Impact of 18F-FDG PET Intensity Normalization on Radiomic Features of Oropharyngeal Squamous Cell Carcinomas and Machine Learning-Generated Biomarkers

We aimed to investigate the effects of 18F-FDG PET voxel intensity normalization on radiomic features of oropharyngeal squamous cell carcinoma (OPSCC) and machine learning-generated radiomic biomarkers. Methods: We extracted 1,037 18F-FDG PET radiomic features quantifying the shape, intensity, and texture of 430 OPSCC primary tumors. The reproducibility of individual features across 3 intensity-normalized images (body-weight SUV, reference tissue activity ratio to lentiform nucleus of brain and cerebellum) and the raw PET data was assessed using an intraclass correlation coefficient (ICC). We investigated the effects of intensity normalization on the features' utility in predicting the human papillomavirus (HPV) status of OPSCCs in univariate logistic regression, receiver-operating-characteristic analysis, and extreme-gradient-boosting (XGBoost) machine-learning classifiers. Results: Of 1,037 features, a high (ICC ≥ 0.90), medium (0.90 > ICC ≥ 0.75), and low (ICC < 0.75) degree of reproducibility across normalization methods was attained in 356 (34.3%), 608 (58.6%), and 73 (7%) features, respectively. In univariate analysis, features from the PET normalized to the lentiform nucleus had the strongest association with HPV status, with 865 of 1,037 (83.4%) significant features after multiple testing corrections and a median area under the receiver-operating-characteristic curve (AUC) of 0.65 (interquartile range, 0.62-0.68). Similar tendencies were observed in XGBoost models, with the lentiform nucleus-normalized model achieving the numerically highest average AUC of 0.72 (SD, 0.07) in the cross validation within the training cohort. The model generalized well to the validation cohorts, attaining an AUC of 0.73 (95% CI, 0.60-0.85) in independent validation and 0.76 (95% CI, 0.58-0.95) in external validation. The AUCs of the XGBoost models were not significantly different. Conclusion: Only one third of the features demonstrated a high degree of reproducibility across intensity-normalization techniques, making uniform normalization a prerequisite for interindividual comparability of radiomic markers. The choice of normalization technique may affect the radiomic features' predictive value with respect to HPV. Our results show trends that normalization to the lentiform nucleus may improve model performance, although more evidence is needed to draw a firm conclusion.

Automated MRI liver segmentation for anatomical segmentation, liver volumetry, and the extraction of radiomics

OBJECTIVES

To develop and evaluate a deep convolutional neural network (DCNN) for automated liver segmentation, volumetry, and radiomic feature extraction on contrast-enhanced portal venous phase magnetic resonance imaging (MRI).

MATERIALS AND METHODS

This retrospective study included hepatocellular carcinoma patients from an institutional database with portal venous MRI. After manual segmentation, the data was randomly split into independent training, validation, and internal testing sets. From a collaborating institution, de-identified scans were used for external testing. The public LiverHccSeg dataset was used for further external validation. A 3D DCNN was trained to automatically segment the liver. Segmentation accuracy was quantified by the Dice similarity coefficient (DSC) with respect to manual segmentation. A Mann-Whitney U test was used to compare the internal and external test sets. Agreement of volumetry and radiomic features was assessed using the intraclass correlation coefficient (ICC).

RESULTS

In total, 470 patients met the inclusion criteria (63.9±8.2 years; 376 males) and 20 patients were used for external validation (41±12 years; 13 males). DSC segmentation accuracy of the DCNN was similarly high between the internal (0.97±0.01) and external (0.96±0.03) test sets (p=0.28) and demonstrated robust segmentation performance on public testing (0.93±0.03). Agreement of liver volumetry was satisfactory in the internal (ICC, 0.99), external (ICC, 0.97), and public (ICC, 0.85) test sets. Radiomic features demonstrated excellent agreement in the internal (mean ICC, 0.98±0.04), external (mean ICC, 0.94±0.10), and public (mean ICC, 0.91±0.09) datasets.

CONCLUSION

Automated liver segmentation yields robust and generalizable segmentation performance on MRI data and can be used for volumetry and radiomic feature extraction.

CLINICAL RELEVANCE STATEMENT

Liver volumetry, anatomic localization, and extraction of quantitative imaging biomarkers require accurate segmentation, but manual segmentation is time-consuming. A deep convolutional neural network demonstrates fast and accurate segmentation performance on T1-weighted portal venous MRI.

KEY POINTS

• This deep convolutional neural network yields robust and generalizable liver segmentation performance on internal, external, and public testing data. • Automated liver volumetry demonstrated excellent agreement with manual volumetry. • Automated liver segmentations can be used for robust and reproducible radiomic feature extraction.

LiverHccSeg: A publicly available multiphasic MRI dataset with liver and HCC tumor segmentations and inter-rater agreement analysis

Accurate segmentation of liver and tumor regions in medical imaging is crucial for the diagnosis, treatment, and monitoring of hepatocellular carcinoma (HCC) patients. However, manual segmentation is time-consuming and subject to inter- and intra-rater variability. Therefore, automated methods are necessary but require rigorous validation of high-quality segmentations based on a consensus of raters. To address the need for reliable and comprehensive data in this domain, we present LiverHccSeg, a dataset that provides liver and tumor segmentations on multiphasic contrast-enhanced magnetic resonance imaging from two board-approved abdominal radiologists, along with an analysis of inter-rater agreement. LiverHccSeg provides a curated resource for liver and HCC tumor segmentation tasks. The dataset includes a scientific reading and co-registered contrast-enhanced multiphasic magnetic resonance imaging (MRI) scans with corresponding manual segmentations by two board-approved abdominal radiologists and relevant metadata and offers researchers a comprehensive foundation for external validation, and benchmarking of liver and tumor segmentation algorithms. The dataset also provides an analysis of the agreement between the two sets of liver and tumor segmentations. Through the calculation of appropriate segmentation metrics, we provide insights into the consistency and variability in liver and tumor segmentations among the radiologists. A total of 17 cases were included for liver segmentation and 14 cases for HCC tumor segmentation. Liver segmentations demonstrates high segmentation agreement (mean Dice, 0.95 ± 0.01 [standard deviation]) and HCC tumor segmentations showed higher variation (mean Dice, 0.85 ± 0.16 [standard deviation]). The applications of LiverHccSeg can be manifold, ranging from testing machine learning algorithms on public external data to radiomic feature analyses. Leveraging the inter-rater agreement analysis within the dataset, researchers can investigate the impact of variability on segmentation performance and explore methods to enhance the accuracy and robustness of liver and tumor segmentation algorithms in HCC patients. By making this dataset publicly available, LiverHccSeg aims to foster collaborations, facilitate innovative solutions, and ultimately improve patient outcomes in the diagnosis and treatment of HCC.

Radiomic markers of intracerebral hemorrhage expansion on non-contrast CT: independent validation and comparison with visual markers

Objective

To devise and validate radiomic signatures of impending hematoma expansion (HE) based on admission non-contrast head computed tomography (CT) of patients with intracerebral hemorrhage (ICH).

Methods

Utilizing a large multicentric clinical trial dataset of hypertensive patients with spontaneous supratentorial ICH, we developed signatures predictive of HE in a discovery cohort (n = 449) and confirmed their performance in an independent validation cohort (n = 448). In addition to n = 1,130 radiomic features, n = 6 clinical variables associated with HE, n = 8 previously defined visual markers of HE, the BAT score, and combinations thereof served as candidate variable sets for signatures. The area under the receiver operating characteristic curve (AUC) quantified signatures' performance.

Results

A signature combining select radiomic features and clinical variables attained the highest AUC (95% confidence interval) of 0.67 (0.61-0.72) and 0.64 (0.59-0.70) in the discovery and independent validation cohort, respectively, significantly outperforming the clinical (pdiscovery = 0.02, pvalidation = 0.01) and visual signature (pdiscovery = 0.03, pvalidation = 0.01) as well as the BAT score (pdiscovery < 0.001, pvalidation < 0.001). Adding visual markers to radiomic features failed to improve prediction performance. All signatures were significantly (p < 0.001) correlated with functional outcome at 3-months, underlining their prognostic relevance.

Conclusion

Radiomic features of ICH on admission non-contrast head CT can predict impending HE with stable generalizability; and combining radiomic with clinical predictors yielded the highest predictive value. By enabling selective anti-expansion treatment of patients at elevated risk of HE in future clinical trials, the proposed markers may increase therapeutic efficacy, and ultimately improve outcomes.

MR Imaging-Based In Vivo Macrophage Imaging to Monitor Immune Response after Radiofrequency Ablation of the Liver

PURPOSE

To establish molecular magnetic resonance (MR) imaging instruments for in vivo characterization of the immune response to hepatic radiofrequency (RF) ablation using cell-specific immunoprobes.

MATERIALS AND METHODS

Seventy-two C57BL/6 wild-type mice underwent standardized hepatic RF ablation (70 °C for 5 minutes) to generate a coagulation area measuring 6-7 mm in diameter. CD68+ macrophage periablational infiltration was characterized with immunohistochemistry 24 hours, 72 hours, 7 days, and 14 days after ablation (n = 24). Twenty-one mice were subjected to a dose-escalation study with either 10, 15, 30, or 60 mg/kg of rhodamine-labeled superparamagnetic iron oxide nanoparticles (SPIONs) or 2.4, 1.2, or 0.6 mg/kg of gadolinium-160 (160Gd)-labeled CD68 antibody for assessment of the optimal in vivo dose of contrast agent. MR imaging experiments included 9 mice, each receiving 10-mg/kg SPIONs to visualize phagocytes using T2∗-weighted imaging in a horizontal-bore 9.4-T MR imaging scanner, 160Gd-CD68 for T1-weighted MR imaging of macrophages, or 0.1-mmol/kg intravenous gadoterate (control group). Radiological-pathological correlation included Prussian blue staining, rhodamine immunofluorescence, imaging mass cytometry, and immunohistochemistry.

RESULTS

RF ablation-induced periablational infiltration (206.92 μm ± 12.2) of CD68+ macrophages peaked at 7 days after ablation (P < .01) compared with the untreated lobe. T2∗-weighted MR imaging with SPION contrast demonstrated curvilinear T2∗ signal in the transitional zone (TZ) (186 μm ± 16.9), corresponsing to Iron Prussian blue staining. T1-weighted MR imaging with 160Gd-CD68 antibody showed curvilinear signal in the TZ (164 μm ± 3.6) corresponding to imaging mass cytometry.

CONCLUSIONS

Both SPION-enhanced T2∗-weighted and 160Gd-enhanced T1-weighted MR imaging allow for in vivo monitoring of macrophages after RF ablation, demonstrating the feasibility of this model to investigate local immune responses.

Machine Learning Models for Prediction of Posttreatment Recurrence in Early-Stage Hepatocellular Carcinoma Using Pretreatment Clinical and MRI Features: A Proof-of-Concept Study

BACKGROUND. Posttreatment recurrence is an unpredictable complication after liver transplant for hepatocellular carcinoma (HCC) that is associated with poor survival. Biomarkers are needed to estimate recurrence risk before organ allocation. OBJECTIVE. This proof-of-concept study evaluated the use of machine learning (ML) to predict recurrence from pretreatment laboratory, clinical, and MRI data in patients with early-stage HCC initially eligible for liver transplant. METHODS. This retrospective study included 120 patients (88 men, 32 women; median age, 60.0 years) with early-stage HCC diagnosed who were initially eligible for liver transplant and underwent treatment by transplant, resection, or thermal ablation between June 2005 and March 2018. Patients underwent pretreatment MRI and posttreatment imaging surveillance. Imaging features were extracted from postcontrast phases of pretreatment MRI examinations using a pretrained convolutional neural network. Pretreatment clinical characteristics (including laboratory data) and extracted imaging features were integrated to develop three ML models (clinical model, imaging model, combined model) for predicting recurrence within six time frames ranging from 1 through 6 years after treatment. Kaplan-Meier analysis with time to recurrence as the endpoint was used to assess the clinical relevance of model predictions. RESULTS. Tumor recurred in 44 of 120 (36.7%) patients during follow-up. The three models predicted recurrence with AUCs across the six time frames of 0.60-0.78 (clinical model), 0.71-0.85 (imaging model), and 0.62-0.86 (combined model). The mean AUC was higher for the imaging model than the clinical model (0.76 vs 0.68, respectively; p = .03), but the mean AUC was not significantly different between the clinical and combined models or between the imaging and combined models (p > .05). Kaplan-Meier curves were significantly different between patients predicted to be at low risk and those predicted to be at high risk by all three models for the 2-, 3-, 4-, 5-, and 6-year time frames (p < .05). CONCLUSION. The findings suggest that ML-based models can predict recurrence before therapy allocation in patients with early-stage HCC initially eligible for liver transplant. Adding MRI data as model input improved predictive performance over clinical parameters alone. The combined model did not surpass the imaging model's performance. CLINICAL IMPACT. ML-based models applied to currently underutilized imaging features may help design more reliable criteria for organ allocation and liver transplant eligibility.

Trust in societal factors and vaccine hesitancy in Western Balkans

Background

Countries in the Western Balkans are facing vaccine hesitancy, trying to bring vaccine acceptance and ways to improve it into the focus. Trust in science and institutions, namely political trust, plays an important role and can significantly affects vaccine acceptance.

Methods

Cross-sectional research was carried out from July to September 2021 in five countries of the Western Balkans (Albania, Bosnia and Herzegovina, North Macedonia, Montenegro and Serbia) and included adult population aged 18 and older (1605 individuals). Convenience sampling was applied using anonymised online questionnaires and Likert scales, shared through online social media, and asking, among others, for trust in societal factors.

Results

In all countries people had more confidence in health authorities than in political officials. There are no gender differences found in showing trust in societal factors, except in Serbia where women compared to men showed greater trust in health authorities (50.6% vs. 34.4%), as well as in political officials (42.8% vs. 28.2%). The lowest trust in pharmaceutical companies was found in Albania where 34,9% respondents believe that vaccination against COVID-19 is largely promoted by pharmaceutical companies due to financial profits. People who put more trust in societal factors were vaccinated to a greater extent. In Serbia and Albania, the older respondents in general put more trust in societal factors. People who assessed themselves as more religious in Serbia and North Macedonia demonstrated less trust towards societal factors.

Conclusions

The study demonstrated moderate trust in societal factors in all countries, with greatest trust in health authorities. This implies that health authorities should have a pivotal role, together with physicians in primary health, in promoting vaccination and educating the general public in the Western Balkans.

Key messages

Impact of Chemoembolic Regimen on Immune Cell Recruitment and Immune Checkpoint Marker Expression following Transcatheter Arterial Chemoembolization in a VX2 Rabbit Liver Tumor Model

PURPOSE

To characterize the effects of commonly used transcatheter arterial chemoembolization (TACE) regimens on the immune response and immune checkpoint marker expression using a VX2 rabbit liver tumor model.

MATERIALS AND METHODS

Twenty-four VX2 liver tumor-bearing New Zealand white rabbits were assigned to 7 groups (n = 3 per group) undergoing locoregional therapy as follows: (a) bicarbonate infusion without embolization, (b) conventional TACE (cTACE) using a water-in-oil emulsion containing doxorubicin mixed 1:2 with Lipiodol, drug-eluting embolic-TACE with either (c) idarubicin-eluting Oncozene microspheres (40 μm) or (d) doxorubicin-eluting Lumi beads (40-90 μm). For each therapy arm (b-d), a tandem set of 3 animals with additional bicarbonate infusion before TACE was added, to evaluate the effect of pH modification on the immune response. Three untreated rabbits served as controls. Tissue was harvested 24 hours after treatment, followed by digital immunohistochemistry quantification (counts/μm² ± SEM) of tumor-infiltrating cluster of differentiation 3⁺ T-lymphocytes, human leukocyte antigen DR type antigen-presenting cells (APCs), cytotoxic T-lymphocyte-associated protein-4 (CTLA-4), and programmed cell death protein-1 (PD-1)/PD-1 ligand (PD-L1) pathway axis expression.

RESULTS

Lumi-bead TACE induced significantly more intratumoral T-cell and APC infiltration than cTACE and Oncozene-microsphere TACE. Additionally, tumors treated with Lumi-bead TACE expressed significantly higher intratumoral immune checkpoint markers compared with cTACE and Oncozene-microsphere TACE. Neoadjuvant bicarbonate demonstrated the most pronounced effect on cTACE and resulted in a significant increase in intratumoral cluster of differentiation 3⁺ T-cell infiltration compared with cTACE alone.

CONCLUSIONS

This preclinical study revealed significant differences in evoked tumor immunogenicity depending on the choice of chemoembolic regimen for TACE.

Improved performance and consistency of deep learning 3D liver segmentation with heterogeneous cancer stages in magnetic resonance imaging

PURPOSE

Accurate liver segmentation is key for volumetry assessment to guide treatment decisions. Moreover, it is an important pre-processing step for cancer detection algorithms. Liver segmentation can be especially challenging in patients with cancer-related tissue changes and shape deformation. The aim of this study was to assess the ability of state-of-the-art deep learning 3D liver segmentation algorithms to generalize across all different Barcelona Clinic Liver Cancer (BCLC) liver cancer stages.

METHODS

This retrospective study, included patients from an institutional database that had arterial-phase T1-weighted magnetic resonance images with corresponding manual liver segmentations. The data was split into 70/15/15% for training/validation/testing each proportionally equal across BCLC stages. Two 3D convolutional neural networks were trained using identical U-net-derived architectures with equal sized training datasets: one spanning all BCLC stages ("All-Stage-Net": AS-Net), and one limited to early and intermediate BCLC stages ("Early-Intermediate-Stage-Net": EIS-Net). Segmentation accuracy was evaluated by the Dice Similarity Coefficient (DSC) on a dataset spanning all BCLC stages and a Wilcoxon signed-rank test was used for pairwise comparisons.

RESULTS

219 subjects met the inclusion criteria (170 males, 49 females, 62.8±9.1 years) from all BCLC stages. Both networks were trained using 129 subjects: AS-Net training comprised 19, 74, 18, 8, and 10 BCLC 0, A, B, C, and D patients, respectively; EIS-Net training comprised 21, 86, and 22 BCLC 0, A, and B patients, respectively. DSCs (mean±SD) were 0.954±0.018 and 0.946±0.032 for AS-Net and EIS-Net (p<0.001), respectively. The AS-Net 0.956±0.014 significantly outperformed the EIS-Net 0.941±0.038 on advanced BCLC stages (p<0.001) and yielded similarly good segmentation performance on early and intermediate stages (AS-Net: 0.952±0.021; EIS-Net: 0.949±0.027; p = 0.107).

CONCLUSION

To ensure robust segmentation performance across cancer stages that is independent of liver shape deformation and tumor burden, it is critical to train deep learning models on heterogeneous imaging data spanning all BCLC stages.

Admission computed tomography radiomic signatures outperform hematoma volume in predicting baseline clinical severity and functional outcome in the ATACH-2 trial intracerebral hemorrhage population

BACKGROUND AND PURPOSE

Radiomics provides a framework for automated extraction of high-dimensional feature sets from medical images. We aimed to determine radiomics signature correlates of admission clinical severity and medium-term outcome from intracerebral hemorrhage (ICH) lesions on baseline head computed tomography (CT).

METHODS

We used the ATACH-2 (Antihypertensive Treatment of Acute Cerebral Hemorrhage II) trial dataset. Patients included in this analysis (n = 895) were randomly allocated to discovery (n = 448) and independent validation (n = 447) cohorts. We extracted 1130 radiomics features from hematoma lesions on baseline noncontrast head CT scans and generated radiomics signatures associated with admission Glasgow Coma Scale (GCS), admission National Institutes of Health Stroke Scale (NIHSS), and 3-month modified Rankin Scale (mRS) scores. Spearman's correlation between radiomics signatures and corresponding target variables was compared with hematoma volume.

RESULTS

In the discovery cohort, radiomics signatures, compared to ICH volume, had a significantly stronger association with admission GCS (0.47 vs. 0.44, p = 0.008), admission NIHSS (0.69 vs. 0.57, p < 0.001), and 3-month mRS scores (0.44 vs. 0.32, p < 0.001). Similarly, in independent validation, radiomics signatures, compared to ICH volume, had a significantly stronger association with admission GCS (0.43 vs. 0.41, p = 0.02), NIHSS (0.64 vs. 0.56, p < 0.001), and 3-month mRS scores (0.43 vs. 0.33, p < 0.001). In multiple regression analysis adjusted for known predictors of ICH outcome, the radiomics signature was an independent predictor of 3-month mRS in both cohorts.

CONCLUSIONS

Limited by the enrollment criteria of the ATACH-2 trial, we showed that radiomics features quantifying hematoma texture, density, and shape on baseline CT can provide imaging correlates for clinical presentation and 3-month outcome. These findings couldtrigger a paradigm shift where imaging biomarkers may improve current modelsfor prognostication, risk-stratification, and treatment triage of ICH patients.

Thermal ablation alone vs thermal ablation combined with transarterial chemoembolization for patients with small (<3 cm) hepatocellular carcinoma

PURPOSE

Thermal ablation (TA) and transarterial chemoembolization (TACE) may be used alone or in combination (TACE+TA) for the treatment of hepatocellular carcinoma (HCC). The aim of our study was to compare the time to tumor progression (TTP) and overall survival (OS) for patients who received TA alone or TACE+TA for HCC tumors under 3 cm.

MATERIALS AND METHODS

This HIPAA-compliant IRB-approved retrospective analysis included 85 therapy-naïve patients from 2010 to 2018 (63 males, 22 females, mean age 62.4 ± 8.5 years) who underwent either TA alone (n = 64) or TA in combination with drug-eluting beads (DEB)-TACE (n = 18) or Lipiodol-TACE (n = 3) for locoregional therapy of early stage HCC with maximum tumor diameter under 3 cm. Kaplan-Meier analysis was performed using the log-rank test to assess TTP and OS.

RESULTS

All TA and TACE+TA treatments included were technically successful. TTP was 23.0 months in the TA group and 22.0 months in the TACE+TA group. There was no statistically significant difference in TTP (p = 0.64). Median OS was 69.7 months in the TA group and 64.6 months in the TACE+TA group. There was no statistically significant difference in OS (p = 0.14). The treatment cohorts had differences in AFP levels (p = 0.03) and BCLC stage (p = 0.047). Complication rates between patient groups were similar (p = 0.61).

CONCLUSION

For patients with HCC under 3 cm, TA alone and TACE+TA have similar outcomes in terms of TTP and OS, suggesting that TACE+TA may not be needed for these tumors unless warranted by tumor location or other technical consideration.

0806 Prescription Correlates of Nightmare Disorder Among Veterans

Introduction

In the James A. Haley Veterans Administration (JAHVA) Vista database, the ICD-9 code 307.47 for Nightmare disorder (ND) is infrequently used and appears independently of codes for PTSD. We wanted to determine if certain drugs that may affect sleep are associated with ND.

Methods

All patients with ND visiting JAHVA between 2007 and 2011 were selected along with control patients who visited JAHVA on one of 20 random days, one day each quarter year. Controls were assigned an index date reflecting their selection quarter. Associations with prescriptions for opioids, antidepressants (SSRI’s, SSNI’s, Tricyclics), antihistamines and benzodiazepine/Z-drugs were initially investigated. Two analyses were performed: risk factor analysis- patients with ND diagnosis dates (cases) or index dates (controls) prior to 2008 were excluded and only prescription dates that preceded the ND diagnosis or index date were considered; treatment analysis- cases and controls with a ND diagnosis date or index date after 2010 were excluded and only prescription dates that were subsequent to the ND diagnosis or index date respectively were considered. Logistic regression adjusting for age, gender, race and Hispanic ethnicity was used to determine the association between drug groups and ND.

Results

In risk factor analysis (667 cases, 14,739 controls), opioids and antihistamines were significantly less prevalent among would-be ND patients than controls (OR=0.627 and 0.610 respectively); no drug group was predictive of ND. In contrast, all drug groups were significantly associated with ND in treatment analysis (803 cases, 15,530 controls). The strongest associations were seen with benzodiazepine (OR=3.026; 95% CI: 2.472, 3.703) and SSRI (OR=2.789; 95% CI=2.316, 3.358) prescriptions.

Conclusion

Our data suggest that some JAHVA providers may be treating ND with medication, most notably with benzodiazepines/Z-drugs and antidepressants. The role of anti-histamine and opioid prescriptions needs further elucidation. The ramifications of these treatment decisions should be explored.

Support

This material is the result of work supported with resources and the use of facilities at the James A. Haley Veterans’ Hospital.

Efficacy of microsurgery in Reinke's oedema evaluated by traditional voice assessment integrated with the Vocal Extent Measure (VEM)

SUMMARY

There are few data analysing to what specific extent phonomicrosurgery improves vocal function in patients suffering from Reinke's oedema (RE). The recently introduced parameter vocal extent measure (VEM) seems to be suitable to objectively quantify vocal performance. The purpose of this clinical prospective study was to investigate the outcomes of phonomicrosurgery in 60 RE patients (6 male, 54 female; 56 ± 8 years ([mean ± SD]) by analysing its effect on subjective and objective vocal parameters with particular regard to VEM. Treatment efficacy was evaluated at three months after surgery by comparing pre- and postoperative videolaryngostroboscopy (VLS), auditory-perceptual assessment (RBH-status), voice range profile (VRP), acoustic-aerodynamic analysis and patient's self-assessment using the voice handicap index (VHI-9i). Phonomicrosurgically, all RE were carefully ablated. VLS revealed removal or substantial reduction of oedema with restored periodic vocal fold vibration. All subjective and most objective acoustic and aerodynamic parameters significantly improved. The VEM increased on average from 64 ± 37 to 88 ± 25 (p #x003C; 0.001) and the dysphonia severity index (DSI) from 0.5 ± 3.4 to 2.9 ± 1.9. Both parameters correlated significantly with each other (rs = 0.70). RBH-status revealed less roughness, breathiness and overall grade of hoarseness (2.0 ± 0.7 vs 1.3 ± 0.7). The VHI-9i-score decreased from 18 ± 8 to 12 ± 9 points. The average total vocal range enlarged by 4 ± 7 semitones, and the mean speaking pitch rose by 2 ± 4 semitones. These results confirm that: (1) the use of VEM in RE patients objectifies and quantifies their vocal capacity as documented in the VRP, and (2) phonomicrosurgery is an effective, objectively and subjectively satisfactory therapy to improve voice in RE patients.

Prestin autoantibodies screening in idiopathic sudden sensorineural hearing loss

OBJECTIVES

To define the clinical association of serum prestin autoantibodies and their impact on prognosis, as specific serum diagnostic markers in patients with idiopathic sudden sensorineural hearing loss (ISSNHL).

DESIGN

Sera from 63 patients with ISSNHL were screened prospectively for the presence of prestin autoantibodies by an enzyme-linked immunosorbent assay (Elisa) test. Serum was assayed for anti-prestin IgG antibodies using recombinant human prestin (SLC26 A5). Demographic, clinical, and audiometric variables were analyzed.

RESULTS

Two patients (3.17%) had demonstrable anti-prestin antibodies in serum (exact 95% CI: -1.16% to 7.5%). No statistically significant association was found between prestin autoantibodies and demographic or audiologic parameters.

CONCLUSIONS

This preliminary and novel study does not support the presence of an active humoral immune reaction against prestin in ISSNHL.

Stabilization of warfarin-binding pocket of VKORC1 and VKORL1 by a peripheral region determines their different sensitivity to warfarin inhibition

Essentials VKORL1 and VKORC1 have a similar overall structure and warfarin-binding pocket. A peripheral region stabilizing this pocket controls warfarin sensitivity of the VKOR paralogs. A human single nucleotide polymorphism in this region renders VKORL1 sensitive to warfarin. A group of warfarin-resistant mutations in VKORC1 acts by disrupting peripheral interactions.

SUMMARY

Background The human genome encodes two paralogs of vitamin-K-epoxide reductase, VKORC1 and VKORL1, that support blood coagulation and other vitamin-K-dependent processes. Warfarin inhibits both enzymes, but VKORL1 is relatively resistant to warfarin. Objectives To understand the difference between VKORL1 and VKORC1, and the cause of warfarin-resistant (WR) mutations in VKORC1. Methods We performed systematic mutagenesis and analyzed warfarin responses with a cell-based activity assay. Mass spectrometry analyses were used to detect cellular redox state. Results VKORC1 and VKORL1 adopt a similar intracellular redox state with four-transmembrane-helix topology. Most WR mutations identified in VKORC1 also confer resistance in VKORL1, indicating that warfarin inhibits these paralogs at a common binding site. A group of WR mutations, distant from the warfarin-binding site, show significantly less resistance in VKORL1 than in VKORC1, implying that their different warfarin responses are determined by peripheral interactions. Remarkably, we identify a critical peripheral region in which single mutations, Glu37Lys or His46Tyr, drastically increase the warfarin sensitivity of VKORL1. In the background of these warfarin-sensitive VKORL1 mutants, WR mutations showing relative less resistance in wild-type VKORL1 become much more resistant, suggesting a structural conversion to resemble VKORC1. At this peripheral region, we also identified a human single nucleotide polymorphism that confers warfarin sensitivity of VKORL1. Conclusions Peripheral regions of VKORC1 and VKORL1 primarily maintain the stability of their common warfarin-binding pocket, and differences of such interactions determine their relative sensitivity to warfarin inhibition. This new model also explains most WR mutations located at the peripheral regions of VKORC1.

Observation of the Self-Modulation Instability via Time-Resolved Measurements

Self-modulation of an electron beam in a plasma has been observed. The propagation of a long (several plasma wavelengths) electron bunch in an overdense plasma resulted in the production of multiple bunches via the self-modulation instability. Using a combination of a radio-frequency deflector and a dipole spectrometer, the time and energy structure of the self-modulated beam was measured. The longitudinal phase space measurement showed the modulation of a long electron bunch into three bunches with an approximately 200  keV/c amplitude momentum modulation. Demonstrating this effect is a breakthrough for proton-driven plasma accelerator schemes aiming to utilize the same physical effect.

Chirp Mitigation of Plasma-Accelerated Beams by a Modulated Plasma Density

Plasma-based accelerators offer the possibility to drive future compact light sources and high-energy physics applications. Achieving good beam quality, especially a small beam energy spread, is still one of the major challenges. Here, we propose to use a periodically modulated plasma density to shape the longitudinal fields acting on an electron bunch in the linear wakefield regime. With simulations, we demonstrate an on-average flat accelerating field that maintains a small beam energy spread.

Selection of the tagged photons by off axis heterodyne holography in ultrasound-modulated optical tomography

Ultrasound-modulated optical tomography (UOT) is a technique that images optical contrast deep inside scattering media. Heterodyne holography is a promising tool that is able to detect UOT-tagged photons with high efficiency. In this work, we describe theoretically the detection of the tagged photon in heterodyne holography-based UOT, show how to filter the untagged photon, and discuss the effect of shot noise. The discussion also considers speckle decorrelation. We show that optimal detection sensitivity can be reached, if the frame exposure time of the camera used to perform the holographic detection is on the order of the decorrelation time.

Thermal fluctuations of an interface near a contact line

The effect of thermal fluctuations near a contact line of a liquid interface partially wetting an impenetrable substrate is studied analytically and numerically. Promoting both the interface profile and the contact line position to random variables, we explore the equilibrium properties of the corresponding fluctuating contact line problem based on an interfacial Hamiltonian involving a "contact" binding potential. To facilitate an analytical treatment, we consider the case of a one-dimensional interface. The effective boundary condition at the contact line is determined by a dimensionless parameter that encodes the relative importance of thermal energy and substrate energy at the microscopic scale. We find that this parameter controls the transition from a partial wetting to a pseudopartial wetting state, the latter being characterized by a thin prewetting film of fixed thickness. In the partial wetting regime, instead, the profile typically approaches the substrate via an exponentially thinning prewetting film. We show that, independently of the physics at the microscopic scale, Young's angle is recovered sufficiently far from the substrate. The fluctuations of the interface and of the contact line give rise to an effective disjoining pressure, exponentially decreasing with height. Fluctuations therefore provide a regularization of the singular contact forces occurring in the corresponding deterministic problem.