Artificial intelligence (AI), digital health technologies, and neuroengineering are rapidly transforming the diagnosis and management of movement disorders. By 2050, these innovations, together with profound demographic, cultural, and economic changes, are expected to reshape the role of neurologists far beyond current clinical practice. movement disorder specialists may evolve from a primarily diagnostic clinician into an orchestrator of personalized neurocare, integrating multimodal digital biomarkers, AI-driven decision support systems, wearable technologies, closed-loop neuromodulation,and disease-modifying therapies. We argue that future neurological care will increasingly rely on continuous home-based monitoring, predictive modeling, and individualized therapeutic strategies based on biological, clinical and 'social' information rather than purely clinical disease definitions. At the same time, population aging, healthcare sustainability, and disparities in access toadvanced technologies will require neurologists to assume broader responsibilities as multidisciplinary coordinators, integrating clinicalexpertise with data science, ethics, and health economics. Despite the growing role of AI, clinical reasoning, empathy, communication,and shared decision-making will remain irreplaceable components of neurological care. Rather than replacing neurologists, AI is likelyto increase the need for highly trained specialists capable of critically interpreting algorithmic outputs, recognizing their limitations, andcontextualizing recommendations within each patient's biological, psychological, and social framework. The neurologist of 2050 willtherefore become not only a clinician, but also a guarantor of trustworthy, equitable, and human centered precision neurology.
The MANAGE-PD tool was developed to identify patients with Parkinson's disease (PD) who may require treatment optimization or consideration of device-aided therapies (DAT). However, real-world studies have suggested substantial discrepancies between MANAGE-PD classifications and routine clinical decision-making. Consecutive PD patients attending a tertiary movement disorders clinic were evaluated using the MANAGE-PD tool. Clinician treatment decisions and DAT recommendations were assessed independently. Clinical factors associated with clinician DAT recommendation were analyzed. A total of 252 patients were included. According to MANAGE-PD, 68 patients (27.0%) were classified as Category 3 (potential DAT candidates), whereas only 12 patients (4.8%) received a clinician recommendation for DAT. Among Category 3 patients, only 10 (14.7%) were recommended for DAT. Patients receiving a clinician recommendation had significantly longer disease duration, higher levodopa equivalent daily dose, and were more likely to experience OFF periods exceeding 2 h per day and troublesome dyskinesia. In exploratory multivariable analyses, disease duration (OR 2.29 per 5 years, 95% CI 1.34-3.91) and OFF periods exceeding 2 h per day (OR 6.80, 95% CI 1.67-27.75) remained independently associated with clinician recommendation. Within the MANAGE-PD Category 3 subgroup, disease duration was the only factor associated with clinician DAT recommendation. MANAGE-PD identified substantially more potential DAT candidates than were recommended for DAT in routine clinical practice. Disease duration emerged as an important determinant of clinician recommendation, suggesting that real-world treatment decisions incorporate factors beyond current algorithm-based criteria.
Background: Artificial intelligence (AI)-driven markerless motion capture (MMC) technologies are increasingly being integrated into pediatric healthcare to improve the assessment and management of movement disorders. These video-based systems enable non-invasive motion analysis without wearable sensors, facilitating more natural movement assessment in children, particularly those with neurological or developmental conditions. Objectives: We evaluated the clinical applicability of AI-based MMC tools in pediatric settings for diagnosis, monitoring of motor development, and rehabilitation. Methods: This systematic review was registered in PROSPERO (CRD42024511787) and conducted by two independent reviewers, with a third reviewer resolving disagreements. The literature published between 2018 and 2025 was systematically searched. Studies involving pediatric populations or clinically relevant pediatric applications of MMC were included. Results: Of 1521 identified studies, 52 were finally selected. The included studies evaluated populations across a wide age range. However, seven of the included articles were specifically focused on underage populations. Infant studies primarily analyzed whole-body movements, emphasizing the relevance of global motor patterns in early development. OpenPose and AlphaPose were the most frequently used frameworks in pediatric research because of their automatic full-body key point detection, whereas DeepLabCut was commonly selected for its customizable labeling capabilities. Theia3D emerged as a promising clinically applicable solution with high accuracy. Most studies evaluated kinematic parameters as objective markers of motor performance and development. However, methodological heterogeneity and limited pediatric-specific validation remain important limitations. Conclusions: AI-driven MMC technologies show considerable potential to support objective, accessible, and child-friendly movement assessment in pediatric clinical practice.
BackgroundAnxiety and other non-motor symptoms can greatly diminish quality of life in people with Parkinson's Disease (PD). Yet, few randomized controlled trials (RCTs) have assessed accessible, non-pharmacological mental health treatments for people with PD.ObjectiveTo assess the feasibility and impact of brief, remotely-delivered meditation and breathwork on neuropsychiatric symptoms in people with PD.MethodsIn this 18-week RCT, intervention participants attended weekly webinars for six weeks, while waitlisted controls maintained their usual routine before crossing over at six weeks. Participants were assessed for weekly compliance trends (primary outcome) and changes in anxiety (primary outcome), depression, perceived stress, and other non-motor symptoms at 6-week intervals: baseline and weeks 6 (T2), 12 (T3), and 18 (T4). Weekly compliance was collected in each group for 12 weeks. Anxiety was measured using the Parkinson's Anxiety Scale.ResultsThere was a significant decrease in the primary outcome, anxiety, in the intervention group (n = 17) compared to the waitlist control (n = 21) after 6 weeks of meditation and breathwork practice (mean difference (MD)= -5.0, 95% CI [-9.5, -0.49], P = .03). Within-group analysis further confirmed a significant reduction in anxiety (MD = -4.7, 95% CI [2.10,7.33], P < .001) in the intervention group (n = 28). Thirty-one percent of participants met the compliance definition.ConclusionRemotely-delivered meditation and breathwork through the BSM program significantly reduced anxiety in individuals with PD in our study and may be a promising non-pharmacological therapy. A larger RCT with continuous wellness coaching is warranted to more rigorously assess efficacy and optimize protocols.Clinical trial registration: ClinicalTrials.gov, NCT05335850, https://clinicaltrials.gov/study/NCT05335850. Study on whether meditation and breathwork practices improves anxiety in people with Parkinson's Disease and if it is possible to keep up with these practicesWhy was the study done?Mental health is greatly affected in those with Parkinson's Disease, reducing quality of life. Accessible treatments specifically for anxiety and other non-motor symptoms other than medications are not well studied. Since previous studies have shown their potential to reduce anxiety safely and at no cost, the research team examined online (virtual) meditation and breathwork for individuals with Parkinsons.What did the researchers do?The research team randomly placed 48 participants in one of two groups (24 participants each) for this 18-week study. The first group learned the meditation and breathwork practices through the Breath, Sound, and Meditation program in the first 6 weeks of the study through guided webinars, while the comparison waited 6 weeks before learning the practices. Participants were asked to answer weekly questions about their practice frequency for 12 weeks. Surveys measured anxiety, depression, stress, and other non-motor symptoms at the beginning of the study, 6, 12, and 18 weeks.What did the researchers find?The research team reported that anxiety levels dropped in the group that learned the meditation and breathwork practices compared to the group that did not learn in the first 6 weeks. The anxiety levels also remained lower 12-weeks after the participants learned the practices compared to baseline. There were no changes in depression, stress, or other non-motor symptoms. Thirty-one percent of participants completed the practices regularly.What do the findings mean?The meditation and breathwork practices decreased anxiety levels in individuals with Parkinson's Disease. The practices could be a promising tool to improve Parkinson's-associated mental health symptoms. However, since this study was done with a small number of participants, a larger research study is needed to understand the full impact of the meditation and breathwork practices.
Approximately 30% of patients with tremor-dominant Parkinson's disease (PD) have rest tremor that persists despite optimal dopaminergic therapy. When deep brain stimulation and focused ultrasound are unavailable or declined, the therapeutic options narrow. Botulinum toxin (BoNT) offers a targeted, titratable, reversible approach, but whether a peripheral neuromuscular blocking agent makes sense for a centrally generated tremor is a legitimate question that deserves a direct answer. This narrative critical review appraises what is currently known across PD and non-PD tremor conditions, defines the technical requirements for safe and effective injection, and provides a practical framework for patient selection and clinical management. The PD-specific literature rests on a single positive double-blind randomized controlled trial of 30 patients; all remaining data are open-label or extrapolated from other tremor conditions, and this narrative synthesis combines heterogeneous conditions, outcome scales, and toxin protocols. A recurring technical observation is that, in the available trials, individualized, EMG-guided injection has been associated with substantially lower rates of hand weakness than fixed-dose injection (reported reductions from roughly 30-70% to below 15%) while maintaining tremor reduction, although the degree of benefit and weakness risk vary with the tremor syndrome, injected muscles, baseline impairment, dose, and guidance method. The careful patient selection this approach requires helps the individual clinician and patient achieve tremor relief, but it departs from the unselected real-world PD population and introduces selection bias that makes a large, statistically representative cohort difficult to assemble. In well-selected patients at centers with the appropriate expertise, BoNT may be a clinically useful option, but routine adoption is not yet supported.
Parkinson's disease (PD) care remains profoundly unequal across and within countries, despite major advances in understanding and treatment. Drawing on PD specialists' personal stories of real-world experiences from diverse regions across Asia, Africa, and South America, this paper highlights the key barriers to equitable PD care and identifies pragmatic, scalable solutions. Across settings, several major barriers consistently emerge: a critical shortage and uneven distribution of trained specialists; geographic disparities limiting access to care; substantial financial barriers, particularly for comprehensive and advanced therapies; fragmented healthcare systems lacking integration and multidisciplinary support; and low public awareness and persistent stigma, leading to delayed diagnosis and treatment. Strategies to address these challenges include workforce development through training, mentorship, and international collaboration; bringing expertise and services closer to patients via outreach programs and the use of "simple" technologies such as telemedicine and mobile communication platforms; ensuring universal access to essential medications, particularly levodopa; integrating multidisciplinary care models; public awareness campaigns and support groups empowering patients and caregivers and reducing stigma; and embedding research into routine care. The experiences presented here illustrate that meaningful progress is achievable through pragmatic solutions, collaborative networks, and sustained commitment to patient-centered care. Working Towards Better Parkinson's Disease Care for All: Stories and Insights from Parkinson's Specialists across the GlobeParkinson's disease (PD) care is not equally available to everyone. Although there have been many advances in understanding and treating PD, many people around the world still struggle to get the care they need. This paper shares real-life experiences from Parkinson's doctors in Asia, Africa, and South America to better understand these challenges and explore practical solutions. Common problems seen across different countries include too few healthcare professionals with expertise in PD, especially outside major cities; difficulties travelling long distances to access care; the high cost of treatments; healthcare services that are not well connected or coordinated; and low public awareness of PD, which can lead to stigma and delays in diagnosis and treatment. Practical approaches to improve access to care include training more healthcare professionals, building local expertise through mentorship and international partnerships, and bringing services closer to patients through outreach clinics and simple technologies such as telemedicine and mobile messaging platforms. Ensuring access to essential medicines, especially levodopa, is also critical. Other important strategies include providing multidisciplinary care, raising public awareness, reducing stigma, and incorporating research into everyday clinical practice. The experiences described here illustrate that meaningful improvements in PD care are possible, even in limited-resource settings. Progress can be achieved through practical solutions, strong partnerships, and a continued focus on the needs of patients and their families.
Continuous subcutaneous foslevodopa/foscarbidopa (FLD/FCD) is the first 24-h levodopa-based subcutaneous infusion therapy for advanced Parkinson's disease (PD). Real-world data from the Middle East region are absent. To report the first Middle Eastern clinical experience with foslevodopa/foscarbidopa, evaluating motor and non-motor outcomes, safety profile, and treatment continuation rates in routine clinical practice. Retrospective, single-center, observational cohort study. Twenty-eight consecutive patients with advanced PD underwent inpatient foslevodopa/foscarbidopa initiation between January 2025 and January 2026 at a quaternary academic medical center in Abu Dhabi, United Arab Emirates. Primary outcomes were Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Part III, daily OFF/ON hours, and Hoehn and Yahr stage; secondary outcomes included Non-Motor Symptoms Scale (NMSS) total and domain scores and MDS-UPDRS Parts I and II. Motor assessments were conducted in the practically defined OFF state at baseline and in the ON state at follow-up on stable foslevodopa/foscarbidopa therapy. Safety was assessed by systematic recording of adverse events. Paired comparisons used Wilcoxon signed-rank tests. Mean age was 61.3 ± 13.5 years; disease duration 10.1 ± 7.4 years; baseline levodopa equivalent daily dose 1018 ± 574 mg. MDS-UPDRS Part III improved from 50.7 ± 14.5 (OFF-state baseline) to 27.1 ± 13.6 (ON-state follow-up; change -23.6 ± 8.3, p < 0.0001), representing elimination of wearing-off disability. Daily OFF time fell from 2.8 ± 1.4 to 0.9 ± 0.6 h (-1.9 ± 1.4 h; p < 0.0001), and good ON time increased by 2.0 ± 1.3 h (p < 0.0001). Hoehn and Yahr stage improved from median 3 (interquartile range (IQR) 3-4) to 2 (IQR 2-3; p < 0.0001). Non-motor symptom burden decreased substantially: MDS-UPDRS Parts I and II fell by 22.7 ± 9.3 points (p = 0.001) and NMSS total score by 29.7 ± 18.7 points (~49% reduction; p = 0.0007). Infusion site reactions occurred in 22 patients (79%) but were generally manageable. Six patients (21%) discontinued therapy (median 4.8 months), primarily because of skin reactions or compliance difficulties. Foslevodopa/foscarbidopa produced clinically meaningful improvements in motor fluctuations and non-motor symptom burden in this first Middle Eastern real-world cohort, supporting its role as an effective and feasible non-surgical device-aided therapy for advanced PD. The first Middle Eastern experience with a new non-surgical pump therapy for advanced Parkinson’s disease Parkinson’s disease causes shaking, stiffness, and slow movement that worsens over time. As the disease advances, standard levodopa tablets stop working smoothly, leaving patients unable to move or function for unpredictable periods each day. Existing advanced treatments, including brain surgery and intestinal tube pumps, require invasive procedures that many patients cannot undergo due to age, cognitive difficulties, or other medical reasons. Foslevodopa/foscarbidopa is a newly approved therapy that delivers levodopa continuously through a small wearable pump placed under the skin, requiring no surgery. This study reports the first real-world experience with this treatment in the Middle East. Twenty-eight patients with advanced Parkinson’s disease were treated at a specialist centre in Abu Dhabi, UAE, and followed for a median of nine months. Patients spent nearly two fewer hours per day unable to move and gained two additional hours of good functional time daily. Motor severity improved substantially. Non-motor symptoms, including anxiety, sleep disturbance, depression, and cognitive difficulties, improved by approximately 49%. Skin reactions at the pump site were common but resolved in all cases. Only 21% of patients discontinued therapy, and 75% reported satisfaction with treatment. These findings show that foslevodopa/foscarbidopa is effective and well-tolerated in a diverse real-world population, supporting its use beyond the settings studied in clinical trials.
Assessing functional movements is important for evaluating shoulder impairments, as these movements directly reflect patients' capacity to perform daily activities. Intelligent rotator cuff injury (RCI) recognition is beneficial for clinical management. This study aimed to develop accurate and cost-effective RCI recognition models by integrating machine learning (ML)/deep learning (DL) algorithms with upper limb kinematic data collected in functional movement, and to explore optimal motion task combinations and model configurations for clinical practice. A total of 102 participants were prospectively enrolled, comprising 51 patients diagnosed with RCI, 25 patients diagnosed with adhesive capsulitis and 26 healthy volunteers. Patients with adhesive capsulitis were included to assess model's ability to distinguish RCI from other shoulder disorders that present with similar function limitations. Each participant performed six functional movements simulating daily activities and two shoulder range of motion (ROM) tests. Upper limb kinematic data were collected by inertial measurement units (IMUs) and analyzed via eight classification models, including the proposed DL based RCI recognition model (RCIRNet) and seven conventional ML models (e.g., KNN, SVM, DT, RF, NB, AdaBoost, and XGBoost). Given the relatively small sample size, data augmentation was applied to mitigate overfitting and improve model generalization. Model performance was evaluated across single and combined motion tasks via Accuracy, Precision, Recall, F1-score, and AUC with its 95% confidence interval (CI). Results demonstrated that shoulder frontal ROM test alone achieved the best performance with RCIRNet, yielding an Accuracy of 0.89, F1-score of 0.89, and AUC of 0.93 (95%CI, 0.88-0.98). Combining four tasks increased Recall to 1.0 with high level Accuracy, F1-score, and AUC. However, adding more movements (5-8 tasks) did not improve model performance. Among different model configurations, RCIRNet performed best, SVM demonstrated overall stability, and KNN excelled in Recall. It is concluded that selecting discriminant movements is more critical for effective RCI recognition than simply increasing the quantity of movement tasks. The shoulder frontal ROM test combined with RCIRNet provides an optimal approach for accurate and efficient clinical screening and rehabilitation monitoring.
Involuntary inspiratory sigh (IIS) is a characteristic symptom of multiple system atrophy (MSA) and a supportive non-motor feature in the current diagnostic criteria. However, IIS during wakefulness has not been systematically investigated. This study explored the clinical significance and neural correlates of IIS by comparing clinical characteristics and cerebral blood flow (CBF) single-photon emission computed tomography findings between patients with and without IIS, and evaluated a practical method for IIS assessment. We retrospectively investigated 90 patients with clinically established or probable MSA. IIS was assessed via structured interview and direct examination. Patients were classified as IIS-positive if either method yielded positive results. Clinical characteristics were compared between IIS-positive and IIS-negative patients. Agreement between the two detection methods was examined. CBF was also compared between groups. IIS was identified in 21 (23.3%) of 90 patients. The IIS-positive patients showed higher Unified Multiple System Atrophy Rating Scale Part I scores and REM Sleep Behavior Disorder Screening Questionnaire scores, and more frequent severe orthostatic hypotension in exploratory comparisons. Interview- and examination-based IIS assessments showed moderate agreement, and IIS was significantly more likely to be detected during interviews than during examinations. The IIS-positive patients exhibited reduced CBF in the bilateral supplementary motor area. In MSA, IIS was associated with greater disease burden, more severe autonomic dysfunction, and reduced perfusion in the supplementary motor area. Interview-based assessment may help capture IIS in routine clinical practice. Clinicians should actively inquire about this clinically relevant symptom when evaluating patients with suspected MSA.
Dance-based interventions are increasingly used to support mental health and well-being across clinical, community, and educational contexts. However, the review-level evidence remains conceptually and methodologically fragmented. This quality-sensitive umbrella review synthesized 90 review articles identified across five databases. Systematic reviews and meta-analyses were appraised using AMSTAR 2, while scoping, narrative, literature, and clinical reviews were assessed using adapted review-appraisal criteria. Findings from systematic reviews and meta-analyses were used primarily to interpret effectiveness evidence, whereas nonsystematic reviews were used to map conceptual definitions, practice traditions, and measurement issues. The synthesis showed that the field is fragmented at three interrelated levels. First, intervention labels and delivery models are inconsistent, ranging from clearly defined dance/movement therapy to broad and underspecified uses of dance. Second, effectiveness is conceptualized differently across intervention traditions, with therapy-oriented reviews emphasizing symptom reduction and non-therapy-oriented reviews foregrounding well-being, vitality, social connection, and embodied experience. Third, outcome assessment relies predominantly on generic psychological scales, with limited use of movement-sensitive or dance-specific measures. Quality-sensitive evidence suggests that dance-based interventions show promise for well-being, social connection, emotional regulation, and selected symptom outcomes, but confidence remains limited by low review quality, heterogeneous populations, inconsistent intervention reporting, and insufficient attention to primary-study overlap. Future research should move beyond asking whether dance works in general and instead specify what kind of dance, for whom, through which mechanisms, and according to which model of mental health and well-being.
Mental disorders impose a disproportionate burden on populations in low- to middle-income countries (LMICs) and low-income countries (LICs), yet longitudinal assessments across these settings remain limited. Using the Global Burden of Disease Study 2023, we investigated prevalence and disability-adjusted life years (DALYs) for 10 mental disorder subcategories across 76 LMICs and LICs from 1990 to 2023 and projected trends through 2050 via a generalized ensemble modeling approach. Age-standardized prevalence rose markedly between 1990 and 2023, from 10,759.9 (95% uncertainty interval, 9,560.1-12,088.6) to 13,929.3 (12,512.5-15,829.0) per 100,000 in LMICs and from 11,723.6 (10,577.7-12,987.5) to 14,979.1 (13,512.9-16,770.7) in LICs, accompanied by corresponding increases in age-standardized DALY rates. The most pronounced increases in both age-standardized prevalence and DALYs were observed during the COVID-19 pandemic. Anxiety disorders (total percentage change: 102.3% in LMICs; 67.3% in LICs), eating disorders (40.7% in LMICs; 4.8% in LICs), depressive disorders (28.6% in LMICs; 29.8% in LICs), and autism spectrum disorders (14.9% in LMICs; 22.5% in LICs) showed the largest increases in prevalence. Under a reference scenario where past trends persist, age-standardized prevalence is projected to reach 17,155.8 (14,449.5-20,212.5) per 100,000 in LMICs and 17,968.4 (15,109.8-21,090.9) in LICs by 2050. These findings reveal persistent and widening disparities in mental health burden across resource-limited settings, substantially exacerbated by the pandemic. Without targeted, scalable, and sustained policy interventions, the burden in LMICs and LICs will continue to worsen, underscoring the critical need for context-specific public health action. This study was funded by the Gates Foundation.
Falls occur across all stages of Huntington's disease (HD) and are associated with poor quality of life and injury. However, there is limited information on falls in HD. The aim was to investigate the clinical features potentially associated with falls in HD. We conducted a cross-sectional, analytical observational study, including consecutive patients with genetically confirmed symptomatic HD, and assessed clinical features, fall characteristics, fear of falling, movement disorder phenomenology, gait characteristics, balance, and cognitive and neuropsychiatric symptoms. Those who had experienced ≥2 falls in the past 6 months were considered fallers. Stepwise forward logistic regression was performed to determine the variables related to recurrent falls. We included 40 individuals, of whom 24 (60%) were considered recurrent fallers. The nonfallers (75%) and fallers (79%) had high fear of falling rates. Seventy-two percent of falls occurred indoors, and 76% were classified as intrinsic. The dose of neuroleptics was higher in the fallers group (10.0 vs. 5.85, P = 0.028). This group also exhibited a higher prevalence of balance disorders, chorea, and executive cognitive impairment than the nonfallers group. No significant differences were observed in the spatiotemporal gait parameters studied. The regression analysis revealed that only the Berg Balance Scale scores were retained in the model (odds ratio: 0.87, 95% confidence interval: 0.78-0.97). Falls and fear of falling were frequent in HD. High doses of neuroleptics, chorea, cognitive and behavioral symptoms, and particularly balance disorders contribute to falls in HD.
To develop and evaluate a simple-to-use checklist to support physicians with the timely diagnosis of Lennox-Gastaut syndrome (LGS). A panel of 10 pediatric and adult epileptologists used the International League Against Epilepsy (ILAE) criteria for LGS classification and definition to develop seven questions for the checklist, through an iterative process. A scoring system formulated for the checklist yielded four distinct outcomes: likely LGS, possible LGS, unlikely LGS, and insufficient data. The checklist was then tested for reliability. Firstly, each expert selected LGS and non-LGS cases from their patient medical records and, for each case, provided the original diagnosis and answers to the checklist questions to an independent third party, who subsequently compared the outcome with the original diagnosis. The checklist questions were then refined based on the initial assessment. All cases were re-evaluated by the panel, and the results were collected as previously. Correct outcomes were defined as a perfect level of agreement with the original diagnosis provided by the expert (both LGS and non-LGS cases) or "possible LGS" outcome matching an original LGS diagnosis. Of the 120 cases evaluated (LGS, n = 64; non-LGS, n = 56), the outcome was correct for 95% (n = 114): 66.7% (n = 80) had a perfect level of agreement with the original diagnosis, and 28.3% (n = 34) were "possible LGS" matching an original LGS diagnosis. Incorrect matches, where the checklist outcome did not match the original LGS diagnosis, were due to the absence of documented characteristic EEG features and, related to young age, lack of cognitive/behavioral impairment. The checklist offers a simple-to-use resource for treating physicians to support earlier LGS diagnosis, using the ILAE criteria as the framework. The findings also highlight the importance of EEG data for LGS diagnosis, that mandatory ILAE diagnostic features are not always initially present and may evolve over time, and the need to re-evaluate patients during follow-up.
Assessing suicidal ideation (SI) in patients with primary temporomandibular disorders (TMD) is crucial for its early identification and intervention. This study aimed to investigate the biopsychosocial factors associated with SI in the overall TMD sample and across subgroups of painful TMD patients. A total of 441 TMD patients were enrolled. TMD was diagnosed using the diagnostic criteria for TMD, and SI was assessed via item 9 of the Patient Health Questionnaire-9. SI prevalence was compared across painful TMD subgroups, stratified by pain duration, and classified according to the International Association for the Study of Pain (IASP) diagnostic criteria. Sociodemographic, Axis I and II, and pain characteristics were compared between the SI and non-SI groups using the Chi-square and Mann-Whitney tests. Binary logistic regression, incorporating pain persistence and depression, was performed to identify the key factors independently associated with SI. In the overall TMD sample, encompassing both painful and non-painful patients, 8.2% reported SI. Among the painful TMD subgroups, SI prevalence was 5.7% in acute cases and 12.0% in chronic cases by pain duration, rising to 20.9% when chronic pain was defined by IASP criteria (p < 0.001). Patients with SI were more frequently divorced, had higher rates of TMD-attributed headache and myofascial pain with referral, and reported greater perceived pain intensity. They also exhibited markedly elevated depression, anxiety, non-specific physical symptoms, pain catastrophizing, and overall distress (p < 0.001), as well as greater disability (p = 0.004). Pain duration was not independently associated with SI, whereas depression emerged as a significant independent predictor (p < 0.001). These findings highlight the substantial psychological burden in TMD patients, particularly those with chronic pain, and underscore the critical role of depression in SI. Incorporating routine biopsychosocial assessment and SI screening into TMD clinical practice is, therefore, highly recommended.
Limited mouth opening (LMO) is a clinical condition that may be relatively commonly encountered in a dental practice. This condition may negatively affect the quality of life and pose a diagnostic challenge for the clinician. It may have a local, systemic, or combined etiology. The authors searched electronic databases (PubMed, Embase, Web of Science, Scopus, Ovid MEDLINE, Google Scholar) for articles focused on the various aspects of LMO and its possible causes, prevention, and management protocols. LMO is a multifactorial condition with congenital and acquired etiologies, involving intracapsular, extracapsular, and musculoskeletal structures. Contributing factors include trauma, systemic diseases, neoplasm, and medication-related conditions. Acute LMO was commonly associated with pain, inflammation, or muscle guarding, whereas chronic LMO was more frequently linked to irreversible changes such as fibrosis, ossification, or persistent muscle spasms, resulting in functional limitation. This article offers a structured, etiology-based framework for assessment and management of LMO, highlighting the importance of early recognition and treatment to prevent long-term complications and improve the quality of life of patients. LMO requires an interdisciplinary approach depending on the underlying etiology. Early diagnosis and conservative management are critical in preventing progression to irreversible LMO and reducing the need for more aggressive therapies. Multiple therapeutic modalities and long-term follow-up play an essential role in restoring and maintaining mandibular function.
SPG46 is an autosomal recessive hereditary spastic paraplegia (HSP) caused by biallelic GBA2 mutations. As a rare and still poorly understood condition, information about its clinical and genetic spectrum is scarce. This study aimed to delineate the clinical, neuroimaging, and genetic characteristics of a Brazilian SPG46 cohort. We conducted a retrospective cross-sectional study at two referral centers, identifying nine patients from six unrelated families harboring pathogenic GBA2 variants through whole-exome sequencing. Demographic, clinical, neuroimaging, and genetic data were systematically analyzed. The cohort (4 males, 5 females) presented disease onset between 6 and 24 years (mean, 10.7). All exhibited progressive spastic paraplegia associated with cerebellar ataxia, dysarthria, and cognitive impairment. Psychiatric manifestations, sleep disturbances, skeletal deformities, and dystonia were frequent. Cerebellar atrophy was observed in four cases, whereas corpus callosum thinning, cataracts, and hearing impairment were absent. The recurrent GBA2 c.1365G>C;p.(Trp455Cys) variant suggests a potential founder effect and expands the phenotypic spectrum of SPG46 in Brazil.
Accurate identification of gait abnormalities is fundamental to clinical evaluation in both veterinary medicine and translational research. Commonly used descriptors such as ataxia, paresis, hypermetria, and lameness are frequently applied as single-word summaries of complex movement disorders, yet these terms lack precise, universally accepted operational definitions. Dictionary definitions are often generic or ambiguous, leading to variability in interpretation among clinicians, students, and researchers. This review evaluates commonly used neurologic and gait-related terminology in dogs, examining historical origins, denotation and connotation, as well as the limitations of current usage in a clinical setting. For each term, clinically observable features are delineated to establish functional, operational definitions grounded in movement analysis and neurophysiologic processes. The goal of this reappraisal is to enhance clarity, consistency, and diagnostic accuracy in veterinary neurology by establishing standardized descriptors for gait abnormalities in clinical practice, teaching, and research.
Sevoflurane is rarely administered as monotherapy in clinical anesthesia practice, yet the safety profile of its drug-drug interactions (DDIs) remains incompletely characterized in real-world settings. To systematically identify and quantify disproportionality signals for central nervous system (CNS) adverse events associated with sevoflurane combinations using the FDA Adverse Event Reporting System (FAERS) database. Reports from 2004 Q1 to 2025 Q2 listing sevoflurane as primary suspect were extracted and deduplicated. Disproportionality analysis employed Reporting Odds Ratio (ROR) and Proportional Reporting Ratio (PRR) to detect signals, supplemented by Bayesian methods (EBGM and IC) for multi-method validation. A hierarchical approach progressed from broad CNS adverse event screening to drug class-level and individual drug-level analyses. Interaction Reporting Odds Ratio (IOR) models quantified synergistic interaction signals under both PS-restricted and role-independent frameworks, with the Ω shrinkage measure applied as a Bayesian validation method for all IOR signals. Among 4,129 sevoflurane reports (2,903 concomitant use, 1,226 quasi-monotherapy), multiple significant signals emerged. Intravenous anesthetics and opioid analgesics demonstrated the highest signal rates (44.4% each). Movement disorders, particularly dystonia, exhibited the strongest signals across multiple drug classes. Morphine combination with sevoflurane showed a notably high dystonia signal (ROR: 66.64). IOR analysis identified supra-additive interactions: morphine-dystonia (IOR: 9.46), fentanyl-dystonia (IOR: 2.96), and propofol-confusional state (IOR: 2.56). Ω shrinkage validation confirmed fentanyl-dystonia and propofol-confusional state as true synergistic signals. The role-independent analysis identified seven additional supra-additive IOR signals, with morphine-dystonia retaining significance across both frameworks. Sensitivity analyses confirmed the robustness of the core signals. Sevoflurane DDIs might be associated with multiple CNS adverse event signals, particularly movement and consciousness disorders. Dual-method validation using IOR and Ω shrinkage measure enhanced signal discrimination and reduced false-positive risk. These findings warrant clinical vigilance in vulnerable populations and prospective validation studies.
Parkinson's disease (PD) progression is highly heterogeneous, complicating clinical management and prognostication. While machine learning models have been developed using research datasets such as Parkinson's Precision Medicine Initiative (PPMI) and Parkinson's Disease Biomarkers Program (PDBP), their clinical translatability is limited due to differences in routinely collected data. The Hoehn and Yahr (H&Y) scale is commonly used in clinical practice to stage PD, yet most predictive models focus on less practical measures. This study developed and validated machine learning models to predict H&Y scores at 5 years post-123I-ioflupane single-photon emission computed tomography (SPECT) imaging, leveraging both a real-world dataset and a subset of the PPMI cohort. The goal was to assess the utility of routinely collected clinical and imaging data for prognostic modeling. Data from medical records and imaging were harmonized from 343 real-world patients and 134 PPMI patients, resulting in a merged dataset with 83 overlapping features. Random Forest and Gradient Boosting models were trained to predict 5-year H&Y scores using varying amounts of longitudinal data and imaging features. Models using 2 years of clinical follow-up data achieved the highest predictive accuracy. The most important predictors were early H&Y scores, gait symptom severity, and select imaging features. Machine learning models can predict 5-year H&Y scores in PD using real-world clinical data, but imaging features add limited prognostic value. This study demonstrated that implementing machine learning models, when using real-world data, did not significantly improve the already known gap between prognostic modeling and real-world implementation. Improvement of models is, however, a promising prospect and further studies are encouraged.