Hasil untuk "Medicine"

T. Mckeown

J. Evans

P. Brukner, K. Khan

M. Angell, J. Kassirer

A. Leplège, S. Hunt

D. Frosch, Robert M Kaplan

A. Albright, M. Franz, G. Hornsby et al.

Daniel P. Kessler, Mark McClellan

Sahibpreet Singh

The study investigates the juridico-technological architecture of international public health instruments, focusing on their implementation across India, the European Union, the United States and low- and middle-income countries (LMICs), particularly in Sub-Saharan Africa. It addresses a research lacuna: the insufficient harmonisation between normative health law and algorithmic public health infrastructures in resource-constrained jurisdictions. The principal objective is to assess how artificial intelligence augments implementation of instruments grounded in IHR 2005 and the WHO FCTC while identifying doctrinal and infrastructural bottlenecks. Using comparative doctrinal analysis and legal-normative mapping, the study triangulates legislative instruments, WHO monitoring frameworks, AI systems including BlueDot, Aarogya Setu and EIOS, and compliance metrics. Preliminary results show that AI has improved early detection, surveillance precision and responsiveness in high-capacity jurisdictions, whereas LMICs face infrastructural deficits, data privacy gaps and fragmented legal scaffolding. The findings highlight the relevance of the EU Artificial Intelligence Act and GDPR as regulatory prototypes for health-oriented algorithmic governance and contrast them with embryonic AI integration and limited internet penetration in many LMICs. The study argues for embedding AI within a rights-compliant, supranationally coordinated regulatory framework to secure equitable health outcomes and stronger compliance. It proposes a model for algorithmic treaty-making inspired by FCTC architecture and calls for WHO-led compliance mechanisms modelled on the WTO Dispute Settlement Body to enhance pandemic preparedness, surveillance equity and transnational governance resilience.

T. Kaptchuk

M. A. Rasel, Sameem Abdul Kareem, Zhenli Kwan et al.

Melanoma, one of the deadliest types of skin cancer, accounts for thousands of fatalities globally. The bluish, blue-whitish, or blue-white veil (BWV) is a critical feature for diagnosing melanoma, yet research into detecting BWV in dermatological images is limited. This study utilizes a non-annotated skin lesion dataset, which is converted into an annotated dataset using a proposed imaging algorithm based on color threshold techniques on lesion patches and color palettes. A Deep Convolutional Neural Network (DCNN) is designed and trained separately on three individual and combined dermoscopic datasets, using custom layers instead of standard activation function layers. The model is developed to categorize skin lesions based on the presence of BWV. The proposed DCNN demonstrates superior performance compared to conventional BWV detection models across different datasets. The model achieves a testing accuracy of 85.71% on the augmented PH2 dataset, 95.00% on the augmented ISIC archive dataset, 95.05% on the combined augmented (PH2+ISIC archive) dataset, and 90.00% on the Derm7pt dataset. An explainable artificial intelligence (XAI) algorithm is subsequently applied to interpret the DCNN's decision-making process regarding BWV detection. The proposed approach, coupled with XAI, significantly improves the detection of BWV in skin lesions, outperforming existing models and providing a robust tool for early melanoma diagnosis.

Abicumaran Uthamacumaran

By use of complex network dynamics and graph-based machine learning, we identified critical determinants of lineage-specific plasticity across the single-cell transcriptomics of pediatric high-grade glioma (pHGGs) subtypes: IDHWT glioblastoma and K27M-mutant glioma. Our study identified network interactions regulating glioma morphogenesis via the tumor-immune microenvironment, including neurodevelopmental programs, calcium dynamics, iron metabolism, metabolic reprogramming, and feedback loops between MAPK/ERK and WNT signaling. These relationships highlight the emergence of a hybrid spectrum of cellular states navigating a disrupted neuro-differentiation hierarchy. We identified transition genes such as DKK3, NOTCH2, GATAD1, GFAP, and SEZ6L in IDHWT glioblastoma, and H3F3A, ANXA6, HES6/7, SIRT2, FXYD6, PTPRZ1, MEIS1, CXXC5, and NDUFAB1 in K27M subtypes. We also identified MTRNR2L1, GAPDH, IGF2, FKBP variants, and FXYD7 as transition genes that influence cell fate decision-making across both subsystems. Our findings suggest pHGGs are developmentally trapped in states exhibiting maladaptive behaviors, and hybrid cellular identities. In effect, tumor heterogeneity (metastability) and plasticity emerge as stress-response patterns to immune-inflammatory microenvironments and oxidative stress. Furthermore, we show that pHGGs are steered by developmental trajectories from radial glia predominantly favoring neocortical cell fates, in telencephalon and prefrontal cortex (PFC) differentiation. By addressing underlying patterning processes and plasticity networks as therapeutic vulnerabilities, our findings provide precision medicine strategies aimed at modulating glioma cell fates and overcoming therapeutic resistance. We suggest transition therapy toward neuronal-like lineage differentiation as a potential therapy to help stabilize pHGG plasticity and aggressivity.

Alan E Guttmacher, Francis S. Collins

G. Stucki, T. Ewert, A. Cieza

Lauren D. Branditz, Andrew P. Kendle, Cynthia G. Leung et al.

Background The transition from medical student to intern is a recognized educational gap. To help address this, the Association of American Medical Colleges developed the Core Entrustable Professional Activities for entering residency. As these metrics outline expectations for all graduating students regardless of specialty, the described procedural expectations are appropriately basic. However, in procedure-heavy specialties such as emergency medicine, the ability to perform advanced procedures continues to contribute to the disconnect between undergraduate and graduate medical education. To prepare our graduating students for their internship in emergency medicine, we developed a simulation-based mastery learning curriculum housed within a specialty-specific program. Our overall goal was to develop the students’ procedural competency for central venous catheter placement and endotracheal intubation before graduation from medical school.Methods Twenty-five students participated in a simulation-based mastery learning procedures curriculum for ultrasound-guided internal jugular central venous catheter placement and endotracheal intubation. Students underwent baseline assessment, deliberate practice, and post-test assessments. Both the baseline and post-test assessments used the same internally developed checklists with pre-established minimum passing scores.Results Despite completing an emergency medicine rotation and a critical care rotation, none of the students met the competency standard during their baseline assessments. All twenty-five students demonstrated competency on both procedures by the end of the curriculum. A second post-test was required to demonstrate achievement of the central venous catheter and endotracheal intubation minimum passing scores by 16% and 28% of students, respectively.Conclusions Students demonstrated procedural competency for central venous catheter placement and endotracheal intubation by engaging in simulation-based mastery learning procedures curriculum as they completed their medical school training. With three instructional hours, students were able to achieve basic procedural competence for two common, high-risk procedures they will need to perform during emergency medicine residency training.

Shalindu Malshan Jayawickrama, Piyumi Madhushani Ranaweera, Ratupaskatiye Gedara Gunaratnege Roshan Pradeep et al.

Abstract Background Precision healthcare has entered a new era because of the developments in personalized medicine, especially in the diagnosis and treatment of head and neck squamous cell carcinoma (HNSCC). This paper explores the dynamic landscape of personalized medicine as applied to HNSCC, encompassing both current developments and future prospects. Recent Findings The integration of personalized medicine strategies into HNSCC diagnosis is driven by the utilization of genetic data and biomarkers. Epigenetic biomarkers, which reflect modifications to DNA that can influence gene expression, have emerged as valuable indicators for early detection and risk assessment. Treatment approaches within the personalized medicine framework are equally promising. Immunotherapy, gene silencing, and editing techniques, including RNA interference and CRISPR/Cas9, offer innovative means to modulate gene expression and correct genetic aberrations driving HNSCC. The integration of stem cell research with personalized medicine presents opportunities for tailored regenerative approaches. The synergy between personalized medicine and technological advancements is exemplified by artificial intelligence (AI) and machine learning (ML) applications. These tools empower clinicians to analyze vast datasets, predict patient responses, and optimize treatment strategies with unprecedented accuracy. Conclusion The developments and prospects of personalized medicine in HNSCC diagnosis and treatment offer a transformative approach to managing this complex malignancy. By harnessing genetic insights, biomarkers, immunotherapy, gene editing, stem cell therapies, and advanced technologies like AI and ML, personalized medicine holds the key to enhancing patient outcomes and ushering in a new era of precision oncology.

Ishu Kataria, Kaushik Ramaiya, Omary Ubuguyu et al.

Objectives Diabetes care remains unavailable and unaffordable for many people. Adapting models of care to low-income and middle-income country contexts is a priority. Digital technology offers substantial potential yet must surmount health system, technological and acceptability issues. This formative research aimed to identify the potential for a digital technology solution (Diabetes Compass) to address diabetes care gaps in primary healthcare.Design Qualitative research was conducted in selected districts of Sri Lanka and Tanzania with practitioners, patients and family members. In-depth interviews assessed how digital solutions may improve diabetes care, acceptability and usability; contextual and clinical observations identified practitioner clinical competencies, strengths and weaknesses, and the influence of the care environment on service delivery; and workshop discussions explored strategies to encourage digital solution uptake and sustain use.Setting The research was undertaken in 2022 at nine health facilities in Sri Lanka’s Southern Province (Galle), and 16 health facilities in Tanzania’s Lindi and Pwani Regions.Participants Participants included primary and secondary care practitioners, facility managers, patients and family members.Results There was striking concordance in the diabetes care gaps and potential for digital solutions in the two countries, and between practitioners, patients and family members. Five main gaps were practitioner training; health information systems and data; service delivery; infrastructure, equipment and medication; and community awareness and knowledge. Practitioners, patients and family members saw strong potential for digital solutions to improve early detection, diagnosis, secondary prevention of complications and improve patients’ and families’ experience of living with diabetes. They identified specific design and implementation considerations to enable the Diabetes Compass to realistically meet these needs and overcome challenges.Conclusion There was a strong appetite among practitioners, patients and family members for a digital solution to strengthen diabetes care. Their experience of challenges and practical recommendations informed the Diabetes Compass design.

Takashi Oshio

Abstract Background Psychological distress (PD) is a major risk factor for mental health among middle-aged and older adults and affects their quality of life and well-being. This study aimed to examine the evolution of PD with age and the relative importance of its determinants, issues that have been insufficiently studied. Methods We used longitudinal data obtained from 17-wave social surveys conducted in Japan from 2005 to 2021, to track 34,128 individuals (16,555 men and 17,573 women) born between 1946 and 1955. We defined PD as a Kessler 6 score (range: 0–24) ≥ 5 and estimated fixed-effects regression models to examine the evolution of its proportion with age. We also conducted a mediation analysis to examine the relative importance of specific mediators such as self-rated health (SRH), activities of daily living (ADL), and social participation, in the association between age and PD. Results Regression model results confirmed an increase in PD with age. Poor SRH, issues with ADL, and no social participation were key mediators of aging on PD, accounting for 34.2% (95% confidence interval [CI]: 21.0–47.3%), 13.7% (95% CI: 8.2–19.3%), and 10.5% (95% CI: 8.0–13.0%), respectively; consequently increasing PD between 50 and 75 years. Conclusion The results suggest the need for policy support to encourage middle-aged and older adults to promote health and increase social participation in order to prevent depression while aging.

Marcelo Matheus Gauy, Larissa Cristina Berti, Arnaldo Cândido et al.

This work investigates Artificial Intelligence (AI) systems that detect respiratory insufficiency (RI) by analyzing speech audios, thus treating speech as a RI biomarker. Previous works collected RI data (P1) from COVID-19 patients during the first phase of the pandemic and trained modern AI models, such as CNNs and Transformers, which achieved $96.5\%$ accuracy, showing the feasibility of RI detection via AI. Here, we collect RI patient data (P2) with several causes besides COVID-19, aiming at extending AI-based RI detection. We also collected control data from hospital patients without RI. We show that the considered models, when trained on P1, do not generalize to P2, indicating that COVID-19 RI has features that may not be found in all RI types.