Browse Articles

Children as controls in research; The voice of the volunteer

Background Modern paediatric healthcare is increasingly dependent on diagnostic methods such as imaging and blood samples. These methods require knowledge of physiology and anatomy to separate signs of early disease development from physiological variants. To acquire this knowledge, researchers depend on studies on children from the general populations, but little is known about how children and their guardians experience participation in studies. This study aims to map the experiences of children participating in research. Materials and methods From a cohort of children and guardians from the general population, participating as controls in a large study on juvenile idiopathic arthritis (NorJIA), a subset was invited to share their experiences. The participants were examined using blood sampling, MRI, x-ray, bone-density scan, anthropometric measurements, and questionnaires. A questionnaire of 26 questions regarding their experiences from the study was sent out 2–4 weeks after the data collection. Results The questionnaire was filled out by 50 children and 50 guardians. A large majority of the children responded that they were positive to help researchers, that blood sample procedures and imaging procedures went well, that they would participate in the study again, and would recommend their friends to take part in similar studies. Guardians generally responded positively, but there were diverging responses between children and guardians. Conclusion Children from the general population reported mainly positive experiences from participation in research, including imaging and blood sampling. The experiences of the children and their guardians are sometimes diverging, underpinning the importance of addressing the child’s opinions.

Developing a novel hybrid model based on GRU deep neural network and Whale optimization algorithm for precise forecasting of river’s streamflow

Role of CCK1 receptor in metabolic benefits of intestinal enteropeptidase inhibition in mice

Enteropeptidase (EP; enterokinase) is a serine protease that regulates intestinal protein digestion by converting trypsinogen into active trypsin, and thus initiates activation of the pancreatic zymogen cascade. Chronic inhibition of EP and trypsin (EP/T) with camostat (Foipan, FOY-305) or its active metabolite (FOY-251) causes weight loss in obese mice by reducing intestinal protein absorption and suppression of food intake, however, the mechanisms leading to appetite suppression are not well understood. We tested the hypothesis that cholecystokinin (CCK) signaling mediates the anorectic effects of EP/T inhibition using a CCK1R inhibitor (loxiglumide) or CCK1R knockout (KO) mice. Acute treatment with loxiglumide was able to partially reverse FOY-251-induced gallbladder contraction and delayed gastric emptying in mice. Chronic co-administration of loxiglumide reversed FOY-251 mediated effects on food intake and metabolism in diet-induced obese (DIO) mice. Chronic dosing of FOY-251 caused similar reductions in food intake but greater weight loss in CCK1R KO mice compared to wildtype (WT) mice, primarily due to fat mass loss. Pair fed (PF) groups revealed food intake-dependent and -independent mechanisms of weight loss by FOY-251. Notably, FOY-251 treatment induced sustained weight loss, whereas body weight loss rebounded in PF animals. In CCKR1 KO mice, FOY-251 caused greater weight loss, and increased protein calorie loss relative to that in WT mice, while having no effect on glycemic control or FGF21. Hence, CCK1R-dependent and -independent mechanisms modulate the metabolic effects of EP/T inhibition and may play a role in maintaining weight loss by this mechanism.

Multimodal deep learning for chemical toxicity prediction and management

Blockchain enabled privacy provisioning scheme for location based services in VANETs

In recent years, vehicular ad hoc networks (VANETs) have emerged as a crucial component of intelligent traffic systems, offering enhanced road safety through autonomous, distributed, and dynamically structured communication. However, ensuring secure and privacy-preserving message broadcasting in VANETs remains a significant challenge due to their open-access nature. Existing solutions have addressed various security and privacy concerns, yet critical issues such as resistance to traffic analysis, unlinkability of messages, computational efficiency, and location privacy remain underexplored. To bridge these gaps, we propose a blockchain-based privacy-preserving scheme that strengthens VANET security while addressing unobservability, unlinkability, and efficiency in authentication. Our approach leverages a cache-based anonymizer server positioned between the On-Board Unit (OBU) and the Roadside Unit (RSU), which enhances privacy by masking communication patterns and improves efficiency by reducing authentication overhead. Performance evaluations demonstrate that our scheme significantly reduces computational costs, achieving 95.17% to 97.00% reduction in V2V and 97.81% to 98.90% reduction in V2RSU communication time compared to referenced schemes. Additionally, our approach reduces communication cost by 67.94% to 81.67% for V2V and 72.40% to 88.00% for V2RSU, while the location leakage probability is minimized to 0.05% which is significantly lower than centralized architectures. Furthermore, our scheme ensures strong privacy protection, attaining a maximum entropy level of 5 which is 95.8% higher than existing schemes. These results confirm that our framework minimizes computational overhead, optimizes communication efficiency, and enhances privacy protection, making it a robust and scalable solution for VANET systems.

Presence of tumor DNA in aqueous humor is correlated with high risk uveal melanoma

Abstract Metastatic risk stratification is critical for uveal melanoma (UM) management, as approximately up to half of patients develop metastatic disease. Current prognostication for patients undergoing eye-preserving therapies relies on tumor staging and molecular analysis of tumor tissue obtained through potentially invasive biopsy, which can be challenging. While liquid biopsy using cell-free DNA (cfDNA) has emerged as a less invasive alternative for other cancers, studies have shown limited utility of blood-derived cfDNA in UM due to low tumor DNA fractions. This study investigates the potential of aqueous humor (AH) and vitreous body (VB) aspirates as alternative sources of tumor DNA for molecular prognostication in UM patients at the time of diagnosis. In this prospective study, AH and/or VB samples were collected from 96 consecutive UM patients undergoing enucleation, transretinal endoresection or transretinal biopsy. DNA was extracted from the ocular fluids and analyzed for the presence of tumor-derived DNA using deep amplicon sequencing targeting mutations in GNAQ and GNA11. This approach achieved an average read depth of 120,000, enabling highly sensitive detection of tumor-specific variants. Tumor DNA was detected in at least one ocular fluid (AH or VB) in 43 of 88 evaluable patients (49%), with variant allele fractions (VAFs) ranging from 0.3 to 50%. Of these positive cases, tumor DNA was identified in VB only in 22 patients, AH only in 5 patients, and both fluids in 16 patients. Importantly, tumor DNA in AH was almost exclusively observed in patients with monosomy 3 UM. No significant correlation was found between the presence of tumor DNA in either ocular fluid and primary tumor size or location. Liquid biopsy of AH and VB offers a promising, minimally invasive strategy for obtaining tumor DNA in nearly half of UM patients at diagnosis. The strong association between detectable tumor DNA in AH and monosomy 3 status warrants further investigation and may offer valuable insights into UM biology and dissemination mechanisms. This approach may improve risk stratification and inform personalized treatment strategies for patients with UM.

Social bonding between humans, animals, and robots: Dogs outperform AIBOs, their robotic replicas, as social companions

In the evolving landscape of technology, robots have emerged as social companions, prompting an investigation into social bonding between humans and robots. While human-animal interactions are well-studied, human-robot interactions (HRI) remain comparatively underexplored. Ethorobotics, a field of social robotic engineering based on ecology and ethology, suggests designing companion robots modeled on animal companions, which are simpler to emulate than humans. However, it is unclear whether these robots can match the social companionship provided by their original models. This study examined social bonding between humans and AIBOs, dog-inspired companion robots, compared to real dogs. Nineteen female participants engaged in 12 affiliative interactions with dogs and AIBOs across two counter-balanced, one-month bonding phases. Social bonding was assessed through urinary oxytocin (OXT) level change over an interaction, self-reported attachment using an adapted version of the Lexington Attachment to Pets Scale, and social companionship evaluations administering the Robot-Dog Questionnaire. To examine OXT level changes and self-reported attachment by comparing the two social companions, we conducted mixed-effects model analyses and planned follow-up comparisons. Frequency comparison, binary logistic regression, and thematic analysis were performed to analyze social companionship evaluations. Results revealed significant differences between dogs and AIBOs in fostering social bonds. OXT level change increased during interactions with dogs but decreased with AIBOs. Participants reported stronger attachment to dogs and rated them as better social companions. These findings highlight the current limitations of AIBOs in fostering social bonding immediately compared to dogs. Our study contributes to the growing HRI research by demonstrating an existing gap between AIBOs and dogs as social companions. It highlights the need for further investigation to understand the complexities of social bonding with companion robots, which is essential to implement successful applications for social robots in diverse domains such as the elderly and health care, education, and entertainment.

Publisher Correction: Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases

Evaluation of an evidence-based practice continuing education course for Canadian Naturopathic Doctors

Background Evidence-based practice (EBP) combines the best available evidence with clinician expertise and patient preference to improve patient outcomes. Recent evidence indicates that Canadian Naturopathic Doctors (NDs) are interested in EBP skill development. The primary objective of the present study was to assess the feasibility and acceptability of a co-designed EBP Continuing Education (CE) course for Canadian NDs. Secondary objectives included assessing changes in EBP skill, use, attitudes, and knowledge. Methods The CE course was evaluated using a pre-post design involving licensed Canadian NDs. The CE course consisted of five weekly, one-hour sessions that were delivered virtually. On course completion, participants reported on their level of satisfaction and provided suggestions for improvement. EBP skill, attitudes and use were assessed using the validated Evidence-Based Practice Attitudes and Utilization Survey. EBP knowledge was objectively assessed using a quiz. Changes in EBP skill, attitudes, use and knowledge were compared between baseline and the end of the course. Use of evidence was reassessed at a 2-month follow up. Results Sixty-one NDs met eligibility criteria. Eighty-nine percent of participants agreed or strongly agreed that they were satisfied with the course. There was a significant increase in self-reported skill and objectively measured EBP knowledge, but no substantive change in EBP attitudes or use of evidence over time. Some participants indicated the level of difficulty was too high while others reported that it could have been more difficult. Participants also wanted more opportunities to practice the skills being taught in the course. Conclusions Delivery of the co-designed EBP CE course was found to be both feasible and acceptable. Preliminary evidence suggests that participation in the course was associated with improvements in EBP knowledge and skill. Participants provided actionable suggestions to improve the course in future iterations.

Supervised optimal control in complex continuous systems with trajectory imitation and reinforcement learning

A dynamic early-warning method for bridge structural safety based on data reconstruction and depth prediction

The structural response of bridges involves a complex interplay of various coupled effects, rendering the identification of long-term variation trends inherently challenging. Consequently, effectively detecting and alerting abnormal monitoring data for bridge structures under complex coupled loads remains a significant difficulty. To address this issue, this study proposes a dynamic early-warning method for bridge structural safety, leveraging data reconstruction and deep learning-based prediction. First, the singular value decomposition (SVD) algorithm is employed to decompose and reconstruct the monitoring data based on the contribution rate of influencing factors, thereby decoupling the data from various coupled effects. Second, a deep learning architecture utilizing a long short-term memory (LSTM) network is applied to establish a prediction model for each group of decomposed monitoring data, significantly enhancing prediction accuracy. Building on this foundation, the dynamic early-warning system for bridge structural safety is realized by integrating anomaly diagnosis theory with both predicted and measured data. A validation case using measured strain data demonstrates that the proposed method accurately predicts bridge strain data and calculates real-time adaptive thresholds, enabling real-time detection of anomalous monitoring data.

Phase-specific determinants of 100 m freestyle performance in elite swimmers

Perish the thawed? EDTA reduces DNA degradation during extraction from frozen tissue

Cryopreservation is the gold standard for preserving high molecular weight (HMW) DNA (>10 kb) in tissue samples. However, frozen tissues are typically thawed either before or during DNA extraction, which can lead to substantial DNA degradation. In this study, we thawed the previously frozen tissues of 10 marine species (five fishes and five invertebrates) in the preservatives EDTA (250 mM, pH 10) or ethanol (EtOH, 95%) and maintained them in their respective preservatives overnight at 4°C before DNA extraction. We then compared the recovery of HMW DNA in these extracts to extracts prepared directly from frozen tissues. To evaluate the effect of these treatments on HMW DNA recovery, we determined the percentage of high molecular weight DNA (%HMW) and yield of HMW DNA normalized by tissue weight (nY) in each DNA extract. The average %HMW values for eight of the 10 species and the average nY values for five of the 10 species were significantly higher in extracts from EDTA-treated tissues compared to extracts from untreated frozen tissues. For all 10 species, we observed no significant decreases in average %HMW or nY values in extracts of EDTA-thawed tissues compared to those extracted directly from frozen tissues. In contrast, EtOH treatment did not significantly improve the average %HMW or nY values in extracts from tissues of nine of the 10 species when compared to extracts prepared directly from frozen tissues. Therefore, investigators may consider EDTA treatment as a simple method for improving HMW DNA recovery from frozen tissues.

Research on the dynamic pressure influence and support engineering strategy of roadway group under multi section collaborative mining conditions

A physician-pharmacist partnership intervention for deprescribing (P3iD) among older adults attending a falls and syncope clinic: Protocol for a randomised controlled trial

Background The concept of deprescribing is gaining traction among clinicians as a formalized approach to improving medication safety for older persons. It has been found to be safe and effective in reducing medication burden. However, its implementation remains challenging. Most research has been conducted in high-income countries, with limited prospective data on deprescribing outcomes in outpatient care settings for older adults in low- and middle-income countries (LMICs). Therefore, evaluating local deprescribing interventions is essential to produce evidence on their effectiveness in older populations. Our study aimed to assess the effectiveness of the Physician-Pharmacist Partnership Intervention for Deprescribing (P3ID) among older persons attending a falls and syncope clinic. Methods This randomised controlled trial will be conducted at a teaching hospital in Kuala Lumpur, Malaysia. Participants will involve individuals aged ≥60 years with at least one chronic disease requiring pharmacological treatment, attending the falls and syncope clinic with ≥1 potentially inappropriate medication (PIM) undergoing systematic multidomain assessment and attending physicians at the clinic. The joint pharmacist-physician intervention comprises five steps: 1) PIM identification, 2) decision on cessation and prioritisation, 3) medication withdrawal, 4) monitoring and support, 5) and documentation. Conclusion The P3ID trial tests the hypothesis that a jointly led pharmacist-physician intervention in an outpatient will reduce the total number of medications, improve medication adherence, reduce falls and improve patients’ and doctors’ satisfaction towards pharmacist services. Findings from this study would inform future deprescribing practices, particularly in LMIC, pertaining to fall prevention as well as aid the development of future deprescribing interventions in other settings.

High CD36 expression in the tumor microenvironmental vasculature correlates with unfavorable overall survival in high grade serous ovarian cancer

Evaluation of pre-analytical specimen rejection using Six Sigma metrics: A retrospective single-center study

Background Up to 60% of errors occur in the pre-analytical stage of laboratory testing, potentially impairing clinical decision-making. This study aimed to assess pre-analytical errors using Six Sigma metrics and identify underlying causes for quality improvement. Methods A retrospective analysis of pre-analytical sample errors was conducted over three years in a clinical laboratory. Errors were categorized, and Sigma values were calculated to assess quality. Trends over time were also analyzed. Results Of 2,068,074 samples, 2,214 (0.107%) were rejected. The top errors were clotted blood specimens (67.34%), insufficient volumes (8.22%), and cancelled test requests (6.28%), with Sigma values of 4.42, 5.25, and 5.32, respectively. The outpatient department performed best (Sigma = 5.47), while other wards required improvement. Conclusion Efforts are needed to reduce specimen rejection, particularly clotted samples, to enhance laboratory quality.

Effect of selenium nanoparticles on intestinal immunity through regulation of NLRP3 signaling pathway

Effects of nutritional interventions on nutritional and immunological status and adherence to antiretroviral treatment among adults living with HIV in low- and middle-income countries: Systematic review and meta-analysis

Background HIV/AIDS may cause malnutrition, both directly and indirectly, through common infections. This systematic review and meta-analysis aim to evaluate the effects of nutritional interventions on nutritional status, immunological status, adherence to antiretroviral treatment (ART), and food security among people living with HIV/AIDS (PLWHA) in low- and middle-income countries (LMICs). Method Five databases—MEDLINE, Embase, Scopus, Web of Science, and CENTRAL—were searched for articles on August 17, 2021, with an updated search conducted on September 30, 2023 to identify new records. Studies were considered eligible if they included adults living with HIV/AIDS who recently initiated ART, if they were controlled trials that provided nutritional interventions, and if they assessed the relevant nutritional, immunological and adherence outcomes. The effects of nutritional interventions were analyzed using a random-effects model. Results The systematic review comprised 22 articles from 12 LMICs, while the meta-analysis included 19 articles. The interventions provided lipid-based nutrient supplements, corn–soy blends, food baskets, conditional cash, prepared meals, micronutrient supplementation, and functional foods to PLWHA. Compared to controls, nutritional interventions for PLWHA significantly improved their body mass index (standardized mean difference, 95% confidence interval) (SMD 0.42; 95% CI: 0.03, 0.81; p =  0.03), fat mass (SMD 0.21; 95% CI: 0.07, 0.34; p =  0.002), fat-free mass (SMD 0.33; 95% CI: 0.19, 0.46; p <  0.0001), and CD4 (SMD 0.54; 95% CI: 0.01, 1.07; p =  0.05), but had no effect on their weight, viral load, or adherence to ART. The baseline nutritional and immunological characteristics of PLWHA, as well as the intervention characteristics, further modified these effects. Conclusion Nutritional interventions improved some nutritional and immunological indicators but not ART adherence among PLWHA. Additionally, their effects were modified by some baseline characteristics and the type and duration of interventions which require consideration before its scaling up.

MULTICAUSENET temporal attention for multimodal emotion cause pair extraction

Abstract In the realm of emotion recognition, understanding the intricate relationships between emotions and their underlying causes remains a significant challenge. This paper presents MultiCauseNet, a novel framework designed to effectively extract emotion-cause pairs by leveraging multimodal data, including text, audio, and video. The proposed approach integrates advanced multimodal feature extraction techniques with attention mechanisms to enhance the understanding of emotional contexts. The key text, audio, and video features are extracted using BERT, Wav2Vec, and Vision transformers (ViTs), which are then employed to construct a comprehensive multimodal graph. The graph encodes the relationships between emotions and potential causes, and Graph Attention Networks (GATs) are used to weigh and prioritize relevant features across the modalities. To further improve performance, Transformers are employed to model intra-modal and inter-modal dependencies through self-attention and cross-attention mechanisms. This enables a more robust multimodal information fusion, capturing the global context of emotional interactions. This dynamic attention mechanism enables MultiCauseNet to capture complex interactions between emotional triggers and causes, improving extraction accuracy. Experiments on emotion benchmark datasets, including IEMOCAP and MELD achieved a WFI score of 73.02 and 53.67 respectively. The results for cause pair analysis are evaluated on ECF and ConvECPE with a Cause recognition F1 score of 65.12 and 84.51, and a Pair extraction F1 score of 55.12 and 51.34.