Construction of a pathological model of skin lesions in acute herpes zoster virus infection and its molecular mechanism.

Varicella-zoster virus (VZV), a common pathogen with humans as the sole host, causes primary infection and undergoes a latent period in sensory ganglia. The recurrence of VZV is often accompanied by severe neuralgia in skin tissue, which has a serious impact on the life of patients. During the acute infection of VZV, there are few related studies on the pathophysiological mechanism of skin tissue. In this study, transcriptome sequencing data from the acute response period within 2 days of VZV antigen stimulation of the skin were used to explore a model of the trajectory of skin tissue changes during VZV infection. It was found that early VZV antigen stimulation caused activation of mainly natural immune-related signaling pathways, while in the late phase activation of mainly active immune-related signaling pathways. JAK-STAT, NFκB, and TNFα signaling pathways are gradually activated with the progression of infection, while Hypoxia is progressively inhibited. In addition, we found that dendritic cell-mediated immune responses play a dominant role in the lesion damage caused by VZV antigen stimulation of the skin. This study provides a theoretical basis for the study of the molecular mechanisms of skin lesions during acute VZV infection.

Evaluation of High-Dimensional Data Classification for Skin Malignancy Detection Using DL-Based Techniques.

Skin cancer can be detected through visual screening and skin analysis based on the biopsy and pathological state of the human body. The survival rate of cancer patients is low, and millions of people are diagnosed annually. By determining the different comparative analyses, the skin malignancy classification is evaluated. Using the Isomap with the vision transformer, we analyze the high-dimensional images with dimensionality reduction. Skin cancer can present with severe cases and life-threatening symptoms. Overall performance evaluation and classification tend to improve the accuracy of the high-dimensional skin lesion dataset when completed. In deep learning methodologies, the distinct phases of skin malignancy classification are determined by its accuracy, specificity, F1 recall, and sensitivity while implementing the classification methodology. A nonlinear dimensionality reduction technique called Isomap preserves the data's underlying nonlinear relationships intact. This is essential for the categorization of skin malignancies, as the features that separate malignant from benign skin lesions may not be linearly separable. Isomap decreases the data's complexity while maintaining its essential characteristics, making it simpler to analyze and explain the findings. High-dimensional datasets for skin lesions have been evaluated and classified more effectively when evaluated and classified using Isomap with the vision transformer.

Correlation and Clinical Significance of HBD-2 and CXCL-1/2 Levels at Skin Lesions with Psoriasis Vulgaris Severity.

OBJECTIVE: This study was performed to explore the clinical significance of the expression of human beta-defensin 2 (HBD-2) and chemokine ligand 1/2 (CXCL-1/2) in psoriasis vulgaris. METHODS: This study retrospectively included the study group (n = 160) and control group (n = 100) for analysis. The levels of inflammatory indicators, blood biochemical indicators, and immune indicators using ELISA. The psoriasis area and severity index (PASI) was used to evaluate disease severity. Levels of HBD-2, CXCL-1, CXCL-2 and CCL20 were determined by RT-PCR. The correlations of HBD-2, CXCL-1 and CXCL-2 levels with CCL20 and PASI scores were analyzed. The diagnostic value of HBD-2, CXCL-1 and CXCL-2 in psoriasis vulgaris was analyzed by ROC curve. RESULTS: HBD-2, CXCL-1 and CXCL-2 were highly expressed in the lesions of psoriasis vulgaris patients, and were positively correlated with CCL20 and PASI score. HBD-2, CXCL-1 and CXCL-2 alone or in combination had high diagnostic value for psoriasis vulgaris and severe psoriasis, and the combined diagnostic value of the three was higher than that of a single indicator. CONCLUSION: HBD-2, CXCL-1, and CXCL-2 levels are closely related to the severity of psoriasis vulgaris and can effectively diagnose the occurrence and progression of psoriasis vulgaris.

Autopsy of tetanus with foot gangrene as portal of entry.

Non-traumatic chronic skin lesions are the second most common cause of tetanus. Herein, we describe an 85-year-old woman who presented with a chronically infected skin lesion. She developed tetanus while in hospital and died of respiratory failure, after refusing mechanical ventilation. Routine immunization against tetanus began in Japan during 1968; hence many people born before 1968 are unvaccinated. Mortality due to tetanus is high and the proportion with protective antibodies is low in older adults. Therefore, we recommend tetanus vaccination for older persons in Japan who have chronic skin lesions and have never been vaccinated.

Enhanced skin cancer diagnosis using optimized CNN architecture and checkpoints for automated dermatological lesion classification.

Skin cancer stands as one of the foremost challenges in oncology, with its early detection being crucial for successful treatment outcomes. Traditional diagnostic methods depend on dermatologist expertise, creating a need for more reliable, automated tools. This study explores deep learning, particularly Convolutional Neural Networks (CNNs), to enhance the accuracy and efficiency of skin cancer diagnosis. Leveraging the HAM10000 dataset, a comprehensive collection of dermatoscopic images encompassing a diverse range of skin lesions, this study introduces a sophisticated CNN model tailored for the nuanced task of skin lesion classification. The model's architecture is intricately designed with multiple convolutional, pooling, and dense layers, aimed at capturing the complex visual features of skin lesions. To address the challenge of class imbalance within the dataset, an innovative data augmentation strategy is employed, ensuring a balanced representation of each lesion category during training. Furthermore, this study introduces a CNN model with optimized layer configuration and data augmentation, significantly boosting diagnostic precision in skin cancer detection. The model's learning process is optimized using the Adam optimizer, with parameters fine-tuned over 50 epochs and a batch size of 128 to enhance the model's ability to discern subtle patterns in the image data. A Model Checkpoint callback ensures the preservation of the best model iteration for future use. The proposed model demonstrates an accuracy of 97.78% with a notable precision of 97.9%, recall of 97.9%, and an F2 score of 97.8%, underscoring its potential as a robust tool in the early detection and classification of skin cancer, thereby supporting clinical decision-making and contributing to improved patient outcomes in dermatology.

The efficacy and safety of autologous epidermal cell suspensions for re-epithelialization of skin lesions: A systematic review and meta-analysis of randomized trials.

BACKGROUND: Successful usage of autologous skin cell suspension (ASCS) has been demonstrated in some clinical trials. However, its efficacy and safety have not been verified. This latest systematic review and meta-analysis aim to examine the effects of autologous epidermal cell suspensions in re-epithelialization of skin lesions. METHODS: Relevant articles were retrieved from PubMed, Embase, Cochrane Database, Web of Science, International Clinical Trials Registry Platform, China National Knowledge Infrastructureris, VIP Database for Chinese Technical Periodicals and Wanfang database. The primary output measure was the healing time, and the secondary outputs were effective rate, size of donor site for treatment, size of study treatment area, operation time, pain scores, repigmentation, complications, scar scale scores and satisfaction scores. Data were pooled and expressed as relative risk (RR), mean difference (MD) and standardized mean difference (SMD) with a 95% confidence interval (CI). RESULTS: Thirty-one studies were included in this systematic review and meta-analysis, with 914 patients who received autologous epidermal cell suspensions (treatment group) and 883 patients who received standard care or placebo (control group). The pooled data from all included studies demonstrated that the treatment group has significantly reduced healing time (SMD = -0.86; 95% CI: -1.59-0.14; p = 0.02, I2 = 95%), size of donar site for treatment (MD = -115.41; 95% CI: -128.74-102.09; p<0.001, I2 = 89%), operation time (MD = 25.35; 95% CI: 23.42-27.29; p<0.001, I2 = 100%), pain scores (SMD = -1.88; 95% CI: -2.86-0.90; p = 0.0002, I2 = 89%) and complications (RR = 0.59; 95% CI: 0.36-0.96; p = 0.03, I2 = 66%), as well as significantly increased effective rate (RR = 1.20; 95% CI: 1.01-1.42; p = 0.04, I2 = 77%). There were no significant differences in the size of study treatment area, repigmentation, scar scale scores and satisfaction scores between the two groups. CONCLUSION: Our meta-analysis showed that autologous epidermal cell suspensions is beneficial for re-epithelialization of skin lesions as they significantly reduce the healing time, size of donar site for treatment, operation time, pain scores and complications, as well as increased effective rate. However, this intervention has minimal impact on size of treatment area, repigmentation, scar scale scores and satisfaction scores.

Intelligent skin lesion segmentation using deformable attention Transformer U-Net with bidirectional attention mechanism in skin cancer images.

BACKGROUND: In recent years, the increasing prevalence of skin cancers, particularly malignant melanoma, has become a major concern for public health. The development of accurate automated segmentation techniques for skin lesions holds immense potential in alleviating the burden on medical professionals. It is of substantial clinical importance for the early identification and intervention of skin cancer. Nevertheless, the irregular shape, uneven color, and noise interference of the skin lesions have presented significant challenges to the precise segmentation. Therefore, it is crucial to develop a high-precision and intelligent skin lesion segmentation framework for clinical treatment. METHODS: A precision-driven segmentation model for skin cancer images is proposed based on the Transformer U-Net, called BiADATU-Net, which integrates the deformable attention Transformer and bidirectional attention blocks into the U-Net. The encoder part utilizes deformable attention Transformer with dual attention block, allowing adaptive learning of global and local features. The decoder part incorporates specifically tailored scSE attention modules within skip connection layers to capture image-specific context information for strong feature fusion. Additionally, deformable convolution is aggregated into two different attention blocks to learn irregular lesion features for high-precision prediction. RESULTS: A series of experiments are conducted on four skin cancer image datasets (i.e., ISIC2016, ISIC2017, ISIC2018, and PH2). The findings show that our model exhibits satisfactory segmentation performance, all achieving an accuracy rate of over 96%. CONCLUSION: Our experiment results validate the proposed BiADATU-Net achieves competitive performance supremacy compared to some state-of-the-art methods. It is potential and valuable in the field of skin lesion segmentation.

A novel Skin lesion prediction and classification technique: ViT-GradCAM.

BACKGROUND: Skin cancer is one of the highly occurring diseases in human life. Early detection and treatment are the prime and necessary points to reduce the malignancy of infections. Deep learning techniques are supplementary tools to assist clinical experts in detecting and localizing skin lesions. Vision transformers (ViT) based on image segmentation classification using multiple classes provide fairly accurate detection and are gaining more popularity due to legitimate multiclass prediction capabilities. MATERIALS AND METHODS: In this research, we propose a new ViT Gradient-Weighted Class Activation Mapping (GradCAM) based architecture named ViT-GradCAM for detecting and classifying skin lesions by spreading ratio on the lesion's surface area. The proposed system is trained and validated using a HAM 10000 dataset by studying seven skin lesions. The database comprises 10 015 dermatoscopic images of varied sizes. The data preprocessing and data augmentation techniques are applied to overcome the class imbalance issues and improve the model's performance. RESULT: The proposed algorithm is based on ViT models that classify the dermatoscopic images into seven classes with an accuracy of 97.28%, precision of 98.51, recall of 95.2%, and an F1 score of 94.6, respectively. The proposed ViT-GradCAM obtains better and more accurate detection and classification than other state-of-the-art deep learning-based skin lesion detection models. The architecture of ViT-GradCAM is extensively visualized to highlight the actual pixels in essential regions associated with skin-specific pathologies. CONCLUSION: This research proposes an alternate solution to overcome the challenges of detecting and classifying skin lesions using ViTs and GradCAM, which play a significant role in detecting and classifying skin lesions accurately rather than relying solely on deep learning models.

In vitro assessment of nanomedicines' propensity to cause palmar-plantar erythrodysesthesia: A Doxil vs. doxorubicin case study.

Palmar-plantar erythrodysesthesia (PPE), also known as hand and foot syndrome, is a condition characterized by inflammation-mediated damage to the skin on the palms and soles of the hands and feet. PPE limits the successful therapeutic applications of anticancer drugs. However, identifying this toxicity during preclinical studies is challenging due to the lack of accurate in vitro and in vivo animal-based models. Therefore, there is a need for reliable models that would allow the detection of this toxicity early during the drug development process. Herein, we describe the use of an in vitro skin explant assay to assess traditional DXR, Doxil reference listed drug (RLD) and two generic PEGylated liposomal DXR formulations for their abilities to cause inflammation and skin damage. We demonstrate that the results obtained with the in vitro skin explant assay model for traditional DXR and Doxil correlate with the clinical data.

Using AI to Differentiate Mpox From Common Skin Lesions in a Sexual Health Clinic: Algorithm Development and Validation Study.

BACKGROUND: The 2022 global outbreak of mpox has significantly impacted health facilities, and necessitated additional infection prevention and control measures and alterations to clinic processes. Early identification of suspected mpox cases will assist in mitigating these impacts. OBJECTIVE: We aimed to develop and evaluate an artificial intelligence (AI)-based tool to differentiate mpox lesion images from other skin lesions seen in a sexual health clinic. METHODS: We used a data set with 2200 images, that included mpox and non-mpox lesions images, collected from Melbourne Sexual Health Centre and web resources. We adopted deep learning approaches which involved 6 different deep learning architectures to train our AI models. We subsequently evaluated the performance of each model using a hold-out data set and an external validation data set to determine the optimal model for differentiating between mpox and non-mpox lesions. RESULTS: The DenseNet-121 model outperformed other models with an overall area under the receiver operating characteristic curve (AUC) of 0.928, an accuracy of 0.848, a precision of 0.942, a recall of 0.742, and an F1-score of 0.834. Implementation of a region of interest approach significantly improved the performance of all models, with the AUC for the DenseNet-121 model increasing to 0.982. This approach resulted in an increase in the correct classification of mpox images from 79% (55/70) to 94% (66/70). The effectiveness of this approach was further validated by a visual analysis with gradient-weighted class activation mapping, demonstrating a reduction in false detection within the background of lesion images. On the external validation data set, ResNet-18 and DenseNet-121 achieved the highest performance. ResNet-18 achieved an AUC of 0.990 and an accuracy of 0.947, and DenseNet-121 achieved an AUC of 0.982 and an accuracy of 0.926. CONCLUSIONS: Our study demonstrated it was possible to use an AI-based image recognition algorithm to accurately differentiate between mpox and common skin lesions. Our findings provide a foundation for future investigations aimed at refining the algorithm and establishing the place of such technology in a sexual health clinic.

Skin lesion segmentation using deep learning algorithm with ant colony optimization.

BACKGROUND: Segmentation of skin lesions remains essential in histological diagnosis and skin cancer surveillance. Recent advances in deep learning have paved the way for greater improvements in medical imaging. The Hybrid Residual Networks (ResUNet) model, supplemented with Ant Colony Optimization (ACO), represents the synergy of these improvements aimed at improving the efficiency and effectiveness of skin lesion diagnosis. OBJECTIVE: This paper seeks to evaluate the effectiveness of the Hybrid ResUNet model for skin lesion classification and assess its impact on optimizing ACO performance to bridge the gap between computational efficiency and clinical utility. METHODS: The study used a deep learning design on a complex dataset that included a variety of skin lesions. The method includes training a Hybrid ResUNet model with standard parameters and fine-tuning using ACO for hyperparameter optimization. Performance was evaluated using traditional metrics such as accuracy, dice coefficient, and Jaccard index compared with existing models such as residual network (ResNet) and U-Net. RESULTS: The proposed hybrid ResUNet model exhibited excellent classification accuracy, reflected in the noticeable improvement in all evaluated metrics. His ability to describe complex lesions was particularly outstanding, improving diagnostic accuracy. Our experimental results demonstrate that the proposed Hybrid ResUNet model outperforms existing state-of-the-art methods, achieving an accuracy of 95.8%, a Dice coefficient of 93.1%, and a Jaccard index of 87.5. CONCLUSION: The addition of ResUNet to ACO in the proposed Hybrid ResUNet model significantly improves the classification of skin lesions. This integration goes beyond traditional paradigms and demonstrates a viable strategy for deploying AI-powered tools in clinical settings. FUTURE WORK: Future investigations will focus on increasing the version's abilities by using multi-modal imaging information, experimenting with alternative optimization algorithms, and comparing real-world medical applicability. There is also a promising scope for enhancing computational performance and exploring the model's interpretability for more clinical adoption.

MASDF-Net: A Multi-Attention Codec Network with Selective and Dynamic Fusion for Skin Lesion Segmentation.

Automated segmentation algorithms for dermoscopic images serve as effective tools that assist dermatologists in clinical diagnosis. While existing deep learning-based skin lesion segmentation algorithms have achieved certain success, challenges remain in accurately delineating the boundaries of lesion regions in dermoscopic images with irregular shapes, blurry edges, and occlusions by artifacts. To address these issues, a multi-attention codec network with selective and dynamic fusion (MASDF-Net) is proposed for skin lesion segmentation in this study. In this network, we use the pyramid vision transformer as the encoder to model the long-range dependencies between features, and we innovatively designed three modules to further enhance the performance of the network. Specifically, the multi-attention fusion (MAF) module allows for attention to be focused on high-level features from various perspectives, thereby capturing more global contextual information. The selective information gathering (SIG) module improves the existing skip-connection structure by eliminating the redundant information in low-level features. The multi-scale cascade fusion (MSCF) module dynamically fuses features from different levels of the decoder part, further refining the segmentation boundaries. We conducted comprehensive experiments on the ISIC 2016, ISIC 2017, ISIC 2018, and PH2 datasets. The experimental results demonstrate the superiority of our approach over existing state-of-the-art methods.

ELA-Net: An Efficient Lightweight Attention Network for Skin Lesion Segmentation.

In clinical conditions limited by equipment, attaining lightweight skin lesion segmentation is pivotal as it facilitates the integration of the model into diverse medical devices, thereby enhancing operational efficiency. However, the lightweight design of the model may face accuracy degradation, especially when dealing with complex images such as skin lesion images with irregular regions, blurred boundaries, and oversized boundaries. To address these challenges, we propose an efficient lightweight attention network (ELANet) for the skin lesion segmentation task. In ELANet, two different attention mechanisms of the bilateral residual module (BRM) can achieve complementary information, which enhances the sensitivity to features in spatial and channel dimensions, respectively, and then multiple BRMs are stacked for efficient feature extraction of the input information. In addition, the network acquires global information and improves segmentation accuracy by putting feature maps of different scales through multi-scale attention fusion (MAF) operations. Finally, we evaluate the performance of ELANet on three publicly available datasets, ISIC2016, ISIC2017, and ISIC2018, and the experimental results show that our algorithm can achieve 89.87%, 81.85%, and 82.87% of the mIoU on the three datasets with a parametric of 0.459 M, which is an excellent balance between accuracy and lightness and is superior to many existing segmentation methods.

Evaluating the Role of Vitamin D in Alleviating Chronic Pruritus: A Meta-Analysis.

Chronic pruritus is a distressing condition that significantly impacts patients' quality of life. Recent research has increasingly focused on the potential role of vitamin D, given its immunomodulatory properties, in managing this condition. This meta-analysis seeks to systematically assess the effectiveness of vitamin D supplementation in alleviating chronic pruritus across diverse clinical contexts. We conducted an extensive search through multiple databases, covering literature up to July 2024, to identify relevant randomized controlled trials (RCTs) that evaluated the effect of vitamin D on chronic pruritus. Eligible studies were those that provided data on changes in pruritus severity, as measured by standardized tools, before and after vitamin D treatment. The data were synthesized using a random-effects model to address variability among the studies. This meta-analysis is registered with PROSPERO (registration number: CRD42024579353). The findings indicate that vitamin D supplementation is associated with a significant reduction in pruritus severity, the skin lesion area, and levels of inflammatory cytokines, including tumor necrosis factor (TNF), interleukin-6 (IL-6), and high-sensitivity C-reactive protein (hs-CRP), compared to controls. These results suggest that vitamin D could be a promising therapeutic option for chronic pruritus, though further rigorous studies are required to validate these findings and to elucidate the mechanisms involved.

Potential inhibition of 12-O-tetradecanoylphorbol-13-acetate-induced inflammation, hyperproliferation, and hyperplasiogenic responses by celecoxib in mouse skin.

PURPOSE: Skin exposure to noxious agents leads to cutaneous lesion marked by an increase in inflammation, cellular proliferation, and hyperplasiogenic reactions. Studies have demonstrated that these damages breach the skin integrity resulting in the aetiology of various cutaneous disorders like atopic dermatitis, eczema, psoriasis, and development of non-melanoma skin cancer. Celecoxib, a cyclooxygenase-2 (COX-2) inhibitor, is an effective treatment for a variety of inflammatory diseases. Its importance in the therapy of skin problems, however, remains under appreciated. METHODS: We tested efficacy of topically applied celecoxib in mitigating skin inflammation, cellular proliferation, and hyperplasia induced by the phorbol ester 12-O-tetradecanoylphorbol-13-acetate (TPA) in Swiss albino mice. RESULTS: Celecoxib (5 and 10 µmol) markedly reduced TPA (10 nmol) induced prostaglandin E2 (PGE2) production, oedema formation, myeloperoxidase (MPO) activity, and levels of pro-inflammatory cytokines such as tumour necrosis factor-alpha (TNF-α), interleukin-1 beta (IL-1ß), and interleukin-6 (IL-6). It also resulted in a considerable decrease in ornithine decarboxylase (ODC) activity and the incorporation of [3H]-thymidine into DNA. In addition, there was a significant reduction in histoarchitectural abnormalities such as epidermal thickness, number of epidermal cell layers, neutrophil infiltration, intercellular oedema, and vasodilation. CONCLUSION: Our results demonstrate that topical celecoxib can reduce the inflammation, hyperproliferation, and hyperplasiogenic events of skin insults suggesting that it may prove to be a valuable management option for cutaneous lesion and associated illnesses such as atopic dermatitis, eczema, and psoriasis, as well as the emergence of non-melanoma cancer.

A study of social isolation in patients with varicose veins of the lower extremities: A scoping review.

OBJECTIVES: To undertake a scoping review of the literature on social alienation in patients with lower extremity varicose veins in order to serve as a reference for future studies in the field. METHODS: With a focus on the phenomenon of social alienation in patients with varicose veins of the lower extremities, a systematic search of Chinese and English databases was carried out using the scoping review methodology as a framework. The included literature was summarized and analyzed with a time frame from database construction to June 24, 2024. RESULTS: A total of 15 publications were included, demonstrating that social alienation is a frequent occurrence in people with varicose veins of the lower extremities but has not yet received much attention. In individuals with varicose veins of the lower limbs, demographic factors, illness issues, psychological problems, and social factors are the key influences on social alienation. CONCLUSION: Social alienation is a common phenomenon that is unevenly distributed in patients with varicose veins of the lower leg and is influenced by a number of different circumstances. In order to better meet the social needs of patients, healthcare professionals should pay attention to the issue of social alienation in patients with varicose veins of the lower extremity, identify and implement intervention strategies quickly, and actively explore a new model of treatment and care for social alienation.

[Skin lesion classification with multi-level fusion of Swin-T and ConvNeXt].

Skin cancer is a significant public health issue, and computer-aided diagnosis technology can effectively alleviate this burden. Accurate identification of skin lesion types is crucial when employing computer-aided diagnosis. This study proposes a multi-level attention cascaded fusion model based on Swin-T and ConvNeXt. It employed hierarchical Swin-T and ConvNeXt to extract global and local features, respectively, and introduced residual channel attention and spatial attention modules for further feature extraction. Multi-level attention mechanisms were utilized to process multi-scale global and local features. To address the problem of shallow features being lost due to their distance from the classifier, a hierarchical inverted residual fusion module was proposed to dynamically adjust the extracted feature information. Balanced sampling strategies and focal loss were employed to tackle the issue of imbalanced categories of skin lesions. Experimental testing on the ISIC2018 and ISIC2019 datasets yielded accuracy, precision, recall, and F1-Score of 96.01%, 93.67%, 92.65%, and 93.11%, respectively, and 92.79%, 91.52%, 88.90%, and 90.15%, respectively. Compared to Swin-T, the proposed method achieved an accuracy improvement of 3.60% and 1.66%, and compared to ConvNeXt, it achieved an accuracy improvement of 2.87% and 3.45%. The experiments demonstrate that the proposed method accurately classifies skin lesion images, providing a new solution for skin cancer diagnosis.

Results of PCR Analysis of Mpox Clinical Samples, Sweden, 2022.

We compared cycle thresholds from mpox skin lesions with other specimen sites and over time from onset of clinical signs among 104 patients in Sweden. Cycle thresholds differed by anatomic site. We identified 2 early mpox cases from anorectal swab specimens after skin samples were negative, indicating necessity of sampling multiple sites.

Dermatologist versus artificial intelligence confidence in dermoscopy diagnosis: Complementary information that may affect decision-making.

In dermatology, deep learning may be applied for skin lesion classification. However, for a given input image, a neural network only outputs a label, obtained using the class probabilities, which do not model uncertainty. Our group developed a novel method to quantify uncertainty in stochastic neural networks. In this study, we aimed to train such network for skin lesion classification and evaluate its diagnostic performance and uncertainty, and compare the results to the assessments by a group of dermatologists. By passing duplicates of an image through such a stochastic neural network, we obtained distributions per class, rather than a single probability value. We interpreted the overlap between these distributions as the output uncertainty, where a high overlap indicated a high uncertainty, and vice versa. We had 29 dermatologists diagnose a series of skin lesions and rate their confidence. We compared these results to those of the network. The network achieved a sensitivity and specificity of 50% and 88%, comparable to the average dermatologist (respectively 68% and 73%). Higher confidence/less uncertainty was associated with better diagnostic performance both in the neural network and in dermatologists. We found no correlation between the uncertainty of the neural network and the confidence of dermatologists (R = -0.06, p = 0.77). Dermatologists should not blindly trust the output of a neural network, especially when its uncertainty is high. The addition of an uncertainty score may stimulate the human-computer interaction.

Clinical and virological features of first human monkeypox cases in Germany.

BACKGROUND: Monkeypox is a zoonotic orthopoxvirus infection endemic in central and western Africa. In May 2022, human monkeypox infections including human-to-human transmission were reported in a multi-country outbreak in Europe and North America. CASE PRESENTATIONS: Here we present the first two cases of monkeypox infection in humans diagnosed in Germany. We present clinical and virological findings, including the detection of monkeypox virus DNA in blood and semen. The clinical presentation and medical history of our patients suggest close physical contact during sexual interactions as the route of infection. CONCLUSION: Monkeypox requires rapid diagnosis and prompt public health response. The disease should be considered in the current situation especially the differential diagnosis of vesicular or pustular rash, particularly in patients with frequent sexual contacts. Most importantly, it is essential to raise awareness among all health professionals for the rapid and correct recognition and diagnosis of this disease, which is probably still underreported in Europe (Adler et al. in Lancet Infect Dis https://doi.org/10.1016/s1473-3099(22)00228-6 , 2022).