Shigehisa Kuriyama has written a brilliant study of how ancient Greek (and Greco-Roman) and Chinese medicine represented the body. Concerned with the visual and literal vocabularies of medicine and the map that they draw of the body, Kuriyama provides detailed studies of how these systems work and of how they differentiate themselves from one another. Thus, the chapter on “the origins of the muscular body” asks why ancient Greek medicine initially did not “see” the muscles, but learned eventually to do so, while ancient Chinese medicine never truly discovered the muscular system. Shigehisa Kuriyama claims that Greek medicine “learned to see” over time; building on anatomical dissection, while Chinese medicine relied on a powerful philosophy of the body that precluded anatomy. Reality testing, however, is sufficient only if there is a philosophy, which demands the accretion and change of knowledge. This is present in Greek medicine, at least until it becomes canonized in the early middle ages. Indeed, one of the most striking things about Shigehisa Kuriyama’s book is that it gives one a strong claim to understand what happens to flexible versus rigid systems of representation in different historical contexts. Thus, the representation and transmission of Greek medicine through Arabic and Hebrew sources into Latin Europe creates a canon of medical truths that exist until Vesalius. Indeed, the power of such models, as has been well noted, is such that even Vesalius, who takes Aristotle’s model of the body to task, is unable to see such anatomical features as the Fallopian tubes because of his mirror-like response to Aristotle. Only his student, Fallopius, is able to make the break. On the other hand, Chinese traditional medicine in Japan (and later in post–Republican China) amalgamates itself into Western medicine. Traditional medicine is banished as part of the Westernization of the Meiji revival, and its reappearance only after the beginning of the 20th century is in very different form. The very nature of the models used in traditional medicine shift. Thus, by the 1950s acupuncture comes to be used as anesthesia during surgical procedures. Given the refusal to open the body in traditional systems of Chinese medicine, such a use would have been literally “unthinkable” before the introduction of Western models of medical thinking.
Model Medicine is the science of understanding, diagnosing, treating, and preventing disorders in AI models, grounded in the principle that AI models -- like biological organisms -- have internal structures, dynamic processes, heritable traits, observable symptoms, classifiable conditions, and treatable states. This paper introduces Model Medicine as a research program, bridging the gap between current AI interpretability research (anatomical observation) and the systematic clinical practice that complex AI systems increasingly require. We present five contributions: (1) a discipline taxonomy organizing 15 subdisciplines across four divisions -- Basic Model Sciences, Clinical Model Sciences, Model Public Health, and Model Architectural Medicine; (2) the Four Shell Model (v3.3), a behavioral genetics framework empirically grounded in 720 agents and 24,923 decisions from the Agora-12 program, explaining how model behavior emerges from Core--Shell interaction; (3) Neural MRI (Model Resonance Imaging), a working open-source diagnostic tool mapping five medical neuroimaging modalities to AI interpretability techniques, validated through four clinical cases demonstrating imaging, comparison, localization, and predictive capability; (4) a five-layer diagnostic framework for comprehensive model assessment; and (5) clinical model sciences including the Model Temperament Index for behavioral profiling, Model Semiology for symptom description, and M-CARE for standardized case reporting. We additionally propose the Layered Core Hypothesis -- a biologically-inspired three-layer parameter architecture -- and a therapeutic framework connecting diagnosis to treatment.
The system identification capabilities of a novel information-theoretic method are examined here. Specifically, this work uses information-theoretic metrics and vibration-based measurements to enhance damping estimation accuracy in mechanical systems. The method refers to a key limitation in system identification, signal processing, monitoring, and alert systems. These systems integrate various components, including sensors, data acquisition devices, and alert mechanisms. They are designed to operate in an environment to calculate key parameters such as peak accelerations and duration of high acceleration values. The current operational modal identification methods, though, suffer from limitations related to obtaining poor damping estimates due to their empirical nature. This has a significant impact on alert warning systems. This occurs when their duration is misestimated; specifically, when using the vibration amplitudes as an indicator of danger alerts for monitoring systems in damage or anomaly detection scenarios. To this end, approaches based on the Shannon entropy and the Kullback-Leibler divergence concept are proposed. The primary objective is to monitor the vibration levels in near real-time and provide immediate alerts when predefined thresholds are exceeded. In considering the proposed approach, both new real-world data from the multi-axis simulation table at the University of Bath, as well as the benchmark International Association for Structural Control-American Society of Civil Engineers (IASC-ASCE) structural health monitoring problem are considered. Importantly, the approach is shown to select the optimal model, which accurately captures the correct alert duration, providing a powerful tool for system identification and monitoring.
Biomaterials are substances other than food or drugs contained in therapeutic or diagnostic systems that are in contact with tissue or biological fluids. Biomaterials play a central role in extra corporeal devices, from contact lenses to kidney dialyses, and are essential components of implants, from vascular grafts to cardiac pacemakers and fracture fixation devices. The development and availability of modern high-tech polymers allowed improving the patients care in all fields of medicine. In this course we will: Gain an overview of the use of polymeric biomaterials in medicine Discuss some examples of permanent and resorbable polymer implants in detail Take a look at legal and regulatory aspects Learn about functional and design requirements when dealing with polymers in medicine, and Look into some modern approaches how polymers improve medical implants.
Djordje S Popovic,1 Theocharis Koufakis,2 Dimitrios Patoulias,2 Anca Pantea Stoian,3 Nikolaos Papanas4 1Clinic for Endocrinology, Diabetes and Metabolic Disorders, Clinical Centre of Vojvodina, Medical Faculty, University of Novi Sad, Novi Sad, Serbia; 2Second Propaedeutic Department of Internal Medicine, Hippokration General Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece; 3Department of Diabetes, Nutrition and Metabolic Diseases, “Carol Davila” University of Medicine and Pharmacy, Bucharest, Romania; 4Diabetes Centre, Second Department of Internal Medicine, Democritus University of Thrace, University Hospital of Alexandroupolis, Alexandroupolis, GreeceCorrespondence: Djordje S Popovic, Clinic for Endocrinology, Diabetes and Metabolic Disorders, Clinical Centre of Vojvodina, Medical Faculty, University of Novi Sad, Hajduk Veljkova 1, Novi Sad, 21000, Serbia, Tel +38163551606, Email pitstop021@gmail.com; djordje.popovic@mf.uns.ac.rsAbstract: Recreational diving with self-contained underwater breathing devices is gaining popularity worldwide as a sport and leisure activity. People living with type 1 diabetes mellitus (PLT1D) are no exception, although historically diabetes mellitus, especially insulin-treated, has been described as an absolute contra-indication for diving. However, based on observational data collected by the Divers Alert Network, the presence of background diabetes mellitus became only a relative contraindication for those without significant co-morbidities or long-term complications. Regarding diving activities among PLT1D, the primary concern is the risk of hypoglycaemia, especially in those with impaired awareness. Furthermore, symptoms consistent with hypoglycaemia could be confused with those originating from other factors related to diving. Although avoidance of hypoglycaemia is imperative among PLT1D practicing diving, the risk of severe hyperglycaemia should also be minimised. Continuous glucose monitoring (CGM) nowadays represents the standard of care for PLT1D, but its accuracy during diving activities is still a matter of debate. This commentary aims to summarize the existing data on accuracy, durability, and underwater performance of different CGM devices among PLT1D who engage in diving, and to call for additional research in the field. Based on available results, the application of real-time CGM still requires extreme caution since none of the existing systems has so far met the standards for accurate use in underwater conditions. Further improvements of contemporary CGM devices, validated through large-scale trials, are necessary before their widespread implementation among PLT1D practicing diving. Such advances should further enhance safety during this popular activity.Keywords: continuous glucose monitoring, diving, type 1 diabetes mellitus
Kira DiClemente-Bosco, Caroline Kuo, Goodman Sibeko
et al.
Background In South Africa, rates of HIV and alcohol use are among the highest globally, with a detrimental synergistic relationship. Screening, Brief Intervention, and Referral to Treatment (SBIRT) is an evidence-based, cost-effective approach to identifying people at risk of alcohol-related problems to deliver early intervention. We developed and deployed a cascading train-the-trainer model to promote SBIRT implementation in a large nongovernmental organization offering HIV services across South Africa. Method Between 2021 and 2022, we completed preparatory activities including designing scalable training resources prior to rolling out the train-the-trainer model across two South African provinces. We conducted a comprehensive assessment of outcomes at the trainer- (knowledge, fidelity), provider- (attitudes, confidence, perceived implementation potential, adoption), and client-encounter (reach) levels over approximately one year. Results We trained 12 novice trainers who then trained 206 providers to implement SBIRT. Trainer SBIRT knowledge increased pre- to posttraining, and fidelity of training delivery was high (99.0% of elements covered across sessions). Provider attitudes, confidence, and perceived implementation potential increased over time, and 64% of providers adopted SBIRT. Reach of the model varied by component, with 41,793 clients screened by trained providers. Of those screening positive for risky alcohol use, 86% received brief intervention (BI) and 53% received referral to treatment (RT). Additionally, 15,353 clients who did not screen as having risky alcohol use received BI and 1,122 received RT. Conclusion Results indicated that the cascading training model was delivered with high fidelity, associated with improvements in all provider outcomes, and reached high numbers of clients for the screening component of the model. Rates of BI and RT delivery were moderate to high, though data suggested over-application of these elements with some clients, highlighting the tension between reach and fidelity. Lessons learned will inform future scale-out of this model in HIV service settings in low- and middle-income countries.
Periklis Petridis, Georgios Margaritis, Vasiliki Stoumpou
et al.
With the increasing interest in deploying Artificial Intelligence in medicine, we previously introduced HAIM (Holistic AI in Medicine), a framework that fuses multimodal data to solve downstream clinical tasks. However, HAIM uses data in a task-agnostic manner and lacks explainability. To address these limitations, we introduce xHAIM (Explainable HAIM), a novel framework leveraging Generative AI to enhance both prediction and explainability through four structured steps: (1) automatically identifying task-relevant patient data across modalities, (2) generating comprehensive patient summaries, (3) using these summaries for improved predictive modeling, and (4) providing clinical explanations by linking predictions to patient-specific medical knowledge. Evaluated on the HAIM-MIMIC-MM dataset, xHAIM improves average AUC from 79.9% to 90.3% across chest pathology and operative tasks. Importantly, xHAIM transforms AI from a black-box predictor into an explainable decision support system, enabling clinicians to interactively trace predictions back to relevant patient data, bridging AI advancements with clinical utility.
This paper addresses the fundamental problem of non-uniform area coverage in multi-agent systems, where different regions require varying levels of attention due to mission-dependent priorities. Existing uniform coverage strategies are insufficient for realistic applications, and many non-uniform approaches either lack optimality guarantees or fail to incorporate crucial real-world constraints such as agent dynamics, limited operation time, the number of agents, and decentralized execution. To resolve these limitations, we propose a novel framework called Density-Driven Optimal Control (D2OC). The central idea of D2OC is the integration of optimal transport theory with multi-agent coverage control, enabling each agent to continuously adjust its trajectory to match a mission-specific reference density map. The proposed formulation establishes optimality by solving a constrained optimization problem that explicitly incorporates physical and operational constraints. The resulting control input is analytically derived from the Lagrangian of the objective function, yielding closed-form optimal solutions for linear systems and a generalizable structure for nonlinear systems. Furthermore, a decentralized data-sharing mechanism is developed to coordinate agents without reliance on global information. Comprehensive simulation studies demonstrate that D2OC achieves significantly improved non-uniform area coverage performance compared to existing methods, while maintaining scalability and decentralized implementability.
Diplopia or double vision has many aetiologies and often presents to the ophthalmologist. The causes of diplopia may be ocular or neurogenic in origin. Any trauma to the eyes in the form of blow-out fractures causes diplopia by damaging the third nerve. The management by the practitioners of contemporary science depends upon the extent of the nerve or muscle injury. Entrapment of muscles requires surgical intervention, whereas others are managed conservatively. The recovery rate is the least for diplopia of traumatic origin.A 50-year-old male who sought Ayurvedic treatment for diplopia in the last three months was diagnosed with partial third nerve palsy after a fracture of the left orbit. He underwent Ayurvedic therapy in the form of internal medicines such as Danadanayanadi kashaya, Ksheerabala 101, Vaishwanara churna, and external treatments such as Pratimarsha Nasya, Siro abhyanga, and Tarpana.At the end of 2 months of Ayurvedic treatment, the patient completely recovered from diplopia.Ayurvedic treatment effectively resolved diplopia in a short time. Patients can benefit from the same if treated promptly in the early stage itself. However, more extensive studies with larger samples will yield more data to prove the potential of Ayurveda in such cases.
Hanna Tania Dwi Crisanti, Rosatyani Puspita Adiati
Perubahan komitmen karyawan saat ini mengarah pada karier mereka daripada organisasi tempat mereka bekerja. Komitmen karier adalah sikap individu terhadap pekerjaan yang meliputi tujuan karier pribadi. Tujuan penelitian ini untuk mengetahui hubungan grit dan persepsi dukungan supervisor dengan komitmen karier karyawan. Partisipan penelitian ini sebanyak 87 partisipan merupakan karyawan/i berusia 24-35 tahun dan sedang bekerja penuh waktu di suatu perusahaan/organisasi. Penelitian ini menggunakan 3 skala, yakni skala Grit yang disusun oleh Duckworth, skala persepsi dukungan supervisor oleh Burns, dan skala komitmen karier oleh Carson dan Bedeian. Analisis data menggunakan bantuan software Jamovi 2.3.28. Hasil penelitian memperoleh terdapat hubungan grit dan persepsi dukungan supervisor dengan komitmen karier pada karyawan milenial. Dengan demikian, organisasi perlu mempertimbangkan pendekatan yang memfokuskan pada pengembangan personal grit karyawan serta memperkuat kualitas hubungan antara supervisor dan karyawan untuk memastikan komitmen jangka panjang karyawan terhadap karier mereka dan kesuksesan organisasi.
Matthias Christenson, Cove Geary, Brian Locke
et al.
The success of precision medicine requires computational models that can effectively process and interpret diverse physiological signals across heterogeneous patient populations. While foundation models have demonstrated remarkable transfer capabilities across various domains, their effectiveness in handling individual-specific physiological signals - crucial for precision medicine - remains largely unexplored. This work introduces a systematic pipeline for rapidly and efficiently evaluating foundation models' transfer capabilities in medical contexts. Our pipeline employs a three-stage approach. First, it leverages physiological simulation software to generate diverse, clinically relevant scenarios, particularly focusing on data-scarce medical conditions. This simulation-based approach enables both targeted capability assessment and subsequent model fine-tuning. Second, the pipeline projects these simulated signals through the foundation model to obtain embeddings, which are then evaluated using linear methods. This evaluation quantifies the model's ability to capture three critical aspects: physiological feature independence, temporal dynamics preservation, and medical scenario differentiation. Finally, the pipeline validates these representations through specific downstream medical tasks. Initial testing of our pipeline on the Moirai time series foundation model revealed significant limitations in physiological signal processing, including feature entanglement, temporal dynamics distortion, and reduced scenario discrimination. These findings suggest that current foundation models may require substantial architectural modifications or targeted fine-tuning before deployment in clinical settings.
Certifying the safety of nonlinear systems, through the lens of set invariance and control barrier functions (CBFs), offers a powerful method for controller synthesis, provided a CBF can be constructed. This paper draws connections between partial feedback linearization and CBF synthesis. We illustrate that when a control affine system is input-output linearizable with respect to a smooth output function, then, under mild regularity conditions, one may extend any safety constraint defined on the output to a CBF for the full-order dynamics. These more general results are specialized to robotic systems where the conditions required to synthesize CBFs simplify. The CBFs constructed from our approach are applied and verified in simulation and hardware experiments on a quadrotor.
Georgios A. Pavlopoulos, P. Kontou, Athanasia Pavlopoulou
et al.
Abstract The latest advances in high-throughput techniques during the past decade allowed the systems biology field to expand significantly. Today, the focus of biologists has shifted from the study of individual biological components to the study of complex biological systems and their dynamics at a larger scale. Through the discovery of novel bioentity relationships, researchers reveal new information about biological functions and processes. Graphs are widely used to represent bioentities such as proteins, genes, small molecules, ligands, and others such as nodes and their connections as edges within a network. In this review, special focus is given to the usability of bipartite graphs and their impact on the field of network biology and medicine. Furthermore, their topological properties and how these can be applied to certain biological case studies are discussed. Finally, available methodologies and software are presented, and useful insights on how bipartite graphs can shape the path toward the solution of challenging biological problems are provided.
Abstract Background Unprescribed and herbal medicines use among pregnant women is a public health concern in low and middle-income countries because of the potential teratogenic effects, insufficient safety and weak drug regulatory systems. Unprescribed and herbal medicines are common among pregnant women in Nigeria, and only a few researchers have documented the prevalence and the risk factors. However, evidence on the associated pregnancy outcomes is still lacking. We assessed the prevalence, predictors and pregnancy outcomes of unprescribed and herbal medicines use among pregnant women in Ibadan, Nigeria. Methods This study was a component of the Ibadan Pregnancy Cohort Study, a prospective cohort study, among pregnant women in Ibadan, Nigeria, who were enrolled at ≤ 20 weeks gestation at their first antenatal visit and followed up till delivery. In all, 571 women participated in the maternal drug use assessment during the third trimester using a pretested interviewer-administered questionnaire. The primary outcomes were unprescribed and herbal medicines use and pregnancy outcomes, secondary outcomes, were abstracted from medical records. The predictors assessed included sociodemographic, obstetric, antenatal care utilization, and lifestyle characteristics. Bi-variate logistic and Poisson regression analyses were used to evaluate the predictors and relative risk for the pregnancy outcomes of unprescribed and herbal medicines at 5% significance. Results The prevalence of unprescribed and herbal medicine use was 31.9% and 21.7%, respectively. On bivariate analysis, the significant predictors of unprescribed medicine (which were protective) were tertiary education, increasing income, adequate antenatal care (≥ 4 visits), and at least two doses of sulfadoxine-pyrimethamine. However, high parity and having an antenatal admission increased the risk. However, after adjusting for confounders, the significant factors associated with unprescribed medicines were; tertiary education (AOR) = 0.23; 95% CI: (0.06 – 0.95); p-value: 0.043] and obtaining at least two doses of sulfadoxine-pyrimethamine [AOR = 0.33; 95% CI: (0.29 – 3.60); p-value: 0.048]. For herbal medicines, the predictors were similar to unprescribed drugs. However, after adjusting for confounders, none was significant for herbal medicines. Unprescribed and herbal medicines were not significantly associated with pregnancy outcomes. Conclusions Unprescribed and herbal medicines use were common among pregnant women in Ibadan, Nigeria, particularly among women with low economic status and those with poor utilization of antenatal care services. These significant predictors can be targeted for public health intervention. Specifically, health education that discourages the use of unprescribed and herbal medications to pregnant women during antenatal care.