Search Results (440)

A 10-year-old intact female Bichon Frise presented with multiple firm skin nodules on all four limbs. The nodules progressively increased in number and size over seven months. Diagnostic tests included cytology of fine-needle aspirates, histopathology of skin biopsies, radiography, and abdominal ultrasonography. Cytology revealed spindle-shaped mesenchymal cells and extracellular matrix components, and histopathology confirmed ND characterized by mature collagen deposition without evidence of malignancy. Ultrasonography detected multiple kidney cysts bilaterally, although their exact nature (benign or malignant) could not be confirmed histologically. Genetic analysis was performed, revealing no mutation in the traditionally implicated FLCN gene but multiple nonsynonymous mutations in the BRCA2 gene. This case suggests a potential association between BRCA2 gene mutations and the development of ND with renal cystic lesions, broadening the known genetic causes beyond the commonly reported FLCN mutation. Regular genetic screening and close monitoring of dermatological and renal conditions in atypical breeds are recommended. To the best of current knowledge, this is the first case report demonstrating ND and renal cysts associated with BRCA2 mutations in a Bichon Frise. Full article

Objective: The classification of skin cancer is very helpful in its early diagnosis and treatment, considering the complexity involved in differentiating AK from BCC and SK. These conditions are generally not easily detectable due to their comparable clinical presentations. Method: This paper presents a new approach to hyperspectral imaging for enhancing the visualization of skin lesions called the Spectrum-Aided Vision Enhancer (SAVE), which has the ability to convert any RGB image into a narrow-band image (NBI) by combining hyperspectral imaging (HSI) to increase the contrast of the area of the cancerous lesions when compared with the normal tissue, thereby increasing the accuracy of classification. The current study investigates the use of ten different machine learning algorithms for the purpose of classification of AK, BCC, and SK, including convolutional neural network (CNN), random forest (RF), you only look once (YOLO) version 8, support vector machine (SVM), ResNet50, MobileNetV2, Logistic Regression, SVM with stochastic gradient descent (SGD) Classifier, SVM with logarithmic (LOG) Classifier and SVM- Polynomial Classifier, in assessing the capability of the system to differentiate AK from BCC and SK with heightened accuracy. Results: The results demonstrated that SAVE enhanced classification performance and increased its accuracy, sensitivity, and specificity compared to a traditional RGB imaging approach. Conclusions: This advanced method offers dermatologists a tool for early and accurate diagnosis, reducing the likelihood of misclassification and improving patient outcomes. Full article

Recently, there has been a rise in skin cancer cases, for which early detection is highly relevant, as it increases the likelihood of a cure. In this context, this work presents a benchmarking study of standard Convolutional Neural Network (CNN) architectures for automated skin lesion classification. A total of 38 CNN architectures from ten families (ConvNeXt, DenseNet, EfficientNet, Inception, InceptionResNet, MobileNet, NASNet, ResNet, VGG, and Xception) were evaluated using transfer learning on the HAM10000 dataset for seven-class skin lesion classification, namely, actinic keratoses, basal cell carcinoma, benign keratosis-like lesions, dermatofibroma, melanoma, melanocytic nevi, and vascular lesions. The comparative analysis used standardized training conditions, with all models utilizing frozen pre-trained weights. Cross-database validation was then conducted using the ISIC 2019 dataset to assess generalizability across different data distributions. The ConvNeXtXLarge architecture achieved the best performance, despite having one of the lowest performance-to-number-of-parameters ratios, with 87.62% overall accuracy and 76.15% F1 score on the test set, demonstrating competitive results within the established performance range of existing HAM10000-based studies. A proof-of-concept multiplatform mobile application was also implemented using a client–server architecture with encrypted image transmission, demonstrating the viability of integrating high-performing models into healthcare screening tools. Full article

Background: Integrating artificial intelligence into clinical photography offers great potential for monitoring skin conditions such as actinic keratosis (AK) and skin field cancerization. Identifying the extent of AK lesions often requires more than analyzing lesion morphology—it also depends on contextual cues, such as surrounding photodamage. This highlights the need for models that can combine fine-grained local features with a comprehensive global view. Methods: To address this challenge, we propose AKTransU-net, a hybrid U-net-based architecture. The model incorporates Transformer blocks to enrich feature representations, which are passed through ConvLSTM modules within the skip connections. This configuration allows the network to maintain semantic coherence and spatial continuity in AK detection. This global awareness is critical when applying the model to whole-image detection via tile-based processing, where continuity across tile boundaries is essential for accurate and reliable lesion segmentation. Results: The effectiveness of AKTransU-net was demonstrated through comparative evaluations with state-of-the-art segmentation models. A proprietary annotated dataset of 569 clinical photographs from 115 patients with actinic keratosis was used to train and evaluate the models. From each photograph, crops of 512 × 512 pixels were extracted using translation lesion boxes that encompassed lesions in different positions and captured different contexts. AKtransU-net exhibited a more robust context awareness and achieved a median Dice score of 65.13%, demonstrating significant progress in whole-image assessments. Conclusions: Transformer-driven context modeling offers a promising approach for robust AK lesion monitoring, supporting its application in real-world clinical settings where accurate, context-aware analysis is crucial for managing skin field cancerization. Full article

Eczema, or atopic dermatitis (AD), is a chronic inflammatory skin condition characterized by persistent itching and scratching, significantly impacting patients’ quality of life. Effective monitoring of scratching behaviour is crucial for assessing disease severity, treatment efficacy, and understanding the relationship between itch and sleep disturbances. This review explores current technological approaches for detecting and monitoring scratching and itching in AD patients, categorising them into contact-based and non-contact-based methods. Contact-based methods primarily involve wearable sensors, such as accelerometers, electromyography (EMG), and piezoelectric sensors, which track limb movements and muscle activity associated with scratching. Non-contact methods include video-based motion tracking, thermal imaging, and acoustic analysis, commonly employed in sleep clinics and controlled environments to assess nocturnal scratching. Furthermore, emerging artificial intelligence (AI)-driven approaches leveraging machine learning for automated scratch detection are discussed. The advantages, limitations, and validation challenges of these technologies, including accuracy, user comfort, data privacy, and real-world applicability, are critically analysed. Finally, we outline future research directions, emphasizing the integration of multimodal monitoring, real-time data analysis, and patient-centric wearable solutions to improve disease management. This review serves as a comprehensive resource for clinicians, researchers, and technology developers seeking to advance objective itch and scratch monitoring in AD patients. Full article

Prunus leaf extracts are rich in phenolic and flavonoid compounds like rutin, and they are known for their antioxidant potential. This study compares the bioactivity and stability of leaf extracts from Prunus domestica L. (EL), Prunus salicina Lindl. (JL), and Prunus cerasifera Ehrh. (CL) and evaluates the dermal safety of a cream containing the extract with the most favorable in vitro properties for potential cosmetic use. Ethanolic extracts were assessed for total phenolic and condensed tannin contents, as well as antioxidants, using DPPH assay and lipid peroxidation inhibitory activities. The CL extract exhibited moderate total phenolic content, the highest condensed tannin content, and strong antioxidant (IC₅₀ = 22.1 ± 3.1 µg/mL) and anti-lipid peroxidation (62.3 ± 1.0%) activities. Based on these results, CL was incorporated into a cream formulation (CCL), which was then evaluated for physicochemical properties, antioxidant retention, and in vitro skin permeation using Franz diffusion cells. The formulation remained physically stable under ambient conditions and retained antioxidant activity above 74.5% under thermal cycling conditions. Rutin from the CCL formulation was retained within the Strat-M™ membrane (4.0 ± 1.1%), which was 5.7-fold higher than that of the control (0.7 ± 0.6%) over 8 h; however, it was not detected in the receptor chamber under these in vitro conditions. A semi-open patch test conducted on 26 healthy volunteers under double-blind conditions revealed no signs of irritation, confirming the formulation’s dermal safety. Overall, the findings support the feasibility of using P. cerasifera extract as a stable antioxidant component in topical skincare formulations. Full article

Cutaneous neoplasia is among the most common illnesses in dogs and can pose significant risks. Accurate morphological diagnosis of these conditions is vital for effective treatment and management. In this retrospective study, a total of 3746 canine skin biopsies were submitted to a veterinary reference diagnostic laboratory and evaluated using histopathology. The variables assessed included age, sex, breed, lesion, location, and histopathological diagnosis. Non-neoplastic lesions accounted for 61% of all analyzed samples, while neoplastic tumors accounted for 39%. When looking at age, dogs ranging 3–6 years and 7–9 years had at least six times higher risk of developing malignant neoplasia compared to those aged 0–2 years. Among the malignant neoplasms, mast cell tumors, hemangiosarcoma, and squamous cell carcinoma were the most observed, representing 30%, 18%, and 12% of cases, respectively. The breeds most frequently affected by malignant neoplasms included Pit Bull Terriers, Boxers, and mixed breeds, all of which comprised the majority of mast cell tumor cases at 50.54%. These findings are novel in this field and may assist small animal veterinarians in making preliminary diagnoses, while also helping pet owners understand the importance of skin cancer and its early detection. Full article

Background/Objectives: The treatment of atopic dermatitis frequently involves using a topical corticosteroid and a moisturizer. While the sequential application of these products is a common dermatological practice, their influence on drug penetration remains poorly understood. There is no clear evidence on how hydration, application sequence, and massage affect cutaneous drug delivery. Hence, this study aimed to evaluate the effects of formulation type, moisturizer composition, application sequence, and mechanical stimulation on betamethasone dipropionate (BET) cutaneous penetration. Methods: Two commercial formulations (cream and ointment) of BET were evaluated in different experimental conditions, including drug application combined with moisturizers (Cetaphil^®, as an emollient; Nivea^®, as an occlusive) pre- or post-application, with or without a 30 s massage. In vitro skin penetration assays were conducted for 12 h using porcine skin mounted in modified Franz diffusion cells. BET levels were extracted from the skin layers and quantified by HPLC. Results: The cutaneous BET penetration was strongly influenced by the application sequence, type of moisturizer, and mechanical stimuli. Pre-application of an occlusive or emollient moisturizer, followed by 30 s physical stimuli, significantly enhanced drug retention in the stratum corneum. For the cream, pre-application of moisturizers followed by massage notably increased BET levels in both the stratum corneum and viable skin. Conversely, post-application of moisturizers hindered BET absorption. The ointment showed limited penetration across all conditions, with no drug detected in the viable skin. Conclusions: The results showed pre-hydrating the skin, combined with a 30 s massage, was the best strategy for BET diffusion into the skin following cream administration. The formulation type and the order of application directly influence the effectiveness of drug therapy and the topical absorption of BET. Full article

This study investigates a noninvasive continuous glucose monitoring (NI-CGM) system optimized for earlobe application, leveraging the site’s anatomical advantages—absence of bone, muscle, and thick skin—for enhanced optical transmission. The system integrates multimodal sensing, combining near-infrared (NIR) diffuse transmission with temperature and pressure sensors. A novel Multi-Wavelength Slope Efficiency Near-Infrared Spectroscopy (MW-SE-NIRS) method is introduced, enhancing noise robustness through the slope efficiency-based parameterization of NIR signal dynamics. By employing three NIR wavelengths with distinct scattering and absorption properties, the method improves glucose detection reliability, addressing tissue heterogeneity and physiological noise in noninvasive monitoring. To validate the feasibility, a pilot clinical trial enrolled five participants with normal or pre-diabetic glucose profiles. Continuous glucose data capturing pre- and postprandial variations were analyzed using a 1D convolutional neural network (Conv1D). For three subjects under stable physiological conditions, the model achieved 97.0% Clarke error grid (CEG) A-Zone accuracy and a mean absolute relative difference (MARD) of 5.2%. Across all participants, results showed 90.9% CEG A-Zone accuracy and a MARD of 8.4%, with performance variations linked to individual factors such as earlobe thickness variability and physical activity. These outcomes demonstrate the potential of the MW-SE-NIRS system for noninvasive glucose monitoring and highlight the importance of future work on personalized modeling, sensor optimization, and larger-scale clinical validation. Full article

Background/Objectives: Quantitative assessments of facial muscle function and cognitive responses can enhance the clinic evaluations in neuromuscular disorders such as Bell’s palsy and psychiatric conditions including anxiety and depression. This study explored the application of Digital Image Speckle Correlation (DISC) in detecting enervation of facial musculature and assessing reaction times in response to visual stimuli. Methods: A consistent video recording setup was used to capture facial movements of human subjects in response to visual stimuli from a calibrated database. The DISC method utilizes the displacement of naturally occurring skin pores to map the specific locus of underlying muscular movement. The technique was applied to two distinct case studies: Patient 1 had unilateral Bell’s palsy and was monitored for 1 month of recovery. Patient 2 had a comorbidity of refractory depression and anxiety disorders with ketamine treatment and was assessed over 3 consecutive weekly visits. For patient 1, facial asymmetry was calculated by comparing left-to-right displacement signals. For patient 2, visual reaction time was measured, and facial motion intensity and response rate were compared with self-reported depression and anxiety scales. Results: DISC effectively mapped biomechanical properties of facial motions, providing detailed spatial and temporal resolution of muscle activity. In a control cohort of 10 subjects, when executing a facial expression, the degree of left/right facial asymmetry was determined to be 13.2 (8)%. And showed a robust response in an average of 275 (81) milliseconds to five out of the five images shown. For patient 1, obtained an initial asymmetry of nearly 100%, which decreased steadily to 20% in one month, demonstrating a progressive recovery. Patient 2 exhibited a prolonged reaction time of 518 (93) milliseconds and reduced response rates compared with controls of 275 (81) milliseconds and a decrease in the overall rate of response relative to the control group. The data obtained before treatment in three visits correlated strongly with selected depression and anxiety scores. Conclusions: These findings highlight the utility of DISC in enhancing clinical monitoring, complementing traditional examinations and self-reported measures. Full article

Sulfamethoxazole (SMZ), a widely used broad-spectrum antibiotic in aquaculture, lacks comprehensive research on its residual elimination kinetics in tilapia. This study investigated SMZ residue depletion, withdrawal periods, and dietary risks in 1-year-old GIFT tilapia (Genetically Improved Farmed Tilapia Oreochromis niloticus) weighing 500 ± 50 g, following oral gavage administration of a loading dose (200 mg/kg BW on day 1) and then 100 mg/kg BW daily for 6 more days, at 22 ± 2 °C. Tissue samples (plasma, muscle, skin, liver, kidney, gill, and remaining tissues) were collected from five fish per time point at intervals from 0.33 to 30 days post-administration, with SMZ residues quantified via HPLC-MS/MS. Results revealed peak SMZ concentrations at 0.33 days (8 h), ordered as liver > skin > plasma > kidney > remaining tissues > gill > muscle. Muscle residues fell below the maximum residue limit (MRL, 100 μg/kg) by day 3, while skin required 10 days. Kidney residues dropped below the limit of detection (LOD) earliest (16 days), followed by muscle, gill, and remaining tissues (25 days), whereas plasma, liver, and skin retained detectable levels until day 30. Elimination equations for SMZ across tissues exhibited first-order kinetics. Based on the specific conditions of this study, a minimum 11-day withdrawal period is recommended for edible tissues (muscle + skin) after SMZ administration. Hazard quotient (HQ) values for all tissues remained below the safety threshold (HQ = 1), indicating low dietary risk. These findings support SMZ use standardization in tilapia aquaculture to ensure food safety compliance. Full article

Skin diseases are common medical conditions, and early detection significantly contributes to improved cure rates. To address the challenges posed by complex lesion morphology, indistinct boundaries, and image artifacts, this paper proposes a skin lesion segmentation method based on multi-scale attention and bidirectional long short-term memory (Bi-LSTM). Built upon the U-Net architecture, the proposed model enhances the encoder with dense convolutions and an adaptive feature fusion module to strengthen feature extraction and multi-scale information integration. Furthermore, it incorporates both channel and spatial attention mechanisms along with temporal modeling to improve boundary delineation and segmentation accuracy. A generative adversarial network (GAN) is also introduced to refine the segmentation output and boost generalization performance. Experimental results on the ISIC2017 dataset demonstrate that the method achieves an accuracy of 0.950, a Dice coefficient of 0.902, and a mean Intersection over Union (mIoU) of 0.865. These results indicate that the proposed approach effectively improves lesion segmentation performance and offers valuable support for computer-aided diagnosis of skin diseases. Full article

Background/Objectives: Actinic keratosis (AK) is considered to be the most common form of in situ carcinoma and typically arises on skin that has been chronically exposed to ultraviolet radiation. The need for early diagnosis, using non-invasive methods, has allowed for a non-surgical approach to these conditions with a significant impact on the quality of life of patients. Methods: A retrospective study was conducted on 58 patients diagnosed with AK who underwent surgical excision at a tertiary center in Bucharest, Romania between 2018 and 2023. Clinical parameters (age, sex, lesion size, anatomical location, comorbidities) and histopathological variables (AK subtype, KIN grade, pleomorphism, solar elastosis, inflammatory infiltrate) were analyzed. Statistical associations between histological findings and clinical features were assessed using Fisher’s exact test. Conclusions: The study confirmed a predominance of AK among elderly patients, with hypertrophic lesions and moderate dysplasia (KIN II) being most common. Higher KIN grades correlated significantly with more severe pleomorphism, solar elastosis, and inflammatory response, suggesting progressive UV-induced skin damage. The findings underscore the importance of clinicopathological correlation for risk stratification and support the integration of non-invasive diagnostic tools to improve early detection and management of AK. Full article

Early and accurate detection of dermatological conditions, particularly melanoma, is critical for effective treatment and improved patient outcomes. Misclassifications may lead to delayed diagnosis, disease progression, and severe complications in medical image processing. Hence, robust and reliable classification techniques are essential to enhance diagnostic precision in clinical practice. This study presents a deep learning-based framework designed to improve feature representation while maintaining computational efficiency. The proposed architecture integrates multi-level feature aggregation with a squeeze-and-excitation attention mechanism to effectively extract salient patterns from dermoscopic medical images. The model is rigorously evaluated on five publicly available benchmark datasets—ISIC-2019, ISIC-2020, SKINL2, MED-NODE, and HAM10000—covering a diverse spectrum of dermatological medical disorders. Experimental results demonstrate that the proposed method consistently outperforms existing approaches in classification performance, achieving accuracy rates of 94.41% and 97.45% on the MED-NODE and HAM10000 datasets, respectively. These results underscore the method’s potential for real-world deployment in automated skin lesion analysis and clinical decision support. Full article

Background/Objectives: In hospital settings, the wide variability of acute and complex chronic conditions—among both adult and pediatric patients—requires advanced approaches to detect early signs of clinical deterioration and the risk of transfer to the intensive care unit (ICU). Nursing diagnoses (NDs), standardized representations of patient responses to actual or potential health problems, reflect nursing complexity. However, most studies have focused on the total number of NDs rather than the individual role each diagnosis may play in relation to outcomes such as ICU transfer. This study aimed to identify and rank the specific NDs most strongly associated with ICU transfers in hospitalized adult and pediatric patients. Methods: A retrospective, monocentric observational study was conducted using electronic health records from an Italian tertiary hospital. The dataset included 42,735 patients (40,649 adults and 2086 pediatric), and sociodemographic, clinical, and nursing data were collected. A random forest model was applied to assess the predictive relevance (i.e., variable importance) of individual NDs in relation to ICU transfers. Results: Among adult patients, the NDs most strongly associated with ICU transfer were Physical mobility impairment, Injury risk, Skin integrity impairment risk, Acute pain, and Fall risk. In the pediatric population, Acute pain, Injury risk, Sleep pattern disturbance, Skin integrity impairment risk, and Airway clearance impairment emerged as the NDs most frequently linked to ICU transfer. The models showed good performance and generalizability, with stable out-of-bag and validation errors across iterations. Conclusions: A prioritized ranking of NDs appears to be associated with ICU transfers, suggesting their potential utility as early warning indicators of clinical deterioration. Patients presenting with high-risk diagnostic profiles should be prioritized for enhanced clinical surveillance and proactive intervention, as they may represent vulnerable populations. Full article