UNASSIGNED: This study aimed to investigate the clinical effects of Orff music therapy on children with Autism Spectrum Disorder (ASD) from the perspectives of parents, evaluators, and therapists.
UNASSIGNED: 93 children with ASD aged 3-6 years participated in the study. They were divided into an observation group (n = 48) receiving comprehensive rehabilitation intervention including Orff music therapy, and a control group (n = 45) receiving only comprehensive rehabilitation intervention. The Autism Behavior Checklist (ABC), Childhood Autism Rating Scale (CARS), and Psycho-educational Profile-3rd edition (PEP-3) were used for assessments before and after the intervention.
UNASSIGNED: There were no significant demographic differences between the two groups. Both groups showed significant improvements in Sensory, Relating, Language, CVP, EL, RL, VMI, AE, SR, and CARS scores at T1, T2, and T3 (T1 vs. T2, T2 vs. T3, T1 vs. T3) (all p < 0.05). The observation group demonstrated significant changes in Body and Object use and FM, while the control group showed some changes in these domains. Social and self-help, GM, CMB, and CVB also significantly improved in both groups after 6 months of intervention (all p < 0.05). In terms of different time intervals, the observation group showed greater improvements in Sensory, Relating, Language, CARS scores, EL, RL, and SR compared to the control group (all p < 0.05). The improvement levels in Body and Object use, CVP, FM, VMI, and AE did not differ significantly between the two groups in the T1-T2 interval, but were significantly higher in the observation group in the T2-T3 and T1-T3 intervals (all p < 0.05). The magnitude of changes in Social and self-help, GM, CMB, and CVB did not differ significantly between the groups.
UNASSIGNED: Orff music therapy showed significant improvements in language expression, language comprehension, social skills, cognitive abilities, imitation abilities, emotional expression and fine motor in children with ASD. These findings provide support for the use of Orff music therapy as an effective intervention for children with ASD.

OBJECTIVE: To investigate the relationship between multi-dimensional aspects of screen exposure and autistic symptoms, as well as neuropsychological development in children with ASD.
METHODS: We compared the ScreenQ and Griffiths Development Scales-Chinese Language Edition (GDS-C) of 636 ASD children (40.79 ± 11.45 months) and 43 typically developing (TD) children (42.44 ± 9.61 months). Then, we analyzed the correlations between ScreenQ and Childhood Autism Rating Scale (CARS), and GDS-C. We further used linear regression model to analyze the risk factors associated with high CARS total scores and low development quotients (DQs) in children with ASD.
RESULTS: The CARS of children with ASD was positively correlated with the ScreenQ total scores and \"access, frequency, co-viewing\" items of ScreenQ. The personal social skills DQ was negatively correlated with the \"access, frequency, content, co-viewing and total scores\" of ScreenQ. The hearing-speech DQ was negatively correlated with the \"frequency, content, co-viewing and total scores\" of ScreenQ. The eye-hand coordination DQ was negatively correlated with the \"frequency and total scores\" of ScreenQ. The performance DQ was negatively correlated with the \"frequency\" item of ScreenQ.
CONCLUSIONS: ScreenQ can be used in the study of screen exposure in children with ASD. The higher the ScreenQ scores, the more severe the autistic symptoms tend to be, and the more delayed the development of children with ASD in the domains of personal-social, hearing-speech and eye-hand coordination. In addition, \"frequency\" has the greatest impact on the domains of personal social skills, hearing-speech, eye-hand coordination and performance of children with ASD.

The phytopathogenic fungus Fusarium fujikuroi has a rich secondary metabolism which includes the synthesis of very different metabolites in response to diverse environmental cues, such as light or nitrogen. Here, we focused our attention on fusarins, a class of mycotoxins whose synthesis is downregulated by nitrogen starvation. Previous data showed that mutants of genes involved in carotenoid regulation (carS, encoding a RING finger protein repressor), light detection (wcoA, White Collar photoreceptor), and cAMP signaling (AcyA, adenylate cyclase) affect the synthesis of different metabolites. We studied the effect of these mutations on fusarin production and the expression of the fus1 gene, which encodes the key polyketide synthase of the pathway. We found that the three proteins are positive regulators of fusarin synthesis, especially WcoA and AcyA, linking light regulation to cAMP signaling. Genes for two other photoreceptors, the cryptochrome CryD and the Vivid flavoprotein VvdA, were not involved in fusarin regulation. In most cases, there was a correspondence between fusarin production and fus1 mRNA, indicating that regulation is mainly exerted at the transcriptional level. We conclude that fusarin synthesis is subject to a complex control involving regulators from different signaling pathways.

Cellular debris resulting from large trauma might overwhelm the scavenger mechanisms and lead to autoimmune reactions. We analysed whether a major well-defined trauma in humans induces laboratory signs of transient autoimmunity in the months after the insult. We included 50 patients with pertrochanteric femur fracture undergoing intramedullary nail osteosynthesis in a prospective cohort study and followed them at 3-4 days, 6 weeks, 12 weeks and 12 months postoperatively. By standard techniques, we assessed levels of total immunoglobulins, anti-nuclear antibodies (ANA), anti-cardiolipin antibodies, anti-dsDNA antibodies and anti-C1q antibodies, as well as antibodies against cytomegalovirus (CMV) as a control. Blood leukocyte differential and lymphocyte subpopulations were determined at baseline and in the first two postoperative samples. The mean age of the patients reached 80.1 years, and 23 (46%) completed all visits. Serum concentrations of total IgG, IgM and IgA increased at all follow-up time points. The ANA fluorescence light intensity units increased at 12 weeks and 12 months postoperatively (p < 0.0001), but the proportion of ANA-positive patients did not change (35%). The values of anti-C1q mildly increased at all follow-up visits, but not the ratio to total IgG. Anti-dsDNA remained negative in all patients, and anti-cardiolipin IgG/IgM antibodies did not change. Anti-CMV IgG antibodies increased significantly at all follow-up visits, without change in the ratio to total IgG. Flow cytometry showed an increased proportion of B-cells 3-4 days postoperatively. In conclusion, major musculoskeletal trauma in elderly patients induces a generalized non-specific increase in immunoglobulin production without laboratory signs for enhanced systemic autoimmunity.

The COVID-19 pandemic triggered the resurgence of synthetic RNA vaccine platforms allowing rapid, scalable, low-cost manufacturing, and safe administration of therapeutic vaccines. Self-amplifying mRNA (SAM), which self-replicates upon delivery into the cellular cytoplasm, leads to a strong and sustained immune response. Such mRNAs are encapsulated within lipid nanoparticles (LNPs) that act as a vehicle for delivery to the cell cytoplasm. A better understanding of LNP-mediated SAM uptake and release mechanisms in different types of cells is critical for designing effective vaccines. Here, we investigated the cellular uptake of a SAM-LNP formulation and subsequent intracellular expression of SAM in baby hamster kidney (BHK-21) cells using hyperspectral coherent anti-Stokes Raman scattering (HS-CARS) microscopy and multiphoton-excited fluorescence lifetime imaging microscopy (FLIM). Cell classification pipelines based on HS-CARS and FLIM features were developed to obtain insights on spectral and metabolic changes associated with SAM-LNPs uptake. We observed elevated lipid intensities with the HS-CARS modality in cells treated with LNPs versus PBS-treated cells, and simultaneous fluorescence images revealed SAM expression inside BHK-21 cell nuclei and cytoplasm within 5 h of treatment. In a separate experiment, we observed a strong correlation between the SAM expression and mean fluorescence lifetime of the bound NAD(P)H population. This work demonstrates the ability and significance of multimodal optical imaging techniques to assess the cellular uptake of SAM-LNPs and the subsequent changes occurring in the cellular microenvironment following the vaccine expression.

Outdoor air pollution has been found to have a significant adverse effect on health. When the authors attempted to monitor air quality that cyclists or e-scooter users\' breath during commuting in different locations for health and safety analysis, it was found that the existence of internal combustion engine (ICE) cars has a significant effect on the pollution levels and the monitoring process. To comprehensively study the effect of cars and traffic on air quality that cyclists and e-scooters users experience, a low-cost and reliable system was needed to detect the proximity of cars that have diesel or petrol engines. Video cameras can be used to visually detect vehicles, but in the modern age with the existence of many electric and hybrid vehicles and the need to reduce the cost of instrumentation, there was a need to determine the passing of vehicles near e-scooter and bike users from the combined engine and tires sounds. To address this issue, this study suggests a novel approach of using sound waves of internal combustion engines and tire sounds during the passing of cars, combined with AI techniques (neural networks), to detect the proximity of cars from cyclists and e-scooter users. Audio-visual data was collected using Go-Pro cameras in order to combine the data with GPS location and pollution levels. Geographical data maps were produced to demonstrate the density of cars that cyclists encounter when on or near the road. This method will enable air quality monitoring research to detect the existence of ICE cars for future correlation with measured pollution levels. The proposed method allows for:•The automated selection of sensitive features from sound waves to detect vehicles.•Low-cost hardware which is independent of orientation that can be integrated with other air quality and GPS sensors.•The successful application of sensor fusion and neural networks.

BACKGROUND: Although people are familiar with loud automobiles, equipped with modified mufflers to increase the volume, it is unclear who is most attracted to these vehicles.
METHODS: A sample of 529 (52% men) undergraduate business students were surveyed and were asked if they viewed their car as an extension of themselves, how much they thought loud cars were \"cool\", and if they would make their car louder with muffler modifications. Cronbach\'s α of the three car items was .76; therefore an aggregate was generated. Also a self-report measure of the dark tetrad was completed.
RESULTS: Using linear regression, the car aggregate was predicted by being a man and having higher scores on psychopathy and sadism, with the model accounting for approximately 29% of the variance.
CONCLUSIONS: As car modifications are illegal in some countries, these findings may be of interest to those heading campaigns to halt these activities.

Fibroblasts are among the most common cell types in the stroma responsible for creating and maintaining the structural organization of the extracellular matrix in the dermis, skin regeneration, and a range of immune responses. Until now, the processes of fibroblast adaptation and functioning in a varying environment have not been fully understood. Modern laser microscopes are capable of studying fibroblasts in vitro and ex vivo. One-photon- and two-photon-excited fluorescence microscopy, Raman spectroscopy/microspectroscopy are well-suited noninvasive optical methods for fibroblast imaging in vitro and ex vivo. In vivo staining-free fibroblast imaging is not still implemented. The exception is fibroblast imaging in tattooed skin. Although in vivo noninvasive staining-free imaging of fibroblasts in the skin has not yet been implemented, it is expected in the future. This review summarizes the state-of-the-art in fibroblast visualization using optical methods and discusses the advantages, limitations, and prospects for future noninvasive imaging.

This review presents the changes that the imaging of articular cartilage has undergone throughout the last decades. It highlights that the expectation is no longer to image the structure and associated functions of articular cartilage but, instead, to devise methods for generating non-invasive, function-depicting images with quantitative information that is useful for detecting the early, pre-clinical stage of diseases such as primary or post-traumatic osteoarthritis (OA/PTOA). In this context, this review summarizes (a) the structure and function of articular cartilage as a molecular imaging target, (b) quantitative MRI for non-invasive assessment of articular cartilage composition, microstructure, and function with the current state of medical diagnostic imaging, (c), non-destructive imaging methods, (c) non-destructive quantitative articular cartilage live-imaging methods, (d) artificial intelligence (AI) classification of degeneration and prediction of OA progression, and (e) our contribution to this field, which is an AI-supported, non-destructive quantitative optical biopsy for early disease detection that operates on a digital tissue architectural fingerprint. Collectively, this review shows that articular cartilage imaging has undergone profound changes in the purpose and expectations for which cartilage imaging is used; the image is becoming an AI-usable biomarker with non-invasive quantitative functional information. This may aid in the development of translational diagnostic applications and preventive or early therapeutic interventions that are yet beyond our reach.

Traditional methods for obtaining soil heavy metal content are expensive, inefficient, and limited in monitoring range. In order to meet the needs of soil environmental quality evaluation and health status assessment, visible near-infrared spectroscopy and XRF spectroscopy for monitoring heavy metal content in soil have attracted much attention, because of their rapid, nondestructive, economical, and environmentally friendly features. The use of either of these spectra alone cannot meet the accuracy requirements of traditional measurements, while the synergistic use of the two spectra can further improve the accuracy of monitoring heavy metal lead content in soil. Therefore, this study applied various spectral transformations and preprocessing to vis-NIR and XRF spectra; used the whale optimization algorithm (WOA) and competitive adaptive re-weighted sampling (CARS) algorithms to identify feature spectra; designed a combination variable model (CVM) based on multi-layer spectral data fusion, which improved the spectral preprocessing and spectral feature screening process to increase the efficiency of spectral fusion; and established a quantitative model for soil Pb concentration using partial least squares regression (PLSR). The estimation performance of three spectral fusion strategies, CVM, outer-product analysis (OPA), and Granger-Ramanathan averaging (GRA), was discussed. The results showed that the accuracy and efficiency of the CARS algorithm in the fused spectra estimation model were superior to those of the WOA algorithm, with an average coefficient of determination (R2) value of 0.9226 and an average root mean square error (RMSE) of 0.1984. The accuracy of the estimation models established, based on the different spectral types, to predict the Pb content of the soil was ranked as follows: the CVM model > the XRF spectral model > the vis-NIR spectral model. Within the CVM fusion strategy, the estimation model based on CARS and PLSR (CARS_D1+D2) performed the best, with R2 and RMSE values of 0.9546 and 0.2035, respectively. Among the three spectral fusion strategies, CVM had the highest accuracy, OPA had the smallest errors, and GRA showed a more balanced performance. This study provides technical means for on-site rapid estimation of Pb content based on multi-source spectral fusion and lays the foundation for subsequent research on dynamic, real-time, and large-scale quantitative monitoring of soil heavy metal pollution using high-spectral remote sensing images.