Citations

Background: About 25% of patients with acute myocardial infarction complicated by cardiogenic shock (AMI-CS) have chronic total occlusion (CTO) at non-culprit artery. However, the prognostic impact of CTO in these patients is still unclear. Aims: We aimed to investigate the impact of CTO at non-culprit artery on 1-year mortality in patients with AMI-CS using the multicenter registry of cardiogenic shock. Methods: A total of 1,247 patients presented with cardiogenic shock (CS) were enrolled in the SMART-RESCUE registry between January 2014 and December 2018. After exclusion of non-ischemic cause shock, ischemic cardiomyopathy, failed or no revascularization, vasospasm, and patients undergoing coronary artery bypass surgery, 695 patients underwent percutaneous coronary intervention (PCI). Among these, we analyzed 536 patients with multivessel disease (MVD) according to the presence of CTO at non-culprit artery. The study endpoint was a 1-year all-cause mortality. We also examined the incidence of 1-year cardiac death, and in-hospital death or renal replacement therapy (RRT). Results: A 141 patients (26.3%) of study population had CTO lesion at non-culprit artery. The CTO group had more patients with diabetes mellitus, and lesser patients with ST-elevation myocardial infarction. Although initial left ventricular ejection fraction was lower in CTO group (28.5 vs. 34.1%, P=0.005), initial systolic blood pressure (73.8 vs. 73.2 mmHg, P=0.822) and the level of lactate (6.6 vs. 6.4 mmol/L, P=0.804) were similar in both groups. Venoarterial extracorporeal membrane oxygenation was applied in 206 patients (38.4%), and its use rate was comparable in 2 groups (40.4 vs. 37.7%, P=0.571). Pre- (32.9 vs. 22.9, P <0.001) and post-PCI SYNTAX score (16.7 vs. 5.3, P <0.001) was significantly higher in CTO group. In CTO group, 53 patients (37.6%) had CTO at left main or left anterior descending artery, and only 19 patients (13.5%) in CTO group received PCI for CTO. In multivariate analysis, all-cause mortality at 1-year was significantly higher in CTO group (51.8 vs. 40.5%, hazard ratio [HR] 1.44, 95% confidence interval [CI] 1.09-1.90, P=0.011). The incidences of 1-year cardiac death (45.4 vs. 32.4%, HR 1.55, 95% CI 1.15-2.10, P=0.004) and in-hospital death or RRT (46.8 vs. 36.5%, P=0.031) were also higher in CTO group. Conclusions: Non-culprit CTO lesion was associated with higher 1-year mortality in patients with AMI-CS and multivessel disease.

Microglia help limit the progression of Alzheimer’s disease (AD) by constraining amyloid-β (Aβ) pathology, effected through a balance of activating and inhibitory intracellular signals delivered by distinct cell surface receptors. Human leukocyte Ig-like receptor B4 (LILRB4) is an inhibitory receptor of the immunoglobulin (Ig) superfamily that is expressed on myeloid cells and recognizes apolipoprotein E (ApoE) among other ligands. Here, we find that LILRB4 is highly expressed in the microglia of patients with AD. Using mice that accumulate Aβ and carry a transgene encompassing a portion of the LILR region that includes LILRB4 , we corroborated abundant LILRB4 expression in microglia wrapping around Aβ plaques. Systemic treatment of these mice with an anti-human LILRB4 monoclonal antibody (mAb) reduced Aβ load, mitigated some Aβ-related behavioral abnormalities, enhanced microglia activity, and attenuated expression of interferon-induced genes. In vitro binding experiments established that human LILRB4 binds both human and mouse ApoE and that anti-human LILRB4 mAb blocks such interaction. In silico modeling, biochemical, and mutagenesis analyses identified a loop between the two extracellular Ig domains of LILRB4 required for interaction with mouse ApoE and further indicated that anti-LILRB4 mAb may block LILRB4-mApoE by directly binding this loop. Thus, targeting LILRB4 may be a potential therapeutic avenue for AD.

Gastrointestinal (GI) bleeding is the most common GI diagnosis leading to hospitalization within the United States. Prompt diagnosis and treatment of GI bleeding is critical to improving patient outcomes and reducing high healthcare utilization and costs. Radiologic techniques including computed tomography angiography, catheter angiography, computed tomography enterography, magnetic resonance enterography, nuclear medicine red blood cell scan, and technetium-99m pertechnetate scintigraphy (Meckel scan) are frequently used to evaluate patients with GI bleeding and are complementary to GI endoscopy. However, multiple management guidelines exist which differ in the recommended utilization of these radiologic examinations. This variability can lead to confusion as to how these tests should be used in the evaluation of GI bleeding. In this document, a panel of experts from the American College of Gastroenterology and Society of Abdominal Radiology provide a review of the radiologic examinations used to evaluate for GI bleeding including nomenclature, technique, performance, advantages, and limitations. A comparison of advantages and limitations relative to endoscopic examinations is also included. Finally, consensus statements and recommendations on technical parameters and utilization of radiologic techniques for GI bleeding are provided.

Objective: This study aims at establishing benchmark values for best achievable outcomes following open major anatomic hepatectomy for liver tumors of all dignities. Background: Outcomes after open major hepatectomies vary widely lacking reference values for comparisons among centers, indications, types of resections, and minimally invasive procedures. Methods: A standard benchmark methodology was used covering consecutive patients, who underwent open major anatomic hepatectomy from 44 high-volume liver centers from 5 continents over a 5-year period (2016–2020). Benchmark cases were low-risk non-cirrhotic patients without significant comorbidities treated in high-volume centers (≥30 major liver resections/year). Benchmark values were set at the 75th percentile of median values of all centers. Minimum follow-up period was 1 year in each patient. Results: Of 8044 patients, 2908 (36%) qualified as benchmark (low-risk) cases. Benchmark cutoffs for all indications include R0 resection ≥78%; liver failure (grade B/C) ≤10%; bile leak (grade B/C) ≤18%; complications ≥grade 3 and CCI ® ≤46% and ≤9 at 3 months, respectively. Benchmark values differed significantly between malignant and benign conditions so that reference values must be adjusted accordingly. Extended right hepatectomy (H1, 4-8 or H4-8) disclosed a higher cutoff for liver failure, while extended left (H1-5,8 or H2-5,8) were associated with higher cutoffs for bile leaks, but had superior oncologic outcomes, when compared to formal left hepatectomy (H1-4 or H2-4). The minimal follow-up for a conclusive outcome evaluation following open anatomic major resection must be 3 months. Conclusion: These new benchmark cutoffs for open major hepatectomy provide a powerful tool to convincingly evaluate other approaches including parenchymal-sparing procedures, laparoscopic/robotic approaches, and alternative treatments, such as ablation therapy, irradiation, or novel chemotherapy regimens.

AbstractDeep learning has the potential to be one of the most transformative technologies to impact orthopedic surgery. Substantial innovation in this area has occurred over the past 5 years, but clinically meaningful advancements remain limited by a disconnect between clinical and technical experts. That is, it is likely that few orthopedic surgeons possess both the clinical knowledge necessary to identify orthopedic problems, and the technical knowledge needed to implement deep learning‐based solutions. To maximize the utilization of rapidly advancing technologies derived from deep learning models, orthopedic surgeons should understand the steps needed to design, organize, implement, and evaluate a deep learning project and its workflow. Equipping surgeons with this knowledge is the objective of this three‐part editorial review. Part I described the processes involved in defining the problem, team building, data acquisition, curation, labeling, and establishing the ground truth. Building on that, this review (Part II) provides guidance on pre‐processing and augmenting the data, making use of open‐source libraries/toolkits, and selecting the required hardware to implement the pipeline. Special considerations regarding model training and evaluation unique to deep learning models relative to “shallow” machine learning models are also reviewed. Finally, guidance pertaining to the clinical deployment of deep learning models in the real world is provided. As in Part I, the focus is on applications of deep learning for computer vision and imaging.

Abstract Measuring prebiotic activities of foods on human microbiome and providing dietary guidelines on one’s health status are an emerging research topic. However, it is difficult to directly compare different foods using the prebiotic index (PI) proposed in 2003 to determine their prebiotic activities. This ambiguity can be resolved with the gut microbiome health index (GMHI), newly built in 2020, using a consortium of 50 microbial species associated with human health. As a translational application of this index, we used this formula to predict the PI of five commercial prebiotics, fructooligosaccharides (FOS), galactooligosaccharides (GOS), xylooligosaccharides (XOS), inulin (IN), and 2`-fucosyllactose (2FL), using in vitro human fecal fermentation. To calculate the GMHI-based PI values, microbial community changes were analyzed at the species level using shotgun sequencing during the fermentation of the prebiotics. As a result, the five compounds exerted a prebiotic effect, increasing the abundance of health-prevalent species, such as Bifidobacterium adolescentis and decreasing health-scarce species. In this context, the GMHI of prebiotics increased after 24 h, but that of the control group without carbon sources decreased. GMHI was highest in the order of IN (0.48 ± 0.06), FOS (0.47 ± 0.03), XOS (0.33 ± 0.02), GOS (0.26 ± 0.02), and 2FL (0.16 ± 0.06), which more accurately reflects the prebiotic effect than PI. Additionally, GMHI stratified the prebiotics more effectively than ecological metrics analyses, such as Shannon diversity, species richness, species evenness, and the inverse Simpson diversity index. This study demonstrates that GMHI-based PI can be used to measure prebiotic activities of several bifidogenic oligosaccharides and perhaps predict the health-beneficial effects of diets after collecting substantial amounts of population-scale meta-datasets.