Traffic intensity monitoring using multiple object detection with traffic surveillance cameras

Object detection and tracking is a field of research that has many applications in the current generation with increasing number of cameras on the streets and lower cost for Internet of Things(IoT). In this paper, a traffic intensity monitoring system is implemented based on the Macroscopic Urban Traffic model is proposed using computer vision as its source. The input of this program is extracted from a traffic surveillance camera which has another program running a neural network classification which can identify and differentiate the vehicle type is implanted. The neural network toolbox is trained with positive and negative input to increase accuracy. The accuracy of the program is compared to other related works done and the trends of the traffic intensity from a road is also calculated. relevant articles in literature searches, great care should be taken in constructing both. Lastly the limitation and the future work is concluded

A novel model based on interleukin 6 and insulin-like growth factor II for detection of hepatocellular carcinoma associated with hepatitis C virus

Abstract Background The coexistence of cirrhosis complicates the early detection of hepatocellular carcinoma (HCC). Thus, novel biomarkers for HCC early detection are needed urgently. Traditionally, HCC detection is carried out by evaluating alpha-fetoprotein (AFP) levels combined with imaging techniques. This work aimed to assess interleukin (IL-6) and insulin-like growth factor 2 (IGF 2) as possible HCC markers in comparison to AFP in patients with and without HCC. Results ROC analysis showed that IGF2 had the highest area under the curve (AUC) for discriminating HCC from liver cirrhosis (0.86), followed by IL6 (0.82), AFP (0.72), and platelet count (0.6). A four-marker model was developed and discriminated HCC from liver cirrhosis with an AUC of 0.97. The best cut-off was 1.28, at which sensitivity and specificity were 90% and 85%, respectively. For small tumor (< 2 cm), the model had an AUC of 0.95 compared to AFP (0.72). Also, the model achieved perfect performance with AUC of 0.93, 0.94, and 0.95 for BCLC (0-A), CLIP (0-1), and Okuda (stage I), respectively, compared to AFP (AUC of 0.71, 0.69, and 0.67, respectively). Conclusions The four markers may serve as a diagnostic model for HCC early stages and help overcome AFP poor sensitivity

A combination of α-fetoprotein, midkine, thioredoxin and a metabolite for predicting hepatocellular carcinoma

Introduction and objectives: The heterogenous nature of hepatocellular carcinoma (HCC) motivated this attempt at developing and validating a model based on combined biomarkers for improving early HCC detection. Patients/materials and methods: This study examined 196 patients for an estimation study (104 patients with HCC, 52 with liver cirrhosis and 40 with liver fibrosis) and 122 patients for the validation study (80 patients with HCC, 42 with liver cirrhosis). All patients were positive for hepatitis C virus. Four markers were measured: Midkine and thioredoxin using ELISA, 1-methyladenosine and 1-methylguanosine using a gas chromatography–mass spectrometry (GC–MS). The results were compared with alpha-fetoprotein (AFP). The performance of the model was estimated in BCLC, CLIP and Okuda staging systems of HCC. Results: The model yielded high performance with an area under ROC (AUC) of 0.94 for predicting HCC in patients with liver cirrhosis, compared with AUC of 0.69 for AFP. This model had AUCs of 0.93, 0.94 and 0.94 in patients who had only one single nodule, absent macrovascular invasion and tumor size <2 cm, respectively, compared with AUCs of 0.71, 0.6 and 0.59 for AFP. The model produced AUCs of 0.91 for BCLC (0-A), 0.92 for CLIP (0–1) and 0.94 for Okuda (stage I) compared with AUCs of 0.56, 0.58 and 0.64 for AFP. No significant difference was found between AUC in the estimation and the validation groups. Conclusion: This model may enhance early-stage HCC detection and help to overcome insufficient sensitivity of AFP

Molecular Biomarkers and Signaling Pathways of Cancer Stem Cells in Colorectal Cancer

Colorectal cancer (CRC) is the third most frequently found cancer in the world, and it is frequently discovered when it is already far along in its development. About 20% of cases of CRC are metastatic and incurable. There is more and more evidence that colorectal cancer stem cells (CCSCs), which are in charge of tumor growth, recurrence, and resistance to treatment, are what make CRC so different. Because we know more about stem cell biology, we quickly learned about the molecular processes and possible cross-talk between signaling pathways that affect the balance of cells in the gut and cancer. Wnt, Notch, TGF-β, and Hedgehog are examples of signaling pathway members whose genes may change to produce CCSCs. These genes control self-renewal and pluripotency in SCs and then decide the function and phenotype of CCSCs. However, in terms of their ability to create tumors and susceptibility to chemotherapeutic drugs, CSCs differ from normal stem cells and the bulk of tumor cells. This may be the reason for the higher rate of cancer recurrence in patients who underwent both surgery and chemotherapy treatment. Scientists have found that a group of uncontrolled miRNAs related to CCSCs affect stemness properties. These miRNAs control CCSC functions like changing the expression of cell cycle genes, metastasis, and drug resistance mechanisms. CCSC-related miRNAs mostly control signal pathways that are known to be important for CCSC biology. The biomarkers (CD markers and miRNA) for CCSCs and their diagnostic roles are the main topics of this review study