Segmentation and classification of skin lesions using hybrid deep learning method in the Internet of Medical Things.

INTRODUCTION: Particularly within the Internet of Medical Things (IoMT) context, skin lesion analysis is critical for precise diagnosis. To improve the accuracy and efficiency of skin lesion analysis, CAD systems play a crucial role. To segment and classify skin lesions from dermoscopy images, this study focuses on using hybrid deep learning techniques. METHOD: This research uses a hybrid deep learning model that combines two cutting-edge approaches: Mask Region-based Convolutional Neural Network (MRCNN) for semantic segmentation and ResNet50 for lesion detection. To pinpoint the precise location of a skin lesion, the MRCNN is used for border delineation. We amass a huge, annotated collection of dermoscopy images for thorough model training. The hybrid deep learning model to capture subtle representations of the images is trained from start to finish using this dataset. RESULTS: The experimental results using dermoscopy images show that the suggested hybrid method outperforms the current state-of-the-art methods. The model's capacity to segment lesions into distinct groups is demonstrated by a segmentation accuracy measurement of 95.49 percent. In addition, the classification of skin lesions shows great accuracy and dependability, which is a notable advancement over traditional methods. The model is put through its paces on the ISIC 2020 Challenge dataset, scoring a perfect 96.75% accuracy. Compared to current best practices in IoMT, segmentation and classification models perform exceptionally well. CONCLUSION: In conclusion, this paper's hybrid deep learning strategy is highly effective in skin lesion segmentation and classification. The results show that the model has the potential to improve diagnostic accuracy in the setting of IoMT, and it outperforms the current gold standards. The excellent results obtained on the ISIC 2020 Challenge dataset further confirm the viability and superiority of the suggested methodology for skin lesion analysis.

Lyme rashes disease classification using deep feature fusion technique.

Automatic classification of Lyme disease rashes on the skin helps clinicians and dermatologists' probe and investigate Lyme skin rashes effectively. This paper proposes a new in-depth features fusion system to classify Lyme disease rashes. The proposed method consists of two main steps. First, three different deep learning models, Densenet201, InceptionV3, and Exception, were trained independently to extract the deep features from the erythema migrans (EM) images. Second, a deep feature fusion mechanism (meta classifier) is developed to integrate the deep features before the final classification output layer. The meta classifier is a basic deep convolutional neural network trained on original images and features extracted from base level three deep learning models. In the feature fusion mechanism, the last three layers of base models are dropped out and connected to the meta classifier. The proposed deep feature fusion method significantly improved the classification process, where the classification accuracy was 98.97%, which is particularly impressive than the other state-of-the-art models.

Real-World Outcomes of Adalimumab Treatment for Moderate and Severe Psoriasis in Korean Patients (RAPSODI Study).

BACKGROUND: Psoriasis imposes a significant treatment burden on patients, particularly impacting well-being and quality of life (QoL). The psychosocial impact of psoriasis treatments remains unexplored in most patient populations. OBJECTIVE: To assess the impact of adalimumab on health-related QoL (HRQoL) in Korean patients with psoriasis. METHODS: This 24-week, multicenter, observational study, assessed HRQoL in Korean patients treated with adalimumab in a real-world setting. Patient-reported outcomes (PROs) including European Quality of Life-5 Dimension scale (EQ-5D), EQ-5D VAS, SF-36, and DLQI were evaluated at week 16 and 24, versus baseline. Patient satisfaction was assessed using TSQM. RESULTS: Among 97 enrolled patients, 77 were assessed for treatment effectiveness. Most patients were male (52, 67.5%) and mean age was 45.4 years. Median baseline body surface area and Psoriasis Area and Severity Index (PASI) scores were 15.00 (range 4.00~80.00) and 12.40 (range 2.70~39.40), respectively. Statistically significant improvements in all PROs were observed between baseline and week 24. Mean EQ-5D score improved from 0.88 (standard deviation [SD], 0.14) at baseline to 0.91 (SD, 0.17) at week 24 (p=0.0067). The number of patients with changes in PASI 75, 90, or 100 from baseline to week 16 and 24 were 65 (84.4%), 17 (22.1%), and 1 (1.3%); and 64 (83.1%), 21 (27.3%), and 2 (2.6%), respectively. Overall treatment satisfaction was reported, including effectiveness and convenience. No unexpected safety findings were noted. CONCLUSION: Adalimumab improved QoL and was well-tolerated in Korean patients with moderate to severe psoriasis, as demonstrated in a real-world setting. Clinical trial registration number (clinicaltrials.gov: NCT03099083).

Role of plant microRNAs and their corresponding pathways in fluctuating light conditions.

In recent years, it has been established that microRNAs (miRNAs) are critical for various plant physiological regulations in numerous species. Next-generation sequencing technologies have aided to our understandings related to the critical role of miRNAs during environmental stress conditions and plant development. Light influences not just miRNA accumulation but also their biological activities via regulating miRNA gene transcription, biosynthesis, and RNA-induced silencing complex (RISC) activity. Light-regulated routes, processes, and activities can all be affected by miRNAs. Here, we will explore how light affects miRNA gene expression and how conserved and novel miRNAs exhibit altered expression across different plant species in response to variable light quality. Here, we will mainly discuss recent advances in understanding how miRNAs are involved in photomorphogenesis, and photoperiod-dependent plant biological processes such as cell proliferation, metabolism, chlorophyll pigment synthesis and axillary bud growth. The review concludes by presenting future prospects via hoping that light-responsive miRNAs can be exploited in a better way to engineer economically important crops to ensure future food security.

Early Efficacy of In Situ Fenestration with a Triple Chimney Technique for High-Risk Stanford Type A Aortic Dissection: A Single-Center Retrospective Study.

PURPOSE: The objective of this investigation was to study the early efficacy of in situ fenestration with triple chimney technique for high-risk type A aortic dissection patients. METHODS: This study included 24 patients who were treated by in situ fenestration with TCT for high-risk TAAD between January 2018 and December 2019. Multiple comorbidities or preoperative critical conditions rendered patients ineligible for open surgery, but all patients that were evaluated and considered had to undergo operation. By analyzing the regular follow-up data, the early postoperative efficacy of the patients was evaluated. RESULTS: The average age of the 24 patients was 65.4 ± 9.3 years. The success rate of the operation was 100%, as all the patients were discharged successfully. There were no serious neurological complications or persistent endoleakage. The mean follow-up time was 21.4 ± 6.9 months. The patency rate of all branching stents was 100%, with no stent displacement, stenosis, or blockage observed. While none presented with type I endoleakage, one patient (4.2%) presented asymptomatic type II endoleakage around the left subclavian artery stent. Currently, 23 of the 24 patients remain alive. CONCLUSION: Initial results are encouraging with TCT for high-risk TAAD. However, due to its high selectivity and potential complexity related to surgical risks, the mid- and long-term efficacy of this technique remains unknown. For patients who are eligible for open heart surgery, we still recommend it be performed.

Preliminary Comparative Assessment of Brucellergene Skin Test for Diagnosis of Brucellosis in Dromedary Camels (Camelus dromedarius).

This study was conducted to evaluate the use of Brucellergene skin test (BST) for the diagnosis of Brucellosis in camels (Camelus dromedarius) in comparison with Rose Bengal test (RBT) and competitive enzyme-linked immunosorbent assay (c-ELISA). A total of 68 apparently healthy adult dromedary camels of either gender from three different geographical locations of Abu Dhabi Emirate, United Arab Emirates (UAE), were included in the study. The skin test was applied on two shaved areas at the middle of the neck: one for the test and the other area was injected with normal saline as a control. Reading was done 72 h postinjection. Results were subjected to Bayesian analysis to assess the test performances in camels. The model estimated the following sensitivity and specificity median values: BST: Se = 70.72%, Sp = 98.82%; RBT: Se = 93.27%, Sp = 97.79%; and c-ELISA: Se = 94.78%, Sp = 98.48%. As the BST investigated in this study proved to be a highly specific test, we propose using it as a confirmatory test in camels particularly when the serological tests give doubtful results on individual animals.

Expression of tissue factor in experimental bovine pneumonic pasteurellosis and endotoxemia.

Tissue factor (TF), a cell surface-associated cofactor and activator of coagulation factor VII, has been implicated in the local and systemic activation of coagulation associated with sepsis. This study describes the pattern of TF expression in experimental bovine pneumonic pasteurellosis and endotoxemia. Immunohistochemical techniques were used to localize TF antigen in tissue sections. Tissue factor expression was not observed in tissues from control animals. In response to Pasteurella haemolytica challenge, TF was expressed within alveolar walls, by mononuclear inflammatory cells within alveoli, and in walls of arteries, arterioles, bronchi, and bronchioles. Tissue factor was not detected in unaffected lung, liver, spleen, lymph node or kidney tissue. Administration of Escherichia coli endotoxin intravenously resulted in tissue factor expression in lung, spleen, and lymph node tissue. Results of this study indicate that TF is expressed locally at sites of inflammation and systemically in endotoxemia. Therefore, TF may be involved in coagulation events associated with pneumonic pasteurellosis.