Functional Significance of Vitamin D Receptor FokI Polymorphism in Human Breast Cancer Cells

The FokI vitamin D receptor (VDR) polymorphism results in different translation initiation sites on VDR. In the VDRff variant, initiation of translation occurs at the first ATG site, giving rise to a full length VDR protein of 427 amino acids. Conversely, in the VDRFF variant, translation begins at the second ATG site, resulting in a truncated protein with three less amino acids. Epidemiological studies have paradoxically implicated this polymorphism with increased breast cancer risk. 1α,25 (OH)(2)D(3), the active metabolite of vitamin D, is known to inhibit cell proliferation, induce apoptosis and potentiate differentiation in human breast cancer cells. It is well documented that 1α,25 (OH)(2)D(3) downregulates estrogen receptor α expression and inhibits estrogen mediated signaling in these cells. The functional significance of the VDR FokI polymorphism in vitamin D action is undefined.To elucidate the functional role of FokI polymorphism in breast cancer, MCF-7-Vector, MCF-7-VDRff and MCF-7-VDRFF stable cell lines were established from parental MCF-7 cells as single-cell clones. In response to 1α,25 (OH)(2)D(3) treatments, cell growth was inhibited by 60% in VDRFF cells compared to 28% in VDRff cells. The induction of the vitamin D target gene CYP24A1 mRNA was 1.8 fold higher in VDRFF cells than in VDRff cells. Estrogen receptor-α protein expression was downregulated by 62% in VDRFF cells compared to 25% in VDRff cells. VDR protein stability was greater in MCF-7-VDRFF cells in the presence of cycloheximide. PCR array analyses of VDRff and VDRFF cells revealed increased basal expression levels of pro-inflammatory genes Cyclooxygenase-2, Interleukin-8 and Chemokine (C-C Motif) Ligand 2 in MCF-7-VDRff cells by 14, 52.7 and 5 fold, respectively.These results suggest that a VDRff genotype may play a role in amplifying aggressive breast cancer, paving the way for understanding why some breast cancer cells respond inefficiently to vitamin D treatment

Enisamium Inhibits SARS-CoV-2 RNA Synthesis.

Pandemic SARS-CoV-2 causes a mild to severe respiratory disease called coronavirus disease 2019 (COVID-19). While control of the SARS-CoV-2 spread partly depends on vaccine-induced or naturally acquired protective herd immunity, antiviral strategies are still needed to manage COVID-19. Enisamium is an inhibitor of influenza A and B viruses in cell culture and clinically approved in countries of the Commonwealth of Independent States. In vitro, enisamium acts through metabolite VR17-04 and inhibits the activity of the influenza A virus RNA polymerase. Here we show that enisamium can inhibit coronavirus infections in NHBE and Caco-2 cells, and the activity of the SARS-CoV-2 RNA polymerase in vitro. Docking and molecular dynamics simulations provide insight into the mechanism of action and indicate that enisamium metabolite VR17-04 prevents GTP and UTP incorporation. Overall, these results suggest that enisamium is an inhibitor of SARS-CoV-2 RNA synthesis in vitro

Reproducing Whisper-Style Training Using an Open-Source Toolkit and Publicly Available Data

Pre-training speech models on large volumes of data has achieved remarkable success. OpenAI Whisper is a multilingual multitask model trained on 680k hours of supervised speech data. It generalizes well to various speech recognition and translation benchmarks even in a zero-shot setup. However, the full pipeline for developing such models (from data collection to training) is not publicly accessible, which makes it difficult for researchers to further improve its performance and address training-related issues such as efficiency, robustness, fairness, and bias. This work presents an Open Whisper-style Speech Model (OWSM), which reproduces Whisper-style training using an open-source toolkit and publicly available data. OWSM even supports more translation directions and can be more efficient to train. We will publicly release all scripts used for data preparation, training, inference, and scoring as well as pre-trained models and training logs to promote open science.Comment: Accepted at ASRU 202

Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification

Failure to recognize samples from the classes unseen during training is a major limit of artificial intelligence (AI) in real-world implementation of retinal anomaly classification. To resolve this obstacle, we propose an uncertainty-inspired open-set (UIOS) model which was trained with fundus images of 9 common retinal conditions. Besides the probability of each category, UIOS also calculates an uncertainty score to express its confidence. Our UIOS model with thresholding strategy achieved an F1 score of 99.55%, 97.01% and 91.91% for the internal testing set, external testing set and non-typical testing set, respectively, compared to the F1 score of 92.20%, 80.69% and 64.74% by the standard AI model. Furthermore, UIOS correctly predicted high uncertainty scores, which prompted the need for a manual check, in the datasets of rare retinal diseases, low-quality fundus images, and non-fundus images. This work provides a robust method for real-world screening of retinal anomalies

Application of the Pipeline Embolization Device for Giant Vertebrobasilar Dissecting Aneurysms in Pediatric Patients

Objective: To evaluate the feasibility and effectiveness of the pipeline embolization device (PED) for the treatment of pediatric giant vertebrobasilar dissecting aneurysms (VBDAs).Methods: We retrospectively reviewed our institutional clinical database and identified 2,706 patients who presented with a diagnosis of intracranial aneurysms from January 2016 to June 2018. Among this group, 153 patients were diagnosed with VBDAs, and 54 of these patients underwent PED therapy. The PED technique was used in four patients who were 18 years old or younger at the time of presentation (two males, two females; mean age 9.25 years; age range 8–11 years).Results: All four included pediatric patients were managed with the PED. One patient (25%) was treated with the PED alone, while three (75%) were treated with the PED and coils. One patient died from brainstem infarction or compression of the brainstem, while follow-up of the other three patients revealed favorable outcomes. The mass effect was reduced in cases 1, 2, and 3 on follow-up MRI performed 6 months after the PED procedure.Conclusions: PEDs could be feasible in the treatment of pediatric giant VBDAs. However, the safety and efficacy of this method have not been clarified in this special pediatric population, and long-term follow-up is still necessary

Still Life

Drawing II Project, dimensions: 30 x 22.5https://spark.parkland.edu/art_collection_2017/1004/thumbnail.jp

Still Life

Drawing II Project, dimensions: 30 x 22.5https://spark.parkland.edu/art_collection_2017/1004/thumbnail.jp

An efficient quantum circuit implementation of Shor’s algorithm for GPU accelerated simulation

In this study, we introduce a novel implementation of Shor’s algorithm specifically designed for the Graphics Processing Unit (GPU) acceleration framework. Our focus lies on achieving efficient execution of the modular multiplication circuit through GPU simulation. To seamlessly integrate our design into the PyQPanda library framework, we made necessary modifications, making a deliberate trade-off by sacrificing a small number of quantum resources to leverage the advantages of GPU acceleration. Subsequently, we conducted simulations and rigorously validated the functionality of our circuit using the PyQPanda library, resulting in a significant speedup compared to a central processing unit-only mode