Restoring the integrity of the external nose presents a complex surgical challenge due to its three-dimensional structure and subunit divisions. The most frequent causes of nasal defects include basal or squamous cell carcinoma, animal bites and trauma. The reconstruction approach varies depending on factors such as the defect’s size, the affected subunit of the nose, the condition and quality of the surrounding tissues and the surgeon’s expertise. Commonly employed surgical techniques for nasal reconstruction include the forehead flap, nasolabial flap, Rieger flap, bilobed flap, and free autologous skin graft. We present a clinical case of a patient with a full-thickness nasal alar defect who underwent three-layer reconstruction with a combination of an inverted skin flap- for the internal lining, a conchal graft to substitute the missing alar cartilage and a forehead flap for external coverage. One of the challenges of nasal reconstruction surgery is to restore a full-thickness defect, especially the internal nasal lining. We offer a different perspective on this problem, showcasing considerable advantages, as there are limited literature data on this method.

Background: Oro-nasal fistula formation remains one of the most common complications following primary palatoplasty, while the influence of surgical technique on fistula incidence and characteristics remains controversial. This study aimed to compare the occurrence and features of oro-nasal fistulas after two primary palatal repair techniques. Methods: A retrospective comparative analysis was conducted in patients undergoing one-stage primary palatoplasty using either closed intravelar palatoplasty or a modified Veau–Wardill–Kilner pushback technique. Oro-nasal fistulas were evaluated according to presence, size, anatomical location, and functional impact. Secondary corrective procedures, including fistula repair and pharyngoplasty, were also analyzed. Results: Oro-nasal fistula formation was significantly associated with the surgical technique. Closed intravelar palatoplasty demonstrated a significantly lower fistula rate compared with the modified Veau–Wardill–Kilner technique. Cleft type and syndromic status were not independently associated with fistula development. Rates of secondary corrective procedures and pharyngoplasty were significantly lower in the intravelar group. Conclusions: Surgical technique plays a decisive role in oro-nasal fistula development after primary palatoplasty. Muscle-oriented repair with limited incisions is associated with reduced fistula formation and a lower need for secondary surgical interventions.

Background/Objectives: The infraclavicular region contains a complex arrangement of the brachial plexus cords and axillary vessels. Accurate ultrasonographic characterization of these relationships is important for anatomical image interpretation and has direct relevance to ultrasound-guided infraclavicular procedures. This study aimed to evaluate the bilateral topographic distribution and morphometric relationships of the lateral, medial, and posterior cords relative to the axillary artery and axillary vein. Methods: This prospective, multicenter, observational anatomical–ultrasonographic study included 50 adults (25 women and 25 men), each examined bilaterally, yielding 100 potential side-specific examinations. The lateral cord (LC), medial cord (MC), posterior cord (PC), and axillary vein (AV) were mapped using a 12-sector radial model centered on the axillary artery (AA). Center-to-center intercord, cord-to-AA, and cord-to-AV distances were measured. Topographic confidence intervals were estimated using participant-level cluster bootstrap resampling, and bilateral morphometric data were analyzed using linear mixed-effects models. Results: The LC was located in sectors 10–11 in 85/94 examinations (90.4%; 95% CI, 84.4–95.7%), the MC in sectors 2–4 in 84/89 (94.4%; 95% CI, 89.2–98.9%), the PC in sectors 6–7 in 78/83 (94.0%; 95% CI, 88.6–98.8%), and the AV in sectors 4–5 in 95/100 (95.0%; 95% CI, 90.0–99.0%). Mean intercord distances were 11.20 ± 1.18 mm for LC/MC, 7.30 ± 1.61 mm for LC/PC, and 5.70 ± 1.10 mm for MC/PC. Mean cord-to-AA distances were 7.60 ± 0.74, 4.60 ± 0.93, and 6.60 ± 0.91 mm for the LC, MC, and PC, respectively. Mean cord-to-AV distances were 15.50 ± 2.01, 6.60 ± 1.22, and 8.50 ± 1.10 mm, respectively. Mixed-effects models demonstrated significant differences among measurements within each distance family (all p < 0.001), without significant overall side or sex effects. Conclusions: The brachial plexus cords and AV demonstrated a consistent topographic organization around the AA during bilateral mid-infraclavicular ultrasonographic assessment. The MC showed the shortest center-to-center distance to both the AA and AV, and the three cords formed a non-equidistant spatial configuration.

Background: Schnyder corneal dystrophy (SCD) is a rare autosomal dominant disorder characterized by bilateral corneal opacification due to abnormal cholesterol and phospholipid deposition. Mutations in the UBIAD1 gene, identified as causative in 2007, underline the condition, although its exact pathogenesis remains unclear. Case Presentation: A 55-year-old female presented with persistent photophobia, blepharospasm, and corneal discomfort. She also reported joint pain related to rheumatoid arthritis (RA), managed with Ro-Actemra (tocilizumab). The ophthalmological evaluation revealed bilateral corneal stromal deposits resembling snowflakes, with visual acuities of 0.8 (right eye) and 0.7 (left eye). Multimodal imaging confirmed stromal hyperreflective deposits. Based on the clinical findings, SCD was diagnosed, although no genetic testing was performed. Symptomatic management with artificial tears was initiated. Discussion: This case illustrates the diagnostic challenges of SCD, particularly in the absence of corneal crystals, a hallmark feature that is not universally present. Advanced imaging techniques aided diagnosis, and the coexistence of SCD and RA highlights the need for multidisciplinary care. Treatment options remain limited, although emerging therapies targeting oxidative stress and lipid metabolism show promise. Conclusions: This case highlights the importance of integrating ophthalmological and systemic care in SCD management and underscores the need for further research to expand diagnostic and therapeutic strategies for this rare disorder.

Mechanical Power (MP) is a critical determinant of Ventilator-Induced Lung Injury (VILI), yet its real-time calculation in pressure-controlled ventilation (PCV) remains a challenge due to the complexity of existing analytical formulas and the inconsistent availability of ICU sensor data. This study proposes a hybrid deep learning architecture, combining a One-Dimensional Convolutional Neural Network (1D CNN) with Bidirectional Long Short-Term Memory (BiLSTM) layers, to estimate MP from 11 standard respiratory parameters. Utilizing research sources from the high-fidelity database VitalDB, the model was trained and validated against four established MP equations. To ensure clinical robustness, a feature-masking augmentation strategy was implemented to simulate signal inconsistencies and sensor failures. The results demonstrate exceptional predictive accuracy, achieving a peak $R^2$ of 0.9994 and a symmetric Mean Absolute Percentage Error (sMAPE) between 3.80% and 4.87% across all target equations. This “sensor-fusion” approach effectively captures both spatial features and temporal dynamics, providing a reliable, real-time decision-support tool for personalized, lung-protective ventilation strategies.

Hysteresis defines a system’s state as a function of its historical trajectory, manifesting as path-dependent loops where variables diverge based on the direction of change. Intrinsic to both information storage and irreversible thermodynamic dissipation, this phenomenon serves as a critical fingerprint for characterizing complex behaviors and dynamic system responses. The automated classification and labeling of dense hysteresis signals is essential for systems engineering, electronics, bio-signal processing, and meteorology. This study presents a comprehensive comparison between two unsupervised learning pipelines, to which a main objective is to discover the capabilities for phenotype discovery between automated feature extraction and expert-driven feature engineering. These methodologies are examined by clustering 7736 respiratory cycles from critical care patients who have undergone invasive mechanical ventilation. Methodology I utilizes the extraction of 17 geometric and spectral features, reduced via Principal Component Analysis (PCA) to ease the choice of high variance parameters for subsequent clustering. Methodology II proposes a Deep Learning (DL) framework utilizing a 1D-convolutional autoencoder (CAE) for automated feature discovery. Both methods utilize UMAP and HDBSCAN for the final clustering stage. After evaluation both models performed with high trustworthiness (0.9974; 0.9853) and Silhouette Scores of 0.6441 and 0.6415. Here, both methodologies obtained a surprisingly low noise ratio (1.5% and 0.74%) by the aformentioned HDBSCAN. The strategy of narrowing discrete features allows for better model interpretability, and the resulting similarity measures reasoned trough a Davies–Bouldin Index are higher. However, a peculiar difference was observed in favor to the automated extraction (CAE), where the method assessed a better quality of clustering when compared for dispersion between clusters and the dispersion of reference clusters.