Midterm Functional Outcomes After Retear of an Arthroscopic Rotator Cuff Repair: A Propensity Score-Matched Comparative Study.

Background: Studies are still limited on the isolated effect of retear after arthroscopic rotator cuff repair (ARCR) on functional outcomes after the midterm period.

Purpose: To assess the effect of retear at midterm follow-up after ARCR and to identify factors associated with the need for revision surgery.

Study Design: Cohort study; Level of evidence, 3.

Cyclin E1/CDK2 activation defines a key vulnerability to WEE1 kinase inhibition in gynecological cancers.

Upregulation of Cyclin E1 and subsequent activation of CDK2 accelerates cell cycle progression from G1 to S phase and is a common oncogenic driver in gynecological malignancies. WEE1 kinase counteracts the effects of Cyclin E1/CDK2 activation by regulating multiple cell cycle checkpoints. Here we characterized the relationship between Cyclin E1/CDK2 activation and sensitivity to the selective WEE1 inhibitor azenosertib.

A disintegrin and metallopeptidase domain (ADAM) 12, ADAM 17 mRNA and ADAM10 protein hold potential as biomarkers for detection of early gastric cancer.

No biomarker can effectively screen for early gastric cancer (EGC). Players in the A disintegrin and metalloproteinase (ADAM)-natural killer group 2 member D (NKG2D) receptor axis may have a role for that. As a proof-of-concept pilot study, the expression of ADAM8, ADAM9, ADAM10, ADAM12, ADAM17, and major histocompatibility complex (MHC) class I chain-related sequence A (MICA), a ligand for NKG2D, in gastric cancer was investigated in silico using The Cancer Genome Atlas (TCGA) database.

Susceptibility map-weighted MRI can distinguish tremor-dominant Parkinson's disease from essential tremor.

Distinguishing between Parkinson's disease (PD) and essential tremor (ET) can be challenging sometimes. Although positron emission tomography can confirm PD diagnosis, its application is limited by high cost and exposure to radioactive isotopes. Patients with PD exhibit loss of the dorsal nigral hyperintensity on brain magnetic resonance imaging (MRI).

Predicting the likelihood of readmission in patients with ischemic stroke: An explainable machine learning approach using common data model data.

Background: Ischemic stroke affects 15 million people worldwide, causing five million deaths annually. Despite declining mortality rates, stroke incidence and readmission risks remain high, highlighting the need for preventing readmission to improve the quality of life of survivors. This study developed a machine-learning model to predict 90-day stroke readmission using electronic medical records converted to the common data model (CDM) from the Regional Accountable Care Hospital in Gangwon state in South Korea.