No abstract

Mitochondria play important roles in regulating cell bioenergetics status and reactive oxygen species (ROS) generation. ROS-induced mitochondrial damage is among the main intracellular signal inducers of autophagy. Autophagy is a cellular catabolic process that regulates protein and organelle turnover, while a selective form of autophagy, mitophagy, specifically targets dysfunctional mitochondrial degradation. This study aims to measure the levels of autophagy, mitophagy, oxidative stress, and apoptosis in invasive breast carcinoma tissues using immunohistochemistry (IHC). Tissue microarrays of 76 patients with breast cancer were stained with six IHC markers (MnSOD, Beclin-1, LC3, BNIP3, Parkin, and cleaved caspase 3). The expression intensity was determined for each tumor tissue and the adjacent tumor-matched control tissues. Intermediate and strong staining scores of MnSOD, Beclin-1, LC-3, BNIP-3, and Parkin were significantly higher in tumor tissues compared to the adjacent matched control. The scoring intensity was further classified into tissues with negative staining and positive staining, which showed that positive scores of Beclin-1 and Parkin were significantly high in tumor tissues compared to other markers. Positive association was also noted between BNIP-3 and Beclin-1 as well as LC-3 and cleaved caspase-3 immunostaining. To our knowledge, this is one of the first studies that measure both mitophagy and autophagy in the same breast cancer tissues and the adjacent matched control. The findings from this study will be of great potential in identifying new cancer biomarkers and inspire significant interest in applying anti-autophagy therapies as a possible treatment for breast cancer.

Prostate cancer diagnosis is based mainly by microscopic evaluation of prostate tissue biopsy which includes assigning cancer grading. The latter is crucial in evaluating the prognosis or cancer progression and treatment. The common grading system used is Gleason grading system that classifies the prostate cancer into five basic grades based on the architecture and pattern of glandular proliferation. However, this process may be subjected to inter and intra observer variation. Therefore, the main aim of this paper is to develop a computer aided diagnosis (CAD) utilizing supervised machine learning techniques for Gleason grading of prostate histology. The proposed procedure utilizes the main tissue components of the images in an ensemble style to correctly classify the input histopathological image into benign or malignant. Moreover, the texture features of the benign and malignant images can be used to build the proposed ensemble framework. However, not all extracted texture features contribute to the improvement of the classification performance of the proposed ensemble framework. Therefore, to select the more informative features from a set is a critical issue. In this study, a new multi-scoring features selection method based on SVM-RFE and conditional mutual information (CMI) is proposed.

Ensemble learning is an effective machine learning approach to improve the prediction performance by fusing several single classifier models. In computer-aided diagnosis system (CAD), machine learning has become one of the dominant solutions for tissue images diagnosis and grading. One problem in a single classifier model for multi-components of the tissue images combination to construct dense feature vectors is the overfitting. In this paper, an ensemble learning for multi-component tissue images classification approach is proposed. The prostate cancer Hematoxylin and Eosin (H&E) histopathology images from HUKM were used to test the proposed ensemble approach for diagnosing and Gleason grading. The experiments results of several prostate classification tasks, namely, benign vs. Grade 3, benign vs.Grade4, and Grade 3vs.Grade 4 show that the proposed ensemble significantly outperforms the previous typical CAD and the naïve approach that combines the texture features of all tissue component directly in dense feature vectors for a classifier.

Skin exposure to ultraviolet (UV) rays in the sun causes premature ageing and may predispose to skin cancers. UV radiation generates excessive free radical species, resulting in oxidative stress, which is responsible for cellular and DNA damage. There is growing evidence that phytonutrients such as flavonoids and carotenoids may impede oxidative stress and prevent photodamage. We conducted a systematic review of the literature to explore the effects of certain phytonutrients in preventing skin photodamage. We searched the electronic Medline (Ovid) and Pubmed databases for relevant studies published between 2002 and 2022. The main inclusion criteria were articles written in English, and studies reporting the effects of phytonutrient-containing plants of interest on the skin or skin cells exposed to UV radiation. We focused on tea, blueberries, lemon, carrot, tomato, and grapes, which are rich in flavonoids and/or carotenoids. Out of 434 articles retrieved, 40 were identified as potentially relevant. Based on our inclusion criteria, nine articles were included in the review. The review comprises three combined in vitro and animal studies, four human studies, one in vitro research, and one mixed in vitro and human study. All the studies reported positive effects of flavonoids and carotenoid-containing plant extract on UV-induced skin damage. This evidence-based review highlights the potential use of flavonoids and carotenoids found in plants in preventing the deleterious effects of UV radiation on the skin. These compounds may have a role in clinical and aesthetic applications for the prevention and treatment of sunburn and photoaging, and may potentially be used against UV-related skin cancers.