Patients´ experiences of TENS as a postoperative pain relief method in the post-anesthesia care unit after laparoscopic cholecystectomy: a qualitative study.

Background: High-frequency, high-intensity transcutaneous electrical nerve stimulation (HFHI TENS, i.e. 80 Hz and 40-60 mA) is an effective, fast-acting pain relief modality after elective surgery, offering pain relief within 5 min.

Idiopathic epiretinal membrane surgery with internal limiting membrane peeling: An optical coherence tomography angiography analysis of macular capillary plexus changes.

Purpose: This study aims to assess retinal vascular changes following internal limiting membrane (ILM) peeling for idiopathic epiretinal membrane (ERM) treatment using optical coherence tomography angiography (OCT-A).

Design: A retrospective study was conducted.

Methods: A cohort of thirty-nine patients was enlisted for this study.

Associations of pulmonary microvascular blood volume with per cent emphysema and CT emphysema subtypes in the community: the MESA Lung study.

Background: Pulmonary microvasculature alterations are implicated in emphysema pathogenesis, but the association between pulmonary microvascular blood volume (PMBV) and emphysema has not been directly assessed at scale, and prior studies have used non-specific measures of emphysema.

Methods: The Multi-Ethnic Study of Atherosclerosis Lung Study invited participants recruited from the community without renal impairment to undergo contrast-enhanced dual-energy CT. Pulmonary blood volume was calculated by material decomposition; PMBV was defined as blood volume in the peripheral 2 cm of the lung.

MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling.

Robust segmentation is critical for deriving quantitative measures from large-scale, multi-center, and longitudinal medical scans. Manually annotating medical scans, however, is expensive and labor-intensive and may not always be available in every domain. Unsupervised domain adaptation (UDA) is a well-studied technique that alleviates this label-scarcity problem by leveraging available labels from another domain.