Applied Sciences

BackgroundNeonatal jaundice management increasingly relies on transcutaneous bilirubinometry (TcB), yet discrepancies with serum bilirubin (TSB) have limited its clinical reliability. This study introduces Skin Residual Bilirubin Volume (SRBV) as a physiologically grounded framework to enhance TcB interpretation. ObjectiveTo evaluate SRBV as an explanation for TcB-TSB discordance and assess whether incorporating SRBV improves the interpretability and reliability of TcB measurements during diagnosis, phototherapy, and recovery. MethodsTcB readings (MBj20) were calibrated against laboratory TSB in non-jaundiced neonates (TSB <3 mg/dL). Neonates undergoing phototherapy were monitored using paired TcB measurements before and after treatment breaks (TBL-out and TBL-return). TSB was measured before treatment, at mid-treatment, and prior to discharge. Patterns of TcB-TSB disparity and an observed reproducible Recovery Value Flip (RVP) phenomenon were analysed. ResultsAcross 102 neonates, TBL consistently equalled or exceeded TSB, supporting the additive SRBV model. Early in phototherapy, TBL-return > TBL-out, indicating persistent cutaneous bilirubin. A reproducible RVP occurred mid-treatment, after which TBL-return < TBL-out coincided with sustained bilirubin decline. Fractional SRBV contribution increased with baseline bilirubin and persisted into recovery, demonstrating dynamic, patient-specific cutaneous bilirubin retention. ConclusionSRBV provides a biologically plausible explanation for TcB-TSB discordance and dynamic TcB behaviour. Incorporating SRBV into TcB interpretation enables physiologically informed monitoring, improving safety and reliability in laboratory-limited neonatal settings. Significance StatementTranscutaneous bilirubinometry is widely used but limited by disagreement with serum bilirubin. This study introduces SRBV as a physiological explanation for TcB variability and proposes an SRBV-adjusted framework that transforms TcB measurements into actionable, non-invasive clinical guidance.

BackgroundAuditory steady-state responses (ASSRs) provide an objective method for estimating hearing thresholds in individuals unable to provide behavioural responses. Bone conduction (BC) testing is required to differentiate conductive from sensorineural hearing loss. Accurate BC ASSR threshold estimation relies on "correction" factors, which are not yet well established. This meta-analysis evaluated the reliability of BC ASSR thresholds to estimate hearing thresholds at 500, 1000, 2000 and 4000 Hz. MethodsA systematic search of PubMed, the Cochrane Library, and Embase was conducted to identify studies involving normal-hearing (NH) and hearing-impaired (HI) participants of all ages. Outcomes were (1) the difference between ASSR behavioural and ASSR thresholds, and (2) ASSR thresholds. The risk of bias was evaluated using the Newcastle-Ottawa Scale. The mean and 95% confidence intervals (CI) were calculated for the thresholds at the four frequencies. The certainty of the evidence was assessed using GRADE approach. ResultsOf records identified, 11 records met the inclusion criteria, yielding a total of 27 studies. Sample sizes ranged from 60 to 249 participants across frequencies and age groups. The quality of records ranged from low to high. Data were synthesised using random-effects models due to heterogeneity. In NH adults, the mean differences ({+/-}95% CI) between BC ASSR thresholds and behavioural thresholds were 17.0 ({+/-}4.8), 15.5 ({+/-}6.0), 13.4 ({+/-}3.3), and 12.1 ({+/-}4.1) dB at 500, 1000, 2000, and 4000 Hz, respectively. In NH infants, mean ({+/-}95% CI) BC ASSR thresholds were 17.2 ({+/-}2.2), 10.5 ({+/-}3.6), 26.4 ({+/-}2.7), and 19.9 ({+/-}4.0) dB HL at the same frequencies. The certainty of the evidence was very low. ConclusionsBC ASSR can be a reliable method for estimating BC thresholds. However, age and frequency significantly impact BC ASSR thresholds, highlighting the need to develop of "correction" factors to accurately predict BC behavioural thresholds. RegistrationPROSPERO CRD42023422150.

BackgroundIndustrial and peri-urban settings present complex health challenges driven by occupational exposures, environmental risks, and socioeconomic vulnerability. Despite ongoing health education and preventive efforts, many populations living and working in such settings continue to experience significant unmet health needs that limit wellbeing and access to care. MethodsThis cross-sectional qualitative study was conducted in Namanve Industrial Park and surrounding communities in Mukono District, Uganda, as part of a baseline assessment to inform a planned health education intervention. Data were collected through focus group discussions (FGDs) and key informant interviews (KIIs) involving industrial workers, supervisors, health and safety personnel, teachers, school administrators, school nurses, and community stakeholders. Data were analysed using an inductive thematic analysis approach to identify unmet health needs and related systemic gaps. ResultsParticipants articulated multiple, interrelated unmet health needs spanning preventive and primary healthcare services, sexual and reproductive health, first aid and occupational safety, water, sanitation and hygiene (WASH), environmental health, and mental health and psychosocial support. Frequently reported gaps included limited access to routine screening and testing services, lack of essential commodities such as first aid supplies, sanitary pads, personal protective equipment, and soap, inadequate WASH infrastructure, insufficient mental health and counselling services, and structural barriers related to informal employment and poor living conditions. These unmet needs were commonly expressed through requests for materials and services, reflecting broader health system and institutional shortcomings rather than individual dependency. ConclusionThe findings demonstrate that unmet health needs in Namanve Industrial Park and surrounding communities are driven by systemic and structural gaps that constrain access to basic healthcare and preventive services. Addressing these needs requires integrated interventions that combine health education with improved service delivery, essential commodities, and supportive environments. Baseline evidence from this study provides critical guidance for designing context-appropriate, sustainable health interventions in industrial and peri-urban settings.

Distributed manufacturing of open-source hardware shows potential to offer accessible, affordable, and customizable solutions for users in low-resource contexts. Their real-world adoption, however, depends not only on the availability of openly shared designs but also on their replicability when fabricated in different local contexts. This work investigates the replicability of open-source hardware through a practical design-driven approach, using the development and experimental evaluation of a two-piece open-source forearm crutch as a case study. Replicability was considered from early-stage design and evaluated by introducing controlled variations from distributed manufacturing contexts, e.g., material feedstock, manufacturing equipment, and fabrication strategies. Four batches of crutches were fabricated and assembled, using virgin and recycled filaments on small- and large-format 3D printers. After the qualitative evaluation, mechanical static load testing was performed following ISO 11334:2007, together with economic analysis. Comparable mean load-bearing and consistent failure behavior were achieved across batches, making them suitable for use in pairs. Limited cost variability was achieved, supporting repairability and product lifecycle extension. Beyond the specific case study, replicability of open-source hardware needs to be considered as an early-stage design constraint by developing products that allow for variability from local contexts and by including product-specific approaches to assess replicability during development.

BackgroundRisk screening for pre-eclampsia relies on accurate gestational age assessment, but routine access to ultrasound-based gestational dating remains challenging in many low- and middle-income countries (LMICs). As part of the formative work for the "Preventing pre-eclampsia: Evaluating AspiRin Low-dose regimens following risk Screening" (PEARLS) trial, we aim to validate and implement an Artificial Intelligence (AI)-based algorithm for estimation of gestational age, using blind sweeps done with a handheld ultrasound device. This study protocol outlines the accuracy cohort for AI-based gestational age estimation in participating facilities in Ghana, Kenya, and South Africa. MethodsThis multi-country prospective cohort study will recruit 969 pregnant women at 13 health facilities across Kenya, Ghana and South Africa. The eligible population are pregnant women presenting for antenatal visit from 11+0 to 13+6 weeks gestation. Eligible women will have a gestational age assessment by a trained sonographer using fetal biometry (reference standard), followed by gestational age estimation conducted by a trained midwife using the AI-based Intelligent Ultrasound ScanNav FetalCheck system (experimental). Both conventional and AI-based gestational age scans will be conducted with the General Electric (GE) VScanTM Air platform. Women will return for a second visit between 14+0 and 27+6 weeks gestation (week of visit is randomly selected) for an assessment with both conventional and AI-based ultrasound. The primary objective is to determine the accuracy and precision of gestational age estimation using an AI ultrasound system in first and second trimesters, as compared to gestational age estimation using crown-rump length (CRL) measurement by conventional ultrasound in first trimester (11+0 to 13+6 weeks). DiscussionThe study will provide critical evidence on the accuracy of a point-of-care, AI-based gestational age estimation ultrasound algorithm in sub-Saharan African settings. This study will inform the design of the PEARLS trial, as well as provide vital evidence for expanding implementation of ultrasound-based gestational age assessment for women in Africa.

Widespread screening for Adolescent Idiopathic Scoliosis (AIS) is critical for timely intervention but is currently constrained by the radiation risks of X-rays and the subjectivity of physical examinations. Here, we present PointScol, a radiation-free triage system leveraging 3D back surface point clouds. To reconcile the conflicting clinical demands for "zero-miss" screening and "fine-grained" severity assessment, we developed a two-stage deep learning framework. First, an automated segmentation module extracts the dorsal region of interest (ROI) to standardize input geometry. Second, the system employs a dual-branch diagnostic strategy: a binary classification network designed for maximal sensitivity to rule out health, and a 5-class grading network designed to stratify severity (0-10{degrees}, 11-20{degrees}, 21-30{degrees}, 31-40{degrees}, >40{degrees}). Validation on a multi-center dataset (n=128) confirmed the distinct utility of this hierarchical approach. For the scoliosis screening task using a 10{degrees} Cobb angle threshold, the binary classification model achieved a sensitivity of 100.00% in the external cohort, ensuring that no cases requiring further clinical attention were missed. While the 5-class grading task inherently faces greater complexity, it successfully achieved an overall accuracy of 84.48% and, crucially, demonstrated a high specificity of 98.42% for severe surgical cases (>40{degrees}). This performance profile establishes PointScol as a safe clinical filter: the binary module reliably excludes healthy individuals, while the 5-class module flags high-risk patients for prioritized intervention, collectively offering a non-invasive, resource-efficient paradigm for scoliosis management.

Medicine historically separates abstract clinical reasoning from physical intervention. We bridge this divide with MedOS, a general-purpose embodied world model. Mimicking human cognition via a dual-system architecture, MedOS demonstrates superior reasoning on biomedical benchmarks and autonomously executes complex clinical research. To extend this intelligence physically, the system simulates medical procedures as a physics-aware model to foresee adverse events. Generating and validating on the MedSuperVision benchmark, MedOS exhibits spatial intelligence for reasoning and action. Crucially, we demonstrate that this platform democratizes clinical expertise and narrows the performance gap between junior and senior physicians. MedOS transforms clinical intervention towards a collaborative discipline where human intuition and machine intelligence co-evolve.

ObjectivePreterm birth (PTB) is a leading cause of neonatal morbidity and mortality worldwide, with India alone contributing nearly 27% of the global PTB burden. Although alterations in the vaginal microbiome have been implicated in PTB, its association in the Indian context is underexplored. This study aimed to investigate the association of vaginal microbiome and PTB in Indian women at the time of delivery. Study designThe vaginal swabs were collected at the time of delivery from 72 women (31 term, 41 preterm) admitted to a tertiary care hospital in Western India. Microbial DNA was extracted, and the V3-V4 region of the 16S rRNA gene was sequenced. Community composition, alpha and beta diversity, and differential taxonomic abundance were assessed using bioinformatics pipelines. ResultsAt the time of delivery, there were no significant differences in alpha or beta diversity between term and preterm groups. Principal coordinate and unsupervised clustering analyses showed no group-wise segregation. The relative abundance of individual Lactobacillus species, including L. iners and L. helveticus, did not differ significantly between the two groups. However, a modest difference in the relative abundance of Streptococcus was observed between the two groups after adjustment. ConclusionThis study found no major microbial shifts in the vaginal microbiome associated with preterm birth in this cross sectional cohort of Indian women, suggesting that vaginal dysbiosis at the time of delivery may not be a principal driver of PTB in this population. These findings underscore the need for larger, longitudinal, and ethnically diverse studies using standardized methodologies better to understand the microbiomes role in PTB risk.

ObjectivesGiven the widespread use of period-tracking applications and evidence that some users rely on fertile-window predictions for pregnancy prevention, we aimed to quantify pregnancy risk arising from misclassification of biologically fertile days by period-tracking applications, and to compare this risk across calendar-based and basal body temperature (BBT)-supported period tracking and a digital contraceptive regulated as a medical device. MethodsWe conducted an observational analysis of cycles of mobile fertility application users who logged urinary luteinizing hormone (LH) tests. Biologically fertile days were defined using an LH-based reference fertile window (days -5 to 0 relative to ovulation). Three approaches were evaluated: a calendar-based period tracking application, a BBT-supported period tracking application, and a FDA-cleared digital contraceptive. Outcomes included day-specific frequency of fertile days misclassified as safe, cycle-level misclassification, and predicted pregnancy risk per cycle. Analyses were repeated in a subgroup of irregular cycles. Results543,167 menstrual cycles with a clear LH surge signature were included in the analysis. Calendar-based period tracking frequently misclassified fertile days as safe, with 67% of cycles containing at least one at-risk day and 25% containing at least one high-risk day. The mean predicted pregnancy risk per cycle was 22%, increasing to 65% in irregular cycles. BBT-supported period tracking reduced misclassification but remained associated with substantial risk (41% of cycles with at least one at-risk day; mean predicted pregnancy risk 9%). In contrast, the digital contraceptive showed consistently low misclassification (3% of cycles with any at-risk day and a mean predicted pregnancy risk of 0.5%). ConclusionsBoth calendar-based and BBT-supported period-tracking applications not intended for contraception frequently misclassify biologically fertile days and should not be considered reliable tools for pregnancy prevention. Regulated digital contraceptives demonstrate substantially lower pregnancy risk. Short condensationPeriod-tracking apps frequently misclassify fertile days as safe, including days with high pregnancy risk. In a large real-world analysis, both calendar- and BBT-supported trackers showed substantial risk, unlike digital contraception methods regulated as a medical device.

A critical challenge in endocrine neurosurgery is intraoperative discrimination between normal pituitary tissue and pituitary neuroendocrine tumors (PitNETs). Suggesting the universal persistence of near-infrared autofluorescence (NIRAF) in endocrine organs and inspired by routine clinical use of NIRAF for parathyroid gland identification, we discovered that pituitary NIRAF can be employed for label-free transsphenoidal surgery guidance. Ex vivo confocal spectral imaging of 33 specimens identified secretory granules as the dominant long-wavelength fluorescence source and showed that normal pituitary had higher granule content than PitNETs. For the first time, we made use of the pituitary NIRAF during surgery and assessed its performance for pituitary/adenoma separation in vivo for 27 surgeries and showed near-perfect separability between pituitary and non-pituitary measurement sites with ROC-AUC of 0.98. The obtained results clearly demonstrate that the suggested method, based on the solid microscopic background, has the potential for clinical translation and paves the way for enhanced gland preservation during resection.

Accurate quantification of individual exposure to air pollutants remains a major challenge in environmental health, as fixed-site monitoring fails to account for mobility, indoor environments, and physiological variability. We deployed TracMyAir, a smartphone-based digital health platform designed to generate time-resolved, personalized exposure and inhaled dose estimates for PM2.5 and ozone under real-world conditions. In an exploratory study of 18 adults contributing more than 1,500 participant-hours, the platform integrated smartphone geolocation, regulatory (AirNow) and community-based (PurpleAir) air quality data, building infiltration modeling, microenvironment classification, and wearable-derived physical activity metrics to compute eight tiers of hourly exposure estimates, culminating in individualized inhaled dose. Hourly dose estimates derived from smartphone-and smartwatch-based step counts were concordant (Spearman correlation p=0.97-0.98), while heart rate-based estimates yielded greater variability and higher mean values (p=0.82-0.92). Exposure explained 51-73% of variance in inhaled dose of PM2.5 and 68-84% of ozone, suggesting that physiological-based modeling approaches improve hyperlocal estimates of personal pollutant burden. Substantial inter-and intra-individual variability reflect dynamic microenvironmental transitions and activity patterns. Modeled doses based on regulatory and community sensor networks were strongly correlated (R=0.84), with community sensors located closer to participants on average, supporting the feasibility of integrating dense, low-cost monitoring networks. No consistent association was observed between outdoor pollutant levels and neighborhood socioeconomic status in this cohort. These findings demonstrate the feasibility of a scalable, smartphone-centered digital health approach for hyperlocal exposure and inhaled dose modeling. By leveraging ubiquitous consumer devices and existing air quality networks, TracMyAir enables personalized environmental exposure assessment with potential applications in epidemiology, population health, and precision environmental medicine.

Thyroid carcinoma is one of the most prevalent endocrine malignancies worldwide, and accurate preoperative differentiation between benign and malignant thyroid nodules remains clinically challenging. Diagnostic methods that medical practitioners use at present depend on their personal judgment to evaluate both imaging results and separate clinical tests, which creates inconsistency that leads to incorrect medical evaluations. The combination of radiological imaging with clinical information systems enables healthcare providers to enhance their capacity to make reliable predictions about patient outcomes while improving their decision-making abilities. The study introduces a deep learning framework that utilizes multiple data sources by combining magnetic resonance imaging (MRI) data with clinical text to predict thyroid cancer. The system uses a Vision Transformer (ViT) to obtain advanced MRI scan features, while a domain-adapted language model processes clinical documents that contain patient medical history and symptoms and laboratory results. The cross-modal attention system enables the system to merge imaging data with textual information from different sources, which helps to identify how the two types of data are interconnected. The system uses a classification layer to classify the fused features, which allows it to determine the probability of cancerous tumors. The experimental results show that the proposed multimodal system achieves better results than the unimodal base systems because it has higher accuracy, sensitivity, specificity, and AUC values, which help medical personnel to make better preoperative decisions.

ObjectivesTo develop and validate pediatric adaptations of the Venous Excess Ultrasound Score (P-VExUS) for noninvasive estimation of central venous pressure (CVP) in critically ill children. DesignProspective observational study. SettingPICU of a tertiary-care teaching hospital. PatientsFifty-six mechanically ventilated children (median age 7.4 months, median weight 6.0 kg) with central venous catheters. InterventionsNone. Measurements and Main ResultsVenous Doppler ultrasonography of the inferior vena cava, hepatic, portal, and intrarenal veins was performed at the bedside. Two P-VExUS models were tested: (1) a categorical grading system (0-III) and (2) a semiquantitative point-based score (0-7). Both models showed significant associations with CVP. For predicting elevated CVP (>12 mmHg), model 1 achieved an AUROC of 0.74 (95% CI 0.61-0.85) with 45% sensitivity and 98% specificity, while model 2 demonstrated superior accuracy with an AUROC of 0.94 (95% CI 0.84-0.98), sensitivity 82%, and specificity 91% (p < 0.001). For detecting low CVP (<7 mmHg), model 2 also outperformed model 1 (AUROC 0.80 vs. 0.69, p = 0.02). Among individual venous Doppler components, intrarenal veins had the highest discriminative ability (AUROC 0.92), followed by hepatic (0.89) and portal (0.80) veins. ConclusionsTwo pediatric-specific P-VExUS models were feasible and accurate for estimating CVP in critically ill children. The point-based model (model 2) demonstrated superior diagnostic performance, supporting its potential as a noninvasive tool to assess venous congestion at the bedside. Research in ContextO_LIVenous congestion, reflected by elevated central venous pressure (CVP), is associated with adverse outcomes in critically ill children, including mortality and renal dysfunction. C_LIO_LIThe Venous Excess Ultrasound Score (VExUS) is validated in adults, but pediatric-specific adaptations and cutoff values remain poorly defined. C_LIO_LIThere is a need for noninvasive, bedside tools to estimate CVP in children and guide fluid management in the PICU. C_LI What This Study MeansO_LIThis study validates pediatric-specific adaptations of the Venous Excess Ultrasound Score (P-VExUS) for estimating CVP in critically ill children. C_LIO_LIThe semiquantitative point-based model provided more consistent and accurate discrimination of venous congestion compared with categorical grading. C_LIO_LIThese findings highlight the feasibility and potential clinical utility of venous Doppler ultrasonography as a noninvasive bedside tool in the PICU. C_LI

BackgroundPulse oximeters are typically validated on cohorts of 200-500 subjects under controlled conditions. Whether these cohorts capture the demographic heterogeneity of national clinical practice -- and whether measurement error is associated with patient outcomes -- has not been established at scale. MethodsWe analyzed paired SpO2/SaO2 readings from three independent sources spanning 209 U.S. hospitals: MIMIC-IV (1 hospital; 12,934 ICU stays), eICU-CRD (208 hospitals; 55,178 stays), and the Open Oximetry Repository (PhysioNet; 52.4 million readings with continuous melanin and perfusion indices). Bias was defined as SpO2 - SaO2. Hidden hypoxemia (SpO2 [&ge;] 94% with SaO2 < 88%) was assessed per ICU stay. Mortality was compared between hidden-hypoxemia-positive and -negative stays with multivariable logistic regression adjusting for age, sex, race, and four laboratory severity markers (cluster-robust SEs by hospital). Sensitivity analyses included landmark restriction (first 48 hours), lactate stratification, alternate thresholds, and patient-level aggregation. PPG signal quality was assessed in 125 ICU patients with demographic-linked waveform data. ResultsBias was minimal at normal perfusion but amplified under low perfusion in high-melanin patients, consistent with known optics: at very low perfusion x high melanin x severe hypoxia, mean bias reached +12.8% (n = 458,571), with 47% of readings constituting hidden severe hypoxemia. National bias in African American patients was +2.76% (n = 529,541; 208 hospitals), 62% higher than academic estimates. Across 55,178 eICU stays, hidden hypoxemia was associated with approximately doubled mortality after adjustment for age, sex, race, and illness severity (adjusted OR 1.86, 95% CI 1.69-2.04, p < 0.001), consistent across all racial groups. Hidden hypoxemia was not a pre-terminal phenomenon: 63% of events occurred >48 hours before death (median first event: 15.3 hours; mean time to death: 151 hours), and the association persisted in landmark analysis (first 48 hours only), in patients with normal lactate (adjusted OR 1.87, 95% CI 1.61-2.16), and when both restrictions were applied simultaneously (16.5% vs. 11.1%). Waveform analysis (n = 125) showed no fixed racial difference in baseline PPG AC/DC ratio (Black: 0.299, White: 0.273), suggesting the signal deficit is conditional on perfusion state. Full extraction (n = 1,545) is in progress. ConclusionsIn this multicenter retrospective analysis, national pulse oximetry variance exceeded published benchmarks and was associated with approximately doubled ICU mortality, replicated across 209 U.S. hospitals. Hidden hypoxemia was not a pre-terminal artifact: events occurred throughout the ICU stay at a constant rate, and mortality associations persisted in landmark and lactate-stratified analyses. These findings suggest that current regulatory validation standards may underestimate the real-world prevalence of demographic bias in pulse oximetry, and that perfusion-dependent mechanisms may offer a target for algorithmic correction.

Background The Hump-nosed pit viper is a recognized but neglected medically significant species causing morbidity and mortality, with non-availability of a specific antivenom. There are many gaps in our understanding of its envenomation, including burden, clinical syndrome, complications and management. Methodology The study is a retrospective sub analysis of the Prospective VENOMS registry and hospital records of Hump Nosed Pit Viper envenomation from a single tertiary care center in coastal Karnataka from May 2018 to March 2024. Epidemiology, syndrome, complications and treatment strategies have been described. A linear mixed model analysis was conducted to study the effect of different therapeutic interventions in combating venom induced consumptive coagulopathy (VICC) Principal Findings Of 46 cases, 24 patients had VICC. The most common complications were AKI (21.7%), TMA (10.9%) and stroke (4.4%). Anaphylaxis to ASV (23.9%) was the most common therapeutic complication. Therapeutic interventions included ASV, administration of blood products and therapeutic plasma exchange along with supportive care. The linear mixed model revealed that administration of blood products (p=<0.001) had the strongest influence on the INR value, however, often resulting in a transient decline in INR value. ASV (p=0.052) caused only marginally significant change in INR. The role of TPE could not be statistically inferred, however, individual cases with severe VICC improved without complications, therefore it required further study but can be considered in critical cases. Conclusions/Significance This study describes the syndrome of hump-nosed pit viper envenomation, while highlighting the urgent need for a species-specific antivenom, recommends treatment strategies that can be used in the interim. Additionally, geo-spatial mapping draws attention to hotspots and the hypothesis that HNPV in coastal Karnataka have regionally distinct toxicity trends.

There are many alternative methods to joystick control for control of Electric Powered Wheelchairs for users with neuromuscular disabilities, such as muscular dystrophy, and spinal cord injuries, such as tetraplegia. However, these methods- which include the sip-and-puff method, head and neck movement, blinking, or tongue movement- hinder social interaction, and are therefore detrimental to user independence. In recent years, research has explored the use of Electromyography (EMG) signals from alternative muscles to control a powered wheelchair, consequently increasing the quality of life of these users. The Auricular Muscles (AM) may be suitable, as they are controlled separately from the facial nerve and are vestigial in humans, making them advantageous for powered wheelchair control for users with tetraplegia. Additionally, they are located around the ear, adding a level of cosmesis when designing wearable sensors and prosthesis. This paper extracts and implements two control strategies from current literature and, for the first time, compares them directly, demonstrating viable implementation approaches for an online EMG-based powered-wheelchair control system. A Support Vector Machine (SVM) was developed and various window lengths were compared, with the most accuracy and real-time effectiveness found at 300ms. A study with three participants demonstrates the feasibility of these methods of control as well as experimental results to guide the potential AM use.

BACKGROUNDDelirium is common in critically ill adults but often goes unrecognized and undertreated. Little is known about the perceptions of ICU nurse and physician leaders regarding ICU delirium detection and management and the potential role of objective continuous delirium monitoring to facilitate ICU delirium care. RESEARCH QUESTIONWhat are the perceptions of ICU leaders regarding the current challenges associated with delirium recognition and management and the potential benefits of continuous delirium monitoring? STUDY DESIGN AND METHODSWe conducted a blinded, cross-sectional, electronic survey of ICU leaders across the U.S., including physician directors and nursing managers with [&ge;]3 years of ICU leadership experience. We asked about perceptions of the effectiveness of current delirium clinical assessment tools, current delirium detection and management challenges, and how an objective, continuous delirium monitoring system might impact clinician practice and patient outcomes in their ICU. RESULTSAmong the 81 respondents (62 physicians, 19 nurses), most (76%) reported that recommended delirium assessment tools (CAM-ICU, ICDSC) are used in their ICUs, though there were mixed perceptions on how reliably they are conducted. A majority (63-90%) perceived that current bedside assessments delay and limit the recognition of ICU delirium. Nearly all (89%) agreed an objective delirium monitoring tool would be more clinically valuable than current delirium assessment tools and that it would support real-time, delirium management by clinicians. CONCLUSIONSICU leaders perceive that there are limitations to using clinical delirium assessment tools in ICU patients to effectively detect and manage ICU delirium. Most felt that an objective delirium monitor could facilitate delirium detection and potentially expedite appropriate delirium management in patients.

PurposeTo investigate the effects of morning and evening narrowband blue light exposure on axial length, and to examine the short-term effect of morning blue light combined with myopic defocus on axial length. MethodsFor objective 1, 18 individuals underwent 60 minutes of narrowband blue light exposure (460nm) in the morning (9:00-11:00AM) and evening (5:00-7:00PM) of the same day. The axial length values were normalized to the average of the morning and evening axial length values. For objective 2, 27 young adults were exposed to 60 minutes of narrowband blue light and broadband white light while wearing a +3.00 D lens over the right eye. Axial length was measured using Lenstar LS900. ResultsA significant reduction in axial length was observed after exposure to morning blue light compared to evening blue light (-10.0{+/-}3.96{micro}m vs.-0.67{+/-}3.30{micro}m; p=0.02), whereas no such effect was observed with broadband white light exposure (0.0{+/-}3.53 {micro}m vs. -2.50{+/-}4.23{micro}m, p=0.70). While the broadband white light exposure did not alter the normal diurnal variation in axial length (+2.35{+/-}1.82{micro}m vs.-6.25{+/-}2.21{micro}m, p=0.04), blue light diminished such a pattern (-4.12{+/-}1.72{micro}m vs. - 2.00{+/-}2.00{micro}m, p=0.48). The myopic defocus did not influence axial length under either narrowband blue or broadband white light conditions. ConclusionThe short-term narrowband blue light exposure led to a significant decrease in axial length in the morning than evening exposure, with a likely influence on the diurnal rhythm of axial length. Morning blue light exposure with lens-induced myopic defocus did not provide additional short-term modulation of axial length.

ObjectiveThis study investigates whether incorporating physiological coupling concepts into neural network design can support stable and interpretable feature learning for histopathological image classification under limited data conditions. MethodsA physiologically inspired architecture, termed CardioPulmoNet, is introduced to model interacting feature streams analogous to pulmonary ventilation and cardiac perfusion. Local and global tissue features are integrated through bidirectional multi-head attention, while a homeostatic regularization term encourages balanced information exchange between streams. The model was evaluated on three histopathological datasets involving oral squamous cell carcinoma, oral submucous fibrosis, and heart failure. In addition to end-to-end training, learned representations were assessed using linear support vector machines to examine feature separability. ResultsCardioPulmoNet achieved performance comparable to several pretrained convolutional neural networks across the evaluated datasets. When combined with a linear classifier, improved classification performance and higher area under the receiver operating characteristic curve were observed, suggesting that the learned feature embeddings are well structured for downstream discrimination. ConclusionThese results indicate that physiologically motivated architectural constraints may contribute to stable and discriminative representation learning in computational pathology, particularly when training data are limited. The proposed framework provides a step toward integrating physiological modeling principles into medical image analysis and may support future development of transferable and interpretable learning systems for histopathological diagnosis.

ObjectiveDespite substantial variability in the severity of post-anoxic encephalopathy, all comatose patients after cardiac arrest are usually treated according to the same standardized intensive care protocol, including sedation, mechanical ventilation, and targeted temperature management (TTM). We hypothesize that patients with a favourable EEG pattern (continuous EEG within 12 hours after cardiac arrest) may not benefit from prolonged sedation and TTM. We studied the feasibility and safety of early cessation of sedation and TTM in this subgroup. MethodsWe conducted a non-randomized, controlled intervention study including 40 adult patients admitted to the ICU with postanoxic encephalopathy after cardiac arrest and an early (< 12 hours) favourable EEG pattern. The control group received standard care with sedation and TTM for at least 24-48 hours, whereas the intervention group underwent early cessation of sedation and TTM as soon as possible after establishing a favourable EEG, followed by weaning from mechanical ventilation. The primary outcome was duration of mechanical ventilation. Secondary outcomes included ICU length of stay, total sedation time, number of ICU complications, and neurological outcomes at 3 and 6 months. ResultsDuration of mechanical ventilation was significantly shorter in the intervention than in the control group (median 12 vs 28 h, p < 0.001). Median ICU length of stay and median total sedation time were also reduced by more than 50% in the intervention group, from respectively 2.5 to 1.2 days (p = 0.001) and 27 to 12 h (p < 0.001). There was no increase in ICU complications in the intervention group. No statistically significant differences in neurological outcomes at 3 or 6 months were observed. ConclusionEarly withdrawal of sedation is feasible and safe in patients with an early favourable EEG following cardiac arrest. The study was underpowered to detect possible differences in long-term neurological recovery. SignificanceShortening sedation and mechanical ventilation is likely to result in direct reductions in healthcare costs and contribute to more appropriate care. Larger studies are needed to evaluate the impact on long-term neurological outcomes.