Highlights Laboratory medicine fulfill a great assistance to early detection of SARAS-CoV-2. Laboratory abnormalities could discriminate between severe and non-severe COVID-19 patients. Abnormal laboratory parameters mirror the evolution of COVID-19 toward an unfavorable outcome. Lymphopenia and increased D-dimer, LDH, ALT, AST reflect poor prognosis in COVID-19.

Background Owing to the COVID-19 pandemic and the imminent collapse of health care systems following the exhaustion of financial, hospital, and medicinal resources, the World Health Organization changed the alert level of the COVID-19 pandemic from high to very high. Meanwhile, more cost-effective and precise COVID-19 detection methods are being preferred worldwide. Objective Machine vision–based COVID-19 detection methods, especially deep learning as a diagnostic method in the early stages of the pandemic, have been assigned great importance during the pandemic. This study aimed to design a highly efficient computer-aided detection (CAD) system for COVID-19 by using a neural search architecture network (NASNet)–based algorithm. Methods NASNet, a state-of-the-art pretrained convolutional neural network for image feature extraction, was adopted to identify patients with COVID-19 in their early stages of the disease. A local data set, comprising 10,153 computed tomography scans of 190 patients with and 59 without COVID-19 was used. Results After fitting on the training data set, hyperparameter tuning, and topological alterations of the classifier block, the proposed NASNet-based model was evaluated on the test data set and yielded remarkable results. The proposed model's performance achieved a detection sensitivity, specificity, and accuracy of 0.999, 0.986, and 0.996, respectively. Conclusions The proposed model achieved acceptable results in the categorization of 2 data classes. Therefore, a CAD system was designed on the basis of this model for COVID-19 detection using multiple lung computed tomography scans. The system differentiated all COVID-19 cases from non–COVID-19 ones without any error in the application phase. Overall, the proposed deep learning–based CAD system can greatly help radiologists detect COVID-19 in its early stages. During the COVID-19 pandemic, the use of a CAD system as a screening tool would accelerate disease detection and prevent the loss of health care resources.

The broad inhibitory effects of melatonin in breast cancer make it a promising agent and may add it to the list of potential drugs in treatment of this cancer.

The toll-like receptor (TLR) family consists of vital receptors responsible for pattern recognition in innate immunity, making them the core proteins involved in pathogen detection and eliciting immune responses. The most studied member of this family, TLR4, has been the center of attention regarding its contributory role in many inflammatory diseases including sepsis shock and asthma. Notably, mounting pieces of evidence have proved that this receptor is aberrantly expressed on the tumor cells and the tumor microenvironment in a wide range of cancer types and it is highly associated with the initiation of tumorigenesis as well as tumor progression and drug resistance. Cancer therapy using TLR4 inhibitors has recently drawn scientists' attention, and the promising results of such studies may pave the way for more investigation in the foreseeable future. This review will introduce the key proteins of the TLR4 pathway and how they interact with major growth factors in the tumor microenvironment. Moreover, we will discuss the many aspects of tumor progression affected by the activation of this receptor and provide an overview of the recent therapeutic approaches using various TLR4 antagonists.

The definitive diagnosis of acute lymphoblastic leukemia (ALL), as a highly prevalent cancer, requires invasive, expensive, and time-consuming diagnostic tests. ALL diagnosis using peripheral blood smear (PBS) images plays a vital role in the initial screening of cancer from noncancer cases. The examination of these PBS images by laboratory users is riddled with problems such as diagnostic error because the nonspecific nature of ALL signs and symptoms often leads to misdiagnosis. Herein, a model based on deep convolutional neural networks (CNNs) is proposed to detect ALL from hematogone cases and then determine ALL subtypes. In this paper, we build a publicly available ALL data set, comprised 3562 PBS images from 89 patients suspected of ALL, including 25 healthy individuals with a benign diagnosis (hematogone) and 64 patients with a definitive diagnosis of ALL subtypes. After color thresholding-based segmentation in the HSV color space by designing a two-channel network, 10 well-known CNN architectures (EfficientNet, Mobile-NetV3, VGG-19, Xception, InceptionV3, ResNet50V2, VGG-16, NASNetLarge, InceptionResNetV2, and Dense-Net201) were employed for feature extraction of different data classes. Of these 10 models, DenseNet201 achieved

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