TROPION-Breast02: Datopotamab deruxtecan for locally recurrent inoperable or metastatic triple-negative breast cancer.

Despite recent treatment advances, the prognosis for patients with locally recurrent inoperable or metastatic triple-negative breast cancer (TNBC) remains poor. The antibody-drug conjugate datopotamab deruxtecan (Dato-DXd) is composed of a humanized anti-TROP2 IgG1 monoclonal antibody linked to a topoisomerase I inhibitor payload via a stable, cleavable linker. The phase III TROPION-Breast02 trial in patients previously untreated for locally recurrent inoperable or metastatic TNBC, who are not candidates for PD-1/PD-L1 inhibitors is evaluating efficacy and safety of Dato-DXd versus investigator's choice of chemotherapy (ICC).

A review and comparative study of cancer detection using machine learning: SBERT and SimCSE application.

Background: Using visual, biological, and electronic health records data as the sole input source, pretrained convolutional neural networks and conventional machine learning methods have been heavily employed for the identification of various malignancies. Initially, a series of preprocessing steps and image segmentation steps are performed to extract region of interest features from noisy features. Then, the extracted features are applied to several machine learning and deep learning methods for the detection of cancer.

Discriminatory Gleason grade group signatures of prostate cancer: An application of machine learning methods.

One of the most precise methods to detect prostate cancer is by evaluation of a stained biopsy by a pathologist under a microscope. Regions of the tissue are assessed and graded according to the observed histological pattern. However, this is not only laborious, but also relies on the experience of the pathologist and tends to suffer from the lack of reproducibility of biopsy outcomes across pathologists.

First-line drug resistance profiling of : a machine learning approach.

The persistence and emergence of new multi-drug resistant Mycobacterium tuberculosis (M. tb) strains continues to advance the devastating tuberculosis (TB) epidemic. Robust systems are needed to accurately and rapidly perform drug-resistance profiling, and machine learning (ML) methods combined with genomic sequence data may provide novel insights into drug-resistance mechanisms.

Classification of COVID-19 and Other Pathogenic Sequences: A Dinucleotide Frequency and Machine Learning Approach.

The world is grappling with the COVID-19 pandemic caused by the 2019 novel SARS-CoV-2. To better understand this novel virus and its relationship with other pathogens, new methods for analyzing the genome are required. In this study, intrinsic dinucleotide genomic signatures were analyzed for whole genome sequence data of eight pathogenic species, including SARS-CoV-2.

A meta-analysis of public microarray data identifies gene regulatory pathways deregulated in peripheral blood mononuclear cells from individuals with Systemic Lupus Erythematosus compared to those without.

Background: Systemic Lupus Erythematosus (SLE) is a complex, multi-systemic, autoimmune disease for which the underlying aetiological mechanisms are poorly understood. The genetic and molecular processes underlying lupus have been extensively investigated using a variety of -omics approaches, including genome-wide association studies, candidate gene studies and microarray experiments of differential gene expression in lupus samples compared to controls.

Methods: This study analyses a combination of existing microarray data sets to identify differentially regulated genetic pathways that are dysregulated in human peripheral blood mononuclear cells from SLE patients compared to unaffected controls.

Identification and characterization of microRNAs expressed in the African malaria vector Anopheles funestus life stages using high throughput sequencing.

Background: Over the past several years, thousands of microRNAs (miRNAs) have been identified in the genomes of various insects through cloning and sequencing or even by computational prediction. However, the number of miRNAs identified in anopheline species is low and little is known about their role. The mosquito Anopheles funestus is one of the dominant malaria vectors in Africa, which infects and kills millions of people every year.

Baseline Predictors of Mortality among Predominantly Rural-Dwelling End-Stage Renal Disease Patients on Chronic Dialysis Therapies in Limpopo, South Africa.

Background: Dialysis therapy for end-stage renal disease (ESRD) continues to be the readily available renal replacement option in developing countries. While the impact of rural/remote dwelling on mortality among dialysis patients in developed countries is known, it remains to be defined in sub-Saharan Africa.

Methods: A single-center database of end-stage renal disease patients on chronic dialysis therapies treated between 2007 and 2014 at the Polokwane Kidney and Dialysis Centre (PKDC) of the Pietersburg Provincial Hospital, Limpopo South Africa, was retrospectively reviewed.

Lupus nephritis is associated with poor pregnancy outcomes in pregnant SLE patients in Cape Town: a retrospective analysis.

Introduction: Systemic lupus erythematosus (SLE) is a multi-system auto-immune disease common in females of child-bearing age. The effect of pregnancy on SLE and vice versa have not been well characterised in Africans. The aim of this study is to describe the pregnancy outcomes of patients with SLE presenting to the maternity department of Groote Schuur Hospital, Cape Town.

Continuous ambulatory peritoneal dialysis in Limpopo province, South Africa: predictors of patient and technique survival.

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Introduction And Aim: Continuous ambulatory peritoneal dialysis (CAPD) is not a frequently used modality of dialysis in many parts of Africa due to several socio-economic factors. Available studies from Africa have shown a strong association between outcome and socio-demographic variables. We sought to assess the outcome of patients treated with CAPD in Limpopo, South Africa.