Summary Hepatitis B and C viruses (HBV, HCV) cause chronic hepatitis and hepatocellular carcinoma (HCC) by poorly understood mechanisms. We show that cytokines lymphotoxin (LT) α, β and their receptor (LTβR) are upregulated in HBV- or HCV-induced hepatitis and HCC. Liver-specific LTαβ expression in mice induces liver inflammation and HCC causally linking hepatic LT overexpression to hepatitis and HCC. Development of HCC, composed in part of A6+ oval cells, depends on lymphocytes and IKappa B kinase β expressed by hepatocytes but is independent of TNFR1. In vivo LTβR stimulation implicates hepatocytes as the major LT-responsive liver cells and LTβR inhibition in LTαβ-transgenic mice with hepatitis suppresses HCC formation. Thus, sustained LT signaling represents a pathway involved in hepatitis-induced HCC.

HCV and HBV affect NK cell subsets according to the status of the diseases, especially CD3(-)CD56(dim)NKG2A(+) and CD3(-)CD56(bright)NKG2A(+) cells, may be of interest for disease monitoring.

Drowsiness or fatigue is a major cause of road accidents and has significant implications for road safety. Several deadly accidents can be prevented if the drowsy drivers are warned in time. A variety of drowsiness detection methods exist that monitor the drivers' drowsiness state while driving and alarm the drivers if they are not concentrating on driving. The relevant features can be extracted from facial expressions such as yawning, eye closure, and head movements for inferring the level of drowsiness. The biological condition of the drivers' body, as well as vehicle behavior, is analyzed for driver drowsiness detection. This paper presents a comprehensive analysis of the existing methods of driver drowsiness detection and presents a detailed analysis of widely used classification techniques in this regard. First, in this paper, we classify the existing techniques into three categories: behavioral, vehicular, and physiological parameters-based techniques. Second, top supervised learning techniques used for drowsiness detection are reviewed. Third, the pros and cons and comparative study of the diverse method are discussed. In addition, the research frameworks are elaborated in diagrams for better understanding. In the end, overall research findings based on the extensive survey are concluded which will help young researchers for finding potential future work in the relevant field.

Dendritic cells (DCs) are central cells in the development of antitumor immune responses, but the number and function of these cells can be altered in various cancers. Whether these cells are affected during the development of melanoma is not known. We investigated the presence, phenotype, and functionality of circulating myeloid DCs (MDCs) and plasmacytoid DCs (PDCs) in newly diagnosed melanoma patients, compared to controls. The frequencies of PDCs and MDCs were equivalent in melanoma patients as compared with normal subjects. Both circulating DC subsets were immature, but on ex vivo stimulation with R848 they efficiently upregulated their expression of costimulatory molecules. We found that circulating DCs from melanoma patients and controls displayed similar pattern of expression of the chemokine receptors CXCR3, CXCR4, CCR7, and CCR10. Strikingly, PDCs from melanoma patients expressed higher levels of CCR6 than control PDCs, and were able to migrate toward CCL20. Further data showed that CCR6-expressing PDCs were present in melanoma primary lesions, and that CCL20 was produced in melanoma tumors. These results suggest that PDCs and MDCs are functional in melanoma patients at the time of diagnosis, and that CCL20 may participate to their recruitment from the blood to the tumor.

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Skin cancer is one of the most dangerous forms of cancer. Skin cancer is caused by un-repaired deoxyribonucleic acid (DNA) in skin cells, which generate genetic defects or mutations on the skin. Skin cancer tends to gradually spread over other body parts, so it is more curable in initial stages, which is why it is best detected at early stages. The increasing rate of skin cancer cases, high mortality rate, and expensive medical treatment require that its symptoms be diagnosed early. Considering the seriousness of these issues, researchers have developed various early detection techniques for skin cancer. Lesion parameters such as symmetry, color, size, shape, etc. are used to detect skin cancer and to distinguish benign skin cancer from melanoma. This paper presents a detailed systematic review of deep learning techniques for the early detection of skin cancer. Research papers published in well-reputed journals, relevant to the topic of skin cancer diagnosis, were analyzed. Research findings are presented in tools, graphs, tables, techniques, and frameworks for better understanding.

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