Hyper-parameter tuned deep learning approach for effective human monkeypox disease detection.

Human monkeypox is a very unusual virus that can devastate society. Early identification and diagnosis are essential to treat and manage an illness effectively. Human monkeypox disease detection using deep learning models has attracted increasing attention recently. The virus that causes monkeypox may be passed to people, making it a zoonotic illness. The latest monkeypox epidemic has hit more than 40 nations. Computer-assisted approaches using Deep Learning techniques for automatically identifying skin lesions have shown to be a viable alternative in light of the fast proliferation and ever-growing problems of supplying PCR (Polymerase Chain Reaction) Testing in places with limited availability. In this research, we introduce a deep learning model for detecting human monkeypoxes that is accurate and resilient by tuning its hyper-parameters. We employed a mixture of convolutional neural networks and transfer learning strategies to extract characteristics from medical photos and properly identify them. We also used hyperparameter optimization strategies to fine-tune the Model and get the best possible results. This paper proposes a Yolov5 model-based method for differentiating between chickenpox and Monkeypox lesions on skin pictures. The Roboflow skin lesion picture dataset was subjected to three different hyperparameter tuning strategies: the SDG optimizer, the Bayesian optimizer, and Learning without Forgetting. The proposed Model had the highest classification accuracy (98.18%) when applied to photos of monkeypox skin lesions. Our findings show that the suggested Model surpasses the current best-in-class models and may be used in clinical settings for actual Human Monkeypox disease detection and diagnosis.

An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean Clustering.

Brain tumors in Magnetic resonance image segmentation is challenging research. With the advent of a new era and research into machine learning, tumor detection and segmentation generated significant interest in the research world. This research presents an efficient tumor detection and segmentation technique using an adaptive moving self-organizing map and Fuzzyk-mean clustering (AMSOM-FKM). The proposed method mainly focused on tumor segmentation using extraction of the tumor region. AMSOM is an artificial neural technique whose training is unsupervised. This research utilized the online Kaggle Brats-18 brain tumor dataset. This dataset consisted of 1691 images. The dataset was partitioned into 70% training, 20% testing, and 10% validation. The proposed model was based on various phases: (a) removal of noise, (b) selection of feature attributes, (c) image classification, and (d) tumor segmentation. At first, the MR images were normalized using the Wiener filtering method, and the Gray level co-occurrences matrix (GLCM) was used to extract the relevant feature attributes. The tumor images were separated from non-tumor images using the AMSOM classification approach. At last, the FKM was used to distinguish the tumor region from the surrounding tissue. The proposed AMSOM-FKM technique and existing methods, i.e., Fuzzy-C-means and K-mean (FMFCM), hybrid self-organization mapping-FKM, were implemented over MATLAB and compared based on comparison parameters, i.e., sensitivity, precision, accuracy, and similarity index values. The proposed technique achieved more than 10% better results than existing methods.

Platform Work and the COVID-19 Pandemic.

Platform business models emerged with the growth of the Internet in the 1990s and are conceptualized as two- or multi-sided markets, as they facilitate exchange between service providers, clients (business) and workers. This article focuses on the impact of COVID-19 on digital labour platforms, such as freelance online web-based platforms and location-based platforms (transportation and delivery platforms), which have grown exponentially over the past decade. The COVID-19 pandemic exposed immediately some of the vulnerabilities that the workers in the platform economy were facing as they were declared as part of the 'emergency services', and this note explores their conditions during the pandemic.

Laparoscopic management of tumor in supernumerary ovary.

Laparoscopic management of most of the adnexal masses has become feasible in the present era of advancing endoscopic techniques. A postmenopausal lady presented with lump in the abdomen, appeared to be a solid ovarian mass on ultrasound, and magnetic resonance imaging. On laparoscopy, both the ovaries were normal and the mass was not connected to uterus or adnexa. The mass was removed and histopathology confirmed it to be ovarian tissue thus confirming it to be a tumor in a supernumerary ovary. Examples of supernumerary ovary are among the rarest of gynecological abnormalities.

Leprosy with Atypical Skin Lesions Masquerading as Relapsing Polychondritis.

Leprosy can present with a variety of clinical manifestations depending on the immune status of the individual. After dermatological and neurological involvement, rheumatic features specially various forms of arthritis are the third most common manifestation of the disease. We describe a unique case of a 22-year-old patient presenting with external ear involvement mimicking relapsing polychondritis along with inflammatory joint symptoms and skin lesions. Ear involvement in relapsing polychondritis characteristically is painful and spares the noncartilaginous ear lobules, in contrast to painless ear involvement in leprosy affecting the lobules as well. Histopathology confirmed the diagnosis, although the ear and skin lesions were not classical of leprosy. Such a presentation of leprosy closely mimicking relapsing polychondritis has not been described previously. Tissue diagnosis should always be attempted whenever possible in patients presenting with autoimmune features, so that inappropriate therapy with immunosuppressants is avoided.

Ellagic acid inhibits PDGF-BB-induced vascular smooth muscle cell proliferation and prevents atheroma formation in streptozotocin-induced diabetic rats.

Plant-derived polyphenolic compounds have beneficial health effects. In the present study, we determined the ability of ellagic acid (EA) to prevent platelet-derived growth factor-BB (PDGF-BB)-induced proliferation of primary cultures of rat aortic smooth muscle cells (RASMCs). We also determined the ability of EA to prevent atherosclerosis in streptozotocin-induced diabetic rats. Proliferation of cells was measured via Alamar Blue assay and through propidium iodide-based cell cycle analysis in flow cytometer. Reactive oxygen species (ROS) were measured via 2',7'-dichlorofluorescin diacetate and Amplex red methods. Expression of proliferation markers and activation of kinases were assessed by immunoblot analysis. Cotreatment of primary cultures of RASMCs with 25 µmol/L of EA significantly reduced PDGF-BB (20 ng/ml)-induced proliferation by blocking S-phase entry. EA effectively blocked PDGF receptor-ß (PDGFR-ß) tyrosine phosphorylation, generation of intracellular ROS and downstream activation of extracellular signal-regulated kinase 1/2. It also blocked PDGF-BB-induced expression of cyclin D1. Computational molecular docking of EA with the PDGFR-ß-PDGF-BB complex revealed two putative inhibitor binding sites which showed similar binding energies with the known PDGFR-ß inhibitor AG1295. In diabetic rats, supplementation of diet with 2% EA significantly blocked diabetes-induced medial thickness, and lipid and collagen deposition in the arch of aorta. These were assessed through haematoxylin and eosin, Oil Red O and Masson's trichome staining, respectively. EA treatment also blocked cyclin D1 expression in medial smooth muscle cells in experimental animals. Thus, EA is effective in reducing atherosclerotic process by blocking proliferation of vascular smooth muscle cells.