Search Results (1,830)

Background: Prebiotics are selectively used by host microorganisms to promote health. Because effective prebiotic doses (1.5–30 g/day) often require inconvenient delivery formats, this study aims to explore whether capsule-compatible doses of galacto-oligosaccharides (GOS) can effectively modulate the gut microbiome. Methods: The impact of Bimuno^® GOS (Reading, UK) at 0.5, 0.75, 1.83, and 3.65 g on the adult gut microbiome was assessed using the ex vivo SIFR^® technology (n = 8), a clinically validated, bioreactor-based technology. Results: The GOS were rapidly fermented and significantly increased beneficial Bifidobacterium species (B. adolescentis, B. bifidum, and B. longum), even at the lowest tested dose. In doing so, GOS strongly promoted SCFA production, particularly acetate (significant from 0.5 g) and butyrate (significant from 0.75 g). Gas production only mildly increased, likely as Bifidobacterium species do not produce gases. Based on the ability of the SIFR^® technology to cultivate strictly anaerobic, hard-to-culture gut microbes, unlike in past in vitro studies, we elucidated that GOS also enriched specific Lachnospiraceae species. Besides Anaerobutyricum hallii, this included Bariatricus comes, Blautia species (B. massiliensis, Blautia_A, B. faecis), Oliverpabstia intestinalis, Mediterraneibacter faecis, and Fusicatenibacter species. Finally, GOS also promoted propionate (significant from 0.75 g), linked to increases in Phocaeicola vulgatus. Conclusions: GOS displayed prebiotic potential at capsule-compatible doses, offering greater flexibility in nutritional product formulation and consumer convenience. Notably, the strong response at the lowest dose suggests effective microbiome modulation at lower levels than previously expected. Full article

This paper proposes an intelligent power adaptation framework for Free-Space Optics (FSO) communication systems operating under different weather conditions exploiting a deep learning (DL) analysis of received eye diagram images. The system incorporates two Convolutional Neural Network (CNN) architectures, LeNet and Wide Residual Network (Wide ResNet) algorithms to perform regression tasks that predict received signal quality metrics such as the Quality Factor (Q-factor) and Bit Error Rate (BER) from the received eye diagram. These models are evaluated using Mean Squared Error (MSE) and the coefficient of determination (${\mathrm{R}}^2$ score) to assess prediction accuracy. Additionally, a custom CNN-based classifier is trained to determine whether the BER reading from the eye diagram exceeds a critical threshold of ${10}^{-4}$; this classifier achieves an overall accuracy of 99%, correctly detecting 194/195 “acceptable” and 4/5 “unacceptable” instances. Based on the predicted signal quality, the framework activates a dual-amplifier configuration comprising a pre-channel amplifier with a maximum gain of 25 dB and a post-channel amplifier with a maximum gain of 10 dB. The total gain of the amplifiers is adjusted to support the operation of the FSO system under all-weather conditions. The FSO system uses a 15 dBm laser source at 1550 nm. The DL models are tested on both internal and external datasets to validate their generalization capability. The results show that the regression models achieve strong predictive performance, and the classifier reliably detects degraded signal conditions, enabling the real-time gain control of the amplifiers to achieve the quality of transmission. The proposed solution supports robust FSO communication under challenging atmospheric conditions including dry snow, making it suitable for deployment in regions like Northern Europe, Canada, and Northern Japan. Full article

The present study examined differences in reading comprehension performance between good and poor comprehenders, across paper-based and computer-based formats. The sample consisted of 197 students (Mage = 10.9, SDage = 1.22), categorized into three groups based on their reading comprehension proficiency: good (n = 73), average (n = 90), and poor (n = 33). Using a pseudo-randomized within-subjects design, participants read two texts and completed both a cloze task and a proofreading task in paper and digital formats. Results showed that poor comprehenders consistently performed worse on both tasks; however, group performances were not influenced by the modality. Both tasks required more time in the digital modality and were associated with greater calibration bias. In the proof-reading task, nouns and adjectives were more difficult to retrieve than verbs and function words, whereas in the cloze task, function words were the easiest to supply. The discussion emphasizes the need to account the for task type and linguistic complexity when evaluating comprehension. Importantly, the lack of interaction between reading proficiency and modality suggests that digital assessments are comparably effective and reliable across different levels of reading ability. Full article

Background/Objectives: Accurate and accessible perioperative health information empowers patients and enhances recovery outcomes. Artificial intelligence tools, such as ChatGPT, have garnered attention for their potential in health communication. This study evaluates the accuracy and readability of responses generated by ChatGPT to questions commonly asked about breast cancer. Methods: Fifteen simulated patient queries about breast cancer surgery preparation and recovery were prepared. Responses generated by ChatGPT (4o version) were evaluated for accuracy by a pool of breast surgeons using a 4-point Likert scale. Readability was assessed with the Flesch–Kincaid Grade Level (FKGL). Descriptive statistics were used to summarize the findings. Results: Of the 15 responses evaluated, 11 were rated as “accurate and comprehensive”, while 4 out of 15 were deemed “correct but incomplete”. No responses were classified as “partially incorrect” or “completely incorrect”. The median FKGL score was 11.2, indicating a high school reading level. While most responses were technically accurate, the complexity of language exceeded the recommended readability levels for patient-directed materials. Conclusions: The model shows potential as a complementary resource for patient education in breast cancer surgery, but should not replace direct interaction with healthcare providers. Future research should focus on enhancing language models’ ability to generate accessible and patient-friendly content. Full article

Background: Despite the growing demand for amyloid PET quantification, practical challenges remain. As automated software platforms are increasingly adopted to address these limitations, we evaluated the reliability of commercial tools for Centiloid quantification against the original Centiloid Project method. Methods: This retrospective study included 332 amyloid PET scans (165 [¹⁸F]Florbetaben; 167 [¹⁸F]Flutemetamol) performed for suspected mild cognitive impairments or dementia, paired with T1-weighted MRI within one year. Centiloid values were calculated using three automated software platforms, BTXBrain, MIMneuro, and SCALE PET, and compared with the original Centiloid method. The agreement was assessed using Pearson’s correlation coefficient, the intraclass correlation coefficient (ICC), a Passing–Bablok regression, and Bland–Altman plots. The concordance with the visual interpretation was evaluated using receiver operating characteristic (ROC) curves. Results: BTXBrain (R = 0.993; ICC = 0.986) and SCALE PET (R = 0.992; ICC = 0.991) demonstrated an excellent correlation with the reference, while MIMneuro showed a slightly lower agreement (R = 0.974; ICC = 0.966). BTXBrain exhibited a proportional underestimation (slope = 0.872 [0.860–0.885]), MIMneuro showed a significant overestimation (slope = 1.053 [1.026–1.081]), and SCALE PET demonstrated a minimal bias (slope = 1.014 [0.999–1.029]). The bias pattern was particularly noted for FMM. All platforms maintained their trends for correlations and biases when focusing on subthreshold-to-low-positive ranges (0–50 Centiloid units). However, all platforms showed an excellent agreement with the visual interpretation (areas under ROC curves > 0.996 for all). Conclusions: Three automated platforms demonstrated an acceptable reliability for Centiloid quantification, although software-specific biases were observed. These differences did not impair their feasibility in aiding the image interpretation, as supported by the concordance with visual readings. Nevertheless, users should recognize the platform-specific characteristics when applying diagnostic thresholds or interpreting longitudinal changes. Full article

Background: Developmental dyslexia (DD) affects reading ability and exacerbates mental health challenges among children. This study examines the relationships between reading ability, depressive symptoms, and internalizing and externalizing behavior problems in Chinese school-age children, focusing on potential mediating effects. Methods: A case–control study was conducted with 44 dyslexic children and 81 controls from Shantou, China. Assessments included phonological processing tasks for reading ability, the Depression Self-Rating Scale for Children (DSRS) for depressive symptoms, and the Child Behavior Checklist/6–18 (CBCL/6–18) for behavior problems. Mediation analyses were performed using the PROCESS macro 4.1 for SPSS. Results: Dyslexic children showed significantly poorer reading ability (all phonological tasks, p < 0.001), higher prevalence of depressive symptoms (40.9% vs. 17.3%, p < 0.01), and greater behavior problems (internalizing and externalizing, both p < 0.001) compared to controls. Both depressive symptoms and behavior problems significantly mediated the effects of reading ability on each other, forming a feedback loop that further impairs reading skills. Externalizing behavior problems showed the strongest mediation effect, explaining up to 33.53% of the relationship between depressive symptoms and reading ability. Conclusions: The study reveals a complex interaction between reading ability, depressive symptoms, and internalizing and externalizing behavior problems in Chinese school-age children, suggesting the need for integrated interventions targeting educational and psychological aspects. Further longitudinal research is needed to clarify causal relationships and refine intervention strategies. Full article

Background/Objectives: While artificial intelligence (AI) is rapidly transforming education, its specific effectiveness for students with learning disabilities (LD) requires rigorous evaluation. This systematic review aims to assess the efficacy of AI-based educational interventions for students with LD, with a specific focus on the methodological quality and risk of bias of the available evidence. Methods: A systematic search was conducted across seven major databases (Google Scholar, ScienceDirect, APA PsycInfo, ERIC, Scopus, PubMed) for experimental studies published between 2022 and 2025. This review followed PRISMA guidelines, using the PICOS framework for inclusion criteria. A formal risk of bias assessment was performed using the ROBINS-I and JBI critical appraisal tools. Results: Eleven studies (representing 10 independent experiments), encompassing 3033 participants, met the inclusion criteria. The most studied disabilities were dyslexia (six studies) and other specific learning disorders (three studies). Personalized/adaptive learning systems and game-based learning were the most common AI interventions. All 11 studies reported positive outcomes. However, the risk of bias assessment revealed significant methodological limitations: no studies were rated as having a low risk of bias, with most presenting a moderate (70%) to high/serious (30%) risk. Despite these limitations, quantitative results from the stronger studies showed large effect sizes, such as in arithmetic fluency (d = 1.63) and reading comprehension (d = −1.66). Conclusions: AI-based interventions demonstrate significant potential for supporting students with learning disabilities, with unanimously positive reported outcomes. However, this conclusion must be tempered by the considerable risk of bias and methodological weaknesses prevalent in the current literature. The limited and potentially biased evidence base warrants cautious interpretation. Future research must prioritize high-quality randomized controlled trials (RCTs) and longitudinal assessments to establish a definitive evidence base and investigate long-term effects, including the risk of cognitive offloading. Full article

Assessing teacher candidates’ self-efficacy in using reading strategies is essential for understanding their academic development. This study developed and validated the Teacher Candidates’ Self-Efficacy Scale for Informational Reading Strategies (TCSES-IRS) using a mixed-methods sequential exploratory design. Initial qualitative data from interviews with 33 candidates and a literature review guided item generation. Lawshe’s method confirmed content validity. The scale was administered to 1176 teacher candidates. Exploratory (n = 496) and confirmatory factor analyses (n = 388) supported a five-factor structure—cognitive, note-taking, exploration and preparation, physical and process-based, and reflective and analytical strategies—explaining 63.71% of total variance, with acceptable fit indices (χ²/df = 2.64, CFI = 0.912, TLI = 0.900, RMSEA = 0.069). Internal consistency was high (α = 0.899 total; subscales α = 0.708–0.906). An additional sample of 294 participants was used for nomological network validation. Convergent validity was demonstrated by significant item-total correlations and strong factor loadings. Discriminant validity was evidenced by moderate inter-factor correlations. Criterion-related validity was confirmed via significant group differences and meaningful correlations with an external self-efficacy measure. The TCSES-IRS emerges as a psychometrically sound tool for assessing informational reading self-efficacy, supporting research and practice in educational psychology. Full article

Background: Microbial communities of insects have distinct roles for their respective hosts. For the black fly (Diptera: Simuliidae), an important vector and ecological indicator, the representative microbiota from the different body regions are not known. Here, we investigated the microbial composition and diversity of the head, thorax, and abdomen of wild-caught Simulium vanluni. Methods: Adult Simulium vanluni were surface-sterilized and dissected into head, thorax, and abdomen. For each body region, 20 individuals were pooled into one sample with six replicates per region. DNA was extracted and sequenced using the 16S rRNA amplification method to assess for possible microbial diversity. Data were analyzed using MicrobiomeAnalyst, where we calculated alpha diversity, beta diversity, and tested compositional differences using PERMANOVA. Results: Across 17 pooled samples, three core genera, Wolbachia (78.33%), Rickettsia (9.74%), and Acinetobacter (9.20%), accounted for more than 97% of the 16S rRNA sequencing reads. Head communities were compositionally distinct compared to the thorax and abdomen (PERMANOVA, p < 0.05). Heads were nearly monodominated by Wolbachia (95–97%), exhibiting significantly lower diversity and evenness compared to other body regions. In contrast, the thoracic and abdominal communities were more even, where thoraces were enriched with Acinetobacter (19.16%) relative to Rickettsia (10.85%), while abdomens harbored higher Rickettsia (10.96%) than Acinetobacter (5.68%). Collectively, the near-monodominance of Wolbachia in heads and inverse abundances of Acinetobacter and Rickettsia in thoraces and abdomens suggest possible anatomical niche partitioning or competition exclusion of microbiota across body regions. Conclusions: Our findings reveal fine-scale anatomical niche partitioning in S. vanluni microbiota, with the heads being almost exclusively colonized by Wolbachia, while the thoracic and abdominal niche regions exhibit distinct enrichment patterns for Acinetobacter and Rickettsia. These spatially distinct microbial distributions suggest potential functional specialization across anatomical regions of S. vanluni. Full article

Aerosol uniformity in the mixing chamber is one of the key factors in evaluating performance of aerosol samplers and accuracy of aerosol monitors which could output the direct reading of particle size or concentration. For obtaining high uniformity and a stable test aerosol sample during evaluation, a portable mixing chamber, where the sample and clean air were dual-vortex turbulent mixed, was designed. By using computational fluid dynamics (CFD), particle motion within the mixing chamber was illustrated or explained. By adjusting critical structure parameters of chamber such as height and diameter, the flow field structure was optimized to improve particle mixing characteristics. Accordingly, a novel portable aerosol mixing chamber with length and inner diameter of 0.7 m and 60 mm was developed. Through a combination of simulations and experiments, the operating conditions, including working flow rate, ratio of carrier/dilution clean air, and mixture duration, were studied. Finally, by using the optimized parameters, a mixing chamber with high spatial uniformity where variation is less than 4% was obtained for aerosol particles ranging from 0.3 μm to 10 μm. Based on this chamber, a standardized testing platform was established to verify the sampling efficiency of aerosol samplers with high flow rate (28.3 L·min⁻¹). The obtained results were consistent with the reference values in the sampler’s manual, confirming the reliability of the evaluation system. The testing platform developed in this study can provide test aerosol particles ranging from sub-micrometers to micrometers and has significant engineering applications, such as atmospheric pollution monitoring and occupational health assessment. Full article

Introduction: The figure-of-eight walk test (F8WT) assesses gait on a curved path, reflecting everyday walking complexity. Despite recognized validity among elderly individuals, its application in neurological disorders remains inadequately explored. This scoping review summarizes evidence regarding F8WT use, validity, and clinical applicability among individuals with neurological disorders. Methods: A systematic literature search was conducted in the PubMed, Scopus, Embase, and Web of Science databases. After reading the full text of the selected studies and applying predefined inclusion criteria, seven studies, involving participants with multiple sclerosis (n = 3 studies), Parkinson’s disease (n = 2 studies), and stroke (n = 2 studies), were included based on pertinence and relevance to the topic. Results: F8WT demonstrated strong reliability and validity across various neurological populations and correlated significantly with established measures of gait, balance, and disease severity. Preliminary evidence supports its ability to discriminate individuals at increased fall risk and detect subtle motor performance changes. Discussion: The F8WT emerges as a valuable tool, capturing multifaceted gait impairments often missed by linear walking assessments. Sensitive to subtle functional changes, it is suitable for tracking disease progression and intervention efficacy. Conclusions: F8WT is reliable and clinically relevant, effectively identifying subtle, complex walking impairments in neurological disorders. Full article

This article examines the strategies employed by Mexican secondary school students to understand unfamiliar academic vocabulary and the relationship between these strategies and their reading proficiency. Within the broader Latin American context—where low reading comprehension levels remain prevalent—the study focused on a sample of 40 first-year secondary students, categorized according to their reading level. Using two instruments, the research identified the vocabulary learning strategies used by students and assessed their effectiveness in deriving word meaning. Findings indicate that while students across reading levels use similar strategies, those with higher reading proficiency more frequently and effectively apply complex strategies such as contextual abstraction, retrieving textual information, rereading the text, and full morphological analysis. Morphological analysis proved to be the most effective strategy, provided students possessed the metalinguistic skills necessary to decompose and reconstruct word meaning from all morphemes. The study concludes that the successful use of vocabulary strategies is closely linked to students’ reading proficiency, and that reading comprehension and academic vocabulary knowledge are mutually reinforcing. These findings highlight the importance of explicitly teaching academic vocabulary in school settings as a means to enhance students’ reading performance. Full article

Rare genetic diseases are often caused by structural variants (SVs), such as insertions, deletions, duplications, inversions, and complex rearrangements. However, due to the technical limitations of short-read sequencing, these variants remain underdiagnosed. Long-read sequencing technologies, including Oxford Nanopore and Pacific Biosciences high-fidelity (HiFi), have recently advanced to the point that they can accurately find SVs throughout the genome, including in previously unreachable areas like repetitive sequences and segmental duplications. This study underscores the transformative role of long-read sequencing in diagnosing rare diseases, emphasizing the bioinformatics tools designed for detecting and interpreting structural variants (SVs). Comprehensive methods are reviewed, including methylation profiling, RNA-seq, phasing analysis, and long-read sequencing. The effectiveness and applications of well-known tools like Sniffles2, SVIM, and cuteSV are also assessed. Case studies illustrate how this technique has revealed new pathogenic pathways and solved cases that were previously undetected. Along with outlining potential future paths like telomere-to-telomere assemblies and pan-genome integration, we also address existing issues, including cost, clinical validation, and computational complexity. For uncommon genetic illnesses, long-read sequencing has the potential to completely change the molecular diagnostic picture as it approaches clinical adoption. Full article

Transparency is a key requirement for some classes of robots, exoskeletons, and assistive devices (READs), where safe and efficient human–robot interaction is crucial. Typical fields that require transparency are rehabilitation and industrial contexts. However, the definitions of transparency adopted in the literature are heterogeneous. It follows that there is a need to clarify, summarize, and assess how transparency is commonly defined and measured. Thus, the goal of this review is to systematically examine how transparency is conceptualized and evaluated across studies. To this end, we performed a structured search across three major scientific databases. After a thorough screening process, 20 out of 400 identified articles were further examined and included in this review. Despite being recognized as a desirable and essential characteristic of READs in many domains of application, our findings reveal that transparency is still inconsistently defined and evaluated, which limits comparability across studies and hinders the development of standardized evaluation frameworks. Indeed, our screening found significant heterogeneity in both terminology and evaluation methods. The majority of the studies used either a mechanical or a kinematic definition, mostly focusing on the intrinsic behavior of the device and frequently giving little attention to the device impact of the user and on the user’s perception. Furthermore, user-centered or physiological assessments could be examined further, since evaluation metrics are usually based on kinematic and robot mechanical metrics. Only a few studies have examined the underlying motor control strategies, using more in-depth methods such as muscle synergy analysis. These findings highlight the need for a shared taxonomy and a standardized framework for transparency evaluation. Such efforts would enable more reliable comparisons between studies and support the development of more effective and user-centered READs. Full article

Background: Hypotension following epidural labor analgesia (ELA) is its most common complication, affecting approximately 20% of patients and posing risks to both maternal and fetal health. As digital tools and predictive analytics increasingly shape perioperative and obstetric anesthesia practices, real-world implementation data are needed to guide their integration into clinical care. Current monitoring practices rely on intermittent non-invasive blood pressure (NIBP) measurements, which may delay recognition and treatment of hypotension. The Hypotension Prediction Index (HPI) algorithm uses continuous arterial waveform monitoring to predict hypotension for potentially earlier intervention. This clinical trial evaluated the feasibility, acceptability, and efficacy of continuous HPI-guided treatment in reducing time-to-treatment for ELA-associated hypotension and improving maternal hemodynamics. Methods: This was a prospective randomized controlled trial design involving healthy pregnant individuals receiving ELA. Participants were randomized into two groups: Group CM (conventional monitoring with NIBP) and Group HPI (continuous noninvasive blood pressure monitoring). In Group HPI, hypotension treatment was guided by HPI output; in Group CM, treatment was based on NIBP readings. Feasibility, appropriateness, and acceptability outcomes were assessed among subjects and their bedside nurse using the Acceptability of Intervention Measure (AIM), Intervention Appropriateness Measure (IAM), and Feasibility of Intervention Measure (FIM) instruments. The primary efficacy outcome was time-to-treatment of hypotension, defined as the duration between onset of hypotension and administration of a vasopressor or fluid therapy. This outcome was chosen to evaluate the clinical responsiveness enabled by HPI monitoring. Hypotension is defined as a mean arterial pressure (MAP) < 65 mmHg for more than 1 min in Group CM and an HPI threshold < 75 for more than 1 min in Group HPI. Secondary outcomes included total time in hypotension, vasopressor doses, and hemodynamic parameters. Results: There were 30 patients (Group HPI, n = 16; Group CM, n = 14) included in the final analysis. Subjects and clinicians alike rated the acceptability, appropriateness, and feasibility of the continuous monitoring device highly, with median scores ≥ 4 across all domains, indicating favorable perceptions of the intervention. The cumulative probability of time-to-treatment of hypotension was lower by 75 min after ELA initiation in Group HPI (65%) than Group CM (71%), although this difference was not statistically significant (log-rank p = 0.66). Mixed models indicated trends that Group HPI had higher cardiac output (β = 0.58, 95% confidence interval −0.18 to 1.34, p = 0.13) and lower systemic vascular resistance (β = −97.22, 95% confidence interval −200.84 to 6.40, p = 0.07) throughout the monitoring period. No differences were found in total vasopressor use or intravenous fluid administration. Conclusions: Continuous monitoring and precision hypotension treatment is feasible, appropriate, and acceptable to both patients and clinicians in a labor and delivery setting. These hypothesis-generating results support that HPI-guided treatment may be associated with hemodynamic trends that warrant further investigation to determine definitive efficacy in labor analgesia contexts. Full article