Search Results (697)

Sleep disorders and stroke are intricately linked through a complex, bidirectional relationship. Sleep disturbances such as obstructive sleep apnea (OSA), insomnia, and restless legs syndrome (RLS) not only increase the risk of stroke but also frequently emerge as consequences of cerebrovascular events. OSA, in particular, is associated with a two- to three-fold increased risk of incident stroke, primarily through mechanisms involving intermittent hypoxia, systemic inflammation, endothelial dysfunction, and autonomic dysregulation. Conversely, stroke can disrupt sleep architecture and trigger or exacerbate sleep disorders, including insomnia, hypersomnia, circadian rhythm disturbances, and breathing-related sleep disorders. These post-stroke sleep disturbances are common and significantly impair rehabilitation, cognitive recovery, and quality of life, yet they remain underdiagnosed and undertreated. Early identification and management of sleep disorders in stroke patients are essential to optimize recovery and reduce the risk of recurrence. Therapeutic strategies include lifestyle modifications, pharmacological treatments, medical devices such as continuous positive airway pressure (CPAP), and emerging alternatives for CPAP-intolerant individuals. Despite growing awareness, significant knowledge gaps persist, particularly regarding non-OSA sleep disorders and their impact on stroke outcomes. Improved diagnostic tools, broader screening protocols, and greater integration of sleep assessments into stroke care are urgently needed. This narrative review synthesizes current evidence on the interplay between sleep and stroke, emphasizing the importance of personalized, multidisciplinary approaches to diagnosis and treatment. Advancing research in this field holds promise for reducing the global burden of stroke and improving long-term outcomes through targeted sleep interventions. Full article

Background/Objectives: Pituitary tumors account for over 90% of all sellar region masses. However, a spectrum of rare neoplastic, inflammatory, infectious, and vascular lesions—benign and malignant—can arise in the intra- and parasellar compartments and clinically and radiologically mimic PitNETs. We report a cohort of 47 such rare and cystic midline intracranial lesions, emphasizing their distinctive morphological, clinical, and imaging features and the personalized treatment strategies applied. Methods: In this retrospective single-center study, we reviewed all patients treated for suspected PitNETs via transsphenoidal approach between 2015 and 2024. Of 529 surgical cases, we excluded confirmed PitNETs, meningiomas, and classical intradural craniopharyngiomas. Collected data encompassed patient demographics, tumor characteristics, presenting symptoms, extent of resection or medical therapy, endocrine outcomes, and follow-up information. Results: Among all 529 patients who underwent surgical treatment for sellar lesions from 2015 to 2024, 47 cases (8.9%) were identified as rare or cystic masses. Forty-six underwent transsphenoidal resection; one patient with hypophysitis received corticosteroid therapy alone. Presenting symptoms included headache (n = 16), dizziness (n = 5), oculomotor disturbances (n = 2), and visual impairment (n = 17). Endocrine dysfunction was found in 30 patients, 27 of whom required hydrocortisone replacement. Histopathological diagnoses were led by colloid cysts (n = 14) and Rathke’s cleft cysts (n = 11). The remaining 22 cases comprised plasmacytoma, germinoma, lymphoma, pituicytoma, inverted papilloma, metastatic carcinoma, chordoma, nasopharyngeal carcinoma, chloroma, and other rare entities. Preoperative imaging diagnosis proved incorrect in 38% (18/47) of cases, with several lesions initially misidentified as PitNETs. Conclusions: Nearly 9% of presumed PitNETs were rare, often benign or inflammatory lesions requiring distinct management. Most could be safely resected and demonstrated excellent long-term outcomes. Yet, despite advanced imaging techniques, accurate preoperative differentiation remains challenging, with over one-third misdiagnosed. Clinical red flags—such as early hormone deficits, rapid progression or atypical imaging findings—should prompt early interdisciplinary evaluation and, when indicated, image-guided biopsy to avoid unnecessary surgery and ensure tailored therapy. Full article

Schistosoma mansoni infection is associated with hepatic inflammation and fibrosis, but its systemic metabolic effects remain poorly understood. This study aimed to investigate changes in the serum lipidomic profile associated with S. mansoni infection and parasite load in individuals from an endemic area. This cross-sectional analysis was nested within a longitudinal cohort study conducted in northeastern Brazil. Parasitological diagnosis and quantification were performed using the Kato–Katz technique. A total of 45 individuals were selected and divided into three groups: high parasite load (HL), low parasite load (LL), and uninfected controls (NegE). Serum samples were analyzed using mass-spectrometry-based lipidomics. The most abundant lipid subclasses across all groups were phosphatidylcholines (PC), triacylglycerols (TAG), and phosphatidylethanolamines (PE). However, individuals in the HL group exhibited distinct lipidomic profiles, with increased levels of specific phosphatidylinositols (PI) and reduced levels of certain TAG species compared to the NegE group. These changes may reflect host–parasite interactions and immune–metabolic alterations driven by intense infection. Our findings suggest that S. mansoni infection, particularly at higher parasite burdens, can influence the host’s serum lipid profile and may contribute to metabolic disturbances in endemic populations. Full article

Background: Charcot–Marie–Tooth disease (CMT) is a genetically and phenotypically heterogeneous hereditary neuropathy. Axonal CMT type 2 (CMT2) subtypes often exhibit overlapping clinical features, which makes molecular genetic analysis essential for accurate diagnosis and subtype differentiation. Methods: This retrospective study included five pediatric patients who presented with gait disturbance, muscle weakness, and foot deformities and were subsequently diagnosed with axonal forms of CMT. Clinical data, electrophysiological studies, neuroimaging, and genetic analyses were evaluated. Whole exome sequencing (WES) was performed in three sporadic cases, while targeted CMT gene panel testing was used for two siblings. Variants were interpreted using ACMG guidelines, supported by public databases (ClinVar, HGMD, and VarSome), and confirmed by Sanger sequencing when available. Results: All had absent deep tendon reflexes and distal muscle weakness; three had intellectual disability. One patient was found to carry a novel homozygous frameshift variant (c.2568_2569del) in the IGHMBP2 gene, consistent with CMT2S. Other variants were identified in the NEFH (CMT2CC), DYNC1H1 (CMT2O), and MPV17 (CMT2EE) genes. Notably, a previously unreported co-occurrence of MPV17 mutation and congenital heart disease was observed in one case. Conclusions: This study expands the clinical and genetic spectrum of pediatric axonal CMT and highlights the role of early physical examination and molecular diagnostics in detecting rare variants. Identification of a novel IGHMBP2 variant and unique phenotypic associations provides new insights for future genotype–phenotype correlation studies. Full article

Background and Clinical Significance: Bilateral diffuse uveal melanocytic proliferation (BDUMP) is a rare paraneoplastic syndrome characterized by bilateral uveal melanocyte proliferation and progressive visual disturbance. While most commonly associated with solid tumors, its occurrence in hematologic malignancies is exceedingly rare. Case Presentation: We report a case of BDUMP in a 64-year-old male recently diagnosed with chronic myelomonocytic leukemia (CMML), who presented with subacute, painless bilateral blurred vision. Multimodal imaging revealed suggestive features of BDUMP, including orange-red subretinal patches, retinal pigment epithelium mottling, and diffuse choroidal thickening, consistent with early structural involvement despite preserved central vision. No intraocular mass or signs of inflammation were observed. The patient did not receive specific treatment for BDUMP, and visual acuity remained stable during follow-up. Conclusions: This case underscores the importance of considering BDUMP in the differential diagnosis of bilateral visual symptoms in patients with hematologic malignancies. Although rare, BDUMP may occur in the context of CMML. Recognition through multimodal imaging and interdisciplinary collaboration is essential, and further research is needed to clarify its pathogenesis and improve management strategies. Full article

Background/Objectives: Infantile hemangioma (IH) is a common vascular tumor in neonates, influenced by multiple prenatal and perinatal factors. This study aimed to identify risk factors in both infants and mothers, assess their link to clinical characteristics and severity, and evaluate treatment outcomes when systemic propranolol therapy was administered. Methods: We conducted a retrospective observational study analyzing 43 infants under 12 months, including 11 neonates (<28 days) diagnosed with IH. Maternal and neonatal factors, diagnostic timelines, clinical presentation, and treatment efficacy were examined. Data analysis included descriptive statistics, focusing on gestational age, birth weight, Apgar scores, and the Infantile Hemangioma Referral Score (IHReS). Results: The study found a female predominance and a correlation between IH and pre-term birth (50%) and low birth weight (<2760 g, 51.16%). Maternal anemia (23%) and gestational hypertension (9%) were present in the cohort, but no statistical association with IH severity was found. A significant number (44.18%) were diagnosed within the first two weeks postpartum. The IHReS was inversely correlated with Apgar scores, with newborns scoring above 8 having a lower IHReS. Treatment with propranolol (1–3 mg/kg/day) was highly effective, resulting in significant lesion regression in most patients. Mild complications included sleep disturbances (12%) and diarrhea (9%). The most affected areas were the face/eyelid (32.55%), limbs (18.6%), and anterior thorax. Additionally, 42% of cases had an IHReS above 4, with multiple hemangiomas increasing severity. Conclusions: IH was common in pre-term and low-birth-weight infants, whereas the maternal comorbidities observed in this small cohort did not show a definitive association, underscoring the need for controlled studies. Early diagnosis, risk stratification, and timely propranolol therapy are crucial in achieving favorable outcomes. Further research is needed to assess long-term effects and evaluate risks of treatment rebound. Full article

Background and clinical significance: Acute reduction in vigilance is a frequent reason for emergency department admissions, especially among the elderly. While intracranial causes or infections with fluid depletion are often responsible, there remain cases where imaging, laboratory tests, and clinical examination fail to provide a clear diagnosis. Case presentation: A 91-year-old woman was presented to the emergency department with recurrent episodes of somnolence to deep coma. On admission, her vital signs were stable, and cerebral CT imaging revealed no intracranial pathology. Laboratory analyses, including blood gas measurements, were unremarkable. Empirical treatment for possible intoxications with benzodiazepines or opioids using flumazenil and naloxone had no effect. An Addison’s crisis was considered but excluded following methylprednisolone administration without improvement in consciousness. Eventually, an isolated elevation of serum ammonia was identified as the cause of the reduced vigilance. Further investigation linked the hyperammonemia to abnormal intestinal bacterial colonization, likely due to a prior ureteroenterostomy. There was no evidence of liver dysfunction, thus classifying the condition as non-hepatic hyperammonemia. Therapy was initiated with rifaximin, supported by aggressive laxative regimens. Ammonia levels and vital parameters were closely monitored. The patient’s condition improved gradually, with serum ammonia levels returning to normal and cognitive function fully restored. Conclusions: This case highlights an uncommon cause of coma due to non-hepatic hyperammonemia in the absence of liver disease, emphasizing the diagnostic challenge when standard evaluations are inconclusive. It underscores the need for broad differential thinking in emergency settings and the importance of considering rare metabolic disturbances as potential causes of altered mental status. Full article

Autistic traits—such as social communication deficits, cognitive rigidity, and repetitive behaviors—are increasingly recognized in individuals with schizophrenia, particularly in early-onset cases and subtypes with predominant negative symptoms. This overlap has prompted investigations into shared pathophysiological mechanisms. One emerging area of focus is the role of neuroinflammation in schizophrenia, which may contribute to the manifestation of autistic features. Immunological research indicates the presence of chronic low-grade inflammation, microglial activation, and disruption of the blood–brain barrier in schizophrenia. In particular, an imbalance in T-helper (Th) cell responses—specifically a shift toward Th2 dominance or concurrent Th1/Th2 activation—may lead to dysregulated cytokine production and disturbances in neural function. These findings highlight the importance of exploring immunological pathways as a basis for specific symptom profiles. Additionally, current efforts aim to identify reliable inflammatory biomarkers in schizophrenia that could support diagnosis, predict disease course, and guide treatment. Evaluating neuroinflammatory markers in patients with autistic features may provide novel insight into schizophrenia subtypes and help tailor immunomodulatory therapies. This review explores the expression of autistic traits in schizophrenia and examines the role of neuroinflammation and Th1/Th2 imbalance as potential mechanisms and biomarkers. Full article

Depressive disorders significantly impact individuals worldwide, including children and adolescents. Despite their widespread occurrence, early and precise diagnosis of depressive disorders remains a complex and challenging task, particularly in younger populations. This study proposes a novel machine learning framework leveraging kinematic handwriting analysis to enhance the detection of psychomotor disturbances indicative of psychomotor retardation in youths with depression. The handwriting data were acquired from 20 youths with depression and 20 healthy controls. All participants completed a simple repetitive handwriting task: continuous writing of the small cursive Latin letter “l”. Segmentation of the handwriting data into individual “Letters” was conducted, and 177 kinematic features were extracted and analyzed. Statistical methods were used to identify significant features. After recursive feature elimination, classification was achieved through machine learning algorithms: logistic regression, support vector machine, and random forest. After the identification of 40 significant features, logistic regression, utilizing an optimal three-feature subset, achieved the highest accuracy in classifying individual letters of 76.7% and the highest accuracy in classifying subjects of 82.5%. The feature selection process revealed that velocity-related features were most effective in distinguishing patients with depression from controls, expectedly reflecting a slowdown in psychomotor functioning among the patients. The findings demonstrate that kinematic handwriting analysis, when combined with machine learning techniques, offers a promising tool to support objective recognition of psychomotor speed, providing insight into psychomotor retardation in youth with depression. Full article

Background: Herpes zoster (HZ), resulting from the reactivation of latent varicella-zoster virus, has been increasingly observed in individuals following COVID-19. Given the shared immunological disturbances between the two conditions, this study aimed to investigate whether HZ following COVID-19 is associated with an elevated risk of renal, infectious, and autoimmune complications. Methods: This retrospective cohort study utilized data from the TriNetX global federated health network, encompassing over 9 million adults diagnosed with COVID-19 between January 2020 and January 2022. Patients who developed HZ within one year following COVID-19 diagnosis were compared to 1:1 propensity score-matched controls without HZ. Time-to-event analyses over a three-year follow-up period were conducted to estimate the risks of major adverse kidney events (MAKE; defined as acute kidney injury, dialysis dependence, or severely reduced kidney function with eGFR <30 mL/min/1.73 m²), sepsis, systemic lupus erythematosus (SLE), and rheumatoid arthritis (RA), using Kaplan–Meier survival curves and Cox proportional hazards models. Results: HZ following COVID-19 was significantly associated with increased risks of all four outcomes: MAKE (HR 1.940, 95% CI: 1.866–2.017), sepsis (HR 2.362, 95% CI: 2.250–2.479), SLE (HR 2.667, 95% CI: 2.254–3.156), and RA (HR 2.484, 95% CI: 2.267–2.730). Subgroup analyses identified older age, diabetes, impaired renal function, and elevated inflammatory markers as key risk-enhancing factors. Conclusions: HZ following COVID-19 may serve as a clinical indicator of systemic immune dysregulation and is independently associated with increased long-term risks of renal, infectious, and autoimmune sequelae. Enhanced monitoring of this high-risk population is warranted. Full article

Objectives: Sleep serves a crucial role in the optimal development of cognitive, emotional, and physical domains. Sleep disturbances and disorders have been reported to frequently occur in many neurodevelopmental conditions, including ADHD and Autism Spectrum Disorder. The connection between dyslexia and sleep, however, is sparsely explored. This community study aimed to enhance knowledge about sleep disturbances in children with dyslexia and explore the potential impact of anxiety. Method: The parents of 160 children aged 7–13 years old with a primary diagnosis of dyslexia completed the Children’s Sleep Habits Questionnaire (CSHQ) and the Spence Children’s Anxiety Scale (SCAS). Results: Sixty-six percent of the children showed pathological levels of sleep disturbances, with clinical scores observed in the subscales of Sleep Onset Delay, Sleep Anxiety, and Daytime Sleepiness. Overall, sleep and anxiety were correlated, but anxiety levels were not elevated and not correlated with Sleep Onset Delay. Conclusions: The current results suggest that the majority of children with dyslexia suffer from sleep disturbances, such as delayed sleep onset and shorter sleep durations, irrespective of the scores given on the anxiety scale. Given the importance of sleep for optimal development, there is an alarming need for more studies to be carried out to explore additional factors that interact with healthy sleep to develop sleep interventions. Full article

Background/Objectives: Cerebral palsy (CP) is a neurodevelopmental disorder associated with sleep disturbances, particularly sleep-disordered breathing (SDB), and is often linked to an increased risk of obstructive sleep apnea (OSA). OSA is underdiagnosed in this population due to the lack of standardized methods and limited access to appropriate diagnostic technologies and appropriate equipment. Thus, this study aimed to investigate the presence and severity of sleep disorders, with a particular focus on OSA, in children and adolescents with CP compared to their typically developing peers. Methods: This observational, clinical, and prospective study included 28 children and adolescents with CP and 32 age- and sex-matched typically developing individuals. Sleep disturbances were assessed using the Sleep Disturbance Scale for Children (SDSC) and a high-resolution oximeter plus actigraphy combined with a cloud-based algorithm for the detection of obstructive sleep apnea (Biologix^® system), which provided data on oxygen saturation, snoring, movement during sleep, and total sleep time. Results: According to the SDSC, 92% of children and adolescents with CP presented scores indicative of sleep disturbances, compared to 31% of typically developing individuals. SDB was the most prevalent subtype (64%) and overnight oximetry revealed that 100% of the CP group presented oxygen desaturation index (ODI) values consistent with a diagnosis of OSA. The CP group also exhibited significantly lower mean SpO₂, longer snoring duration, shorter total sleep time, and prolonged sleep latency compared to the typically developing group. Conclusions: Children and adolescents with cerebral palsy (CP) exhibit a high prevalence of sleep disturbances, with increasing evidence indicating a significant occurrence of sleep-disordered breathing (SDB), particularly obstructive sleep apnea (OSA). Full article

Alzheimer’s disease (AD) and schizophrenia are traditionally considered distinct clinical entities, yet growing evidence highlights substantial overlap in their molecular and neuroinflammatory pathogenesis. This review explores current insights into the shared and divergent mechanisms underlying these disorders, with emphasis on neuroinflammation, autophagy dysfunction, blood–brain barrier (BBB) disruption, and cognitive impairment. We examine key signaling pathways, particularly spleen tyrosine kinase (SYK), the mechanistic (or mammalian) target of rapamycin (mTOR), and the S100 calcium-binding protein B (S100B)/receptor for advanced glycation end-products (RAGE) axis, that link glial activation, excitatory/inhibitory neurotransmitter imbalances, and impaired proteostasis across both disorders. Specific biomarkers such as S100B, matrix metalloproteinase 9 (MMP9), and soluble RAGE show promise for stratifying disease subtypes and predicting treatment response. Moreover, psychiatric symptoms frequently precede cognitive decline in both AD and schizophrenia, suggesting that mood and behavioral disturbances may serve as early diagnostic indicators. The roles of autophagic failure, cellular senescence, and impaired glymphatic clearance are also explored as contributors to chronic inflammation and neurodegeneration. Current treatments, including cholinesterase inhibitors and antipsychotics, primarily offer symptomatic relief, while emerging therapeutic approaches target upstream molecular drivers, such as mTOR inhibition and RAGE antagonism. Finally, we discuss the future potential of personalized medicine guided by genetic, neuroimaging, and biomarker profiles to optimize diagnosis and treatment strategies in both AD and schizophrenia. A greater understanding of the pathophysiological convergence between these disorders may pave the way for cross-diagnostic interventions and improved clinical outcomes. Full article

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

Syncope of unclear cause (SUC) presents a significant diagnostic challenge, with a considerable proportion of patients remaining without a definitive diagnosis despite comprehensive clinical evaluation. This study aims to explore the potential of unsupervised machine learning (ML), specifically clustering algorithms, to identify clinically meaningful subgroups within a cohort of 123 patients with SUC. Patients were prospectively recruited from the cardiology, neurology, and emergency departments, and clustering was performed using the k-prototypes algorithm, which is suitable for mixed-type data. The number of clusters was determined through cost function analysis and silhouette index, and visual validation was performed using UMAP. Five distinct patient clusters were identified, each exhibiting unique profiles in terms of age, comorbidities, and symptomatology. After clustering, nocturnal cardiorespiratory polygraphy and heart rate variability (HRV) parameters were analyzed across groups to uncover potential physiological differences. The results suggest distinct autonomic and respiratory patterns in specific clusters, pointing toward possible links among sympathetic dysregulation, sleep-related disturbances, and syncope. While the sample size imposes limitations on generalizability, this pilot study demonstrates the feasibility of applying unsupervised ML to complex clinical syndromes. The integration of clinical, autonomic, and sleep-related data may provide a foundation for future, larger-scale studies aiming to improve diagnostic precision and guide personalized management strategies in patients with SUC. Full article