Unfolded Protein Response Signaling in Hepatic Stem Cell Activation in Liver Fibrosis.

Frequent exposure to various external and internal adverse forces (stresses) disrupts cell protein homeostasis through endoplasmic reticulum (ER) capacity saturation. This process leads to the unfolded protein response (UPR), which aims to re-establish/maintain optimal cellular equilibrium. This complex mechanism is involved in the pathogenesis of various disorders, such as metabolic syndrome, fibrotic diseases, neurodegeneration, and cancer, by altering cellular metabolic changes integral to activating the hepatic stellate cells (HSCs). The development of hepatic fibrosis is one of the consequences of UPR activation. Therefore, novel therapies that target the UPR pathway effectively and specifically are being studied. This article covers the involvement of the UPR signaling pathway in cellular damage in liver fibrosis. Investigating the pathogenic pathways related to the ER/UPR stress axis that contribute to liver fibrosis can help to guide future drug therapy approaches.

Sperm DNA fragmentation and apoptosis in the sperm of men with oligozoospermia are closely related to anti-ODF2 autoantibodies.

BACKGROUND: Around 15% of couples of childbearing age suffer from infertility; in 50% of these cases, the male factor is present. In this study, we investigated the association between anti-ODF2 autoantibody existence and the DNA fragmentation and apoptosis of sperm in oligozoospermia men. MATERIAL AND METHODS: 35 fertile men and 57 oligozoospermia men are enrolled in this study as control and case groups, respectively. After the identification of ODF2 as a possible target of anti-sperm antibodies in sera of oligozoospermia men using two-dimensional gel electrophoresis followed by western blotting and mass spectrometry, the case group serums were screened for anti-ODF2 autoantibodies and divided into anti-ODF2 negative (N = 24) and positive (N = 33) subgroups to follow assays. The mRNA expression levels of ODF2, Caspases 3, 8, 9, BAX, and BCL-2 were evaluated via qRT-PCR in spermatozoa samples of mentioned groups. DNA fragmentation and apoptosis rate of spermatozoa in studied groups were assessed using an SDF kit and flow cytometry, respectively. RESULTS: Mass spectrometry showed that ODF2 is one of the anti-sperm antibodies targeted in oligozoospermia patients. 33 of 57 oligozoospermia men had anti-ODF2 autoantibody in their sera. An elevated expression of ODF2 mRNA was observed in spermatozoa of anti-ODF2+ patients compared to anti-ODF2- patients and controls. There was an increased expression level of Caspase 3, 8, 9, and BAX and decreased expression of BCL-2 in spermatozoa of anti-ODF2+ patients compared to anti-ODF2- patients and controls. Noticeable increases in DNA fragmentation and apoptosis rate of anti-ODF2+ patients' spermatozoa were observed compared to anti-ODF2- patients and healthy controls spermatozoa. A positive correlation was observed between ODF-2 expression and DNF fragmentation and apoptosis rate of anti-ODF2+ patients' spermatozoa. CONCLUSION: Our results revealed that ODF2 is one of the main spermatozoa structural proteins, which is one of the anti-sperm antibodies targets, and its dysregulated expression may result in an increased rate of sperm DNA fragmentation and apoptosis.

The evaluation of CD39, CD73, and HIF-1 α expression besides their related miRNAs in PBMCs of women with recurrent pregnancy loss.

The molecular mechanisms involved in the pathogenesis of recurrent pregnancy loss (RPL) are not completely recognized. The present study aimed to assess the molecules associated with ATP catabolism and hypoxia besides their related miRNAs in patients with RPL. The frequency of Th17 and Treg cells in PBMCs of RPL women and healthy pregnant women were evaluated with Flow cytometry. The expression levels of CD39, CD73, and Hypoxia-inducible factor-alpha (HIF-1α), miR-18a, miR-30a, and miR-206 in PBMCs of two groups were measured with real-time PCR and western blotting. Then, serum levels of IGF-1, TGF-ß, and HIF-1α were measured by ELISA. Our results indicated a higher (p = 0.0002) and lower (p < 0.0001) frequency of Th17 and Treg lymphocytes in RPL women, respectively. The expression level of CD39 decreased in PBMCs of RPL women whereas the level of CD73 and HIF-α increased (p = 0.0010, 0.0023, 0.0006 respectively). The results of CD39 and CD37 were also confirmed by protein analysis (p = 0.0047, 0.0364 respectively). Almost, the same results for CD39 and CD73 expression at mRNA and protein levels were observed in isolated Treg cells. Moreover, we found the higher expression of miR-206 and miRNA-30a (p = 0.0038, 0.0123), but the lower expression of miRNA-18a (p = 0.0101) in RPL. The concentration level of IGF-1, and TGF-ß reduced (p = 0.0017, 0.0065 respectively) while the level of HIF-α elevated (p = 0.0235) in serum samples of RPL. In conclusion, we observed the dysregulation of molecules that are involved in ATP catabolism and hypoxia, including CD39, CD73, and HIF-1a which is related to miR-18a, miR-30a, and miR-206 change in RPL women. It may be potentially used for RPL prognosis by more comprehensive future studies.

Targeting AMPK by Statins: A Potential Therapeutic Approach.

Statins are a group of lipid-lowering drugs that inhibit cholesterol biosynthesis and have anti-inflammatory, anti-tumor, and immunomodulatory properties. Several lines of evidence indicate that statins regulate multiple proteins associated with the regulation of differing cellular pathways. The 5'-adenosine monophosphate-activated protein kinase (AMPK) pathway plays an important role in metabolism homeostasis with effects on cellular processes including apoptosis and the inflammatory responses through several pathways. Recently, it has been shown that statins can affect the AMPK pathway in differing physiological and pathological ways, resulting in anti-cancer, cardio-protective, neuro-protective, and anti-tubercular effects; additionally, they have therapeutic effects on non-alcoholic fatty liver disease and diabetes mellitus-associated complications. Statins activate AMPK as an energy sensor that inhibits cell proliferation and induces apoptosis in cancer cells, whilst exerting its cardio-protective effects through inhibition of inflammation and fibrosis, and promotion of angiogenesis. Furthermore, statin-associated AMPK activation leads to decreased lipid accumulation and decreased amyloid beta deposition in the liver and brain, respectively, and may have therapeutic effects on the liver and neurons. In this review, we summarize the results of studies of AMPK-associated therapeutic effects of statins in different pathological conditions.

CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV.

Tuberculosis (TB) is the leading cause of preventable death in HIV-positive patients, and yet often remains undiagnosed and untreated. Chest x-ray is often used to assist in diagnosis, yet this presents additional challenges due to atypical radiographic presentation and radiologist shortages in regions where co-infection is most common. We developed a deep learning algorithm to diagnose TB using clinical information and chest x-ray images from 677 HIV-positive patients with suspected TB from two hospitals in South Africa. We then sought to determine whether the algorithm could assist clinicians in the diagnosis of TB in HIV-positive patients as a web-based diagnostic assistant. Use of the algorithm resulted in a modest but statistically significant improvement in clinician accuracy (p = 0.002), increasing the mean clinician accuracy from 0.60 (95% CI 0.57, 0.63) without assistance to 0.65 (95% CI 0.60, 0.70) with assistance. However, the accuracy of assisted clinicians was significantly lower (p < 0.001) than that of the stand-alone algorithm, which had an accuracy of 0.79 (95% CI 0.77, 0.82) on the same unseen test cases. These results suggest that deep learning assistance may improve clinician accuracy in TB diagnosis using chest x-rays, which would be valuable in settings with a high burden of HIV/TB co-infection. Moreover, the high accuracy of the stand-alone algorithm suggests a potential value particularly in settings with a scarcity of radiological expertise.

Impact of a deep learning assistant on the histopathologic classification of liver cancer.

Artificial intelligence (AI) algorithms continue to rival human performance on a variety of clinical tasks, while their actual impact on human diagnosticians, when incorporated into clinical workflows, remains relatively unexplored. In this study, we developed a deep learning-based assistant to help pathologists differentiate between two subtypes of primary liver cancer, hepatocellular carcinoma and cholangiocarcinoma, on hematoxylin and eosin-stained whole-slide images (WSI), and evaluated its effect on the diagnostic performance of 11 pathologists with varying levels of expertise. Our model achieved accuracies of 0.885 on a validation set of 26 WSI, and 0.842 on an independent test set of 80 WSI. Although use of the assistant did not change the mean accuracy of the 11 pathologists (p = 0.184, OR = 1.281), it significantly improved the accuracy (p = 0.045, OR = 1.499) of a subset of nine pathologists who fell within well-defined experience levels (GI subspecialists, non-GI subspecialists, and trainees). In the assisted state, model accuracy significantly impacted the diagnostic decisions of all 11 pathologists. As expected, when the model's prediction was correct, assistance significantly improved accuracy (p = 0.000, OR = 4.289), whereas when the model's prediction was incorrect, assistance significantly decreased accuracy (p = 0.000, OR = 0.253), with both effects holding across all pathologist experience levels and case difficulty levels. Our results highlight the challenges of translating AI models into the clinical setting, and emphasize the importance of taking into account potential unintended negative consequences of model assistance when designing and testing medical AI-assistance tools.

Fast and accurate read alignment for resequencing.

MOTIVATION: Next-generation sequence analysis has become an important task both in laboratory and clinical settings. A key stage in the majority sequence analysis workflows, such as resequencing, is the alignment of genomic reads to a reference genome. The accurate alignment of reads with large indels is a computationally challenging task for researchers. RESULTS: We introduce SeqAlto as a new algorithm for read alignment. For reads longer than or equal to 100 bp, SeqAlto is up to 10 × faster than existing algorithms, while retaining high accuracy and the ability to align reads with large (up to 50 bp) indels. This improvement in efficiency is particularly important in the analysis of future sequencing data where the number of reads approaches many billions. Furthermore, SeqAlto uses less than 8 GB of memory to align against the human genome. SeqAlto is benchmarked against several existing tools with both real and simulated data. AVAILABILITY: Linux and Mac OS X binaries free for academic use are available at http://www.stanford.edu/group/wonglab/seqalto CONTACT: whwong@stanford.edu.