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Aberrant nuclear protein transport, often observed in cancer, causes mislocalization dependent inactivation of critical cellular proteins. Earlier we showed that over-expression of the nuclear export protein exportin 1 (XPO1) is linked to higher grade and Gleason score in metastatic castration resistant prostate cancer (mCRPC). We also showed that the selective inhibitor of nuclear export (SINE) compound selinexor and the second generation SINE eltanexor (KPT-8602) could suppress mCRPC growth, reduce androgen receptor (AR), and re-sensitize to androgen deprivation therapy. Here we evaluated the combination of KPT-8602 with three different PARP inhibitors (PARPi) namely olaparib, veliparib and rucaparib in a mCRPC model. Growth inhibition was determined by MTT assay. Drug synergy analysis was done and the isobolograms were generated from Calcusyn 2.1 software (Biosoft, Cambridge, UK). Other techniques include: colony formation assay, apoptosis determination by flow cytometry (annexin V-propidium iodide), real time RT-qPCR (SYBR green I), western blotting, immunofluorescence etc. For the determination of band density, NIH ImageJ 1.5Oi software was utilized. KPT-8602 synergized with all three studied PARPi (CI Taken together, this study revealed the therapeutic potential of a novel combination including second generation SINE compound KPT-8602 and PARP inhibitors for the growth inhibition of mCRPC cells. Pre-clinical xenograft studies testing the efficacy of SINE-PARPi as well as a phase II clinical study combining KPT-8602-PARPi is planned. Citation Format: Md. Hafiz Uddin, Yiwei Li, Amro Aboukameel, Husain Y. Khan, Vijendra Singh, Shriya Reddy, Suresh Kumar Balasubramanian, Yosef Landesman, Trinayan Kashyap, Elisabeth I. Heath, Asfar S. Azmi. Nuclear export inhibitor KPT-8602 synergize with PARP inhibitors in castration resistant metastatic prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1091.

This paper shows a charging scheme for electric vehicles that would be faster, effective, and reliable than the existing system. This work proposed the method of changing the current parameters at discrete intervals dependent on the battery’s using the five-level battery charging method. To validate the results and design technique, the proposed work is executed by Matrix Laboratory, the obtained output results were extremely gratifying. Further motivated to build a practical working model and verify it with the obtained software values. Both the results were convincing and confirmed to be much faster than the existing schemes.

Gliomas constitute about 80% of brain tumors and have a meager two-year survival rate. The treatment options available are very few because of poor prognosis and a lack of targeted nanodelivery systems that can cross the blood-brain barrier (BBB) and the blood-tumor barrier. This short review attempts to clarify the challenges for delivery systems designed to cross the BBB, and provides a brief description of the different types of targeted nanodelivery system that have shown potential for success in delivering drugs to the brain. Further, this review describes the most recent studies that have developed nanoparticles for brain delivery in the past five years. We also provide an insight into the most recent clinical trials designed to assess the efficacy of these nanodelivery systems for glioma.

In today’s digital world of information, a fact verification system to disprove assertions made in speech, print media or online content is the need of the hour. We propose a system which would verify a claim against a source and classify the claim to be true, false, out-of-context or an inappropriate claim with respect to the textual source provided to the system. A true label is used if the claim is true, false if it is false, if the claim has no relation with the source then it is classified as out-of-context and if the claim cannot be verified at all then it is classified as inappropriate. This would help us to verify a claim or a fact as well as know about the source or our knowledge base against which we are trying to verify our facts. We used a two-step approach to achieve our goal. At first, we retrieved evidence related to the claims from the textual source using the Term Frequency-Inverse Document Frequency(TF-IDF) vectors. Later we classified the claim-evidence pairs as true, false, inappropriate and out of context using a modified version of textual entailment module. Textual entailment module calculates the probability of each sentence supporting the claim, contradicting the claim or not providing any relevant information using Bi-LSTM network to assess the veracity of the claim. The accuracy of the best performing system is 64.49%

Melanoma is the deadliest form of skin cancer and remains a diagnostic challenge in the dermatology clinic. Several non-invasive imaging techniques have been developed to identify melanoma. The signal source in each of these modalities is based on the alteration of physical characteristics of the tissue from healthy/benign to melanoma. However, as these characteristics are not always sufficiently specific, the current imaging techniques are not adequate for use in the clinical setting. A more robust way of melanoma diagnosis is to “stain” or selectively target the suspect tissue with a melanoma biomarker attached to a contrast enhancer of one imaging modality. Here, we categorize and review known melanoma diagnostic biomarkers with the goal of guiding skin imaging experts to design an appropriate diagnostic tool for differentiating between melanoma and benign lesions with a high specificity and sensitivity.

Voice-based smart home applications have taken a rapid growth in the past few years. Voice recognition is the ability of the system to receive, interpret, analyze and carry out the spoken commands. This paper proposes a ROS based voice - controlled robot, which runs using a differential drive mechanism and takes the voice commands from the user and moves a wheeled robot accordingly. The system is based on a statistical model such as the Hidden Markov Model (HMM). The HMM approach to speech enhancement relies on the Viterbi Algorithm.