clinical outcomes, including progression, stability, or spontaneous regression. Understanding these outcomes' underlying mechanisms is crucial for enhancing patient care, particularly with the increasing use of computer simulations in medical diagnostics and treatment planning. The aim of this study is to utilize computational analysis of blood flow and vascular wall to: (1) understand the pathophysiology of stroke-like episodes in patients with carotid artery dissection; and (2) assess the effectiveness of this method in predicting the evolution of carotid dissection. Methods: Utilizing contrast-enhanced magnetic resonance angiography (MRA), we segmented images of the patient's right internal carotid artery. These images were transformed into 3D solids for simulation in Ansys multifisic software, employing a two-way fluid structure interaction (FSI) analysis. Simulations were conducted across two wall conditions (atherosclerotic and normal) and three pressure states (hypotension, normotension, hypertension). Results: The simulations indicated a significant pressure discrepancy between the true and false lumens of the artery. This suggests that flap motion and functional occlusion under hypertensive conditions could be the cause of the clinical episodes. Thrombotic risk and potential for dissection extension were not found to be critical concerns. However, a non-negligible risk of vessel dilation was assessed, aligning with the patient's clinical follow-up data. Conclusion: This study highlights specific hemodynamic parameters that could elucidate carotid artery dissection's mechanisms, offering a potential predictive tool for assessing dissection progression and informing personalized patient care strategies. [ABSTRACT FROM AUTHOR]

The transformation of healthcare from a fee-for-service model to value-based care is particularly crucial in managing complex and rare diseases like sarcoma, where data fragmentation and variability present significant challenges. This manuscript reviews strategies for structured and harmonized data integration—a critical precursor to precision medicine in sarcoma care. We demonstrate how standardizing data formats, ontologies, and coding systems enable seamless integration of clinical, economic, and patient-reported outcomes across institutions, paving the way for comprehensive predictive analytics. By establishing robust value-based healthcare (VBHC) frameworks through digital transformation and predictive models, including digital twins, we create the foundation for personalized sarcoma treatment and real-world-time clinical decision-making. The manuscript also addresses practical challenges, including the need for system standardization, overcoming regulatory and privacy concerns, and managing high costs. We propose actionable strategies to overcome these barriers and discuss the role of advanced analytics and future research directions that further enhance VBHC and precision medicine. This work outlines the necessary steps to build a cohesive, data-driven approach that supports the transition to precision medicine, fundamentally improving outcomes for sarcoma patients. [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]

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]

Simple Summary: The aim of the study was to prospectively assess the microbiome and its influence on radiotherapy toxicity in patients with laryngeal cancer. Statistically significant risk factors for complications after radiotherapy were the percentage of Porphyromonas of at least 6.7%, the percentage of Fusobacterium of at least 2.6% and the percentage of Catonella of at least 2.6%. The importance of the microbiome in oncology has been confirmed in many studies. Effective radiotherapy treatment and the prevention of radiation-induced oral mucositis is a challenge in oncology. The microbiome may be an important part of personalized cancer treatment. The assessment of the microbiome of patients diagnosed with cancer may provide the opportunity to predict the response to treatment and its effectiveness. The influence of the microbiome may be important in predicting the risk group for radiotherapy treatment failure. The possibility of modifying the microbiome may become a goal to improve the prognosis of patients with laryngeal cancer. Fusobacterium, Porphyromonas and Catonella are important risk factors for radiation-induced oral mucositis in patients with laryngeal cancer. Introduction: Radiotherapy is an effective method of treating cancer and affects 50% of patients. Intensity-modulated radiotherapy (IMRT) is a modernized method of classical radiation used in the treatment of laryngeal cancer. Treatment with intent to preserve the larynx is not always safe or complication-free. The microbiome may significantly influence the effectiveness of oncological treatment, especially radiotherapy, and may also be modified by the toxic response to radiation. Objective: The aim of the study was to prospectively assess the microbiome and its influence on radiotherapy toxicity in patients with laryngeal cancer. Results: Statistically significant risk factors for complications after radiotherapy were the percentage of Porphyromonas of at least 6.7%, the percentage of Fusobacterium of at least 2.6% and the percentage of Catonella of at least 2.6%. Conclusions: The importance of the microbiome in oncology has been confirmed in many studies. Effective radiotherapy treatment and the prevention of radiation-induced oral mucositis is a challenge in oncology. The microbiome may be an important part of personalized cancer treatment. The assessment of the microbiome of patients diagnosed with cancer may provide the opportunity to predict the response to treatment and its effectiveness. The influence of the microbiome may be important in predicting the risk group for radiotherapy treatment failure. The possibility of modifying the microbiome may become a goal to improve the prognosis of patients with laryngeal cancer. Fusobacterium, Porphyromonas and Catonella are important risk factors for radiation-induced oral mucositis in patients with laryngeal cancer. [ABSTRACT FROM AUTHOR]

Simple Summary: Cancer continues to be a leading cause of death globally. Advances in effective treatment have been hindered by difficulties in personalized therapy, especially among patients with comorbid conditions. The use of artificial intelligence (AI) in patient profiling presents a promising strategy for enhancing individualized cancer therapy. AI technologies, such as machine learning (ML), deep learning (DL), and natural language processing (NLP), have become crucial in identifying genetic and molecular biomarkers for cancer and migraine. These technologies facilitate predictive analytics to evaluate the impact of migraine on cancer treatment in patients with comorbidities, helping to forecast outcomes and supporting clinical decision-making through real-time treatment adjustments. AI has considerable potential to enhance the precision and efficacy of managing cancer patients with comorbid migraine. However, challenges related to data integration, clinical validation, and ethical considerations must be addressed. Background: Cancer remains a leading cause of death worldwide. Progress in its effective treatment has been hampered by challenges in personalized therapy, particularly in patients with comorbid conditions. The integration of artificial intelligence (AI) into patient profiling offers a promising approach to enhancing individualized anticancer therapy. Objective: This narrative review explores the role of AI in refining anticancer therapy through personalized profiling, with a specific focus on cancer patients with comorbid migraine. Methods: A comprehensive literature search was conducted across multiple databases, including PubMed, Scopus, and Google Scholar. Studies were selected based on their relevance to AI applications in oncology and migraine management, with a focus on personalized medicine and predictive modeling. Key themes were synthesized to provide an overview of recent developments, challenges, and emerging directions. Results: AI technologies, such as machine learning (ML), deep learning (DL), and natural language processing (NLP), have become instrumental in the discovery of genetic and molecular biomarkers of cancer and migraine. These technologies also enable predictive analytics for assessing the impact of migraine on cancer therapy in comorbid cases, predicting outcomes and provide clinical decision support systems (CDSS) for real-time treatment adjustments. Conclusions: AI holds significant potential to improve the precision and effectiveness of the management and therapy of cancer patients with comorbid migraine. Nevertheless, challenges remain over data integration, clinical validation, and ethical consideration, which must be addressed to appreciate the full potential for the approach outlined herein. [ABSTRACT FROM AUTHOR]

Simple Summary: Non-small cell lung cancer (NSCLC) is a leading cause of cancer-related deaths, particularly among patients with a specific mutation in the KRAS gene known as KRAS G12C. Two targeted therapies, adagrasib and sotorasib, have been developed for this mutation. This manuscript reviews three key clinical trials that evaluated the efficacy of these drugs in patients with KRAS G12C-mutated NSCLC. We compare their effectiveness in slowing disease progression and examine their differing side effect profiles, both of which are crucial for treatment decisions. While both drugs demonstrate efficacy, their side effects can influence patient tolerance and overall experience. Understanding these differences enables physicians to make more informed choices about the most appropriate treatment for each patient. Our analysis indicates that, although the overall survival rates for both drugs are comparable, certain patients may derive greater benefits from one therapy over the other, depending on individual characteristics and the management of side effects. Background: The KRAS G12C mutation, prevalent in various malignancies, including non-small cell lung cancer (NSCLC), represents a unique therapeutic target. Adagrasib and sotorasib, two FDA-approved agents specifically targeting this mutation, have shown promise in clinical trials. This study aims to compare their efficacy in treating KRAS G12C-mutated NSCLC, drawing insights from pivotal clinical trials. Methods: We analyzed data from three key clinical trials: KRYSTAL-1, CodeBreak100, and CodeBreak200. Our methodology involved reconstructing individual patient data from published Kaplan–Meier curves using the IPDfromKM tool (Version 0.1.10). The primary endpoints were progression-free survival (PFS) and overall survival (OS), evaluated through hazard ratios (HRs) and the restricted mean survival time (RMST) method. Results: The HR for PFS favored adagrasib (HR: 0.90 [95% CI: 0.69, 1.19], p = 0.473), suggesting a non-significant trend toward better disease control compared to sotorasib. For OS, the HR was 0.99 [95% CI: 0.75, 1.33] (p = 0.969), indicating no significant difference between the two drugs. RMST analysis supported these findings, with adagrasib showing a consistently higher RMST in PFS at 6, 12, and 18 months. However, OS benefits converged over time, with adagrasib marginally surpassing sotorasib by the 18-month mark. Conclusions: This comprehensive analysis reveals that while adagrasib may offer a slight advantage in PFS, both drugs demonstrate comparable efficacy in OS for KRAS G12C-mutated NSCLC. The subtle differences observed, particularly in PFS, could inform clinical decision-making, emphasizing the need for personalized treatment strategies. Future research should focus on long-term effects and identifying patient subgroups that may benefit more from one drug over the other. [ABSTRACT FROM AUTHOR]

Simple Summary: In liver surgery, the complex and individualized nature of liver vascular anatomy makes planning and execution challenging. Traditional 2D intraoperative ultrasonography (IOUS) often suffers from interpretability issues due to noise and artifacts. This paper introduces an AI-based model, the "2D-weighted U-Net model," designed to enhance real-time IOUS navigation by accurately segmenting key blood vessels, including the inferior vena cava, hepatic veins, portal vein, and its major branches. Our deep learning model demonstrated high performance, with Dice scores ranging from 0.84 to 0.96 across different vessels. This advancement promises improved precision in liver resection procedures and sets the stage for future development of real-time multi-label segmentation for broader liver vasculature. Background/Objectives: In the field of surgical medicine, the planning and execution of liver resection procedures present formidable challenges, primarily attributable to the intricate and highly individualized nature of liver vascular anatomy. In the current surgical milieu, intraoperative ultrasonography (IOUS) has become indispensable; however, traditional 2D ultrasound imaging's interpretability is hindered by noise and speckle artifacts. Accurate identification of critical structures for preservation during hepatectomy requires advanced surgical skills. Methods: An AI-based model that can help detect and recognize vessels including the inferior vena cava (IVC); the right (RHV), middle (MHV), and left (LVH) hepatic veins; the portal vein (PV) and its major first and second order branches the left portal vein (LPV), right portal vein (RPV), and right anterior (RAPV) and posterior (RPPV) portal veins, for real-time IOUS navigation can be of immense value in liver surgery. This research aims to advance the capabilities of IOUS-guided interventions by applying an innovative AI-based approach named the "2D-weigthed U-Net model" for the segmentation of multiple blood vessels in real-time IOUS video frames. Results: Our proposed deep learning (DL) model achieved a mean Dice score of 0.92 for IVC, 0.90 for RHV, 0.89 for MHV, 0.86 for LHV, 0.95 for PV, 0.93 for LPV, 0.84 for RPV, 0.85 for RAPV, and 0.96 for RPPV. Conclusion: In the future, this research will be extended for real-time multi-label segmentation of extended vasculature in the liver, followed by the translation of our model into the surgical suite. [ABSTRACT FROM AUTHOR]

Simple Summary: This review highlights the recent advances in the development and application of dual function kinase inhibitors that also bear fluorescent properties (fluorescent kinase inhibitors), thus can be used as theranostics in the field of cancer. This is a rapidly growing field with significant potential for cancer therapy and diagnosis. This work mainly focuses on the key design principles that guide the development of these multifunctional compounds, emphasizing the integration of essential components such as the kinase cytotoxic warhead, the fluorophore, the linkers, and additional modular elements to enhance the efficacy of the final assembled compound. We anticipate this review to propel the advancement of this field by improving the understanding of the design principles and ultimately leading to the development of more effective tools for the concurrent diagnosis and treatment of cancer. Kinase inhibitors are potent therapeutic agents in cancer treatment, but their effectiveness is frequently restricted by the inability to image the tumor microenvironment. To address this constraint, kinase inhibitor–fluorophore conjugates have emerged as promising theranostic agents, allowing for simultaneous cancer diagnosis and treatment. These conjugates are gaining attention for their ability to visualize malignant tissues and concurrently enhance therapeutic interventions. This review explores the design principles governing the development of multimodal inhibitors, highlighting their potential as platforms for kinase tracking and inhibition via bioimaging. The structural aspects of constructing such theranostic agents are critically analyzed. This work could shed light on this intriguing field and provide adequate impetus for developing novel theranostic compounds based on small molecule inhibitors and fluorophores. [ABSTRACT FROM AUTHOR]

Simple Summary: Cardiovascular diseases are among the most frequent, although rare, long-term sequalae in adolescents and young adult survivors of breast cancer. However, no dedicated tool exists to help clinicians with planning personalized follow-up strategies for these patients. To make up for this lack, in this work, we developed a Bayesian network, an artificial intelligence model, to predict the 5-year risk for cardiovascular diseases in these patients, leveraging real-world data from two different cohorts. The model showed a very good ability to identify patients at risk and select those that should be prioritized because they are at higher risk, making it useful for guiding clinicians in everyday practice. Finally, the methodological approach proposed in this work is particularly interesting for all researchers who aim at developing causally interpretable tools, also dealing with real-world data and their biases. Background: In the last decades, the increasing number of adolescent and young adult (AYA) survivors of breast cancer (BC) has highlighted the cardiotoxic role of cancer therapies, making cardiovascular diseases (CVDs) among the most frequent, although rare, long-term sequalae. Leveraging innovative artificial intelligence (AI) tools and real-world data (RWD), we aimed to develop a causally interpretable model to identify young BC survivors at risk of developing CVDs. Methods: We designed and trained a Bayesian network (BN), an AI model, making use of expert knowledge and data from population-based (1036 patients) and clinical (339 patient) cohorts of female AYA (i.e., aged 18 to 39 years) 1-year survivors of BC, diagnosed in 2009–2019. The performance achieved by the BN model was validated against standard classification metrics, and two clinical applications were proposed. Results: The model showed a very good classification performance and a clear causal semantic. According to the predictions made by the model, focusing on the 25% of AYA BC survivors at higher risk of developing CVDs, we could identify 81% of the patients who would actually develop it. Moreover, a desktop-based app was implemented to calculate the individual patient's risk. Conclusions: In this study, we developed the first causal model for predicting the CVD risk in AYA survivors of BC, also proposing an innovative AI approach that could be useful for all researchers dealing with RWD. The model could be pivotal for clinicians who aim to plan personalized follow-up strategies for AYA BC survivors. [ABSTRACT FROM AUTHOR]

Simple Summary: Cancer treatments traditionally focus on the type of tissue where the cancer starts, but recent research has shifted to targeting genetic changes that drive cancer growth, regardless of where the tumor originates. This approach is known as histology-agnostic therapy. In this review, we provide an update on these therapies, focusing on several newly developed drugs that are changing the way cancer is treated. Our goal was to explain how these drugs work, the benefits they offer, and the challenges that remain, such as resistance to treatment. This research may help doctors and researchers better understand how to choose the right treatments for patients with different types of cancer. By addressing the strengths and limitations of histology-agnostic therapies, we hope to support ongoing efforts in cancer treatment and encourage further studies to improve outcomes for patients. Recent advancements in oncology have led to the development of histology-agnostic therapies, which target genetic alterations irrespective of the tumor's tissue of origin. This review aimed to provide a comprehensive update on the current state of histology-agnostic drug development, focusing on key therapies, including pembrolizumab, larotrectinib, entrectinib, dostarlimab, dabrafenib plus trametinib, selpercatinib, trastuzumab deruxtecan, and reprotrectinib. We performed a detailed analysis of each therapy's mechanism of action, clinical trial outcomes, and associated biomarkers. The review further explores challenges in drug resistance, such as adaptive signaling pathways and neoantigen variability, as well as diagnostic limitations in identifying optimal patient populations. While these therapies have demonstrated efficacy in various malignancies, significant hurdles remain, including intratumoral heterogeneity and resistance mechanisms that diminish treatment effectiveness. We propose considerations for refining trial designs and emerging biomarkers, such as tumor neoantigen burden, to enhance patient selection. These findings illustrate the transformative potential of histology-agnostic therapies in precision oncology but highlight the need for continued research to optimize their use and overcome existing barriers. [ABSTRACT FROM AUTHOR]

Simple Summary: Glioblastoma is an extremely lethal malignant brain tumor. Finding ways to improve current treatments and outcomes for patients is crucial. Molecular profiling has become essential in diagnosis and management, with new technologies in areas of histopathology and radiogenomics being currently developed. Molecular biomarkers are the target of new therapies that hold great potential for refined and personalized treatments that aim to improve patient survival. This review summarizes the latest advances in the fields of histopathology and radiogenomics and the development of targeted therapies, providing an overview of the results of recent trials and the future directions of molecular targeted therapies in glioblastoma. Glioblastoma is the most commonly occurring malignant brain tumor, with a high mortality rate despite current treatments. Its classification has evolved over the years to include not only histopathological features but also molecular findings. Given the heterogeneity of glioblastoma, molecular biomarkers for diagnosis have become essential for initiating treatment with current therapies, while new technologies for detecting specific variations using computational tools are being rapidly developed. Advances in molecular genetics have made possible the creation of tailored therapies based on specific molecular targets, with various degrees of success. This review provides an overview of the latest advances in the fields of histopathology and radiogenomics and the use of molecular markers for management of glioblastoma, as well as the development of new therapies targeting the most common molecular markers. Furthermore, we offer a summary of the results of recent preclinical and clinical trials to recognize the current trends of investigation and understand the possible future directions of molecular targeted therapies in glioblastoma. [ABSTRACT FROM AUTHOR]

Simple Summary: In this study, we applied translational informatics for intelligent medicine of acute myeloid leukemia, a type of cancer characterized by disease relapses even after seemingly successful treatments. Treatment failure is associated, at least in part, with the fact that targeting individual proteins often promotes rewiring of relevant networks and re-organization of interactions of, among others, non-targeted proteins to eventually evade single-target therapies. To develop efficient therapies, these dynamics should be taken into account and target whole network modules instead of singleton genes in order to prevent the establishment of compensating signaling circuits. Therefore, we integrated network-based methods, structural pharmacology, and molecular modeling to establish two complementary multitargeting strategies, one in the form of repurposable drug combinations and the other as a de novo synthesized triple-targeting agent. Of note, our study exploits, for the first time, a greedy algorithm to identify optimal combinations of drugs and therapeutic protein targets. Background/Objectives: Acute myeloid leukemia (AML) is characterized by therapeutic failure and long-term risk for disease relapses. As several therapeutic targets participate in networks, they can rewire to eventually evade single-target drugs. Hence, multi-targeting approaches are considered on the expectation that interference with many different components could synergistically hinder activation of alternative pathways and demolish the network one-off, leading to complete disease remission. Methods: Herein, we established a network-based, computer-aided approach for the rational design of drug combinations and de novo agents that interact with many AML network components simultaneously. Results: A reconstructed AML network guided the selection of suitable protein hubs and corresponding multi-targeting strategies. For proteins responsive to existing drugs, a greedy algorithm identified the minimum amount of compounds targeting the maximum number of hubs. We predicted permissible combinations of amiodarone, artenimol, fostamatinib, ponatinib, procaine, and vismodegib that interfere with 3–8 hubs, and we elucidated the pharmacological mode of action of procaine on DNMT3A. For proteins that do not respond to any approved drugs, namely cyclins A1, D2, and E1, we used structure-based de novo drug design to generate a novel triple-targeting compound of the chemical formula C15H15NO5, with favorable pharmacological and drug-like properties. Conclusions: Overall, by integrating network and structural pharmacology with molecular modeling, we determined two complementary strategies with the potential to annihilate the AML network, one in the form of repurposable drug combinations and the other as a de novo synthesized triple-targeting agent. These target–drug interactions could be prioritized for preclinical and clinical testing toward precision medicine for AML. [ABSTRACT FROM AUTHOR]

Simple Summary: Pancreatic ductal adenocarcinoma (PDAC) is one of the most challenging cancers to diagnose and treat. Despite over 4,300 clinical trials for pancreatic cancer exploring various treatments, progress in developing effective options for PDAC remains slow. This review seeks to offer a detailed and insightful analysis of the current landscape of PDAC-related clinical trials, with the primary goal of identifying ways to expedite drug development and enhance patient outcomes in the battle against this aggressive disease. Despite many decades of research, pancreatic ductal adenocarcinoma (PDAC) remains one of the most difficult cancers to diagnose and treat effectively. Although there have been improvements in the 5-year overall survival rate, it is still very low at 12.5%. The limited efficacy of current therapies, even when PDAC is detected early, underscores the aggressive nature of the disease and the urgent need for more effective treatments. Clinical management of PDAC still relies heavily on a limited repertoire of therapeutic interventions, highlighting a significant gap between research efforts and available treatments. Over 4300 clinical trials have been or are currently investigating different treatment modalities and diagnostic strategies for PDAC, including targeted therapies, immunotherapies, and precision medicine approaches. These trials aim to develop more effective treatments and improve early detection methods through advanced imaging techniques and blood-based biomarkers. This review seeks to categorize and analyze PDAC-related clinical trials across various dimensions to understand why so few chemotherapeutic options are available to patients despite the numerous trials being conducted. This review aims to provide a comprehensive and nuanced understanding of the landscape of PDAC-related clinical trials, with the overarching goal of identifying opportunities to accelerate progress in drug development and improve patient outcomes in the fight against this devastating disease. [ABSTRACT FROM AUTHOR]

In modern healthcare services, patient safety is a primary goal. With technological advancements, the complexity of healthcare services increases, which in turn creates increased pressure on health professionals during decision-making processes and can lead to errors worldwide. Communication gaps, human factors, patientoriginated issues, technical failures, and inadequate policies have been identified as the main causes of medical errors. While research shows that errors stem from human nature and are inevitable, it is emphasized that it is possible to develop methods that enhance patient safety. Utilizing digital technologies to improve the quality and efficiency of healthcare services is a crucial strategy. Innovations such as wearable technologies, mobile devices, digital media-delivered education and consulting services, telehealth applications, 3D printers, clinical decision support systems, and implantable biosensors encompass advancements in the digital health field. This research aims to analyze the complex and dynamic structure of healthcare services in the 21st century, especially considering the opportunities presented by the integration of artificial intelligence and genomic data, within the scope of the relevant literature. [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]

OBJECTIVES: To determine if ChatGPT can incorporate patient-specific information to provide high-quality answers to parental questions in the PICU. We hypothesized that ChatGPT would generate high-quality, patient-specific responses. METHODS: In this cross-sectional study, we generated assessments and plans for 3 PICU patients with respiratory failure, septic shock, and status epilepticus and paired them with 8 typical parental questions. We prompted ChatGPT with instructions, an assessment and plan, and 1 question. Six PICU physicians evaluated the responses for accuracy (1-6), completeness (yes/no), empathy (1-6), and understandability (Patient Education Materials Assessment Tool, PEMAT, 0% to 100%; Flesch-Kincaid grade level). We compared answer quality among scenarios and question types using the Kruskal-Wallis and Fischer's exact tests. We used percent agreement, Cohen's Kappa, and Gwet's agreement coefficient to estimate inter-rater reliability. RESULTS: All answers incorporated patient details, utilizing them for reasoning in 59% of sentences. Responses had high accuracy (median 5.0, [interquartile range (IQR), 4.0-6.0]), empathy (median 5.0, [IQR, 5.0-6.0]), completeness (97% of all questions), and understandability (PEMAT % median 100, [IQR, 87.5-100]; Flesch-Kincaid level 8.7). Only 4/144 reviewer scores were <4/6 in accuracy, and no response was deemed likely to cause harm. There was no difference in accuracy, completeness, empathy, or understandability among scenarios or question types. We found fair, substantial, and almost perfect agreement among reviewers for accuracy, empathy, and understandability, respectively. CONCLUSIONS: ChatGPT used patient-specific information to provide high-quality answers to parental questions in PICU clinical scenarios. [ABSTRACT FROM AUTHOR]

Purpose: This review highlights the importance of functional connectivity in pediatric neuroscience, focusing on its role in understanding neurodevelopment and potential applications in clinical practice. It discusses various techniques for analyzing brain connectivity and their implications for clinical interventions in neurodevelopmental disorders. Methods: The principles and applications of independent component analysis and seed-based connectivity analysis in pediatric brain studies are outlined. Additionally, the use of graph analysis to enhance understanding of network organization and topology is reviewed, providing a comprehensive overview of connectivity methods across developmental stages, from fetuses to adolescents. Results: Findings from the reviewed studies reveal that functional connectivity research has uncovered significant insights into the early formation of brain circuits in fetuses and neonates, particularly the prenatal origins of cognitive and sensory systems. Longitudinal research across childhood and adolescence demonstrates dynamic changes in brain connectivity, identifying critical periods of development and maturation that are essential for understanding neurodevelopmental trajectories and disorders. Conclusion: Functional connectivity methods are crucial for advancing pediatric neuroscience. Techniques such as independent component analysis, seed-based connectivity analysis, and graph analysis offer valuable perspectives on brain development, creating new opportunities for early diagnosis and targeted interventions in neurodevelopmental disorders, thereby paving the way for personalized therapeutic strategies. [ABSTRACT FROM AUTHOR]

Purpose: This study investigated treatment-induced performance gains in memory function following therapy through the Intensive Cognitive Communication Rehabilitation (ICCR) program for young adults with acquired brain injury (ABI). We aimed to determine the utility of a novel approach to measuring memory performance improvement across semesters of therapy using (a) systematic treatment tasks called Individualized Quantitative Protocols (IQPs) as compared to (b) standardized measures of memory function. Method: Retrospective IQP data spanning five consecutive ICCR semesters were collected from patients (N = 13) with ABI. Raw task-accuracy data were scaled to account for task difficulty gradation. Linear mixed-effects models (LMMs) were used to evaluate the degree of memory improvement—measured by scaled IQP scores—as a function of therapy duration, age, time postinjury, and aphasia; pre- to posttreatment effect sizes were also calculated. For comparison, similar LMMs were run using standardized metrics of memory abilities as the outcome measure. Results: Results showed significant treatment-induced improvements, with gains at the session level (β = 2.76; t = 2.23; p = .047), when improvement was measured using IQP scaled scores. Standardized metrics did not show significant improvement as a function of therapy. Effect size analysis mirrored LMM results, with a large (d = 0.92, 95% confidence interval [0.35, 1.49]) pre- to posttreatment effect when change was measured using IQP scaled scores and a small effect for standardized measures. Conclusions: This preliminary study demonstrates the utility of a granular, individualized metric to index significant impairment-based performance gains following ICCR treatment. These results introduce promise for future analysis of complex treatment data. Additionally, they provide another lens with which to assess treatment progress and its significance. [ABSTRACT FROM AUTHOR]

Purpose: The Functional External Memory Aid Tool (FEMAT) is an 11-item performance-based measure that simulates everyday tasks (e.g., medication management) to measure one’s use of compensation (e.g., pill organizer). The FEMAT is not a norm-referenced diagnostic tool; rather, it is designed to provide clinicians with information about a patient’s function and compensation in a standardized format. To provide further evidence for validity of the FEMAT, the purpose of this study was to conduct a mixed-methods analysis of FEMAT responses to operationalize the types of behaviors elicited during test administration. Method: We adopted an embedded single-case study design to analyze the FEMAT administrations of 12 community-dwelling female participants, ages 73– 90 years, who met criteria for mild cognitive impairment. These participants were part of the original validation sample during which they completed the FEMAT (Version 1.0) in a single session with an assessor. Sessions were videorecorded and transcribed. We analyzed the data qualitatively in two phases using theme and subtheme codes and then quantitatively analyzed the data for response trends. Results: Our thematic analysis revealed four themes to operationalize participants’ verbal and behavioral FEMAT responses: (a) used an external memory aid (EMA), (b) described using a strategy, (c) discussed memory and comprehension, and (d) discussed functional information. While completing the FEMAT, most participants described using a strategy (85%) and discussed their selfperceived memory and comprehension (33%). Several subthemes also emerged within each theme (e.g., self-perceived memory strengths and barrier). Conclusions: Our analysis suggests that beyond a 4-point item score, the FEMAT elicits behavioral (e.g., used an EMA) and verbal (e.g., discussion of health information) content that may be useful for person-centered treatment planning. This study is part of a comprehensive research agenda establishing the evidence for the validity of the FEMAT to support its use to inform personcentered treatment approaches. [ABSTRACT FROM AUTHOR]

Polygenic risk scores (PRS) have emerged as a promising tool for predicting disease risk and treatment outcomes using genomic data. Thousands of genome-wide association studies (GWAS), primarily involving populations of European ancestry, have supported the development of PRS models. However, these models have not been adequately evaluated in non-European populations, raising concerns about their clinical validity and predictive power across diverse groups. Addressing this issue requires developing novel risk prediction frameworks that leverage genetic characteristics across diverse populations, considering host-microbiome interactions and a broad range of health measures. One of the key aspects in evaluating PRS is understanding the strengths and limitations of various methods for constructing them. In this review, we analyze strengths and limitations of different methods for constructing PRS, including traditional weighted approaches and new methods such as Bayesian and Frequentist penalized regression approaches. Finally, we summarize recent advances in PRS calculation methods development, and highlight key areas for future research, including development of models robust across diverse populations by underlining the complex interplay between genetic variants across diverse ancestral backgrounds in disease risk as well as treatment response prediction. PRS hold great promise for improving disease risk prediction and personalized medicine; therefore, their implementation must be guided by careful consideration of their limitations, biases, and ethical implications to ensure that they are used in a fair, equitable, and responsible manner. [ABSTRACT FROM AUTHOR]

The increase in the incidence of antibiotic-resistant bacteria poses a global public health threat. According to a 2019 WHO report, approximately 1.27 million deaths were attributed to antibiotic-resistant bacteria, with many cases linked to specific bacterial species, such as drug-resistant Pseudomonas aeruginosa and Staphylococcus aureus. By 2050, the number of deaths caused by these bacteria is predicted to surpass that caused by cancer. In response to this serious situation, phage therapy, an alternative to antibiotic treatment, has gained attention. Phage therapy involves the use of viruses that target specific bacteria to treat infections. This method has proven effective in multiple clinical cases, particularly for patients with severe infections caused by multidrug-resistant bacteria. For example, there are reports of patients with systemic infections caused by multidrug-resistant Acinetobacter who recovered following phage administration and patients infected with panresistant Pseudomonas aeruginosa who were cured by phage therapy. A key feature of phage therapy is its high specificity. Phages infect only specific bacteria and eliminate them. However, this specificity can also be a disadvantage, as careful selection of the appropriate phage for the target bacteria is needed. Additionally, bacteria can develop resistance to phages, potentially reducing treatment effectiveness over time. Efforts are underway to select, combine, and improve phages to address these challenges. In Belgium, a national phage bank has been established, and in the United States, the University of California, San Diego, has founded Innovative Phage Applications and Therapeutics (IPATH), marking significant progress toward the clinical application of phage therapy in the country. As a result, phage therapy is emerging as a component of personalized medicine, offering a new treatment option against antibiotic-resistant bacteria. The clinical application of phage therapy is particularly important in life-saving treatments for patients with severe bacterial infections, and its use in conjunction with antibiotics could enhance therapeutic outcomes. Continued research and development of this therapy could provide hope for many more patients in the future. [ABSTRACT FROM AUTHOR]

Why you should read this article: • To understand how lower limb immobilisation creates a risk of deep vein thrombosis and the individual risk factors that can compound this risk • To familiarise yourself with the methods of pharmacological thromboprophylaxis that may be used • To count towards revalidation as part of your 35 hours of CPD, or you may wish to write a reflective account (UK readers) • To contribute towards your professional development and local registration renewal requirements (non-UK readers). Deep vein thrombosis (DVT) is the formation of a blood clot in a deep vein, causing disruption or complete occlusion of blood flow. People who require lower limb immobilisation – for example after an injury such as metatarsal fracture, Achilles tendon rupture or malleolar fracture – are at increased risk of DVT, since epithelial injury and stasis will be present. Various patient-related and admission-related risk factors can further increase the risk of DVT. It is crucial that patients are individually assessed to determine their risk of DVT and their need for pharmacological thromboprophylaxis. This article discusses the indications for lower limb immobilisation, the mechanisms by which lower limb immobilisation creates a risk of DVT and the individual risk factors that compound the risk of thrombosis. It also outlines the importance of individualised risk assessments, the methods of pharmacological thromboprophylaxis and the nurse’s role in providing patient education. [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]

Theranostics is a new term for long‐established principles in nuclear medicine. The generalisability of the term means there is a very broad use of the term across the medical literature, not all of which is consistent with the intent in nuclear medicine. The term molecular theranostics better reflects the philosophy and application in nuclear medicine. Even with a clearer definition, there are a number of challenges or controversies whose debate provides a richer understanding of the principles and applications of molecular theranostics. Radioiodine imaging and therapy of hyperthyroidism and thyroid cancer provide the historical context for theranostics. The prototype molecular theranostic is the 68Ga/177Lu DOTATATE pair that targets somatostatin receptor subtype 2 in neuroendocrine tumors. The potential value of precision medicine of radiation dosimetry in molecular theranostics needs a balanced discussion with limitations of reactive dosimetry and the opportunities for predictive or pre‐treatment dosimetry. Despite challenges and limitations, molecular theranostics is a powerful tool in the precision medicine landscape. Molecular theranostics is a vehicle for improved outcomes in cancer patients with a future‐facing portfolio of opportunities. [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]

Attaining a true diabetes cure hinges on restoring functional β-cell mass, with regeneration offering advantages over transplantation, yet remaining elusive. The regenerative pathway is likely influenced by the severity of β-cell injury, involving replication or redifferentiation after mild-to-severe loss and neogenesis or reprogramming after extreme loss. Precision in understanding pathophysiological mechanisms and quantifying residual β-cell mass is essential for advancing personalized medicine and integrating regenerative therapies for diabetes. Safety in regenerative therapies demands targeted compound delivery, while efficacy and treatment personalization require a combination of therapies. The pandemic scale of diabetes mellitus is alarming, its complications remain devastating, and current treatments still pose a major burden on those affected and on the healthcare system as a whole. As the disease emanates from the destruction or dysfunction of insulin-producing pancreatic β-cells, a real cure requires their restoration and protection. An attractive strategy is to regenerate β-cells directly within the pancreas; however, while several approaches for β-cell regeneration have been proposed in the past, clinical translation has proven challenging. This review scrutinizes recent findings in β-cell regeneration and discusses their potential clinical implementation. Hereby, we aim to delineate a path for innovative, targeted therapies to help shift from 'caring for' to 'curing' diabetes. [ABSTRACT FROM AUTHOR]

Objective: Achieving remission in severe asthma holds paramount importance in elevating patient quality of life and reducing both individual and societal burdens associated with this chronic condition. This study centers on identifying pivotal patient-relevant endpoints through standardized, reproducible methods, while also developing a patient-centric definition of remission, essential for effective disease management. Methods: A discrete choice experiment (DCE) was conducted to assess patients' perceptions on the four primary criteria for defining severe asthma remission, as outlined by the SANI survey. Additionally, it investigated the correlation between these perceptions and improvements in the doctor-patient therapeutic alliance during treatment decision-making. Results: 249 patients (70% aged between 31–60, 59% women and 82% without other pathologies requiring corticosteroids) prioritize the use of oral corticosteroids (OCS, 48%) and the Asthma Control Test (ACT, 27%) in defining their condition, ranking these above lung function and exacerbations. This preference for OCS stems from its direct role in treatment, tangible tracking, immediate symptom relief, and being a concrete measure of disease severity compared to the less predictable and quantifiable exacerbations. Conclusions: This study explores severe asthma remission from patients' perspectives using clinician-evaluated parameters. The DCE revealed that most patients highly value OCS and the ACT, prefer moderate improvement, and avoid cortisone cycles. No definitive preference was found for lung function status. Integrating patient-reported information with professional insights is crucial for effective management and future research. Personalized treatment plans focusing on patient preferences, adherence, and alternative therapies aim to achieve remission and enhance quality of life. [ABSTRACT FROM AUTHOR]

To improve outcomes for youth who do not respond optimally to existing treatments, we need to identify robust predictors, moderators, and mediators that are ideal targets for personalisation in mental health care. We propose a solution to leverage the Individual Patient Data Meta‐analysis (IPDMA) approach to allow broader access to individual‐level data while maintaining methodological rigour. Such a resource has the potential to answer questions that are unable to be addressed by single studies, reduce researcher burden, and enable the application of newer statistical techniques, all to provide data‐driven strategies for clinical decision‐making. Using childhood anxiety as the worked example, the editorial perspective outlines the rationale for leveraging IPDMA methodology to build a data repository, the Platform for Anxiety Disorder Data in Youth. We also include recommendations to address the methods and challenges inherent in this endeavour. [ABSTRACT FROM AUTHOR]

Chronic pain is a constantly recurring and persistent illness, presenting a formidable healthcare challenge for patients and physicians alike. Current first‐line analgesics offer only low‐modest efficacy when averaged across populations, further contributing to this debilitating disease burden. Moreover, many recent trials for novel analgesics have not met primary efficacy endpoints, which is particularly striking considering the pharmacological advances have provided a range of highly relevant new drug targets. Heterogeneity within chronic pain cohorts is increasingly understood to play a critical role in these failures of treatment and drug discovery, with some patients deriving substantial benefits from a given intervention while it has little‐to‐no effect on others. As such, current treatment failures may not result from a true lack of efficacy, but rather a failure to target individuals whose pain is driven by mechanisms which it therapeutically modulates. This necessitates a move towards phenotypical stratification of patients to delineate responders and non‐responders in a mechanistically driven manner. In this article, we outline a bench‐to‐bedside roadmap for this transition to mechanistically informed personalised pain medicine. We emphasise how the successful identification of novel analgesics is dependent on rigorous experimental design as well as the validity of models and translatability of outcome measures between the animal model and patients. Subsequently, we discuss general and specific aspects of human trial design to address heterogeneity in patient populations to increase the chance of identifying effective analgesics. Finally, we show how stratification approaches can be brought into clinical routine to the benefit of patients. [ABSTRACT FROM AUTHOR]

Advances in precision oncology led to approval of tumour-agnostic molecularly guided treatment options (MGTOs). The minimum requirements for claiming tumour-agnostic potential remain elusive. The European Society for Medical Oncology (ESMO) Precision Medicine Working Group (PMWG) coordinated a project to optimise tumour-agnostic drug development. International experts examined and summarised the publicly available data used for regulatory assessment of the tumour-agnostic indications approved by the US Food and Drug Administration and/or the European Medicines Agency as of December 2023. Different scenarios of minimum objective response rate (ORR), number of tumour types investigated, and number of evaluable patients per tumour type were assessed for developing a screening tool for tumour-agnostic potential. This tool was tested using the tumour-agnostic indications approved during the first half of 2024. A taxonomy for MGTOs and a framework for tumour-agnostic drug development were conceptualised. Each tumour-agnostic indication had data establishing objective response in at least one out of five patients (ORR ≥ 20%) in two-thirds (≥4) of the investigated tumour types, with at least five evaluable patients in each tumour type. These minimum requirements were met by tested indications and may serve as a screening tool for tumour-agnostic potential, requiring further validation. We propose a conceptual taxonomy classifying MGTOs based on the therapeutic effect obtained by targeting a driver molecular aberration across tumours and its modulation by tumour-specific biology: tumour-agnostic, tumour-modulated, or tumour-restricted. The presence of biology-informed mechanistic rationale, early regulatory advice, and adequate trial design demonstrating signs of biology-driven tumour-agnostic activity, followed by confirmatory evidence, should be the principles for tumour-agnostic drug development. The ESMO Tumour-Agnostic Classifier (ETAC) focuses on the interplay of targeted driver molecular aberration and tumour-specific biology modulating the therapeutic effect of MGTOs. We propose minimum requirements to screen for tumour-agnostic potential (ETAC-S) as part of tumour-agnostic drug development. Definition of ETAC cut-offs is warranted. • Advances in precision oncology led to the approval of tumour-agnostic therapies, challenging conventional drug development. • There is a need to establish a minimum set of requirements for a therapy to be eligible for tumour-agnostic potential. • Approved tumour-agnostic therapies had ORR ≥ 20% in 2/3 of tumour types (≥4) with ≥5 evaluable patients per tumour type. • Tumour-agnostic effect results from targeted driver molecular aberration interplay with tumour-specific modulating biology. • The ESMO PMWG proposes a framework to foster and accelerate tumour-agnostic drug development for patients with cancer. [ABSTRACT FROM AUTHOR]