Search Results (19,284)

Background/Objectives: Insulin resistance (IR) is a relevant metabolic concern in patients with rheumatic diseases; however, data regarding its clinical influence on the systemic sclerosis (SSc) phenotype is lacking. This study aimed to evaluate the characteristics of patients exhibiting IR in a monocentric SSc cohort. Methods: We conducted a cross-sectional study on 178 SSc patients, stratified according to the presence of IR, defined as a HOMA-IR value >1.85 for men and >2.07 for women, based on thresholds previously validated in the Estudio Epidemiológico de la Insuficiencia Renal en España (EPIRCE) cross-sectional study. The rationale for applying the current cut-offs is based on its discriminative potential when using sex- and age-specific thresholds in a nondiabetic population. This approach is particularly applicable to SSc, where the prevalence of diabetes is very low and the median ages of the two cohorts are comparable. Data collected included demographic-, clinical-, laboratory-, pulmonary function-, capillaroscopic-, and treatment-related parameters. A multivariable logistic regression model was used to identify independent predictors of IR. Results: Patients with IR (n = 76) had a significantly higher prevalence of diffuse cutaneous subset (26.3% vs. 11.8%, p = 0.012) and interstitial lung disease (39.5% vs. 17.6%, p = 0.001), along with the positivity for anti-Scl70 antibodies and the current presence of musculoskeletal symptoms (p = 0.021) and digital ulcers (p = 0.037). As expected, body mass index (BMI) was significantly higher in the IR population (24.6 ± 5.2 vs. 22.9 ± 4.1, p = 0.012), along with fasting glucose, insulin, HOMA-IR, and HbA1c levels. IR patients exhibited higher percentages of dyslipidemia and liver steatosis. Medications such as hydroxychloroquine, statins, and Iloprost were more frequently used in the IR group; as for corticosteroids usage (21.1% vs. 5.9%, p = 0.002), however, cumulative glucocorticoid dosage did not differ between the groups. In multivariable analysis, BMI (OR 1.09; p = 0.038) and interstitial lung disease (ILD) (OR 3.03; p = 0.034) were independent predictors of IR. Conclusions: In SSc, IR is associated with ILD, digital ulcers, musculoskeletal involvement, and anti-Scl70 autoantibodies. Full article

Background and Objectives: Subclinical paroxysmal atrial fibrillation (AF) is often undetected by conventional screening strategies, until complications emerge. Artificial intelligence (AI) applied to sinus rhythm electrocardiograms has emerged as a promising tool to identify individuals with occult AF and to predict the risk of future incident AF. This scoping review synthesizes evidence from original studies evaluating AI models trained on sinus rhythm ECGs for AF detection or AF prediction. Materials and Methods: A comprehensive search of MEDLINE, Embase, Web of Science, Scopus, and IEEE Xplore was conducted to identify peer-reviewed studies from inception to November 2025. Eligible studies included original investigations in which the model input was a sinus rhythm ECG and the outcome was either paroxysmal AF or new-onset AF. Extracted variables included cohort characteristics, ECG acquisition parameters, AI architecture, model predictive performance, AF prediction horizon, clinical outcomes, and validation strategy. Risk of bias was assessed using PROBAST. Results: Nineteen studies met the inclusion criteria. Retrospective datasets ranging from several thousand to over one million ECGs and convolutional or deep neural network AI architectures were used in most studies. AI-ECG models demonstrated high diagnostic accuracy for detecting subclinical AF (ten studies; AUROC 0.75–0.90) and for predicting long-term new-onset AF (six studies; AUROC 0.69–0.85) from a single sinus rhythm ECG. Robust external validation was reported in eleven studies. Combining AI-ECG models with clinical risk factors improved AF predictive performance in several reports. Key limitations across studies included retrospective design, patient selection, limited calibration reporting, and sparse prospective impact data. Conclusions: AI-based analysis of sinus rhythm ECGs can detect occult AF and stratify future AF risk with moderate-to-high accuracy across multiple populations and healthcare systems. However, rigorous prospective trials, evaluating clinical benefit, cost-effectiveness, calibration across demographic groups, and real-world implementation, are required before broad adoption in clinical practice. Full article

American tegumentary leishmaniasis (ATL) and other infectious granulomatous diseases (IGDs) may present with oral/oropharyngeal mucosal lesions (OOPML). IGD-OOPML can result from fungal, parasitic, or bacterial infections, and squamous cell carcinoma (SCC) represents the main differential diagnosis. ATL, other IGD, and SCC share overlapping clinical and epidemiological features, making diagnostic suspicion challenging. This study compared sociodemographic and clinical characteristics among ATL, other IGD, and SCC. Descriptive, comparative, and multivariable logistic regression analyses were performed. Among 7551 patients, 213 met inclusion criteria (83-SCC and 130-IGD). Except for smoking, which differed only between ATL and SCC, most IGD parameters were similar. Male patients predominated in all groups. SCC patients were significantly older (p < 0.001) and had a shorter median disease duration (p = 0.007). The presence of pain increased the odds of SCC-OOPML by 3.96 times (95% CI 1.97–12.51). SCC patients were more likely to present lesions in a single subsite, either the oral cavity or oropharynx. Painful, ulcerated, or exophytic lesions favored SCC diagnosis, whereas infiltrative, granular, or mulberry-like lesions, involvement of multiple subsites, or associated nasal and laryngeal lesions suggested IGDs. Although clinical differentiation remains difficult, these findings may support early diagnostic suspicion, prompt treatment, and reduced sequelae. Full article

Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation technique that utilizes magnetic fields to induce cortical electric currents, enabling both the measurement and modulation of neuronal activity. Initially developed as a diagnostic tool, TMS now serves dual roles in clinical neurology, offering insight into neurophysiological dysfunctions and the therapeutic modulation of abnormal cortical excitability. This review examines key TMS outcome measures, including motor thresholds (MT), input–output (I/O) curves, cortical silent periods (CSP), and paired-pulse paradigms such as short-interval intracortical inhibition (SICI), short-interval intracortical facilitation (SICF), intracortical facilitation (ICF), long interval cortical inhibition (LICI), interhemispheric inhibition (IHI), and short-latency afferent inhibition (SAI). These biomarkers reflect underlying neurotransmitter systems and can aid in differentiating neurological conditions. Diagnostic applications of TMS are explored in Parkinson’s disease (PD), dystonia, essential tremor (ET), Alzheimer’s disease (AD), and mild cognitive impairment (MCI). Each condition displays characteristic neurophysiological profiles, highlighting the potential for TMS-derived biomarkers in early or differential diagnosis. Therapeutically, repetitive TMS (rTMS) has shown promise in modulating cortical circuits and improving motor and cognitive symptoms. High- and low-frequency stimulation protocols have demonstrated efficacy in PD, dystonia, ET, AD, and MCI, targeting the specific cortical regions implicated in each disorder. Moreover, the successful application of TMS in differentiating and treating AD and MCI underscores its clinical utility and translational potential across all neurodegenerative conditions. As research advances, increased attention and investment in TMS could facilitate similar diagnostic and therapeutic breakthroughs for other neurological disorders that currently lack robust tools for early detection and effective intervention. Moreover, this review also aims to underscore the importance of maintaining standardized TMS protocols. By highlighting inconsistencies and variability in outcomes across studies, we emphasize that careful methodological design is critical for ensuring the reproducibility, comparability, and reliable interpretation of TMS findings. In summary, this review emphasizes the value of TMS as a distinctive, non-invasive approach to probing brain function and highlights its considerable promise as both a diagnostic and therapeutic modality in neurology—roles that are often considered separately. Full article

Background/Objectives: Acute ischemic stroke (AIS) associated with cervical carotid artery pathology remains a therapeutic challenge due to uncertainty regarding emergent carotid artery stenting (eCAS) and the need for intensified antithrombotic therapy, which may increase the risk of hemorrhagic transformation (HT). This retrospective cohort study evaluated the functional and safety outcomes of eCAS within an extended treatment time window. Methods: We analyzed 139 consecutive patients with anterior circulation AIS and large vessel occlusion treated with mechanical thrombectomy between 2019 and 2024. Patients were eligible for MT within 24 h based on clinical–core mismatch (DAWN) or perfusion–core mismatch (DEFUSE 3) criteria. Outcomes were compared between patients treated with eCAS and those undergoing MT without stenting. Results: Twenty-five patients underwent eCAS, predominantly for tandem lesions (80%). Median age was 66 years, median baseline NIHSS was 14, and median infarct core volume on DWI/CTP was 15 mL. Baseline characteristics were comparable between groups, except for the site of occlusion (p < 0.001). A good functional outcome (modified Rankin Scale, mRS 0–2 at 90 days) was observed in 60% of patients in the eCAS group versus 43% in the non-stenting group, without statistical significance (p = 0.067). Rates of parenchymal hematoma (12% vs. 18.4%) and symptomatic intracerebral hemorrhage (8% vs. 3.5%) were similar between groups. Conclusions: In this single-center cohort, eCAS performed in an extended time window did not demonstrate a clear signal of increased hemorrhagic risk. However, residual confounding and imbalance between treatment groups persisted despite the application of inverse probability weighting (IPW), and the findings should be interpreted cautiously. Full article

Autologous fat grafting is the main surgical technique for soft tissue reconstruction. However, its clinical use with more extended volumes is limited by repeated procedures due to the little possibility of banking tissue, donor-site morbidity and unpredictable graft resorption rates. To overcome these problems, adipose tissue engineering has focused on developing injectable scaffolds. Most of them are hydrogels that closely mimic the biological, structural and mechanical characteristics of native adipose tissue. This review provides an overview of current injectable scaffolds designed to restore soft tissue volume defects, emphasizing their translational potential and future directions. Natural injectable scaffolds exhibit excellent biocompatibility but degrade rapidly and lack mechanical strength. Synthetic injectable scaffolds provide tunable elasticity and degradation rates but require biofunctionalization to support cell adhesion and tissue integration. Adipose extracellular matrix-derived injectable scaffolds are fabricated by decellularization of adipose tissue. Accordingly, they combine bio-mimetic structure with intrinsic biological cues that stimulate host-driven adipogenesis and angiogenesis, thus representing a translatable “off-the-shelf” alternative to autologous fat grafting. However, despite this broad spectrum of available injectable scaffolds, the establishment of clinically reliable soft tissue substitutes capable of supporting large-volume and long-lasting soft tissue reconstruction still remains an open challenge. Full article

Periapical lesions (PALs) are a common sequela of pulpal pathology, and accurate radiographic detection is essential for successful endodontic diagnosis and treatment outcome. With recent advancements in Artificial Intelligence (AI), deep learning systems have shown remarkable potential to enhance the diagnostic accuracy of PALs. This study highlights recent evidence on the use of AI-based systems in detecting PALs across various imaging modalities. These include intraoral periapical radiographs (IOPAs), panoramic radiographs (OPGs), and cone-beam computed tomography (CBCT). A literature search was conducted for peer-reviewed studies published from January 2021 to July 2025 evaluating artificial intelligence for detecting periapical lesions on IOPA, OPGs, or CBCT. PubMed/MEDLINE and Google Scholar were searched using relevant MeSH terms, and reference lists were hand screened. Data were extracted on imaging modality, AI model type, sample size, subgroup characteristics, ground truth, and outcomes, and then qualitatively synthesized by imaging modality and clinically relevant moderators (i.e., lesion size, tooth type and anatomical surroundings, root-filling status and effect on clinician’s performance). Thirty-four studies investigating AI models for detecting periapical lesions on IOPA, OPG, and CBCT images were summarized. Reported diagnostic performance was generally high across radiographic modalities. The study results indicated that AI assistance improved clinicians’ performance and reduced interpretation time. Performance varied by clinical context: it was higher for larger lesions and lower around complex surrounding anatomy, such as posterior maxilla. Heterogeneity in datasets, reference standards, and metrics limited pooling and underscores the need for external validation and standardized reporting. Current evidence supports the use of AI as a valuable diagnostic platform adjunct for detecting periapical lesions. However, well-designed, high-quality randomized clinical trials are required to assess the potential implementation of AI in the routine practice of periapical lesion diagnosis. Full article

In the last year, great advances in the treatment outcomes of advanced EGFR-mutant NSCLC have been achieved. Two combination regimens, amivantamab plus lazertinib and platin-based chemotherapy plus osimertinib, have yielded, in the phase III randomized trials named MARIPOSA and FLAURA 2, statistically and clinically significant improvements in overall survival compared with monotherapy with osimertinib. However, translation to clinical practice of these relevant results is challenging for two main reasons. The first is that we have no evidence-based tools to choose among the two combinations, except their different safety profiles. The second is that combinations are significantly more toxic than osimertinib alone. Thus, osimertinib remains an effective treatment with an excellent safety profile, perhaps to be considered as still the best option in the majority of elderly patients and in all patients that do not intend to trade-off an excess of toxicity with survival prolongment. The safety and efficacy characteristics of the three treatment options are the basis for a patient-tailored treatment choice, but in a significant proportion of patients, a personal and intimate approach to quality of life and survival prolongment is to be considered the main driver within a well-structured shared decision-making process. Full article

Background: The vestibular schwannoma (VS) is the most common cerebellopontine angle tumor in adults, exhibiting a highly variable natural history, from stability to rapid growth. Accurate, the non-invasive prediction of tumor behavior is essential to guide personalized management and avoid overtreatment or delayed intervention. Objective: To systematically review and synthesize the evidence on MRI-based biomarkers for predicting VS growth and treatment responses. Methods: We conducted a PRISMA-compliant search of PubMed, EMBASE, and Cochrane databases for studies published between 1 January 2000 and 1 January 2025, addressing MRI predictors of VS growth. Cohort studies evaluating texture features, signal intensity ratios, perfusion parameters, and apparent diffusion coefficient (ADC) metrics were included. Study quality was assessed using the NOS (Newcastle–Ottawa Scale) score, GRADE (Grading of Recommendations, Assessment, Development and Evaluation), and ROBIS (Risk of Bias in Systematic reviews) tool. Data on diagnostic performance, including the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, and p value, were extracted and descriptively analyzed. Results: Ten cohort studies (five retrospective, five prospective, total n = 525 patients) met the inclusion criteria. Texture analysis metrics, such as kurtosis and gray-level co-occurrence matrix (GLCM) features, yielded AUCs of 0.65–0.99 for predicting volumetric or linear growth thresholds. Signal intensity ratios on gadolinium-enhanced T1-weighted images for tumor/temporalis muscle achieved a 100% sensitivity and 93.75% specificity. Perfusion MRI parameters (K^trans, v_e, ASL, and DSC derived blood-flow metrics) differentiated growing from stable tumors with AUCs up to 0.85. ADC changes post-gamma knife surgery predicted a favorable response, though the baseline ADC had limited value for natural growth prediction. The heterogeneity in growth definitions, MRI protocols, and retrospective designs remains a key limitation. Conclusions: MRI-based biomarkers may provide exploratory signals associated with VS growth and treatment responses. However, substantial heterogeneity in growth definitions and MRI protocols, small single-center cohorts, and the absence of external validation currently limit clinical implementation. Full article

Background/Objectives: Interstitial pneumonia with autoimmune features (IPAF) is a recently defined entity characterized by interstitial lung disease (ILD) with clinical, serological, and radiological features suggestive of autoimmunity that do not fulfil the criteria for a defined connective tissue disease (CTD). This study aimed to evaluate the clinical characteristics, treatment modalities, and outcomes of patients with IPAF in a tertiary referral center. Methods: We retrospectively analyzed 72 patients who fulfilled the IPAF classification criteria. Demographic, clinical, serological, radiological, pulmonary function, treatment, and survival data were collected and evaluated. Logistic regression analysis was performed to identify factors associated with mortality. Results: The cohort consisted of 62.5% female patients, with a mean age of 62.7 (SD, 10.4) years at diagnosis. The most frequent radiological pattern was nonspecific interstitial pneumonia (83.3%). Raynaud’s phenomenon (6.9%) and arthritis (2.8%) were the most common rheumatological manifestations. Antinuclear antibodies positivity at titers ≥1:320 was observed in 27.8% of patients. Azathioprine was the most frequently prescribed agent (20.8%), followed by mycophenolate mofetil (11.1%). After a median follow-up of 30.1 months (IQR, 52.8), 16 patients (22.22%) died, with a 5-year survival rate of 70%. Glucocorticoid therapy at doses ≥20 mg/day was independently associated with increased mortality (OR 6.13 (95% CI 1.17–32.21). Conclusions: IPAF predominantly affects middle-aged females. Glucocorticoid use at doses ≥20 mg/day was associated with mortality; however, this observational association may reflect underlying disease severity rather than a causal effect of high-dose treatment. Further prospective studies are needed to optimize management strategies in patients with IPAF. Full article

Background and Objectives: Cognitive disorders are a significant health problem in patients undergoing peritoneal dialysis and can profoundly impair both quality of life and treatment outcomes. Early identification of risk factors for the development of cognitive disorders in this population is therefore essential. This study aimed to (1) determine the prevalence of cognitive dysfunction in patients on peritoneal dialysis, (2) examine its association with sociodemographic characteristics, and (3) assess whether anaemia is associated with cognitive dysfunction in these patients. Materials and Methods: A cross-sectional study was conducted in November 2024 at the University Clinical Centre of Vojvodina, Clinic for Nephrology and Clinical Immunology, and included 36 patients on peritoneal dialysis. The Montreal Cognitive Assessment (MoCA) was used to evaluate cognitive function, while a structured questionnaire was used to collect sociodemographic data. Anaemia was determined based on haemoglobin levels. Results: Cognitive dysfunction was present in 69.4% of patients on peritoneal dialysis, while anaemia, as indicated by haemoglobin values, was present in 58.3% of the sample. Older age, rural residence, and lower haemoglobin levels were significantly associated with cognitive dysfunction in patients on peritoneal dialysis. Conclusions: Preserved cognitive function is a key prerequisite for the adequate implementation of peritoneal dialysis and for maintaining patients’ quality of life. The findings indicate the need for further research to identify effective strategies for preventing and treating anaemia, a factor associated with cognitive dysfunction in this patient population. Full article

Introduction: Pneumonitis represents one of the clinically significant adverse events observed in patients with non-small-cell lung cancer (NSCLC) who receive durvalumab as consolidation therapy after chemoradiotherapy (CRT). Although clinical factors such as radiation dose (e.g., V20) and interstitial lung abnormalities (ILAs) have been reported as risk predictors, accurate and objective prognostication remains difficult. This study aimed to develop a radiomics-based machine learning model to predict grade ≥ 2 pneumonitis. Methods: This retrospective study included patients with unresectable NSCLC who received CRT followed by durvalumab. Radiomic features, including first-order and texture and shape-based features with wavelet transformation were extracted from whole-lung regions on pre-durvalumab computed tomography (CT) images. Machine learning models, support vector machines, k-nearest neighbor, neural networks, and naïve Bayes classifiers were developed and evaluated using a testing cohort. Model performance was assessed using five-fold cross-validation. Conventional predictors, including V20 and ILAs, were also assessed using logistic regression and receiver operating characteristic analysis. Results: Among 123 patients, 44 (35.8%) developed grade ≥ 2 pneumonitis. The best-performing model, a support vector machine, achieved an AUC of 0.88 and accuracy of 0.81, the conventional model showed lower performance with an AUC of 0.71 and accuracy of 0.64. Conclusions: Radiomics-based machine learning demonstrated superior performance over clinical parameters in predicting pneumonitis. This approach may enable individualized risk stratification and support early intervention in patients with NSCLC. Full article

Schistosomiasis remains a major neglected tropical disease globally and presents particular challenges for countries transitioning from control to elimination. Saudi Arabia represents a unique epidemiological setting, having shifted from historical endemic transmission to very low reported incidence, yet long-term national analyses remain limited. A retrospective longitudinal analysis of national schistosomiasis surveillance data from 2002 to 2024 was conducted to evaluate temporal trends, clinical subtypes, regional distribution, and demographic characteristics. Joinpoint regression was used to identify significant changes in temporal trends, and autoregressive integrated moving average (ARIMA) models were applied to forecast national and regional trajectories. National incidence declined markedly from 5.5 per 100,000 in 2002 to 0.12 per 100,000 in 2024, with a notable change around 2010, followed by sustained low-level incidence. Intestinal schistosomiasis accounted for most cases, with increasing concentration among adult non-Saudi males and near-elimination among children. Regionally, cases were confined to a limited number of western and southwestern regions, particularly Ta’if, Al Baha, Jazan, and Madinah. Forecasting analyses indicated continued low-level detection without evidence of national resurgence. These findings demonstrate a transition to an elimination-maintenance phase and highlight the need for sustained surveillance in historically endemic regions and mobile populations. Full article

Background: Neurogenic bowel dysfunction (NBD) is a major chronic sequela of spinal cord injury (SCI) with substantial implications for rehabilitation and long-term management. However, population-level evidence describing how gastrointestinal (GI) diagnostic codes are used following SCI, particularly within administrative healthcare systems, remains limited. Methods: We conducted a nationwide retrospective cohort study using administrative claims data from the Korean National Health Insurance Service (NHIS). A total of 584,266 adults with trauma-related SCI encounters between 2009 and 2019 were identified. GI diagnostic codes—paralytic ileus (K56), irritable bowel syndrome (K58), and functional bowel disorders (K59)—were evaluated as administrative proxies for bowel dysfunction. Demographic characteristics, disability status, regional factors, and health behaviors were analyzed using multivariable logistic regression. Results: GI diagnostic codes were frequently recorded after SCI, most commonly irritable bowel syndrome (approximately 30%) and functional bowel disorders (approximately 37%), whereas paralytic ileus was uncommon. Greater disability severity, female sex, older age, and rural residence were consistently associated with higher odds of GI diagnostic coding. Physical activity showed robust inverse associations across all models. Inverse associations observed with smoking and alcohol consumption were interpreted as reflecting residual confounding or health-related selection, rather than biological protective effects. Conclusions: Patterns of GI diagnostic coding after SCI likely reflect the clinical burden and management needs of neurogenic bowel dysfunction within healthcare systems, rather than the development of new gastrointestinal diseases. These findings underscore the importance of individualized bowel management, incorporation of structured physical activity into rehabilitation programs, and equitable access to SCI rehabilitation services, particularly for individuals with greater disability or those living in rural areas. Full article

Chimeric antigen receptor (CAR) T cell therapy has demonstrated clinical success in hematologic malignancies but has limited efficacy in solid tumors due to tumor microenvironment (TME) barriers that impede CAR T cell recognition, infiltration, and sustained function. Traditional 2D assays inadequately recapitulate these constraints, necessitating improved in vitro models. This study validated a 3D tumor spheroid platform using an agarose microwell system to generate uniform B7-H3-positive spheroids from multiple solid tumor cell lines, enabling the evaluation of CAR T cell activity. TME-relevant immune modulation under 3D conditions was analyzed by flow cytometry for B7-H3, MHC I/II, and antigen processing machinery (APM), followed by co-culture with B7-H3 CAR T cells to assess cytotoxicity, spheroid integrity, tumor viability, and CAR T cell activation, exhaustion, and cytokine production. Two human cancer-cell-line-derived spheroids, DU 145 (prostate cancer) and SUM159 (breast cancer), retained B7-H3 expression, while MC38 (mouse colon cancer)-derived spheroids served as a B7-H3 negative control. Under 3D culture conditions, DU 145 and SUM159 spheroids acquire TME-like immune evasion characteristics and specifically downregulated MHC-I and APM (TAP1, TAP2, LMP7) with concurrent upregulation of MHC-II and calreticulin. Co-culture showed effective spheroid infiltration, cytotoxicity, and structural disruption, with infiltrating CAR T cells displaying higher CD4⁺ fraction, activation, exhaustion, effector/terminal differentiation, and IFN-γ/TNF-α production. This 3D platform recapitulates critical TME constraints and provides a cost-effective, feasible preclinical tool to assess CAR T therapies beyond conventional 2D assays. Full article