Search Results (2,617)

Background: Parkinson’s disease (PD) is characterized by progressive degeneration of dopaminergic neurons in the substantia nigra pars compacta, accompanied by neuromelanin loss. Neuromelanin-sensitive magnetic resonance imaging (NM-MRI) enables in vivo visualization of these changes; however, its diagnostic and clinical utility remains incompletely defined. This study evaluated the feasibility, reliability, and biological sensitivity of semi-automated NM-MRI–based substantia nigra volumetry in PD. Methods: In this prospective case–control study, 50 participants (25 PD patients and 25 healthy controls) underwent 3T NM-sensitive MRI using a high-resolution T1-weighted spin-echo sequence. Semi-automated segmentation of hyperintense substantia nigra regions was performed using Mango v3.5.1, with intracranial volume normalization derived from FreeSurfer v7.3. Four participants were excluded due to motion artifacts, yielding a final cohort of 46 subjects. Clinical assessment included the Unified Parkinson’s Disease Rating Scale (UPDRS) Part III and Hoehn and Yahr (H&Y) staging. Group comparisons, receiver operating characteristic (ROC) analysis, and reliability testing using intraclass correlation coefficients (ICC) were performed. Results: Corrected substantia nigra volume was significantly reduced in PD patients compared with controls (18% reduction; p = 0.039, Mann–Whitney U test). Semi-automated measurements demonstrated excellent agreement with manual segmentation (ICC = 0.945). ROC analysis showed moderate discriminative performance for corrected volume (AUC = 0.700; sensitivity 68.4%, specificity 74.1%). No significant correlation was observed between corrected substantia nigra volume and UPDRS-III motor scores, while a trend toward lower SNc volume was observed with advancing H&Y stage. Conclusions: Semi-automated NM-MRI volumetry detects biologically meaningful substantia nigra volume loss in early-stage Parkinson’s disease with high measurement reliability. However, diagnostic performance was moderate and insufficient for standalone clinical diagnosis or motor severity prediction. These findings support the role of NM-MRI as a complementary imaging marker within multimodal diagnostic and research frameworks rather than as an independent diagnostic tool. Full article

Background: Retinal and optic nerve disorders are a leading cause of irreversible visual impairment worldwide. Advances in molecular genetics—including next-generation sequencing, genome-wide association studies, and gene-based therapeutic technologies—have reshaped understanding of both inherited and complex retinal diseases. However, translating genetic discovery into sustained clinical benefit remains biologically and practically constrained. Methods: A structured literature search was conducted using PubMed and Scopus to identify relevant studies published between 2015 and 2025. The search focused on molecular genetics, epigenetic modulation, mitochondrial biology, and translational applications in inherited retinal dystrophies and selected complex retinal diseases, prioritizing high-impact original research and systematic reviews addressing diagnostic innovation and therapeutic development. Results: Inherited retinal dystrophies represent the most advanced model of precision ophthalmology, with diagnostic yields approaching 70–80% in well-characterized cohorts. Gene augmentation and genome-editing strategies have demonstrated proof-of-concept efficacy, yet clinical benefit depends on residual cellular viability, delivery efficiency, and durability of expression. Emerging platforms include AAV-mediated gene transfer, in vivo CRISPR-based editing, RNA-directed splice modulation, and mitochondrial-targeted approaches. Persistent barriers include unresolved non-coding and structural variants, variant interpretation uncertainty, and endpoint selection in clinical trials. In contrast, complex retinal diseases such as glaucoma, age-related macular degeneration, and pathological myopia reflect polygenic susceptibility interacting with environmental and aging-related factors. Although polygenic risk scores refine probabilistic prediction, their utility is limited by ancestry bias and incomplete predictive performance. Epigenetic and mitochondrial mechanisms further modulate disease expression but remain largely non-actionable in routine practice. Conclusions: Retinal genetics has progressed from gene discovery to early therapeutic implementation. Future advances will depend on improved variant detection, functional validation, biomarker-guided staging, and integration of genomics with imaging and longitudinal modeling to achieve durable and equitable precision ophthalmology. Full article

Objective: To assess the safety and diagnostic outcomes of image-guided, non-target renal biopsies performed in cancer patients. Materials and Methods: We retrospectively identified patients who underwent percutaneous, image-guided, non-target renal biopsy between January 2017 and December 2020 in our institution. We recorded demographics, clinical, procedural, and pathologic details. Univariate and multivariable logistic regression models were used to assess the association between various variables and diagnostic yield or development of adverse events. Results: A total of 318 biopsies were performed in 318 patients (178 male, 140 female) with a median BMI of 28.4 kg/m². Median systolic and diastolic BP at the time of biopsy were 133 mmHg and 74 mmHg, respectively. Tissue was obtained using 18-gauge needles (99%). Adverse events were documented in 57 cases (18%), with 12 cases (3.8%) classified as grade 2 or higher per SIR classification. Diagnosis was achieved in 310 biopsies (97%). The median number of the glomeruli identified by light microscopy, immunofluorescence, and electron microscopy was 25, 8, and 3, respectively, and a higher number of identified glomeruli was associated with diagnostic yield in univariate analysis, although not in multivariable analysis. Diastolic BP higher than 80 mmHg and CT imaging guidance were associated with the development of adverse events in univariate analysis, and CT use remained so in the final multivariable analysis (p < 0.001). No other variables, including pre-biopsy anticancer or immunotherapy medications, were associated with increased risk of adverse events. Conclusions: Percutaneous, image-guided, non-target renal biopsy in cancer patients using an 18-gauge needle has a high diagnostic yield and safety profile. Full article

Background/Objectives: This study aims to develop a comprehensive Clinical Decision Support System (CDSS) that integrates multi-sequence sacroiliac joint (SIJ) MRIs with rheumatological, clinical, and laboratory findings into the decision-making process for the early diagnosis of axial spondyloarthritis (axSpA), incorporating segmentation-supported explainability. Methods: Multi-sequence SIJ MRI data (T1-WI, T2-WI, STIR, and PD-WI) were analysed from 367 participants (n = 193 axSpA; n = 174 non-axSpA controls). Sequence-based classification was performed using VGG16, ResNet50, DenseNet121, and InceptionV3 models; additionally, a lightweight and parameter-efficient SacroNet architecture was developed. Slice-level probability scores were converted to patient-level scores using the Dynamic Top-K Averaging method. Image-based scores were combined with a logistic regression-based clinical risk score using weighted linear integration (0.60 image/0.40 clinical) and a conservative threshold (τ = 0.70). Grad-CAM was applied for visual interpretability. Furthermore, to support the diagnostic outcomes with precise spatial data, active inflammation in STIR and T2-WI sequences was segmented. For this purpose, the MDC-UNet model was employed and compared with baseline U-Net derivatives. Results: Sequence-specific analysis showed VGG16 performing best on T1-WI (AUC = 0.920; Accuracy = 0.878) and DenseNet121 on STIR (AUC = 0.793; Accuracy = 0.771). The SacroNet architecture provided competitive classification performance at the patient level despite its low number of parameters (~110 K). Furthermore, MDC-UNet successfully segmented active inflammation, yielding Dice scores of 0.752 (HD95: 19.25) for STIR and 0.682 (HD95: 26.21) for T2-WI. Conclusions: The findings demonstrate that patient-level decision integration based on multi-sequence MRI, when used in conjunction with clinical risk scoring and segmentation-assisted interpretability, can provide a feasible and interpretable DSS framework for the early diagnosis of axSpA. Full article

Background: Schistosomiasis remains a significant neglected tropical disease in Kenya, but its presence in the western/northern Lake Turkana region is poorly characterised. We conducted an exploratory serosurvey to assess evidence of Schistosoma spp. exposure and a diagnostic accuracy study to evaluate dried blood spots (DBSs) for field serology. Methods: We performed a cross-sectional survey in adults (≥18 years) from six communities in the western/northern and shoreline area of Turkana Lake, excluding individuals with >6 months of residence in other Kenyan endemic areas. Capillary blood was collected on DBSs and tested centrally using ELISA for Schistosoma spp. IgG. In parallel, DBS cards performance was compared with paired routine serum ELISA in 23 patients assessed for suspected schistosomiasis at our centre. Results: We enrolled 155 participants (60% men; median age 30 years), with nearly universal reported freshwater contact (154/155, 99.4%). In the validation study, DBS values were lower than serum values (mean bias 0.27), with moderate correlation (r = 0.54) and modest discrimination (AUC 0.65; sensitivity 80% and specificity 50% at OD index >0.8). The median DBS ELISA OD index for the 155 participants was 0.55 (IQR, 0.34–0.79). Six samples exceeded 0.8, but these values were low, and all had negative IHA (<1/80), yielding no confirmed seropositive cases. Conclusions: These findings suggest low or absent sustained transmission in the sampled communities during the study period and indicate that DBS-based serology is operationally feasible but requires careful calibration and confirmatory testing for robust field inference. Full article

Background and Objectives: The authors aimed to describe their experience performing frameless stereotactic biopsies using an Autoguide Robotic Platform and to compare the outcomes with a frame-based stereotactic technique. We would like to emphasize the importance of this study, as it is the first in the literature to use sodium fluorescein for confirmation in intracranial biopsies taken with a Stealth Autoguide Robotic Platform. Materials and Methods: We retrospectively evaluated 30 patients who underwent a stereotactic intracranial biopsy between June 2018 and March 2024. The patients were divided into two groups: The robotic biopsy group (n = 15) underwent a frameless image-guided stereotactic intracranial biopsy with a Stealth Autoguide Robotic Platform and optical neuronavigation system (Stealth-Station S8, Medtronic, Minneapolis, MN, USA) using intraoperative sodium fluorescein. The frame-based (Integra, CRW, New Jersey, USA) stereotactic biopsy group (n = 15) underwent a stereotactic biopsy with the use of a stereotactic planning system (Atlas Integra Software, NJ, USA and Brainlab AG, Munich, Germany) without sodium fluorescein. Preoperative MRI and CT scans were performed in all the patients. Their external cranial anatomy was registered using either facial tracing or O-Arm (Medtronic Sofamor Danek, Inc., Memphis, TN, USA). Results: The robotic biopsy group demonstrated a diagnostic yield of 93.3% (14/15), while the frame-based group achieved 100% (15/15), with no significant difference (p = 0.609). The mean calculated tip error in the robotic biopsy group was 0.42 ± 0.19 mm (range: 0.1–0.7 mm) and the postoperative targeting accuracy in the frame-based biopsy group was 0.51 ± 0.23 mm (range: 0.2–0.9 mm), with no significant difference (p = 0.287). The robotic biopsy group demonstrated a significantly shorter mean surgical time (40.26 ± 6.13 vs. 52.47 ± 8.92 min, p = 0.002). Conclusions: Both frame-based and robotic-assisted stereotactic biopsy techniques achieve comparable diagnostic accuracy and targeting precision. However, a robotic biopsy significantly reduces the surgical time compared to a frame-based technique. The use of intraoperative sodium fluorescein is a valuable adjunct method for confirming that biopsy specimens are obtained from the intended target site. Full article

Objective: Anxiety symptoms frequently occur alongside mood disorders and are associated with poorer clinical outcomes, highlighting the importance of early and accurate detection. This study evaluated the diagnostic accuracy and psychometric properties of the Anxious Distress Assessment Scale (ADS), a newly developed brief self-report instrument designed to detect anxious distress. Method: The study was conducted in two phases. Phase 1 involved the development of the ADS as a five-item instrument reflecting the DSM-5-TR anxious distress criteria. In Phase 2, 105 adults diagnosed with major depressive disorder (MDD) completed the ADS alongside the Generalized Anxiety Disorder-7 (GAD-7) and the Montgomery–Åsberg Depression Rating Scale (MADRS). Psychometric evaluation included internal consistency reliability (Cronbach’s α), analyses of convergent validity, and diagnostic accuracy assessment using correlation and receiver operating characteristic (ROC) analyses. Results: Anxious distress was highly prevalent, with 71% of participants meeting DSM-5-TR criteria. The ADS demonstrated strong diagnostic performance, with sensitivity of 88.0%, specificity of 90.0%, positive predictive value of 95.7%, and negative predictive value of 75.0%. ROC analysis yielded an area under the curve (AUC) of 0.97 (95% CI: 0.943–0.997), with an optimal cut-off score of ≥10. Internal consistency was excellent (Cronbach’s α = 0.897). Principal component analysis supported a unidimensional structure, accounting for 71.5% of the total variance, with all items loading above 0.80. The ADS also demonstrated strong convergent validity, correlating significantly with the GAD-7 (r = 0.82) and MADRS (r = 0.68). Conclusions: The ADS demonstrates promising psychometric properties, including strong reliability, meaningful convergent validity, and excellent diagnostic accuracy. Its brief format and direct alignment with DSM-5-TR anxious distress criteria support its potential utility as a practical screening tool in clinical settings. However, these findings should be interpreted in light of the study’s focus on English-speaking Malaysian adults with MDD recruited from a tertiary-care setting. Further validation across diagnostic groups, clinical contexts, and cultural and linguistic populations is warranted. Full article

Background/Objectives: Early-onset high myopia (eoHM), defined as high myopia manifesting before 10 years of age, is largely attributed to genetic defects. This study aimed to investigate the genetic underpinnings of eoHM in a cohort of Chinese patients. Methods: We recruited 64 Chinese patients with eoHM. Comprehensive clinical evaluations were performed, and whole exome sequencing (WES) was conducted to identify potential pathogenic variants. The genetic findings were analyzed and correlated with the clinical phenotypes. Results: A total of 64 unrelated Chinese patients with suspected early-onset high myopia were initially recruited. Following whole exome sequencing (WES) and variant annotation, final 37 patients with variants in known myopia-associated genes were included in the analytical cohort. The mean age of onset for the cohort was 5 years (IQR, 4–7), with a mean spherical equivalent refraction of −7 D (IQR, (−8)–(−6)). Genetic analysis revealed variants in 28 known myopia-associated genes. We identified pathogenic or likely pathogenic variants in 11 of the 37 patients (29.7%, 95%CI: 0.1737–0.4590), while the overall diagnostic yield was 17.2% (11/64, 95%CI: 0.0970–0.2839) in initial 64 recruited patients. These genes included seven well-established eoHM-related genes, such as ARR3, CACNA1F, P4HA2, TRPM1, COL11A1, COL2A1, and PAX6. Additionally, variants of uncertain significance (VUS) in seven other candidate genes were detected in patients with eoHM. Conclusions: Our findings expand the genetic spectrum of eoHM and reinforce the critical role of genetic testing in its etiological diagnosis and clinical management. Observed patterns of genotype–phenotype associations are descriptive and should be considered hypothesis-generating, requiring validation in larger cohorts. Additionally, we identify several candidate genes that may serve as prospective biomarkers, though these findings require validation in larger cohorts and functional studies. Full article

Modern healthcare remains structurally and conceptually fragmented, with profound clinical and policy implications. At its root lies an ontological fracture: the prevailing biomedical model reduces patients to discrete biological systems (organs, biomarkers, and symptoms) detached from the psychological, social, and ecological contexts in which health and illness are experienced. This is compounded by epistemological fragmentation, where medical knowledge is compartmentalized into increasingly narrow specialties, limiting holistic understanding. These philosophical divisions manifest in downstream operational, informational, financial, and policy dysfunctions duplicative testing, misaligned incentives, disconnected care pathways, and population health failures. To address these multilevel fractures, we propose a unified architecture grounded in three interlocking components. First, the Precision and Personalized Population Health (P3H) framework offers a principle-based realignment toward care that is integrated, personalized, proactive, and population wide. P3H addresses the conceptual shortcomings of fragmented care by focusing on the full human trajectory across time, systems, and determinants. Second, General Purpose Technologies including artificial intelligence, biosensors, mobile diagnostics, and multimodal data systems enable the operationalization of whole-person care at scale, especially in low-resource settings. Third, the AI-WHOLE policy framework (Alignment, Integration, Workflow, Holism, Outcomes, Learning, and Equity) provides governance principles to guide ethical, equitable, and context-specific implementation. We argue that this triadic blueprint is particularly critical for Global South nations, where the lack of legacy infrastructure offers an opportunity for leapfrogging toward integrated, intelligent systems of care. Early models illustrate how policy-aligned, technology-enabled care rooted in whole-person principles can yield improvements in continuity, cost-efficiency, and chronic disease outcomes. This manuscript offers a systems-level strategy to overcome fragmentation and reimagine healthcare delivery, not only by refining clinical tools, but by redefining what it means to care for the human being in full. Full article

Against the global carbon neutrality target, predictive maintenance (PdM) of automotive engines represents a core technical strategy to advance the sustainable development of the automotive industry. Conventional single-modal diagnostic approaches for engine abnormal sound defects suffer from low accuracy and weak anti-interference capability. Existing multi-modal fusion methods fail to deeply mine the physical coupling between cross-modal features and often entail excessive model complexity, hindering deployment on resource-constrained on-board edge devices. To resolve these limitations, this study proposes a Physical Prior-Embedded Cross-Modal Attention (PPE-CMA) mechanism for lightweight multi-modal fusion diagnosis of engine abnormal sound defects. First, wavelet packet decomposition (WPD) and mel-frequency cepstral coefficients (MFCC) are integrated to extract time-frequency features from engine audio signals, while a channel-pruned ResNet18 is employed to extract spatial features from engine thermal imaging and vibration visualization images. Second, the PPE-CMA module is designed to adaptively assign attention weights to audio and image features by exploiting the physical coupling between engine fault acoustic and visual characteristics, enabling efficient cross-modal feature fusion with redundant information suppression. A rigorous theoretical derivation is provided to link cosine similarity with the physical correlation of engine fault acoustic-visual features, justifying the attention weight constraint (β = 1 − α) from the perspective of fault feature physical coupling. Third, an improved lightweight XGBoost classifier is constructed for fault classification, and a hybrid data augmentation strategy customized for engine multi-modal data is proposed to address the small-sample challenge in industrial applications. Ablation experiments on ResNet18 pruning ratios verify the optimal trade-off between diagnostic performance and computational efficiency, while feature distribution analysis validates the authenticity and effectiveness of the hybrid augmentation strategy. Experimental results on a self-constructed multi-modal dataset show that the proposed method achieves 98.7% diagnostic accuracy and a 98.2% F1-score, retaining 96.5% accuracy under 90 dB high-level environmental noise, with an end-to-end inference speed of 0.8 ms per sample (including preprocessing, feature extraction, and classification). Cross-engine and cross-domain validation on a 2.0T diesel engine small-sample dataset and the open-source SEMFault-2024 dataset yield average accuracies of 94.8% and 95.2%, respectively, demonstrating strong generalization. This method effectively enhances the accuracy and robustness of engine abnormal sound defect diagnosis, offering a lightweight technical solution for on-board real-time fault diagnosis and in-plant online quality inspection. By reducing engine fault-induced energy loss and spare parts waste, it further promotes energy conservation and emission reduction in the automotive industry. Quantified experimental data on fuel efficiency improvement and carbon emission reduction are provided to substantiate the ecological benefits of the proposed framework. Full article

Background: The clinical intra-anesthetic changes of perioperative hypersensitivity (POH) are not specific and require a thorough differential diagnosis with other mimicking conditions. Biomarkers such as tryptase and histamine provide supportive evidence for POH. From the suggested cutoffs, a common decision threshold has not been validated for use in daily practice. The aim of this systematic review and meta-analysis is to identify biomarkers investigated for POH and to evaluate their diagnostic performance. Methods: This meta-analysis included original diagnostic accuracy studies comparing patients with clinically suspected POH and controls with no signs of intraoperative hypersensitivity reactions, in whom allergy biomarkers were evaluated, aiming to investigate diagnostic performance of the assays. Data was pooled to evaluate sensitivity and specificity. Results: In seven studies on tryptase and three studies on histamine dosing for the diagnosis of POH/POA, different fixed or dynamic thresholds for positivity were proposed. For tryptase, fixed thresholds had 59.8% sensitivity and 95.2% specificity for an optimal cutoff of 12.68 ng/mL, while dynamic thresholds yielded 77.2% sensitivity and 88.5% specificity. For histamine, fixed cutoffs presented 78% sensitivity and 85% specificity, while dynamic thresholds investigated in a single study yielded 78.2% sensitivity and 91.1% specificity. Estimates for histamine are unreliable due to limited data. Conclusions: From published data, tryptase is clearly the most robust biomarker, dynamic thresholds boost sensitivity without major specificity loss and confirm the added diagnostic value of relative changes over fixed absolute cutoffs. Preliminary results suggest that histamine might have optimal diagnostic performance, but estimates are severely limited by small sample sizes. Full article

Chitooligosaccharides (COS) are short-chain chitosan derivatives with a wide range of biomedical, agricultural, and environmental applications, including antimicrobial therapy, wound healing, and pollutant removal. Reliable quantification of COS is essential but currently relies on high-performance liquid chromatography, mass spectrometry, or capillary electrophoresis, which require costly equipment, complex sample preparation, and are unsuitable for routine or on-site applications. This study reports a rapid, solvent-free, colorimetric assay for COS based on the reaction of 5% aqueous ninhydrin with free amino groups in McIlvaine buffer. The assay was optimized using glucosamine as a model analyte, yielding maximal sensitivity at pH 7.0. The chromophore generated (Ruhemann’s purple) remained stable for over 120 min after reaction, allowing measurements to be taken without strict time constraints. Calibration was linear from 0.4 to 2.2 mM (R² = 0.9926), with low limits of detection (0.006 mM) and quantification (0.018 mM). Increasing absorbance with COS polymerization degree (DP1–DP6) demonstrates specificity for free amino groups, while N-acetyl glucosamine showed a negligible response. Furthermore, the assay was successfully adapted for solid-phase detection on ninhydrin-pretreated filter paper and nitrocellulose, with enhanced sensitivity. This simple, efficient, and low-cost method provides an accessible alternative to instrumental techniques, supporting COS monitoring in laboratory workflows and enabling portable applications in biomedicine, agriculture, and environmental diagnostics. Full article

Low-temperature stress during the jointing stage severely disrupts the coordination of the source–flow–sink system in winter wheat. To elucidate the underlying mechanism, three wheat cultivars with different winter habits (Zhenmai 12, Jimai 22, and Shannong 38) were selected and subjected to six temperature levels (−6 °C to 8 °C) and three stress durations (2–6 days). The effects of vascular bundle traits on the transport of photosynthetic products, dry matter distribution, and yield formation were analyzed. The results showed that Zhenmai 12 and Jimai 22 completely ceased photosynthesis under 0 °C and −3 °C, respectively. The leaf vascular bundle area continuously decreased with increasing low-temperature stress, while the proportion of xylem and phloem initially increased by approximately 15% and 10%, respectively, before rapidly decreasing to 65% of the control value. In the stem, the three vascular bundle parameters initially increased by 20%, 25%, and 20%, respectively, before quickly decreasing to 50%. Changes in the vascular bundle structure weakened the transport capacity of assimilates, with dry matter in leaves and stems decreasing by 15–20% and 10%, respectively, while the root dry matter increased by 20–30%. Correlation analysis revealed highly significant relationships (p < 0.001) between vascular bundle parameters and yield components. Principal component and cluster analyses indicate that the area of leaf and stem vascular bundles, maximum net photosynthetic rate, and water use efficiency may be key indicators in explaining the variation in yield. Radar plots further validated this finding, showing that Zhenmai 12 and Jimai 22 are more sensitive to changes in the maximum net photosynthetic rate, while Shannong 38 exhibits a greater sensitivity to changes in water use efficiency. Based on existing research on photosynthetic pathways and dry matter distribution, this study innovatively investigates the potential relationship between material transport and yield formation under low-temperature stress during the jointing stage from the perspective of anatomical structure and functional coupling. The findings provide new insights into understanding the structural impact of low-temperature stress on crop yield formation and offer theoretical support for identifying the structural basis of limited material transport under stress and for developing disaster diagnostic models driven by structural parameters. Full article

Background: The contribution of intronic variants to the etiology of Mendelian diseases is still underrecognized, impacting the diagnostic yield. Whole genome sequencing (WGS) detects intronic variants, but besides canonical splice-sites, intronic variants are frequently excluded from the interpretation step or are classified as variants of uncertain significance (VUS). In fact, assessing their clinical significance often requires validation via RNA-sequencing (RNA-seq) or in vitro studies. Methods: We studied a 31-year-old patient with spinocerebellar ataxia who lacked a molecular diagnosis after WGS analysis. We applied the Detection of RNA Outliers Pipeline (DROP) to analyze RNA-seq data from patient fibroblasts. DROP integrates OUTRIDER and FRASER 2.0 algorithms designed to identify aberrant gene expression and splicing, respectively. Results: DROP identified differential expression and aberrant splicing of SNX14. Retrospective WGS data analysis revealed a homozygous NM_153816.6(SNX14): c.867+288A>G deep intronic variant, which caused pseudo-exon activation and reduced transcript levels. Biallelic loss-of-function variants in SNX14 cause autosomal recessive spinocerebellar ataxia type 20 (SCAR20; OMIM 616354), consistent with the clinical presentation of this case. Conclusions: We identify a deep intronic SNX14 variant as the genetic basis of SCAR20. We demonstrate the utility of RNA-seq to increase the diagnostic yield by identifying and resolving the pathogenicity of deep intronic variants. Defining aberrant splicing events is therapeutically relevant, as these mechanisms are targets for antisense oligonucleotide (ASO) based interventions. Full article

Background: Artificial intelligence (AI) is poised to fundamentally reshape healthcare delivery, offering unprecedented advancements in diagnostics, treatment personalization, and operational efficiency. However, a growing body of international research reveals a critical “optimism–knowledge gap”: healthcare professionals are enthusiastic about AI’s potential but possess limited technical knowledge and practical experience. This gap compromises the safe and effective implementation of AI tools. The Italian healthcare context presents a unique and amplifying challenge, as it is defined by the stringent “human-in-the-loop” oversight mandated by the Garante per la protezione dei dati personali (Italy’s Data Protection Authority). This legal framework makes clinician competence not just a goal, but a prerequisite for regulatory compliance. Objective: This study aimed to provide an exploratory quantitative assessment of AI awareness, practical application, and understanding of its limitations among a sample of clinicians in Italy. It specifically sought to compare the preparedness of hospital-based clinicians and general practitioners (GPs) and to identify the workforce’s perceived educational needs within this unique legal environment. Methods: A descriptive, cross-sectional survey was conducted from February to August 2025. Using a non-probability convenience sampling method via professional networks, the survey yielded 362 total responses. Data were analyzed descriptively and inferentially using Chi-square (${\chi }^2$) tests to compare cohort responses on familiarity, practical exposure, knowledge of limitations, and interest in further training. Results: A universal and high demand for education was found, with 89.9% of all respondents being “Moderately” or “Very” interested in learning more about AI. This optimism coexists with dangerously low practical exposure. The gap was most profound among GPs, 44.1% of whom have “Never” used an AI tool—a rate significantly higher than hospital clinicians (34.9%; ${\chi }^2=3.14$, p = 0.045). Furthermore, 32.6% of GPs admitted that they “understand some benefits but not the limitations.” Conclusions: Italian clinicians mirror the global optimism–knowledge gap. These findings underscore the urgent need for structured, continuous education in AI literacy to address ethical and regulatory imperatives within the Italian healthcare system. Full article