An 83-year-old male presented to our Digestive System Department with a 5-day history of severe gastrointestinal (GI) bleeding and a 14-year history of idiopathic thrombocytopenic purpura (ITP) with low platelet levels. Colonoscopy revealed extensive telangiectasias throughout the colon, particularly in the transverse and ascending segments. Standard treatment with proton-pump inhibitors and somatostatin proved ineffective.
View Article and Find Full Text PDFBackground: Heart rate, diastolic pressure, and pulse pressure are key modifiable factors influencing heart failure prognosis. While heart failure with mildly reduced ejection fraction (HFmrEF) is a distinct subgroup of heart failure, the prognostic impact of these hemodynamic parameters in this population remains unclear, necessitating focused investigation. This study aims to elucidate their effects on HFmrEF patient outcomes.
View Article and Find Full Text PDFOxaliplatin-induced hypersensitivity reactions (HSRs) are commonly encountered in first-line therapies for various malignancies. Recent research indicates that these reactions can include cytokine release reactions (CRRs), which are characterized by a marked increase in interleukin-6 (IL-6) levels, sometimes rising as much as 40-fold. Standard management strategies for HSRs typically involve desensitization protocols and routine treatments.
View Article and Find Full Text PDFJ Exp Clin Cancer Res
January 2025
Background: Colorectal cancer (CRC) has high incidence and mortality rates, with severe prognoses during invasion and metastasis stages. Despite advancements in diagnostic and therapeutic technologies, the impact of the tumour microenvironment, particularly extracellular matrix (ECM) stiffness, on CRC progression and metastasis is not fully understood.
Methods: This study included 107 CRC patients.
Analog In-memory Computing (IMC) has demonstrated energy-efficient and low latency implementation of convolution and fully-connected layers in deep neural networks (DNN) by using physics for computing in parallel resistive memory arrays. However, recurrent neural networks (RNN) that are widely used for speech-recognition and natural language processing have tasted limited success with this approach. This can be attributed to the significant time and energy penalties incurred in implementing nonlinear activation functions that are abundant in such models.
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