Search Results (333)

Previous studies have shown that plasma amyloid-beta oligomers (AβOs), the toxic form of amyloid-beta (Aβ), are a critical issue in the development or worsening of Alzheimer’s disease (AD) and can be regarded as a blood marker for screening in dementia. We examined plasma AβOs with their related biomarkers in a case–control study to clarify these issues. A total of 16 patients diagnosed with Alzheimer’s dementia (AD) and 16 cognitively normal controls (NCs) were recruited to compare their plasma biomarkers, AβO, Aβ_1-40, and Aβ_1-42, also referring to other parameters like APOE ε4 status, Clinical Dementia Rating^®-Sum of Boxes (CDR^®-SB), and Mini Mental Status Examination (MMSE) scores. In plasma concentrations of Aβ_1-40, Aβ_1-42, and AβO, the mean concentrations were significantly different between the two groups. There is a significant increase in the concentrations of Aβ_1-40 and AβO, while Aβ_1-42 is decreased in individuals with AD compared to NC. AβO was statistically associated with the Aβ_1-40 and Aβ_1-42/Aβ_1-40 ratio. Higher plasma concentrations of AβO were significantly associated with AD compared to non-dementia controls. This suggests that AβOs can be potential plasma biomarkers to screen in AD. However, a study recruiting more individuals is necessary to examine the association, if any. Full article

Introduction: “The dog is a man’s best friend” and research has showed that this idea is extended beyond the degree of loyalty. Dog ownership has been linked to several positive health outcomes for the owner. The aim of the present cross-sectional case–control study was to assess differences in the physical activity level (PAL), body composition, quality of life (QoL), and diet quality and dietary knowledge between dog owners and non-owners. Methods: A total of 55 dog owners and an equal amount of non-dog owners (all aged between 18 and 60 years old) formed the case and control groups, respectively. Basic anthropometric measurements were performed, including body fat (BF) and diet, assessed with the Mediterranean Diet Score (MedDietScore) and the Eating Assessment Table (EAT). Physical activity was recorded for 3 consecutive days using activity monitors. QoL was evaluated using the brief version of the World Health Organization QoL (WHOQOL-BREF) tool. Results: The two groups demonstrated a similar PAL, but lower BF% (p = 0.009), hip circumference (p < 0.001), triceps (p = 0.012), and subscapular skinfolds (p = 0.003) were recorded among dog owners. The EAT score was greater among dog owners (p = 0.0023), indicating improved dietary intake and knowledge, even after adjustment for education attained and BMI (p = 0.026). On the other hand, greater adherence to the Mediterranean diet was exhibited among those not having dogs (p = 0.018). Regarding dog measurements and their owners’ anthropometry, dog neck circumference was negatively correlated to the owners’ biceps and triceps skinfolds (r = −0.327, p = 0.016; r = −0.320, p = 0.018, respectively). Additionally, dog breed size was negatively correlated to the owners’ triceps skinfold (r = −0.325, p = 0.015), sum of skinfolds (r = −0.311, p = 0.021), hip circumference (r = −0.341, p = 0.011), body fat (r = −0.357, p = 0.007), and fat mass index (r = −0.307, p = 0.023). Conclusions: Dog ownership is associated with improved body composition and smaller skinfold thickness at specific body sites, as well as with a more health-conscious lifestyle, including better diet quality and knowledge. Full article

The evaluation of myocardial perfusion imaging (MPI) studies is based on the visual interpretation of the reconstructed images, while the measurements obtained through software packages may contribute to the investigation, mainly in cases of ambiguous scintigraphic findings. We aimed to investigate the long-term prognostic value of expert reading of Summed Stress Score (SSS), Summed Rest Score (SRS), and Summed Difference Score (SDS), combined with the automated measurements of these parameters, in comparison to the prognostic ability of the angiographic score for soft and hard cardiac events. The study was conducted at the Nuclear Medicine Laboratory of the University of Thessaly, in Larissa, Greece. Overall, 378 consecutive patients with known or suspected coronary artery disease (CAD) were enrolled. Automated measurements of SSS, SRS, and SDS were obtained using the Emory Cardiac Toolbox, Myovation, and Quantitative Perfusion SPECT software packages. Coronary angiographies were scored according to a four-point scoring system (angiographic score). Follow-up data were recorded after phone contact, as well as through review of hospital records. All participants were followed up for at least 36 months. Soft and hard cardiac events were recorded in 31.7% and 11.6% of the sample, respectively, while any cardiac event was recorded in 36.5%. For hard cardiac events, the prognostic value of expert scoring, combined with the prognostic value of the automated measurements, was significantly greater compared to the prognostic ability of the angiographic score (p < 0.001). As far as any cardiac event, the prognostic value of expert scoring, combined with the prognostic value of the automated analyses, was significantly greater compared to the prognostic ability of the angiographic score (p < 0.001). According to our results, in patients with known or suspected CAD, the combination of expert reading and automated measurements of SSS, SRS, and SDS shows a superior prognostic ability in comparison to the angiographic score. Full article

This study investigated whether supplying rumen-protected lysine (RPL), alone or in combination with tannins, could modify the fatty acid (FA) profile, physicochemical characteristics, carcass traits, and sensory attributes of lamb meat. Forty Santa Inês × Dorper lambs (≈23 kg, 4 months old) were assigned to four dietary treatments for 55 days: a control diet, free lysine (0.44%), RPL microencapsulated in a carnauba-wax matrix, and RPL + tannins blend (1.34%). Feed intake, carcass weight, and quantitative carcass measurements did not differ among treatments (p > 0.05). Likewise, pH, color, proximate composition, water-holding capacity, cooking losses, and shear force remained unchanged. Dietary supplementation influenced the FA composition of the meat. RPL, especially when added with tannins, increased concentrations of conjugated linoleic acid (C18:2 cis–9, trans–11), eicosapentaenoic (C20:5 n–3), and docosahexaenoic acids (C22:6 n–3), improving the n–6:n–3 ratio (p < 0.05). The sum and ratio of other FA and cardiometabolic indices were not altered. Lipid oxidation was reduced in RPL treatments, indicating enhanced oxidative stability. Sensory attributes scores were not affected (p > 0.05), ranging from “liked slightly” to “liked very much”. RPL, particularly when combined with tannins, improved specific health-related FA without adversely affecting carcass characteristics or consumer acceptance. Full article

Food recommender systems are pivotal in helping people make optimal dietary choices based on tremendous amounts of data. Extant studies offer different methods and techniques, but the combination of similarity search, large language models (LLMs), and multi-criteria decision-making (MCDM) remains underexplored. This study proposes a new system that leverages all three. First, we utilize an LLM to suggest queries from the same domain as the dish database. Then, the queries are vectorized and used for similarity search to generate a preliminary list of suggested menu items. Next, multiple LLMs provide scores for each item, which become the MCDM inputs, where Lin’s concordance correlation coefficient (LCCC) enhances the weighted sum scalarization technique. We evaluated the prototype on three publicly available dish datasets and at classification thresholds of 0.25, 0.50, and 0.75, and the proposed domain-adaptation approach consistently outperformed the baseline query. For example, at the 0.50 threshold, precision ranged from 49.11% to 56.60%, compared with 35.40% for the baseline. Furthermore, aggregating multiple LLMs mitigates single-model bias in recommendations. To substantiate this, a bootstrap evaluation of the proposed LCCC-based consensus weighting confirms that both the estimated weights and the induced rankings are numerically stable under sampling perturbations. To further ensure the robustness and reliability of the proposed system, we validate the results against other established weighting schemes and state-of-the-art MCDM methods. Moreover, Kendall’s $\tau$-based comparisons across weighting schemes and multiple MCDM methods confirm that the proposed LCCC-based framework produces highly consistent and statistically significant rankings, demonstrating strong robustness to methodological choices. This paper contributes a system architecture and design that can be adopted for other domains of recommender systems where the capability of multiple LLMs can benefit complex and multifaceted decision-making processes. Full article

Background: Accurate confirmation of nasogastric (NG) tubes is essential for patient safety, but delays and variability in interpretation remain common in clinical practice. Deep learning (DL) models have shown potential for assisting in this task, but real-world performance, particularly in detecting malpositioned tubes, remains insufficiently characterized. Methods: We conducted a pilot evaluation of a previously developed DL model using 135 chest radiographs from Kangwon National University Hospital. Expert physicians established the reference standard. Model performance was assessed and receiver operating characteristic (ROC) curve and precision recall curve (PRC) analyses were performed. Differences between correctly classified and misclassified cases were examined using Wilcoxon rank-sum and Fisher’s exact tests to explore potential clinical or radiographic contributors to model failure. Results: The model correctly classified 129 of 135 cases. The sensitivity was 96.1% (95% confidence interval (CI): 92.2–98.9%), specificity was 85.7% (95% CI: 42.2–97.7%), positive predictive value (PPV) was 99.2% (95% CI: 96.1–99.9%), negative predictive value (NPV) was 54.5% (95% CI: 25.4–80.8%), balanced accuracy was 90.8%, and F1-score was 0.976. The area under the ROC curve was 0.970 (95% CI: 0.929–1.000) and that under the PRC was 0.727 (95% CI: 0.289–1.000), reflecting substantial uncertainty related to the very small number of incomplete cases (n = 6). No statistically significant differences in clinical or radiographic characteristics were observed between correctly classified and misclassified cases. Conclusions: The DL model performed well in identifying correctly positioned NG tubes but demonstrated limited and unstable performance for detecting incomplete placements. Given the safety implications of misclassification, the model should be used only as an assistive tool with mandatory physician oversight. Larger, multi-center studies with greater representation of incomplete cases are required to obtain more reliable estimates and support safe clinical implementation. Full article

Quantitative vascular analysis involves the measurements of arterial tortuosity and branch angle in a region of interest in cerebral arteries to assess vascular risks associated with cerebral aneurysm. The measurements themselves are not a simple process since they are made on the three-dimensional (3D) structures of the arteries. The aim of this study was to develop a deep convolutional neural network (CNN) model to predict a probability score of aneurysm without direct measurements of the artery’s geometry. A total of 204 subjects’ image data were considered. In all, 585 gray-scale three-dimensional (3D) patches with the bifurcations near the center of the patches were extracted and labeled as either an aneurysm or a non-aneurysm class. Three-dimensional CNN architectures were developed and validated for the binary classification of the 3D patches. Accuracy, precision, recall, F1-score, receiver operating characteristics area under the curve (ROC-AUC), and precision recall AUC (PR-AUC) were calculated for test data. Deep learning predictions were compared with vessel geometry measurements. Deep learning probability scores were dichotomized into high-score and low-score groups. For both groups, bifurcation angles and sum-of-angles-metric (SOAM) were calculated and compared. ResNetV2_18 with translation as data augmentation achieved the highest mean ROC-AUC (0.735) and PR-AUC (0.472). The independent t-test indicated that for the bifurcation angle sum feature there was a statistically significant difference (t = −2.280, p-value < 0.05) between the low-score and the high-score groups. In conclusion, we have demonstrated a deep learning-based approach to the prediction of aneurysmal risks in the bifurcation regions of interest. Deep learning predictions were associated with vessel geometry measurements. This suggests that deep learning on 3D patches centered around the bifurcations has the potential to screen bifurcations with a high aneurysm risk. Full article

Operation and maintenance (O&M) events resulting from environmental factors (e.g., precipitation, temperature, seasonality, and unexpected weather conditions) are among the primary sources of operating costs and downtime in run-of-river small hydropower plants (SHPs). This paper presents a data-driven methodology for predicting such long events using machine learning models trained on historical power production, weather radar, and forecast data. Case studies on two Slovenian SHPs with different structural designs and levels of automation demonstrate how environmental features—such as day of year, rain duration, cumulative amount of rain, and rolling precipitation sums—can be used to forecast long events or shutdowns. The proposed approach integrates probabilistic classification outputs with threshold-consistency smoothing to reduce noise and stabilize predictions. Several algorithms were tested—including Logistic Regression, Support Vector Machine (SVM), Random Forest, Gradient Boosting, and k-Nearest Neighbors (k-NN)—across varying feature combinations for O&M model development, with cross-validation ensuring robust evaluation. The models achieved an F1-score of up to 0.58 in SHP1 (k-NN), showing strong seasonality dependence, and up to 0.68 in SHP2 (Gradient Boosting). For SHP1, the best model (k-NN) correctly detected 36 long events, while 15 were misclassified as no events and 38 false alarms were produced. For SHP2, the best model (Gradient Boosting) correctly detected 69 long events, misclassified 23 as no events, and produced 42 false alarms. The findings highlight that probabilistic machine learning-based forecasting can effectively support predictive O&M planning, particularly for manually operated or service-operated SHPs. Full article

Aims: To describe lung ultrasound (LUS) features of Mycoplasma pneumoniae pneumonia and their distribution in pediatric age, and to correlate imaging findings with clinical and laboratory data. Methods: This is a multicenter, prospective, pilot study that involved three hospitals. In total, 35 patients aged 1 month to 17 years, admitted with a diagnosis of Mycoplasma pneumoniae infection, were enrolled. History, clinical, microbiological, and ultrasound data were collected. The LUS examination was performed at admission, recording the following features: presence of subpleural consolidation, bronchograms, B lines, or pleural effusion, and their characteristics. The scans were performed using a standardized approach, in which a composite score was obtained by summing the scores of the different parameters. Results: Consolidations were seen in 97% of children (mostly located in basal, posterior, and lateral fields), and 65% of patients had multiple ones. Non-perilesional B lines were found in 43% of cases, principally in the posterior and basal fields. Pleural effusion was found in 37% of children. The univariate logistic regression showed a correlation between the age of the patient and large-sized consolidations. Moreover, increased lymphocyte count was associated with a lower risk of large-sized consolidations. Conclusions: LUS is a low-cost, non-invasive tool that can reveal findings suggestive of Mycoplasma pneumoniae infection and help physicians better manage children with lower respiratory tract infections, supporting a more personalized diagnostic and therapeutic approach, including antibiotic selection. These preliminary findings also indicate that a larger, comparative study involving other bacterial and viral etiologic agents is warranted to confirm whether LUS patterns are pathogen-specific and whether they can predict clinical outcomes. Full article

Background/Objectives: Low back pain (LBP) is the most prevalent musculoskeletal condition, significantly impacting quality of life and incurring high social costs. Although non-specific (without anatomical abnormalities) LBP accounts for nearly 80% of cases, LBP due to adult spinal deformities (ASDs), including scoliosis, remains a major concern. Several patient-reported outcome measures (PROMs)—notably the Oswestry Disability Index (ODI), Scoliosis Research Society-22 questionnaire (SRS-22), and Core Outcome Measure Instrument (COMI)—are recommended for assessment in these populations. This study aims to verify if these PROMs can effectively distinguish between adults with scoliosis-associated LBP (SLBP) and those with non-specific LBP (LBP). Methods: subjects were categorised as either having idiopathic/degenerative scoliosis (>10° Cobb angle in the coronal plane) with LBP, or non-specific LBP. Statistical comparisons applied non-parametric tests (Wilcoxon rank-sum, Mood’s median, chi-square), Spearman’s correlation, and generalised linear regression analyses. Results: Among 1092 subjects (552 SLBP; 540 LBP), median ODI scores were similar between groups, while SRS-22 scores were modestly higher in the SLBP cohort. Females consistently reported higher ODI and lower SRS-22 scores. Significant correlations arose between ODI and COMI, with moderate inverse associations with SRS-22. Regression analysis demonstrated that pathology group, gender, age, and BMI weakly predicted PROM scores. Conclusions: ODI and SRS-22 perform comparably in assessing disability in adults with LBP regardless of scoliosis, suggesting they cannot discriminate different pathologies. These findings underscore the importance of employing multiple PROMs to capture clinical dimensions. Full article

Photoplethysmography (PPG) is widely used in health monitoring, but its reliability is often compromised by artifacts, limiting accurate peripheral arterial oxygen saturation (SpO2) estimation. Moreover, physiological and demographic factors can substantially alter PPG waveform morphology. We propose a lightweight, real-time predictive modeling approach that adapts to subject-specific PPG signal dynamics to improve monitoring robustness under conditions prone to artifacts. A total of 459 min of dual-wavelength PPG signals, together with reference SpO2 values, were collected from 17 healthy volunteers (2 female, 15 male, mean age $27\pm 3$ years old) undergoing controlled desaturation in the 85–100% range after being instructed to remain still. Cardiac pulses were segmented and decomposed into AC and DC components, and the adequacy of several signal models, ranging from sums of Gaussians to Fourier series, and polynomial expansions of different orders, was evaluated. A space of representative signal features was built from the best-performing model, and used to generate machine learning-based predictions for each pulse using the preceding four clean pulses. Predicted pulses could be directly compared with their originals, enabling accurate error estimation without simulated data. The predicted signals closely matched the originals, achieving mean $R^2$ scores above 0.9, and an SpO2 estimation RMSE of 1.28%. In practical use, the same approach could be applied to overcome artifact-corrupted segments if combined with a signal quality assessment module. Therefore, this algorithm provides a promising pathway toward more reliable SpO2 monitoring in wearable systems, particularly under hypoxic conditions. Full article

Generalised joint hypermobility (GJH) describes an excessive range of joint movement and is associated with increased musculoskeletal injury risk, joint pain, and instability. This study compares lower limb biomechanical characteristics between children with and without GJH. Children aged 5–18 years with GJH (Beighton score ≥ 6/9 pre-puberty, ≥5/9 post-puberty) were age- and sex-matched with controls (Beighton score ≤ 2/9). Biomechanical measures included internal hip rotation, quadriceps (Q) angle, tibial torsion, ankle range of motion (ROM), and foot posture index (FPI). Wilcoxon rank sum test and chi-square were used to assess group differences. Fifty-two participants (median age 11 years, 69% females) included 27 children with GJH and 25 healthy children. Internal hip rotation, Q-angle, ankle ROM and FPI were significantly higher for children with GJH (p < 0.001) than healthy peers. While tibial torsion showed no difference in males, females had greater internal tibial torsion [median difference: right −4° (95%CI:−7,−2), p = 0.002; left −5° (95%CI:−7,−1), p = 0.010]. The largest differences were in ankle ROM [median difference: right 9° (95%CI:7,12); left 9° (95%CI:6,12)]. Children with GJH present different biomechanical measures than non-GJH peers. Further research into the clinical relevance of ROM at the hip, ankle and foot for children with GJH which are movement planes not assessed in Beighton score is warranted. Full article

Background/Objectives: Despite growing evidence on quantitative computed tomography (CT) analysis of coronary plaques and pericoronary adipose tissue (PCAT), their association with myocardial perfusion (MP) in patients with first acute myocardial infarction (AMI) with obstructive coronary artery disease (MICAD) and non-obstructive coronary arteries (MINOCA) remain unclear. The aim of this study was to assess the relationship between quantitative CT coronary plaque components and PCAT characteristics with MP, myocardial blood flow (MBF) and coronary flow reserve (CFR) obtained by dynamic single-photon emission computed tomography (SPECT) in patients with AMI. Methods: Patients with a first episode of AMI were included in the study. All patients underwent coronary CT angiography with quantitative assessment of plaque volume (PV) and burden (PB), as well as PCAT volume and attenuation. Dynamic SPECT was performed on cadmium–zinc–telluride gamma-camera for quantitative assessment of MP parameters, stress and rest MBF, and CFR. Results: A total of 31 patients (median age 62 [56–70] years) were analyzed, including MICAD (n = 21) and MINOCA (n = 10). MICAD patients had significantly higher total PV and PB, mainly due to non-calcified and fibrofatty components (p < 0.05), while low-attenuation (LAP) and calcified plaques (CP) did not differ between groups. PCAT volumes were higher in MICAD (p < 0.05), whereas PCAT attenuation showed no differences. Dynamic SPECT revealed lower stress MBF and CFR in MICAD (p < 0.05). Correlation analysis showed positive associations of PV and PB with MP summed stress and rest scores, except LAP or CP; PB was negatively associated with MBF. In addition, PCAT volume correlated negatively with stress and rest MBF and CFR, as well as PCAT attenuation correlated positively with stress-induced MP abnormalities. Conclusions: Patients with MICAD demonstrated a greater extent of atherosclerosis and larger PCAT volume compared with MINOCA. Moreover, PCAT volume demonstrated inverse associations with MBF and CFR, indicating a potential link between PCAT characteristics and microvascular dysfunction. Full article

Rice, as one of the world’s three major staple crops, provides a food source for nearly half of the global population. Timely and accurate acquisition of rice cultivation information is crucial for optimizing spatial distribution, guiding production practices, and safeguarding food security. Taking Bishan District of Chongqing as the study area, NDVI values were derived from Sentinel-2 satellite imagery to construct standard NDVI time-series curves for typical land-cover types, including paddy fields, dryland, water bodies, construction land, and forest and grassland. These curves were then used in the NDVI time-series characteristics method to identify paddy fields. First, the Euclidean distance between the standard NDVI time series of paddy fields and those of other land-cover types was calculated. The sum of these element-wise differences was used to determine the upper threshold for paddy field extraction. Second, the mean absolute deviation between elements of the rice sample dataset and the standard NDVI time series was calculated for each time step. The sum of these average deviations was used as the lower threshold to extract the initial paddy field data. On this basis, an extreme-value constraint was introduced to reduce the interference of mixed pixels from forest and grassland and construction land, effectively eliminating anomalous pixels and improving the accuracy of paddy field identification. Finally, the results were validated and compared with those from other extraction methods. The results indicate that: (1) Paddy fields exhibit distinct NDVI time-series characteristics throughout the entire growing season, which can serve as a reference standard. By calculating the Euclidean distance between the NDVI curves of other land-cover types and those of paddy fields, similarity can be quantified, enabling rice identification. (2) The extraction method based on NDVI time-series characteristics successfully identified paddy fields through the appropriate setting of thresholds. The overall accuracy and Kappa coefficient remained high, while the F1-score consistently exceeded 0.8, indicating a good balance between precision and recall. Furthermore, the bootstrap uncertainty analysis revealed narrow 95% confidence intervals across all metrics, confirming the robustness and statistical reliability of the results. Overall, the proposed method demonstrated excellent performance in paddy field classification and significantly outperformed traditional machine learning methods implemented on the GEE platform. (3) Mixed pixels considerably affected the accuracy of rice classification; however, the introduction of the extreme-value constraint effectively mitigated this influence and further improved classification results. Full article

Background/Objectives: Peripheral intravenous catheters are commonly employed to administer intravenous therapy to hospitalized patients. However, their use can result in complications, with phlebitis occurring in approximately 11% of cases and bloodstream infections in about 0.18%. This study aimed to enhance PIVC management in a local hospital by implementing a comprehensive care bundle to mitigate these complications. Methods: This quality improvement study involved the collection of data from 1330 PIVCs in adult patients, both prior to and following the implementation of the intervention. Data collection occurred between June 2022 and November 2023, employing the validated Peripheral Intravenous Catheter-Mini Questionnaire (PIVC-miniQ). This instrument comprises 16 observation points that assess phlebitis-related signs and symptoms, the integrity of PIVC dressings and IV connections, and the adequacy of documentation. Results: The prevalence of phlebitis decreased from 15.1% at baseline to 9.4% post-intervention (p = 0.018). Significant predictors of phlebitis included the intervention, ward, gender, and PIVC gauge. Improvements were also noted in PIVC dressing and IV connection practices, as well as documentation standards (p < 0.001). Closed integrated PIVCs outperformed ported PIVCs in the PIVC-miniQ scores after the intervention (p < 0.001). A statistically significant difference was observed in the mean PIVC-miniQ sum score post-intervention compared to baseline (p < 0.001). Conclusions: This study indicates that implementing a care bundle can enhance the quality of PIVCs and reduce the prevalence of phlebitis. Further high-quality research is needed to identify the most effective care bundles for preventing PIVC-related complications. Full article