Citations

Abstract Novel treatment strategies are needed for the treatment of Philadelphia chromosome–positive (Ph+) acute lymphoblastic leukemia (ALL) in older patients. This trial evaluated the feasibility and outcomes with the anti-CD19 bispecific T-cell–engaging antibody, blinatumomab, in combination with dasatinib and steroids. Patients 65 years of age or older with Ph+ or Ph-like ALL (with dasatinib-sensitive fusions/mutations) were eligible and could be newly diagnosed or relapsed/refractory. Induction therapy consisted of dasatinib/prednisone. Patients not achieving response by day 56 proceeded to blinatumomab reinduction therapy. Patients achieving response with induction or reinduction therapy proceeded to blinatumomab/dasatinib postremission therapy for 3 cycles followed by dasatinib/prednisone maintenance. All patients received central nervous system prophylaxis with intrathecal methotrexate for a total of 8 doses. Response was assessed at days 28, 56, and 84 and at additional time points based on response parameters. Measurable residual disease was assessed centrally by 8-color flow cytometry at day 28. A total of 24 eligible patients with newly diagnosed Ph+ ALL were enrolled with a median age of 73 years (range, 65-87 years). This combination was safe and feasible. With a median of 2.7 years of follow-up, 3-year overall survival and disease-free survival were 87% (95% confidence interval [CI], 64-96) and 77% (95% CI, 54-90), respectively. Although longer follow-up is needed, these results are encouraging, and future trials are building on this backbone regimen. This trial was registered at www.clinicaltrials.gov as #NCT02143414.

Abstract Objective: Scar tissue is an identified cause for the development of malignant ventricular arrhythmias in patients of myocardial infarction, which ultimately leads to cardiac death, a fatal outcome. We aim to evaluate the left ventricular endocardial Scar tissue pattern using Radon descriptor-based machine learning. We performed automated LV segmentation to find the LV endocardial wall, performed morphological operations, and marked the region of the scar tissue on the endocardial wall of LV. Motivated by a Radon descriptor-based machine learning approach; the patches of 17 patients from CT images of the heart were used and categorized into “endocardial Scar tissue” and “normal tissue” groups. The ten feature vectors are extracted from patches using Radon descriptors and fed into a traditional machine learning model. Results: The decision tree has shown the best performance with 98.07% accuracy. This study is the first attempt to provide a Radon transform-based machine learning method to distinguish patterns between “endocardial Scar tissue” and “normal tissue” groups. Our proposed research method could be potentially used in advanced interventions.

Heart disease has a higher fatality rate than any other disease. Increased Atrial fat on the left atrium has been discovered to cause Atrial Fibrillation (AF) in most patients. AF can put one’s life at risk and eventually lead to death. AF might worsen over time; therefore, it is crucial to have an early diagnosis and treatment. To evaluate the left atrium fat tissue pattern using Radon descriptor-based machine learning. This study developed a bridge between the Radon transform framework and machine learning to distinguish two distinct patterns. Motivated by a Radon descriptor-based machine learning approach, the patches of eight patients from CT images of the heart were used and categorized into “epicardial fat tissue” and “nonfat tissue” groups. The 10 feature vectors are extracted from each big patch using Radon descriptors and then fed into a traditional machine learning model. The results show that the proposed methodology discriminates between fat tissues and nonfat tissues clearly. KNN has shown the best performance with 96.77% specificity, 98.28% sensitivity, and 97.50% accuracy. To our knowledge, this study is the first attempt to provide a Radon transform-based machine learning method to distinguish between fat tissue and nonfat tissue on the left atrium. Our proposed research method could be potentially used in advanced interventions.

Domestic violence is highly prevalent in Australia and has serious and complex impacts. This study aimed to analyse research outputs on domestic violence in Australia from the period of 1984 to 2019. Articles relevant to domestic violence in Australia that met specified inclusion criteria were retrieved using the Scopus database. Bibliometric analysis of the output was conducted to examine trends in publications. A trend of an increase in publications relating to domestic violence in Australia over time was identified, with the majority published in institutions located in densely populated capital cities. Significant diversity was found in the subject matter of highly cited articles, reflecting the far-reaching impacts of domestic violence. The increase in social attention to domestic violence over time was reflected in an increase in publications. Future research would benefit from examining trends in the reporting of domestic violence, and analysing the effectiveness of interventions for perpetrators and victims.

Peripheral T-cell lymphomas (PTCLs) are a heterogeneous group of lymphoproliferative disorders arising from mature T cells, accounting for about 10% of non-Hodgkin lymphomas. PTCL-not otherwise specified is the most common subtype, followed by angioimmunoblastic T-cell lymphoma, anaplastic large cell lymphoma, anaplastic lymphoma kinase–positive, anaplastic large cell lymphoma, anaplastic lymphoma kinase–negative, and enteropathy-associated T-cell lymphoma. This discussion section focuses on the diagnosis and treatment of PTCLs as outlined in the NCCN Guidelines for T-Cell Lymphomas.

Background: The most often diagnosed malignancy in women worldwide is cancer of the cervix. It is also the most prevalent kind of gynecological cancer in women. This cancer originates in the opening of the cervix and spreads through sexual contact. Even though human papillomavirus (HPV) may not cause cancer immediately, it does develop over time as a result of the virus’s lengthy persistence to cause dysplastic changes overtime, particularly in high-risk kinds. The primary objective of this research is to see if miRNAs are dysregulated as a result of treatment resistance in cervical cancer (CC). The aim is to see if these microRNAs may be utilized as biomarkers for detecting chemoresistance in CC, particularly for clinical applications. Methods: The recommended protocol for comprehensive study and meta-analysis (PRISMA-P) standards will be utilized for the analysis and data interpretation. The bibliographic databases will be methodically searched using a combination of search keywords. Based on established inclusion and exclusion criteria, the acquired findings will be reviewed, and data retrieved from the selected scientific papers for systematic review. We will then construct a forest from the pooled Hazard ratio (HR) and 95% C.I. values, data obtained using the random-effects model. Discussion: The focus of this study is to identify the function of miRNAs as a chemoresistance regulator and determine if they have the potential scope to be considered as biomarkers for cervical cancer. Through this systematic review and meta-analysis, the goal is to collect, compare, and analyze the data pertaining to the role of miRNAs in cervical cancer, thereby, enabling us to understand the role they play in chemosensitivity.

miRNAs biomarkers are emerging as an essential part of clinical oncology. Their oncogenic and tumour suppressor properties playing a role in malignancy has generated interest in their potential for use in disease prognosis. While several studies on miRNA have been carried out across the globe, evaluating the clinical implications of miRNAs in cancer diagnosis and prognosis research has currently not been attempted. A study delineating the area of miRNA research, including the topics presently being focused on, the seminal papers in this field, and the direction of research interest, does not exist. This study aims to conduct a large-scale, global data analysis and bibliometric profiling analysis of studies to evaluate the research output of clinical implications of miRNAs in cancer diagnosis and prognosis listed in the SCOPUS database. A systematic search strategy was followed to identify and extract all relevant studies, subsequently analysed to generate a bibliometric map. SPSS software (version 27) was used to calculate bibliometric indicators or parameters for analysis, such as year and country of affiliation with leading authors, journals, and institutions. It is also used to analyse annual research outputs, including total citations and the number of times it has been cited with productive nations and H-index. The number of global research articles retrieved for miRNA-Cancer research over the study period 2003 to 2019 was 18,636. Between 2012 and 2019, the growth rate of global publications is six times (n = 15,959; 90.71 percent articles) that of 2003 to 2011. (2704; 9.29 per cent articles). China published the most publications in the field of miRNA in cancer (n = 7782; 41%), while the United States had the most citations (n = 327,538; 48%) during the time span. Of these journals, Oncotarget has the highest percentage of article publications. The journal Cancer Research had the most citations (n = 41,876), with 6.20 per cent (n = 41,876). This study revealed a wide variety of journals in which miRNA-Cancer research are published; these bibliometric parameters exhibit crucial clinical information on performance assessment of research productivity and quality of research output. Therefore, this study provides a helpful reference for clinical oncologists, cancer scientists, policy decision-makers and clinical data researchers.