Buttock pain in lumbar disc herniation: clinical characteristics, risk factors, and surgical outcomes.

Objective: Buttock pain is a common symptom in patients with lumbar degenerative disorders. While the nature of buttock pain remains unclear, clinicians consider it as either nociceptive back pain or radicular leg pain. Some have proposed that buttock pain may be a mixed symptom of radicular pain and referred pain, although buttock pain in spine practice is less studied.

Prognostic impact of initial platelet count and post-induction platelet recovery in elderly AML patients: associated with circulating cytokines.

Objective: Acute myeloid leukemia (AML) is a highly heterogeneous hematologic malignancy, with various clinical features influencing its prognosis. The aim of this study to evaluate the impact of platelet count at diagnosis and platelet recovery after induction chemotherapy on the survival outcomes of elderly AML patients.

Methods: A total of 109 elderly patients with AML who were treated in our center between 2017 and 2023 were evaluated.

Blockade of glucagon receptor induces α-cell hypersecretion by hyperaminoacidemia in mice.

Blockade of the glucagon receptor (GCGR) has been shown to improve glycemic control. However, this therapeutic approach also brings side effects, such as α-cell hyperplasia and hyperglucagonemia, and the mechanisms underlying these side effects remain elusive. Here, we conduct single-cell transcriptomic sequencing of islets from male GCGR knockout (GCGR-KO) mice.

Spatial 3D genome organization reveals intratumor heterogeneity in primary glioblastoma samples.

Glioblastoma (GBM) is the most prevalent malignant brain tumor with poor prognosis. Although chromatin intratumoral heterogeneity is a characteristic feature of GBM, most current studies are conducted at a single tumor site. To investigate the GBM-specific 3D genome organization and its heterogeneity, we conducted Hi-C experiments in 21 GBM samples from nine patients, along with three normal brain samples.

Development and Validation of Machine Learning Models for Adverse Events after Cardiac Surgery.

Importance: Early recognition of adverse events after cardiac surgery is vital for treatment. However, the widely used Society of Thoracic Surgery (STS) risk model has modest performance in predicting adverse events and only applies <80% of cardiac surgeries.

Objective: To develop and validate machine learning (ML) models for predicting outcomes after cardiac surgery.