Search Results (55)

Informal settlements accommodate more than one billion people worldwide, yet their intricate urban forms are frequently perceived as chaotic, which impedes the formulation of sustainable upgrading strategies. The main objective of this research is to bridge a major methodological gap by developing analytical tools that can systematically decode the inherent spatial logic of such environments. This paper develops and applies an integrated four-part morphological framework designed to provide a deep, form-based reading of informal urbanism. The framework’s indicators were systematically derived from an extensive review of the literature and subsequently validated through the Fuzzy Delphi Method (FDM) with a panel of 15 experts, ensuring analytical robustness. The validated framework was then applied to the Saadi neighborhood, a representative informal settlement in Shiraz, Iran, using a multi-scalar, mixed-methods approach that integrated GIS, remote sensing, and in-depth field surveys. The analysis produced a comprehensive analytical atlas, culminating in a detailed morphological profile. The findings identify Saadi’s urban form not as disordered, but as a ‘consolidating, low-rise, fine-grained fabric shaped by topography,’ revealing a clear, self-organized spatial logic. The study concludes that the proposed framework is a robust and replicable tool for moving beyond pejorative descriptions of informality. By providing an evidence-based method to read the physical language of these settlements, the approach offers a crucial foundation for developing more context-sensitive and sustainable urban upgrading strategies. Full article

Dyslexia is a neurodevelopmental disorder that impairs reading, affecting 5–17.5% of children and representing the most common learning disability. Individuals with dyslexia experience decoding, reading fluency, and comprehension difficulties, hindering vocabulary development and learning. Early and accurate identification is essential for targeted interventions. Traditional diagnostic methods rely on behavioral assessments and neuropsychological tests, which can be time-consuming and subjective. Recent studies suggest that physiological signals, such as electrooculography (EOG), can provide objective insights into reading-related cognitive and visual processes. Despite this potential, there is limited research on how typeface and font characteristics influence reading performance in dyslexic children using EOG measurements. To address this gap, we investigated the most suitable typefaces for Turkish-speaking children with dyslexia by analyzing EOG signals recorded during reading tasks. We developed a novel deep learning framework, DyslexiaNet, using scalogram images from horizontal and vertical EOG channels, and compared it with AlexNet, MobileNet, and ResNet. Reading performance indicators, including reading time, blink rate, regression rate, and EOG signal energy, were evaluated across multiple typefaces and font sizes. Results showed that typeface significantly affects reading efficiency in dyslexic children. The BonvenoCF font was associated with shorter reading times, fewer regressions, and lower cognitive load. DyslexiaNet achieved the highest classification accuracy (99.96% for horizontal channels) while requiring lower computational load than other networks. These findings demonstrate that EOG-based physiological measurements combined with deep learning offer a non-invasive, objective approach for dyslexia detection and personalized typeface selection. This method can provide practical guidance for designing educational materials and support clinicians in early diagnosis and individualized intervention strategies for children with dyslexia. Full article

Advancements in genomic technologies have transformed prenatal genetic testing, offering more accurate, comprehensive, and noninvasive approaches to reproductive care. This review provides an in-depth overview of current methodologies and emerging innovations, including expanded carrier screening (ECS), cell-free DNA (cfDNA) testing, chromosomal microarray analysis (CMA), and sequencing-based diagnostics. We highlight how next-generation sequencing (NGS) technologies have revolutionized carrier screening and fetal genome analysis, enabling detection of a broad spectrum of genetic conditions. The clinical implementation of cfDNA has expanded from common aneuploidies to include copy number variants (CNVs), and single-gene disorders. Diagnostic testing has similarly evolved, with genome sequencing outperforming traditional CMA and exome sequencing through its ability to detect both sequence and structural variants in a single assay. Emerging tools such as optical genome mapping, RNA sequencing, and long-read sequencing further enhance diagnostic yield and variant interpretation. This review summarizes major technological advancements, assesses their clinical utility and limitations, and outlines future directions in prenatal genomics. Full article

Next-Generation Sequencing (NGS) techniques have become a cornerstone of molecular diagnostics, enabling high-throughput, parallel analysis of multiple disease-associated genes. Their targeted design allows streamlined interpretation and optimised diagnostic yield, especially in disorders with known genetic heterogeneity. In this review, we provide a comprehensive overview of the clinical application of NGS techniques—targeted gene panels, whole exome sequencing (WES) and whole genome sequencing (WGS)—detailing the methodological workflow and the critical steps involved in their implementation. Particular emphasis is placed on the genes identified through NGS that are implicated in neurodevelopmental, neurodegenerative, psychiatric, neuromuscular, cardiovascular, and metabolic disorders. We also compare the advantages and limitations of panel-based diagnostics versus WES and WGS, and discuss future directions, including the integration of long-read sequencing technologies into multidisciplinary clinical practice. Finally, we consider how these advances may ultimately bridge biomedical research and clinical practise to improve the diagnosis and management of multifactorial diseases. Full article

This study investigated the cognitive profiles of Italian university students with dyslexia using the WAIS-IV, comparing them to peers without specific learning disorders. Seventy-one participants took part: 36 with a diagnosis of dyslexia and 35 matched controls. While dyslexic adults showed lower Full Scale IQ (FSIQ) scores compared to controls, their scores remained within the average range. They showed deficits in Working Memory Index (WMI) and Processing Speed Index (PSI) but performed similarly to controls in Verbal Comprehension Index (VCI) and Perceptual Reasoning Index (PRI). Significant group differences also emerged in Arithmetic Reasoning, Symbol Search, and Coding subtests. Logistic regression identified WMI and PSI as the most reliable predictors of dyslexia, showing a good predictive value in discriminating between adults with and without dyslexia. Additionally, dyslexic adults displayed lower Cognitive Proficiency Index (CPI) scores relative to their General Ability Index (GAI), and lower FSIQ scores compared to controls. Overall, dyslexic adults exhibit a distinctive cognitive profile with strengths and weaknesses. This pattern can aid in dyslexia diagnosis, particularly in individuals who have compensated through extensive reading experience in a highly regular orthography. 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

Background/Objectives: The increased accessibility of information has resulted in a rise in patients trying to self-diagnose and opting for self-medication, either as a primary treatment or as a supplement to medical care. Our objective was to evaluate the reliability, comprehensibility, and readability of the responses provided by ChatGPT 4.0 when queried about the most prevalent orthopaedic problems, thus ascertaining the occurrence of misguidance and the necessity for an audit of the disseminated information. Methods: ChatGPT 4.0 was presented with 26 open-ended questions. The responses were evaluated by two observers using a Likert scale in the categories of diagnosis, recommendation, and referral. The scores from the responses were subjected to subgroup analysis according to the area of interest (AoI) and anatomical region. The readability and comprehensibility of the chatbot’s responses were analyzed using the Flesch–Kincaid Reading Ease Score (FRES) and Flesch–Kincaid Grade Level (FKGL). Results: The majority of the responses were rated as either ‘adequate’ or ‘excellent’. However, in the diagnosis category, a significant difference was found in the evaluation made according to the AoI (p = 0.007), which is attributed to trauma-related questions. No significant difference was identified in any other category. The mean FKGL score was 7.8 ± 1.267, and the mean FRES was 52.68 ± 8.6. The average estimated reading level required to understand the text was considered as “high school”. Conclusions: ChatGPT 4.0 facilitates the self-diagnosis and self-treatment tendencies of patients with musculoskeletal disorders. However, it is imperative for patients to have a robust understanding of the limitations of chatbot-generated advice, particularly in trauma-related conditions. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder leading to movement impairment, cognitive decline, and psychiatric symptoms. Key manifestations of PD include bradykinesia (the slowness of movement), changes in voice or speech, and gait disturbances. The quantification of neurological disorders through voice analysis has emerged as a rapidly expanding research domain, offering the potential for non-invasive and large-scale monitoring. This review explores existing research on the application of machine learning (ML) in speech, voice, and language processing for the diagnosis of PD. It comprehensively analyzes current methodologies, highlights key findings and their associated limitations, and proposes strategies to address existing challenges. A systematic review was conducted following PRISMA guidelines. We searched four databases: PubMed, Web of Science, Scopus, and IEEE Xplore. The primary focus was on the diagnosis, detection, or identification of PD through voice, speech, and language characteristics. We included 34 studies that used ML techniques to detect or classify PD based on vocal features. The most used approaches involved free speech and reading-speech tasks. In addition to widely used feature extraction toolkits, several studies implemented custom-built feature sets. Although nearly all studies reported high classification performance, significant limitations were identified, including challenges in comparability and incomplete integration with clinical applications. Emerging trends in this field include the collection of real-world, everyday speech data to facilitate longitudinal tracking and capture participants’ natural behaviors. Another promising direction involves the incorporation of additional modalities alongside voice analysis, which may enhance both analytical performance and clinical applicability. Further research is required to determine optimal methodologies for leveraging speech and voice changes as early biomarkers of PD, thereby enhancing early detection and informing clinical intervention strategies. Full article

This study aimed to validate the usefulness of the Simplified Computerized Comprehensive Learning Ability Screening Test (SCLST) in schools and homes, to facilitate early detection and intervention for children with reading disorder (RD), math disorder (MD), or attention-deficit hyperactivity disorder (ADHD). Participants included 207 children and adolescents diagnosed with ADHD, RD, or MD and the healthy control group that was matched 1:1 by gender, age, and educational years. Higher rates of omission errors, commission errors, and standard deviation of response times were observed in the ADHD group (p < 0.001) in the SCLST-ADHD. The accuracy rates of the SCLST-RD and SCLST-MD were lower in the RD (p < 0.001) and MD group (p < 0.01), respectively. The mean response times were higher in the MD group (p < 0.001). In addition, the optimal sensitivity and specificity values were 84.6% and 88.5%, and the positive and negative predictive values were 88.0% and 85.2%, respectively, in the SCLST-ADHD. In the SCLTS-RD, the sensitivity and specificity values were 81.1% and 85.6%, and the positive and negative predictive values were 84.9% and 81.9%, respectively. In the SCLST-MD, the sensitivity and specificity values were 97.4% and 76.9%, and the positive and negative predictive values were 80.9% and 96.8%, respectively. Thus, by supporting timely assessment and intervention, this tool can support clinicians and educators in early-stage learning disabilities screening and reduce long-term psychosocial impairments. Full article

Background: Primary progressive aphasia (PPA) is a neurodegenerative disorder that worsens over time without appropriate treatment. Although referral to a speech and language pathologist is essential for diagnosing language deficits and developing effective treatment plans, there is no scientific consensus regarding the most effective treatment. Thus, our study aims to assess the efficacy and safety of various therapeutic interventions for PPA. Methods: Google Scholar, PubMed, Web of Science, and the Cochrane Library databases were systematically searched to identify articles assessing different therapeutic interventions for PPA. To ensure comprehensive coverage, the search strategy employed specific medical subject headings. The primary outcome measure was language gain; the secondary outcome assessed overall therapeutic effects. Data on study characteristics, patient demographics, PPA subtypes, therapeutic modalities, and treatment patterns were collected. Results: Fifty-seven studies with 655 patients were included. For naming and word finding, errorless learning therapy, lexical retrieval cascade (LRC), semantic feature training, smartphone-based cognitive therapy, picture-naming therapy, and repetitive transcranial magnetic stimulation (rTMS) maintained effects for up to six months. Repetitive rTMS, video-implemented script training for aphasia (VISTA), and structured oral reading therapy improved speech fluency. Sole transcranial treatments enhanced auditory verbal comprehension, whereas transcranial direct current stimulation (tDCS) combined with language or cognitive therapy improved repetition abilities. Phonological and orthographic treatments improved reading accuracy across PPA subtypes. tDCS combined with speech therapy enhanced mini-mental state examination (MMSE) scores and cognitive function. Several therapies, including smartphone-based cognitive therapy and VISTA therapy, demonstrated sustained language improvements over six months. Conclusions: Various therapeutic interventions offer potential benefits for individuals with PPA. However, due to the heterogeneity in study designs, administration methods, small sample sizes, and lack of standardized measurement methods, drawing a firm conclusion is difficult. Further studies are warranted to establish evidence-based treatment protocols. Full article

Background/Objectives: Augmented reality (AR) has emerged as a promising educational tool for individuals with autism spectrum disorder (ASD), offering interactive and engaging learning experiences. While AR interventions have been widely explored in educational contexts, their specific impact on learning outcomes in individuals with ASD remains unclear. This systematic review aimed to explore preliminary indications of the efficacy of augmented reality (AR)-based interventions in improving cognitive and academic skills in children, adolescents, and adults with ASD. Methods: A comprehensive literature search identified studies published between 2014 and 2024 that assessed AR interventions targeting learning outcomes in individuals with ASD. Results: A total of 12 studies (9 were single-subject studies), comprising 123 participants, met the inclusion criteria. The findings revealed that AR interventions contributed to improvements in multiple learning domains, including language acquisition, reading comprehension, mathematics, science education, executive functioning, and attention. AR-based applications were particularly effective in enhancing engagement, motivation, and interactive learning, addressing challenges commonly faced by individuals with ASD. Conclusions: Findings suggest that AR can be a valuable tool for improving learning outcomes in individuals with ASD, and it could contribute to the inclusion and functional development of students with special needs. Full article

Molar incisor hypomineralization (MIH) is a dental condition that affects the enamel of permanent molars and/or incisors, often leading to tooth decay. Although several etiological hypotheses have come forward, including prenatal medical problems and postnatal illness, the pathogenesis of MIH is yet unclear. Aimed at exploring the epigenomic landscape of this dental condition, we collected dental tissue from a MIH-affected child and an age-matched control patient and investigated their DNA methylation status through an in-depth analysis of nanopore long-read sequencing data. We identified 780,141 CpGs with significantly different methylation levels between the samples; intriguingly, the density of these dinucleotides was higher in the regions containing genes involved in dental morphogenesis and inflammatory processes leading to periodontitis. Further examination of 54 genes associated with MIH or hypomineralized second primary molar disorders revealed very distinct methylation of intragenic transposable elements (SINEs, LINEs, and LTRs), while functional profiling analysis of 571 differentially methylated regions genome-wide uncovered significant enrichment processes including ameloblasts differentiation and calcium ion binding, as well as SP1 and other zinc finger transcription factors. Taken together, our findings suggest that DNA methylation could play a role in the pathogenesis of MIH and represent a stepping stone towards a comprehensive understanding of this multifactorial disorder. Full article

Existing studies have revealed that the reading comprehension ability of readers can be adversely affected by their psychosocial stress. Yet, the detailed impact of stress on various stages of text processing is understudied. This study aims to explore how the higher-level text processing ability, including syntactic parsing, sentence integration, and global text processing, of first language (L1) and second language (L2) English readers is affected under stress situations. In addition, the roles of trait anxiety, the central executive function moderating stress effects, in text processing were also examined. Twenty-two L1 readers and twenty-one L2 readers were asked to perform reading comprehension tasks under different stress situations. Eye-tracking technology was adopted to record participants’ visual behaviors while reading, and ten eye-movement measurements were computed to represent the effect of different types of text processing. The results demonstrate that the stress reduced the efficiency of syntactic parsing and sentence integration in both L1 and L2 groups, but only impaired global text processing in L2 readers. Specifically, L2 readers focused more on the topic structure of text to facilitate comprehension under stress situations. Moreover, only L1 readers’ higher-level text processing was affected by trait anxiety, while L2 readers’ processing was mainly related to their reading proficiency level. Future studies and applications were discussed. The findings advance our understanding of stress effects on different stages of higher-level text processing. They also have practical implications for developing interventions to help language learners suffering from stress disorders. Full article

The use of augmented reality, assistive technology (AT), and augmentative and alternative communication (AAC) offers a promising opportunity to significantly enhance the general reading abilities of students with specific learning disorders (SLDs) by providing effective learning tools. This study aimed to assess students’ learning experiences to understand the effectiveness of AT and AAC in language skills development and identify the AT tools and devices commonly used in classroom settings, with the goal of better informing practitioners. A systematic literature review was conducted across various databases, resulting in the inclusion of 22 relevant articles, focusing on multiple implications of AT and AAC. Common factors associated with the implementation of AT in teaching students with SLDs were identified, and a thematic analysis revealed recurring patterns regarding the impact of AT solutions on students with SLDs. The findings indicate notable improvements in language skills among students with SLDs, including vocabulary, spelling, orthography, phonological awareness, and reading comprehension. However, two studies reported limited effects or no effects on language skills, self-efficacy, and self-esteem. This review shows that AT and AAC effectively support language skills development and outcomes for students with SLDs. Nevertheless, given the limited number of studies and the complexity of the factors explored, these conclusions should be interpreted with caution. Full article

Background/Objectives: It is often challenging for patients to understand treatment options, their mechanisms of action, and the potential side effects of each treatment option for glomerular disorders. This study explored the ability of ChatGPT to simplify these treatment options to enhance patient understanding. Methods: GPT-4 was queried on sixty-seven glomerular disorders using two distinct queries for a general explanation and an explanation adjusted for an 8th grade level or lower. Accuracy was rated on a scale of 1 (incorrect) to 5 (correct and comprehensive). Readability was measured using the average of the Flesch–Kincaid Grade (FKG) and SMOG indices, along with the Flesch Reading Ease (FRE) score. The understandability score (%) was determined using the Patient Education Materials Assessment Tool for Printable Materials (PEMAT-P). Results: GPT-4’s general explanations had an average readability level of 12.85 ± 0.93, corresponding to the upper end of high school. When tailored for patients at or below an 8th-grade level, the readability improved to a middle school level of 8.44 ± 0.72. The FRE and PEMAT-P scores also reflected improved readability and understandability, increasing from 25.73 ± 6.98 to 60.75 ± 4.56 and from 60.7% to 76.8% (p < 0.0001 for both), respectively. The accuracy of GPT-4’s tailored explanations was significantly lower compared to the general explanations (3.99 ± 0.39 versus 4.56 ± 0.66, p < 0.0001). Conclusions: ChatGPT shows significant potential for enhancing the readability and understandability of glomerular disorder therapies for patients, but at a cost of reduced comprehensiveness. Further research is needed to refine the performance, evaluate the real-world impact, and ensure the ethical use of ChatGPT in healthcare settings. Full article