Personalized medicine could transform healthcare

Benchmarking feature selection and feature extraction methods to improve the performances of machine-learning algorithms for patient classification using metabolomics biomedical data

Objective

Classification tasks are an open challenge in the field of biomedicine. While several machine-learning techniques exist to accomplish this objective, several peculiarities associated with biomedical data, especially when it comes to omics measurements, prevent their use or good performance achievements. Omics approaches aim to understand a complex biological system through systematic analysis of its content at the molecular level. On the other hand, omics data are heterogeneous, sparse and affected by the classical "curse of dimensionality" problem, i.e. having much fewer observation, samples (n) than omics features (p). Furthermore, a major problem with multi-omics data is the imbalance either at the class or feature level. The objective of this work is to study whether feature extraction and/or feature selection techniques can improve the performances of classification machine-learning algorithms on omics measurements.

Methods

Among all omics, metabolomics has emerged as a powerful tool in cancer research, facilitating a deeper understanding of the complex metabolic landscape associated with tumorigenesis and tumor progression. Thus, we selected three publicly available metabolomics datasets, and we applied several feature extraction techniques both linear and non-linear, coupled or not with feature selection methods, and evaluated the performances regarding patient classification in the different configurations for the three datasets.

Results

We provide general workflow and guidelines on when to use those techniques depending on the characteristics of the data available. To further test the extension of our approach to other omics data, we have included a transcriptomics and a proteomics data. Overall, for all datasets, we showed that applying supervised feature selection improves the performances of feature extraction methods for classification purposes. Scripts used to perform all analyses are available at: https://github.com/Plant-Net/Metabolomic_project/.

Advancing personalized medicine: Integrating statistical algorithms with omics and nano-omics for enhanced diagnostic accuracy and treatment efficacy

Medical laboratory services enable precise measurement of thousands of biomolecules and have become an inseparable part of high-quality healthcare services, exerting a profound influence on global health outcomes. The integration of omics technologies into laboratory medicine has transformed healthcare, enabling personalized treatments and interventions based on individuals' distinct genetic and metabolic profiles. Interpreting laboratory data relies on reliable reference values. Presently, population-derived references are used for individuals, risking misinterpretation due to population heterogeneity, and leading to medical errors. Thus, personalized references are crucial for precise interpretation of individual laboratory results, and the interpretation of omics data should be based on individualized reference values. We reviewed recent advancements in personalized laboratory medicine, focusing on personalized omics, and discussed strategies for implementing personalized statistical approaches in omics technologies to improve global health and concluded that personalized statistical algorithms for interpretation of omics data have great potential to enhance global health. Finally, we demonstrated that the convergence of nanotechnology and omics sciences is transforming personalized laboratory medicine by providing unparalleled diagnostic precision and innovative therapeutic strategies.

Venous thromboembolism in the era of machine learning and artificial intelligence in medicine

In this review, we embark on a comprehensive exploration of venous thromboembolism (VTE) in the context of medical history and its current practice within medicine. We delve into the landscape of artificial intelligence (AI), exploring its present utility and envisioning its transformative roles within VTE management, from prevention to screening and beyond. Central to our discourse is a forward-looking perspective on the integration of AI within VTE in medicine, advocating for rigorous study design, robust validation processes, and meticulous statistical analysis to gauge the efficacy of AI applications. We further illuminate the potential of large language models and generative AI in revolutionizing VTE care, while acknowledging their inherent limitations and proposing innovative solutions to overcome challenges related to data availability and integrity, including the strategic use of synthetic data. The critical importance of navigating ethical, legal, and privacy concerns associated with AI is underscored, alongside the imperative for comprehensive governance and policy frameworks to regulate its deployment in VTE treatment. We conclude on a note of cautious optimism, where we highlight the significance of proactively addressing the myriad challenges that accompany the advent of AI in healthcare. Through diligent design, stringent validation, extensive education, and prudent regulation, we can harness AI's potential to significantly enhance our understanding and management of VTE. As we stand on the cusp of a new era, our commitment to these principles will be instrumental in ensuring that the promise of AI is fully realized within the realm of VTE care.

Precision medicine activities and opportunities for shaping maternal and neonatal health in Qatar

Precision Medicine (PM) is a transformative clinical medicine strategy that aims to revolutionize healthcare by leveraging biological information and biomarkers. In the context of maternal and neonatal health, PM enables personalized care from preconception through the postnatal period. Qatar has emerged as a key player in PM research, with dedicated programs driving advancements and translating cutting-edge research into clinical applications. This article delves into neonatal and maternal health in Qatar, emphasizing PM programs and initiatives that have been implemented. It also features noteworthy clinical cases that demonstrate the effectiveness of precision interventions. Furthermore, the article highlights the role of pharmacogenomics in addressing various maternal health conditions. The review further explores potential advancements in the application of PM in maternal and neonatal healthcare in Qatar.

Scientometric analysis of global research on delayed and nonunion of fractures (2004-2023): Insights from the Web of Science core collections

BACKGROUND AND AIMS

Nonunion of fractures continue to be the most challenging complication. The increased interest in this area has been anecdotally recognized through published research. The study aimed to conduct a comprehensive bibliometric analysis of global research on nonunion and delayed union of fractures to identify key trends, hotspots, and potential areas for future research development.

METHODS

Using a pre-defined search strategy, articles related to "Nonunion and Delayed Union of Fractures" that were published from January 1st, 2004 to December 31st, 2023, were retrieved from the Web of Science Core Collection for scientometric analysis. The bibliometrics method was used to analyze the publication dates, countries, institutions, journals, authors, highly cited literature and research hotspots.

RESULTS

A total of 1997 publications were identified, which registered an average of 21.42 citations per paper (CPP). The 30.39 % and 3.55 % of the 1997 global publications have received external funding support and were highly-cited papers, respectively. The USA, China and UK, were the most productive countries, while Canada, Scotland, and Belgium registered the highest citation impact. University Leeds, Shanghai Jiao Tong University and Kobe University were the most productive organizations, while Vanderbilt University, University of Edinburgh and University Leeds registered the highest citation impact. Giannoudis PV, Egol KA, and Konda SR were the most productive authors, while Bhandari M, Mehta S, and Schemitsch EH registered the highest citation impact. Injury, Journal of Orthopaedic Trauma and Archives of Orthopaedic and Trauma Surgery contributed the most papers, while Journal of Bone and Joint Surgery-American, Injury and Clinical Orthopaedics and Related Research registered the highest citation impact per paper.

CONCLUSION

The present bibliometric analysis shows the characteristics and trends of non-union fracture research and illuminates the current research situation and developmental trends.

Improving systemic therapy selection for inflammatory skin diseases: A clinical need survey

Background

Empirical decisions to select therapies for psoriasis (PSO) and atopic dermatitis (AD) can lead to delays in disease control and increased health care costs. However, routine molecular testing for AD and PSO are lacking.

Objective

To examine (1) how clinicians choose systemic therapies for patients with PSO and AD without molecular testing and (2) to determine how often the current approach leads to patients switching medications.

Methods

A 20-question survey designed to assess clinician strategies for systemic treatment of AD and PSO was made available to attendees of a national dermatology conference in 2022.

Results

Clinicians participating in the survey (265/414, 64% response rate) ranked "reported efficacy" as the most important factor governing treatment choice (P < .001). However, 62% (165/265) of clinicians estimated that 2 or more systemic medications were typically required to achieve efficacy. Over 90% (239/265) of respondents would or would likely find a molecular test to guide therapeutic selection useful.

Limitations

To facilitate ease of recall, questions focused on systemic therapies as a whole and not individual therapies.

Conclusion

Clinicians want a molecular test to help determine the most efficacious drug for individual patients.

Effect of predictive nursing based on risk early warning system on patients with acute respiratory failure in Intensive Care Unit

Background & Objective

Acute respiratory failure (ARF) is a life-threatening condition that necessitates intensive care and often results in high morbidity and mortality. Predictive nursing, combined with a risk early warning system, offers a proactive approach to patient care that could potentially improve outcomes in patients with ARF. However, the efficiency of this approach in intensive care settings is still unclear. This study aimed to analyze the effect of predictive nursing based on risk early warning system in patients with acute respiratory failure in intensive care unit (ICU) setting.

Methods

A retrospective cohort study included records of 368 patients admitted to ICU of a tertiary care hospital due to ARF from January 2021 to January 2023. Patients were divided into two groups based on the received care: standard care (control group, n=197) and predictive nursing care based on a risk early warning system (observation group, n=171). Data on demographics, clinical characteristics, complications, Acute Physiology, Age and Chronic Health Evaluation-II (APACHE-II) and Sequential Organ Failure Assessment (SOFA) scores, and duration of hospitalization were collected and analyzed.

Results

The observation group exhibited significantly lower incidence of complications related to ventilator use and shorter durations of mechanical ventilation, ICU stay, and total hospitalization compared to the control group (p<0.001). Furthermore, patients in the observation group had significantly lower APACHE-II and SOFA scores and blood lactate levels at both one week and two weeks post-intervention.

Conclusion

Predictive nursing care based on a risk early warning system significantly improved clinical outcomes and reduced mortality rates in ICU patients with ARF. The results underscore the potential of integrating predictive nursing care into routine practice, thereby transforming the care paradigm for ICU patients with ARF. Future research should explore the applicability of predictive nursing for other clinical conditions and in various healthcare settings.

Optimizing Cardiovascular Health: A Comprehensive Review of Risk Assessment Strategies for Primary Prevention

Cardiovascular disease (CVD) is a leading global health concern, and effective primary prevention strategies are essential to mitigate its impact. This comprehensive review examines current risk assessment strategies for primary prevention of CVD, emphasizing the importance of early identification and intervention to reduce disease incidence. Traditional risk factors such as hypertension, hyperlipidemia, smoking, and lifestyle choices are discussed alongside emerging factors, including genetic predispositions and biomarkers. The review evaluates various risk assessment tools and models, such as the Framingham risk score, atherosclerotic CVD risk calculator, QRISK, and Reynolds risk score, highlighting their methodologies, strengths, and limitations. Additionally, the review explores lifestyle modifications, including dietary changes, physical activity, weight management, smoking cessation, and pharmacological interventions like statins and antihypertensives. Special considerations for different populations, including the elderly, women, and those with a family history of CVD, are addressed. Future directions in cardiovascular risk assessment are also discussed, focusing on technological advancements and personalized medicine. This review aims to enhance the implementation of effective primary prevention measures and improve cardiovascular health outcomes by providing a thorough analysis of risk assessment strategies.

A Comprehensive Review of the Role of Stem Cells in Neuroregeneration: Potential Therapies for Neurological Disorders

Stem cell research has emerged as a groundbreaking field with significant potential for advancing neuroregeneration and neurological disorder treatment. Neurological conditions such as Alzheimer's disease, Parkinson's disease, stroke, and spinal cord injuries pose severe challenges due to their impact on quality of life and the limited efficacy of current treatments, which primarily focus on symptom management rather than addressing the underlying damage. Neuroregeneration, the process of repairing and restoring damaged neural tissues, is crucial for improving patient outcomes, given the central nervous system's limited intrinsic repair capacity. Stem cells offer a promising solution due to their ability to self-renew and differentiate into various neural cell types, providing opportunities for innovative therapies. This review provides a comprehensive analysis of the role of stem cells in neuroregeneration, exploring different types of stem cells, including embryonic stem cells (ESCs), induced pluripotent stem cells (iPSCs), and adult stem cells, and their mechanisms of action in neural repair. It examines current clinical trials and translational research efforts, highlighting successes and ongoing challenges such as ethical considerations, immunogenicity, and technical limitations. The review also discusses future directions in stem cell research, including advancements in gene editing, tissue engineering, and personalized medicine. By addressing these aspects, the review aims to offer a thorough understanding of the potential and challenges of stem cell-based therapies, contributing to the development of effective treatments for neurological disorders and ultimately enhancing patient quality of life.

Know your molecule: pharmacological characterization of drug candidates to enhance efficacy and reduce late-stage attrition

Despite advances in chemical, computational and biological sciences, the rate of attrition of drug candidates in clinical development is still high. A key point in the small-molecule discovery process that could provide opportunities to help address this challenge is the pharmacological characterization of hit and lead compounds, culminating in the selection of a drug candidate. Deeper characterization is increasingly important, because the 'quality' of drug efficacy, at least for G protein-coupled receptors (GPCRs), is now understood to be much more than activation of commonly evaluated pathways such as cAMP signalling, with many more 'efficacies' of ligands that could be harnessed therapeutically. Such characterization is being enabled by novel assays to characterize the complex behaviour of GPCRs, such as biased signalling and allosteric modulation, as well as advances in structural biology, such as cryo-electron microscopy. This article discusses key factors in the assessments of the pharmacology of hit and lead compounds in the context of GPCRs as a target class, highlighting opportunities to identify drug candidates with the potential to address limitations of current therapies and to improve the probability of them succeeding in clinical development.

DeepDRA: Drug repurposing using multi-omics data integration with autoencoders

Cancer treatment has become one of the biggest challenges in the world today. Different treatments are used against cancer; drug-based treatments have shown better results. On the other hand, designing new drugs for cancer is costly and time-consuming. Some computational methods, such as machine learning and deep learning, have been suggested to solve these challenges using drug repurposing. Despite the promise of classical machine-learning methods in repurposing cancer drugs and predicting responses, deep-learning methods performed better. This study aims to develop a deep-learning model that predicts cancer drug response based on multi-omics data, drug descriptors, and drug fingerprints and facilitates the repurposing of drugs based on those responses. To reduce multi-omics data's dimensionality, we use autoencoders. As a multi-task learning model, autoencoders are connected to MLPs. We extensively tested our model using three primary datasets: GDSC, CTRP, and CCLE to determine its efficacy. In multiple experiments, our model consistently outperforms existing state-of-the-art methods. Compared to state-of-the-art models, our model achieves an impressive AUPRC of 0.99. Furthermore, in a cross-dataset evaluation, where the model is trained on GDSC and tested on CCLE, it surpasses the performance of three previous works, achieving an AUPRC of 0.72. In conclusion, we presented a deep learning model that outperforms the current state-of-the-art regarding generalization. Using this model, we could assess drug responses and explore drug repurposing, leading to the discovery of novel cancer drugs. Our study highlights the potential for advanced deep learning to advance cancer therapeutic precision.

Towards a lifelong personalized brain health program: empowering individuals to define, pursue, and monitor meaningful outcomes

Incorporating person-centered outcomes into clinical trials for neurodegenerative diseases has been challenging due to a deficiency in quantitative measures. Meanwhile, the integration of personally meaningful treatment targets in clinical practice remains qualitative, failing to truly inform evaluations, therapeutic interventions and longitudinal monitoring and support. We discuss the current advances and future directions in capturing individualized brain health outcomes and present an approach to integrate person-centered outcome in a scalable manner. Our approach stems from the evidence-based electronic Person-Specific Outcome Measure (ePSOM) program which prompts an individual to define personally meaningful treatment priorities and report level of confidence in managing items that matter to the individual the most (e.g., "Do I feel confident in my ability to contribute to a conversation?"). Deployed either as a single version (person only) or a dyad version (person and care partner), our proposed tool could be used as an endpoint in clinical trials, offering proof of meaningful intervention benefits and in clinical practice, by establishing an anchor for the therapeutic objectives sought by the individual.

The Roadmap toward Personalized Medicine: Challenges and Opportunities

In 2019, the International Consortium for Personalised Medicine (ICPerMed) developed a vision on how the use of personalized medicine (PM) approaches will promote "next-generation" medicine in 2030 more firmly centered on the individual's personal characteristics, leading to improved health outcomes within sustainable healthcare systems through research, development, innovation, and implementation for the benefit of patients, citizens, and society. Nevertheless, there are significant hurdles that healthcare professionals, researchers, policy makers, and patients must overcome to implement PM. The ICPerMed aims to provide recommendations to increase stakeholders' awareness on actionable measures to be implemented for the realization of PM. Starting with best practice examples of PM together with consultation of experts and stakeholders, a careful analysis that underlined hurdles, opportunities, recommendations, and information, aiming at developing knowledge on the requirements for PM implementation in healthcare practices, has been provided. A pragmatic roadmap has been defined for PM integration into healthcare systems, suggesting actions to overcome existing barriers and harness the potential of PM for improved health outcomes. In fact, to facilitate the adoption of PM by diverse stakeholders, it is mandatory to have a comprehensive set of resources tailored to stakeholder needs in critical areas of PM. These include engagement strategies, collaboration frameworks, infrastructure development, education and training programs, ethical considerations, resource allocation guidelines, regulatory compliance, and data management and privacy.

Personalizing Injury Management and Recovery: A Cross-Sectional Investigation of Musculoskeletal Injuries and Quality of Life in Athletes

Purpose

The study explores the impact of Musculoskeletal Injuries on the quality of life in youth athletes, aiming to understand the extent of these injuries' effects on their physical and mental wellbeing.

Patients and Methods

This cross-sectional study included 130 youth athletes, using questionnaires to collect data on demographics, training exposures (averaging 11±3.8 hours/week), overuse symptoms (using the Oslo Sports Trauma Research Center Overuse Injury Questionnaire), acute injury history, and overall wellbeing (assessed by the RAND 36-item Short Form Health Survey).

Results

Findings indicated that 55.4% of participants had suffered an acute injury in the past 6 months, leading to an average of 4 weeks of time loss. The mean score for OSTRC-O Scores was 16.8±6.4, with knee overuse averaging 21.3± 8.8. In terms of wellbeing, physical and mental health scores were 82.4±15.3 and 81.7±14.1 respectively. There were significant correlations between higher overuse scores and poorer physical functioning (r=-0.42), bodily pain (r=-0.38), vitality (r=-0.32), and mental health (r=-0.31). Acute injuries were linked with worse physical functioning and role limitations. Regression analysis showed that both overuse and acute injuries predicted poorer physical health.

Conclusion

The study underscores the significant prevalence of musculoskeletal injuries among youth athletes and delineates their profound impact on the quality of life, encompassing both the physical and mental health realms. These findings advocate for the critical integration of preventive measures and personalized training protocols, spotlighting the pivotal role of comprehensive biopsychosocial strategies in nurturing athletes' overall wellbeing. By prioritizing the quality of life as a key outcome, this research advocates for a more nuanced approach to injury management and recovery.

Precision Medicine in Veterinary Science

Precision medicine focuses on the clinical management of the individual patient, not on population-based findings. Successes from human precision medicine inform veterinary oncology. Early evidence of success for canines shows how precision medicine can be integrated into practice. Decreasing genomic profiling costs will allow increased utilization and subsequent improvement of knowledge base from which to make better informed decisions. Utility of precision medicine in canine oncology will only increase for improved cancer characterization, enhanced therapy selection, and overall more successful management of canine cancer. As such, practitioners are called to interpret and leverage precision medicine reports for their patients.

From the Gut to the Brain: Is Microbiota a New Paradigm in Parkinson's Disease Treatment?

Parkinson's disease (PD) is recognized as the second most prevalent primary chronic neurodegenerative disorder of the central nervous system. Clinically, PD is characterized as a movement disorder, exhibiting an incidence and mortality rate that is increasing faster than any other neurological condition. In recent years, there has been a growing interest concerning the role of the gut microbiota in the etiology and pathophysiology of PD. The establishment of a brain-gut microbiota axis is now real, with evidence denoting a bidirectional communication between the brain and the gut microbiota through metabolic, immune, neuronal, and endocrine mechanisms and pathways. Among these, the vagus nerve represents the most direct form of communication between the brain and the gut. Given the potential interactions between bacteria and drugs, it has been observed that the therapies for PD can have an impact on the composition of the microbiota. Therefore, in the scope of the present review, we will discuss the current understanding of gut microbiota on PD and whether this may be a new paradigm for treating this devastating disease.

Precision Medicine-Are We There Yet? A Narrative Review of Precision Medicine's Applicability in Primary Care

Precision medicine (PM), also termed stratified, individualised, targeted, or personalised medicine, embraces a rapidly expanding area of research, knowledge, and practice. It brings together two emerging health technologies to deliver better individualised care: the many "-omics" arising from increased capacity to understand the human genome and "big data" and data analytics, including artificial intelligence (AI). PM has the potential to transform an individual's health, moving from population-based disease prevention to more personalised management. There is however a tension between the two, with a real risk that this will exacerbate health inequalities and divert funds and attention from basic healthcare requirements leading to worse health outcomes for many. All areas of medicine should consider how this will affect their practice, with PM now strongly encouraged and supported by government initiatives and research funding. In this review, we discuss examples of PM in current practice and its emerging applications in primary care, such as clinical prediction tools that incorporate genomic markers and pharmacogenomic testing. We look towards potential future applications and consider some key questions for PM, including evidence of its real-world impact, its affordability, the risk of exacerbating health inequalities, and the computational and storage challenges of applying PM technologies at scale.

Interplay of precision therapeutics and MD study: Calocybe indica's potentials against cervical cancer and its interaction with VEGF via octadecanoic acid

The evolving landscape of personalized medicine necessitates a shift from traditional therapeutic interventions towards precision-driven approaches. Embracing this paradigm, our research probes the therapeutic efficacy of the aqueous crude extract (ACE) of Calocybe indica in cervical cancer treatment, merging botanical insights with advanced molecular research. We observed that ACE exerts significant influences on nuclear morphology and cell cycle modulation, further inducing early apoptosis and showcasing prebiotic attributes. Characterization of ACE have identified several phytochemicals including significant presence of octadeconoic acid. Simultaneously, utilizing advanced Molecular Dynamics (MD) simulations, we deciphered the intricate molecular interactions between Vascular Endothelial Growth Factor (VEGF) and Octadecanoic acid to establish C.indica's role as an anticancer agent. Our study delineates Octadecanoic acid's potential as a robust binding partner for VEGF, with comprehensive analyses from RMSD and RMSF profiles highlighting the stability and adaptability of the protein-ligand interactions. Further in-depth thermodynamic explorations via MM-GBSA calculations reveal the binding landscape of the VEGF-Octadecanoic acid complex. Emerging therapeutic innovations, encompassing proteolysis-targeting chimeras (PROTACs) and avant-garde nanocarriers, are discussed in the context of their synergy with compounds like Calocybe indica P&C. This convergence underscores the profound therapeutic potential awaiting clinical exploration. This study offers a holistic perspective on the promising therapeutic avenues facilitated by C. indica against cervical cancer, intricately woven with advanced molecular interactions and the prospective integration of precision therapeutics in modern oncology.

Pollutants to pathogens: The role of heavy metals in modulating TGF-β signaling and lung cancer risk

Lung cancer is a malignant tumor that develops in the lungs due to the uncontrolled growth of aberrant cells. Heavy metals, such as arsenic, cadmium, mercury, and lead, are metallic elements characterized by their high atomic weights and densities. Anthropogenic activities, such as industrial operations and pollution, have the potential to discharge heavy metals into the environment, hence presenting hazards to ecosystems and human well-being. The TGF-β signalling pathways have a crucial function in controlling several cellular processes, with the ability to both prevent and promote tumor growth. TGF-β regulates cellular responses by interacting in both canonical and non-canonical signalling pathways. Research employing both in vitro and in vivo models has shown that heavy metals may trigger TGF-β signalling via complex molecular pathways. Experiments conducted in a controlled laboratory environment show that heavy metals like cadmium and arsenic may directly bind to TGF-β receptors, leading to alterations in their structure that enable the receptor to be phosphorylated. Activation of this route sets in motion subsequent signalling cascades, most notably the canonical Smad pathway. The development of lung cancer has been linked to heavy metals, which are ubiquitous environmental pollutants. To grasp the underlying processes, it is necessary to comprehend their molecular effect on TGF-β pathways. With a particular emphasis on its consequences for lung cancer, this abstract delves into the complex connection between exposure to heavy metals and the stimulation of TGF-β signalling.

Are non-protein coding RNAs junk or treasure?: An attempt to explain and reconcile opposing viewpoints of whether the human genome is mostly transcribed into non-functional or functional RNAs

The human genome project's lasting legacies are the emerging insights into human physiology and disease, and the ascendance of biology as the dominant science of the 21st century. Sequencing revealed that >90% of the human genome is not coding for proteins, as originally thought, but rather is overwhelmingly transcribed into non-protein coding, or non-coding, RNAs (ncRNAs). This discovery initially led to the hypothesis that most genomic DNA is "junk", a term still championed by some geneticists and evolutionary biologists. In contrast, molecular biologists and biochemists studying the vast number of transcripts produced from most of this genome "junk" often surmise that these ncRNAs have biological significance. What gives? This essay contrasts the two opposing, extant viewpoints, aiming to explain their bases, which arise from distinct reference frames of the underlying scientific disciplines. Finally, it aims to reconcile these divergent mindsets in hopes of stimulating synergy between scientific fields.

Unlocking the Potential of Therapy-Induced Cytokine Responses: Illuminating New Pathways in Cancer Precision Medicine

Precision cancer medicine primarily aims to identify individual patient genomic variations and exploit vulnerabilities in cancer cells to select suitable patients for specific drugs. These genomic features are commonly determined by gene sequencing prior to therapy, to identify individuals who would be most responsive. This precision approach in cancer therapeutics remains a powerful tool that benefits a smaller pool of patients, sparing others from unnecessary treatments. A limitation of this approach is that proteins, not genes, are the ultimate effectors of biological functions, and therefore the targets of therapeutics. An additional dimension in precision medicine that considers an individual's cytokine response to cancer therapeutics is proposed. Cytokine responses to therapy are multifactorial and vary among individuals. Thus, precision is dictated by the nature and magnitude of cytokine responses in the tumor microenvironment exposed to therapy. This review highlights cytokine responses as modules for precision medicine in cancer therapy, including potential challenges. For solid tumors, both detectability of cytokines in tissue fluids and their being amenable to routine sensitive analyses could address the difficulty of specimen collection for diagnosis and monitoring. Therefore, in precision cancer medicine, cytokines offer rational targets that can be utilized to enhance the efficacy of cancer therapy.

Community Outreach and Engagement in Cancer Research Through a Biobank Clinic at Shaukat Khanum Memorial Cancer Hospital and Research Centre, Pakistan

INTRODUCTION

Cancer's increasing prevalence across the globe emphasizes the urgency for continued research, prevention, and accessible healthcare to mitigate its impact on individuals and communities. While there have been significant advances made towards controlling cancer morbidity and mortality in recent decades, Pakistan continues to experience a markedly elevated burden of the disease. With this study, we aim to raise awareness about biobank research within the cancer patient community, fostering participation and collaboration to advance the fight against cancer through vital research contributions.

METHODS

In October 2022, we initiated the biobank clinic at Shaukat Khanum Memorial Cancer Hospital and Research Centre (SKMCH&RC). Here, patients underwent screening and received invitations to voluntarily participate in biobank research. During these interactions, we engaged patients in discussions about the significance of biobank research, addressed their concerns, and encouraged their participation in advancing our research endeavors. Two-sample independent t-tests were performed to compare the mean number of participants in pre-clinic and post-clinic cohorts.

RESULTS

This research involved a total of 958 participants, with 312 participants enrolled before the clinic and 646 participants enrolled after the clinic. We have observed a noticeable increase in the participation of cancer patients in our research endeavors since the inception of the biobank clinic (p-value<0.001). Over an 11-month time frame, we scheduled appointments for 759 patients, and out of those, 656 patients availed themselves to visit the clinic. Impressively, we achieved the enrollment of 646 patients into the clinic, reflecting an exceptional consent rate of 98.47% for their active involvement in our research initiatives. This underscores our commitment to conducting comprehensive discussions and providing thorough explanations regarding the ethical and procedural aspects of our research.

CONCLUSION

Biobank clinic plays a pivotal role in raising cancer awareness and fostering research participation, especially in regions with limited healthcare infrastructure and lower literacy rates. It emerges as a community-engagement model that aligns research with local needs, ensuring its relevance and benefit to the population.

Barriers and Facilitators in Continuous Medical Education Related to Allogeneic Stem Cell Transplantation: A Qualitative Study of Physicians

INTRODUCTION

This study explored qualitatively, in a sample of German hematologists working in clinical allogeneic hematopoietic stem cell transplantation (alloHSCT), perceptions of barriers and facilitators to participate in continuous medical education (CME), to provide detailed information on how to improve participation in CME activities related to alloHSCT, which may also be applicable to other areas of medicine.

METHODS

Based on a recruitment campaign of the German Association for Hematopoietic Stem Cell Transplantation (DAG-HSZT), 21 semi-structured telephone interviews were conducted, transcribed, and analyzed using framework analysis.

RESULTS

Three clusters of barriers were identified that explain why alloHSCT physicians may or may not participate in CME: individual constraints (e.g., better networking, young physicians being overwhelmed by the complexity of alloHSCT), structural constraints (e.g., time and financial issues, tailoring CME courses according to the targeted audience), and content-related constraints (e.g., requirement of CME sessions, provision of an overview of CME courses, more flexible offers). We discuss the ten most frequently raised issues, including the use of incentives and the need for support at the start of residency, staff shortages, and requirements for learning sessions.

CONCLUSION

There is a need for a paradigm shift in CME related to alloHSCT toward a more individualized and needs-based approach. Close monitoring of residents' needs and learning progress, as well as feedback systems, could help identify appropriate CME courses that should be integrated into a tiered learning system. CME should be more targeted to specific audiences (i.e., residents, fellows, and attendees) to provide training that is tailored to individual CME needs. On-demand courses can help balance work and family obligations. Finally, peer-reviewed, up-to-date information platforms should be expanded.

Levothyroxine personalized treatment: is it still a dream?

Levothyroxine is a milestone in the treatment of all causes of hypothyroidism. From 19th century till today, Levothyroxine experienced a great advancement, from hypodermic injections of an extract of the thyroid gland of a sheep to novel formulations, known to circumvent malabsorption issue. However, the rate of patients on suboptimal therapy is still high. Current Guidelines are clear, daily Levothyroxine dosage should be calculated based on body weight. However, we are still far away from the possibility to administer the right dosage to the right patient, for several reasons. We retrace the history of treatment with levothyroxine, pointing out strengths and weaknesses of different formulations, with particular attention to what keeps us away from tailored therapy. In the age of digitalization, the pharmaceutical industry has been giving rising importance to Digital therapeutics, that are known to be effective in reaching target therapies. By combining current knowledge of hypothyroidism therapy with cutting-edge technology, we also hypothesized what could be the future strategies to be developed in this field.

A Review of the Common Neurodegenerative Disorders: Current Therapeutic Approaches and the Potential Role of Bioactive Peptides

Neurodegenerative disorders, which include Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS), represent a significant and growing global health challenge. Current therapies predominantly focus on symptom management rather than altering disease progression. In this review, we discuss the major therapeutic strategies in practice for these disorders, highlighting their limitations. For AD, the mainstay treatments are cholinesterase inhibitors and N-methyl-D-aspartate (NMDA) receptor antagonists. For PD, dopamine replacement therapies, including levodopa, are commonly used. HD is managed primarily with symptomatic treatments, and reusable extends survival in ALS. However, none of these therapies halts or substantially slows the neurodegenerative process. In contrast, this review highlights emerging research into bioactive peptides as potential therapeutic agents. These naturally occurring or synthetically designed molecules can interact with specific cellular targets, potentially modulating disease processes. Preclinical studies suggest that bioactive peptides may mitigate oxidative stress, inflammation, and protein misfolding, which are common pathological features in neurodegenerative diseases. Clinical trials using bioactive peptides for neurodegeneration are limited but show promising initial results. For instance, hemiacetal, a γ-secretase inhibitor peptide, has shown potential in AD by reducing amyloid-beta production, though its development was discontinued due to side effects. Despite these advancements, many challenges remain, including identifying optimal peptides, confirming their mechanisms of action, and overcoming obstacles related to their delivery to the brain. Future research should prioritize the discovery and development of novel bioactive peptides and improve our understanding of their pharmacokinetics and pharmacodynamics. Ultimately, this approach may lead to more effective therapies for neurodegenerative disorders, moving beyond symptom management to potentially modify the course of these devastating diseases.

HeNeCOn: An ontology for integrative research in Head and Neck cancer

BACKGROUND

Head and Neck Cancer (HNC) has a high incidence and prevalence in the worldwide population. The broad terminology associated with these diseases and their multimodality treatments generates large amounts of heterogeneous clinical data, which motivates the construction of a high-quality harmonization model to standardize this multi-source clinical data in terms of format and semantics. The use of ontologies and semantic techniques is a well-known approach to face this challenge.

OBJECTIVE

This work aims to provide a clinically reliable data model for HNC processes during all phases of the disease: prognosis, treatment, and follow-up. Therefore, we built the first ontology specifically focused on the HNC domain, named HeNeCOn (Head and Neck Cancer Ontology).

METHODS

First, an annotated dataset was established to provide a formal reference description of HNC. Then, 170 clinical variables were organized into a taxonomy, and later expanded and mapped to formalize and integrate multiple databases into the HeNeCOn ontology. The outcomes of this iterative process were reviewed and validated by clinicians and statisticians.

RESULTS

HeNeCOn is an ontology consisting of 502 classes, a taxonomy with a hierarchical structure, semantic definitions of 283 medical terms and detailed relations between them, which can be used as a tool for information extraction and knowledge management.

CONCLUSION

HeNeCOn is a reusable, extendible and standardized ontology which establishes a reference data model for terminology structure and standard definitions in the Head and Neck Cancer domain. This ontology allows handling both current and newly generated knowledge in Head and Neck cancer research, by means of data linking and mapping with other public ontologies.

An Assessment of the Knowledge and Perceptions of Precision Medicine (PM) in the Rwandan Healthcare Setting

INTRODUCTION

Precision medicine (PM) or personalized medicine is an innovative approach that aims to tailor disease prevention and treatment to consider the differences in people's genes, environments, and lifestyles. Although many efforts have been made to accelerate the universal adoption of PM, several challenges need to be addressed in order to advance PM in Africa. Therefore, our study aimed to establish baseline data on the knowledge and perceptions of the implementation of PM in the Rwandan healthcare setting.

METHOD

A descriptive qualitative study was conducted in five hospitals offering diagnostics and oncology services to cancer patients in Rwanda. To understand the existing policies regarding PM implementation in the country, two additional institutions were surveyed: the Ministry of Health (MOH), which creates and sets policies for the overall vision of the health sector, and the Rwanda Biomedical Center (RBC), which coordinates the implementation of health sector policies in the country. The researchers conducted 32 key informant interviews and assessed the functionality of available PM equipment in the 5 selected health facilities. The data were thematically categorized and analyzed.

RESULTS

The study revealed that PM is perceived as a complex and expensive program by most health managers and health providers. The most cited challenges to implementing PM included the following: the lack of policies and guidelines; the lack of supportive infrastructures and limited suppliers of required equipment and laboratory consumables; financial constraints; cultural, behavioral, and religious beliefs; and limited trained, motivated, and specialized healthcare providers. Regarding access to health services for cancer treatment, patients with health insurance pay 10% of their medical costs, which is still too expensive for Rwandans.

CONCLUSION

The study participants highlighted the importance of PM to enhance healthcare delivery if the identified barriers are addressed. For instance, Rwandan health sector leadership might consider the creation of specialized oncology centers in all or some referral hospitals with all the necessary genomic equipment and trained staff to serve the needs of the country and implement a PM program.

Decision rules for personalized statin treatment prescriptions over multi-objectives

In our previous study, we demonstrated the feasibility of producing a proactive statin prescription strategy - a personalized statin treatment plan (PSTP) - using neural networks with big data. However, its non-transparency limited result interpretations and clinical usability. To improve the transparency of our previous approach with minimal compromise to the maximal statin treatment benefit-to-risk ratio, this study proposed a five-step pipeline approach called the decision rules for statin treatment (DRST). Steps 1-3 of our proposed pipeline improved our previous PSTP model in optimizing individual benefit-to-risk ratio; Step 4 used a decision tree model (DRST) to provide straightforward rules in the initial statin treatment plan; Step 5 aimed to evaluate the efficacy of these decision rules by conducting a clinical trial simulation. We included 107,739 de-identified patient data from Optum Labs Database Warehouse in this study. The final decision rules were compact and efficient, resulting from a decision tree with only a maximum depth of 3 and 11 nodes. The DRST identified three factors that are easily obtainable at the point of care: age, low-density lipoprotein cholesterol (LDL-C) level, and age-adjusted Charlson score. Moreover, it also identified six subpopulations that can benefit most from these decision rules. In our clinical trial simulations, DRST was found to improve statin benefit in LDL-C reduction by 4.15 percentage points (pp) and reduce risks of statin-associated symptoms (SAS) and statin discontinuation by 11.71 and 3.96 pp, respectively, when compared to the standard of care. Moreover, these DRST results were only less than 0.6 pp suboptimal to PSTP, demonstrating that building DRST that provide transparency with minimal compromise to the maximal benefit-to-risk ratio of statin treatments is feasible.

Advances in neuroproteomics for neurotrauma: unraveling insights for personalized medicine and future prospects

Neuroproteomics, an emerging field at the intersection of neuroscience and proteomics, has garnered significant attention in the context of neurotrauma research. Neuroproteomics involves the quantitative and qualitative analysis of nervous system components, essential for understanding the dynamic events involved in the vast areas of neuroscience, including, but not limited to, neuropsychiatric disorders, neurodegenerative disorders, mental illness, traumatic brain injury, chronic traumatic encephalopathy, and other neurodegenerative diseases. With advancements in mass spectrometry coupled with bioinformatics and systems biology, neuroproteomics has led to the development of innovative techniques such as microproteomics, single-cell proteomics, and imaging mass spectrometry, which have significantly impacted neuronal biomarker research. By analyzing the complex protein interactions and alterations that occur in the injured brain, neuroproteomics provides valuable insights into the pathophysiological mechanisms underlying neurotrauma. This review explores how such insights can be harnessed to advance personalized medicine (PM) approaches, tailoring treatments based on individual patient profiles. Additionally, we highlight the potential future prospects of neuroproteomics, such as identifying novel biomarkers and developing targeted therapies by employing artificial intelligence (AI) and machine learning (ML). By shedding light on neurotrauma's current state and future directions, this review aims to stimulate further research and collaboration in this promising and transformative field.

The molecular landscape of neurological disorders: insights from single-cell RNA sequencing in neurology and neurosurgery

Single-cell ribonucleic acid sequencing (scRNA-seq) has emerged as a transformative technology in neurological and neurosurgical research, revolutionising our comprehension of complex neurological disorders. In brain tumours, scRNA-seq has provided valuable insights into cancer heterogeneity, the tumour microenvironment, treatment resistance, and invasion patterns. It has also elucidated the brain tri-lineage cancer hierarchy and addressed limitations of current models. Neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis have been molecularly subtyped, dysregulated pathways have been identified, and potential therapeutic targets have been revealed using scRNA-seq. In epilepsy, scRNA-seq has explored the cellular and molecular heterogeneity underlying the condition, uncovering unique glial subpopulations and dysregulation of the immune system. ScRNA-seq has characterised distinct cellular constituents and responses to spinal cord injury in spinal cord diseases, as well as provided molecular signatures of various cell types and identified interactions involved in vascular remodelling. Furthermore, scRNA-seq has shed light on the molecular complexities of cerebrovascular diseases, such as stroke, providing insights into specific genes, cell-specific expression patterns, and potential therapeutic interventions. This review highlights the potential of scRNA-seq in guiding precision medicine approaches, identifying clinical biomarkers, and facilitating therapeutic discovery. However, challenges related to data analysis, standardisation, sample acquisition, scalability, and cost-effectiveness need to be addressed. Despite these challenges, scRNA-seq has the potential to transform clinical practice in neurological and neurosurgical research by providing personalised insights and improving patient outcomes.

The Impact of Antibiotics and Steroids on the Nasal Microbiome in Patients with Chronic Rhinosinusitis: A Systematic Review According to PICO Criteria

BACKGROUND

The nasal microbiome represents the main environmental factor of the inflammatory process in chronic rhinosinusitis (CRS). Antibiotics and steroids constitute the mainstay of CRS therapies. However, their impact on microbial communities needs to be better understood. This systematic review summarizes the evidence about antibiotics' and steroids' impact on the nasal microbiota in patients with CRS.

METHODS

The search strategy was conducted in accordance with the PRISMA guidelines for systematic reviews. The authors searched all papers in the three major medical databases (PubMed, Scopus, and Cochrane Library) using the PICO tool (population, intervention, comparison, and outcomes). The search was carried out using a combination of the key terms "Microbiota" or "Microbiome" and "Chronic Rhinosinusitis".

RESULTS

Overall, 402 papers were identified, and after duplicate removal (127 papers), excluding papers off-topic (154) and for other structural reasons (110), papers were assessed for eligibility; finally, only 11 papers were included and summarized in the present systematic review. Some authors used only steroids, other researchers used only antibiotics, and others used both antibiotics and steroids. With regard to the use of steroids as exclusive medical treatment, topical mometasone and budesonide were investigated. With regard to the use of antibiotics as exclusive medical treatments, clarithromycin, doxycycline, roxithromycin, and amoxicillin clavulanate were investigated. Regarding the use of both antibiotics and steroids, two associations were investigated: systemic prednisone combined with amoxicillin clavulanate and topical budesonide combined with azithromycin.

CONCLUSIONS

The impact that therapies can have on the nasal microbiome of CRS patients is very varied. Further studies are needed to understand the role of the nasal microbiome, prevent CRS, and improve therapeutic tools for personalized medicine tailored to the individual patient.

Mastering the Art of Dermatosurgery: Aesthetic Alchemy in Medical Excellence

Dermatosurgery, a specialized branch within dermatology, has traversed an extraordinary journey through time, shaped by ancient practices, technological leaps, and shifting societal perceptions. This review explores the evolution of dermatosurgery, highlighting its profound transformation from addressing solely medical concerns to seamlessly integrating aesthetics. From its roots in ancient civilizations, where cultural traditions laid the foundation for modern techniques, to the twentieth-century technological renaissance, marked by innovative tools and enhanced understanding of skin anatomy, dermatosurgery has emerged as a dynamic field. Societal notions of beauty and health have significantly influenced dermatosurgery, blurring the lines between medical necessity and elective aesthetic procedures. The delicate balance between satisfying aesthetic desires and upholding medical ethics is a central challenge that dermatosurgeons face today. Open dialogue between practitioners and patients as well as psychological support plays a pivotal role in navigating this terrain. The training and ethics associated with dermatosurgery have evolved to meet the increasing demand for specialized procedures. Maintaining a focus on patient safety and satisfaction remains paramount as commercial pressures and disparities in access to care loom. Upholding best practices and standards in the field is essential for ensuring consistent, high-quality care for all patients. Looking ahead, dermatosurgery stands on the brink of a transformative era, marked by non-invasive techniques, artificial intelligence (AI) integration, and personalized medicine. The field's ability to harmonize medical science with aesthetic artistry is evident in various case studies, showcasing the intricate balance dermatosurgeons strike between addressing medical concerns and fulfilling aesthetic desires. As dermatosurgery continues to evolve, it promises to provide patients with even more precise, tailored treatments that enhance both their physical well-being and aesthetic satisfaction.

Maintaining High-Touch in High-Tech Digital Health Monitoring and Multi-Omics Prognostication: Ethical, Equity, and Societal Considerations in Precision Health for Palliative Care

Advances in digital health, systems biology, environmental monitoring, and artificial intelligence (AI) continue to revolutionize health care, ushering a precision health future. More than disease treatment and prevention, precision health aims at maintaining good health throughout the lifespan. However, how can precision health impact care for people with a terminal or life-limiting condition? We examine here the ethical, equity, and societal/relational implications of two precision health modalities, (1) integrated systems biology/multi-omics analysis for disease prognostication and (2) digital health technologies for health status monitoring and communication. We focus on three main ethical and societal considerations: benefits and risks associated with integration of these modalities into the palliative care system; inclusion of underrepresented and marginalized groups in technology development and deployment; and the impact of high-tech modalities on palliative care's highly personalized and "high-touch" practice. We conclude with 10 recommendations for ensuring that precision health technologies, such as multi-omics prognostication and digital health monitoring, for palliative care are developed, tested, and implemented ethically, inclusively, and equitably.

Artificial Intelligence in Regenerative Medicine: Applications and Implications

The field of regenerative medicine is constantly advancing and aims to repair, regenerate, or substitute impaired or unhealthy tissues and organs using cutting-edge approaches such as stem cell-based therapies, gene therapy, and tissue engineering. Nevertheless, incorporating artificial intelligence (AI) technologies has opened new doors for research in this field. AI refers to the ability of machines to perform tasks that typically require human intelligence in ways such as learning the patterns in the data and applying that to the new data without being explicitly programmed. AI has the potential to improve and accelerate various aspects of regenerative medicine research and development, particularly, although not exclusively, when complex patterns are involved. This review paper provides an overview of AI in the context of regenerative medicine, discusses its potential applications with a focus on personalized medicine, and highlights the challenges and opportunities in this field.

Exploring the potential impact of multi-factor precision interventions in Alzheimer's disease with system dynamics

Numerous clinical trials based on a single-cause paradigm have not resulted in efficacious treatments for Alzheimer's disease (AD). Recently, prevention trials that simultaneously intervened on multiple risk factors have shown mixed results, suggesting that careful design is necessary. Moreover, intensive pilot precision medicine (PM) trial results have been promising but may not generalize to a broader population. These observations suggest that a model-based approach to multi-factor precision medicine (PM) is warranted. We systematically developed a system dynamics model (SDM) of AD for PM using data from two longitudinal studies (N=3660). This method involved a model selection procedure in identifying interaction terms between the SDM components and estimating individualized parameters. We used the SDM to explore simulated single- and double-factor interventions on 14 modifiable risk factors. We quantified the potential impact of double-factor interventions over single-factor interventions as 1.5 [95% CI: 1.5-2.6] and of SDM-based PM over a one-size-fits-all approach as 3.5 [3.1, 3.8] ADAS-cog-13 points in 12 years. Although the model remains to be validated, we tentatively conclude that multi-factor PM could come to play an important role in AD prevention.

The Constrained-Disorder Principle Assists in Overcoming Significant Challenges in Digital Health: Moving from "Nice to Have" to Mandatory Systems

The success of artificial intelligence depends on whether it can penetrate the boundaries of evidence-based medicine, the lack of policies, and the resistance of medical professionals to its use. The failure of digital health to meet expectations requires rethinking some of the challenges faced. We discuss some of the most significant challenges faced by patients, physicians, payers, pharmaceutical companies, and health systems in the digital world. The goal of healthcare systems is to improve outcomes. Assisting in diagnosing, collecting data, and simplifying processes is a "nice to have" tool, but it is not essential. Many of these systems have yet to be shown to improve outcomes. Current outcome-based expectations and economic constraints make "nice to have," "assists," and "ease processes" insufficient. Complex biological systems are defined by their inherent disorder, bounded by dynamic boundaries, as described by the constrained disorder principle (CDP). It provides a platform for correcting systems' malfunctions by regulating their degree of variability. A CDP-based second-generation artificial intelligence system provides solutions to some challenges digital health faces. Therapeutic interventions are held to improve outcomes with these systems. In addition to improving clinically meaningful endpoints, CDP-based second-generation algorithms ensure patient and physician engagement and reduce the health system's costs.

Unveiling the Genetic Footprint: Exploring Somatic Mutations in Peripheral Arterial Disease Progression

Peripheral arterial diseases (PADs) are complex cardiovascular conditions influenced by environmental factors and somatic mutations in multiple genes involved in hematopoiesis and inflammation. While traditional risk factors, such as smoking, hypercholesterolemia, and hypertension, have been extensively studied, the role of somatic mutations in PAD progression remains underexplored. The present article intends to provide a comprehensive commentary of the molecular mechanisms, genetic landscape, prognostic significance, and clinical implications of somatic mutations in PADs. The expansion of clonal hematopoiesis of indeterminate potential (CHIP) clones in the circulating blood, named clonal hematopoiesis (CH), leads to the infiltration of these clones into atherosclerotic plaques and the production of inflammatory cytokines, increasing the risk of cardiovascular diseases, including PADs. Furthermore, recent experimental evidence has demonstrated the involvement of somatically mutated TP53 genes with a high variant allele frequency (VAF) in PAD development and prognosis. This review delves into the relationship between CH and PADs, elucidating the prevalence, impact, and underlying mechanisms of this association. This understanding paves the way for novel therapeutic approaches targeting CHIP to promote tissue regeneration and improve outcomes in PAD patients. It emphasizes the need for further research to fully unravel the genetic footprint of the disease and highlights potential clinical implications. The findings presented in this article lay the foundation for personalized medicine approaches and open avenues for the development of targeted therapies based on somatic mutation profiling.

Unraveling patient heterogeneity in complex diseases through individualized co-expression networks: a perspective

This perspective highlights the potential of individualized networks as a novel strategy for studying complex diseases through patient stratification, enabling advancements in precision medicine. We emphasize the impact of interpatient heterogeneity resulting from genetic and environmental factors and discuss how individualized networks improve our ability to develop treatments and enhance diagnostics. Integrating system biology, combining multimodal information such as genomic and clinical data has reached a tipping point, allowing the inference of biological networks at a single-individual resolution. This approach generates a specific biological network per sample, representing the individual from which the sample originated. The availability of individualized networks enables applications in personalized medicine, such as identifying malfunctions and selecting tailored treatments. In essence, reliable, individualized networks can expedite research progress in understanding drug response variability by modeling heterogeneity among individuals and enabling the personalized selection of pharmacological targets for treatment. Therefore, developing diverse and cost-effective approaches for generating these networks is crucial for widespread application in clinical services.

The Role of Gender Pharmacogenetics in the Personalization of Drug Treatment

The use of pharmacogenetic guidelines in personalizing treatments has shown the potential to reduce interindividual variability in drug response by enabling genotype-matched dosing and drug selection. However, other important factors, such as patient gender, may interact strongly with pharmacogenetics in determining the individual profile of toxicity and efficacy but are still rarely considered when planning pharmacological treatment. The literature indicates that males and females respond differently to drugs, with women being at higher risk for toxicity and having different plasma exposure to drugs at standard doses. Recent studies have shown that pharmacogenetic variants may have different predictive value in different sexes, as in the case of treatment with opioids, angiotensin-converting enzyme inhibitors, or proton pump inhibitors. Of particular interest is the case of treatment with fluoropyrimidines for cancer. A significant increase in toxicity has been described in female patients, with a more pronounced effect of specific DPYD and TYMS polymorphisms also noted. This manuscript reviews the major findings in the field of sex-specific pharmacogenomics. SIGNIFICANCE STATEMENT: Interindividual variability in drug response is an emerging issue in pharmacology. The genetic profile of patients, as well as their gender, may play a role in the identification of patients more exposed to the risk of adverse drug reactions or poor efficacy. This article reviews the current state of research on the interaction between gender and pharmacogenetics in addressing interindividual variability.

Recent Advances in Organ-on-Chips Integrated with Bioprinting Technologies for Drug Screening

Currently, the demand for more reliable drug screening devices has made scientists and researchers develop novel potential approaches to offer an alternative to animal studies. Organ-on-chips are newly emerged platforms for drug screening and disease metabolism investigation. These microfluidic devices attempt to recapitulate the physiological and biological properties of different organs and tissues using human-derived cells. Recently, the synergistic combination of additive manufacturing and microfluidics has shown a promising impact on improving a wide array of biological models. In this review, different methods are classified using bioprinting to achieve the relevant biomimetic models in organ-on-chips, boosting the efficiency of these devices to produce more reliable data for drug investigations. In addition to the tissue models, the influence of additive manufacturing on microfluidic chip fabrication is discussed, and their biomedical applications are reviewed.

Computational Mechanics of Form-Fitting 3D-Printed Lattice-Based Wrist-Hand Orthosis for Motor Neuron Disease

Motor neuron disease (MND) patients often experience hand-wrist muscle atrophy resulting in severe social consequences and hampering their daily activities. Although hand-wrist orthosis is commonly used to assist weakened muscles, its effectiveness is limited due to the rapid progression of the disease and the need for customization to suit individual patient requirements. To address these challenges, this study investigates the application of three-dimensional (3D) printing technology to design and fabricate two lattice structures inspired by silkworm cocoons, using poly-ε-caprolactone as feedstock material. Finite element method (FEM) analysis is employed to study the mechanical behavior, enabling control over the geometric configuration incorporated into the hand-wrist orthosis. Through tensile displacement and three-point bending simulations, the stress distribution is examined for both lattice geometries. Geometry-1 demonstrates anisotropic behavior, while geometry-2 exhibits no strict directional dependence due to its symmetry and uniform node positioning. Moreover, the biocompatibility of lattices with human skin fibroblasts is investigated, confirming excellent biocompatibility. Lastly, the study involves semi-structured interviews with MND patients to gather feedback and develop prototypes of form-fitting 3D-printed lattice-based hand-wrist orthosis. By utilizing 3D printing technology, this study aims to provide customized orthosis that can effectively support weakened muscles and reposition the hand for individuals with MND.

Lincomycin HCl-Loaded Borneol-Based In Situ Gel for Periodontitis Treatment

Solvent exchange-induced in situ forming gel (ISG) has emerged as a versatile drug delivery system, particularly for periodontal pocket applications. In this study, we developed lincomycin HCl-loaded ISGs using a 40% borneol-based matrix and N-methyl pyrrolidone (NMP) as a solvent. The physicochemical properties and antimicrobial activities of the ISGs were evaluated. The prepared ISGs exhibited low viscosity and reduced surface tension, allowing for easy injection and spreadability. Gel formation increased the contact angle on agarose gel, while higher lincomycin HCl content decreased water tolerance and facilitated phase separation. The drug-loading influenced solvent exchange and matrix formation, resulting in thinner and inhomogeneous borneol matrices with slower gel formation and lower gel hardness. The lincomycin HCl-loaded borneol-based ISGs demonstrated sustained drug release above the minimum inhibitory concentration (MIC) for 8 days, following Fickian diffusion and fitting well with Higuchi's equation. These formulations exhibited dose-dependent inhibition of Staphylococcus aureus ATCC 25923, Escherichia coli ATCC 8739, and Prophyromonas gingivalis ATCC 33277, and the release of NMP effectively inhibited Candida albicans ATCC 10231. Overall, the 7.5% lincomycin HCl-loaded 40% borneol-based ISGs hold promise as localized drug delivery systems for periodontitis treatment.

The Misguided Veneration of Averageness in Clinical Neuroscience: A Call to Value Diversity over Typicality

Research and practice in clinical neurosciences often involve cognitive assessment. However, this has traditionally used a nomothetic approach, comparing the performance of patients to normative samples. This method of defining abnormality places the average test performance of neurologically healthy individuals at its center. However, evidence suggests that neurological 'abnormalities' are very common, as is the diversity of cognitive abilities. The veneration of central tendency in cognitive assessment, i.e., equating typicality with healthy or ideal, is, I argue, misguided on neurodiversity, bio-evolutionary, and cognitive neuroscientific grounds. Furthermore, the use of average performance as an anchor point for normal performance is unreliable in practice and frequently leads to the mischaracterization of cognitive impairments. Examples are explored of how individuals who are already vulnerable for socioeconomic reasons can easily be over-pathologized. At a practical level, by valuing diversity rather than typicality, cognitive assessments can become more idiographic and focused on change at the level of the individual. The use of existing methods that approach cognitive assessment ideographically is briefly discussed, including premorbid estimation methods and informant reports. Moving the focus away from averageness to valuing diversity for both clinical cognitive assessments and inclusion of diverse groups in research is, I argue, a more just and effective way forward for clinical neurosciences.

Nafcillin-Loaded Photocrosslinkable Nanocomposite Hydrogels for Biomedical Applications

Skin infections are frequently treated via intravenous or oral administration of antibiotics, which can lead to serious adverse effects and may sometimes contribute to the proliferation of resistant bacterial strains. Skin represents a convenient pathway for delivering therapeutic compounds, ensured by the high number of blood vessels and amount of lymphatic fluids in the cutaneous tissues, which are systematically connected to the rest of the body. This study provides a novel, straightforward method to obtain nafcillin-loaded photocrosslinkable nanocomposite hydrogels and demonstrates their performance as drug carriers and antimicrobial efficacy against Gram-positive bacteria. The novel formulations obtained, based on polyvinylpyrrolidone, tri(ethylene glycol) divinyl ether crosslinker, hydrophilic bentonite nanoclay, and/or two types of photoactive (TiO₂ and ZnO) nanofillers, were characterized using various analytical methods (transmission electron microscopy (TEM), scanning electron microscopy-energy-dispersive X-ray analysis (SEM-EDX), mechanical tests (tension, compression, and shear), ultraviolet-visible spectroscopy (UV-Vis), swelling investigations, and via specific microbiological assays ("agar disc diffusion method" and "time-kill test"). The results reveal that the nanocomposite hydrogel possessed high mechanical resistance, good swelling abilities, and good antimicrobial activity, demonstrating a decrease in the bacteria growth between 3log₁₀ and 2log₁₀ after one hour of direct contact with S. aureus.

Metabolomics of infectious diseases in the era of personalized medicine

Infectious diseases continue to be a major cause of morbidity and mortality worldwide. Diseases cause perturbation of the host's immune system provoking a response that involves genes, proteins and metabolites. While genes are regulated by epigenetic or other host factors, proteins can undergo post-translational modification to enable/modify function. As a result, it is difficult to correlate the disease phenotype based solely on genetic and proteomic information only. Metabolites, however, can provide direct information on the biochemical activity during diseased state. Therefore, metabolites may, potentially, represent a phenotypic signature of a diseased state. Measuring and assessing metabolites in large scale falls under the omics technology known as "metabolomics". Comprehensive and/or specific metabolic profiling in biological fluids can be used as biomarkers of disease diagnosis. In addition, metabolomics together with genomics can be used to differentiate patients with differential treatment response and development of host targeted therapy instead of pathogen targeted therapy where pathogens are more prone to mutation and lead to antimicrobial resistance. Thus, metabolomics can be used for patient stratification, personalized drug formulation and disease control and management. Currently, several therapeutics and in vitro diagnostics kits have been approved by US Food and Drug Administration (FDA) for personalized treatment and diagnosis of infectious diseases. However, the actual number of therapeutics or diagnostics kits required for tailored treatment is limited as metabolomics and personalized medicine require the involvement of personnel from multidisciplinary fields ranging from technological development, bioscience, bioinformatics, biostatistics, clinicians, and biotechnology companies. Given the significance of metabolomics, in this review, we discussed different aspects of metabolomics particularly potentials of metabolomics as diagnostic biomarkers and use of small molecules for host targeted treatment for infectious diseases, and their scopes and challenges in personalized medicine.

Corticosteroids in ARDS

Acute respiratory distress syndrome (ARDS) is frequently associated with sepsis. ARDS and sepsis exhibit a common pathobiology, namely excessive inflammation. Corticosteroids are powerful anti-inflammatory agents that are routinely used in septic shock and in oxygen-dependent SARS-CoV-2 related acute respiratory failure. Recently, corticosteroids were found to reduce mortality in severe community-acquired pneumonia. Corticosteroids may therefore also have a role to play in the treatment of ARDS. This narrative review was undertaken following a PubMed search for English language reports published before January 2023 using the terms acute respiratory distress syndrome, sepsis and steroids. Additional reports were identified by examining the reference lists of selected articles and based on personnel knowledge of the authors of the field. High-quality research is needed to fully understand the role of corticosteroids in the treatment of ARDS and to determine the optimal timing, dosing and duration of treatment.

3D Bioprinting for Next-Generation Personalized Medicine

In the past decade, immense progress has been made in advancing personalized medicine to effectively address patient-specific disease complexities in order to develop individualized treatment strategies. In particular, the emergence of 3D bioprinting for in vitro models of tissue and organ engineering presents novel opportunities to improve personalized medicine. However, the existing bioprinted constructs are not yet able to fulfill the ultimate goal: an anatomically realistic organ with mature biological functions. Current bioprinting approaches have technical challenges in terms of precise cell deposition, effective differentiation, proper vascularization, and innervation. This review introduces the principles and realizations of bioprinting with a strong focus on the predominant techniques, including extrusion printing and digital light processing (DLP). We further discussed the applications of bioprinted constructs, including the engraftment of stem cells as personalized implants for regenerative medicine and in vitro high-throughput drug development models for drug discovery. While no one-size-fits-all approach to bioprinting has emerged, the rapid progress and promising results of preliminary studies have demonstrated that bioprinting could serve as an empowering technology to resolve critical challenges in personalized medicine.

Potentiality of algorithms and artificial intelligence adoption to improve medication management in primary care: a systematic review

OBJECTIVES

The aim of this study is to investigate the effect of artificial intelligence (AI) and/or algorithms on drug management in primary care settings comparing AI and/or algorithms with standard clinical practice. Second, we evaluated what is the most frequently reported type of medication error and the most used AI machine type.

METHODS

A systematic review of literature was conducted querying PubMed, Cochrane and ISI Web of Science until November 2021. The search strategy and the study selection were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and the Population, Intervention, Comparator, Outcome framework. Specifically, the Population chosen was general population of all ages (ie, including paediatric patients) in primary care settings (ie, home setting, ambulatory and nursery homes); the Intervention considered was the analysis AI and/or algorithms (ie, intelligent programs or software) application in primary care for reducing medications errors, the Comparator was the general practice and, lastly, the Outcome was the reduction of preventable medication errors (eg, overprescribing, inappropriate medication, drug interaction, risk of injury, dosing errors or in an increase in adherence to therapy). The methodological quality of included studies was appraised adopting the Quality Assessment of Controlled Intervention Studies of the National Institute of Health for randomised controlled trials.

RESULTS

Studies reported in different ways the effective reduction of medication error. Ten out of 14 included studies, corresponding to 71% of articles, reported a reduction of medication errors, supporting the hypothesis that AI is an important tool for patient safety.

CONCLUSION

This study highlights how a proper application of AI in primary care is possible, since it provides an important tool to support the physician with drug management in non-hospital environments.

Evaluation of important human CYP450 isoforms and P-glycoprotein phenotype changes and genotype in type 2 diabetic patients, before and after intensifying treatment regimen using Geneva cocktail

The present study evaluates the influence of type 2 diabetes (T2D) on important CYP450 (CYP) isoforms and P-glycoprotein (Pgp) transporter activities before and 3 months after an intensifying treatment regimen involving 40 patients. Results have been compared with 21 non-T2D healthy participants (the control group). CYPs and Pgp activities were assessed after administering the Geneva cocktail. The mean metabolic ratios (MR) for CYP2B6 (1.81 ± 0.93 versus 2.68 ± 0.87), CYP2C19 (0.420 ± 0.360 versus 0.687 ± 0.558) and CYP3A4/5 (0.487 ± 0.226 versus 0.633 ± 0.254) significantly decreased in T2D patients compared to the control group (p < 0.05). CYP2C9 (0.089 ± 0.037 versus 0.069 ± 0.017) activities slightly increased in diabetic patients, and no difference was observed regarding CYP1A2 (0.154 ± 0.085 versus 0.136 ± 0.065), CYP2D6 (1.17 ± 0.56 versus 1.24 ± 0.83), and Pgp activities in comparison to the control group. Three months after the intensifying treatment regimen, MRs of CYP2C9 (0.080 ± 0.030) and CYP3A4/5 (0.592 ± 0.268) improved significantly and were not statistically different compared to the control group (P > 0.05). Several covariables, such as inflammatory markers (IL-1β and IL-6), genotypes, diabetes and demographic-related factors, were considered in the analyses. The results indicate that chronic inflammatory status associated with T2D modulates CYP450 activities in an isoform-specific manner.