Approaches of wearable and implantable biosensor towards of developing in precision medicine.

In the relentless pursuit of precision medicine, the intersection of cutting-edge technology and healthcare has given rise to a transformative era. At the forefront of this revolution stands the burgeoning field of wearable and implantable biosensors, promising a paradigm shift in how we monitor, analyze, and tailor medical interventions. As these miniature marvels seamlessly integrate with the human body, they weave a tapestry of real-time health data, offering unprecedented insights into individual physiological landscapes. This log embarks on a journey into the realm of wearable and implantable biosensors, where the convergence of biology and technology heralds a new dawn in personalized healthcare. Here, we explore the intricate web of innovations, challenges, and the immense potential these bioelectronics sentinels hold in sculpting the future of precision medicine.

Extracellular vesicles and cancer stem cells: a deadly duo in tumor progression.

The global incidence of cancer is increasing, with estimates suggesting that there will be 26 million new cases and 17 million deaths per year by 2030. Cancer stem cells (CSCs) and extracellular vesicles (EVs) are key to the resistance and advancement of cancer. They play a crucial role in tumor dynamics and resistance to therapy. CSCs, initially discovered in acute myeloid leukemia, are well-known for their involvement in tumor initiation, progression, and relapse, mostly because of their distinct characteristics, such as resistance to drugs and the ability to self-renew. EVs, which include exosomes, microvesicles, and apoptotic bodies, play a vital role in facilitating communication between cells within the tumor microenvironment (TME). They have a significant impact on cellular behaviors and contribute to genetic and epigenetic changes. This paper analyzes the mutually beneficial association between CSCs and EVs, emphasizing their role in promoting tumor spread and developing resistance mechanisms. This review aims to investigate the interaction between these entities in order to discover new approaches for attacking the complex machinery of cancer cells. It highlights the significance of CSCs and EVs as crucial targets in the advancement of novel cancer treatments, which helps stimulate additional research, promote progress in ideas for cancer treatment, and provide renewed optimism in the effort to reduce the burden of cancer.

Renal cancer: signaling pathways and advances in targeted therapies.

Renal cancer is a highlyheterogeneous malignancy characterized by rising global incidence and mortalityrates. The complex interplay and dysregulation of multiple signaling pathways,including von Hippel-Lindau (VHL)/hypoxia-inducible factor (HIF), phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR), Hippo-yes-associated protein (YAP), Wnt/ß-catenin, cyclic adenosine monophosphate (cAMP), and hepatocyte growth factor (HGF)/c-Met, contribute to theinitiation and progression of renal cancer. Although surgical resection is thestandard treatment for localized renal cancer, recurrence and metastasiscontinue to pose significant challenges. Advanced renal cancer is associatedwith a poor prognosis, and current therapies, such as targeted agents andimmunotherapies, have limitations. This review presents a comprehensiveoverview of the molecular mechanisms underlying aberrant signaling pathways inrenal cancer, emphasizing their intricate crosstalk and synergisticinteractions. We discuss recent advancements in targeted therapies, includingtyrosine kinase inhibitors, and immunotherapies, such as checkpoint inhibitors.Moreover, we underscore the importance of multiomics approaches and networkanalysis in elucidating the complex regulatory networks governing renal cancerpathogenesis. By integrating cutting-edge research and clinical insights, this review contributesto the development of innovative diagnostic and therapeutic strategies, whichhave the potential to improve risk stratification, precision medicine, andultimately, patient outcomes in renal cancer.

Prediction of Response to FDA-Approved Targeted Therapy and Immunotherapy in Acute Lymphoblastic Leukemia (ALL).

OPINION STATEMENT: Acute lymphoblastic leukemia (ALL) represents the predominant cancer in pediatric populations, though its occurrence in adults is relatively rare. Pre-treatment risk stratification is crucial for predicting prognosis. Important factors for assessment include patient age, white blood cell (WBC) count at diagnosis, extramedullary involvement, immunophenotype, and cytogenetic aberrations. Minimal residual disease (MRD), primarily assessed by flow cytometry following remission, plays a substantial role in guiding management plans. Over the past decade, significant advancements in ALL outcomes have been witnessed. Conventional chemotherapy has remarkably reduced mortality rates; however, its intensive nature raises safety concerns and has led to the emergence of treatment-resistant cases with recurrence of relapses. Consequently, The U.S. Food and Drug Administration (FDA) has approved several novel treatments for relapsed/refractory ALL due to their demonstrated efficacy, as indicated by improved complete remission and survival rates. These treatments include tyrosine kinase inhibitors (TKIs), the anti-CD19 monoclonal antibody blinatumomab, anti-CD22 inotuzumab ozogamicin, anti-CD20 rituximab, and chimeric antigen receptor (CAR) T-cell therapy. Identifying the variables that influence treatment decisions is a pressing necessity for tailoring therapy based on heterogeneous patient characteristics. Key predictive factors identified in various observational studies and clinical trials include prelymphodepletion disease burden, complex genetic abnormalities, and MRD. Furthermore, the development of serious adverse events following treatment could be anticipated through predictive models, allowing for appropriate prophylactic measures to be considered. The ultimate aim is to incorporate the concept of precision medicine in the field of ALL through valid prediction platform to facilitate the selection of the most suitable treatment approach.

Immunotherapy in melanoma: advances, pitfalls, and future perspectives.

Cutaneous melanoma is the deadliest and most aggressive form of skin cancer owing to its high capacity for metastasis. Over the past few decades, the management of this type of malignancy has undergone a significant revolution with the advent of both targeted therapies and immunotherapy, which have greatly improved patient quality of life and survival. Nevertheless, the response rates are still unsatisfactory for the presence of side effects and development of resistance mechanisms. In this context, tumor microenvironment has emerged as a factor affecting the responsiveness and efficacy of immunotherapy, and the study of its interplay with the immune system has offered new promising clinical strategies. This review provides a brief overview of the currently available immunotherapeutic strategies for melanoma treatment by analyzing both the positive aspects and those that require further improvement. Indeed, a better understanding of the mechanisms involved in the immune evasion of melanoma cells, with particular attention on the role of the tumor microenvironment, could provide the basis for improving current therapies and identifying new predictive biomarkers.

Intra-arterial PSMA injection using hepatic arterial infusion pump in intrahepatic cholangiocarcinoma: a proof-of-concept study.

Prostate-specific membrane antigen (PSMA) targeted tracers show increased uptake in several malignancies, indicating a potential for peptide radioligand therapy. Intra-arterial injection of radiotracers can increase the therapeutic window. This study aimed to evaluate the feasibility of intra-arterial injection of [68Ga]Ga-PSMA-11 for intrahepatic cholangiocarcinoma and compare tracer uptake after intrahepatic arterial injection and intravenous injection. Three patients with intrahepatic cholangiocarcinoma received [68Ga]Ga-PSMA-11 through a hepatic arterial infusion pump, followed by positron emission tomography/computed tomography (PET/CT). Two-three days later, patients underwent PET/CT after intravenous [68Ga]Ga-PSMA-11 injection. All tumours showed higher uptake on the intra-arterial scan compared with the intravenous scan: the intra-arterial / intravenous standardised uptake value normalised by lean body mass ratios were 1.40, 1.46, and 1.54. Local intra-arterial PSMA injection is possible in patients with intrahepatic cholangiocarcinoma. Local injection increases tumour-to-normal tissue ratios, increasing the therapeutic window for theranostic applications. RELEVANCE STATEMENT: Intra-arterial Prostate specific membrane antigen (PSMA) injection increases the therapeutic window for potential theranostic application in intrahepatic cholangiocarcinoma. KEY POINTS: Three patients with intrahepatic cholangiocarcinoma underwent PET/CT after intra-arterial and intravenous injection of [68Ga]Ga-PSMA-11. Intra-arterial injection showed higher uptake than intravenous injection. PSMA-targeted imaging could be valuable for a subset of intrahepatic cholangiocarcinoma patients.

The prognostic role of MRI-based radiomics in tongue carcinoma: a multicentric validation study.

PURPOSE: Radiomics is an emerging field that utilizes quantitative features extracted from medical images to predict clinically meaningful outcomes. Validating findings is crucial to assess radiomics applicability. We aimed to validate previously published magnetic resonance imaging (MRI) radiomics models to predict oncological outcomes in oral tongue squamous cell carcinoma (OTSCC). MATERIALS AND METHODS: Retrospective multicentric study on OTSCC surgically treated from 2010 to 2019. All patients performed preoperative MRI, including contrast-enhanced T1-weighted (CE-T1), diffusion-weighted sequences and apparent diffusion coefficient map. We evaluated overall survival (OS), locoregional recurrence-free survival (LRRFS), cause-specific mortality (CSM). We elaborated different models based on clinical and radiomic data. C-indexes assessed the prediction accuracy of the models. RESULTS: We collected 112 consecutive independent patients from three Italian Institutions to validate the previously published MRI radiomic models based on 79 different patients. The C-indexes for the hybrid clinical-radiomic models in the validation cohort were lower than those in the training cohort but remained > 0.5 in most cases. CE-T1 sequence provided the best fit to the models: the C-indexes obtained were 0.61, 0.59, 0.64 (pretreatment model) and 0.65, 0.69, 0.70 (posttreatment model) for OS, LRRFS and CSM, respectively. CONCLUSION: Our clinical-radiomic models retain a potential to predict OS, LRRFS and CSM in heterogeneous cohorts across different centers. These findings encourage further research, aimed at overcoming current limitations, due to the variability of imaging acquisition, processing and tumor volume delineation.

Application of pharmacogenetic/pharmacogenomic data to personalize treatment in routine clinical practice. A narrative review. / Aplicación de datos farmacogenéticos/farmacogenómicos para personalizar el tratamiento en la práctica clínica habitual. Revisión narrativa.

OBJECTIVE: The aim of this article is to perform a narrative review of how pharmacogenetics and pharmacogenomics is being applied in the clinic, especially in Spain. METHOD: Publications and websites of major interest have been reviewed. RESULTS: Pharmacogenes and variants used in several hospitals, available methodologies, and the implementation process are discussed.

[Translated article] Application of pharmacogenetic/pharmacogenomic data to personalise treatment in routine clinical practice. A narrative review.

OBJECTIVE: The aim of this article was to perform a narrative review of how pharmacogenetics and pharmacogenomics is being applied in the clinics, especially in Spain. METHOD: Publications and websites of major interest have been reviewed. RESULTS: Pharmacogenes and variants used in several hospitals, available methodologies, and the implementation process are discussed.

Prescription Precision: A Comprehensive Review of Intelligent Prescription Systems.

Intelligent Prescription Systems (IPS) represent a promising frontier in healthcare, offering the potential to optimize medication selection, dosing, and monitoring tailored to individual patient needs. This comprehensive review explores the current landscape of IPS, encompassing various technological approaches, applications, benefits, and challenges. IPS leverages advanced computational algorithms, machine learning techniques, and big data analytics to analyze patient-specific factors, such as medical history, genetic makeup, biomarkers, and lifestyle variables. By integrating this information with evidence-based guidelines, clinical decision support systems, and real-time patient data, IPS generates personalized treatment recommendations that enhance therapeutic outcomes while minimizing adverse effects and drug interactions. Key components of IPS include predictive modeling, Drug-Drug Interaction detection, adverse event prediction, dose optimization, and medication adherence monitoring. These systems offer clinicians invaluable decision-support tools to navigate the complexities of medication management, particularly in the context of polypharmacy and chronic disease management. While IPS holds immense promise for improving patient care and reducing healthcare costs, several challenges must be addressed. These include data privacy and security concerns, interoperability issues, integration with existing electronic health record systems, and clinician adoption barriers. Additionally, the regulatory landscape surrounding IPS requires clarification to ensure compliance with evolving healthcare regulations. Despite these challenges, the rapid advancements in artificial intelligence, data analytics, and digital health technologies are driving the continued evolution and adoption of IPS. As precision medicine gains momentum, IPS is poised to play a central role in revolutionizing medication management, ultimately leading to more effective, personalized, and patient-centric healthcare delivery.

Improving Social Recovery Capital Research To Enhance Clinical Utility: A Proposed Agenda.

Social recovery capital (SRC) is the combination of social resources that can be used to initiate and sustain addiction recovery through friends, family, and peers. Broadly, understanding one's SRC allows us to get a sense of where one has social support for recovery and where there may be social barriers to their recovery process. SRC is often a vital component of many people's recovery journey, yet our understanding of how best to use this concept in research and practice remains underdeveloped. To improve understanding of the role of social recovery capital and strategies to measure and increase it, we present a roadmap involving a five-pronged research agenda to: (1) Refine the measurement of social recovery capital; (2) Model the complexity of social recovery capital empirically; (3) Integrate personality science with social recovery capital research; (4) Optimize evidence-based behavior change techniques of social recovery capital; and (5) Incorporate an intersectional framework when examining or applying social recovery capital. Overall, this five-pronged research agenda seeks to enhance the clinical utility of SRC research to maximize the impact of SRC on one's recovery.

Translational Research Platform for Malignant Central Nervous System Tumors.

Some central nervous system (CNS) malignancies are highly aggressive and urgently need innovative treatment strategies to improve prognosis. A significant concern for therapeutic development is the time-consuming nature of developing treatments for CNS tumors. Therefore, a rapid and efficient translational approach is needed to address this problem. Translational and reverse translational research aims to bridge the gap between laboratory data and clinical applications and has been developed in the field of neuro-oncology. This study presents our translational platform systems for malignant CNS tumors, which combine an intraoperative integrated diagnostic system and comprehensive in vitro and in vivo assay systems. These laboratory systems may contribute to a better understanding of tumor biology and the development of novel therapeutic strategies for the poor prognosis of CNS tumors.

ESMO Recommendations on clinical reporting of genomic test results for solid cancers.

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

Cell-free DNA from ascites identifies clinically relevant variants and tumour evolution in patients with advanced ovarian cancer.

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

[Healthcare 4.0-Medicine in transition]. / Healthcare 4.0 ­ Medizin im Wandel.

Healthcare 4.0 describes the future transformation of the healthcare sector driven by the combination of digital technologies, such as artificial intelligence (AI), big data and the Internet of Medical Things, enabling the advancement of precision medicine. This overview article addresses various areas such as large language models (LLM), diagnostics and robotics, shedding light on the positive aspects of Healthcare 4.0 and showcasing exciting methods and application examples in cardiology. It delves into the broad knowledge base and enormous potential of LLMs, highlighting their immediate benefits as digital assistants or for administrative tasks. In diagnostics, the increasing usefulness of wearables is emphasized and an AI for predicting heart filling pressures based on cardiac magnetic resonance imaging (MRI) is introduced. Additionally, it discusses the revolutionary methodology of a digital simulation of the physical heart (digital twin). Finally, it addresses both regulatory frameworks and a brief vision of data-driven healthcare delivery, explaining the need for investments in technical personnel and infrastructure to achieve a more effective medicine.

Precision Medicine and Clinical Trials in Advanced and Metastatic Oral Cancer.

Purpose: Oral cancer is a significant global health concern, with high morbidity and mortality rates, particularly in regions with prevalent tobacco usage such as Asia. Majority of oral cancers are detected at an advanced stage resulting in poor survival outcomes. Moreover, the treatment modalities of oral cancers have remained constant with surgery and concurrent chemoradiotherapy being mainstays of the treatment. This review provides a significant progress made in understanding the molecular landscape of oral cancers and the evolution of therapeutic strategies toward precision medicine. Methods: A comprehensive literature review was conducted to gather recent studies on the molecular landscape of oral cancers, genomic insights, and clinical trials. Results: Firstly, genomic insights into oral cancers, including key driver mutations and copy number alterations, are discussed in the context of personalized medicine approaches. Subsequently, advancements in therapeutic strategies, particularly focusing on clinical trials investigating immunotherapy and targeted agents, are highlighted. Conclusion: Despite promising results, challenges persist in identifying reliable biomarkers for treatment response and resistance. Continued research efforts are warranted to validate biomarkers and optimize therapeutic interventions, with the goal of enhancing patient outcomes and reducing the global burden of oral cancer.

Machine learning-enabled detection of attention-deficit/hyperactivity disorder with multimodal physiological data: a case-control study.

BACKGROUND: Attention-Deficit/Hyperactivity Disorder (ADHD) is a multifaceted neurodevelopmental psychiatric condition that typically emerges during childhood but often persists into adulthood, significantly impacting individuals' functioning, relationships, productivity, and overall quality of life. However, the current diagnostic process exhibits limitations that can significantly affect its overall effectiveness. Notably, its face-to-face and time-consuming nature, coupled with the reliance on subjective recall of historical information and clinician subjectivity, stand out as key challenges. To address these limitations, objective measures such as neuropsychological evaluations, imaging techniques and physiological monitoring of the Autonomic Nervous System functioning, have been explored. METHODS: The main aim of this study was to investigate whether physiological data (i.e., Electrodermal Activity, Heart Rate Variability, and Skin Temperature) can serve as meaningful indicators of ADHD, evaluating its utility in distinguishing adult ADHD patients. This observational, case-control study included a total of 76 adult participants (32 ADHD patients and 44 healthy controls) who underwent a series of Stroop tests, while their physiological data was passively collected using a multi-sensor wearable device. Univariate feature analysis was employed to identify the tests that triggered significant signal responses, while the Informative k-Nearest Neighbors (KNN) algorithm was used to filter out less informative data points. Finally, a machine-learning decision pipeline incorporating various classification algorithms, including Logistic Regression, KNN, Random Forests, and Support Vector Machines (SVM), was utilized for ADHD patient detection. RESULTS: Results indicate that the SVM-based model yielded the optimal performance, achieving 81.6% accuracy, maintaining a balance between the experimental and control groups, with sensitivity and specificity of 81.4% and 81.9%, respectively. Additionally, integration of data from all physiological signals yielded the best results, suggesting that each modality captures unique aspects of ADHD. CONCLUSIONS: This study underscores the potential of physiological signals as valuable diagnostic indicators of adult ADHD. For the first time, to the best of our knowledge, our findings demonstrate that multimodal physiological data collected via wearable devices can complement traditional diagnostic approaches. Further research is warranted to explore the clinical applications and long-term implications of utilizing physiological markers in ADHD diagnosis and management.