Search Results (145)

Background: Parkinson’s disease (PD) is a chronic, slowly progressive, and irreversible neuropathological disorder characterized by the progressive degeneration of specific neurons responsible for producing neurotransmitters essential for motor control. Although PD primarily affects motor function, various non-motor symptoms commonly emerge during the prodromal phase. These include autonomic dysfunction, cognitive and neurobehavioral disorders, and sensory and sleep abnormalities. Notably, speech and voice alterations, particularly hypokinetic dysarthria, are frequent manifestations. This research presents a methodology to distinguish between individuals with PD and healthy controls using voice signals through speech recognition and machine learning (ML) techniques. A dataset comprising 81 voice samples (41 healthy controls and 40 PD patients) was utilized to extract two types of cepstral features: Mel-frequency cepstral coefficients (MFCCs) and subband-based cepstral coefficients (SBCs). These extracted features were used to train and evaluate three ML algorithms: Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machines (SVM). Results: Among the algorithms evaluated, the SVM-SBC model exhibited the highest performance, achieving an accuracy of 79%, a sensitivity of 75.5%, and an Area Under the ROC Curve (AUC-ROC) of 84%. Conclusions: This study highlights the potential of integrating cepstral features with machine learning algorithms to develop reliable, non-invasive tools for the early detection of PD. Full article

Neurological disorders like Alzheimer’s disease, Parkinson’s disease, and epilepsy are becoming major causes of disability and mortality worldwide, and their prevalence is expected to rapidly increase with the aging of the population. These diseases develop silently, with irreversible neuronal damage often occurring decades before any clinical signs of illness are noticed, making early diagnosis and treatment difficult. The presymptomatic period greatly restricts the effectiveness of therapeutic interventions and reduces the possibility of disease-modifying interventions. Traditional diagnostic methods based on clinical assessment, neuroimaging, and invasive biomarkers are not sensitive enough to identify the disease at an early stage and are expensive to the healthcare system. The latest artificial intelligence (AI) technology and machine learning (ML) approaches, together with digital biomarkers obtained from eye tracking, facial expressions, speech analysis, motor dynamics, electrophysiology, wearable devices, and passive sensing, offer promising non-invasive alternatives for early detection of diseases. However, most reported performance metrics are derived from retrospective or pre-validated datasets, and prospective external validation remains limited. This narrative review synthesizes current evidence on AI-driven digital biomarkers for early detection of neurological diseases, examining disease-specific applications, methodological approaches, and challenges in clinical practices. We emphasize that clinical utility is task specific and dependent on disease stage, validation design, clinical endpoints, cost, workflow integration, and availability of disease-modifying therapies. We also note that much of the evidence summarized here derives from retrospective, case-control, or internally validated datasets and that prospective, patient-independent, and external validation with clinically meaningful endpoints remains limited. Reported performance figures should be read as proof-of-concept evidence rather than as evidence of demonstrated clinical readiness. We highlight promising future directions, including federated learning, explainable AI, and precision neurology approaches, while acknowledging that most applications remain investigational and require prospective validation before broad clinical deployment. Full article

Purpose: Childhood apraxia of speech (CAS) is a motor speech disorder characterized by deficits in speech planning and programming. However, current diagnostic frameworks have been developed primarily from studies of English-speaking children, raising questions regarding their applicability to other languages. This study examined the common and diagnostic speech characteristics of Hebrew-speaking children with CAS as perceived by Israeli speech–language pathologists (SLPs). Method: Two hundred Israeli SLPs with experience diagnosing and/or treating children with CAS completed an online questionnaire. Participants rated 26 speech characteristics on 7-point scales reflecting their perceived frequency and diagnostic specificity in Hebrew-speaking children with CAS. Descriptive analyses included mean ratings and the percentage of clinicians endorsing each characteristic. Results: Increased difficulty with multisyllabic words (M = 6.37) and inconsistent errors (M = 6.32) received the highest frequency ratings and were endorsed by 87.0% and 84.0% of clinicians, respectively, as common characteristics of CAS. Inconsistent errors received the highest diagnostic specificity rating (M = 6.12), followed by groping movements (M = 5.73) and difficulty sequencing syllables (M = 5.48). Prosodic features, including lexical stress errors (M = 3.72) and equal stress patterns (M = 3.73), received ratings at or below the midpoint of the scale and were endorsed by fewer than 20% of clinicians as common characteristics of CAS. Breathy voice (M = 3.11) and consistent hypernasality (M = 2.79), two control characteristics not typically associated with CAS, received the lowest ratings. Conclusions: Israeli SLPs identified syllable sequencing difficulties and inconsistency as the most salient and diagnostically informative characteristics of CAS in Hebrew-speaking children. In contrast, prosodic features, particularly lexical stress errors, were perceived as less common and less central to diagnosis than suggested by English-based diagnostic frameworks. These findings suggest that the clinical manifestation of CAS may be influenced by language-specific phonological and prosodic properties and underscore the need for direct investigation of CAS characteristics in Hebrew-speaking children. Full article

Background: Juvenile-onset Huntington disease (JoHD) is a rare form of Huntington disease characterized by symptom onset at or before 20 years of age. Early manifestations are often non-choreic and may be attributed to developmental, psychiatric, movement, metabolic, or epileptic disorders. We described three molecularly confirmed cases and examined early manifestations, diagnostic pathways, and epilepsy. Methods: We conducted a retrospective case series and a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 systematic review of PubMed, Scopus, and Web of Science Core Collection through 5 July 2026. The strict patient-level synthesis required attributable onset at or before 20 years, patient-specific molecular confirmation of a pathogenic HTT repeat expansion, and extractable clinical data. Complementary aggregate or linked reports using closely aligned JoHD criteria were retained for context but excluded from patient-level calculations. Results: The cases included childhood-onset JoHD with drug-resistant epilepsy, adolescent-onset JoHD with progressive motor-cognitive decline and epilepsy in a known Huntington disease pedigree, and childhood-onset JoHD without available family history, in whom status epilepticus prompted renewed diagnostic evaluation. Ninety-three reports were included; of these, 81 contributed 228 unique patients and 12 provided complementary data. Early manifestations were heterogeneous and broadly consistent with previously described childhood-onset JoHD phenotypes. Diagnostic delay was extractable in 180/228 patients; among 172 with point estimates, the median was 4.0 years. Definite epilepsy was reported in 60/145 patients with ascertainable seizure status and was descriptively more frequent in childhood-onset (<10 years) than adolescent-onset (10–20 years) JoHD (49/84 [58.3%] vs. 11/57 [19.3%]). Conclusions: JoHD should be considered in children and adolescents with progressive multisystem neurological involvement, particularly when epilepsy occurs with developmental regression, gait or speech deterioration, pyramidal or extrapyramidal signs, basal-ganglia abnormalities, or a compatible family history. Full article

Background: Pathogenic variants in ZNF292 cause intellectual disability type 64 (MIM#619188), characterized by intellectual impairment ranging from mild to severe, speech delay, and autism spectrum disorder. However, reports of ZNF292-related neurodevelopmental disorders remain scarce, and most published studies are based on multi-center cohorts. Methods: We performed whole-exome sequencing in eight unrelated individuals presenting with unexplained neurodevelopmental disorders, including global developmental delay and/or intellectual disability. Detailed clinical characterization, neuroimaging, and developmental assessments were conducted. Results: Seven de novo variants in ZNF292 were identified, including two nonsense and five frameshift variants, namely, c.4189C>T (p.Arg1397Ter), c.6343C>T (p.Arg2115Ter), c.1533del (p.Ile511Metfs*11), c.3094dup (p.Ser1032Phefs*18), c.3997_3998del (p.Thr1333Glnfs*8), c.6028_6031del (p.Ala2010Ter) and c.6160_6161del (p.Glu2054Lysfs*14). Among these, c.1533del (p.Ile511Metfs11), c.3094dup (p.Ser1032Phefs18) and c.3997_3998del (p.Thr1333Glnfs*8) are reported here for the first time. All variants were classified as pathogenic. All individuals exhibited global developmental delay, intellectual disability, and short stature. The majority presented with language impairment, motor delays, autism spectrum features, and dysmorphic facial features, while brain magnetic resonance imaging revealed nonspecific abnormalities such as ventriculomegaly. Conclusions: This study contributes additional cases to the expanding phenotypic and mutational spectrum of ZNF292-related neurodevelopmental disorder. Growth retardation was observed in all eight individuals, but given the limitations of a single-center referral cohort, this observation should be interpreted with caution and requires validation in larger studies. Full article

Background/Objectives: Autism spectrum disorder (ASD) is currently singularly described as a behaviourally defined neurodevelopmental diagnosis with symptoms affecting social communication and social understanding alongside the presence of restricted patterns of behaviour. Wide, and often very personal, variations in symptom intensities and differing developmental trajectories, reflective of large heterogeneity, are an important feature of the label. ASD carries enhanced risks for multiple over-represented, sometimes overlapping, comorbidities spanning behavioural, psychiatric and somatic domains. Said comorbidities often have numerous and far-reaching effects on quality of life by way of their impacts and, in extreme cases, their potential effects on life expectancy. It is of paramount importance that data on the risks of such comorbidities are available and, where possible, screening, preventive and/or treatment options implemented. Methods: In this narrative review, the authors searched databases (PubMed/Medline, ScienceDirect, Google Scholar) for papers published between 1 January 2015 and 16 February 2026 that were pertinent to comorbidity and autism. Results: We present data on the frequency of various somatic (gastrointestinal, immunological, metabolic, neurological, motor-sensory disorders), psychiatric (anxiety, mood, schizophrenia spectrum, personality, substance abuse, trauma, feeding and eating, sleeping, self-harm, neurocognitive disorders) and neurodevelopmental comorbidities (intellectual, attentional, speech and language, motor disorders) that can present alongside a diagnosis of ASD. We provide accompanying data on the magnitude of potential risk and, where available, information on comorbidity risks according to sex and age. Conclusions: This review paper deals with an extremely broad field. Limitations of the narrative review strategy are discussed alongside how the variable risk of comorbidity mimics the heterogeneity present in autism, thus inviting further investigations. Full article

The cerebellum is traditionally considered a structure responsible for motor control, but it is also involved in auditory perception, vocalization, speech, memory, emotional control, and social cognition. Due to its high intrinsic synaptic plasticity and complex connectivity with other brain regions, it is of potential interest for monitoring adaptive responses under various physiological and pathological conditions to capture global brain dynamics. Nevertheless, the use of electrocerebellography (ECeG) to detect changes in cerebellar electrical activity is limited, and a systematic evaluation of ECeG data to inform future research directions is lacking. This review summarizes recent ECeG research to explore the contribution of this time-honored method to deciphering the cerebellum’s spatial and temporal dynamics in health and disease. ECeG studies from the past three decades examining the slow and fast adaptive responses of the cerebellum in different cerebellar layers during sleep, anesthesia, brain injury, epilepsy, neurodegenerative diseases, and neuropsychiatric disorders are compiled from the PubMed, Scopus, and Google Scholar databases and discussed accordingly. It can be concluded that, despite certain limitations, ECeG is a practical, valuable, and reliable technique for detecting and predicting the complex spatial and temporal features of cerebellar electrical activity. Full article

Background and Clinical Significance: Cerebral amyloid angiopathy (CAA) is a neurovascular disorder characterized by the deposition of amyloid beta (Aβ) peptides within the walls of small-to-medium-sized cerebral vessels, leading to vascular fragility and an increased risk of lobar intracerebral hemorrhage (ICH), cognitive decline, and recurrent stroke. CAA is an important cause of spontaneous ICH in elderly patients and may be underrecognized, particularly when presenting with acute neurologic symptoms that mimic ischemic stroke. Early identification has significant implications for management, prognosis, and secondary prevention. Case Presentation: An 80-year-old male presented to the emergency department with incoherent speech, rambling, and severe headache concerning for acute stroke. His medical history was notable for a prior cerebrovascular accident, hypertension, diabetes mellitus, benign prostatic hyperplasia, and recent evaluation for dementia-like symptoms. Initial neuroimaging revealed a 3.2 cm intraparenchymal hemorrhage in the left occipital lobe with surrounding edema. Subsequent MRI demonstrated a lobar hemorrhage pattern suggestive of CAA based on imaging findings and clinical context. The patient was admitted to the intensive care unit (ICU) for close neurologic monitoring. He remained hemodynamically stable with no new motor or sensory deficits. Over a three-day hospital course, his speech and visual deficits improved. Blood pressure was carefully controlled, and repeat imaging demonstrated stable hemorrhage without progression. He was diagnosed with probable CAA and discharged home with supportive services. Conclusions: This case highlights the importance of considering cerebral amyloid angiopathy in elderly patients presenting with spontaneous lobar intracerebral hemorrhage and cognitive symptoms. Prompt recognition and appropriate neuroimaging are critical for diagnosis, risk stratification, and guiding management. Full article

Background: Parkinson’s disease (PD) is a progressive neurodegenerative disorder that affects millions globally, particularly those in the elderly population. Several occupational exposures typical of maritime environments are recognized or suspected risk factors for PD, warranting attention within occupational health frameworks. The disease is characterized by motor symptoms such as tremor, rigidity, and bradykinesia, as well as non-motor impairments including speech abnormalities. Objective: Early diagnosis is crucial for effective disease management but remains challenging due to symptoms overlapping with normal aging and other neurological conditions. This study presents a machine learning (ML)-based approach for the early diagnosis of PD using speech signal analysis. Methods: We employed six supervised ML classifiers to differentiate between PD patients and healthy controls based on vocal features. The experimental dataset, MDVR-KCL, consists of speech recordings from both reading tasks and spontaneous dialogs, collected via mobile devices. From these recordings, we extracted Mel-Frequency Cepstral Coefficients (MFCCs), Gammatone Frequency Cepstral Coefficients (GTCCs), and acoustic features such as jitter, shimmer, and harmonic-to-noise ratio. These features capture a broad range of prosodic, spectral, and articulatory characteristics associated with PD-related speech impairments. Speaker diarization was applied in spontaneous dialog recordings to separate participant speech. Hyperparameter tuning was performed using GridSearchCV with 10-fold cross-validation, while final model evaluation was conducted using Leave-One-Subject-Out Cross-Validation (LOSOCV) to ensure subject-independent performance assessment. Results: In the read-text task, the SVM model performed exceptionally, yielding 95.45% accuracy, 94.62% sensitivity, 95.97% specificity, an F1-score of 94.12%, and an AUC of 0.98 with an MCC value of 0.90, for GTCCs with the acoustic features. In the spontaneous dialog task, the XGB model demonstrated the highest overall performance across all metrics, with a test accuracy of 83.7%, a sensitivity of 76.3.9%, a specificity of 88.9%, an F1-score of 79.5%, an AUC value of 0.88, and an MCC value of 0.66. Conclusions: Comparable results were obtained on both spontaneous dialog and reading speech subsets, demonstrating the robustness of the approach across different speaking contexts. These results demonstrate the effectiveness of integrating cepstral and acoustic features with machine learning models for non-invasive PD classification. The findings support the use of speech-based digital biomarkers in early PD detection and highlight the potential for developing scalable tools. This work highlights the potential of speech-based digital diagnostics to support clinical decision-making and improve patient outcomes. Full article

Background: Pediatric epilepsy is a clinically and genetically heterogeneous group of disorders, particularly in early-onset and syndromic presentations. Data integrating molecular findings with detailed clinical phenotyping remain limited in Kazakhstan and Central Asia. Methods: We conducted a retrospective, single-center, clinically selected, referral-based case series study of 31 pediatric patients evaluated at a tertiary center in Kazakhstan for epilepsy or epilepsy-associated neurodevelopmental disorders and found to have clinically relevant or potentially relevant genetic findings. Clinical records were reviewed for demographics, age at seizure onset, seizure semiology, developmental profile, EEG, MRI, extra-neurological features, treatment response, and family history. Variants were interpreted using ACMG-based criteria, and inheritance/segregation data were incorporated where available. Results: The cohort included 15 males and 16 females. Median seizure onset was 5.0 months (IQR 2.0–13.0), and 22/31 patients (71.0%) presented within the first year of life. Developmental delay/intellectual impairment was observed in 20/31 cases (64.5%), speech delay in 17/31 (54.8%), motor delay in 15/31 (48.4%), and hypotonia in 12/31 (38.7%). Variants were identified in 23 genes, including several variants with limited or no prior support in ClinVar or peer-reviewed reports. Ion channelopathies were the largest mechanistic group (12/31, 38.7%), followed by mitochondrial/metabolic disorders (6/31, 19.4%), mTOR pathway disorders (5/31, 16.1%), and neurodevelopmental/chromatin/transcriptional disorders (4/31, 12.9%). SCN1A was the most recurrent gene (8/31, 25.8%) and showed a broad phenotypic continuum from Dravet-compatible to non-Dravet presentations. EEG and MRI abnormalities were common and often syndromic in pattern. Treatment response was frequently partial or poor, although selected genotype-linked treatment observations were noted in PRRT2 (carbamazepine), PNPO (pyridoxine), and one SCN1A case (stiripentol). Conclusions: This study expands the clinicogenetic characterization of pediatric epilepsy cases with clinically relevant or potentially relevant genetic findings in Kazakhstan and highlights the value of integrated molecular, phenotypic, and segregation analysis in underrepresented populations. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder primarily caused by the degeneration of dopamine-producing neurons in the substantia nigra, leading to characteristic motor symptoms such as tremors, rigidity, and bradykinesia, as well as non-motor manifestations including depression, sleep disturbances, and speech impairments. Among these symptoms, speech abnormalities affect approximately 90% of individuals with PD, making acoustic analysis a promising non-invasive cue for early detection. However, subtle speech variations are often imperceptible to the human ear, and speech-only analysis may overlook complementary visual manifestations, such as hypomimia—reduced facial expressivity commonly observed in PD patients. To address these limitations, we propose Parkinson’s Detection via Attentional Fusion Network (PDAF-Net), a novel multimodal deep learning framework for early PD detection that jointly models acoustic and facial dynamic features in a binary classification setting. The proposed architecture consists of a Dual-Stream Feature Encoder (DSFE), with an audio branch based on a one-dimensional convolutional neural network (1D-CNN) and bidirectional long short-term memory (BiLSTM), and a visual branch built upon a two-dimensional convolutional neural network (2D-CNN) and a Transformer encoder. Multimodal integration is achieved through a Cross-Attention-guided Attentional Feature Fusion (CA-AFF) module, which explicitly models bidirectional cross-modal interactions and performs adaptive feature recalibration via an iterative attentional fusion mechanism. We conducted experiments on a self-collected Chinese multimodal dataset comprising 100 PD patients and 100 healthy controls. Although the data are balanced at the subject level, sliding-window segmentation introduces sample-level imbalance; to address this issue, a class-balanced focal loss is employed. Model performance was evaluated using subject-wise five-fold cross-validation. The results demonstrate that PDAF-Net consistently outperforms unimodal baselines across multiple evaluation metrics, achieving an accuracy of 89.3%, an F1-score of 0.884, and an AUC of 0.916. These findings highlight the effectiveness of explicit cross-modal interaction modeling and adaptive feature fusion for improving automated early PD screening in real-world clinical settings. Full article

Background: Communication, language, and speech disorders are highly prevalent in children with cerebral palsy (CP) and substantially impact social, educational, and community participation. However, few studies have systematically characterized communicative and linguistic profiles using standardized assessments. This paper outlines the work of the ‘Italian CP & Language Network’ over the last two years, focusing on identifying research priorities, developing specialized assessment protocols, and proposing a shared classification system for speech and language disorders in children with CP. Methods: A survey was sent to 11 specialized centers to investigate clinical practices and assessment tools. Based on the results and an extensive literature review, the group developed three age- and complexity-based diagnostic protocols and a shared classification system. Results: The survey highlighted high variability in test selection, especially for speech and pragmatic assessment, and a significant need for ad hoc tools for augmentative and alternative communication (AAC). Three standardized protocols were defined: (1) early language (<48 months), (2) school-age language and pragmatics (4–12 years), and (3) minimally verbal children (6–12 years). A multi-level classification system for language and speech disorders was proposed to improve diagnostic consistency. Conclusions: Standardizing assessment is a critical step toward early identification of communicative vulnerabilities to guide tailored interventions and promote participation and quality of life across developmental stages. The group provides a framework for prospective multicenter data collection to correlate linguistic and speech phenotypes with neuroradiological features and motor outcomes. Full article

Automated video-based detection of cognitive disorders can enable a scalable non-invasive health monitoring. However, existing methods focus on a single disease and provide limited interpretability, whereas real-world videos often contain co-occurring conditions. We propose a novel unified multi-task method to detect depression and Parkinson’s disease (PD) from in-the-wild video data called DEPART (DEpression and PArkinson’s Recognition Technique). It performs body region extraction, Contrastive Language-Image Pre-training (CLIP)-based visual encoding, Transformer-based temporal modeling, and prototype-aware classification with a gated fusion technique. Gradient-based attention maps are used to visualize task-specific regions that drive predictions. Experiments on the In-the-Wild Speech Medical (WSM) corpus demonstrate competitive performance: the multi-task model achieves Recall of 82.39% for depression and 78.20% for PD, compared with 87.76% and 78.20%, for the best single-task models. The multi-task learning initially increases false positives for healthy persons in the PD subset, mainly due to annotation–modality mismatches, static visual content misinterpreted as motor impairments, and occasional body detection failures. After cleaning the test data, Recall for healthy individuals becomes comparable across models; the multi-task model improves Recall for both depression (from 82.39% to 87.50%) and PD (from 78.20% to 86.14%), suggesting better robustness for real-life clinical applications. Full article

STT3A encodes the catalytic subunit of the oligosaccharyltransferase A (OST-A) complex and is classically linked to severe autosomal-recessive congenital disorder of glycosylation (CDG). To define the distinct autosomal-dominant disorder, we reviewed all published cases and integrated three previously unpublished individuals from the CDG natural history study. Across 21 individuals, abnormal transferrin glycosylation was present in nearly all individuals (20/21), and subtle facial dysmorphism was common (18/21). Neurodevelopmental involvement was frequent, including motor delay (13/21), learning difficulties (13/21), speech delay (12/21), and intellectual disability (10/21). Musculoskeletal manifestations were also common, including skeletal abnormalities (12/21), short stature (11/21), muscle cramps (8/21), and early-onset osteoarthritis in adults (6/21). Less frequent features included congenital heart defects (5/21) and coagulation factor deficiency (5/21). Importantly, the newly reported individuals expand dominant STT3A-CDG with previously unreported features, including anorectal malformation, morbid obesity, and clinically significant bleeding diathesis with von Willebrand factor and factor VIII deficiency. Biochemical signatures ranged from classic type I transferrin patterns to subtle or atypical abnormalities, emphasizing that near-normal transferrin testing does not exclude the diagnosis. Variants clustered in conserved catalytic regions, with recurrent p.Arg405 across de novo, inherited, and mosaic cases supporting a mutational hotspot and likely dominant-negative mechanism. Full article

Background/Objectives: Amyotrophic lateral sclerosis (ALS) is a clinically heterogeneous neurodegenerative disease in which bulbar involvement frequently affects speech and voice production. Although acoustic voice analysis can detect phonatory alterations in ALS, its ability to differentiate clinical phenotypes remains limited. This study investigated whether biomechanical voice parameters provide complementary information for characterizing bulbar involvement across bulbar-onset ALS (ALS-B) and spinal-onset ALS (ALS-S) and explored their association with clinical and functional measures. Methods: This cross-sectional observational study included 50 patients with ALS (20 ALS-B, 30 ALS-S) and 50 controls with non-neurological voice disorders. Sustained vowel phonation was analyzed using acoustic measures and biomechanical voice parameters derived from a standardized model of vocal fold vibration. Perceptual voice severity was assessed using the GRBAS scale, while functional status was evaluated with the ALS Functional Rating Scale–Revised (ALSFRS-R) and the Barthel Index. Associations with clinical measures were explored in secondary analyses. Results: Compared with controls, ALS patients showed significant differences in acoustic measures and several biomechanical parameters related to glottal closure and vibratory stability. Biomechanical analysis revealed significant differences between ALS-B and ALS-S, particularly in parameters reflecting vibratory asymmetry, glottal tension and cycle-to-cycle instability. Unexpectedly, ALS-B showed greater perceptual voice severity and higher Barthel Index scores than ALS-S, while no differences were observed in global ALSFRS-R total scores. Conclusions: Biomechanical voice analysis appears to capture physiologically meaningful alterations in vocal fold function in ALS and provides complementary information for characterizing bulbar motor involvement across clinical phenotypes, particularly ALS-B disease. When combined with acoustic and clinical assessments, this approach may enhance the evaluation of bulbar involvement and functional status in ALS. Full article