Browse Articles

Disability disclosure in healthcare settings for individuals with developmental disabilities: A qualitative study of patient and caregiver perspectives

Background People with disabilities experience significant healthcare disparities, including missed opportunities for preventive, inaccessible services, and inadequate communication with providers. These challenges often lead to unmet healthcare needs and poor health outcomes. Disability disclosure is one strategy that may aid in closing this healthcare equity gap, though limited research sheds light on patient and caregiver feelings towards and preferences for disclosure. Objective This study assessed comfort with and preferences for disability disclosure within healthcare settings among individuals with developmental disabilities and caregivers of individuals with developmental disabilities. Methods An exploratory qualitative research design was employed, utilizing semi-structured interviews with 22 participants (10 patients and 12 caregivers) in South Florida. Data were transcribed and analyzed through thematic analysis to identify key themes related to disability disclosure in healthcare settings. Results Five main themes emerged. Two themes centered on the downside of disclosure (harm avoidance and disclosure utility), while two themes illuminated the upside of disclosure (disclosure necessity and reduced stigma). The final theme focused on disclosure preferences. Conclusions Comfort with disability disclosure among patients and caregivers was largely motivated by a desire to avoid perceived pitfalls and secure quality healthcare. Findings confirm the persistence of inadequate healthcare delivered to patients with disabilities, and the beneficial role disability disclosure can play in addressing current deficiencies. With support of healthcare system leadership and other salient stakeholder groups, further research can inform development, implementation, and evaluation of disclosure systems that facilitate equitable care delivery and improve health outcomes among patients with developmental disabilities.

Advancing smart communities with a deep learning framework for sustainable resource management

Background The rapid development of urban systems and rising requirements for sustainable development lift resource management issues in smart communities. A fundamental problem for contemporary communities involves effectively using energy and water resources and waste management systems under environmental limitations. Artificial intelligence (AI) techniques at an advanced level deliver new methods that optimize resource management systems. Objective The research builds and examines a deep-learning framework that optimizes the management of smart community resources. The framework leverages long short-term memory (LSTM) networks for temporal data, convolutional neural networks (CNNs) for spatial analysis, and autoencoders for anomaly detection. The system focuses on two main objectives, which include better forecasting precision, optimum resource distribution, and efficient detection of operational problems. Methods Research validation employed data from the Amsterdam Open Data Platform and Singapore Government Open Data Portal joined by crowdsourced platforms FixMyStreet and OneService. The preprocessing phase involved three stages, i.e., cleaning and normalization and feature engineering steps, before model training and testing phases. Predictive models received assessment based on Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R². A comparison with traditional methods revealed the proposed approach delivered superior performance results. Results The deep learning framework demonstrated superior performance, achieving an average reduction of 18.7% in resource consumption and a 16.2% reduction in operational costs. The models outperformed baseline methods, with LSTMs achieving an MAE of 1.8 for water demand prediction and autoencoders detecting anomalies with an F1-score of 95.5%. Conclusion Due to its effective capabilities, the proposed framework solves challenges in resource management for smart communities while showing the potential of AI-driven solutions for sustainable urban development. Research results demonstrate that integrating sophisticated deep-learning methods yields more significant potential for optimizing resource utilization while improving operational effectiveness.

Reducing bias in coronary heart disease prediction using Smote-ENN and PCA

Coronary heart disease (CHD) is a major cardiovascular disorder that poses significant threats to global health and is increasingly affecting younger populations. Its treatment and prevention face challenges such as high costs, prolonged recovery periods, and limited efficacy of traditional methods. Additionally, the complexity of diagnostic indicators and the global shortage of medical professionals further complicate accurate diagnosis. This study employs machine learning techniques to analyze CHD-related pathogenic factors and proposes an efficient diagnostic and predictive framework. To address the data imbalance issue, SMOTE-ENN is utilized, and five machine learning algorithms—Decision Trees, KNN, SVM, XGBoost, and Random Forest—are applied for classification tasks. Principal Component Analysis (PCA) and Grid Search are used to optimize the models, with evaluation metrics including accuracy, precision, recall, F1-score, and AUC. According to the random forest model’s optimization experiment, the initial unbalanced data’s accuracy was 85.26%, and the F1-score was 12.58%. The accuracy increased to 92.16% and the F1-score reached 93.85% after using SMOTE-ENN for data balancing, which is an increase of 6.90% and 81.27%, respectively; the model accuracy increased to 97.91% and the F1-score increased to 97.88% after adding PCA feature dimensionality reduction processing, which is an increase of 5.75% and 4.03%, respectively, compared with the SMOTE-ENN stage. This indicates that combining data balancing and feature dimensionality reduction techniques significantly improves model accuracy and makes the random forest model the best model. This study provides an efficient diagnostic tool for CHD, alleviates the challenges posed by limited medical resources, and offers a scientific foundation for precise prevention and intervention strategies.

Daily briefing: Lithium supplements reverse Alzheimer’s symptoms in mice

1-4-2: Evaluation of applied mechanical power to individual lungs in a simulator-based setting of one ventilator for two patients

Background The concept of ventilating multiple patients concurrently using a single ventilator has been proposed as a solution when the demand for ventilators surpasses the available supply. While the practicality of this approach has been established, a thorough evaluation of the risks involved has yet to be comprehensively addressed. Methods Two circuits, a simple one (circuit-1) and another with an adjustable resistance valve (circuit-2), were evaluated within an experimental framework utilizing two computer-controlled lung simulators (TestChest and ASL 5000). These simulators were ventilated by an ICU (intensive care unit) ventilator (Servo-u) employing various ventilation modes (volume- and pressure-controlled ventilation). The study was conducted under differing respiratory conditions, characterized by low compliance (20 ml/cmH2O) as well as normal-high compliance (100 ml/cmH2O), in order to ascertain the applied tidal volume (VT), pressures, and the resultant mechanical power (MP). Results Circuit-1: The applied VT, pressures, and MP differed significantly between the two simulators, as well as in relation to ventilation mode, compliance, and respiratory rate (RR) (p < 0.001); the differences were most pronounced in settings with differing compliance levels. Circuit-2: Differences in VT, pressures, and MP were observed between simulators concerning valve settings (p < 0.001). The VT demonstrated a negative correlation, with volumes derived from valve closure spanning from 50 to 100 ml across all settings. In the design of circuit-2, MP exceeded the 12 J/min threshold in both lung simulators at elevated RR and could only be decreased through valve closure followed by a consequential hypoventilation in one simulator. Conclusion The simultaneous ventilation of two patients using a single ventilator is technically viable, yet it presents considerable risks. Even with the integration of an adjustable resistance valve to accommodate varying lung complexities, the likelihood of unilateral hypoventilation and elevated mechanical stress remains high.

Expanding the identification of key resource combinations for mid- to long-term growth in electric vehicle market entry

This study examines the key resource combinations influencing electric vehicle (EV) adoption, differentiating between short-term market entry and mid- to long-term growth, using the Technology-Organization-Environment (TOE) framework and the Resource-Based View (RBV). Analyzing 12 companies from 2012 to 2022, we find that firms with a well-balanced combination of technological capabilities, organizational strategies, and environmental adaptability achieve sustained market diffusion. A deficiency in any one of these areas hinders long-term success, and excellence in a single resource alone is insufficient. Moreover, the resource requirements for initial market entry differ from those needed for sustained growth, highlighting the need for firms to dynamically adjust their resource strategies based on evolving technological advancements, organizational capabilities, and regulatory and market conditions. Theoretically, this study integrates innovation diffusion theory, RBV, and TOE to offer a comprehensive perspective on EV market dynamics. From a managerial standpoint, companies must develop technological advancements, organizational capabilities, and environmental adaptability to ensure long-term success. Policy-wise, governments can accelerate EV adoption by implementing targeted infrastructure investments, standardizing digital and charging networks, and supporting sustainable innovation through incentives and regulatory frameworks.

These genes can have the opposite effects depending on which parent they came from

FCMI-YOLO: An efficient deep learning-based algorithm for real-time fire detection on edge devices

The rapid development of Internet of Things (IoT) technology and deep learning has propelled the deployment of vision-based fire detection algorithms on edge devices, significantly exacerbating the trade-off between accuracy and inference speed under hardware resource constraints. To address this issue, this paper proposes FCMI-YOLO, a real-time fire detection algorithm optimized for edge devices. Firstly, the FasterNext module is proposed to reduce computational cost and enhance detection precision through lightweight design. Secondly, the Cross-Scale Feature Fusion Module (CCFM) and the Mixed Local Channel Attention (MLCA) mechanism are incorporated into the neck network to improve detection performance for small fire targets and reduce resource consumption. Finally, the Inner-DIoU loss function is proposed to optimize bounding box regression. Experimental results on a custom fire dataset demonstrate that FCMI-YOLO increases mAP@50 by 1.5%, reduces parameters by 40%, and lowers GFLOPs to 28.9% of YOLOv5s, demonstrating its practical value for real-time fire detection in edge scenarios with limited computational resources. The core code and dataset are available at https://github.com/ JunJieLu20230823/code.git.

Factors associated with intubation and heated high-flow nasal cannula use in hospitalized respiratory syncytial virus infected children: A single-center retrospective cohort study

Background Respiratory syncytial virus (RSV) is a leading cause of severe lower respiratory tract illness (LRTI) in children, often requiring hospitalization and respiratory support. This study, therefore, aims to identify factors associated with intubation and heated high-flow nasal cannula (HHFNC) use in children hospitalized with RSV infection. Methods This retrospective study reviewed medical records of children aged 0 month to 15 years hospitalized with RSV infection at Chiang Mai University Hospital between January 2018 and December 2022. Baseline characteristics, clinical features, and laboratory findings were analyzed. Factors associated with intubation or HHFNC use were analyzed using univariable and multivariable logistic regression with significance set at p < 0.05. Result Among 260 children (53.8% male; median age 28 months, IQR 12–44), 76.5% required low-flow oxygen therapy, 11.5% required HHFNC, and 11.9% required intubation, respectively. Prematurity (22.7%) and respiratory comorbidities (17.6%) were common. HHFNC use was significantly associated with prematurity (adjusted odds ratio [aOR] 3.11, p = 0.016), chest retractions (aOR 5.42, p = 0.017), and multi-lobar infiltrates on chest X-ray (aOR 7.52, p < 0.001). Factors associated with intubation included age ≤ 2 years (aOR 3.70, p = 0.008), prematurity (aOR 5.68, p < 0.001), chest retractions (aOR 4.39, p = 0.033), and multi-lobar infiltrates (aOR 8.83, p < 0.001). Conclusions Prematurity, younger age, chest retractions, and multi-lobar infiltrates on chest X-ray were key predictors for HHFNC and intubation in RSV-infected children. These findings may inform risk stratification and management strategies for severe RSV-related illness in pediatric patients.

The peer-review crisis: how to fix an overloaded system

Safety assessment of Osilodrostat: The adverse event analysis based on FAERS database by means of disproportionality analysis

Background Osilodrostat is a medication recently approved for the treatment of Cushing’s syndrome. However, there is a current dearth of large-scale studies on the adverse events associated with Osilodrostat. Consequently, this study aims to comprehensively evaluate these adverse events using data from the FDA Adverse Event Reporting System (FAERS). Methods A disproportionality analysis was utilized to identify signals of adverse events linked to Osilodrostat. Furthermore, a Weibull distribution analysis was conducted to evaluate the temporal evolution of adverse events, and subgroup analyses were performed. The Wilcoxon test was applied to investigate differences in the temporal patterns of adverse events across different genders. Results A total of 1,078 cases related to Osilodrostat were identified, including 3,744 adverse events. The most frequent and severe signals of adverse events were investigations, off-label use, fatigue, nausea, and adrenal insufficiency. The median time to onset of adverse events related to Osilodrostat was 52 days after starting the medication. There was a gender difference in the median time to onset of adverse events, with a median of 15 days for males and 34 days for females. Conclusion This study provides a comprehensive evaluation of adverse events related to Osilodrostat, confirming some known side effects and revealing other potential risks. This information offers valuable insights for the clinical application of Osilodrostat.

Cytokines interferon−γ− inducible protein 10 and granulocyte−macrophage colony−stimulating factor are associated with psychiatric symptoms in opioid−dependent patients: A cross− sectional study

Background Psychiatric disorders and chronic hepatitis virus C infection are known to alter blood cytokines levels. However, little is known about the association between cytokines and psychiatric symptoms in patients with chronic hepatitis C virus infection. This study aimed at exploring this association. Moreover, since nearly half of the patients receive opioid maintenance treatment, we also investigated if long−term opioid treatment had any impact on these associations. Methods We conducted a cross−sectional study on 120 outpatients referred for antiviral hepatitis C treatment. Serum level of 27 cytokines was measured using multiplex technology, and psychiatric symptom clusters were assessed using the Symptoms Check−List−90−R. Data on confounding factors including age, gender, weight, height, current medication and smoking habits were collected. Multiple linear regression analysis was performed to examine associations, adjusting for confounding factors. Results After adjusting for the most commonly known confounding factors, IP−10 and GM−CSF were negatively associated with depression, and GM−CSF was negatively associated with phobic anxiety. Subgroup analyses revealed that these associations were present only in patients receiving opioid maintenance treatment, as demonstrated by repeated regression analysis. Conclusions In patients with chronic hepatitis C viral infection, only IP−10 and GM−CSF were negatively associated with self−reported psychiatric symptom clusters. These associations were observed exclusively in patients receiving opioid maintenance treatment. Our study contributes to others investigations pointing to a possible immune dampening caused by long−term opioid treatment.

Variability of test parameters from mice of different age groups in published data sets

The use of mice as animal models in biomedical research allows the standardization of genetic background, housing conditions as well as experimental protocols, which all affect phenotypic variability. In this study, the phenotypic variability of test parameters was analyzed in genetically identical mice of different age groups, i.e., early adults versus late adults. Therefore, published data sets of genetically identical mice of different age groups collected from the same investigator/ project were retrospectively analyzed. Morphological parameters, blood parameters and behavioral tests were analyzed which are predominantly used in biomedical research. The JaxKOMP project examined C57BL/6NJ mice with an age of 7–20 weeks and 66–81 weeks. Further substrains of C57BL/6N mice with an age of 8–16 weeks and 49–63 weeks were examined as wild-type controls from various investigators of the International Mouse Phenotyping Consortium (IMPC). Additional data sets of young and old groups of genetically identical mice were derived from the Mouse Phenome Database (MPD) and the RIKEN BioResource Research Center (RBRC). The phenotypic variability of the chosen traits and parameters was measured by calculating the coefficient of variation (CV = standard deviation/ mean) of the animals with the same sex of a given mouse strain. Subsequently, the CVs of the young and the old mouse group were compared. The comparison of the phenotypic variability of the late adults versus early adults revealed the appearance of unpredictable interactions between genotype, environment and experiment. Overall, a higher phenotypic variability of the late adults appeared almost consistently for body weight including lean mass and fat mass for females as well as for hematology and immunology parameters, particularly for females. Clinical chemistry often appeared inconspicuous. No noticeable differences were detected for the traits echocardiography and electrocardiogram, whereas late adults also often showed a higher phenotypic variability for behavioral tests.

Early identification of the efficacy of 0.125% atropine treatment for children with Myopia: A prospective pilot study

Purpose This study aimed to investigate whether early axial length (AL) changes in the short term after 0.125% atropine treatment could predict long-term axial elongation in children with myopia. Methods This was a prospective cohort study involving children aged 5–15 years with myopia who were treated with 0.125% atropine for myopia control. AL was measured 1–2 months after starting treatment and then every 3 months for follow-up visits. Regression analysis was used to develop a model of AL changes with time. A generalized estimating equation (GEE) model was then used to identify correlations between the early AL changes and long-term AL changes. Results Eighty eyes of 40 patients (mean age 8.4 years) were included in the final analysis. The estimation curve of AL changes with time indicated that the AL decreased at 67 days (the turning point in the regression model) after 0.125% atropine treatment and then increased gradually with time. Univariate GEE showed that a larger AL elongation in the initial 4 months was significantly associated with AL changes at 6 months (β = 0.354, P = 0.020, 6 ~ 12 months period from baseline) and 12 months (β = 0.560, P = 0.045, 6 ~ 18 months period from baseline) after that period in all myopic eyes. Conclusions The magnitude of AL elongation in the initial 4 months of 0.125% atropine treatment correlated positively with the further half-year and one-year AL changes. Identifying these changes may be useful for controlling refractory myopia in children.

Study on the effect of light distribution on the greenhouse environment in Chinese solar greenhouse

Solar greenhouse is a primary agricultural facility in northern China during winter, providing a certain level of security for the demand for vegetables and melons in the northern regions. However, there remains a lack of uniformity between crop requirements and the light and thermal environment within the planting area of the greenhouse, resulting in non-uniform growth and development of crops. The present study set out with the objective of investigating the impact of the light environment on the internal conditions of a solar greenhouse. To this end, experimental measurements were employed in conjunction with deep learning models. The results showed that rates of change in air temperature and light intensity were significantly higher in the vertical than the horizontal direction, especially below 1,800 metres, where significant differenced in temperature and light distribution existIn the horizontal direction, the impact of light distribution on soil temperature was significant within a range of less than 4,500 mm from the southern base of the greenhouse. By contrast, the impact was less pronounced within a range of 4,500 to 9,000 mm, In the temporal dimension, light variation significantly affected soil temperatures within 150 mm of the surface, but had no significant effect on temperatures within the 300–600 mm range. Similarly, light variation significantly affected temperatures within 200 mm of the inner wall surface, but had no significant effect on temperatures within the 400–800 mm range.Furthermore, vertical differences in light intensity significantly affected temperatures within the 800 mm height range from the indoor ground level, whereas the impact at other heights was less pronounced. The LSTM prediction model was highly accurate, and this study provided the necessary data and theoretical basis for regulating the light and temperature environments in solar greenhouse.

Improving pediatric care in Uganda with a digital platform and quality improvement initiative: A retrospective review of Smart Triage + QI

Objective This is a retrospective review of the feasibility study and implementation of the Smart Triage and Quality Improvement (QI) initiative at Holy Innocents Children’s Hospital (HICH), a dedicated pediatric hospital in Mbarara, Uganda, over a 5-year period. The aim of this QI initiative was to improve triaging rates and the time-to-antimicrobials in HICH’s outpatient department (OPD). Methods Smart Triage is a risk prediction algorithm and digital platform that enables healthcare workers to triage patients and track treatments effectively. Following the feasibility study, the QI program was implemented in September 2021 using three Plan-Do-Study-Act cycles: 1) Standardize Training, 2) Adjust Workflows, and 3) QI Team Communication. Data sources were triage and hospital reports. Monthly run charts of OPD attendance, acuity of illness, triaging rates, median-time-to-antimicrobials, and mortality rates of admitted patients were created. The trajectories of the variables were assessed using linear regression with time as the explanatory variable. Results 121,521 children attended HICH OPD from November 2018 to October 2023. The OPD triaging rate increased to 91% by October 2023, with a sustained plateau above 90% since July 2022. There was a significant reduction in the median time-to-antimicrobials during the 5-year period, from 77.6 to 53.6 minutes, with a slope of −0.4 minutes per month (CI: −0.73 to −0.04, p-value: 0.029). The inpatient mortality rate decreased from 5.1% in August 2018 to 2.6% in October 2023, with a significant increase in the number of cases with comparable illness severity. Conclusion The impact of Smart Triage was sustained beyond the end of the feasibility trial and showed sustained improvements in processes such as treatment times and clinical outcomes including a reduction in mortality. HICH’s leadership integrated a culture of QI across disciplines and departments, contributing to this initiative’s sustainability and impact.

Health-related quality of life and QALY loss under COVID-19 lockdown: The case of Spain

Objectives The COVID-19 pandemic forced many countries to implement confinement measures to limit the spread of the virus. Measuring the loss in terms of quality-adjusted life-years (QALYs) may provide a commensurable basis for comparing the impact of COVID-19. The aim of this research was to explore the impact of the first 21 days of COVID-19 lockdown on health-related quality of life (HRQoL) and associated QALY loss of the Spanish general population. Methods A quota-based online survey was conducted in four waves with 500 general population respondents each: one conducted shortly before the lockdown (baseline) and 3 follow-ups conducted weekly. HRQoL data were collected using EQ-5D-5L. For comparison with pre-covid responses, data from the 2011–2012 National Health Survey was taken as reference. Data were analyzed using frequency analysis and logistic regression. QALY loss was estimated over the follow-up period and for the entire duration of the lockdown. Results Comparing the baseline results to the follow-up results shows little change with respect to the distributions of reported problems in any of the 5 dimensions during the follow-up period. However, results for anxiety/depression show a 32% increase in the proportion of reported problems. The Spanish population was estimated to accrue a total of 1,994,216 QALYs over the study period. Based on the reference data, the population should have accrued 2,054,737 QALYs, leading to a loss of 60,520 QALYs over 21 days. For the entire lockdown, the corresponding loss would be 285,310 QALYs. Conclusions A population under a lockdown situation reported higher rates of anxiety/depression problems than in a regular situation. On a country-wide scope, this may lead to a substantial loss in terms of QALYs, especially over longer periods of time. This is the first study to directly assess the impact of the lockdown in terms of QALY loss on a country-wide level.

Data anomalies and the economic commitment of climate change

Robust skeletal motion tracking using temporal and spatial synchronization of two video streams

Accurate and reliable skeletal motion tracking is essential for rehabilitation monitoring, enabling objective assessment of patient progress and facilitating telerehabilitation applications. Traditional marker-based motion capture systems, while highly accurate, are costly and impractical for home rehabilitation, whereas marker-less methods often suffer from depth estimation errors and occlusions. Recent studies have explored various computer vision and deep learning approaches for human pose estimation, yet challenges remain in ensuring robust depth accuracy and tracking under occlusion conditions. This study proposes a three-dimensional human skeleton tracking system for upper limb activities that integrates temporal and spatial synchronization to improve depth estimation accuracy for rehabilitation exercises. The proposed system combines a 90° secondary camera to compensate for the depth prediction inaccuracies inherent in single-camera systems, reducing error margins by up to 0.4 m. In addition, a linear regression-based depth error correction model is implemented to refine depth coordinates, further improving tracking precision. The Kalman filtering framework is employed to enhance temporal consistency, allowing real-time interpolation of missing joint positions. Experimental results demonstrate that the proposed method significantly reduces depth estimation errors of the elbow and wrist joint (p < 0.001) compared to single camera setups, particularly in scenarios involving occlusions and non-frontal perspectives. This study provides a cost-effective and scalable solution for remote patient monitoring and motor function evaluation.

Reply to: Inconclusive proof of ferroelectricity in peptide-VDF ribbons