Tag: pubmed

BMC Pediatr. 2024 Oct 28;24(1):683. doi: 10.1186/s12887-024-05162-7.

ABSTRACT

BACKGROUND: Impaired visuomotor integration (VMI) is commonly observed in children with developmental delay (DD). This pilot study aimed to evaluate the effects of tablet computer-based cognitive training on the VMI in children with DD.

METHODS: This study included children aged 4 to under 18 years diagnosed with DD. The children participated in a 12-week tablet computer-based visual-spatial and visuomotor training program. They were administered the Mind Rx Kids Program (Brain Academy, Seoul, South Korea). The participants underwent daily 30-min tablet computer-based training for 12 weeks. The primary visuomotor function was measured using the Beery-Buktenica Developmental Test of Visual-Motor Integration, 6th Edition (VMI-6). For secondary outcomes, measurements were taken before and after 12-week treatment using the Quality of Upper Extremity Skills Test (QUEST), Functional Independence Measure for Children (WeeFIM), Childhood Autism Rating Scale (CARS), Attention deficit hyperactivity disorder Rating Scale (ARS), and Child Smartphone Addiction Observer Scale. The Wilcoxon signed-rank test was used to compare the pre- and post-treatment outcomes.

RESULTS: Ten children with DD participated in this study. The results of the 12-week tablet computer-based cognitive training showed significant improvements in the raw score, standard score, percentile score, and equivalent age of the Beery VMI-6. Additionally, there were significant improvements in QUEST and WeeFIM scores. Although there were improvements in the CARS, ARS, and smartphone addiction observer scale, these were not statistically significant.

CONCLUSION: This pilot study confirmed that applying tablet computer-based cognitive training to children with DD not only improves VMI, but also enhances fine motor skills and activities of daily living. Furthermore, the results of this study indicate that tablet computer-based cognitive training does not increase digital media addiction. Therefore, children with DD can engage in tablet computer-based cognitive training at home without concerns about digital media addiction.

PMID:39465386 | DOI:10.1186/s12887-024-05162-7

BMC Plant Biol. 2024 Oct 28;24(1):1018. doi: 10.1186/s12870-024-05739-5.

ABSTRACT

BACKGROUND: Light is a critical environmental factor in plants, encompassing two vital aspects: intensity and quality. To assess the influence of different light intensities on Origanum majorana L., pots containing the herb were subjected to four levels of light intensity: 20, 50, 70, and 100% natural light. After a 60-day treatment period, the plants were evaluated for metabolite production, including total sugar content, protein, dry weight, antioxidant indices, expression of monoterpenes biosynthesis genes, and essential oil compounds. The experimental design followed a randomized complete blocks format, and statistical analysis of variance was conducted.

RESULTS: The results indicated a correlation between increased light intensity and elevated total sugar and protein content, which contributed to improved plant dry weight. The highest levels of hydrogen peroxide and malondialdehyde (MDA) were observed under 100% light intensity. Catalase and superoxide dismutase enzymes exhibited increased activity, with a 4.23-fold and 2.14-fold increase, respectively, under full light. In contrast, peroxidase and polyphenol oxidase enzyme activities decreased by 3.29-fold and 3.24-fold, respectively. As light intensity increases, the expression level of the 1-deoxy-D-xylulose 5-phosphate reductoisomerase (DXR) gene increases. However, beyond a light intensity of 70%, the DXR gene expression level decreased. Furthermore, the expression levels of the cytochrome P450 genes CYP71D178 and CYP71D179 exhibited an increasing trend in response to elevated light intensity. Essential oil content increased from 0.02 to 0.5% until reaching 70% light intensity. However, with further increases in light intensity, the essential oil content decreased by 54 to 0.23%.

CONCLUSIONS: These findings emphasize the importance of balancing plant growth promotion and stress management under different light conditions. The research suggests that sweet marjoram plants thrive best in unshaded open spaces, resulting in maximum biomass. However, essential oil production decreases under the same conditions. For farmers in areas with an average light intensity of approximately 1700 µmol m^-2s^-1, it is recommended to cultivate sweet marjoram in shade-free fields to optimize biomass and essential oil production. Towards the end of the growth cycle, it is advisable to use shades that allow 70% of light to pass through. The specific duration of shade implementation can be further explored in future research.

PMID:39465361 | DOI:10.1186/s12870-024-05739-5

Sci Rep. 2024 Oct 27;14(1):25623. doi: 10.1038/s41598-024-77268-y.

ABSTRACT

Adolescence is a crucial period of growth and the best time to recognize, rebuild, and improve different psychological and social aspects of a person’s life. Anxiety is one of the variables that affect a person’s mental health. Also, there is a connection between parenting styles and mental health during adolescence. The purpose of this study was to determine the relationship between perceived parenting styles and anxiety of adolescents in Isfahan (Iran). This descriptive-analytical study was conducted with the participation of 197 teenagers in the age group of 12-18 years in Isfahan, Iran. The participants were selected by cluster random sampling. The Parenting Styles Questionnaire (PSQ) and March Children’s Anxiety Questionnaire were used to collect information. Data were analyzed using descriptive and analytical statistics through SPSS 26. The mean and standard deviation of anxiety in adolescents were 45.96 and 16.51, respectively (at a low level). The participants evaluated their parenting style in order as permissive (32%), authoritative (24.9%), neglectful (21.8%), and authoritarian (21.3%). A significant difference was observed between the anxiety level of adolescents and their parenting style (p < 0.001). In this way, the highest anxiety was related to the children of permissive parents, and the most minor anxiety was associated with the authoritarian parents (p < 0.001). The findings of this research indicate that there is a significant difference between the perceived parenting styles in terms of the level of children’s anxiety. Therefore, considering the importance of parenting styles on children’s psychological characteristics, It is recommended to educate parents about the importance of their parenting style on their children’s health.

PMID:39465351 | DOI:10.1038/s41598-024-77268-y

Sci Rep. 2024 Oct 27;14(1):25658. doi: 10.1038/s41598-024-77449-9.

ABSTRACT

This study aimed to compare polishing systems on color stability, surface roughness, and gloss of additively manufactured permanent and conventional resin composites. Totally 250 disc specimens (6 mm*2 mm) were prepared from resin-based materials [G-ænial Posterior (GP), Clearfil Majesty Esthetic (CME), SonicFill-2 (SF), Tescera (Tes), and Crowntec (CT)]. Following baseline color (ΔE₀₀), gloss (GU), and surface roughness (Ra) measurements, the specimens were randomly divided into 5 groups (n = 10/group) according to polishing systems: Control (mylar strips); OneGloss; OneGloss + Platina Hi-Gloss; OptiDisc; and OptiDisc + Platina Hi-Gloss. Specimens were immersed in coffee for 144 h following polishing. ΔE₀₀, GU, and Ra measurements were repeated. Atomic force microscopy images were taken in all groups. Spearman’s rho correlation coefficient, Robust ANOVA, and Bonferroni correction were used for statistical analysis. Significance level was taken as p < 0.050. Significant differences in ΔE₀₀ values were found among resin-based materials, polishing systems, and their interactions (p < 0.001,p < 0.01, and p = 0.001). Regardless of polishing system, the lowest ΔE₀₀ values were observed in CT, while lowest gloss (GU) values were found in Tes. The lowest surface roughness (Ra) values were detected at OptiDisc group (p < 0.001). A single type of polishing system may not be sufficient to achieve optimal results in resin-based materials.

PMID:39465348 | DOI:10.1038/s41598-024-77449-9

Sci Rep. 2024 Oct 27;14(1):25641. doi: 10.1038/s41598-024-77033-1.

ABSTRACT

This study aims to develop machine learning (ML)-assisted models for analyzing datasets related to Gleason scores in prostate cancer, conducting statistical analyses on the datasets, and identifying meaningful features. We retrospectively collected data from 717 hormone-sensitive prostate cancer (HSPC) patients at Yunnan Cancer Hospital. Of these, data from 526 patients were used for modeling. Seven auxiliary models were established using Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Extreme gradient boosting tree (XGBoost), Adaptive Boosting (Adaboost), and artificial neural network (ANN) based on 21 clinical biochemical indicators and features. Evaluation metrics included accuracy (ACC), precision (PRE), specificity (SPE), sensitivity (SEN) or regression rate(Recall), and f1 score. Evaluation metrics for the models primarily included ACC, PRE, SPE, SEN or Recall, f1 score, and area under the curve(AUC). Evaluation metrics were visualized using confusion matrices and ROC curves. Among the ensemble learning methods, RF, XGBoost, and Adaboost performed the best. RF achieved a training dataset score of 0.769 (95% CI: 0.759-0.835) and a testing dataset score of 0.755 (95% CI: 0.660-0.760) (AUC: 0.786, 95%CI: 0.722-0.803), while XGBoost achieved a training dataset score of 0.755 (95% CI: 95%CI: 0.711-0.809) and a testing dataset score of 0.745 (95% CI: 0.660-0.764) (AUC: 0.777, 95% CI: 0.726-0.798). Adaboost scored 0.789 on the training dataset (95% CI: 0.782-0.857) and 0.774 on the testing dataset (95% CI: 0.651-0.774) (AUC: 0.799, 95% CI: 0.703-0.802). In terms of feature importance (FI) in ensemble learning, Bone metastases at first visit, prostatic volume, age, and T1-T2 have significant proportions in RF’s FI. fPSA, TPSA, and tumor burden have significant proportions in Adaboost’s FI, while f/TPSA, LDH, and testosterone have the highest proportions in XGBoost. Our findings indicate that ensemble learning methods demonstrate good performance in classifying HSPC patient data, with TNM staging and fPSA being important classification indicators. These discoveries provide valuable references for distinguishing different Gleason scores, facilitating more accurate patient assessments and personalized treatment plans.

PMID:39465343 | DOI:10.1038/s41598-024-77033-1

Sci Rep. 2024 Oct 27;14(1):25624. doi: 10.1038/s41598-024-77513-4.

ABSTRACT

Currently, lightweight small object detection algorithms for unmanned aerial vehicles (UAVs) often employ group convolutions, resulting in high Memory Access Cost (MAC) and rendering them unsuitable for edge devices that rely on parallel computing. To address this issue, we propose the SOD-YOLO model based on YOLOv7, which incorporates a DSDM-LFIM backbone network and includes a small object detection branch. The DSDM-LFIM backbone network, which combines Deep-Shallow Downsampling Modules (DSD Modules) and Lightweight Feature Integration Modules (LFI Modules), avoids excessive use of group convolutions and element-wise operations. The DSD Module focuses on extracting both deep and shallow features from feature maps using fewer parameters to obtain richer feature representations. The LFI Module, is a dual-branch feature integration module designed to consolidate feature information. Experimental results demonstrate that the SOD-YOLO model achieves an AP50 of 50.7% and a FPS of 72.5 on the VisDrone validation set. Compared to YOLOv7, our model reduces computational costs by 20.25% and decreases the number of parameters by 17.89%. After scaling the number of channels in the model, it achieves an AP50 of 33.4% with an inference time of 27.3ms on the Atlas 200I DK A2. These experimental results indicate that the SOD-YOLO model can effectively perform small object detection tasks in a large number of aerial images captured by UAVs.

PMID:39465334 | DOI:10.1038/s41598-024-77513-4

Sci Rep. 2024 Oct 27;14(1):25670. doi: 10.1038/s41598-024-77043-z.

ABSTRACT

This study was aim to determine the prognostic value of triglyceride-glucose (TyG) index and coronary computed tomography angiography (CTA) features for major adverse cardiovascular events (MACE). In addition, we investigate the incremental prognostic value of TyG index beyond coronary CTA features in patients with suspected or known coronary artery disease (CAD). The present study ultimately includes 3528 patients who met the enrollment criteria. The TyG index was calculated based on measured levels of triglycerides and fasting blood glucose. Primary combined endpoint consisted of MACE, which defined as myocardial infraction (MI), all-cause mortality and stroke. Three multivariate Cox proportional hazard regression models were performed to assess the association between TyG index and MACE. C-statistic was performed to assess the discriminatory value of models. 212 (6.0%) patients developed MACE during a median follow-up of 50.4 months (IQR, 39.4-55.1). TyG index remained to be a significantly and independent risk factors for predicting MACE after adjusting by different models (clinical variables alone or plus coronary CTA features) in multivariable analysis. Both the addition of TyG index to clinical model plus Coronary Artery Disease Reporting and Data System (CAD-RADS) and to clinical model plus CAD-RADS 2.0 slightly but not significantly increased the C-statistic index (0.725 vs. 0.721, p = 0.223; 0.733 vs. 0.731, p = 0.505). TyG index was associated with an increased risk of MACE. However, no incremental prognostic benefit of TyG index over CAD-RADS or CAD-RADS 2.0 was detected for MACE in patients with suspected or known CAD.

PMID:39465316 | DOI:10.1038/s41598-024-77043-z

Oral Dis. 2024 Oct 27. doi: 10.1111/odi.15171. Online ahead of print.

ABSTRACT

INTRODUCTION: The clinical potential of extracellular vesicles (EVs) is widely acknowledged, yet the standardization and reproducibility of its separation remain challenging. This study compares three protocols: ultracentrifugation (UC), UC with purification step (UC + PS), and a combined protocol using polymer-based precipitation and UC (PBP + UC).

METHODS: Salivary samples were collected from healthy donors. EVs were separated (UC, UC + PS, and PBP + UC) and characterized using transmission electron microscopy, nanoparticle tracking analysis, EV purity, RNA concentration, and Western blotting. miRNA expression was evaluated by quantitative RT-PCR. Statistical analyses comparing groups were performed using ANOVA.

RESULTS: All methods successfully separated CD9+ and CD63+ EVs from saliva. The UC + PS and PBP + UC protocols yielded the highest concentrations of EVs, enriched in < 200 nm vesicles. EV purity and RNA recovery were comparable among all methods. Expression of miR-16, miR-27a, and miR-99a was successfully detected using all methods.

CONCLUSIONS: The UC + PS and PBP + UC protocols demonstrate comparable efficiency in separating salivary EVs. However, the combined PBP + UC protocol, with its simplified processing capability, offers a significant advantage, particularly in the initial phase of EV separation. This finding suggests its potential application in clinical settings where time-sensitive simple processing is critical. Further validation is needed to confirm its effectiveness for transcriptomic and proteomic analyses.

PMID:39462790 | DOI:10.1111/odi.15171

J Formos Med Assoc. 2024 Oct 26:S0929-6646(24)00508-4. doi: 10.1016/j.jfma.2024.10.021. Online ahead of print.

ABSTRACT

This short communication presents an audit of anesthetic gas usage at National Taiwan University Hospital from 2018 to 2023. Using descriptive statistics and trend analysis, the data reveals trends in the consumption of sevoflurane and desflurane, associated costs, and their corresponding carbon emissions. A significant decrease in desflurane usage contributed to a 42.4% reduction in carbon dioxide equivalent (CO₂e) emissions per general anesthesia case from 2018 to 2023. The findings underscore the importance of sustainable practices in anesthesia to align with global efforts to reduce carbon emissions.

PMID:39462739 | DOI:10.1016/j.jfma.2024.10.021

Trends Plant Sci. 2024 Oct 26:S1360-1385(24)00259-0. doi: 10.1016/j.tplants.2024.09.011. Online ahead of print.

ABSTRACT

Statistical machine learning (ML) extracts patterns from extensive genomic, phenotypic, and environmental data. ML algorithms automatically identify relevant features and use cross-validation to ensure robust models and improve prediction reliability in new lines. Furthermore, ML analyses of genotype-by-environment (G×E) interactions can offer insights into the genetic factors that affect performance in specific environments. By leveraging historical breeding data, ML streamlines strategies and automates analyses to reveal genomic patterns. In this review we examine the transformative impact of big data, including multi-trait genomics, phenomics, and environmental covariables, on genomic-enabled prediction in plant breeding. We discuss how big data and ML are revolutionizing the field by enhancing prediction accuracy, deepening our understanding of G×E interactions, and optimizing breeding strategies through the analysis of extensive and diverse datasets.

PMID:39462718 | DOI:10.1016/j.tplants.2024.09.011