CAR T-cell therapies are an increasingly popular treatment modality in relapsed/refractory B-cell malignancies, but responses are highly variable. This review presents results from recent studies investigating CD19-directed CAR T-cell therapies in order to provide an overview of the current therapeutic landscape and identify directions for future research. Relevant studies published between 2015 and 2020 were identified using PubMed and the Cochrane Central Register of Controlled Trials. Database searches identified 2,281 studies, and two additional records were included in the final analysis. Results were too heterogeneous to determine publication bias. This analysis included 21 studies, in which 1245 patients were infused with a CAR T-cell product. The most common diagnosis was non-Hodgkin’s lymphoma (62%, n=762); 576 patients (56%) recorded a complete response. Cytokine release syndrome was the most commonly reported acute toxicity, occurring in 70% (n=853) of patients. Neurological toxicity occurred in 42% (n=477) of patients. Severe CRS and neurological toxicity, defined as grade ≥3, were recorded in 18% (n=217) and 19% (n=204) of patients, respectively. Aggregation of results was complicated by highly heterogeneous outcomes, likely due to inconsistent standards of reporting. To allow for more reliable comparison of CAR T-cell therapies, it is suggested that future investigators implement universal standards regarding dosage, grading of toxicity and assessment of biomarkers.

Convolutional Neural Networks has been around for a few years, and it’s one of the most used neural networks to training data, from simple to complicated one. While Backpropagation (or backward propagation of errors) algorithm has experienced a recent resurgence given the widespread adoption of deep neural networks for image recognition and speech recognition, as it is considered an efficient algorithm, and modern implementations take advantage of specialized GPU to further improve performance. So which one is better to use? In order to find out, we used convolutional neural network and backpropagation (regular) algorithm to train the computer to classify the 70000 images of the Modified National Institute of Standards and Technology (MNIST) dataset. Throughout the test data, we evaluate the efficiency of both neural networks throughout individual variables to find out individual influences of each variables before combining all together.

This paper examines the etiology of dissociative identity disorder. Dissociative identity disorder (DID) is a mental disorder defined by the presence of more than one personality state within an individual. Although there is consensus among experts that DID follows from severe childhood trauma, there is currently disagreement about the exact mechanisms underlying DID symptoms. Some researchers argue that DID arises directly as a result of trauma. These researchers contend that victims dissociate as a way of protecting their childhood self, who is unable to cope with sexual or physical violence. Other researchers argue that DID results as an artifact of treatment for DID. This paper reviews evidence for each of these theories and concludes with recommendations for treatment.

Upper airway diseases are one of the most frequent ailments in and outside of hospitals, as the region is exposed to environmental stressors and essential for life. Some diseases, such as subglottic stenosis and laryngeal cancer, cause the constriction of the airway necessitating stenting. One concern with medical airway stent usage is the attachment of bacterial biofilms on stent surfaces. After stent implantation, biofilm communities trigger inflammatory responses and other complications such as tissue granulation within patients. Researchers have developed drug-eluting stents either coated with anti-biofilm chemicals or incorporated within the stent for sustained release, to alleviate infection. This review examines published literature on the recent development of drug-eluting biomaterials to disrupt biofilm formation in upper airway applications, by assessing outcomes in both in vitro and in vivo models. Promising results have been shown in nanoparticle-mediated drug-delivery pathways; specifically, inorganic metallic nanoparticles such as silver nanoparticles that display both intrinsic and bio-responsive anti-biofilm properties after applying them to drug-eluting stents. The review also discusses the effectiveness of synthetic polymers such as PLGA, small molecules such as Sirolimus, and natural polymers such as chitosan in the biofabrication of stents to reduce biofilms. These molecules can act as potential therapeutic strategies and improve the efficiency of these necessary medical devices. Overall, drug-eluting airway stents are advancing rapidly in efficacy, complexity, and success to prevent bacterial infection, reduce associated comorbidities, and improve patient outcomes.

Looking up into the night sky, we see thousands of stars, with billions more in our galaxy and an untold amount more in our universe. But while they may seem to be static unchanging points of light, scientists know that they are not. We know that stars change, they are born, they mature, they grow old, and they eventually die. This Journey, from the birth to the death of a star is called stellar evolution and it is a very important part of astronomy. Understanding the process helps to give us the ability to answer important questions, such as understanding the synthesis of heavy elements through star death, and the creation of new stars, and galaxies.

The true nature of dark matter is an extremely important and fundamental problem in the study of astrophysics, particle physics, cosmology and many other areas within the study of physics. This paper presents experimental evidence for the existence of dark matter through discussing the experimental results of mass profiling a galaxy and gravitational lensing. The fundamental properties of dark matter are then discussed, and evidence for these properties is presented. This allows further discussion of one of the most promising models of dark matter - the axion. The purpose of this paper is to present the evidence for the axion model, describe the nature of the theoretical axion particle, and to highlight the effects this model would have on other theories in physics such as solving the Strong CP Problem in the theory of quantum chromodynamics.