Comparative assessment of morphological, cytological, and photosynthetic characteristics of the induced octoploid and its tetraploid counterpart of Celosia argentea L.

Background: Celosia argentea is a widely recognized plant for its ornamental qualities and therapeutic uses in traditional medicine. As demand for such multipurpose plants grows, enhancing its phenotypic and physiological traits could further expand its commercial potential. Polyploidization, particularly through chemical treatments like oryzalin, offers a method to induce genetic variation and potentially improve desirable traits in plants.

Skeletal Stem Cells: A Basis for Orthopaedic Pathology and Tissue Repair.

➢ Skeletal stem cells (SSCs) continually replenish mature cell populations to support skeletal homeostasis.➢ SSCs repopulate by self-renewal, have multilineage potential, and are long-lived in vivo.➢ SSCs express specific combinations of cell surface markers that reflect their lineage identity.

Multiparametric MRI-based Machine Learning Radiomics for Predicting Treatment Response to Transarterial Chemoembolization Combined with Targeted and Immunotherapy in Unresectable Hepatocellular Carcinoma: A Multicenter Study.

Rationale And Objectives: To develop and validate multiple machine learning predictive models incorporating clinical features and pretreatment multiparametric magnetic resonance imaging (MRI) radiomic features for predicting treatment response to transarterial chemoembolization combined with molecular targeted therapy plus immunotherapy in unresectable hepatocellular carcinoma (HCC).

Materials And Methods: This retrospective study involved 276 patients with unresectable HCC who received combination therapy from 4 medical centers. Patients were divided into one training cohort and two independent external validation cohorts.

Predicting Unplanned Return to Operating Room Following Primary Total Shoulder Arthroplasty: Insights from Fair and Explainable Ensemble Machine Learning.

Reoperation is the most significant complication following any surgical procedure. Developing machine learning methods that predict the need for reoperation will allow for improved shared surgical decision making and patient-specific and preoperative optimisation. Yet, no precise machine learning models have been published to perform well in predicting the need for reoperation within 30 days following primary total shoulder arthroplasty (TSA).