Napiercastaneda4643

Signal transducer and activator of transcription 3 (STAT3) and nuclear factor-κB (NF-κB) are two representative transcription factors that play a critical role in inflammation-associated tumorigenesis through multi-level cooperation. Unlike other types of tumors, breast carcinomas have shown a significant dependency on the non-classical NF-κB pathway as well as the classical one. The α subunit of the inhibitor of the κB kinase (IKK) complex, IKKα, is involved in both classical and non-classical activation of NF-κB. Although the cross-talk between STAT3 and NF-κB has been suggested in several studies, the interplay between STAT3 and the regulators of NF-κB including IKKα has not been fully clarified yet. In this study, we observed overexpression and co-localization of IKKα and STAT3 in human breast cancer tissues as well as in H-Ras transformed human breast epithelial (H-Ras MCF-10A) and breast cancer (MDA-MB-231) cells. By utilizing small interfering RNA (siRNA) technology, we were able to demonstrate that STAT3 up-regulated IKKα, but not IKKβ or IKKγ, in these cells. This was attributable to direct binding to and subsequent stabilization of IKKα protein by blocking the ubiquitin-proteasome system. Notably, we identified the lysine 44 residue of IKKα as a putative binding site for STAT3. Moreover, siRNA knockdown of IKKα attenuated viability, anchorage-independent growth and migratory capabilities of H-Ras MCF-10A cells. Taken together, these findings propose a novel mechanism responsible for NF-κB activation by STAT3 through stabilization of IKKα, which contributes to breast cancer promotion and progression. Thus, breaking the STAT3-IKKα alliance can be an alternative therapeutic strategy for the treatment of breast cancer.One of the major challenges in the treatment of cancer are differential responses of patients to existing standard of care anti-cancer drugs. These differential responses may, in part, be due to a diverse range of genomic, epigenomic, proteomic, and metabolic alterations among individuals suffering from the same type of cancer. Precision medicine is an emerging approach in cancer therapeutics that takes into account specific molecular alterations, environmental factors as well as lifestyle of individual patients. This approach allows clinicians and researchers to select or predict treatments that would most likely benefit the patient based on their individual tumor characteristics. One class of precision medicine tools are predictive, in vitro drug-response assays designed to test the sensitivity of patient tumor cells to existing or novel therapies. These assays have the potential to rapidly identify the most effective treatments for cancer patients and thus hold great promise in the field of precision medicine. compound991 In this review, we have highlighted several drug-response assays developed in ovarian cancer and discussed the current challenges and future prospects of these assays in the clinical management of this disease.In March 2020, the World Health Organization (WHO) declared the outbreak of Coronavirus disease 2019 (COVID-19) as a pandemic, which affected all countries worldwide. During the outbreak, public sentiment analyses contributed valuable information toward making appropriate public health responses. This study aims to develop a model that predicts an individual's awareness of the precautionary procedures in five main regions in Saudi Arabia. In this study, a dataset of Arabic COVID-19 related tweets was collected, which fell in the period of the curfew. The dataset was processed, based on several machine learning predictive models Support Vector Machine (SVM), K-nearest neighbors (KNN), and Naïve Bayes (NB), along with the N-gram feature extraction technique. The results show that applying the SVM classifier along with bigram in Term Frequency-Inverse Document Frequency (TF-IDF) outperformed other models with an accuracy of 85%. The results of awareness prediction showed that the south region observed the highest level of awareness towards COVID-19 containment measures, whereas the middle region was the least. The proposed model can support the medical sectors and decision-makers to decide the appropriate procedures for each region based on their attitudes towards the pandemic.

OS2966 is a first-in-class, humanized and de-immunized monoclonal antibody which targets the adhesion receptor subunit, CD29/β1 integrin. CD29 expression is highly upregulated in glioblastoma and has been shown to drive tumor progression, invasion, and resistance to multiple modalities of therapy. Here, we present a novel Phase I clinical trial design addressing several factors plaguing effective treatment of high-grade gliomas (HGG).

This 2-part, ascending-dose, Phase I clinical trial will enroll patients with recurrent/progressive HGG requiring a clinically indicated resection. In Study Part 1, patients will undergo stereotactic tumor biopsy followed by placement of a purpose-built catheter which will be used for the intratumoral, convection-enhanced delivery (CED) of OS2966. Gadolinium contrast will be added to OS2966 before each infusion, enabling the real-time visualization of therapeutic distribution via MRI. Subsequently, patients will undergo their clinically indicated tumor resection followed by CED of OS2966 to the surrounding tumor-infiltrated brain. Matched pre- and post-infusion tumor specimens will be utilized for biomarker development and validation of target engagement by receptor occupancy. Dose escalation will be achieved using a unique concentration-based accelerated titration design.

The present study design leverages multiple innovations including (1) the latest CED technology, (2) 2-part design including neoadjuvant intratumoral administration, (3) a first-in-class investigational therapeutic, and (4) concentration-based dosing.

A U.S. Food and Drug Administration (FDA) Investigational New Drug application (IND) for the above protocol is now active.

A U.S. Food and Drug Administration (FDA) Investigational New Drug application (IND) for the above protocol is now active.Wheat head detection can estimate various wheat traits, such as density, health, and the presence of wheat head. However, traditional detection methods have a huge array of problems, including low efficiency, strong subjectivity, and poor accuracy. In this paper, a method of wheat-head detection based on a deep neural network is proposed to enhance the speed and accuracy of detection. The YOLOv4 is taken as the basic network. The backbone part in the basic network is enhanced by adding dual spatial pyramid pooling (SPP) networks to improve the ability of feature learning and increase the receptive field of the convolutional network. Multilevel features are obtained by a multipath neck part using a top-down to bottom-up strategy. Finally, YOLOv3's head structures are used to predict the boxes of wheat heads. For training images, some data augmentation technologies are used. The experimental results demonstrate that the proposed method has a significant advantage in accuracy and speed. The mean average precision of our method is 94.