The next generation of evidence-based medicine

Digital health competences and AI beliefs as conditions for the practice of evidence-based medicine: a study of prospective physicians in Canada

BACKGROUND

The practice of evidence-based medicine (EBM) has become pivotal in enhancing medical care and patient outcomes. With the diffusion of innovation in healthcare organizations, EBM can be expected to depend on medical professionals' competences with digital health (dHealth) and artificial intelligence (AI) technologies.

OBJECTIVE

We aim to investigate the effect of dHealth competences and perceptions of AI on the adoption of EBM among prospective physicians. By focusing on dHealth and AI technologies, the study seeks to inform the redesign of medical curricula to better prepare students for the demands of evidence-based medical practice.

METHODS

A cross-sectional survey was administered online to students at the University of Montreal's medical school, which has approximately 1,400 enrolled students. The survey included questions on students' dHealth competences, perceptions of AI, and their practice of EBM. Using structural equation modeling (SEM), we analyzed data from 177 respondents to test our research model.

RESULTS

Our analysis indicates that medical students possess foundational knowledge competences of dHealth technologies and perceive AI to play an important role in the future of medicine. Yet, their experiential competences with dHealth technologies are limited. Our findings reveal that experiential dHealth competences are significantly related to the practice of EBM (β = 0.42, p < 0.001), as well as students' perceptions of the role of AI in the future of medicine (β = 0.39, p < 0.001), which, in turn, also affect EBM (β = 0.19, p < 0.05).

CONCLUSIONS

The study underscores the necessity of enhancing students' competences related to dHealth and considering their perceptions of the role of AI in the medical profession. In particular, the low levels of experiential dHealth competences highlight a promising starting point for training future physicians while simultaneously strengthening their practice of EBM. Accordingly, we suggest revising medical curricula to focus on providing students with practical experiences with dHealth and AI technologies.

Designing Clinical Trials for Patients With Rare Cancers: Connecting the Zebras

The field of rare cancer research is rapidly transforming, marked by significant progress in clinical trials and treatment strategies. Rare cancers, as defined by the National Cancer Institute, occur in fewer than 150 cases per million people each year, yet they collectively represent a significant portion of all cancer diagnoses. Because of their infrequency, these cancers pose distinct challenges for clinical trials, including limited patient populations, geographical dispersion, and a general lack of awareness of treatment options. Economic limitations further complicate drug development, making initiatives such as the Orphan Drug Act essential for incentivizing research. The advent of next-generation sequencing (NGS) and precision medicine has been instrumental in identifying actionable genetic alterations in parallel with an explosion in the development of genomically targeted therapies, immunotherapies, and antibody drug conjugates. Advances in clinical NGS, precision medicine, and tumor-agnostic therapies have become central to the progress in rare cancer research. The development and approval of tumor-agnostic drugs, such as BRAF, NTRK, and RET inhibitors, and immunotherapy for mismatch repair deficient/microsatellite instability-high status cancers highlight the potential of personalized treatments across diverse cancer types and across the age spectrum. Collaborative trials from cooperative groups including SWOG DART, ASCO TAPUR, NCI-MATCH, pediatric COG-match, DRUP, IMPRESS, and innovative registrational basket and platform trials (eg, VE-Basket, ROAR, LIBRETTO-001, ARROW), along with patient advocacy group-run trials like TRACK, are enhancing access to clinical trials. In addition, artificial intelligence has the potential to improve the trial matching process. An integrated approach, combining these innovations in collaboration with multiple stakeholders, is crucial for advancing rare cancer research, offering hope for better patient outcomes and quality of life.

Multi-omic profiling in breast cancer: utility for advancing diagnostics and clinical care

INTRODUCTION

Breast cancer remains a major global health challenge. While advances in precision oncology have contributed to improvements in patient outcomes and provided a deeper understanding of the biological mechanisms that drive the disease, historically, research and patients' allocation to treatment have heavily relied on single-omic approaches, analyzing individual molecular dimensions such as genomics, transcriptomics, or proteomics. While these have provided deep insights into breast cancer biology, they often fail to offer a complete understanding of the disease's complex molecular landscape.

AREAS COVERED

In this review, the authors explore the recent advancements in multi-omic research in the realm of breast cancer and use clinical data to show how multi-omic integration can offer a more holistic understanding of the molecular alterations and their functional consequences underlying breast cancer.

EXPERT OPINION

The overall developments in multi-omic research and AI are expected to complement precision diagnostics through potentially refining prognostic models, and treatment selection. Overcoming challenges such as cost, data complexity, and lack of standardization is crucial for unlocking the full potential of multi-omics and AI in breast cancer patient care to enable the advancement of personalized treatments and improve patient outcomes.

Next questions on gastrointestinal stromal tumors: unresolved challenges and future directions

PURPOSE OF REVIEW

Despite remarkable progress in the management of gastrointestinal stromal tumors (GISTs), critical challenges persist. Key aspects such as risk stratification, the optimal duration of adjuvant therapy, and strategies to enhance the efficacy of first-line treatment remain subjects of ongoing debate. This review explores emerging concepts and innovative approaches aimed at refining patient selection and optimizing therapeutic decision-making to further improve clinical outcomes.

RECENT FINDINGS

Molecular and genomic parameters have the potential to enhance traditional risk models, enabling more precise stratification of high-risk patients. Innovations in artificial intelligence and liquid biopsy are emerging as powerful tools for refining predictions of recurrence and treatment response. Meanwhile, the definition and prognostic significance of tumor rupture remain pivotal challenges that influence both risk assessment and adjuvant therapy decisions. Furthermore, transcriptomic and multiomic analyses have unveiled distinct GIST subtypes with significant prognostic and therapeutic implications, paving the way for more tailored treatment strategies.

SUMMARY

Integrating molecular features into clinical decision making may refine risk assessment and personalize the treatment in patients with GIST. Future research should focus on validating these tools and redefine clinical trial designs to accelerate drug development for this rare disease.

Assessing Infant Gross Motor Performance With an At-Home Wearable

BACKGROUND

Early development of gross motor skills is foundational for the upcoming neurocognitive performance. Here, we studied whether at-home wearable measurements performed by the parents could be used to quantify and track infants' developing motor abilities.

METHODS

Unsupervised at-home measurements of the infants' spontaneous activity were made repeatedly by the parents using a multisensor wearable suit (altogether 620 measurements from 134 infants at age 4-22 months). Machine learning-based algorithms were developed to detect the reaching of gross motor milestones (GMM), to measure times spent in key postures, and to track the overall motor development longitudinally. Parental questionnaires regarding GMMs were used for developing the algorithms, and the results were benchmarked with the interrater agreement levels established by World Health Organization (WHO). A total of 97 infants were used for the algorithm development and cross-validation, whereas an external validation was done using 37 infants from an independent recruitment in the same hospital.

RESULTS

The algorithms detected the reaching of GMMs very accurately (cross-validation: accuracy, 90.9%-95.5%; external validation, 92.4%-96.8%), which compares well with the human experts in the WHO reference study. The wearable-derived postural times showed strong correlation to parental assessments (ρ = .48-.81). Individual trajectories of motor maturation showed strong correlation to infants' age (ρ = .93).

CONCLUSIONS

These findings suggest that infants' gross motor skills can be quantified reliably and automatically from unsupervised at-home wearable recordings. Such methodology could be used in health care practice and in all developmental studies for gaining real-world quantitation and tracking of infants' motor abilities.

Population-based, first-tier genomic newborn screening in the maternity ward

The rapid development of therapies for severe and rare genetic conditions underlines the need to incorporate first-tier genetic testing into newborn screening (NBS) programs. A workflow was developed to screen newborns for 165 treatable pediatric disorders by deep sequencing of regions of interest in 405 genes. The prospective observational BabyDetect pilot project was launched in September 2022 in a maternity ward of a public hospital in the Liege area, Belgium. In this ongoing observational study, 4,260 families have been informed of the project, and 3,847 consented to participate. To date, 71 disease cases have been identified, 30 of which were not detected by conventional NBS. Glucose-6-phosphate dehydrogenase deficiency was the most frequent disorder detected, with 44 positive individuals. Of the remaining 27 cases, 17 were recessive disorders. We also identified one false-positive case in a newborn in whom two variants in the AGXT gene were identified, which were subsequently shown to be located on the maternal allele. Nine heterozygous variants were identified in genes associated with dominant conditions. Results from the BabyDetect project demonstrate the importance of integrating biochemical and genomic methods in NBS programs. Challenges must be addressed in variant interpretation within a presymptomatic population and in result reporting and diagnostic confirmation.

Challenges and applications in generative AI for clinical tabular data in physiology

Recent advancements in generative approaches in AI have opened up the prospect of synthetic tabular clinical data generation. From filling in missing values in real-world data, these approaches have now advanced to creating complex multi-tables. This review explores the development of techniques capable of synthesizing patient data and modeling multiple tables. We highlight the challenges and opportunities of these methods for analyzing patient data in physiology. Additionally, it discusses the challenges and potential of these approaches in improving clinical research, personalized medicine, and healthcare policy. The integration of these generative models into physiological settings may represent both a theoretical advancement and a practical tool that has the potential to improve mechanistic understanding and patient care. By providing a reliable source of synthetic data, these models can also help mitigate privacy concerns and facilitate large-scale data sharing.

Guidelines for Designing and Conducting Delphi Consensus Studies: An Expert Consensus Delphi Study

PURPOSE

The purpose of this study was to conduct a Delphi project to develop guidelines for the design and execution of Delphi studies within medical and surgical specialties.

METHODS

Open-ended questions in round 1 and open-ended and semi-open questions in round 2 were answered. The results of the first two rounds were used to develop a Likert style questionnaire for round 3. The level of agreement and consensus was defined as 80%. Consensus was further categorized into specific percentage ranges for clarity: 100% unanimous consensus, 90-99% very strong consensus, 80-89% consensus.

RESULTS

Consensus was achieved for 35 out of 63 items (56%). Unanimous agreement was reached for 4 items (6.3%), while very strong consensus was established for 12 items (19%). Consensus was reached for an additional 19 items (30.1%), and the panel remained undecided on 7 items (11.1%).

CONCLUSION

Unanimous agreement was reached for iteration, the ability to establish treatment guidelines, a proven track record of panel members, and the requirement for at least one steering committee member to be a Delphi expert. Very strong consensus was reached on several key requirements: a clear definition of consensus, controlled feedback between rounds, precise definitions of expert and expertise, and the need for panel members to demonstrate experience through publications and clinical practice. Criteria for panel selection should ensure diversity and specialization, with steering committee members being content experts and a minimum of 20-30 panel members for broader topics. Regional experts should provide consensus on specific topics only. The steering committee should develop questions, with open-ended questions in Round 1 and both types in Round 2. Limiting the process to three rounds is advisable, aiming for at least 80% consensus in the final round.

Redefining clinical trial strategic design to support drug approval in medical oncology

Randomized clinical trials represent the gold standard for the introduction of innovative therapies in medical oncology, and they provide the highest level of evidence to ascertain the clinical activity of new drugs or novel combinations. However, the current infrastructure of clinical trials supporting innovative drug approvals is challenged by an increased body of knowledge concerning tumor biology and therapy resistance, a fast-growing armamentarium of novel anticancer compounds, an impressively upscaled data analysis capacity, as well as increasing costs related to clinical trials management. In this scenario, modern clinical trial designs need to evolve to expedite new drug approvals by tailoring patients' treatment strategies according to their medical needs. Balanced, patient-oriented clinical trial designs are eagerly warranted to increase their efficiency, to include the fast pace of technological innovations and scientific discoveries, and, ultimately, to face the challenges of the modern medical oncology field.

Molecular and immune landscape of hepatocellular carcinoma for therapeutic development

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, with an estimated 750,000 deaths in 2022. Recent emergence of molecular targeted agents and immune checkpoint inhibitors and their combination therapies have been transforming HCC care, but their prognostic impact in advanced-stage disease remains unsatisfactory. In addition, their application to early-stage disease is still an unmet need. Omics profiling studies have elucidated recurrent and heterogeneously present molecular aberrations involved in pro-cancer tumor (immune) microenvironment that may guide therapeutic strategies. Recurrent aberrations such somatic mutations in TERT promoter and TP53 have been regarded undruggable, but recent studies have suggested that these may serve as new classes of therapeutic targets. HCC markers such as alpha-fetoprotein, glypican-3, and epithelial cell adhesion molecule have also been explored as therapeutic targets. These molecular features may be utilized as biomarkers to guide the application of new approaches as companion biomarkers to maximize therapeutic benefits in patients who are likely to benefit from the therapies, while minimizing unnecessary harm in patients who will not respond. The explosive number of new agents in the pipelines have posed challenges in their clinical testing. Novel clinical trial designs guided by predictive biomarkers have been proposed to enable their efficient and cost-effective evaluation. These new developments collectively facilitate clinical translation of personalized molecular-targeted therapies in HCC and substantially improve prognosis of HCC patients.

Embracing Generative Artificial Intelligence in Clinical Research and Beyond: Opportunities, Challenges, and Solutions

To explore threats and opportunities and to chart a path for safely navigating the rapid changes that generative artificial intelligence (AI) will bring to clinical research, the Duke Clinical Research Institute convened a multidisciplinary think tank in January 2024. Leading experts from academia, industry, nonprofits, and government agencies highlighted the potential opportunities of generative AI in automation of documentation, strengthening of participant and community engagement, and improvement of trial accuracy and efficiency. Challenges include technical hurdles, ethical dilemmas, and regulatory uncertainties. Success is expected to require establishing rigorous data management and security protocols, fostering integrity and trust among stakeholders, and sharing information about the safety and effectiveness of AI applications. Meeting insights point towards a future where, through collaboration and transparency, generative AI will help to shorten the translational pipeline and increase the inclusivity and equitability of clinical research.

Molecular and immune landscape of hepatocellular carcinoma to guide therapeutic decision-making

Liver cancer, primarily HCC, exhibits highly heterogeneous histological and molecular aberrations across tumors and within individual tumor nodules. Such intertumor and intratumor heterogeneities may lead to diversity in the natural history of disease progression and various clinical disparities across the patients. Recently developed multimodality, single-cell, and spatial omics profiling technologies have enabled interrogation of the intertumor/intratumor heterogeneity in the cancer cells and the tumor immune microenvironment. These features may influence the natural history and efficacy of emerging therapies targeting novel molecular and immune pathways, some of which had been deemed undruggable. Thus, comprehensive characterization of the heterogeneities at various levels may facilitate the discovery of biomarkers that enable personalized and rational treatment decisions, and optimize treatment efficacy while minimizing the risk of adverse effects. Such companion biomarkers will also refine HCC treatment algorithms across disease stages for cost-effective patient management by optimizing the allocation of limited medical resources. Despite this promise, the complexity of the intertumor/intratumor heterogeneity and ever-expanding inventory of therapeutic agents and regimens have made clinical evaluation and translation of biomarkers increasingly challenging. To address this issue, novel clinical trial designs have been proposed and incorporated into recent studies. In this review, we discuss the latest findings in the molecular and immune landscape of HCC for their potential and utility as biomarkers, the framework of evaluation and clinical application of predictive/prognostic biomarkers, and ongoing biomarker-guided therapeutic clinical trials. These new developments may revolutionize patient care and substantially impact the still dismal HCC mortality.

Evaluating the impact of the Radiomics Quality Score: a systematic review and meta-analysis

OBJECTIVES

Conduct a systematic review and meta-analysis on the application of the Radiomics Quality Score (RQS).

MATERIALS AND METHODS

A search was conducted from January 1, 2022, to December 31, 2023, for systematic reviews which implemented the RQS. Identification of articles prior to 2022 was via a previously published review. Quality scores of individual radiomics papers, their associated criteria scores, and these scores from all readers were extracted. Errors in the application of RQS criteria were noted and corrected. The RQS of radiomics papers were matched with the publication date, imaging modality, and country, where available.

RESULTS

A total of 130 systematic reviews were included, and individual quality scores 117/130 (90.0%), criteria scores 98/130 (75.4%), and multiple reader data 24/130 (18.5%) were extracted. 3258 quality scores were correlated with the radiomics study date of publication. Criteria scoring errors were discovered in 39/98 (39.8%) of articles. Overall mean RQS was 9.4 ± 6.4 (95% CI, 9.1-9.6) (26.1% ± 17.8% (25.3%-26.7%)). Quality scores were positively correlated with publication year (Pearson R = 0.32, p < 0.01) and significantly higher after publication of the RQS (year < 2018, 5.6 ± 6.1 (5.1-6.1); year ≥ 2018, 10.1 ± 6.1 (9.9-10.4); p < 0.01). Only 233/3258 (7.2%) scores were ≥ 50% of the maximum RQS. Quality scores were significantly different across imaging modalities (p < 0.01). Ten criteria were positively correlated with publication year, and one was negatively correlated.

CONCLUSION

Radiomics study adherence to the RQS is increasing with time, although a vast majority of studies are developmental and rarely provide a high level of evidence to justify the clinical translation of proposed models.

KEY POINTS

Question What level of adherence to the Radiomics Quality Score have radiomics studies achieved to date, has it increased with time, and is it sufficient? Findings A meta-analysis of 3258 quality scores extracted from 130 review articles resulted in a mean score of 9.4 ± 6.4. Quality scores were positively correlated with time. Clinical relevance Although quality scores of radiomics studies have increased with time, many studies have not demonstrated sufficient evidence for clinical translation. As new appraisal tools emerge, the current role of the Radiomics Quality Score may change.

International Precision Child Health Partnership (IPCHiP): an initiative to accelerate discovery and improve outcomes in rare pediatric disease

Advances in genomic technologies have revolutionized the diagnosis of rare genetic diseases, leading to the emergence of precision therapies. However, there remains significant effort ahead to ensure the promise of precision medicine translates to improved outcomes. Here, we discuss the challenges in advancing precision child health and highlight how international collaborations such as the International Precision Child Health Partnership, which embed research into clinical care, can maximize benefits for children globally.

Accelerating the Future of Oncology Drug Development: The Role of Consortia in the Delivery of Precision Oncology Early Phase Clinical Trials

PURPOSE

Over the past 15 years, the landscape of early phase clinical trials (EPCTs) has undergone a remarkable expansion in both quantity and intricacy. The proliferation of sites, trials, sponsors, and contract research organizations has surged exponentially, marking a significant shift in research conduct. However, EPCT operations suffer from numerous inefficiencies, such as cumbersome start-up processes, which are particularly critical when drug safety and the recommended phase II dose need to be established in a timely manner. Networks and consortia may overcome some of these challenges of enrolling suitable patients and streamlining start-up, particularly when distance and disease trajectory come into play.

DESIGN

In this article, we provide an overview of EPCT consortia in adult oncology across different continents assembled through systematic review of the literature and snowball sampling methodology. We illustrate their scope, structure, funding, and achievements.

RESULTS

Fifteen EPCT consortia were identified including two in the United States, three in Europe, five in Asia-Pacific, two intercontinental consortia, and three within private oncology networks. These consortia vary in their scope, funding, and structure from government-funded models such as the National Cancer Institute Experimental Therapeutics Clinical Trials Networks through charitably funded and private research organizations. EPCT consortia play a role in collaborative research, molecular tumor boards to provide patient-centric biomarker-matched treatments, and streamlining trial conduct to improve timelines and cost efficiency.

CONCLUSION

The growth in EPCT activity and complexity has resulted in expansion in the number of EPCT consortia globally. By actively engaging with regulatory bodies and pharmaceutical and contract research organization industries, consortia have an opportunity to address the evolving challenges faced in this field and to accelerate the translation of scientific discoveries into clinical practice.

Digital twins as global learning health and disease models for preventive and personalized medicine

Ineffective medication is a major healthcare problem causing significant patient suffering and economic costs. This issue stems from the complex nature of diseases, which involve altered interactions among thousands of genes across multiple cell types and organs. Disease progression can vary between patients and over time, influenced by genetic and environmental factors. To address this challenge, digital twins have emerged as a promising approach, which have led to international initiatives aiming at clinical implementations. Digital twins are virtual representations of health and disease processes that can integrate real-time data and simulations to predict, prevent, and personalize treatments. Early clinical applications of DTs have shown potential in areas like artificial organs, cancer, cardiology, and hospital workflow optimization. However, widespread implementation faces several challenges: (1) characterizing dynamic molecular changes across multiple biological scales; (2) developing computational methods to integrate data into DTs; (3) prioritizing disease mechanisms and therapeutic targets; (4) creating interoperable DT systems that can learn from each other; (5) designing user-friendly interfaces for patients and clinicians; (6) scaling DT technology globally for equitable healthcare access; (7) addressing ethical, regulatory, and financial considerations. Overcoming these hurdles could pave the way for more predictive, preventive, and personalized medicine, potentially transforming healthcare delivery and improving patient outcomes.

Service Lines, Neurology, and Academic Medicine: Departmental Perspectives, Implementation Strategies, and Keys to Success

Service lines are increasingly common for organizing multidisciplinary patient care. Concerns regarding impacts of neuroscience service lines were voiced at several national neurology department chair summits, prompting the American Academy of Neurology to convene a Service Lines Workgroup. Neurology department leaders nationally at institutions that had created or considered a neuroscience service line were interviewed to elicit their experiences and lessons learned. Potential benefits identified stemmed from additional resources that the service line structure yielded (patient navigators, quality improvement staff, technicians) and strengthening of cross-department collaboration. Potential pitfalls included top-down institutional decision-making regarding service line creation, lack of explicit goals, late involvement of neurology, imbalances in neurology representation in leadership, unclear impacts on department finances, and lack of education and research mission integration into service lines. Establishing a satisfactory decision-making structure in a matrixed arrangement and ensuring that funds flow allocations acknowledged neurology's "upstream" contributions were also challenges.

Effectiveness of Pelvic Floor Muscle and Education-based Therapies on Bladder, Bowel, Vaginal, Sexual, Psychological Function, Quality of Life, and Pelvic Floor Muscle Function in Females Treated for Breast Cancer: A Systematic Review

PURPOSE OF REVIEW

Breast malignancy is the most common cancer in females. Symptoms of pelvic floor disorders and sexual dysfunction secondary to systemic cancer treatment may occur. Non-surgical, non-pharmaceutical conservative therapies, namely pelvic floor muscle (PFM) and education-based therapies, could be beneficial to reduce these symptoms in this population. This systematic review aimed to examine the evidence regarding their effectiveness on bladder, bowel, vaginal, sexual, psychological function, quality of life, and PFM function in breast cancer populations.

RECENT FINDINGS

Six databases were searched to identify interventional studies on the effect of PFM therapies, education-based therapies, or combined (multimodal) therapies on any outcome of interest. The search yielded 603 results, from which 12 studies were included. Of these, six (50%) were RCTs, one (8%) was a non-RCT with two groups, and five (42%) were non-RCTs with a single group. Findings suggest that PFM therapies (active) may be beneficial, and education in the format of CBT may improve bladder function. No data were found for bowel function and results from two RCTs were inconclusive to draw conclusions for vaginal function. Sexual function was the most frequently reported outcome. PFM therapies (active > passive) may be beneficial, and education is more likely than not to improve sexual function. For psychological function, PFM therapies (active + passive) may be beneficial, and education is more unlikely than likely to improve psychological function. For quality of life, PFM therapies (active + passive) may be beneficial, and education is more unlikely than likely to improve quality of life, although CBT combined with physical exercise may provide further improvement. PFM therapies (active ± passive) may improve PFM function. Given the limited number of studies and their methodological limitations, caution should be exercised when interpreting these study results. More research is needed to confirm findings and to investigate the clinical value of PFM therapies and combined, multimodal therapies for breast cancer populations. Non-surgical, non-pharmaceutical conservative therapies may be helpful for breast cancer populations. Clinicians should consider the highest level of available evidence to guide their practice and use their clinical judgement to select the treatment components and appropriate dosages.

[AI-supported decision-making in obstetrics - a feasibility study on the medical accuracy and reliability of ChatGPT]

The aim of this study is to investigate the feasibility of artificial intelligence in the interpretation and application of medical guidelines to support clinical decision-making in obstetrics. ChatGPT was provided with guidelines on specific obstetric issues. Using several clinical scenarios as examples, the AI was then evaluated for its ability to make accurate diagnoses and appropriate clinical decisions. The results varied, with ChatGPT providing predominantly correct answers in some fictional scenarios but performing inadequately in others. Despite ChatGPT's ability to grasp complex medical information, the study revealed limitations in the precision and reliability of its interpretations and recommendations. These discrepancies highlight the need for careful review by healthcare professionals and underscore the importance of clear, unambiguous guideline recommendations. Furthermore, continuous technical development is required to harness artificial intelligence as a supportive tool in clinical practice. Overall, while the use of AI in medicine shows promise, its current suitability primarily lies in controlled scientific settings due to potential error susceptibility and interpretation weaknesses, aiming to safeguard the safety and accuracy of patient care.

Nanotechnological approaches to improve corticosteroids ocular therapy

The administration of corticosteroids is the first-line treatment of the clinical conditions with ocular inflammation. Nonetheless, ocular physiological mechanisms, anatomical barriers and corticosteroid properties prevent it from reaching the target site. Thus, frequent topical administered doses or ocular injections are required, leading to a higher risk of adverse events and poor patient compliance. Designing novel drug delivery systems based on nanotechnological tools is a useful approach to overcome disadvantages associated with the ocular delivery of corticosteroids. Nanoparticle-based drug delivery systems represent an alternative to the current dosage forms for the ocular administration of corticosteroids, since due to their particle size and the properties of their materials, they can increase their solubility, improve ocular permeability, control their release and increase bioavailability after their ocular administration. In this way, lipid and polymer-based nanoparticles have been the main strategies developed, giving rise to novel patent applications to protect these innovative drug delivery systems as a product, its preparation or administration method. Additionally, it should be noted that at least 10 clinical trials are being carried out to evaluate the ocular application of different pharmaceutical formulations based on corticosteroid-loaded nanoparticles. Through a comprehensive and extensive analysis, this review highlights the impact of nanotechnology applications in ocular inflammation therapy with corticosteroids.

Philanthropic drug development: understanding its importance, mechanisms, and future prospects

Philanthropic drug development (PDD) addresses gaps in traditional pharmaceutical innovation, particularly for rare and underserved diseases. Cost and timeline challenges discourage new investments, especially in niche therapeutic areas. Patient organizations (POs) are uniquely positioned to help to reduce development challenges by providing expertise, supporting early research, fostering collaborations, and driving patient-centered clinical trials. PDD relies on effective partnerships between POs, pharmaceutical companies, and other stakeholders, ensuring that patient perspectives inform the drug development process. PDD is poised to relieve the pressure on the traditional drug development process and thereby foster beneficial patient-focused innovations. In doing so, PDD allows pharmaceutical companies to expand their drug development activities into commercially unrewarding {} areas, diversifying their portfolios beyond competitive fields.

Adaptive Universal Principles for Real-world Observational Studies (AUPROS): an approach to designing real-world observational studies for clinical, epidemiologic, and precision oncology research

The field of precision oncology has witnessed several advances that stimulated the development of new clinical trial designs and the emergence of real-world data (RWD) as an important resource for evidence generation in healthcare decision-making. Here, we highlight our experience with an innovative approach to a set of Adaptive, Universal Principles for Real-world Observational Studies (AUPROS). To demonstrate the utility of these principles, we used a mixed-methods approach to assess three studies that follow AUPROS at Princess Margaret Cancer Centre: (1) Molecular Epidemiology of ThorAcic Lesions (METAL), (2) Translational Head And NecK Study (THANKS), and (3) CAnadian CAncers With Rare Molecular Alterations (CARMA; NCT04151342). We performed resource assessments, stakeholder-directed surveys and discussions, analysis of funding, research output, collaborations, and a Strengths-Weaknesses-Opportunities-Threats (SWOT) analysis. Based on these analyses, AUPROS is an approach that is applicable to a wide range of observational study designs. The universality of AUPROS allows for multi-purpose analyses of various RWD, and the adaptive nature creates opportunities for multi-source funding and collaborations. Following AUPROS can offer cost and logistical benefits and may lead to increased research productivity. Several challenges were identified pertinent to ethics approvals, sustainability, complex coordination, and data quality that require local adaptation of these principles.

Analyzing determinants of premature trial discontinuation in leukemia clinical trials

Clinical trials are crucial for improving patient outcomes. Although a significant number of trials are discontinued prematurely, our understanding of factors influencing early termination is limited. We conducted a comprehensive search of ClinicalTrials.gov to identify leukemia trials from 2000 to 2020, followed by data abstraction performed by two independent reviewers. Among 3522 leukemia clinical trials identified, 28.4% were terminated prematurely. Slow accrual was the leading cause of termination 38.2%. The termination rate increased significantly from 17.0% between 2000 and 2005 to 30.9% between 2010 and 2015 (p < .001). Large trials had a lower termination rate than small trials (p < .001). Academic-sponsored trials had the highest termination rates compared to other sponsors' trials (p < .001). Early-phase trials showed higher termination rates compared to late-phase (p < .001). Other significant factors included a sequential assignment, single-center, and non-randomized trials (p < .001). Much of leukemia trials are terminated prematurely, with slow accrual being the most common reason for early termination.

Predicting accrual success for better clinical trial resource allocation

Accrual success is one key determining factor for the success of clinical trials. Global data analyses of all terminated trials reported that 55% of trials were terminated due to low accrual rates. Failure to meet accrual goals have a significant impact on costs for sponsors, academic institutions, investigators, and society at large. The ability to predict trial accrual success with high precision before the trial starts would be highly valuable, preventing the allocation of critical resources for trials unlikely to meet accrual goals. In the present study, we constructed a dataset for predicting clinical trial failure based on poor accrual using clinicaltrial.gov data containing information on 57,846 trials. Features of the dataset were informed by prior literature and constructed using data-driven natural language processing methods. We built predictive models for accrual failure using state-of-the-art supervised machine learning protocols and methods. Models resulted in good predictive performance that was stable over a 10-year time period, with predictive performance of cross-validation AUC = 0.744 (+/-0.018) and prospective validation AUC = 0.737 (+/-0.038). We also improved model calibration and examined model performance with the reject option. These modifications enable model translation into decision support tools for various real-world settings. To the best of our knowledge, this is the first study to develop models for predicting clinical trial failure due to accrual based on a large dataset with a comprehensive set of trial features.

Perceptions of Cardiovascular Healthcare Professionals Regarding Clinical Trials: A Survey-Based Study from the Middle East

Background

Low-middle income countries harbor the highest burden of cardiovascular diseases globally, but there is an under-representation of these countries in cardiovascular clinical trials. This limits the generalizability of the trial results to these countries. There is a lack of data on insights of cardiologists in these countries regarding conducting and participating in clinical trials. We sought the views of cardiovascular healthcare professionals in Iraq on participation in clinical trials.

Method

Cardiovascular professionals in Iraq were identified and contacted, via special platforms on social media specified for them, to answer a 30-item online survey.

Results

We surveyed n = 255specialists (20% were women); interventional cardiologists constituted 44.7%, followed by cardiology trainees at 31%. Almost 30% reported having been involved in clinical trials, with data collection being the more frequently reported role (21.2%). Prior participation was not significantly associated with respondent gender, academic affiliation, or presence of institutional ethical committee. Of the total, 95.7% thought that clinical trials should be conducted in Iraq, with 58.8% reporting that they would participate if invited. The most common barriers to respondents' participation in trials were lack of electronic health records (52.2% of those surveyed) and time (51.4%), followed by the requirement of additional follow-up visits or investigations (34.1%). The most common motivators were establishing electronic health records (86.27%), education and training of the general population about clinical trials (84.7%), and dedicated training for healthcare providers about clinical trial basics (84.3%).

Conclusion

Our work helps pave the path to implementing a robust clinical trial ecosystem in Iraq. Institutional and financial factors and a lack of dedicated research time are related to the cardiovascular clinical trial lag in Iraq. Future qualitative research can help in getting a deeper understanding of what is needed to create the right infrastructure.

Oxidative Stress and Cardiovascular Complications in Type 2 Diabetes: From Pathophysiology to Lifestyle Modifications

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder that significantly increases the risk of cardiovascular disease, which is the leading cause of morbidity and mortality among diabetic patients. A central pathophysiological mechanism linking T2DM to cardiovascular complications is oxidative stress, defined as an imbalance between reactive oxygen species (ROS) production and the body's antioxidant defenses. Hyperglycemia in T2DM promotes oxidative stress through various pathways, including the formation of advanced glycation end products, the activation of protein kinase C, mitochondrial dysfunction, and the polyol pathway. These processes enhance ROS generation, leading to endothelial dysfunction, vascular inflammation, and the exacerbation of cardiovascular damage. Additionally, oxidative stress disrupts nitric oxide signaling, impairing vasodilation and promoting vasoconstriction, which contributes to vascular complications. This review explores the molecular mechanisms by which oxidative stress contributes to the pathogenesis of cardiovascular disease in T2DM. It also examines the potential of lifestyle modifications, such as dietary changes and physical activity, in reducing oxidative stress and mitigating cardiovascular risks in this high-risk population. Understanding these mechanisms is critical for developing targeted therapeutic strategies to improve cardiovascular outcomes in diabetic patients.

Ongoing prospective studies on reirradiation: A systematic review of a clinical trials database

INTRODUCTION

Reirradiation has gained increasing interest, as advances in systemic therapy increase the survival of patients with cancer, and modern radiation techniques allow more precise treatments. However, high-quality prospective evidence on the safety and efficacy of reirradiation to guide clinical practice remains scarce. This systematic review evaluates ongoing prospective studies on reirradiation to identify research gaps and priorities.

METHODS

A systematic review of ClinicalTrials.gov was conducted on July 11, 2024, using search terms related to reirradiation. Inclusion criteria were prospective studies that were "recruiting," "not yet recruiting," or "active, not recruiting." Studies with published results, retrospective, and in-silico studies were excluded. The review followed PRISMA 2020 guidelines and recommendations for systematic searches of clinical trial registries.

RESULTS

Among 1026 identified studies, 307 were screened, 99 were included. Fourty (40%) focused on central nervous system (CNS), 23 (23%) head and neck, and 17 (17%) on pelvic reirradiation. Most studies (90%) were interventional, with 32 (32%) phase II and 4 (4%) phase III trials. Sixteen trials were randomized (RCTs), including the 4 phase III trials for recurrent glioblastoma, rectal and nasopharyngeal cancer. Ten dose escalation trials focus on recurrent prostate, rectal, and non-small cell lung cancer as well as glioma. Modern high-precision radiotherapy techniques were frequently used, with 21 (21%) studies using stereotactic radiotherapy and 17 (17%) using particle therapy. Combinations with systemic therapies were investigated in 41 (41%) studies.

CONCLUSION

Ongoing studies most frequently focus on CNS, head and neck, and pelvic reirradiation. There remains a critical need for RCTs, in particular for lung, breast, and gynecological cancers. Dose escalation trials, application of precision radiation techniques and combinations with modern systemic therapy may help define the optimal multimodality treatment schedules.

Pharmacovigilance in the Age of Legalized Cannabis: Using Social Media to Monitor Drug-Drug Interactions Between Immunosuppressants and Cannabis-Derived Products

INTRODUCTION

A clinical trial of Epidiolex®, the only US FDA-approved cannabis-derived consumer product (CDP), discovered an interaction with an immunosuppressant (tacrolimus) that led to drug toxicity, highlighting the unique intersection of prescription and commonly unregulated consumer products.

OBJECTIVE

We aimed to identify if similar drug-drug interactions (DDIs) are occurring among the consumer CDP market, even though they cannot be identified through trials.

METHODS

We searched Reddit for subreddits related to CDPs or health, resulting in 63,561,233 posts. From these, we identified 190 posts discussing both immunosuppressants and CDPs. Two blinded investigators evaluated the following. (1) Was there a concern about a potential DDI between consumer CDPs and immunosuppressants? (2) Was there a unique adverse event attributed to a DDI between consumer CDPs and immunosuppressants?

RESULTS

Of these, 66 posts (35%) expressed concern about a potential DDI, such as "Hey, my partner wants to try my edibles … she's on Prograf [tacrolimus] and wants to talk to a stoner who's had a heart transplant." Four posts (2%) reported a unique DDI, such as "I have clinical results that are semi-anecdotal, showing the coordination to my halting substance use … It's the CBD. Shot my prograf to 30 at like 4 mg." Two of the four reported DDIs are similar to those first reported for Epidiolex. The remaining two reported DDIs include a potential cannabidiol (CBD)/sirolimus or delta-9-tetrahydrocannabinol (THC)/sirolimus interaction and a THC/tacrolimus interaction, both resulting in drug toxicity.

CONCLUSION

This case study is the first to report on DDIs involving consumer CDPs, including both CBD and THC products, as well as a broader class of immunosuppressants. This demonstrates the risks associated with using consumer CDPs alongside prescription medications while highlighting the need for development of increased surveillance to monitor consumer CDPs for drug safety signals, as well as comprehensive regulations that take into account the unique characteristics of the consumer marketplace.

Artificial intelligence in drug development

Drug development is a complex and time-consuming endeavor that traditionally relies on the experience of drug developers and trial-and-error experimentation. The advent of artificial intelligence (AI) technologies, particularly emerging large language models and generative AI, is poised to redefine this paradigm. The integration of AI-driven methodologies into the drug development pipeline has already heralded subtle yet meaningful enhancements in both the efficiency and effectiveness of this process. Here we present an overview of recent advancements in AI applications across the entire drug development workflow, encompassing the identification of disease targets, drug discovery, preclinical and clinical studies, and post-market surveillance. Lastly, we critically examine the prevailing challenges to highlight promising future research directions in AI-augmented drug development.

Enhancing Nursing Practices in Critical Care for Older Adults: A Systematic Review of Age-Friendly Nursing Interventions

OBJECTIVES

To synthesize evidence on the implementation and effectiveness of age-friendly interventions in intensive care units (ICUs) to optimize care for older adults.

DESIGN

Systematic review of studies published up to February 2024, focusing on interventions tailored to meet the needs of older adults in ICU settings.

SETTING AND PARTICIPANTS

Comparative studies conducted in ICUs worldwide, involving older adults receiving critical care, were reviewed.

METHODS

A systematic search of databases including Embase, MEDLINE, and Cochrane was performed. The quality of studies was assessed using the ROSVIS-II tool, and findings were synthesized narratively and thematically.

RESULTS

Out of 1200 articles initially identified, 45 studies met inclusion criteria. Age-friendly interventions (eg, geriatric assessment teams, multimodal care bundles) demonstrated significant benefits, including a 19% reduction in major in-hospital complications and shorter hospital stays by an average of 3 days.

CONCLUSIONS AND IMPLICATIONS

The review supports the effectiveness of age-friendly interventions in improving clinical outcomes for older ICU patients. However, consistent reporting of effect sizes was lacking, and more high-quality comparative effectiveness research is needed. Implications for practice include integrating these interventions into standard ICU protocols to enhance older adult care. Policy implications involve advocating for health care policies that support the dissemination and implementation of effective age-friendly practices. Further research should focus on establishing a robust evidence base to guide implementation and policy decisions.

Artificial Intelligence in Nephrology: Clinical Applications and Challenges

Artificial intelligence (AI) is increasingly used in many medical specialties. However, nephrology has lagged in adopting and incorporating machine learning techniques. Nephrology is well positioned to capitalize on the benefits of AI. The abundance of structured clinical data, combined with the mathematical nature of this specialty, makes it an attractive option for AI applications. AI can also play a significant role in addressing health inequities, especially in organ transplantation. It has also been used to detect rare diseases such as Fabry disease early. This review article aims to increase awareness on the basic concepts in machine learning and discuss AI applications in nephrology. It also addresses the challenges in integrating AI into clinical practice and the need for creating an AI-competent nephrology workforce. Even though AI will not replace nephrologists, those who are able to incorporate AI into their practice effectively will undoubtedly provide better care to their patients. The integration of AI technology is no longer just an option but a necessity for staying ahead in the field of nephrology. Finally, AI can contribute as a force multiplier in transitioning to a value-based care model.

Artificial intelligence for personalized nanomedicine; from material selection to patient outcomes

INTRODUCTION

Artificial intelligence (AI) is changing the field of nanomedicine by exploring novel nanomaterials for developing therapies of high efficacy. AI works on larger datasets, finding sought-after nano-properties for different therapeutic aims and eventually enhancing nanomaterials' safety and effectiveness. AI leverages patient clinical and genetic data to predict outcomes, guide treatments, and optimize drug dosages and forms, enhancing benefits while minimizing side effects. AI-supported nanomedicine faces challenges like data fusion, ethics, and regulation, requiring better tools and interdisciplinary collaboration. This review highlights the importance of AI regarding patient care and urges scientists, medical professionals, and regulators to adopt AI for better outcomes.

AREAS COVERED

Personalized Nanomedicine, Material Discovery, AI-Driven Therapeutics, Data Integration, Drug Delivery, Patient Centric Care.

EXPERT OPINION

Today, AI can improve personalized health wellness through the discovery of new types of drug nanocarriers, nanomedicine of specific properties to tackle targeted medical needs, and an increment in efficacy along with safety. Nevertheless, problems such as ethical issues, data security, or unbalanced data sets need to be addressed. Potential future developments involve using AI and quantum computing together and exploring telemedicine i.e. the Internet-of-Medical-Things (IoMT) approach can enhance the quality of patient care in a personalized manner by timely decision-making.

Single-arm clinical trials: design, ethics, principles

Although randomised controlled trials are considered the gold standard in clinical research, they are not always feasible due to limitations in the study population, challenges in obtaining evidence, high costs and ethical considerations. As a result, single-arm trial designs have emerged as one of the methods to address these issues. Single-arm trials are commonly applied to study advanced-stage cancer, rare diseases, emerging infectious diseases, new treatment methods and medical devices. Single-arm trials have certain ethical advantages over randomised controlled trials, such as providing equitable treatment, respecting patient preferences, addressing rare diseases and timely management of adverse events. While single-arm trials do not adhere to the principles of randomisation and blinding in terms of scientific rigour, they still incorporate principles of control, balance and replication, making the design scientifically reasonable. Compared with randomised controlled trials, single-arm trials require fewer sample sizes and have shorter trial durations, which can help save costs. Compared with cohort studies, single-arm trials involve intervention measures and reduce external interference, resulting in higher levels of evidence. However, single-arm trials also have limitations. Without a parallel control group, there may be biases in interpreting the results. In addition, single-arm trials cannot meet the requirements of randomisation and blinding, thereby limiting their evidence capacity compared with randomised controlled trials. Therefore, researchers consider using single-arm trials as a trial design method only when randomised controlled trials are not feasible.

The Digital Transformation in Health: How AI Can Improve the Performance of Health Systems

Mobile health has the potential to revolutionize health care delivery and patient engagement. In this work, we discuss how integrating Artificial Intelligence into digital health applications focused on supply chain operation, patient management, and capacity building, among other use cases, can improve the health system and public health performance. We present the Causal Foundry Artificial Intelligence and Reinforcement Learning platform, which allows the delivery of adaptive interventions whose impact can be optimized through experimentation and real-time monitoring. The system can integrate multiple data sources and digital health applications. The flexibility of this platform to connect to various mobile health applications and digital devices, and to send personalized recommendations based on past data and predictions, can significantly improve the impact of digital tools on health system outcomes. The potential for resource-poor settings, where the impact of this approach on health outcomes could be decisive, is discussed. This framework is similarly applicable to improving efficiency in health systems where scarcity is not an issue.

Data-knowledge co-driven innovations in engineering and management

Modern intelligent engineering and management scenarios require advanced data utilization methodologies. Here, we propose and discuss data-knowledge co-driven innovations that could address emerging challenges, and we advocate for the adoption of interdisciplinary methodologies in numerous engineering and management applications.

Advancing the role of real-world evidence in comparative effectiveness research

Aim: Comparative effectiveness research (CER) is essential for making informed decisions about drug access. It provides insights into the effectiveness and safety of new drugs compared with existing treatments, thereby guiding better healthcare decisions and ensuring that new therapies meet the real-world needs of patients and healthcare systems. Objective: To provide a tool that assists analysts and decision-makers in identifying the most suitable analytical approach for answering a CER question, given specific data availability contexts. Methods: A systematic literature review of the scientific literature was performed and existing regulatory and health technology assessment (HTA) guidance were evaluated to identify and compare recommendations and best practices. Based on this review a methods flowchart that synthesizes current practices and requirements was proposed. Results: The review did not find any papers that clearly identified the most appropriate analytical approach for answering CER questions under various conditions. Therefore, a methods flowchart was designed to inform analyst and decision makers choices starting from a well-defined scientific question. Conclusion: The proposed methods flowchart offers clear guidance on CER methodologies across a range of settings and research needs. It begins with a well-defined research question and considers multiple feasibility aspects related to CER. This tool aims to standardize methods, ensure rigorous and consistent research quality and promote a culture of evidence-based decision-making in healthcare.

Assessment of the organ function as the primary intention of clinical reasoning applied to the critically ill patient

This article examines how clinical reasoning about the critical patient is currently treated and draws attention to some critical issues already often highlighted in the literature. Traditional approaches to clinical reasoning, even when applied to critical patients, prioritize identifying structured diseases. In contrast, the critical care setting demands an alternative approach that aligns with the intensivist's goal of supporting or substituting vital organ functions. In this manuscript, we emphasized the reasons that make it primary for intensivists to obtain a diagnosis of function in order to act therapeutically. Moreover, we highlighted the challenges posed by diagnostic errors, often attributed to cognitive biases and shortcomings in clinical reasoning, which can adversely affect patient outcomes and resource utilization. We also discussed the complexities of clinical decision-making in emergency medical services, where physicians must perform rapid actions in the face of incomplete information and high uncertainty. We underscore the limitations of traditional information technology tools in facilitating practical clinical reasoning, advocating for the integration of relevant data that directly informs on organ function and pathophysiological mechanisms. This discourse emphasizes a deep understanding of physiology and pathophysiology as foundational for practical clinical reasoning in critical care. Finally, we propose a structured assessment method that prioritizes pinpointing the compromised organ function, elucidating the pathophysiological mechanism responsible, hypothesizing potential causes, and testing these hypotheses to guide therapeutic interventions. This approach aligns clinical reasoning with the intensivist's goal: supporting and restoring vital functions in the critically ill patient.

Rapid SARS-CoV-2 sensing through oxygen reduction reaction catalysed by Au@Pt/Au core@shell nanoparticles

The development of rapid, accurate, sensitive, and low-cost diagnostic methods for COVID-19 detection in real-time is the unique way to control infection sources and monitor illness progression. In this work, we propose an electrochemical biosensor for the rapid and accuracy diagnosis of COVID-19, through the determination of ORF1ab specific sequence. The biosensor is based on the immobilization of a thiolated sequence partially complementary (domain 1) to ORF1ab on gold screen-printed electrodes and the use of bifunctional Au@Pt/Au core@shell nanoparticles modified with a second thiolated sequence partially complementary to ORF1ab (domain 2) as electrochemical indicator of the hybridization of DNA sequences. The synthesized Au@Pt/Au nanoparticles consist of an Au core, a shell of Pt (Au@Pt NPs), that provides an excellent electrocatalytic activity toward the oxygen reduction reaction (ORR) even after formation of hybrid biomaterials by modification, through the Au protuberances growth on the NPs surface, with an oligonucleotide with recognition ability. The ORR electrochemical activity, enhanced by the label element (Au@Pt/Au NPs), has been employed, for the first time, as indicator of the hybridization event. Based on this strategy, target sequences of the SARS-CoV-2 virus have been detected with a detection limit of 32 pM. The selectivity of the biosensor was confirmed by analysing ORF1ab sequence in the presence of DNA sequences from other viruses. The biosensor has been successfully applied to the direct detection of the virus in non-amplified samples of nasopharyngeal swabs from infected and non-infected patients. Results compare well with those obtained through RT-qPCR but our method is more rapid since does not need any amplification process.

Translating proof-of-concept for platelet slip into improved antithrombotic therapeutic regimens

Platelets are central to thrombosis. Research at the intersection of biological and physical sciences provides proof-of-concept for shear rate-dependent platelet slip at vascular stenosis and near device surfaces. Platelet slip extends the observed biological "slip-bonds" to the boundary of functional gliding without contact. As a result, there is diminished engagement of the coagulation cascade by platelets at these surfaces. Comprehending platelet slip would more precisely direct antithrombotic regimens for different shear environments, including for percutaneous coronary intervention (PCI). In this brief report we promote translation of the proof-of-concept for platelet slip into improved antithrombotic regimens by: (1) reviewing new supporting basic biological science and clinical research for platelet slip; (2) hypothesizing the principal variables that affect platelet slip; (3) applying the consequent construct model in support of-and in some cases to challenge-relevant contemporary guidelines and their foundations (including for urgent, higher-risk PCI); and (4) suggesting future research pathways (both basic and clinical). Should future research demonstrate, explain and control platelet slip, then a paradigm shift for choosing and recommending antithrombotic regimens based on predicted shear rate should follow. Improved clinical outcomes with decreased complications accompanying this paradigm shift for higher-risk PCI would also result in substantive cost savings.

Charting the therapeutic landscape: a comprehensive evidence map on medical cannabis for health outcomes

The therapeutic potential of medical cannabis has garnered significant attention in recent years, prompting an urgent need for a comprehensive understanding of its effectiveness across various health outcomes. This article presents an Evidence Map that systematically summarizes clinical evidence on the use of medical cannabis, including the health conditions it addresses, the interventions employed, and the resulting clinical outcomes. The objective is to map the effectiveness of medical cannabis in relation to a wide range of health outcomes. The systematic review process involved two independent, blinded literature researchers who screened the search output using Rayyan software. For studies deemed relevant, full texts were obtained to clarify inclusion or exclusion criteria, and any disagreements were resolved through group discussion. Out of 1,840 initial references, 279 potential studies were selected and read in full, resulting in the inclusion of 194 studies in this evidence map. The results highlight the use of various cannabis formulations, including those based on isolated cannabidiol (CBD). Seventy-one distinct health outcomes were identified in the systematic reviews, with the most reported outcomes being related to various types of pain and patient safety. Other frequently studied outcomes included appetite regulation, chemotherapy-induced nausea and vomiting, and muscle spasticity. Notably, 278 out of 489 descriptions of treatment effects for these health outcomes reported either "Positive" or "Potentially Positive" effects. When considering only high-quality systematic reviews, as evaluated by the AMSTAR 2 tool, 42 out of 67 descriptions of treatment effects for up to 20 health outcomes were classified as "Positive" or "Potentially Positive." These outcomes included pain, insomnia, seizures, anxiety, muscle spasticity, multiple sclerosis, urinary incontinence, anorexia, and patient safety. This evidence map provides a comprehensive overview of the current clinical evidence on medical cannabis, highlighting its potential therapeutic benefits across a range of health conditions and emphasizing the need for further high-quality research.

Risk factors for surgical site infection after general surgery in HIV-infected patients: a retrospective study

BACKGROUND

As the number of HIV-infected patients increased, the number of patients requiring general surgery has subsequently increased. However, impairment of immune function due to HIV infection increases the risk of postoperative surgical-site infection and significant harm to patient health. This study aimed to examine the risk factors for surgical-site infection after general surgery.

METHODS

The patients' data were from Zunyi fourth hospital medical information system. Machine learning based Boruta algorithm were used for variable screening. Univariable and multivariable logistic regression and restricted cubic spline analysis were performed to examine the relationship between significant variables and surgical-site infection.

RESULTS

A total of 125 general surgery postoperative HIV-infected patients participated in the study. Surgical-site pathogen culture identified Escherichia coli, Klebsiella pneumoniae, and mixed bacteria as the three most common pathogens causing Surgical-site infection. Univariable and multivariable logistic regression analysis to adjust for risk factors identified type III surgical incision (OR = 9.92, 95% CI = 1.28-76.75) and elevated preoperative white blood cell (WBC) count (OR = 1.30, 95% CI = 1.12-1.51) as independent risk factors for postoperative surgical-site infection, whereas CD4 + T lymphocyte count greater than 400 cells/µL was identified as a protective factor (OR = 0.23, 95% CI = 0.09-0.60) while. The restricted cubic spline analysis results directly reflected the dose-response relationship between continuous variables and postoperative surgical-site infection.

CONCLUSIONS

Type III incision and an elevated WBC count pose a higher risk of postoperative surgical-site infection. A CD4 + T lymphocyte counts greater than 400 cells/µL provided a protective effect of lower risk of surgical site infection. Preoperative serum neutrophil percentage, albumin level, red blood cell count, and serum urea level within a specific range were beneficial in reducing the risk of incisional infections. Our research provides a theoretical basis for clinical practice.

Advancing Precision Oncology with Digital and Virtual Twins: A Scoping Review

Digital twins (DTHs) and virtual twins (VTHs) in healthcare represent emerging technologies towards precision medicine, providing opportunities for patient-centric healthcare. Our scoping review aimed to map the current DTH and VTH technologies in oncology, summarize their technical solutions, and assess their credibility. A systematic search was conducted in the main bibliographic databases, identifying 441 records, of which 30 were included. The studies covered a wide range of cancers, including breast, lung, colorectal, and gastrointestinal malignancies, with DTH and VTH applications focusing on diagnosis, therapy, and monitoring. The results revealed heterogeneity in targeted topics, technical approaches, and outcomes. Most twining solutions use synthetic or limited real-world data, raising concerns regarding their reliability. Few studies have integrated real-time data and machine learning for predictive modeling. Technical challenges include data integration, scalability, and ethical considerations, such as data privacy and security. Moreover, the evidence lacks sufficient clinical validation, with only partial credibility in most cases. Our findings underscore the need for multidisciplinary collaboration among end-users and developers to address the technical and ethical challenges of DTH and VTH systems. Although promising for the future of personalized oncology, substantial steps are required to move beyond experimental frameworks and to achieve clinical implementation.

Interoception, Insula, and Autonomic Integration: Relevance to the Expression and Treatment of Psychiatric Symptoms

The mind is embodied, and this is relevant to mental health and psychiatric illness. Interoception is the body-to-brain axis of sensory information flow and its representation at the neural and psychological levels. Interoception is the purported basis for motivation and emotion, and as an inescapable stream of information about the health and functioning of the whole organism, it is proposed to be the foundation to the conscious unitary sense of self. Correspondingly, this central representation of internal state is relevant to understanding the expression of psychological symptoms and behaviours and ultimately psychiatric disorders. Here we review interoception, particularly from a cardiovascular perspective, and how understanding theoretical neural and psychological aspects of interoception relates to perceptions, thoughts, and feelings. We examine how perturbations in interoceptive processing are expressed in mental symptoms and psychiatric disorders and show how this knowledge may yield new treatment targets.

Nutritional guidance needs and influence factors for gastric cancer survivors in primary healthcare setting: a cross-sectional survey

PURPOSE

This study aimed to build a comprehensive understanding of the nutritional guidance needs of gastric cancer survivors in the primary healthcare setting that could be fulfilled by community health service centers as part of their primary healthcare services.

METHODS

Gastric cancer survivors were invited to participate in a questionnaire-based survey by convenience sampling method from the community health center. Relationships between nutritional demand and potential influence factors were examined by a multivariable logistic regression model.

RESULTS

A total of 200 gastric cancer survivors were recruited from the community health service center in Shanghai, China, from whom we obtained 194 valid questionnaires, resulting in a 97.0% response rate. Of these gastric survivor participants, 48 individuals (24.7%) expressed a need for nutritional guidance administered by community health service centers, whereas 146 participants (75.3%) held the perspective that such guidance was unnecessary. Preferences for nutritional guidance included having a dietitian as the provider (68.8%), home-based engagement (72.9%), face-to-face consultation (68.8%), individual counseling (87.5%), and beginning immediately post-discharge (89.6%). A notable reluctance (66.7%) towards financial contribution was also observed. After adjusting for confounders, participants with higher income level (odds ratio (OR) = 4.45, 95% confidence interval (CI) = 1.39-15.50), history of food intake reduction (OR = 14.96, 95% CI = 3.49-82.28), and gastrointestinal symptoms (OR = 4.40, 95% CI = 1.35-14.33) were more likely to seek nutritional guidance.

CONCLUSIONS

Gastric cancer survivors have a certain need for nutritional guidance administered by primary health service centers. Personal guidance by the primary health service center should be provided to gastric cancer survivors to support their dietary and nutritional intake needs.

IMPLICATIONS FOR CANCER SURVIVORS

There is a need to develop and implement nutritional guidance programs in community health service centers as part of their primary healthcare services for gastric cancer survivors.