BackgroundThe Viral hepatitis elimination by 2030 is uncertain in resource-limited settings (RLS), due to high burdens and poor diagnostic coverage. This sounds more challenging for hepatitis C virus (HCV) given that antibody (HCVAb) sero-positivity still lacks wide access to HCV RNA molecular testing. This warrants context-specific strategies for appropriate management of liver impairment in RLS. We herein determine the association between anti-HCV positivity and liver impairment in an African RLS.MethodsA facility-based observational study was conducted from July-August 2021 among individuals attending the “St Monique” Health Center at Ottou, a rural community of Yaounde,Cameroon. Following a consecutive sampling, consenting individuals were tested for anti-HCV antibodies, hepatitis B surface antigen (HBsAg) and HIV antibodies (HIVAb) as per the national guidelines. After excluding positive cases for HBsAg and/or HIVAb, liver function tests (ALT/AST) were performed on eligible participants (HBsAg and HIVAb negative) and outcomes were compared according to HCVAb status; with p < 0.05 considered statistically significant.ResultsOut of 306 eligible participants (negative for HBsAg and HIVAb) enrolled, the mean age was 34.35 ± 3.67 years. 252(82.35%) were female and 129 (42.17%) were single. The overall HCVAb sero-positivity was 15.68%(48/306), with 17.86% (45/252) among women vs. 5.55%(3/54) among men [OR (95%CI) = 3.69(2.11-9.29),p = 0.04]. HCVAb Carriage was greater among participants aged > 50 years compared to younger ones [38.46%(15/39) versus 12.36% (33/267) respectively, OR(95%CI) = 4.43(2.11-9.29), p < 0.000] and in multipartnership [26.67%(12/45)vs.13.79%(36/261) monopartnership, OR (95%CI) = 2.27(1.07-4.80),p = 0.03]. The liver impairment rate (abnormal ALT+AST levels) was 30.39%(93/306), with 40.19%(123/306) of abnormal ALT alone. Moreover, the burden of Liver impairment was significantly with aged> 50 versus younger ones [69.23% (27/39) versus 24.72%(66/267) respectively, p < 0.000). Interestingly, the burden of liver impairment (abnormal AST + ALAT) was significantly higher in HCVAb positive (62.5%, 30/48) versus HCVAb negative (24.42%, 63/258) participants, OR: 3.90 [1.96; 7.79], p = 0.0001.ConclusionsIn this rural health facility, HCVAb is highly endemic and the burden of liver impairment is concerning. Interestingly, HCVAb carriage is associated with abnormal liver levels of enzyme (ALT/AST), especially among the elderly populations. Hence, in the absence of nuclei acid testing, ALT/AST are relevant sentinel markers to screen HCVAb carriers who require monitoring/care for HCV-associated hepatocellular carcinoma in RLS.

ABSTRACTBackgroundThe global Viral hepatitis elimination by 2030 is uncertain in resource-limited settings (RLS), due to high burdens and poor diagnostic coverage. This sounds more challenging for hepatitis C virus (HCV) given that antibody (HCVAb) sero-positivity still lacks wide access to HCV RNA molecular testing. This warrants context-specific strategies for appropriate management of liver impairment in RLS. We herein determine the association between anti-HCV positivity and liver impairment in an African RLS.MethodsA facility-based observational study was conducted from July-August 2021 among individuals attending the “St Monique” Health Center at Ottou, a rural community of Yaounde,Cameroon. Following a consecutive sampling, consenting individuals were tested for anti-HCV antibodies, hepatitis B surface antigen (HBsAg) and HIV antibodies (HIVAb) as per the national guidelines. After excluding positive cases for HBsAg and/or HIVAb, liver function tests (ALT/AST) were performed on eligible participants (HBsAg and HIVAb negative) and outcomes were compared according to HCVAb status; with p&lt;0.05 considered statistically significant.ResultsOut of 306 eligible participants (negative for HBsAg and HIVAb) enrolled, the mean age was 34.35±3.67 years. 252(82.35%) were female and 129 (42.17%) were single. The overall HCVAb sero-positivity was 15.68%(48/306), with 17.86% (45/252) among women vs. 5.55%(3/54) among men [OR (95%CI)=3.69(2.11-9.29),p=0.04]. HCVAb Carriage was greater among participants aged &gt;50 years compared to younger ones [38.46%(15/39) versus 12.36% (33/267) respectively, OR(95%CI)=4.43(2.11-9.29), p&lt;0.000] and in multipartnership [26.67%(12/45)vs.13.79%(36/261) monopartnership, OR (95%CI)= 2.27(1.07-4.80),p=0.03]. The liver impairment rate (abnormal ALT+AST levels) was 30.39%(93/306), with 40.19%(123/306) of abnormal ALT alone. Moreover, the burden of Liver impairment was significantly with aged&gt;50 versus younger ones [69.23% (27/39) versus 24.72%(66/267) respectively, p&lt;0.000). Interestingly, the burden of liver impairment (abnormal AST+ALAT) was significantly higher in HCVAb positive (62.5%, 30/48) versus HCVAb negative (24.42%, 63/258) participants, OR: 3.90 [1.96; 7.79], p=0.0001.ConclusionsIn this rural health facility, HCVAb is highly endemic and the burden of liver impairment is concerning. Interestingly, HCVAb carriage is associated with abnormal liver levels of enzyme (ALT/AST), especially among the elderly populations. Hence, in the absence of nuclei acid testing, ALT/AST are relevant sentinel markers to screen HCVAb carriers who require monitoring/care for HCV-associated hepatocellular carcinoma in RLS.

Theology is a science unlike any other, moreover, it is the mother of many because many were born from the misunderstandings that it presented, hence in this article I am trying to resolve one of them, particularly the one relating to the persecution in the book of the prophet Daniel showing the faithfulness of these men to their God makes the most powerful ruler of their time King Nebuchadnezzar king of Babylon.

In this work, we propose to address the unsupervised domain adaptive (UDA) person re-id problem in which the model learns from an unlabeled target domain using a fully annotated source domain. Current approaches mainly address domain shift problem or the inter/intra-domain variation of the two domains. However, they have neglected to integrate the-easy-to-learn label distribution of the target domain into the model to improve its performance. Moreover, the automatic label assignment for the unlabeled target data currently used in UDA methods does not reflect the underlying data. To address these issues, we introduce a technique that enforces three properties: (1) target instance invariance that considers the target data and uses a key–value memory to guess the label distribution that is later used as the supervision signal. (2) a camera invariance, formed by unlabeled target images, and their camera-style transferred. Here, a new loss function is proposed to control overconfident predictions on the styled images. Lastly, (3) a hierarchical clustering-based optimization technique that exploits the similarities between the target images to constrain the supervision information of the first property. Here, we randomly allocate each target image to a separate cluster, then gradually incorporate similarity within each identity as we group similar images into clusters and use the cluster-IDs as the new target labels. We iteratively refine the guessed label distribution of the target domain by making predictions on the unlabeled target domain and then train the network with these new samples. Extensive experimental results on the concurrent use of these three properties demonstrate that the proposed model can achieve the state-of-the-art on unsupervised domain adaptive person re-id. Our work is important for knowledge discovery and knowledge transfer.

Person re-identification is the task of recognizing an individual across heterogeneous non-overlapping camera views. It has become a crucial capability needed by many applications in public space video surveillance. However, it remains a challenging task due to the subtle inter-class similarity and large intra-class variation found in person images. Current CNN-based approaches have focused and investigated traditional identification or verification frameworks. Such approaches typically use the whole input image including the background and fail to pay attention to specific body parts, deviating the feature representation learning from informative parts. In this article, we introduce a self-attention mechanism coupled with cross-resolution to improve the feature representation learning of person re-identification task. The proposed self-attention module reinforces the most informative parts from a high-resolution image using its internal representation at the low-resolution. In particular, the model is fed with a pair of images on a different scale and consists of two branches. The upper branch processes the high-resolution image and learns high dimensional feature representation while the lower branch processes the low-resolution image and learns a filtering attention heatmap. The feature maps on the lower branch are subsequently weighted to reflect the importance of each patch of the input image using a softmax operation; whereas, on the upper branch, we apply a max pooling operation to downsample the high-resolution feature map before element-wise multiplied with the attention heatmap. Our attention module helps the network learn the most discriminative visual features of multiple regions of the image and is specifically optimized to attend and enforce feature representation at different scales. Extensive experiments on three large-scale datasets show that network architectures augmented with our self-attention module systematically improve their accuracy and outperform various state-of-the-art models by a large margin.

Person re-identification (re-id) is a cross-camera retrieval task which establishes a correspondence between images of a person from multiple cameras. Deep Learning methods have been successfully applied to this problem and have achieved impressive results. However, these methods require a large amount of labeled training data. Currently labeled datasets in person re-id are limited in their scale and manual acquisition of such large-scale datasets from surveillance cameras is a tedious and labor-intensive task. In this paper, we propose a framework that performs intelligent data augmentation and assigns partial smoothing label to generated data. Our approach first exploits the clustering property of existing person re-id datasets to create groups of similar objects that model cross-view variations. Each group is then used to generate realistic images through adversarial training. Our aim is to emphasize feature similarity between generated samples and the original samples. Finally, we assign a non-uniform label distribution to the generated samples and define a regularized loss function for training. The proposed approach tackles two problems (1) how to efficiently use the generated data and (2) how to address the over-smoothness problem found in current regularization methods. Extensive experiments on four larges cale datasets show that our regularization method significantly improves the Re-ID accuracy compared to existing methods.

Person re-identification (re-id) is a cross-camera retrieval task which establishes a correspondence between images of a person from multiple cameras. Deep Learning methods have been successfully applied to this problem and have achieved impressive results. However, these methods require a large amount of labeled training data. Currently labeled datasets in person re-id are limited in their scale and manual acquisition of such large-scale datasets from surveillance cameras is a tedious and labor-intensive task. In this paper, we propose a framework that performs intelligent data augmentation and assigns partial smoothing label to generated data. Our approach first exploits the clustering property of existing person re-id datasets to create groups of similar objects that model cross-view variations. Each group is then used to generate realistic images through adversarial training. Our aim is to emphasize feature similarity between generated samples and the original samples. Finally, we assign a non-uniform label distribution to the generated samples and define a regularized loss function for training. The proposed approach tackles two problems (1) how to efficiently use the generated data and (2) how to address the over-smoothness problem found in current regularization methods. Extensive experiments on four larges cale datasets show that our regularization method significantly improves the Re-ID accuracy compared to existing methods.