Sandip Bhattacharya, Vanessa da Silva, Christina Kohlmann
Personal care formulations often contain synthetic and non-biodegradable ingredients, such as silicone and mineral oils, which can offer a unique performance. However, due to regulations like the EU ban of Octamethylcyclotetrasiloxane (D4), Decamethyl-cyclopentasiloxane (D5), Dodecamethylcyclohexasiloxane (D6) already in effect for rinse off and for leave on cosmetics by June 2027 coupled with growing consumer awareness and expectations on sustainability, personal care brands face significant pressure to replace these synthetic ingredients with natural alternatives without compromising performance and cost. As a result, formulators are confronted with the challenge to find natural-based solutions within a short timeframe. In this study, we propose a pioneering approach that utilizes predicting modelling and simulation-based digital services to obtain natural-based ingredient combinations as recommendations to commonly used synthetic ingredients. We will demonstrate the effectiveness of our predictions through the application of these proposals in specific formulations. By offering a platform of digital services, it is aimed to empower formulators to explore good performing novel and environmentally friendly alternatives, ultimately driving a substantial and genuine transformation in the personal care industry.
Beatriz Araújo, André Rivera, Suzany Martins
et al.
Background The utilization of video laryngoscopy (VL) has demonstrated superiority over direct laryngoscopy (DL) for intubation in surgical settings. However, its effectiveness in the intensive care unit and emergency department settings remains uncertain. Methods We systematically searched PubMed, Embase, Cochrane, and ClinicalTrials.gov databases for randomized controlled trials (RCTs) comparing VL versus DL in critically ill patients. Critical setting was defined as emergency department and intensive care unit. This systematic review and meta-analysis followed Cochrane and PRISMA recommendations. R version 4.3.1 was used for statistical analysis and heterogeneity was examined with I^2 statistics. All outcomes were submitted to random-effect models. Results Our meta-analysis of 14 RCTs, compromising 3981 patients assigned to VL ( n = 2002) or DL ( n = 1979). Compared with DL, VL significantly increased successful intubations on the first attempt (RR 1.12; 95% CI 1.04–1.20; p < 0.01; I ^2 = 82%). Regarding adverse events, VL reduced the number of esophageal intubations (RR 0.44; 95% CI 0.24–0.80; p < 0.01; I ^2 = 0%) and incidence of aspiration episodes (RR 0.63; 95% CI 0.41–0.96; p = 0.03; I ^2 = 0%) compared to DL. Conclusion VL is a more effective and safer strategy compared with DL for increasing successful intubations on the first attempt and reducing esophageal intubations in critically ill patients. Our findings support the routine use of VL in critically ill patients. Registration CRD42023439685 https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023439685 . Registered 6 July 2023.
Harsha Sammangi, Aditya Jagatha, Giridhar Reddy Bojja
et al.
AI Innovations in the IoT for Real-Time Patient Monitoring On one hand, the current traditional centralized healthcare architecture poses numerous issues, including data privacy, delay, and security. Here, we present an AI-enabled decentralized IoT architecture that can address such challenges during a pandemic and critical care settings. This work presents our architecture to enhance the effectiveness of the current available federated learning, blockchain, and edge computing approach, maximizing data privacy, minimizing latency, and improving other general system metrics. Experimental results demonstrate transaction latency, energy consumption, and data throughput orders of magnitude lower than competitive cloud solutions.
Davis Byamugisha, Francis Kamuganga, Adones Rukundo
et al.
Effective treatment of cancer requires early diagnosis which involves the patient's awareness of the early signs and symptoms, leading to a consultation with a health provider, who would then promptly refer the patient for confirmation of the diagnosis and thereafter treatment. However, this is not always the case because of delays arising from limited skilled manpower and health information management systems that are neither integrated nor organized in their design hence leading to information gap among care groups. Existing methods focus on using accumulated data to support decision making, enhancing the sharing of secondary data while others exclude some critical stakeholders like patient caretakers and administrators thus, leaving an information gap that creates delays and miscommunication during case management. We however notice some similarities between cancer treatment and software engineering information management especially when progress history needs to be maintained (versioning). We analyze the similarities and propose a model for information sharing among cancer care groups using the software engineering principles approach. We model for reducing delays and improving coordination among care groups in cancer case management. Model design was guided by software engineering principles adopted in GitHub version control system for bug fixing in open-source code projects. Any-Logic simulation software was used to mimic the model realism in a virtual environment. Results show that bug resolution principles from software engineering and GitHub version control system can be adopted to coordinate collaboration and information sharing among care groups in a cancer case management environment while involving all stakeholders to improve care treatment outcomes, ensure early diagnosis and increase patient's survival chances.
Amal Elgammal, Bernd J. Krämer, Michael P. Papazoglou
et al.
Personalized chronic care requires the integration of multimodal health data to enable precise, adaptive, and preventive decision-making. Yet most current digital twin (DT) applications remain organ-specific or tied to isolated data types, lacking a unified and privacy-preserving foundation. This paper introduces the Patient Medical Digital Twin (PMDT), an ontology-driven in silico patient framework that integrates physiological, psychosocial, behavioral, and genomic information into a coherent, extensible model. Implemented in OWL 2.0, the PMDT ensures semantic interoperability, supports automated reasoning, and enables reuse across diverse clinical contexts. Its ontology is structured around modular Blueprints (patient, disease and diagnosis, treatment and follow-up, trajectories, safety, pathways, and adverse events), formalized through dedicated conceptual views. These were iteratively refined and validated through expert workshops, questionnaires, and a pilot study in the EU H2020 QUALITOP project with real-world immunotherapy patients. Evaluation confirmed ontology coverage, reasoning correctness, usability, and GDPR compliance. Results demonstrate the PMDT's ability to unify heterogeneous data, operationalize competency questions, and support descriptive, predictive, and prescriptive analytics in a federated, privacy-preserving manner. By bridging gaps in data fragmentation and semantic standardization, the PMDT provides a validated foundation for next-generation digital health ecosystems, transforming chronic care toward proactive, continuously optimized, and equitable management.
Dementia significantly impacts cognitive, behavioral, and functional abilities, creating challenges for both individuals and caregivers. Recent advancements in HCI have introduced innovative technological solutions to support people with dementia (PwD) and their caregivers. This scoping review systematically examines 32 recent publications from leading digital libraries, categorizing technological interventions into four key domains: Assistive and Smart Technology for Daily Life, Social Interaction and Communication, Well-being and Psychological Support, and Caregiver Support and Training. Our analysis highlights how emerging technologies are transforming dementia care. These technologies enhance quality of life by promoting independence, fostering social engagement, and providing emotional and cognitive support. However, the review also identifies critical gaps, particularly in addressing the needs of individuals with early-stage dementia and the lack of individualized support mechanisms. By emphasizing user-centered design, accessibility, and ethical considerations, this paper offers a structured roadmap for future research and practice in dementia care. It bridges the gap between technological innovation and the real-world needs of PwD and their caregivers, providing valuable insights for researchers, practitioners, and policymakers. This review not only synthesizes current advancements but also sets the stage for future HCI-driven innovations in dementia care, aiming to improve outcomes for an aging global population.
Group-relative reinforcement learning with verifiable rewards (RLVR) often wastes the most informative data it already has the failures. When all rollouts are wrong, gradients stall; when one happens to be correct, the update usually ignores why the others are close-but-wrong, and credit can be misassigned to spurious chains. We present CARE (Contrastive Anchored REflection), a failure-centric post-training framework for multimodal reasoning that turns errors into supervision. CARE combines: (i) an anchored-contrastive objective that forms a compact subgroup around the best rollout and a set of semantically proximate hard negatives, performs within-subgroup z-score normalization with negative-only scaling, and includes an all-negative rescue to prevent zero-signal batches; and (ii) Reflection-Guided Resampling (RGR), a one-shot structured self-repair that rewrites a representative failure and re-scores it with the same verifier, converting near-misses into usable positives without any test-time reflection. CARE improves accuracy and training smoothness while explicitly increasing the share of learning signal that comes from failures. On Qwen2.5-VL-7B, CARE lifts macro-averaged accuracy by 4.6 points over GRPO across six verifiable visual-reasoning benchmarks; with Qwen3-VL-8B it reaches competitive or state-of-the-art results on MathVista and MMMU-Pro under an identical evaluation protocol.
Enhancing emotional well-being has become a significant focus in HCI and CSCW, with technologies increasingly designed to track, visualize, and manage emotions. However, these approaches have faced criticism for potentially suppressing certain emotional experiences. Through a scoping review of 53 empirical studies from ACM proceedings implementing Technology-Mediated Emotion Intervention (TMEI), we critically examine current practices through lenses drawn from HCI critical theories. Our analysis reveals emotion intervention mechanisms that extend beyond traditional emotion regulation paradigms, identifying care-centered goals that prioritize non-judgmental emotional support and preserve users' identities. The findings demonstrate how researchers design technologies for generating artificial care, intervening in power dynamics, and nudging behavioral changes. We contribute the concept of "emotion support" as an alternative approach to "emotion regulation," emphasizing human-centered approaches to emotional well-being. This work advances the understanding of diverse human emotional needs beyond individual and cognitive perspectives, offering design implications that critically reimagine how technologies can honor emotional complexity, preserve human agency, and transform power dynamics in care contexts.
We propose CARE (Collision Avoidance via Repulsive Estimation) to improve the robustness of learning-based visual navigation methods. Recently, visual navigation models, particularly foundation models, have demonstrated promising performance by generating viable trajectories using only RGB images. However, these policies can generalize poorly to environments containing out-of-distribution (OOD) scenes characterized by unseen objects or different camera setups (e.g., variations in field of view, camera pose, or focal length). Without fine-tuning, such models could produce trajectories that lead to collisions, necessitating substantial efforts in data collection and additional training. To address this limitation, we introduce CARE, an attachable module that enhances the safety of visual navigation without requiring additional range sensors or fine-tuning of pretrained models. CARE can be integrated seamlessly into any RGB-based navigation model that generates local robot trajectories. It dynamically adjusts trajectories produced by a pretrained model using repulsive force vectors computed from depth images estimated directly from RGB inputs. We evaluate CARE by integrating it with state-of-the-art visual navigation models across diverse robot platforms. Real-world experiments show that CARE significantly reduces collisions (up to 100%) without compromising navigation performance in goal-conditioned navigation, and further improves collision-free travel distance (up to 10.7x) in exploration tasks. Project page: https://airlab-sogang.github.io/CARE/
Julia Ive, Olatomiwa Olukoya, Jonathan P. Funnell
et al.
Introduction: Timely care in a specialised neuro-intensive therapy unit (ITU) reduces mortality and hospital stays, with planned admissions being safer than unplanned ones. However, post-operative care decisions remain subjective. This study used artificial intelligence (AI), specifically natural language processing (NLP) to analyse electronic health records (EHRs) and predict ITU admissions for elective surgery patients. Methods: This study analysed the EHRs of elective neurosurgery patients from University College London Hospital (UCLH) using NLP. Patients were categorised into planned high dependency unit (HDU) or ITU admission; unplanned HDU or ITU admission; or ward / overnight recovery (ONR). The Medical Concept Annotation Tool (MedCAT) was used to identify SNOMED-CT concepts within the clinical notes. We then explored the utility of these identified concepts for a range of AI algorithms trained to predict ITU admission. Results: The CogStack-MedCAT NLP model, initially trained on hospital-wide EHRs, underwent two refinements: first with data from patients with Normal Pressure Hydrocephalus (NPH) and then with data from Vestibular Schwannoma (VS) patients, achieving a concept detection F1-score of 0.93. This refined model was then used to extract concepts from EHR notes of 2,268 eligible neurosurgical patients. We integrated the extracted concepts into AI models, including a decision tree model and a neural time-series model. Using the simpler decision tree model, we achieved a recall of 0.87 (CI 0.82 - 0.91) for ITU admissions, reducing the proportion of unplanned ITU cases missed by human experts from 36% to 4%. Conclusion: The NLP model, refined for accuracy, has proven its efficiency in extracting relevant concepts, providing a reliable basis for predictive AI models to use in clinically valid applications.
BACKGROUND Delirium is a common and severe complication in intensive care unit (ICU) patients with acute ischemic stroke, exacerbating cognitive and physical impairments. It prolongs hospitalization, increases healthcare costs, and raises mortality risk. Early prediction is crucial because it facilitates prompt interventions that could possibly reverse or alleviate the detrimental consequences of delirium. Braden scores, traditionally used to assess pressure injury risk, could also signal frailty, providing an early warning of delirium and aiding in prompt and effective patient management. OBJECTIVE To examine the association between the Braden score and delirium. METHODS A retrospective analysis of adult ischemic stroke patients in the ICU of a tertiary academic medical center in Boston from 2008 to 2019 was performed. Braden scores were obtained on admission for each patient. Delirium, the primary study outcome, was assessed using the Confusion Assessment Method for Intensive Care Unit and a review of nursing notes. The association between Braden score and delirium was determined using Cox proportional hazards modeling, with hazard ratios (HR) and 95% confidence intervals (CI) calculated. RESULTS The study included 3,680 patients with a median age of 72 years, of whom 1,798 were women (48.9 %). The median Braden score at ICU admission was 15 (interquartile range 13-17). After adjustment for demographics, laboratory tests, severity of illness, and comorbidities, the Braden score was inversely associated with the risk of delirium (adjusted HR: 0.94, 95 % CI: 0.92-0.96, P < 0.001). CONCLUSIONS The Braden score may serve as a convenient and simple screening tool to identify the risk of delirium in ICU patients with ischemic stroke. IMPLICATION FOR CLINICAL PRACTICE The use of the Braden score as a predictor of delirium in ischemic stroke patients in the ICU allows early identification of high-risk patients. This facilitates timely intervention, thereby improving patient outcomes and potentially reducing healthcare costs.
Introduction: Implantable cardioverter-defibrillator (ICD) lead perforation through the myocardium may result in chest pain and electrocardiogram (ECG) changes concerning for ST-segment elevation myocardial infarction. The clinical context of the ECG aids in appropriate management. Case Report: We report the case of a 71-year-old woman experiencing chest pain after an ICD placement two weeks earlier. On presentation, she exhibited ST-segment elevation on her ECG. Computed tomography confirmed ICD lead migration. The patient’s hemodynamics were normal, and she was discharged home after a five-day hospital stay following a lead revision. Conclusion: Although rare, ICD lead perforation is a potential cause of chest pain and ischemic ECG changes. Emergency physicians should consider lead perforation as a potential differential diagnosis when evaluating chest pain in patients with ICDs, taking into account the potential complications of coronary angiography.
Medical emergencies. Critical care. Intensive care. First aid
The advancement of technology has significantly impacted the student population, with many young people now spending a large portion of their time engaging with various forms of technology (1). As such, it is imperative for educational systems to adapt and integrate new technologies into their frameworks in order to meet the evolving needs of students (2). Virtual reality (VR) technology has gained substantial popularity among students and addressing its integration could be a crucial step towards bridging educational gaps (3). However, this poses the question: does the current educational system possess the capacity to embrace this expansive platform? Extensive investigations have revealed that for an educational system to effectively incorporate virtual reality technology, it must address key issues including education needs assessment, defining clear educational goals, implementing effective game design patterns, assessing practicality and applicability within the system along with providing necessary support and opportunities while also grappling with implementation challenges as well as ethical considerations related to integrating such advanced technologies into curricula (4). Consequently, this letter aims to delve deeper into these critical concerns surrounding designing virtual reality games specifically tailored for medical courses.
Medical emergencies. Critical care. Intensive care. First aid
Kh. G. Alidzhanova, K. A. Popugyaev, G. R. Ramazanov
et al.
Endovascular thrombectomy (ET) effectively and safely recanalizes the occluded artery and restores the ischemic area in patients with acute ischemic stroke (IS), improving the clinical prognosis of stroke in the anterior and posterior circulation system, expanding the time therapeutic window from no more than 6 to 24 hours, greatly increasing the chances of functional independence and survival. However, some patients develop an unfavorable postoperative outcome, complications and “ineffectiveness” of revascularization. The thrombectomy result depends not only on the patient selection criteria, timing and success of the procedure, but on many other factors as well. Despite the advances in stroke treatment, the issues of neuroimaging and patient selection for ET remain relevant; the pathophysiological mechanisms of the influence of some factors on the effectiveness of the procedure are not completely clear; the causes of “uneffective” revascularization, unfavorable outcome and mortality after ET are unclear. An analysis of global experience in treating ischemic stroke with ET showed the heterogeneity of the patient selection criteria, clinical and neuroimaging variables, prognostic factors and treatment outcomes, which makes it difficult to draw a general conclusion and requires further targeted research. The article discusses the issues of patient selection, pathophysiological mechanisms of the influence of some risk factors on the outcome of ischemic stroke and the causes of unfavorable outcome and death after ET.
Medical emergencies. Critical care. Intensive care. First aid
As the aging population increases and the shortage of healthcare workers increases, the need to examine other means for caring for the aging population increases. One such means is the use of humanoid robots to care for social, emotional, and physical wellbeing of the people above 65. Understanding skilled and long term care nursing home administrators' perspectives on humanoid robots in caregiving is crucial as their insights shape the implementation of robots and their potential impact on resident well-being and quality of life. This authors surveyed two hundred and sixty nine nursing homes executives to understand their perspectives on the use of humanoid robots in their nursing home facilities. The data was coded and results revealed that the executives were keen on exploring other avenues for care such as robotics that would enhance their nursing homes abilities to care for their residents. Qualitative analysis reveals diverse perspectives on integrating humanoid robots in nursing homes. While acknowledging benefits like improved engagement and staff support, concerns persist about costs, impacts on human interaction, and doubts about robot effectiveness. This highlights complex barriers financial, technical, and human and emphasizes the need for strategic implementation. It underscores the importance of thorough training, role clarity, and showcasing technology benefits to ensure efficiency and satisfaction among staff and residents.