Effects of newer anti-hyperglycemic agents on cardiovascular outcomes in older adults: Systematic review and meta-analysis.

Aim: To demonstrate cardiovascular safety of dipeptidyl peptidase-4 inhibitors (DPP-4i), glucagon-like peptide-1 receptor agonists (GLP-1RA), and sodium/glucose cotransporter 2 inhibitors (SGLT-2i) across age-groups.

Methods: PubMed, Embase and Cochrane were searched for cardiovascular outcome trials (CVOTs) testing newer agents until August 31, 2022 (PROSPERO ID CRD42021260167). Studies with ≥1000 T2D participants enrolled for ≥12 months were included.

Utilization of anonymization techniques to create an external control arm for clinical trial data.

Background: Subject-level real-world data (RWD) collected during daily healthcare practices are increasingly used in medical research to assess questions that cannot be addressed in the context of a randomized controlled trial (RCT). A novel application of RWD arises from the need to create external control arms (ECAs) for single-arm RCTs. In the analysis of ECAs against RCT data, there is an evident need to manage and analyze RCT data and RWD in the same technical environment.

Classification and Locoregional Treatment of Rectal Neuroendocrine Tumors.

A 43-year-old male presented to his primary care physician's office with a complaint of painless rectal bleeding with a concomitant weight loss of 10-15 pounds and intermittent abdominal pain. Endoscopic evaluation was remarkable for a 5 mm rectal polyp roughly 10 cm from the anal verge. Resection was performed and the pathology was consistent with a low-grade neuroendocrine/carcinoid tumor.

Treatment Outcomes Of Patients With Newly Diagnosed Acute Promyelocytic Leukemia; Experience From A Developing Country.

Background: Acute promyelocytic leukaemia (APL) characterized by t (15;17) leading to formation of fusion protein PML-RARA is an acute leukaemia with highest mortality. A remarkable improvement in the outcomes has been witnessed due to evolution of highly effective targeted therapies replacing the traditional chemotherapy is most patients. However limited data is available regarding treatment outcomes of APL using various novel regimens from developing countries like Pakistan.

Machine learning and feature selection for drug response prediction in precision oncology applications.

In-depth modeling of the complex interplay among multiple omics data measured from cancer cell lines or patient tumors is providing new opportunities toward identification of tailored therapies for individual cancer patients. Supervised machine learning algorithms are increasingly being applied to the omics profiles as they enable integrative analyses among the high-dimensional data sets, as well as personalized predictions of therapy responses using multi-omics panels of response-predictive biomarkers identified through feature selection and cross-validation. However, technical variability and frequent missingness in input "big data" require the application of dedicated data preprocessing pipelines that often lead to some loss of information and compressed view of the biological signal.

Drug Target Commons: A Community Effort to Build a Consensus Knowledge Base for Drug-Target Interactions.

Knowledge of the full target space of bioactive substances, approved and investigational drugs as well as chemical probes, provides important insights into therapeutic potential and possible adverse effects. The existing compound-target bioactivity data resources are often incomparable due to non-standardized and heterogeneous assay types and variability in endpoint measurements. To extract higher value from the existing and future compound target-profiling data, we implemented an open-data web platform, named Drug Target Commons (DTC), which features tools for crowd-sourced compound-target bioactivity data annotation, standardization, curation, and intra-resource integration.

Global proteomics profiling improves drug sensitivity prediction: results from a multi-omics, pan-cancer modeling approach.

Motivation: Proteomics profiling is increasingly being used for molecular stratification of cancer patients and cell-line panels. However, systematic assessment of the predictive power of large-scale proteomic technologies across various drug classes and cancer types is currently lacking. To that end, we carried out the first pan-cancer, multi-omics comparative analysis of the relative performance of two proteomic technologies, targeted reverse phase protein array (RPPA) and global mass spectrometry (MS), in terms of their accuracy for predicting the sensitivity of cancer cells to both cytotoxic chemotherapeutics and molecularly targeted anticancer compounds.