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Background: The differentiation of pheochromocytoma (PCC) from other adrenal lesions, particularly in incidentalomas with non-benign radiological characteristics (size > 4 cm or density > 10 HU), remains a clinical challenge. The study aimed to develop and validate an interpretable machine learning (ML) model for pairwise differentiation of PCC from adrenocortical carcinomas (ACCs) and non-functioning adrenal adenomas (NAAs) and to identify the most important clinical features.

Methods: We analyzed a dataset of 50 clinical, laboratory, and radiological parameters from 123 patients with histologically verified adrenal tumors (63 PCC, 30 ACC, 30 NAA). Four classifiers-Logistic Regression (LR), Random Forest (RF), Linear Discriminant Analysis (LDA), and Extreme Gradient Boosting (XGBoost)-were trained for binary classification tasks (PCC vs. ACC, PCC vs. NAA, ACC vs. NAA) using a robust nested stratified cross-validation pipeline to ensure generalizability and avoid overfitting.

Results: All four models showed strong predictive performance, with discrimination (AUC) more than 0.8. Our analysis, based on the interpretable LR model, identified the key discriminators differentiated PCC from both ACC and NAA: maximum systolic blood pressure, grade 3 hypertension, headache, palpitation, tachycardia, male sex, and concomitant gastric and duodenal ulcers. In contrast, lower back pain and general weakness were strong signs of lower probability of PCC. The tumor density specifically differentiated PCC from NAA, whereas tumor size was an important marker for distinguishing PCC and ACC.

Conclusions: We developed robust ML models capable of accurately differentiating PCC from other adrenal tumors in complex cases. The models provide a clinically actionable tool for pre-surgical decision support. Furthermore, the identification of key discriminative features enhances the clinical understanding of PCC and facilitates its differential diagnosis prior to histological verification.

Geographic atrophy (GA) is a progressive cause of central vision loss with limited rehabilitation options. This prospective case series aimed to evaluate the effects of biofeedback fixation training (BFT) on visual function and vision-related quality of life (QoL) in patients with GA. Eighteen patients with total central vision loss in one eye underwent BFT on the fellow eye (study eye) using the Macular Integrity Assessment (MAIA) system, which was used to select a new, previously chosen preferred retinal locus (PRL) to stabilize fixation or adopt a new fixation locus. Patients were followed for an average of 13.2 months (range 3-26 months). Functional outcomes included best corrected visual acuity (ETDRS chart), reading performance (Radner test), and contrast sensitivity (Spot Checks test). MAIA parameters comprised average retinal sensitivity, fixation distance and stability (P1, P2), and changes in the bivariate contour ellipse area (BCEA). Vision-related quality of life was assessed using the National Eye Institute Visual Functioning Questionnaire-25 (NEI-VFQ-25). Following BFT, visual acuity, reading ability and contrast sensitivity improved significantly (p value: p < 0.02), and fixation stability and NEI-VFQ-25 scores showed a positive trend. These findings indicate that BFT is a feasible and promising rehabilitation approach for patients with GA.

(1) Background: Hemifacial spasm (HFS) is most commonly caused by neurovascular compression at the root exit zone (REZ) of the facial nerve; however, isolated compression along the distal cisternal segment is uncommon and remains poorly characterized. This study aimed to analyze the clinical features, intraoperative neurophysiological patterns, and surgical outcomes of patients with HFS caused by cisternal segment arterial compression. (2) Methods: Among 874 patients who underwent microvascular decompression (MVD) for HFS, 18 (2.1%) were identified as having isolated neurovascular conflict at the cisternal segment, all involving the anterior inferior cerebellar artery (AICA). Clinical characteristics, offender location, intraoperative monitoring results including lateral spread response (LSR), brainstem auditory evoked potentials, and postoperative outcomes were retrospectively evaluated. A standardized Teflon interposition technique was used in all cases. (3) Results: Postoperatively, 83.3% of patients experienced immediate spasm relief, and at the latest available follow-up, 94.4% achieved significant improvement without severe complications. (4) Conclusions: Although rare, cisternal segment arterial compression can produce typical HFS and should be considered when REZ compression is unclear or when intraoperative neuromonitoring does not respond as expected. Microvascular decompression using Teflon interposition is a safe and effective treatment option for this anatomically challenging offender location.

Axial spondyloarthritis (axSpA) is a chronic inflammatory disorder causing structural spinal damage and pathological thoracic kyphosis. Accurate quantification of spinal curvature is crucial for monitoring disease progression and guiding treatment. Conventional Cobb angle measurement on radiographs or DEXA images is widely used but is time-consuming and prone to inter-observer variability. This study evaluates an automated deep learning-based approach using a You Only Look Once (YOLO) model for vertebral detection on lateral morphometric DEXA scans and estimation of thoracic kyphosis angles. A dataset of 512 annotated DEXA images, including 182 from axSpA patients, was used to train and test the model. Kyphosis angles were computed by fitting a circle through detected vertebral centroids (Th4-Th12) and calculating the corresponding curvature angle. Model-predicted angles demonstrated strong agreement with physician-measured Cobb angles (r = 0.92, p < 0.001), low mean squared error (4.2°) and high sensitivity and specificity for detecting clinically significant kyphosis. Automated lateral DEXA morphometry provides a rapid, reproducible and clinically interpretable method for assessing thoracic kyphosis and bone density in axSpA, representing a practical tool for integrated structural and metabolic evaluation.

This study systematically investigated the influence of approach kinematics on the subsequent kinetics and power production strategies during the approach to running jumps with a single leg (ARJSL). Twenty-five physically active male university students performed ARJSL trials under two prescribed approach speeds (fast and slow) and three approach distances (3, 6, and 9 m) in a 2 × 3 within-subjects design. Three-dimensional motion capture synchronized with force platform data was used to quantify jump height (JH), vertical touchdown velocity (TDv), reactive strength index (RSI), peak joint power (hip, knee, and ankle), and joint stiffness. Significant approach speed × distance interactions were observed for JH (p = 0.006), TDv (p < 0.001), RSI (p = 0.014), ankle stiffness (p = 0.006), and peak power generation at all lower-limb joints (all p < 0.034). The results demonstrate that changes in approach strategy systematically alter the distribution of mechanical power among the hip, knee, and ankle joints, thereby influencing the effectiveness of horizontal-to-vertical momentum conversion during take-off. Notably, RSI and ankle stiffness were particularly sensitive to combined manipulations of speed and distance, highlighting their value as neuromechanical indicators of stretch-shortening cycle intensity and joint loading demands. In conclusion, ARJSL performance depends on finely tuned, speed- and distance-specific biomechanical adaptations within the lower extremity. These findings provide a constrained, joint-level mechanical characterization of how approach speed and distance interact to influence power redistribution and stiffness behavior during ARJSL, without implying optimal or performance-maximizing strategies.

Background: Functional dizziness and persistent postural-perceptual dizziness (PPPD) involve mutually reinforcing vestibular symptoms and anxiety. Non-pharmacological interventions, such as vestibular rehabilitation therapy (VRT) and cognitive behavioral therapy (CBT), aim to address both mechanisms, yet their overall effectiveness remains unclear. Methods: We systematically examined randomized controlled trials (RCTs) published between 2000 and 2025 that evaluated VRT, CBT, or multimodal approaches for adults with functional or chronic dizziness (including PPPD and related functional dizziness constructs) accompanied by significant anxiety. Twelve RCTs (513 participants) met the criteria, involving individuals with PPPD, chronic subjective dizziness, chronic vestibular disorders with prominent anxiety, and residual dizziness after benign paroxysmal positional vertigo. Results: Conventional VRT delivered in clinic or as structured home-based programs produced small-to-moderate improvements in dizziness-related disability versus usual care. Combining VRT with CBT or psychologically informed components yielded larger and more consistent reductions in disability and maladaptive dizziness-related beliefs. CBT-based interventions reduced anxiety and dizziness-related distress compared with supportive controls. Emerging modalities, including virtual-reality-based VRT, non-invasive neuromodulation, and heart-rate-variability biofeedback, showed potential, although they were limited by small samples and methodological issues. Most trials had some risk-of-bias concerns and evidence certainty ranged from very low to moderate. Conclusions: Integrated multimodal rehabilitation shows promise, although larger, high-quality RCTs using standardized procedures and outcome measures are required.

Background:Stenotrophomonas maltophilia is an increasingly important multidrug-resistant opportunistic pathogen frequently isolated from clinical, environmental, and plant-associated niches. Despite its medical relevance, the global population structure, species-complex boundaries, and genomic determinants of antimicrobial resistance (AMR) and ecological adaptation remain poorly resolved, partly due to inconsistent annotations and fragmented genomic datasets. Methods: Approximately 2400 genome assemblies annotated as Stenotrophomonas maltophilia were available in the NCBI Assembly database at the time of query. After pre-download filtering to exclude metagenome-assembled genomes and atypical lineages, 1750 isolate genomes were retrieved and subjected to stringent quality control (completeness ≥ 90%, contamination ≤ 5%, ≤500 contigs, N50 ≥ 10 kb, and ≤1% ambiguous bases), yielding a final curated dataset of 1518 high-quality genomes used for downstream analyses. Genomes were assessed using CheckM, annotated with Prokka, and compared using average nucleotide identity (ANI), pan-genome analysis, core-genome phylogenomics, and functional annotation. AMR genes, mobile genetic elements (MGEs), and metadata (source, host, and geographic origin) were integrated to assess lineage-specific genomic features and ecological distributions. Results: ANI-based clustering resolved the S. maltophilia complex into multiple distinct genomospecies and revealed extensive misidentification of publicly deposited genomes. The pan-genome was highly open, reflecting strong genomic plasticity driven by accessory gene acquisition. Core-genome phylogeny resolved well-supported clades associated with clinical, environmental, and plant-related niches. Resistome profiling showed widespread intrinsic MDR determinants, with certain lineages enriched for efflux pumps, β-lactamases, and trimethoprim-sulfamethoxazole resistance markers. MGE analysis identified lineage-specific integrative conjugative elements, prophages, and transposases that correlated with source and geographic distribution. Conclusions: This large-scale analysis provides the most comprehensive genomic overview of the S. maltophilia complex to date. Our findings clarify species boundaries, highlight substantial taxonomic misannotation in public databases, and reveal lineage-specific AMR and mobilome patterns linked to ecological and clinical origins. The curated dataset and evolutionary insights generated here establish a foundation for global genomic surveillance, epidemiological tracking, and future studies on the evolution of antimicrobial resistance in S. maltophilia.

Lipodystrophy is a rare group of metabolic disorders characterized by the abnormal distribution of body fat, which can lead to various metabolic complications due to the body's inability to adequately process carbohydrates and fat. We report the case of a female, aged 53 years, who was admitted as an outpatient for progressive weight loss of the upper part of the body (face, neck, arms, and chest), dyspeptic complaints, fatigue, mild insomnia, and anxious behavior. Her medical history was characterized by the presence of dyslipidemia, hypertension, and a minor stroke episode. However, she denied any family-relevant medical history. Although the clinical perspective suggested a possible late onset of partial acquired lipodystrophy, due to the imaging exam that revealed an enlarged liver with inhomogeneous structure with multiple nodular lesions, scattered over both lobes, a lot of lab work-ups and complementary studies were performed. Eventually, a liver biopsy was performed by a laparoscopic approach during cholecystectomy, the histology consistent with metabolic disease-associated steatohepatitis (MASH). In conclusion, given their heterogeneity and rarity, lipodystrophies may be either overlooked or misdiagnosed for other entities. Barraquer-Simons syndrome (BSS) may be associated with liver disease, including cirrhosis and liver failure. Liver lipodystrophy in BSS may sometimes feature steatosis with a focal, multi-nodular aspect, multiplying the diagnostic burden. Liver lipodystrophy may manifest as asymptomatic fat accumulation but may progress to severe conditions, representing one of the major causes of mortality in BSS, apart from the cardio-vascular comorbidities. Given the potential of severe outcomes, it is mandatory to correctly assess the stage of liver disease since the first diagnosis.

Background: The COVID-19 pandemic substantially altered the epidemiology of respiratory infections. Its impact on the clinical course of influenza in hospitalised children remains insufficiently characterised.

Objectives: We aimed to compare the clinical course, complications, and selected laboratory parameters of influenza in children before, during, and after the COVID-19 pandemic.

Methods: This single-centre retrospective study included 553 children hospitalised with laboratory-confirmed influenza between September 2017 and August 2025. Patients were divided into three groups: pre-pandemic, pandemic, and post-pandemic. Clinical complications and inflammatory markers (CRP, PCT, neutrophil counts) were analysed.

Results: Influenza-related complications occurred in 59.5% of patients and were significantly more frequent after the pandemic compared to the pre-pandemic period (64.3% vs. 52.9%, p = 0.02). Pneumonia was the most common complication across all groups, but its incidence was lowest during the pandemic. Myositis occurred most frequently during the pandemic and appears to coincide with a higher proportion of influenza B infections. No significant differences were observed in CRP, PCT concentrations, or neutropenia rates between groups.

Conclusions: The COVID-19 pandemic influenced the clinical presentation of influenza in children, with a post-pandemic increase in complications. These findings may reflect delayed access to healthcare and the phenomenon of immunity debt, highlighting the need for continued surveillance and preventive strategies.

Behavior-related factors represent a major cause of cat relinquishment to shelters, highlighting the need for reliable tools to support appropriate matching between cats and adopters. The present study applied the ASPCA^® Meet Your Match^® Feline-ality™ assessment to evaluate personality profiles of shelter cats and to examine factors associated with variation in personality expression across shelters. A total of 113 cats housed in six shelters in the south of France were assessed using a standardized behavioral protocol. Differences between shelters were evaluated using one-way ANOVA for behavioral scale scores, while associations between personality type and shelter affiliation, sex, coat color, and age were analyzed using χ² tests of independence. Significant differences between shelters were observed for the majority of behavioral assessment items, as well as for composite valiance and independent-gregarious scale scores. Shelter affiliation was significantly associated with the distribution of Feline-ality™ personality types, indicating that personality profiles were not uniformly distributed across shelters. No statistically detectable association was found between personality type and sex. In contrast, significant associations were observed between personality type and both coat color category and age category, suggesting non-random variation in personality distribution across these factors. These findings indicate that shelter-related and individual factors are associated with variation in feline personality expression. While causal relationships cannot be inferred, the results underscore the importance of considering environmental context and population characteristics when interpreting shelter-based behavioral assessments. The Feline-ality™ framework appears to be a useful tool for characterizing personality variation in shelter cats and may support improved adoption matching when applied with appropriate caution.