Citations

Osteoarthritis (OA) is a common shoulder disorder that impacts shoulder functions. Shoulder arthroplasty is often required to restore function and quality of life. Reverse total shoulder arthroplasty (RSA), which was originally designed mainly for irreparable rotator cuff damage, has gained popularity in recent years for the treatment of advanced shoulder OA instead of the clinically standard total shoulder arthroplasty (TSA). However, this RSA has some nonnegligible flaws such as higher complications rate and economic cost, not mention the following problems caused by irreversible physical structural damage. Therefore, the employment of RSA needs to be carefully considered. This study aimed to compare TSA and RSA in OA patients with or without rotator cuff damage to better guide clinical decision making. We believe the radical use of RSA in patients without rotator cuff deficiency may cause more harm than good. We queried the Nationwide Inpatient Sample (NIS) database from 2011 to 2014 to collect information on OA patients who received TSA and RSA. Patients were divided into 2 groups of comparison according to the presence of rotator cuff deficiency and matched with propensity score analysis. A total of 57,156 shoulder arthroplasties were identified. RSA patients in the rotator cuff deficiency group had significant higher transfusion rates and longer hospital stays. RSA patients without rotator cuff deficiency had a statistically significantly higher number of implant-related mechanical complications, acute upper respiratory infections and postoperative pain. Overall, RSA incurred higher costs in both groups. For OA patients with rotator cuff deficiencies, RSA has its benefits as complication rates were comparable to TSA. For those patients without rotator cuff deficiencies, the use of RSA should be reconsidered as there were more complications with higher severity.

Abstract Funding Acknowledgements Type of funding sources: None. Background Aortic regurgitation (AR) is a common valvular lesion associated with left ventricular (LV) enlargement and dysfunction and increased risk of death with onset of symptoms, significant LV enlargement, or systolic dysfunction. The current guidelines are based on symptoms, LV size and LV function to evaluate the candidacy for surgery in patients with severe AR. Purpose We propose a machine-learning (ML) based algorithm to identify patients at risk for death from AR independent of aortic valve replacement (AVR) surgery (patients were censored at AVR event unless the operation occurred within 100 days of the first visit, in which case baseline AVR is included as a predictor). Methods The model was trained with 5-fold cross-validation on a dataset of 1035 patients. Model performance is reported on an independent dataset of 207 patients. Optimal predictive performance was observed with a Conditional Random Survival Forest model. A subset of 19 / 41 variables were selected for inclusion in the final model. Variable selection was performed with 10-fold cross validation using Random Survival Forest model; variables that were on an average selected by less than 6/10 splits were excluded. Results The mean age of patients was 60 ± 17 years and 198 (18%) were females. The variables included in the models were age, end-diastolic volume, end systolic dimension, body surface area, ejection fraction, NYHA class, body mass index, diastolic blood pressure, bicuspid valve, regurgitant volume, filling pressure, mid ascending aorta diameter, mitral tissue early relaxation velocity, pulmonary artery systolic pressure, and comorbidities, and the relative variable importances averaged across five splits of cross validation in each repeat were evaluated (Figure). The predictive AUC for predicting survival of the best-performing model was 0.84 at 1 year, 0.86 at 2 years, and 0.87 overall, respectively. Conclusion Using commonly observed echo parameters and patient characteristics we successfully trained a ML model to predict survival in patients with severe AR. This technique could identify high-risk patients who would benefit from early intervention, thereby improving patient outcomes. Abstract Figure.

PurposeTo develop a robust, accurate, and accelerated T1ρ quantification solution for submillimeter in vivo whole‐brain imaging.MethodsA multislice T1ρ mapping solution (MS‐T1ρ) was developed based on a two‐acquisition scheme using turbo spin echo with RF cycling to allow for whole‐brain coverage with 0.8‐mm in‐plane resolution. A compressed sensing–based fast imaging method, SCOPE, was used to accelerate the MS‐T1ρ acquisition time to a total scan time of 3 minutes 31 seconds. A phantom experiment was conducted to assess the accuracy of MS‐T1ρ by comparing the T1ρ value obtained using MS‐T1ρ with the reference value obtained using the standard single‐slice T1ρ mapping method. In vivo scans of 13 volunteers were acquired prospectively to validate the robustness of MS‐T1ρ.ResultsIn the phantom study, the T1ρ values obtained with MS‐T1ρ were in good agreement with the reference T1ρ values (R2 = 0.9991) and showed high consistency throughout all slices (coefficient of variation = 2.2 ± 2.43%). In the in vivo experiments, T1ρ maps were successfully acquired for all volunteers with no visually noticeable artifacts. There was no significant difference in T1ρ values between MS‐T1ρ acquisitions and fully sampled acquisitions for all brain tissues (p‐value > .05). In the intraclass correlation coefficient and Bland‐Altman analyses, the accelerated T1ρ measurements show moderate to good agreement to the fully sampled reference values.ConclusionThe proposed MS‐T1ρ solution allows for high‐resolution whole‐brain T1ρ mapping within 4 minutes and may provide a potential tool for investigating neural diseases.

Whereas VHL inactivation is a primary event in clear cell renal cell carcinoma (ccRCC), the precise mechanism(s) of how this interacts with the secondary mutations in tumor suppressor genes, including PBRM1 , KDM5C / JARID1C , SETD2 , and/or BAP1 , remains unclear. Gene expression analyses reveal that VHL, PBRM1, or KDM5C share a common regulation of interferon response expression signature. Loss of HIF2α, PBRM1, or KDM5C in VHL-/- cells reduces the expression of interferon stimulated gene factor 3 (ISGF3), a transcription factor that regulates the interferon signature. Moreover, loss of SETD2 or BAP1 also reduces the ISGF3 level. Finally, ISGF3 is strongly tumor-suppressive in a xenograft model as its loss significantly enhances tumor growth. Conversely, reactivation of ISGF3 retards tumor growth by PBRM1-deficient ccRCC cells. Thus after VHL inactivation, HIF induces ISGF3, which is reversed by the loss of secondary tumor suppressors, suggesting that this is a key negative feedback loop in ccRCC.

Quantitation of circulating tumor cells (CTCs) constitutes an emerging tool for the diagnosis and staging of cancer, assessment of response to therapy, and evaluation of residual disease after surgery. Unfortunately, no existing technology has the sensitivity to measure the low numbers of tumor cells (<1 CTC per ml of whole blood) that characterize minimal levels of disease. We present a method, intravital flow cytometry, that noninvasively counts rare CTCs in vivo as they flow through the peripheral vasculature. The method involves i.v. injection of a tumor-specific fluorescent ligand followed by multiphoton fluorescence imaging of superficial blood vessels to quantitate the flowing CTCs. Studies in mice with metastatic tumors demonstrate that CTCs can be quantitated weeks before metastatic disease is detected by other means. Analysis of whole blood samples from cancer patients further establishes that human CTCs can be selectively labeled and quantitated when present at ≈2 CTCs per ml, opening opportunities for earlier assessment of metastatic disease.