Role of the anillin-like protein in growth of Cryptococcus neoformans at human host temperature.

Cryptococcus neoformans, a basidiomycete yeast, causes lethal meningitis in immunocompromised individuals. The ability of C. neoformans to proliferate at 37°C is essential for virulence. We identified anillin-like protein, CnBud4, as essential for proliferation of C. neoformans at 37°C and for virulence in a heterologous host Galleria mellonella at 25°C. C. neoformans cells lacking CnBud4 were inviable at 25°C in the absence of active calcineurin and were hypersensitive to membrane stress and an anti-fungal agent fluconazole, phenotypes previously described for C. neoformans mutants lacking septins. CnBud4 localized to the mother-bud neck during cytokinesis in a septin-dependent manner. In the absence of CnBud4, septin complex failed to transition from a collar-like single ring to the double ring during cytokinesis. In an ascomycete yeast, Saccharomyces cerevisiae, the anillin-like homologue ScBud4 participates in the organization of the septin ring at the mother-bud neck and plays an important role in specifying location for new bud emergence, known as axial budding pattern. In contrast to their role in S. cerevisiae, neither septins nor CnBud4 were needed to direct the position of the new bud in C. neoformans, suggesting that this function is not conserved in basidiomycetous yeasts. Our data suggest that the requirement of CnBud4 for growth at 37°C and pathogenicity in C. neoformans is based on its conserved role in septin complex organization.

Utility of Functional and Volumetric Left Atrial Parameters Derived From Preprocedural Cardiac CTA in Predicting Mortality After Transcatheter Aortic Valve Replacement.

BACKGROUND. Cardiac CTA is required for preprocedural workup before transcatheter aortic valve replacement (TAVR) and can be used to assess functional parameters of the left atrium (LA). OBJECTIVE. We aimed to evaluate the utility of functional and volumetric LA parameters derived from cardiac CTA to predict mortality in patients with severe aortic stenosis (AS) undergoing TAVR. METHODS. This retrospective study included 175 patients with severe AS (92 men, 83 women; median age, 79.0 years) who underwent cardiac CTA for clinical pre-TAVR assessment. A postdoctoral research fellow calculated maximum and minimum LA volumes using biplane area-length measurements; these values were indexed to body surface area, and maximum and minimum LA volume index (LAVImax and LAVImin, respectively) values were calculated. The LA emptying fraction (LAEF) was automatically calculated. All-cause mortality within a 24-month follow-up period after TAVR was recorded. To identify parameters predictive of mortality, Cox regression analysis was performed, and results were summarized by hazard ratio (HR) and 95% CI. The Harrell C-index was used to assess model performance. A radiology resident repeated the measurements in a random sample of 20% (n = 35) of the cases, and interobserver agreement was computed using the intraclass correlation coefficient (ICC). RESULTS. Thirty-eight deaths (21.7%) were recorded within a median follow-up of 21 months. LAVImax (HR, 1.02 [95% CI, 1.01-1.04]; p = .01), LAVImin (HR, 1.02 [95% CI, 1.01-1.04]; p < .001), and LAEF (HR, 0.97 [95% CI, 0.95-0.99]; p = .002) were predictive of mortality in univariable analysis. After adjusting for clinical parameters, only LAEF (HR, 0.97 [95% CI, 0.94-0.99]; p = .02) independently predicted mortality. The C-index of the Society of Thoracic Surgeons Predicted Risk of Mortality (STS-PROM) significantly increased from 0.636 to 0.683, 0.694, and 0.700 when incorporating into the model LAVImax, LAVImin, and LAEF, respectively. The ICC for maximum and minimum LA volumes and LAEF ranged from 0.94 to 0.99. CONCLUSION. LAEF derived from preprocedural cardiac CTA independently predicts mortality in patients with severe AS undergoing TAVR. CLINICAL IMPACT. Cardiac CTA-derived LA function, evaluated during pre-TAVR workup, can be used to assess preprocedural risk and may improve risk stratification in post-TAVR surveillance.

Automated diagnosis and prognosis of COVID-19 pneumonia from initial ER chest X-rays using deep learning.

BACKGROUND: Airspace disease as seen on chest X-rays is an important point in triage for patients initially presenting to the emergency department with suspected COVID-19 infection. The purpose of this study is to evaluate a previously trained interpretable deep learning algorithm for the diagnosis and prognosis of COVID-19 pneumonia from chest X-rays obtained in the ED. METHODS: This retrospective study included 2456 (50% RT-PCR positive for COVID-19) adult patients who received both a chest X-ray and SARS-CoV-2 RT-PCR test from January 2020 to March of 2021 in the emergency department at a single U.S. INSTITUTION: A total of 2000 patients were included as an additional training cohort and 456 patients in the randomized internal holdout testing cohort for a previously trained Siemens AI-Radiology Companion deep learning convolutional neural network algorithm. Three cardiothoracic fellowship-trained radiologists systematically evaluated each chest X-ray and generated an airspace disease area-based severity score which was compared against the same score produced by artificial intelligence. The interobserver agreement, diagnostic accuracy, and predictive capability for inpatient outcomes were assessed. Principal statistical tests used in this study include both univariate and multivariate logistic regression. RESULTS: Overall ICC was 0.820 (95% CI 0.790-0.840). The diagnostic AUC for SARS-CoV-2 RT-PCR positivity was 0.890 (95% CI 0.861-0.920) for the neural network and 0.936 (95% CI 0.918-0.960) for radiologists. Airspace opacities score by AI alone predicted ICU admission (AUC = 0.870) and mortality (0.829) in all patients. Addition of age and BMI into a multivariate log model improved mortality prediction (AUC = 0.906). CONCLUSION: The deep learning algorithm provides an accurate and interpretable assessment of the disease burden in COVID-19 pneumonia on chest radiographs. The reported severity scores correlate with expert assessment and accurately predicts important clinical outcomes. The algorithm contributes additional prognostic information not currently incorporated into patient management.

Adhesion barriers in cardiac surgery: A systematic review of efficacy.

BACKGROUND: Postoperative pericardial adhesions have been associated with increased morbidity, mortality, and surgical difficulty. Barriers exist to limit adhesion formation, yet little is known about their use in cardiac surgery. The study presented here provides the first major systematic review of adhesion barriers in cardiac surgery. METHODS: Scopus and PubMed were assessed on November 20, 2020. Inclusion criteria were clinical studies on human subjects, and exclusion criteria were studies not published in English and case reports. Risk of bias was evaluated with the Cochrane Risk of Bias Tool. Barrier efficacy data was assessed with Excel and GraphPad Prism 5. RESULTS: Twenty-five studies were identified with a total of 13 barriers and 2928 patients. Polytetrafluoroethylene (PTFE) was the most frequently evaluated barrier (13 studies, 67% of patients) with adhesion formation rate of 37.31% and standardized tenacity score of 26.50. Several barriers had improved efficacy. In particular, Cova CARD had a standardized tenacity score of 15.00. CONCLUSIONS: Overall, the data varied considerably in terms of study design and reporting bias. The amount of data was also limited for the non-PTFE studies. PTFE has historically been effective in preventing adhesions. More recent barriers may be superior, yet the current data is nonconfirmatory. No ideal adhesion barrier currently exists, and future barriers must focus on the requirements unique to operating in and around the heart.

Feasibility of Coronary CT Angiography-derived Left Ventricular Long-Axis Shortening as an Early Marker of Ventricular Dysfunction in Transcatheter Aortic Valve Replacement.

Purpose: To evaluate the value of using left ventricular (LV) long-axis shortening (LAS) derived from coronary CT angiography (CCTA) to predict mortality in patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR). Materials and Methods: Patients with severe AS who underwent CCTA for preprocedural TAVR planning between September 2014 and December 2019 were included in this retrospective study. CCTA covered the whole cardiac cycle in 10% increments. Image series reconstructed at end systole and end diastole were used to measure LV-LAS. All-cause mortality within 24 months of follow-up after TAVR was recorded. Cox regression analysis was performed, and hazard ratios (HRs) are presented with 95% CIs. The C index was used to evaluate model performance, and the likelihood ratio χ2 test was performed to compare nested models. Results: The study included 175 patients (median age, 79 years [IQR, 73-85 years]; 92 men). The mortality rate was 22% (38 of 175). When adjusting for predictive clinical confounders, it was found that LV-LAS could be used independently to predict mortality (adjusted HR, 2.83 [95% CI: 1.13, 7.07]; P = .03). In another model using the Society of Thoracic Surgeons Predicted Risk of Mortality (STS-PROM), LV-LAS remained significant (adjusted HR, 3.38 [95 CI: 1.48, 7.72]; P = .004), and its use improved the predictive value of the STS-PROM, increasing the STS-PROM C index from 0.64 to 0.71 (χ2 = 29.9 vs 19.7, P = .001). In a subanalysis of patients with a normal LV ejection fraction (LVEF), the significance of LV-LAS persisted (adjusted HR, 3.98 [95 CI: 1.56, 10.17]; P = .004). Conclusion: LV-LAS can be used independently to predict mortality in patients undergoing TAVR, including those with a normal LVEF.Keywords: CT Angiography, Transcatheter Aortic Valve Implantation/Replacement (TAVI/TAVR), Cardiac, Outcomes Analysis, Cardiomyopathies, Left Ventricle, Aortic Valve Supplemental material is available for this article. © RSNA, 2022See also the commentary by Everett and Leipsic in this issue.

Computed tomographic assessment of right ventricular long axis strain for prognosis after transcatheter aortic valve replacement.

OBJECTIVES: To investigate the predictive value of right ventricular long axis strain (RV-LAS) derived by cardiac computed tomography angiography (CCTA) for mortality in patients with aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR). METHODS: We retrospectively included patients with severe AS undergoing TAVR (n = 168, median 79 years). Parameters of RV function including RV-LAS and RV ejection fraction (RVEF) were assessed using pre-procedural systolic and diastolic CCTA series. The tricuspid annulus diameter (TAD) and diameter of the main pulmonary artery (mPA) were also assessed. All-cause mortality was recorded post-TAVR. Cox regression was used and results are presented with hazard ratio (HR) and 95% confidence interval (CI). Harrell's c-index was used to assess the performance of different models and the likelihood ratio test was used to compare nested models. RESULTS: Thirty-eight deaths (22.6%) occurred over a median follow-up of 21 months. RV-LAS > -11.42% (HR 2.86, 95% CI 1.44-5.67, p = 0.003), LVEF (HR 0.98, 95% CI 0.96-0.996; p = 0.02), TAD (HR 1.05, 95% CI 1.01-1.10, p = 0.02) and mPA diameter (HR 1.09, 95% CI 1.02-1.16, p = 0.01) were associated with mortality on univariable analysis. In a multivariable model, only RV-LAS (HR 2.36, 95% CI 1.04-5.36, p = 0.04) remained as an independent predictor of all-cause mortality. RV-LAS significantly improved the predictive power of the Society of Thoracic Surgeons Predicted Risk of Mortality (STS-PROM) (c-index 0.700 vs 0.637; p = 0.01). CONCLUSION: RV-LAS was an independent predictor of all-cause mortality in patients with severe AS undergoing TAVR, outperformed anatomical markers such as TAD and mPA diameter, and could potentially improve the current risk-stratifying tool.