Changes in the tumour microenvironment mark the transition from serous borderline tumour to low-grade serous carcinoma.

Low-grade serous ovarian carcinoma (LGSC) is a rare and lethal subtype of ovarian cancer. LGSC is pathologically, biologically, and clinically distinct from the more common high-grade serous ovarian carcinoma (HGSC). LGSC arises from serous borderline ovarian tumours (SBTs). The mechanism of transformation for SBTs to LGSC remains poorly understood. To better understand the biology of LGSC, we performed whole proteome profiling of formalin-fixed, paraffin-embedded tissue blocks of LGSC (n = 11), HGSC (n = 19), and SBTs (n = 26). We identified that the whole proteome is able to distinguish between histotypes of the ovarian epithelial tumours. Proteins associated with the tumour microenvironment were differentially expressed between LGSC and SBTs. Fibroblast activation protein (FAP), a protein expressed in cancer-associated fibroblasts, is the most differentially abundant protein in LGSC compared with SBT. Multiplex immunohistochemistry (IHC) for immune markers (CD20, CD79a, CD3, CD8, and CD68) was performed to determine the presence of B cells, T cells, and macrophages. The LGSC FAP+ stroma was associated with greater abundance of Tregs and M2 macrophages, features not present in SBTs. Our proteomics cohort reveals that there are changes in the tumour microenvironment in LGSC compared with its putative precursor lesion, SBT. These changes suggest that the tumour microenvironment provides a supportive environment for LGSC tumourigenesis and progression. Thus, targeting the tumour microenvironment of LGSC may be a viable therapeutic strategy. © 2024 The Pathological Society of Great Britain and Ireland.

Contemporary Risk Models for In-Hospital and 30-Day Mortality After Percutaneous Coronary Intervention.

PURPOSE OF REVIEW: Risk models for mortality after percutaneous coronary intervention (PCI) are underutilized in clinical practice though they may be useful during informed consent, risk mitigation planning, and risk adjustment of hospital and operator outcomes. This review analyzed contemporary risk models for in-hospital and 30-day mortality after PCI. RECENT FINDINGS: We reviewed eight contemporary risk models. Age, sex, hemodynamic status, acute coronary syndrome type, heart failure, and kidney disease were consistently found to be independent risk factors for mortality. These models provided good discrimination (C-statistic 0.85-0.95) for both pre-catheterization and comprehensive risk models that included anatomic variables. There are several excellent models for PCI mortality risk prediction. Choice of the model will depend on the use case and population, though the CathPCI model should be the default for in-hospital mortality risk prediction in the United States. Future interventions should focus on the integration of risk prediction into clinical care.

Incorporation of Drone Technology Into the Chain of Survival for OHCA: Estimation of Time Needed for Bystander Treatment of OHCA and CPR Performance.

BACKGROUND: Drone-delivered automated external defibrillators (AEDs) hold promises in the treatment of out-of-hospital cardiac arrest. Our objective was to estimate the time needed to perform resuscitation with a drone-delivered AED and to measure cardiopulmonary resuscitation (CPR) quality. METHODS: Mock out-of-hospital cardiac arrest simulations that included a 9-1-1 call, CPR, and drone-delivered AED were conducted. Each simulation was timed and video-recorded. CPR performance metrics were recorded by a Laerdal Resusci Anne Quality Feedback System. Multivariable regression modeling examined factors associated with time from 9-1-1 call to AED shock and CPR quality metrics (compression rate, depth, recoil, and chest compression fraction). Comparisons were made among those with recent CPR training (≤2 years) versus no recent (>2 years) or prior CPR training. RESULTS: We recruited 51 research participants between September 2019 and March 2020. The median age was 34 (Q1-Q3, 23-54) years, 56.9% were female, and 41.2% had recent CPR training. The median time from 9-1-1 call to initiation of CPR was 1:19 (Q1-Q3, 1:06-1:26) minutes. A median time of 1:59 (Q1-Q3, 01:50-02:20) minutes was needed to retrieve a drone-delivered AED and deliver a shock. The median CPR compression rate was 115 (Q1-Q3, 109-124) beats per minute, the correct compression depth percentage was 92% (Q1-Q3, 25-98), and the chest compression fraction was 46.7% (Q1-Q3, 39.9%-50.6%). Recent CPR training was not associated with CPR quality or time from 9-1-1 call to AED shock. Younger age (per 10-year increase; ß, 9.97 [95% CI, 4.63-15.31] s; P<0.001) and prior experience with AED (ß, -30.0 [95% CI, -50.1 to -10.0] s; P=0.004) were associated with more rapid time from 9-1-1 call to AED shock. Prior AED use (ß, 6.71 [95% CI, 1.62-11.79]; P=0.011) was associated with improved chest compression fraction percentage. CONCLUSION: Research participants were able to rapidly retrieve an AED from a drone while largely maintaining CPR quality according to American Heart Association guidelines. Chest compression fraction was lower than expected.