skin lesion

Kimura’s disease (KD) is a rare chronic inflammatory condition of unknown origin. This case report described a unique presentation of KD in a 35-year-old Iranian woman who presented to the Emergency Department in March 2021 with hematuria, proteinuria, diffuse painless, and non-tender white cutaneous lesions affecting the head, neck, chest, and hands, with no swelling. Laboratory assessments showed micro-proteinuria, without oliguria, as well as peripheral serum eosinophilia (10%). Other laboratory findings were unremarkable. Histopathological analysis of skin lesion biopsies showed epidermal focal erosion, hyperkeratosis, parakeratosis, acanthosis, and intact basal layer, as well as dermal interstitial and perivascular infiltration of lymphocytes, histiocytes, and a few eosinophils, with plump endothelial cells in vessels. The diagnosis of KD was confirmed. Hematuria and proteinuria resolved without specific treatment within two days. The skin lesions remained persistent. This case deviated from the typical presentation of KD, as it involved a non-oriental female patient with diffuse cutaneous involvement. Increased awareness of atypical presentations is crucial for preventing misdiagnosis in the evaluation of suspected KD cases.

Coronavirus disease 2019 (COVID-19) was first identified in 2019 in Wuhan, China. Initially, although the number of COVID-19-infected individuals was very low, the infected cases increased as the virus spread worldwide. Skin manifestation is one of the symptoms observed in COVID-19 patients.

This study investigated the critical genes and molecular pathways involved in skin manifestations in COVID-19 patients through a biological system approach.

In this study, the microarray dataset was downloaded from the Gene Expression Omnibus (GEO) database and analyzed for identifying differentially expressed genes (DEGs). The enrichment analysis of DEGs was evaluated using the DAVID database. Afterward, protein-protein interaction (PPI) networks were constructed via the STRING database and visualized using Cytoscape software. The hub genes were recognized using the cytoHubba. The interaction of the microRNA (miRNA)-hub genes, transcription factor (TF)-hub genes, and drug-hub genes was also evaluated in this study.

After analysis, some genes with the highest degree of connectivity, which were involved in the pathogenesis of HELLP syndrome were identified, and they were known as hub genes. These genes are as follows: IFN-γ, CXCL1, CCL2, CCL3, TLR2, IL-1B, CXCL6, IL-6, CCL4, and CXCL2. has-mir-34a-5p, has-mir-20a-5p, and has-mir-27a-3p as miRNA, as well as RELA as TF had the most interaction with the hub genes.

Finally, IL-6 and CXCL10 that were compared to the other hub genes had the highest interaction with other genes; therefore, their role in Shamgir's pathogenesis is significant. Targeting the cited genes would be a strategy to prevent symptom manifestation and better patient management.

One of the common limitations in the treatment of cancer is in the early detection of this disease. The customary medical practice of cancer examination is a visual examination by the dermatologist followed by an invasive biopsy. Nonetheless, this symptomatic approach is timeconsuming and prone to human errors. An automated machine learning model is essential to capacitate fast diagnoses and early treatment.

The key objective of this study is to establish a fully automatic model that helps Dermatologists in skin cancer handling process in a way that could improve skin lesion classification accuracy.

The work is conducted following an implementation of a Deep Convolutional Generative Adversarial Network (DCGAN) using the Python-based deep learning library Keras. We incorporated effective image filtering and enhancement algorithms such as bilateral filter to enhance feature detection and extraction during training. The Deep Convolutional Generative Adversarial Network (DCGAN) needed slightly more fine-tuning to ripe a better return. Hyperparameter optimization was utilized for selecting the best-performed hyperparameter combinations and several network hyperparameters. In this work, we decreased the learning rate from the default 0.001 to 0.0002, and the momentum for Adam optimization algorithm from 0.9 to 0.5, in trying to reduce the instability issues related to GAN models and at each iteration the weights of the discriminative and generative network were updated to balance the loss between them. We endeavour to address a binary classification which predicts two classes present in our dataset, namely benign and malignant. More so, some wellknown metrics such as the receiver operating characteristic -area under the curve and confusion matrix were incorporated for evaluating the results and classification accuracy.

The model generated very conceivable lesions during the early stages of the experiment and we could easily visualise a smooth transition in resolution along the way. Thus, we have achieved an overall test accuracy of 93.5% after fine-tuning most parameters of our network.

This classification model provides spatial intelligence that could be useful in the future for cancer risk prediction. Unfortunately, it is difficult to generate high quality images that are much like the synthetic real samples and to compare different classification methods given the fact that some methods use non-public datasets for training.

COVID-19 was first discovered in Wuhan, China, and has spread rapidly around the world. The most important manifestation of COVID-19 was ARDS-like lung injury at first, but the involvement of other organs, such as kidney, heart, liver, and skin, was gradually reported. It is important to report and share all atypical manifestations of this disease to help other physicians to gain more knowledge about this new viral disease. As mentioned, there are also studies that show different types of cutaneous involvement in these patients, but due to the lack of more detailed studies in this field, and on the other hand, the possible usefulness of skin lesions as a diagnostic or alarming sign in the COVID-19 era, in this study we report a COVID-19 patient with a large hemorrhagic blister similar to sepsis-induced skin lesion. Despite the lack of common symptoms of the disease, the lung scan of the patient was positive for COVID-19.

Multiple myeloma can have different clinical manifestations, and not all patients present with a classic CRAB component. The acronym CRAB stands for the following traits of multiple myeloma: C = Calcium (elevated), R = Renal failure, A = Anemia, B = Bone lesions (bone pain). We describe a 53-year-old man who was referred to our hospital with a complaint of a rapidly progressive purpura/petechiae on the lower limb and abdomen, and multiple lytic lesions in the skull and spine, and multiple fractures. We documented an increased presence of plasmacytes in bone marrow aspiration and multiple lytic bone lesions, which led to a diagnosis of multiple myeloma. Although multiple myeloma presenting with purpura/petechiae is uncommon, it must always be considered as a differential diagnosis with this clinical finding.

Atopic dermatitis (AD) is a heterogeneous disease with regard to clinical phenotype and natural history. We investigated T cell subtypes and cytokine responses in peripheral blood and skin lesions of AD patients with various sensitivities. Immunological studies were performed in 27 subjects: 9 house dust mite (HDM)-sensitized; 6 subjects with sensitizations other than HDM; 7 non-allergic AD patients and 5 healthy controls. Among those, skin biopsy samples of 13 subjects were evaluated for immunohistochemical analyses, as well. The mean age was 8.93±5.17 years. HDM-allergic AD emerged as a distinct immunologic phenotype, with higher production of interleukin (IL)-4, -5, -2 both at rest and when stimulated by Der p1 or SEB along with higher Th17. As for TH17 cell percentage, it was increased in all AD groups compared to healthy controls, while HDM-allergic group was distinguished with a significantly lower production of IL-17. Patients with sensitizations other than HDM were mostly similar to non-allergic AD, with increased Th17 and CD4࠾뷺⧭窷�gamma (IFN-γ) T cells percentage. The biopsy of lesional skin showed that HDM-allergic AD had lower IFN-γ and IFN-γ co-expressing CD8 T cells compared to patients with other sensitizations (p=0.03 and p=0.04, respectively). Among the HDM allergic patients, pairwise comparison of lesional versus non-lesional skin revealed higher CD4 T cells numbers, expression of forkhead box P3 (Foxp3) and T-cell-specific transcription factor (T-bet) (p=0.018, p=0.018, p=0.018, respectively). HDM-allergic AD is a distinct subtype with a predominant skewing in Th2 and higher Th17 cell percentage along with a blunted Th1 response in the skin, all of which may have therapeutic implications.