Search Results (16,775)

Objectives: Three stability-indicating analytical methods featuring outstanding sensitivity, selectivity, and precision were set up for the quantification of salicylamide (SAD) and ascorbic acid (ASC) in the presence of salicylic acid (SAL), which represents a possible impurity and degradation product of SAD. The aim was to develop sensitive, selective, precise, and eco-friendly assays appropriate for routine quality control of pharmaceuticals. Methods: Method (A) was a spectrophotometric technique of a successive derivative of ratio spectra built upon a two-step derivatization of ratio spectra utilizing double-distilled water as a solvent. SAD was quantified at 247.2 nm and 257.0 nm, and ASC at 251.8 and 259.8 nm, while SAL was quantified at 305.6 nm. Technique (B) relied on ratio spectra for the mean centering analytical process applied via two sequential stages, where the amplitudes derived after the second ratio spectra of the mean centering have been recorded on 291.0, 266.0, and 241.0 nm for SAD, ASC, and SAL, in that order. Method (C) involved TLC densitometric analysis, in which the separation was carried out upon plates of silica gel with chloroform–hexane–methanol–acetone–formic acid (5:3:2:1:0.2, in volumes) as a mobile phase, monitored by densitometric detection at 240 nm. The linear relationships were observed over concentration ranges of (0.2–2 µg/band) for SAD with ASC and (0.1–1 µg/band) for SAL. Validation of the presented techniques was performed in accordance with ICH strategies. Results: These developed techniques have been effectively analyzed for SAD with ASC in pharmaceutical dosage forms with non-interfering ingredients. A statistical comparison with the previously used HPLC technique revealed no considerable difference in terms of accuracy and precision. Greenness assessment using the AGREE platform produced scores of 0.72 for the spectrophotometric approach (benefiting from aqueous solvent) and 0.62 for HPTLC (limited by chloroform). Practical applicability (BAGI = 80 for both spectrophotometry and HPTLC) and overall quality indices (CACI = 83 for spectrophotometry; 80 for HPTLC) supported routine QC suitability. Conclusions: The three developed stability-indicating methods are accurate, precise, and selective for simultaneous assay of SAD and ASC in the presence of SAL and are suitable for quality control use. The spectrophotometric procedures combine high analytical performance with an improved environmental profile, while HPTLC offers comparable analytical reliability with slightly lower greenness due to organic solvent use. Full article

University orientation programmes are a primary mechanism through which new students become familiar with campus facilities, academic spaces, and institutional procedures. However, many orientation activities are delivered as single in-person sessions, limiting opportunities for students to revisit spatial and procedural information after the event. To help address this constraint, a digital twin-inspired metaverse orientation application, the Digital Twin Metaverse Orientation (DTMO), was designed in Unity and hosted on Spatial.io as a spatially faithful virtual replica of a faculty environment. An exploratory pilot evaluation was conducted with 30 university students from multiple faculties after a facilitator-guided orientation session. The System Usability Scale (SUS), Net Promoter Score (NPS), and two open-ended questions were used to examine perceived usability, recommendation intention, and the reasons underpinning recommendation decisions. The application obtained a mean SUS score of 86.83, corresponding to an excellent perceived-usability rating, and an NPS of 53.33, indicating positive immediate recommendation intention. Qualitative responses suggested that participants valued the DTMO for engagement, accessibility, ease of navigation, and support for spatial familiarisation, while some participants emphasised that it should complement rather than replace physical orientation. These pilot findings indicate promising user reception in a small, guided-session sample, but they do not establish orientation effectiveness, learning transfer, wayfinding performance, retention, belonging, institutional integration, or sustained use. Further research with broader samples and outcome-based measures is therefore needed. Full article

Objective: To evaluate the feasibility of adding antigravity treadmill training (ATT) or harness-based body-weight-supported treadmill training (BWSTT) to standard inpatient rehabilitation after primary hip or knee arthroplasty and to explore preliminary effects on osteoarthritis-related outcomes, balance, and psychological status. Methods: In this single-center, assessor-blinded pilot randomized trial, 60 adults within 3 months after primary hip or knee arthroplasty for osteoarthritis were allocated 1:1:1 to ATT, BWSTT, or standard inpatient rehabilitation over 6 weeks. Feasibility outcomes included recruitment, retention, and adherence. ATT and BWSTT additionally included unloading-based treadmill gait training using lower-body positive pressure or a harness system. Exploratory clinical outcomes included WOMAC total and subscale scores, analyzed using baseline-adjusted ANCOVA estimated marginal means. Secondary exploratory outcomes were BBS, FES-I, PHQ-9, and PSS-10. Results: Post-intervention data were available for 47 participants, with differential attrition across groups. Exploratory ANCOVA suggested between-group differences for WOMAC total (p = 0.004) and WOMAC function (p < 0.001). Compared with standard rehabilitation, ATT showed lower adjusted WOMAC total and function scores (both p < 0.01). ATT versus BWSTT contrasts for WOMAC total and function were statistically significant in the primary exploratory model but attenuated after hypertension adjustment. Exploratory signals were also observed for BBS and FES-I, although FES-I was less robust in sensitivity analysis. No clear between-group differences were observed for WOMAC pain, stiffness, PHQ-9, or PSS-10. No formal multiplicity adjustment was applied across exploratory endpoints. Conclusions: In this single-center pilot randomized trial, ATT suggested preliminary function- and balance-related signals that require confirmation in adequately powered multicenter trials. Full article

Background/Objectives: Foods and beverages consumed during school lunch periods contribute substantially to children’s dietary intake; however, Canadian evidence of their nutritional contribution and quality remains limited. This study assessed nutrient content, nutrient density, and contributions to dietary recommendations among Saskatchewan elementary students. Methods: A descriptive quantitative study was conducted among 379 students aged 5–13. Dietary intake during school lunch periods was assessed using a photography-assisted plate waste method. Nutrient content was estimated using standard nutrient databases, nutrient density was evaluated using the Nutrient-Rich Food (NRF) 9.3 Index, and contributions to dietary recommendations were examined. Results: Median lunch energy was 411.4 kcal (IQR: 296.7), and the mean NRF 9.3 score was 292.6 (SD: 130.7). Compared with home-packed and mixed lunches, school-provided lunches contained less energy, carbohydrate, fat, and sugar, while protein was similar across lunch types. Overall, lunches contributed <25% of daily requirements for key nutrients, including fibre, vitamin D, calcium, and potassium. Older students had lower proportional nutrient contributions relative to their higher nutritional requirements. Nutrient density differed by lunch provenance, but not by age or reported gender, with school-provided lunches achieving the highest NRF 9.3 scores. Conclusions: Lunches contributed modestly to daily nutrient requirements, particularly among older students. In this sample, school-provided lunches were associated with higher nutrient density than home-packed lunches, although their absolute contributions to several nutrients remained limited. These findings provide baseline evidence on lunches consumed during school lunch periods among Saskatchewan elementary students and may inform future evaluations of school food systems and policies. Full article

Millimeter-wave radar enables non-contact monitoring of cardiac activity and therefore has the potential to reconstruct electrocardiogram signals without surface electrodes. However, existing radar-based electrocardiogram reconstruction methods still suffer from incomplete extraction of heartbeat-related information and insufficient modeling of electrocardiogram-related features, which limits reconstruction accuracy. To address these issues, this study proposes a millimeter-wave radar-based electrocardiogram reconstruction method that integrates a respiratory-harmonic-suppressed multi-channel signal-processing frontend with the proposed CA-WTBNet deep reconstruction network. First, based on maximal overlap discrete wavelet transform-based multi-resolution analysis, respiratory harmonics mixed into heartbeat-related components are suppressed by combining respiratory harmonic detection with a heart-rate frequency protection strategy, while cardiac-related information is preserved as much as possible. A multi-channel input representation is then constructed. Meanwhile, the proposed deep reconstruction network is developed to jointly model complementary channel-wise features, local waveform morphology, and temporal dependencies by integrating channel-attention mechanisms, convolutional residual modules, window-based Transformer blocks, and bidirectional long short-term memory. Experiments conducted on the public dataset show that our method achieves an average Pearson correlation coefficient of 0.9641, a mean normalized root mean square error of 0.0458, an average R-peak F1 score of 0.9956, and an average R-peak timing error of 3.13 ms on the test set. In comparison with related studies on the same public Resting dataset, the proposed method achieves the best overall performance among the compared methods, with a 0.53% improvement in Pearson correlation coefficient and a 10.20% reduction in normalized root mean square error over the best-performing compared method. Full article

Background: Gastroparesis presents with frequently debilitating symptoms of nausea, vomiting, abdominal pain, bloating and early satiety, resulting in high healthcare utilization. Resilience, defined as the inherent and modifiable ability of an individual to adapt and recover positively to stress, is crucial for patients with chronic diseases but has not been studied in gastroparesis. We aimed to investigate if resilience correlates with acute care utilization and symptom severity in patients with gastroparesis. Methods: We conducted a single-center prospective observational study of patients with gastroparesis. Resilience was assessed using the 10-item Connor–Davidson Resilience scale (CD-RISC). Symptom severity was assessed through the Gastroparesis Cardinal Symptom Index (GCSI). Gastric emptying severity using scintigraphy or wireless motility capsule was categorized as mild, moderate, or severe based on consensus recommendations. Acute care utilization and hospitalizations in the last 12 months, comorbidities, medications, and demographic information were collected. Count outcomes were modeled using negative binomial regression due to overdispersion. Models were adjusted for age, sex, and symptom severity. Results: Among 40 consecutive patients (mean age 39 ± 16, 88% female), gastric emptying severity was mild in 35%, moderate in 15%, severe in 30%, and unknown in 20%. Mean resilience score was 29 ± 8 and mean GCSI was 2.96 ± 1.14. Gastroparesis symptoms did not correlate with gastric emptying severity (p = 0.5). In a linear regression model, no statistically significant correlation was observed between resilience and mean GCSI score in unadjusted or adjusted models. In negative binomial regression models, greater symptom severity was strongly associated with higher Emergency Department (ED)/urgent care visits (IRR 3.12; 95% CI 1.60–6.98; p < 0.001) and hospitalization rates (IRR 3.36; 95% CI 1.62–8.57, p = 0.006). Resilience was not a significant predictor of either (IRR 1.07; 0.95–1.22; p = 0.2 and IRR 1.02; 0.89–1.18; p = 0.7). Conclusions: Among patients with gastroparesis, no statistically significant association was detected between resilience and symptom severity, gastric emptying, or acute-care utilization after accounting for clinical and demographic factors. Symptom severity was the dominant predictor of ED visits and hospitalizations. These findings suggest that symptomatic disease burden, rather than objective gastric emptying severity, is the primary driver of acute healthcare utilization in this cohort. Full article

Background: Acute pain after video-assisted thoracoscopic surgery (VATS) promotes respiratory splinting, impaired cough, and pulmonary complications, and predicts persistent opioid use. Surgeon-administered intercostal nerve block (ICNB) is a simple regional technique, but its independent effect on early pain and opioid requirement in a contemporary uniportal VATS (UVATS) pathway is incompletely defined. Methods: We performed a retrospective cohort study of 456 consecutive patients undergoing UVATS at a single Israeli center between 2017 and 30 May 2025. Patients receiving an intercostal block were compared with those who did not. Baseline covariates were balanced by 1:1 nearest-neighbor propensity-score matching (caliper 0.2 SD of the logit propensity score). The primary endpoints were pain on postoperative day (POD) 1 (visual analog scale, VAS) and postoperative opioid use; secondary endpoints included later pain, analgesic regimen, postoperative pneumonia, and mortality. Results: Matching yielded 159 patients per group (n = 318) with all clinically relevant covariates balanced (standardized mean difference [SMD] < 0.13). Median POD1 VAS was lower with the block (4 [IQR 3–4] vs. 5 [5–7]; p < 0.001), and 76.1% of block patients were opioid-free versus 10.7% who were not (p < 0.001). The effect was concentrated early and attenuated by POD3. In multivariable analysis the block was independently associated with lower POD1 VAS (adjusted β = −1.64, 95% CI −2.00 to −1.29; p < 0.001). Postoperative pneumonia was less frequent in the block group (5.7% vs. 20.1%; p < 0.001). Thirty-day and one-year mortality did not differ significantly. Conclusions: In UVATS, a surgeon-placed intercostal nerve block was associated with lower early postoperative pain that persisted after adjustment for operating surgeon and surgical era, concordant with pooled meta-analytic estimates; associated reductions in opioid use and pneumonia were confounded with surgeon and secular trend and are hypothesis-generating. These single-center, retrospective findings require prospective, protocol-randomized confirmation. Full article

Background: Endocrine therapy for hormone receptor-positive breast cancer is associated with accelerated bone loss and increased fracture risk. Vertebral fractures (VFs) are frequently asymptomatic and remain underdiagnosed, potentially leading to underestimation of fracture risk. Methods: We conducted a cross-sectional real-world study that included 172 women with breast cancer (mean age 58.2 ± 12.0 years), the majority receiving aromatase inhibitors. Vertebral fractures were assessed using vertebral fracture assessment (VFA) during dual-energy X-ray absorptiometry (DXA). Bone mineral density (BMD), trabecular bone score (TBS), quantitative ultrasound (QUS), and FRAX^® scores were evaluated. Results: Vertebral fractures were identified in 13% of patients, and 78% of these occurred in women with normal or osteopenic BMD. Age was independently associated with VFs, while conventional densitometric and non-densitometric parameters showed limited discriminatory ability. The incorporation of VFA-detected fractures into FRAX significantly increased estimated fracture risk (hip fracture risk: 0.8% vs. 4.1%, p = 0.008). Conclusions: Vertebral fractures are common and frequently unrecognized in women receiving endocrine therapy and are not adequately captured by BMD. Routine use of VFA during DXA substantially improves fracture risk assessment and leads to a clinically meaningful reclassification of FRAX estimates. These findings support a more comprehensive approach to skeletal risk evaluation in this population. Full article

Background/Objectives: Sepsis detection relies on clinical variables and scoring systems assumed to perform uniformly across hospital settings. However, sepsis phenotype distributions shift between clinical environments, suggesting that variable importance may be setting dependent. This study aimed to quantify service-specific variability in the discriminatory capacity of clinical variables for sepsis detection and to evaluate whether this heterogeneity translates into differential performance of machine learning models compared to traditional clinical scoring systems. Methods: This stratified sub-analysis of the BIAlert Sepsis cohort (203,755 patients; 11,864 sepsis episodes, 2014–2018) evaluated 61 structured quantitative variables across nine hospital services (≥90 sepsis episodes each). Within each service, the Mann–Whitney–Wilcoxon test (p < 0.01, Holm-corrected) assessed differences between septic and non-septic episodes. Five machine learning models (Random Forest/BIAlert, XGBoost, CatBoost, SVM, Neural Network) and three clinical rules (NEWS, SIRS, qSOFA) were evaluated globally and stratified across four clinical environments. Results: The proportion of significant variables ranged from 95.1% in the Emergency Department (58/61) to 37.7% in the Intensive Care Unit (23/61). Lactate was the only universally significant variable (9/9 services). Clinical scoring systems collapsed in Critical Care (qSOFA and NEWS AUC 0.459). BIAlert maintained the highest AUC across all environments (0.975–0.857). The Friedman test confirmed significant differences (χ² = 28.00, p < 0.001), with BIAlert achieving a mean rank of 1.0. Conclusions: The discriminatory capacity of clinical variables for sepsis detection is not uniform across hospital services. ML models, particularly BIAlert, maintained robust performance where fixed-rule scoring systems failed. Full article

Temperament has been associated with reproductive success in beef cattle, with excitable animals often exhibiting reduced fertility. This study evaluated whether acclimating heifers to human handling during an ovulation synchronization protocol improves temperament and pregnancy rates to timed artificial insemination (TAI). A total of 622 Bos taurus yearling beef heifers across five locations and two breeding seasons (eight herd-year observations) were stratified according to reproductive maturity and temperament and were assigned to either acclimation (TRT; n = 307) or control (CTRL; n = 315). Acclimated heifers were moved through handling facilities without restraint prior to each protocol event (days 0, 7, and 10). Temperament was assessed using chute score (CS) and exit velocity (EV), and plasma cortisol was measured in a subset of animals. Acclimated heifers had lower CS on days 7 and 10 (p = 0.011 and p = 0.010, respectively) and greater pregnancy rates to TAI compared with control heifers (54.5% vs. 45.2%; p = 0.018). Exit velocity and cortisol concentrations did not differ between treatments (p ≥ 0.13). These results indicate that acclimation during handling events can improve behavioral responses and pregnancy rates to TAI with modest additional handling time (a mean of 17 s per heifer; no more than 18 min per location/day), providing a practical and scalable strategy for beef producers. Full article

Background: Laparoscopic cholecystectomy is the gold standard for gallbladder disease but is often associated with significant postoperative pain. Opioid analgesia is effective and is the mainstay of treatment. However, opioids are limited by multiple adverse effects such as nausea and respiratory depression. Esketamine is an NMDA receptor antagonist that has emerged as a potential analgesic adjunct. This systematic review and meta-analysis aims to evaluate the efficacy and safety of perioperative esketamine for patients undergoing laparoscopic cholecystectomy. Methods: We conducted a systematic review and meta-analysis of randomized controlled trials comparing perioperative esketamine with control regimens in adults undergoing laparoscopic cholecystectomy. Five databases were searched from inception to 25 August 2025. Random-effects meta-analyses were performed for pain, hemodynamic, recovery, and safety outcomes. Results: Six randomized controlled trials including 569 patients were eligible. Esketamine was associated with lower pain scores at rest and during movement, although neither were statistically significant. No significant clinical differences were observed in mean arterial pressure or heart rate changes during surgery or after surgery. However, esketamine significantly shortened wake-up time (MD = −3.55 min, 95% CI [−6.09 to −1.02]), improved postoperative sleep quality (MD = −5.78, 95% CI [−6.80 to −4.76]), and reduced PONV (RR = 0.47, 95% CI [0.24 to 0.92]) and respiratory depression (RR = 0.18, 95% CI [0.03 to 0.98]). Conclusions: Esketamine improved selected recovery and safety outcomes but did not significantly reduce hemodynamic parameters, postoperative pain or rescue analgesia. Larger high-quality trials are needed to confirm its role in laparoscopic cholecystectomy. Full article

Indoor Air Quality (IAQ) monitoring, especially in multi-zone smart buildings, is typically limited by the high computational and energy requirements of continuous sensor processing, which makes event-driven methods desirable for efficiency. Energy proportionality, in this context, refers to a system whose computational cost scales with the significance of detected environmental changes rather than with the fixed sampling rate. This paper presents a spike-driven neuromorphic sensing framework for decentralized IAQ monitoring that combines adaptive Kalman filter preprocessing, dynamic threshold-based asynchronous spike encoding, and a Leaky Integrate-and-Fire neural network with Spike-Timing-Dependent Plasticity (STDP) learning. Multiple-parameter IAQ data including PM₁, PM_2.5, PM₁₀, CO₂, CO, TVOCs, and O₃ were sampled from nine functionally differing zones of an educational building in Metro Manila, Philippines. The neuromorphic model yielded a mean Sparse Firing Ratio of 10.94%, a Mean Response Time of 10.62 timesteps, and an energy efficiency proxy score of 9.28. Neuron population scaling and parameter robustness analyses revealed that the four neurons per parameter were enough to saturate the performance, and FLOP-based estimation indicated an 8.9-fold computational reduction (approximately 89% fewer FLOPs) compared to LSTM inference. In addition, the revised Performance Efficiency Index and composite efficiency score corroborated the stable and energy-proportional nature of behavior in all zones. These results illustrate that spike-based neuromorphic computation is an energy-efficient and scalable way for decentralized smart-building IAQ monitoring, though hardware-level validation on dedicated neuromorphic processors remains necessary for absolute power saving verification. Multi-seed validation (five seeds) with expanded baselines including GRU, Temporal CNN, XGBoost, and Logistic Regression confirmed the robustness and repeatability of reported metrics. Full article

Real-time assessment of reading comprehension remains a challenge in educational research. Traditional evaluation methods, such as questionnaires, provide delayed and retrospective measures and therefore do not capture the dynamic nature of comprehension during reading. This exploratory study investigates whether beta-band electroencephalography (EEG) activity can be used to estimate EEG-derived indicators related to reading comprehension during academic reading. The study included 40 university students who read a conceptually demanding scientific text while EEG signals were continuously recorded. Beta-band activity (13–30 Hz) was extracted from six cognition-related channels and segmented into non-overlapping 2 s windows. Principal component analysis (PCA) was applied for dimensionality reduction, followed by kernel ridge regression (KRR) for prediction. At the window level, the proposed KRR–PCA framework achieved a mean absolute error (MAE) of 5.797, a root mean square error (RMSE) of 7.783, an MAE-based accuracy of 94.2%, and an explained variance of R² = 0.275 on a held-out test set. At the participant level, aggregated predictions showed a significant correlation with questionnaire-based comprehension scores (r = 0.59), indicating that EEG-derived features captured meaningful inter-individual differences. The framework also generated time-resolved prediction profiles that reflected fluctuations in EEG-derived comprehension estimates during reading. These findings suggest that beta-band EEG contains information related to reading comprehension and may support the development of future EEG-based educational monitoring systems. Further validation using larger cohorts and time-resolved comprehension measures is needed to confirm the practical applicability of the approach. Full article

Neighbourhood-scale sustainable urban mobility assessment requires analytical tools that evaluate walking, cycling, and public transport together rather than as separate modes. Existing studies often rely on single-mode indicators or aggregated urban-scale measures, which limit their ability to reveal micro-scale spatial inequalities and multimodal performance imbalances. This study addresses this gap by developing an integrated Geographic Information Systems (GIS)–Analytic Hierarchy Process (AHP)–correlation–clustering framework for six sample areas in Kayseri, Türkiye. The framework evaluates three main criteria—walkability, bikeability, and public transport accessibility—through ten sub-criteria. In addition, seven land-use and urban design variables are used to examine built environment relationships. A 100 × 100 m grid-based spatial database was created; criteria weights were determined using AHP; mobility scores were examined through correlation analysis; and spatial mobility typologies were identified using K-means clustering. The findings indicate that development density and land-use diversity support walkability. However, similar density patterns do not automatically improve cycling performance or public transport integration. The clustering results reveal persistent modal imbalances, even in areas with medium-to-high overall performance. The study demonstrates that density alone is insufficient for multimodal sustainability and offers an adaptable decision-support framework for context-sensitive neighbourhood planning. Full article

Background: Advances in monitoring systems featuring wearable sensors, computer vision, and artificial intelligence (AI) have been increasingly used in sports science and rehabilitation practices as a means of movement pattern analysis, injury prevention, and training optimization. These technologies are becoming essential components of athlete-performance analysis and rehabilitation-monitoring systems designed to support biomechanical assessment, athlete development, and movement-quality evaluation. Athlete-performance analysis and rehabilitation monitoring increasingly rely on intelligent multimodal sensing systems capable of continuously evaluating movement quality, biomechanical patterns, training execution, and recovery progress. Human activity recognition (HAR) serves as a key enabling technology for these applications by providing automated assessment of human movement using wearable and vision-based sensing modalities. Therefore, the purpose of this study was to develop and evaluate an attention-based multimodal framework that integrates wearable inertial sensing and RGB video analysis for robust athlete-performance assessment and rehabilitation monitoring through accurate recognition of human movement patterns. Methods: Athlete-performance analysis and rehabilitation monitoring combining inertial sensor data and RGB-based visual information was introduced. Inertial signals were segmented with adaptive windowing, whereas silhouette refinement was performed to analyze motion structures from visual inputs in support of athlete-performance analysis and rehabilitation monitoring. Temporal, spatial, and motion features such as trajectory, orientation, and skeleton-based space-time representations were calculated from multimodal inputs. The proposed framework was designed to capture complex movement dynamics associated with rehabilitation exercises and sports-related motion patterns across heterogeneous sensing environments. Extracted features were then combined and optimized with a multimodal feature fusion approach, while the Ranger optimization algorithm was utilized during the process. An attention-based deep learning classifier was implemented to classify movement activities. Results: The results showed that the proposed framework reached accuracy scores of 88.40% and 87.96% on the VIDIMU dataset and the UTD-MHAD dataset respectively. Recognition performance across both inertial and vision-based modalities provided greater robustness than single-modality solutions. The integration of wearable sensing and computer vision modalities further improved the ability of the framework to analyze complex movement behaviors under varying execution conditions and environmental variations. Conclusion: The proposed multimodal framework provides a foundation for intelligent athlete-performance and rehabilitation-monitoring systems by integrating wearable sensing, computer vision, and attention-based artificial intelligence for robust movement analysis. The findings highlight its potential to support biomechanical assessment, movement-quality evaluation, training-performance monitoring, rehabilitation tracking, and injury-risk management in modern sports and healthcare environments. Full article