Goal: Current methodologies for assessing cerebral compliance using pressure sensor technologies are prone to errors and issues with inter- and intra-observer consistency. RAP, a metric for measuring intracranial compensatory reserve (and therefore compliance), holds promise. It is derived using the moving correlation between intracranial pressure (ICP) and the pulse amplitude of ICP (AMP).
View Article and Find Full Text PDFEndosulfan (ESN) is an organophosphate insecticidal agent that is documented to induce various organ toxicities. Genistein (GEN) is a plant derived polyphenolic compound with excellent biological as well as pharmacological properties. This research was planned to assess the palliative potential of GEN to avert ENS prompted colonic toxicity.
View Article and Find Full Text PDFFerritin nanocarriers, which can penetrate the blood-brain barrier (BBB), have gained significant research interest for the diagnosis and treatment of central nervous system (CNS) diseases, including gliomas, Alzheimer's disease, and brain metastases. In recent years, ferritin has been proved as a candidate to cross the BBB using receptor-mediated transcytosis (RMT) mechanism through transferrin receptor 1 (TfR1) which is overexpressed in the cells of the BBB. Various types of cargo molecules, including therapeutics, imaging agents, nucleic acids, and metal nanoparticles, have been incorporated into ferritin nanocages for the diagnosis and treatment of CNS diseases.
View Article and Find Full Text PDFBackground: Appendicectomy is a common procedure in children. Regional anaesthesia helps reduce requirements for opioids and hospital stay and enhances recovery. Laparoscopic-assisted Transversus Abdominus Plane block (L-TAP) was shown to be efficient and potentially superior to port site infiltration (PSI); however, this was not previously studied in paediatric appendicitis.
View Article and Find Full Text PDFBackground: Gastrointestinal (GI) diseases pose significant challenges for healthcare systems, largely due to the complexities involved in their detection and treatment. Despite the advancements in deep neural networks, their high computational demands hinder their practical use in clinical environments.
Objective: This study aims to address the computational inefficiencies of deep neural networks by proposing a lightweight model that integrates model compression techniques, ConvLSTM layers, and ConvNext Blocks, all optimized through Knowledge Distillation (KD).