Search Results (7,161)

Background: Dynamic risk stratification is fundamental to the modern management of pulmonary arterial hypertension (PAH). However, data on the impact of sequential add-on therapy in patients with Group 1.4 PAH—particularly in Latin American populations—remains limited. This study evaluated changes in risk classification using COMPERA 2.0 and REVEAL Lite 2 scores in patients treated with endothelin receptor antagonist (ERA) and phosphodiesterase type 5 inhibitor (PDE5i) combination therapy (macitentan + sildenafil) at a referral center in Mexico. Methods: In this single-center, retrospective cohort study, 25 patients with a confirmed diagnosis of PAH between 1st January 2022 and 31st December 2024 were evaluated at baseline and after 24 weeks of treatment. Clinical, functional, and biochemical parameters were recorded. Within-patient changes were analyzed using the Wilcoxon signed-rank test, and agreement between risk assessment tools was assessed using Spearman’s correlation coefficient. Results: At 24 weeks, patients demonstrated significant improvement in World Health Organization functional class (p = 0.002) and a significant reduction in brain natriuretic peptide levels (p = 0.003). Both COMPERA 2.0 and REVEAL Lite 2 scores showed a consistent shift toward lower-risk categories. A strong concordance between the two tools was observed. Conclusions: Sequential add-on ERA + PDE5i therapy was associated with meaningful improvement in risk stratification among patients with Group 1.4 PAH. These findings support the clinical utility of simplified, noninvasive risk assessment tools in real-world settings, particularly in resource-constrained environments. Full article

Background: Early identification of balance impairments is critical for detecting the fall risk in older adults. Force plates are the standard for measuring postural sway, but are restricted in practice because they are cumbersome and expensive. The Balance Mat is a portable device that requires comprehensive validation against force plates and clinical benchmarks in older adult populations. Objective: The objective of this study was to evaluate the technical validity and clinical discriminative ability of the Balance Mat against a laboratory-grade force plate, clinical tests, and the fall history in an older adult cohort. Methods: Fifty-six community-dwelling older adults performed static balance assessments across six stance conditions. Postural sway data were recorded simultaneously using the Balance Mat and a force plate. The technical validity was assessed using Spearman’s rank correlation and intraclass correlation coefficients. Linear regression models were applied to calibrate the Balance Mat outputs against the force plate. The diagnostic accuracy for classifying the fall risk against the timed up and go test, the Falls Efficacy Scale-International, and the retrospective fall history was evaluated using an area-under-the-curve analysis. Results: The Balance Mat demonstrated strong associations with force plate measurements, particularly for the sway path/velocity, variance and Area95 ($r>0.80$). Following calibration, the absolute agreement for the sway path/velocity reached excellent levels ($ICC=0.93$) and good levels for Area95 and RMS ($ICC>0.75$), whereas the mean sway demonstrated a poor agreement and was excluded. For fall-risk classification, the calibrated Balance Mat achieved a fair accuracy for the retrospective fall history and a high Falls Efficacy Scale-International concern (area under the curve, 0.76-0.78), and a fair accuracy for the timed up and go thresholds (area under the curve, 0.70). Conclusions: The calibrated Balance Mat provided valid measurements of postural sway that aligned with the force plate parameters, particularly for the sway path/velocity and Area95. The within-stance agreement for the sway path/velocity ranged from ICC= 0.44 to 0.88, and the pooled value should not be interpreted as the uniform performance across all stance conditions. Given its fair diagnostic accuracy, the device is best utilised as a portable screening tool in combination with standard clinical assessments and the fall history rather than as a standalone diagnostic test. Full article

Background/Objectives: Oral cancer is one of the most common types of cancer, with high mortality rates if not detected early. Traditional diagnostic methods based on clinical examination rely on experience, leading to delays in early and reliable diagnosis. In recent years, medical imaging and AI-based computer-aided diagnostic systems have shown promising results in the automated identification of oral cancer. In particular, the efficient management of high-dimensional feature spaces in machine learning and deep learning approaches directly impacts classification performance. In this context, metaheuristic-based feature selection technics is a critical component because of eliminating redundant and irrelevant features. To address these challenges, this study proposes a metaheuristic-based feature selection method to reduce feature dimensionality and enhance the classification performance of oral cancer detection. Methods: This study proposes an improved Honey Badger Algorithm-based feature selection approach for the automated detection of oral cancer. In the proposed method, the distance vector used in the HBA method has been redefined to improve the balance between exploration and exploitation. Additionally, a new Cauchy mutation-based migration strategy was integrated into the proposed method to increase diversity in the search space and avoid getting stuck in local minima. The continuous-valued iHBA method was discretized with a modified sin–cos transfer function for feature selection. Oral cancer images were filtered using the CLAHE method, and after extracting deep features with the ResNet50 architecture, the proposed metaheuristic-based method was used to select discriminative features. Results: The proposed method was first tested for reliability and limitations through repeated runs on problems with different characteristics, such as unimodal and multimodal classical test functions. Then, the method was applied to extract significant features for oral cancer detection using a Histopathological Imaging Database containing 1224 histopathological oral tissue images at 100× and 400× magnification levels from 230 patients. The proposed approach was assessed in terms of accuracy, precision, recall, F1-score, and convergence curves in comparison with various classical feature selection techniques, such as wrapper-based, filter-based, and embedded-based methods, as well as other metaheuristic-based methods. The experimental results demonstrated that the suggested strategy outperformed both traditional feature selection techniques and alternative metaheuristic approaches. Conclusions: The effectiveness of the proposed method in improving diagnostic accuracy was evaluated through comprehensive experimental analyses. The obtained findings show that the proposed iHBA-based feature selection approach can reduce feature dimensionality, eliminate redundant and irrelevant features, and improve the classification performance of oral cancer detection. Therefore, the proposed method provides an effective and competitive computer-aided diagnostic framework for the automated classification of histopathological oral cancer images. Full article

Indian metropolitan cities such as Mumbai grapple with rapid urbanisation, extreme urban density, high built-up areas, loss of green cover, and shrinking open spaces, resulting in increased impermeable surfaces, urban heat island effects, and frequent flooding occurrences. Modern stormwater management has increasingly been characterised by integrated grey-green approaches; however, cities in the Global North benefit from established policies, technical expertise, and financial resources that enable the systematic and large-scale integration of Blue-Green Infrastructure (BGI) through district-wide geospatial assessment frameworks, unlike many cities in the Global South. Despite growing interest in nature-based stormwater solutions, there remains a dearth of geospatial empirical research from India examining the placement, distribution, performance, and functionality of BGI integrated with existing stormwater management systems in cities such as Mumbai. Furthermore, hydrological modelling using tools such as the Storm Water Management Model (SWMM) for the design, planning, and implementation of BGI in Indian cities remains largely unexplored. This study explores the role of BGI strategies in improving urban stormwater management within high-density Indian cities under a 25-year return period extreme rainfall scenario. Using an integrated approach that combines QGIS-based spatial analysis with EPA-SWMM hydrologic-hydraulic modelling, the research examines runoff behaviour, identifies flooding hotspots, and evaluates the effectiveness of Low Impact Development (LID)-based BGI measures such as permeable pavements, infiltration trenches, and green roofs applied at the ward level in Mumbai’s F/North and G/North Wards. Detailed land use classification, spatial mapping, and rainfall simulation corresponding specifically to a 25-year return period rainfall event was used to assess pre- and post-intervention conditions. The findings indicate that the applied BGI measures led to a 12.6% reduction in peak runoff (137.6 m³/s to 120.2 m³/s) and a 5.5% decrease in total runoff volume (783,510 m³ to 740,410 m³). More importantly, the peak flooding flow rate decreased by 45% (94.1 m³/s to 51.7 m³/s), demonstrating that BGI measures can efficiently reduce peak flooding flows by extending runoff hydrographs during extreme rainfall events. These findings are specifically applicable to the simulated 25-year return period extreme rainfall scenario and may vary under different rainfall intensities or return periods. Less extreme events could potentially experience even greater relative reductions or prevent flooding altogether, while also easing downstream hydraulic loads. Overall, strategically placed BGI interventions can significantly reduce surface runoff and peak flow, thereby enhancing stormwater resilience within spatially constrained urban environments. This study provides a replicable, data-driven framework for catchment-scale stormwater planning in dense Indian cities under extreme rainfall conditions, offering practical insights into methods, local contextual considerations, and spatial planning strategies for policymakers and urban planners seeking to retrofit and adapt existing infrastructure under increasing hydrologic stress and climate variability. Full article

Background: Childhood oral hygiene behaviors are crucial to preventing oral diseases and can influence the development and progression of malocclusions. The World Health Organization (WHO) Oral Health Questionnaire is a standardized tool for assessing oral hygiene behaviors, oral health-related behaviors, and preventive dental awareness in children. Aim: This study aimed to assess oral hygiene behaviours and examine associations between WHO Oral Health Questionnaire variables and Angle malocclusion classes among children aged 6–9 years. Materials and Methods: This cross-sectional study included 90 children aged 6–9 years from the Pristina region, Kosovo. Data were collected using the WHO Oral Health Questionnaire for Children, which assessed oral hygiene habits, toothbrushing frequency, fluoride awareness, dental attendance, dietary behaviors, oral symptoms, and oral-health-related quality of life. Malocclusion was classified according to Angle classification into Class I, II, and III malocclusions with 3D intraoral scanners, Aerolscan 3. Descriptive statistical analysis, Chi-square (χ²) test, Spearman correlation analysis, and reliability analysis using Cronbach’s Alpha were performed using SPSS Statistics 23.0 (IBM Corp., Armonk, NY, USA) and Statistica 7.1 (StatSoft Inc., Tusla, OK, USA). Results: Most participants reported regular oral hygiene practices, with 46.7% brushing their teeth two or more times daily. However, limited awareness regarding fluoride-containing toothpaste was observed, as most children answered “don’t know” regarding fluoride use. Occasional toothache or oral discomfort was reported by 33.3% of participants, while 23.3% reported dissatisfaction with dental appearance. Difficulty biting hard foods was present in 34.4% of children. Reliability analysis of the Q10 section demonstrated moderate internal consistency (Cronbach’s Alpha = 0.500). Chi-square analysis demonstrated no statistically significant association between Angle malocclusion classes and WHO questionnaire variables (p > 0.05). The highest χ² value was observed for tooth-cleaning frequency (Q7) (χ² = 11.97; p = 0.152), although the association remained statistically non-significant. Psychosocial impact questions and oral health-related quality of life questions also demonstrated no statistically significant association with malocclusion classes. Conclusions: oral hygiene practices, preventative oral health practices, and oral health-related experiences were comparatively similar among children in different Angle malocclusion classes. Although there were no statistically significant correlations found between malocclusion classes and WHO questionnaire variables, the results show that some children have psychosocial concerns about their dental appearance and insufficient awareness of preventive oral health. The WHO Oral Health Questionnaire is a useful epidemiological tool for evaluating pediatric oral health behaviors and may help build youth orthodontic and preventive oral health policies. Full article

Background: Hydatid disease, caused by Echinococcus granulosus, remains a significant public health concern in endemic regions. This study aimed to evaluate the role of ultrasound in the diagnosis, staging, and clinical management of liver hydatid cysts in the eastern city of Jalalabad, Afghanistan. Method: A cross-sectional study was conducted between February and November 2024 among 159 patients diagnosed with liver hydatid cysts. Demographic, clinical, laboratory, and imaging data were collected. Cysts were classified according to the WHO Informal Working Group on Echinococcosis (WHO-IWGE) and Gharbi systems. Ultrasound findings were compared with computed tomography (CT), and their association with treatment decisions was assessed. Result: A total of 159 patients with liver hydatid cysts were included in the study. Among them, 91 (57.2%) were female, 80 (50.3%) were aged 20–39 years, and 128 (80.5%) resided in rural areas. Most patients presented with a single cyst (144/159, 90.6%), while multiple cysts were observed in 15 (9.4%). The majority of cysts measured 5–9.9 cm in diameter (43.4%), followed by 1–4.9 cm (42.1%) and ≥10 cm (14.5%). According to the WHO-IWGE classification, CE1 (25.8%) and CE4 (24.5%) were the most common stages, followed by CE2 (17.6%), CE3a (13.8%), CE3b (11.3%), and CE5 (7.0%). Common exposure-related factors included dog ownership, poor hygiene practices, and consumption of raw vegetables. Ultrasound accurately identified cyst stages and demonstrated a significant association between WHO-IWGE staging and treatment modality (χ² = 63.56, p < 0.001). Almost perfect agreement was observed between ultrasound and CT for cyst classification (Cohen’s κ > 0.90), although CT provided additional anatomical information in selected complex cases. Conclusions: Ultrasound is an accessible, accurate, and reliable imaging modality for the diagnosis, staging, and management of liver hydatid cysts. In resource-limited settings, it serves as the primary imaging modality for guiding clinical decision-making, with CT reserved for complex or uncertain cases. Full article

Background: Accurate assessment of neonatal sleep is critical for monitoring brain development and identifying potential neurological disorders, yet manual scoring of multi-channel EEG recordings is labor-intensive and prone to variability. Methods: To address this, we propose a lightweight temporal–spatial feature fusion network for automatic neonatal sleep staging. The model employs a dual-branch architecture to separately capture temporal dependencies and spatial correlations in EEG signals, which are then integrated through feature concatenation and a compact classifier to obtain comprehensive feature representations while maintaining low computational complexity. Results: The framework was evaluated on a clinical neonatal dataset (CHFD) for tasks including sleep–wake classification, quiet sleep detection, and three-stage sleep staging, achieving superior performance compared with several state-of-the-art methods. Additional evaluation on the MASS-S3 adult dataset demonstrate that the model retains competitive accuracy and F1-score, indicating strong generalization across populations. Conclusions: These results suggest that jointly modeling temporal and spatial features enables robust and efficient automatic sleep staging. The proposed approach offers a practical solution for clinical applications and edge deployment, providing reliable, multi-dimensional assessment of neonatal brain activity and laying the groundwork for future studies integrating larger datasets or multimodal physiological signals. Full article

Background: Artificial intelligence (AI) adoption is increasingly reported as a priority for organizations, yet they face a growing, fragmented body of evidence concerning the factors that influence successful AI integration. Method: To identify the relevant factors for organizational AI adoption, we conducted a systematic literature review (SLR) following PRISMA guidelines, which yielded 37 quantitative empirical studies. From these studies we extracted 1229 paper-item instances, of which 810 were retained after applying structured exclusion criteria to develop a domain model relevant to organizational AI adoption. The model’s content validity was assessed and supported through expert feedback using the Content Validity Index (CVI) methodology. Results: We organized 24 subclusters into nine main clusters across the three dimensions Technology (Enablers, Usability, Trust), Organization (Leadership, People, Process), and Environment (Market, Regulatory, Partner). Our analysis suggests that workforce skills, perceived intelligence, and resources are among the most frequently studied and positively associated antecedents of AI adoption, and that constructs related to AI explainability and control (human-in-the-loop oversight) have received little research attention and remain underrepresented despite growing regulatory requirements such as the EU AI Act. Conclusions: The resulting domain model provides an empirically grounded classification of organizational AI adoption factors and can serve as a foundation for future measurement instruments. Full article

Background/Objectives: Dysphagia is common in tracheostomized patients with traumatic brain injury (TBI) and may be influenced by airway pathology and tracheostomy-related factors. This study investigated whether tracheostomy cannula type is independently associated with swallowing function and pharyngeal residue after accounting for airway stenosis and clinical variables. Methods: This retrospective observational study included 80 tracheostomized TBI patients using a speaking valve. Participants were grouped according to cannula type (non-fenestrated vs. fenestrated). Swallowing function was evaluated using Fiberoptic Endoscopic Evaluation of Swallowing (FEES), and pharyngeal residue severity was assessed using the Yale Pharyngeal Residue Severity Rating Scale (YPRSRS). Airway stenosis severity was graded using the Cotton–Meyer classification. Multivariable ordinal logistic regression analyses were performed to identify independent predictors of pharyngeal residue. Results: Higher pharyngeal residue scores were observed in the fenestrated cannula group under selected conditions, particularly for 5 mL liquid (p = 0.039) and 5 mL semi-solid boluses (p = 0.004) in the vallecular region, and for 5 mL semi-solid boluses in the pyriform sinuses (p < 0.001). Airway stenosis grade was strongly associated with increased pharyngeal residue and reduced SpO₂ levels (p < 0.001). In multivariable analyses, airway stenosis emerged as the factor most consistently associated with pharyngeal residue severity (e.g., OR = 4.909, 95% CI: 1.646–14.646, p = 0.004), whereas cannula type was not independently associated with most outcomes. Condition-specific associations were identified between fenestrated cannula use and pharyngeal residue in two models (vallecular residue for 5 mL semi-solid: OR = 0.354, 95% CI: 0.143–0.876, p = 0.025; pyriform sinus residue for 10 mL liquid: OR = 0.190, 95% CI: 0.073–0.495, p = 0.001); however, the direction of these associations differed from unadjusted comparisons, indicating prominent confounding by stenosis severity. Conclusions: FEES-estimated airway stenosis appeared to be the factor most consistently associated with pharyngeal residue severity in tracheostomized TBI patients, whereas the effect of cannula type appeared to be limited. Comprehensive airway assessment may therefore be important in dysphagia management. Full article

Rural public art is increasingly used to improve living environments and reactivate place-based culture in rural communities. However, existing evaluations remain fragmented and provide limited support for assessing intervention effectiveness and formulating targeted strategies. To address this gap, this study constructs a multidimensional evaluation system for rural public art interventions and empirically tests it through case studies of 11 villages in Songyang County, China. The system covers three dimensions: material space creation, cultural heritage and innovation, and the reconstruction of social relations. A mixed-methods approach was adopted, combining literature review, field investigation, expert consultation, AHP weighting, and Kano demand classification. The results support the validity of the proposed evaluation system and identify cultural heritage preservation and transmission, basic and cultural facilities, funding safeguards, spatial accessibility, cultural affinity, and local cultural aesthetic compatibility as stable priority indicators. The comparison between expert weighting and stakeholder sensitivity further reveals differences between strategic importance and locally perceived demand. This study provides an operational evaluation system for assessing rural public art interventions and translates the evaluation results into targeted strategies, offering empirical support for more sustainable and context-sensitive rural public art practices. Full article

Background/Objectives: Artificial intelligence (AI) offers a promising approach for detecting and classifying symptom severity in patients with Parkinson’s disease (PD). The objective was to provide an overview of AI methods performance used for this classification through a systematic review and meta-analysis conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Methods: The Google Scholar, IEEE Xplore, PubMed/MedLine, and ScienceDirect databases were searched for the period 2015–2025. The studies included were original, peer-reviewed studies written in English that addressed an AI method based on machine learning (ML) or deep learning (DL) for the classification of PD patients. The dataset used had to be “Gait in Parkinson’s Disease,” in which the severity of disease symptoms was assessed using the Hoehn and Yahr (H&Y) scale. Studies had to report at least one of the five performance metrics: accuracy, sensitivity, specificity, precision, and F1 score. Two reviewers independently selected articles, assessed the risk of bias using PROBAST (Prediction Model Study Risk of Bias Assessment Tool), and extracted data. The logit-transformed values were pooled separately by performance metrics and by severity level using a random-effects model. Cochran’s Q test, the I² statistic, and inter-study variability (τ²), computed using the generalized inverse variance method with the restricted maximum likelihood model, were used to assess heterogeneity. Forest plots with 95% confidence intervals were used to present the results. Possible causes of heterogeneity were explored using a subgroup analysis (ML vs. DL) and a sensitivity analysis. Finally, publication bias (Egger’s test) and the certainty of the evidence (using GRADE—Grading of Recommendations Assessment, Development, and Evaluation) were assessed to verify the generalizability of the results. Results: Among the 257 unique records, 12 studies were included. The methods demonstrated very high overall performance (>92%): accuracy (96.4%, 95% CI: 95.9–96.9%), specificity (97.7%, 95% CI: 97.3–98.1%), sensitivity (94.0%, 95% CI: 92.7–95.2%), precision (93.4%, 95% CI: 92.0–94.6%), F1 score (92.1%, 95% CI: 90.6–93.4%). Accuracy, specificity, and precision were high for all H&Y levels. However, the more advanced the symptoms, the lower the sensitivity (97.3% for H&Y0 vs. 92.1% for H&Y3). ML models achieved the best results for classifying healthy patients (H&Y0: 95.7% to 98.2%), while DL approaches performed better for classifying higher severity levels (>92%). Heterogeneity and inter-study variability were moderate (I²: 40–50% and τ²: 0.3–0.4) for precision and F1 score, and high (I² > 90% and τ² > 0.6) for accuracy, specificity, and sensitivity. The GRADE analysis revealed low-quality evidence for precision and F1 score and very-low quality for accuracy, specificity, and sensitivity. Conclusions: Thus, AI-based wearable gait assessment devices show great promise in terms of aiding clinical decision-making and treatment personalization. However, further research using a rigorous methodology (PROBAST) is needed to ensure the generalizability of the results and the clinical viability of the proposed solutions. Full article

Background/Objectives: Absent septum pellucidum (ASP) is a rare fetal midline brain finding that may occur in isolation or alongside broader central nervous system (CNS) malformations, genetic disorders, or septo-optic dysplasia (SOD). Accurate prenatal diagnosis and counseling remain challenging because apparently isolated ASP may be reclassified following fetal magnetic resonance imaging (MRI), postnatal neuroimaging, or specialist assessment. This structured narrative review aimed to synthesize current evidence on prenatal imaging findings, associated anomalies, genetic evaluation, and postnatal outcomes in fetuses with ASP. Methods: This structured narrative review used PRISMA-informed reporting. PubMed and Google Scholar were searched for full-text English-language studies published from 2014 through the updated search date (8 June 2026). Data on gestational age at diagnosis, imaging classification, associated anomalies, genetic testing, postnatal assessment, and neurodevelopmental, ophthalmological, and endocrine outcomes were extracted. Study methodological quality was appraised using Joanna Briggs Institute tools. Results: Seven studies comprising 342 fetal ASP cases were included. Of these, 94 cases (27.5%) were classified as isolated ASP prenatally, but only 57 remained isolated postnatally when follow-up data were available. SOD was confirmed after birth in 11 of 94 (11.7%) fetuses with prenatally isolated ASP. As definitions, imaging protocols, genetic testing strategies, and follow-up duration differed substantially across studies, these pooled values are descriptive observations rather than formal quantitative estimates. Conclusions: ASP is a heterogeneous prenatal finding. The prognosis is most favorable when ASP remains isolated following a detailed prenatal and postnatal evaluation. Multidisciplinary follow-up involving fetal medicine, neuroradiology, genetics, ophthalmology, endocrinology, and neurology is essential for risk stratification and counseling. Full article

Background/Objectives: Pediatric IgA nephropathy (IgAN) is often considered to have a favorable early course. However, its progression is variable, and the prognostic value of histopathological classifications, such as MEST-C, remains incompletely defined in children. This study aimed to characterize clinicopathological features and the early disease course in pediatric IgAN and to descriptively examine histopathological findings and clinical outcomes. Methods: This retrospective, single-center study included children with biopsy-confirmed IgAN diagnosed between 2016 and 2025. Clinical, laboratory, and histopathological data were collected, and biopsies were assessed using the Oxford MEST-C classification. Follow-up data, including estimated glomerular filtration rate (eGFR), were analyzed descriptively, with follow-up extending from diagnosis to early 2026. Results: Fourteen patients were included, showing heterogeneous clinical presentations. Mesangial hypercellularity was observed in all cases (100%), with frequent endocapillary hypercellularity (78.6%) and segmental sclerosis (57.1%), consistent with a predominance of active lesions. Over a median follow-up of approximately five years, renal function remained stable in 57.1% of patients, declined in 21.4%, and improved in 14.3%, indicating variability in renal function during follow-up and potential reversibility in a subset of patients. One patient (7.1%) developed severe acute kidney injury requiring temporary dialysis, followed by full recovery. Given the descriptive design and limited sample size, no conclusions regarding associations between histopathological findings and renal outcomes can be drawn. Conclusions: Within this small cohort, pediatric IgAN showed variable renal function courses ranging from stability to decline or partial recovery. These findings should be considered descriptive and hypothesis-generating, supporting longitudinal monitoring in larger pediatric cohorts. Full article

Background: SARS-CoV-2 infection has been associated with metabolic disturbances and endocrine alterations, including effects on pancreatic β-cell function and thyroid hormone regulation. However, the relationship between thyroid function and β-cell compensatory capacity in the post-COVID state remains unclear. Methods: In this cross-sectional study, we evaluated β-cell compensation (HOMA-B/HOMA-IR) and thyroid parameters in three groups: patients with active COVID-19, individuals with post-COVID metabolic disturbances, and a COVID-negative metabolic syndrome reference group. Thyroid status was assessed using both comprehensive clinical classification and biochemical criteria. Associations between thyroid hormones and β-cell function were analyzed using Spearman correlation. Results: β-cell compensatory capacity differed significantly across groups, with the lowest values observed during active COVID-19 and intermediate impairment in the post-COVID cohort compared with the metabolic syndrome group. FT3 concentrations and the FT3/FT4 ratio were significantly reduced during active infection and were positively associated with β-cell compensation in the post-COVID group (ρ = 0.421, p = 0.018 and ρ = 0.382, p = 0.031, respectively). Although thyroid dysfunction appeared more prevalent in the post-COVID cohort when defined by overall clinical classification, no significant differences were observed when thyroid status was evaluated based solely on biochemical criteria, excluding clinical history and euthyroid sick syndrome. Conclusions: Post-COVID metabolic disturbances are characterized by impaired β-cell compensatory capacity and alterations in peripheral thyroid hormone dynamics. The apparent increase in thyroid dysfunction is largely driven by pre-existing thyroid disease and non-thyroidal illness effects rather than intrinsic thyroid pathology. These findings are consistent with the hypothesis of a potential post-COVID immunometabolic phenotype involving both pancreatic and thyroid-related mechanisms. Full article

Objective: Minimally invasive surgery in Hirschsprung disease (HSCR) management was introduced in the mid-1990s. Despite decades of clinical application of various laparoscopic approaches, there remains a paucity of high-powered prospective studies and comprehensive systematic reviews in the literature. This study aimed to systematically review and summarize published techniques and outcomes of laparoscopic- and robotic-assisted surgery in HSCR. Methods: A systematic literature review was conducted using PubMed and the Cochrane Library. Studies reporting technical and outcome data of laparoscopic- or robotic-assisted surgery for HSCR were included. Data extraction and analysis were performed in accordance with the PRISMA 2020 guidelines. Parameters of interest included surgical technique, age at primary pull-through (PT), operative time, and functional outcomes. Outcomes of laparoscopic- versus robotic-assisted Yancey–Soave PT were compared. Results: A total of 700 publications were screened, of which seven studies met the inclusion criteria. Data from 556 patients were analyzed. A total of 338 underwent laparoscopic-assisted, and 218 underwent robotic-assisted pull-through. Large variability of the reported transanal resection technique (modified Yancey–Soave PT) was reported. Four studies reported functional outcomes in patients aged over four years. Three studies directly compared laparoscopic- and robotic-assisted PT; two reported no difference in the incidence of postoperative Hirschsprung-associated enterocolitis (HAEC). Functional outcomes were assessed using the Krickenbeck classification in three studies and the bowel function score in one study, with no significant differences reported in patients aged >4 years. Conclusions: Laparoscopic- and robotic-assisted Yancey–Soave PT appears to be safe for HSCR. Large variability in the applied surgical technique—despite being commonly classified as modified Yancey–Soave PT—as well as heterogeneity in the bowel function assessment, limit direct comparability between studies. To date, no single minimally invasive approach has demonstrated clear superiority over others. Prospective, randomized controlled studies are required to enable robust comparative evaluation of techniques, overall costs, and outcomes. Full article