Comparison of the effect of intraoperative dexmedetomidine versus ketamine on postoperative analgesia in fracture femur patients operated under subarachnoid block - A prospective randomized double-blind controlled trial.

Background And Aims: Intravenous sedation during spinal anesthesia has the advantages of increased duration of spinal anesthesia and better postoperative pain control. The aim of this study was to compare the effect of intravenous bolus and infusion of dexmedetomidine versus ketamine given intraoperative on the postoperative analgesia in fracture femur patients operated under subarachnoid block.

Material And Methods: In this prospective randomized double-blind controlled study, 75 patients aged 18-65 years posted for elective surgery were selected and randomly divided into three groups to receive ketamine (group K), dexmedetomidine (group D), and saline (control group C).

SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathology.

Introducing interpretability and reasoning into Multiple Instance Learning (MIL) methods for Whole Slide Image (WSI) analysis is challenging, given the complexity of gigapixel slides. Traditionally, MIL interpretability is limited to identifying salient regions deemed pertinent for downstream tasks, offering little insight to the end-user (pathologist) regarding the rationale behind these selections. To address this, we propose Self-Interpretable MIL (SI-MIL), a method intrinsically designed for interpretability from the very outset.

Adopting a model of antimicrobial stewardship program to anti-tubercular treatment stewardship: A single-centre experience from a private tertiary care hospital in South India.

Tuberculosis (TB) remains a significant public health challenge in Low- and Middle-Income Countries (LMIC). Inappropriate use of Anti-Tubercular Treatment (ATT) undermines treatment efficacy and could contribute to drug resistance. While antimicrobial stewardship programs (AMSP) are well established, anti-tubercular treatment stewardship programs (ATTSP) in private hospitals do not have an established model.

Topology-Aware Uncertainty for Image Segmentation.

Segmentation of curvilinear structures such as vasculature and road networks is challenging due to relatively weak signals and complex geometry/topology. To facilitate and accelerate large scale annotation, one has to adopt semi-automatic approaches such as proofreading by experts. In this work, we focus on uncertainty estimation for such tasks, so that highly uncertain, and thus error-prone structures can be identified for human annotators to verify.