The most prevalent malignancy that can occur in the gastrointestinal tract is colon cancer. The current treatment options for colon cancer patients include chemotherapy, surgery, radiotherapy, immunotherapy, and targeted therapy. Although the chance of curing the disease in the early stages is high, there is no cure for almost all patients with advanced and metastatic disease. It has been found that over-activation of cyclooxygenase 2 (COX-2), followed by the production of prostaglandin E2 (PGE2) in patients with colon cancer are significantly increased. The tumorigenic function of COX-2 is mainly due to its role in the production of PGE2. PGE2, as a main generated prostanoid, has an essential role in growth and survival of colon cancer cell's. PGE2 exerts various effects in colon cancer cells including enhanced expansion, angiogenesis, survival, invasion, and migration. The signaling of PGE2 via the EP4 receptor has been shown to induce colon tumorigenesis. Moreover, the expression levels of the EP4 receptor significantly affect tumor growth and development. Overexpression of EP4 by various mechanisms increases survival and tumor vasculature in colon cancer cells. It seems that the pathway starting with COX2, continuing with PGE2, and ending with EP4 can promote the spread and growth of colon cancer. Therefore, targeting the COX-2/PGE2/EP4 axis can be considered as a worthy therapeutic approach to treat colon cancer. In this review, we have examined the role and different mechanisms that the EP4 receptor is involved in the development of colon cancer.Overgrowth syndromes are characterized by a global or regional excess growth of various tissue types, especially of mesenchymal nature. The CLOVES (Congenital Lipomatous asymmetric Overgrowth of the trunk with lymphatic, capillary, venous, and combined-type Vascular malformations, Epidermal naevi, Scoliosis/Skeletal and spinal anomalies) syndrome is characterized by an asymmetric growth excess associated with PIK3CA mutations, found in mosaic, affecting the lesional, but not the normal tissues. Herein, we report the case of a patient affected by CLOVES syndrome, harboring a 13 cm leiomyoma of the uterine broad ligament. The leiomyoma tissue was examined by next-generation sequencing showing the absence of related mutations.The nuclei segmentation of hematoxylin and eosin (H&E) stained histopathology images is an important prerequisite in designing a computer-aided diagnostics (CAD) system for cancer diagnosis and prognosis. Automated nuclei segmentation methods enable the qualitative and quantitative analysis of tens of thousands of nuclei within H&E stained histopathology images. However, a major challenge during nuclei segmentation is the segmentation of variable sized, touching nuclei. To address this challenge, we present NucleiSegNet - a robust deep learning network architecture for the nuclei segmentation of H&E stained liver cancer histopathology images. Our proposed architecture includes three blocks a robust residual block, a bottleneck block, and an attention decoder block. The robust residual block is a newly proposed block for the efficient extraction of high-level semantic maps. The attention decoder block uses a new attention mechanism for efficient object localization, and it improves the proposed architecture's performance by reducing false positives. When applied to nuclei segmentation tasks, the proposed deep-learning architecture yielded superior results compared to state-of-the-art nuclei segmentation methods. We applied our proposed deep learning architecture for nuclei segmentation to a set of H&E stained histopathology images from two datasets, and our comprehensive results show that our proposed architecture outperforms state-of-the-art methods. As part of this work, we also introduced a new liver dataset (KMC liver dataset) of H&E stained liver cancer histopathology image tiles, containing 80 images with annotated nuclei procured from Kasturba Medical College (KMC), Mangalore, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India. The proposed model's source code is available at https//github.com/shyamfec/NucleiSegNet. Coronavirus disease-2019 (COVID-19) is an infectious pandemic caused by SARS-CoV-2. SARS-CoV-2 main protease (M ) and spike protein are crucial for viral replication and transmission. Spike protein recognizes the human ACE2 receptor and transmits SARS-CoV-2 into the human body. Thus, M , spike protein, and ACE2 receptor act as appropriate targets for the development of therapeutics against SARS-CoV-2. Spices are traditionally known to have anti-viral and immune-boosting activities. Therefore, we investigated the possible use of selected spice bioactives against the potential targets of SARS-CoV-2 using computational analysis. Molecular docking analysis was performed to analyze the binding efficiency of spice bioactives against SARS-CoV-2 target proteins along with the standard drugs. Drug-likeness properties of selected spice bioactives were investigated using Lipinski's rule of five and the SWISSADME database. Pharmacological properties such as ADME/T, biological functions, and toxicity were analyzed u9.Conventional one-layer models have yet to achieve clinically relevant classification rates in predicting exacerbations for patients with COPD. The present study investigates whether a two-layer probabilistic model can increase classification rates compared to a one-layer model. Continuous measurements of oxygen saturation, pulse rate, and blood pressure from nine patients with COPD were structured into 17 prodromal exacerbation periods and 398 control periods. A one-layer model was compared to a two-layer model based on prior probabilities using double cross-validation. DOX inhibitor mouse The two models were compared by the area under the receiver operating characteristics curve and sensitivity at an arbitrarily set specificity of 0.95. This comparison was carried out across nine different classification algorithms. The area under the receiver operating characteristics curve was increased across all nine classification algorithms and by a mean value of 0.11. Sensitivity at an arbitrarily set specificity of 0.95 was also increased by a mean value of 0.