Ovarian cancer is the most lethal gynecologic malignancy, and patient prognosis has not improved significantly over the last several decades. In order to improve therapeutic approaches and patient outcomes, there is a critical need for focused research towards better understanding of the disease. Recent findings have revealed that the tumor microenvironment plays an essential role in promoting cancer progression and metastasis. The tumor microenvironment consists of cancer cells and several different types of normal cells recruited and reprogrammed by the cancer cells to produce factors beneficial to tumor growth and spread. These normal cells present within the tumor, along with the various extracellular matrix proteins and secreted factors, constitute the tumor stroma and can compose 10–60% of the tumor volume. Cancer associated fibroblasts (CAFs) are a major constituent of the tumor microenvironment, and play a critical role in promoting many aspects of tumor function. This review will describe the various hypotheses about the origin of CAFs, their major functions in the tumor microenvironment in ovarian cancer, and will discuss the potential of targeting CAFs as a possible therapeutic approach.

TMPyP4 is widely considered as a potential photosensitizer in photodynamic therapy and a G-quadruplex stabilizer for telomerase-based cancer therapeutics. However, its biological effects including a possible adverse-effect are poorly understood. In this study, whole genome RNA-seq analysis was used to explore the alteration in gene expression induced by TMPyP4. Unexpectedly, we find that 27.67% of changed genes were functionally related to cell adhesion. Experimental evidences from cell adhesion assay, scratch-wound and transwell assay indicate that TMPyP4 at conventional doses (≤0.5 μM) increases cell-matrix adhesion and promotes the migration of tumor cells. In contrast, a high dose of TMPyP4 (≥2 μM) inhibits cell proliferation and induces cell death. The unintended “side-effect” of TMPyP4 on promoting cell migration suggests that a relative high dose of TMPyP4 is preferred for therapeutic purpose. These findings contribute to better understanding of biological effects induced by TMPyP4 and provide a new insight into the complexity and implication for TMPyP4 based cancer therapy.

A survey on five kinds of cocoa beans from new cocoa planting countries was conducted to analyze each kind’s basic quality. The average bean weight and butter content of Hainan cocoa beans were the lowest, at less than 1.1 g, and 39.24% to 43.44%, respectively. Cocoa beans from Indonesia where shown to be about 8.0% and 9.0% higher in average bean weight and butter content, respectively, than that of Papua New Guinea and about 20.0% and 25.0% higher in average bean weight and butter content than Chinese dried beans, respectively. The average total polyphenolic content ranged from 81.22 mg/10 g to 301.01 mg/10 g. The Hainan 2011 sample had the highest total polyphenolic content, followed by the unfermented sample from Indonesia and the Papua New Guinea sample. The polyphenolic levels found in the Hainan 2010 sample were 123.61 mg/10 g and lower than the other three samples, but the Indonesian fermented sample had the lowest total polyphenolic content of 81.22 mg/10 g. The average total amino acid content ranged from 11.58 g/100 g to 18.17 g/100 g. The total amino acid content was the highest in the Indonesian unfermented sample, followed by the Hainan 2011 sample and the Papua New Guinea sample. The levels found in the Hainan 2010 sample were lower; the Indonesian fermented sample had the lowest total amino acid content.

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These results reveal the potential of Tetra-Pt(bpy) as a novel oncotherapeutic agent for targeting ALT cancer cells.

Knot detection is a challenging problem for the wood industry. Traditional methodologies depend heavily on the features selected manually and therefore were not always accurate due to the variety of knot appearances. This paper proposes an automated framework for addressing the aforementioned problem by using the state-of-the-art YOLO-v5 (the fifth version of You Only Look Once) detector. The features of surface knots were learned and extracted adaptively, and then the knot defects were identified accurately even though the knots vary in terms of color and texture. The proposed method was compared with YOLO-v3 SPP and Faster R-CNN on two datasets. Experimental results demonstrated that YOLO-v5 model achieved the best performance for detecting surface knot defects. F-Score on Dataset 1 was 91.7% and that of Dataset 2 was up to 97.7%. Moreover, YOLO-v5 has clear advantages in terms of training speed and the size of the weight file. These advantages made YOLO-v5 more suitable for the detection of surface knots on sawn timbers and potential for timber grading.

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