Community-Level Factors Associated with Hepatocellular Carcinoma Incidence and Mortality: An Observational Registry Study

BACKGROUND

Hepatocellular carcinoma (HCC) incidence and outcomes vary across populations in the United States, but few studies evaluate local drivers of observed disparities. We measured HCC incidence at the community level and assessed community-level HCC risk factors with the goal of informing resource allocation to improve early case detection, which is associated with improved outcomes.

METHODS

Clinical and demographic data including census tract of residence for all adults diagnosed with HCC in the Connecticut Tumor Registry between 2008 and 2019 were combined with publicly available U.S. Census and Centers for Disease Control and Prevention (CDC) data at the ZIP Code tabulation area (ZCTA) level. The average annual incidence of HCC was calculated for each ZCTA and associations between community-level characteristics, HCC incidence, stage at diagnosis, and survival were evaluated.

RESULTS

Average annual HCC incidence during the study period was 8.9/100,000 adults and varied from 0 to 97.7 per 100,000 adults by ZCTA. At the community level, lower rates of high school graduation, higher rates of poverty, and rural community type were associated with higher HCC incidence. Persons with HCC living in the highest incidence ZCTAs were diagnosed at a younger age and were less likely to be alive at 1, 2, and 5 years after diagnosis.

CONCLUSIONS

Community-level socioeconomic factors are strongly associated with HCC incidence and survival in Connecticut.

IMPACT

This reproducible geo-localization approach using cancer registry, Census, and CDC data can be used to identify communities most likely to benefit from health system investments to reduce disparities in HCC.

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.

Advancing diagnosis and management of liver disease in adults through exome sequencing

BACKGROUND

Whole-exome sequencing (WES) is an effective tool for diagnosis in patients who remain undiagnosed despite a comprehensive clinical work-up. While WES is being used increasingly in pediatrics and oncology, it remains underutilized in non-oncological adult medicine, including in patients with liver disease, in part based on the faulty premise that adults are unlikely to harbor rare genetic variants with large effect size. Here, we aim to assess the burden of rare genetic variants underlying liver disease in adults at two major tertiary referral academic medical centers.

METHODS

WES analysis paired with comprehensive clinical evaluation was performed in fifty-two adult patients with liver disease of unknown etiology evaluated at two US tertiary academic health care centers.

FINDINGS

Exome analysis uncovered a definitive or presumed diagnosis in 33% of patients (17/52) providing insight into their disease pathogenesis, with most of these patients (12/17) not having a known family history of liver disease. Our data shows that over two-thirds of undiagnosed liver disease patients attaining a genetic diagnosis were being evaluated for cholestasis or hepatic steatosis of unknown etiology.

INTERPRETATION

This study reveals an underappreciated incidence and spectrum of genetic diseases presenting in adulthood and underscores the clinical value of incorporating exome sequencing in the evaluation and management of adults with liver disease of unknown etiology.

FUNDING

S.V. is supported by the NIH/NIDDK (K08 DK113109 and R01 DK131033-01A1) and the Doris Duke Charitable Foundation Grant #2019081. This work was supported in part by NIH-funded Yale Liver Center, P30 DK34989.

Spectral top-down recovery of latent tree models

Modeling the distribution of high-dimensional data by a latent tree graphical model is a prevalent approach in multiple scientific domains. A common task is to infer the underlying tree structure, given only observations of its terminal nodes. Many algorithms for tree recovery are computationally intensive, which limits their applicability to trees of moderate size. For large trees, a common approach, termed divide-and-conquer, is to recover the tree structure in two steps. First, separately recover the structure of multiple, possibly random subsets of the terminal nodes. Second, merge the resulting subtrees to form a full tree. Here, we develop spectral top-down recovery (STDR), a deterministic divide-and-conquer approach to infer large latent tree models. Unlike previous methods, STDR partitions the terminal nodes in a non random way, based on the Fiedler vector of a suitable Laplacian matrix related to the observed nodes. We prove that under certain conditions, this partitioning is consistent with the tree structure. This, in turn, leads to a significantly simpler merging procedure of the small subtrees. We prove that STDR is statistically consistent and bound the number of samples required to accurately recover the tree with high probability. Using simulated data from several common tree models in phylogenetics, we demonstrate that STDR has a significant advantage in terms of runtime, with improved or similar accuracy.

Adherence to Tumor Board Recommendations in the Treatment of Patients with Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is a heterogeneous disease that typically arises in the setting of chronic liver disease, making treatment selection complex. Multidisciplinary liver tumor boards (MDLTB) have been shown to improve outcomes in patients with HCC. However, in many cases, patients evaluated by MDLTBs ultimately do not receive the board's recommended treatment.

Purpose

This study aims to assess adherence to MDLTB recommendations for the treatment of HCC, the reasons for non-adherence, and the survival of Barcelona Clinic Liver Cancer (BCLC) Stage A patients treated with curative treatment compared to palliative locoregional therapy.

Patients and Methods

A single-site, retrospective cohort study was conducted of all patients with treatment-naïve HCC who were evaluated by an MDLTB at a tertiary care center in Connecticut between 2013 and 2016, of which 225 patients met inclusion criteria. Investigators conducted a chart review and recorded adherence to the MDLTB's recommendations, and in cases of discordance, evaluated and recorded the underlying cause; investigators assessed MDLTB recommendations' compliance with BCLC guidelines. Survival data was accrued through February 1st of 2022 and analyzed via Kaplan-Meier analysis and multivariate Cox regression.

Results

Treatment adherent to MDLTB recommendations occurred in 85.3% of patients (n=192). The majority of non-adherence occurred in the management of BCLC Stage A disease. In cases where adherence was possible but the recommendation was not followed, most discrepancies were whether to treat with curative or palliative intent (20/24), with almost all discrepancies occurring in patients (19/20) with BCLC Stage A disease. For patients with Stage A unifocal HCC, those who received curative therapy lived significantly longer than patients who received palliative locoregional therapy (5.55 years vs 4.26 years, p=0.037).

Conclusion

Most forms of non-adherence to MDLTB recommendations were unavoidable; however, treatment discordance in the management of patients with BCLC Stage A unifocal disease may present an opportunity for clinically significant quality improvement.

Immune checkpoint inhibitors in malignancies after liver transplantation: A systematic review and pooled analysis

BACKGROUND AND AIMS

Treatment of de novo malignancies and recurrent hepatocellular carcinoma with immune checkpoint inhibitors (ICI) in liver transplant recipients (LT) is an attractive strategy that is infrequently pursued because of the lack of strong evidence regarding their safety and efficacy. In this systematic review with pooled analysis, we aimed to assess safety and efficacy of ICI therapy following LT.

METHODS

We performed a systematic search of case reports and series published until January 2022. We included 31 publications reporting a total of 52 patients treated with ICIs after LT and assessed in a pooled analysis the risk of graft rejection and the outcome of ICI therapy.

RESULTS

Acute graft rejection occurred in 15 patients (28.8%) and 7 patients (13.4% of the total cohort) died because of graft loss. Rejection was associated with shorter overall survival (OS) (17.2 months, confidence interval [CI] 12.1-22.2 vs. 3.5 months, CI 1.6-5.4, p < 0.001). Disease control rate was 44.2% (n = 23), and in these patients, OS was longer than in non-responders (26.4 months, CI 20.8-32.0 vs. 3.4 months, CI 2.1-4.7, p < 0.001).

CONCLUSIONS

Observational, off-label experience suggests that treatment with ICI for advanced malignancies in LT recipients might not be discarded a priori. This notwithstanding, ICI treatment in these patients is associated with a substantial risk of graft rejection and mortality. Prospective studies are needed to provide adequate safety and efficacy figures of ICI treatment in this fragile population.

Holistic management of hepatocellular carcinoma: The hepatologist's comprehensive playbook

Hepatocellular carcinoma (HCC) is a common complication in patients with chronic liver disease and leads to significant morbidity and mortality. Liver disease and liver cancer are preventable by mitigating and managing common risk factors, including chronic hepatitis B and C infection, alcohol use, diabetes, obesity and other components of the metabolic syndrome. The management of patients with HCC requires treatment of the malignancy and adequate control of the underlying liver disease, as preserving liver function is critical for successful cancer treatment and may have a relevant prognostic role independent of HCC management. Hepatologists are the ideal providers to guide the care of patients with HCC as they are trained to identify patients at risk, apply appropriate surveillance strategies, assess and improve residual liver function, evaluate candidacy for transplant, provide longitudinal care to optimize and preserve liver function during and after HCC treatment, survey for cancer recurrence and manage its risk factors, and prevent and treat decompensating events. We highlight the need for a team-based holistic approach to the patient with liver disease and HCC and identify necessary gaps in current care and knowledge.

Diabetes medications and risk of HCC

Type 2 diabetes mellitus is a recognized risk factor for HCC in patients with liver disease, independent from the etiology of their liver disease. Hence, prevention and treatment of type 2 diabetes mellitus and its underlying cause, insulin resistance, should be considered a treatment target for patients with liver disease. The drug armamentarium for diabetes is wide and consists of agents with insulin-sensitizing activity, agents that stimulate insulin secretion, insulin itself, and agents that reduce gastrointestinal and urinary glucose absorption. From an endocrinology perspective, the main goal of treatment is the achievement of euglycemia; however, in patients at risk of, or with known underlying liver disease, the choice of diabetic medication as it relates to potential hepatic carcinogenesis remains complex and should be carefully considered. In the last decade, increasing evidence has suggested that metformin may reduce the risk of HCC, whereas evidence for other classes of diabetic medications, particularly some of the newer agents including the sodium glucose cotransporter-2 inhibitors and glucagon-like peptide-1 receptor agonists, is fewer and often inconsistent. In this review, we aim to summarize the current evidence on the potential effects of the most widely used diabetic agents on liver cancer tumorigenesis.

Artificial intelligence-enabled electrocardiographic algorithm for the detection of left ventricular dysfunction in emergency department patients undergoing high-sensitivity cardiac troponin T

Background

Artificial intelligence-augmented electrocardiogram (AI-ECG) algorithms have been developed from the standard 12-lead ECG and validated for the recognition of left ventricular systolic dysfunction (LVSD), defined as LV ejection fraction (LVEF)≤35%. Whether AI-ECG facilitates identification of LVSD and is associated with adverse outcomes in emergency department (ED) patients undergoing high-sensitivity cardiac troponin (hs-cTnT) testing is uncertain.

Purpose

To investigate the diagnostic and prognostic performance of AI-ECG in ED patients undergoing hs-cTnT measurement.

Methods

Observational US cohort study of ED patients undergoing hs-cTnT measurement. Cases with hs-cTnT increases &gt;sex-specific 99th percentiles were adjudicated following the Fourth Universal Definition of Myocardial Infarction (MI). Post-discharge major adverse cardiac events (MACE) included death, MI, heart failure (HF) hospitalization, stroke or transient ischemic attack, and new onset atrial fibrillation/flutter during 2-years follow-up. The AI-ECG network output, which is a continuous number between 0–1, that provides a probability of LVSD, was obtained for each patient from the first ECG during the index presentation. An AI-ECG threshold of ≥0.256 indicates a positive screen that correlates with a high probability of LVSD.

Results

Among 1977 patients, 1729 (87%) had a negative AI-ECG screen, while 248 (13%) had a positive AI-ECG screen. Patients with a positive AI-ECG screen were older and had more comorbidities. As compared to patients with hs-cTnT≤99th percentile in whom AI-ECG was positive in 5.8%, those with hs-cTnT&gt;99th percentile had a positive AI-ECG in 22% of cases (p&lt;0.0001). Based on adjudicated diagnoses, the frequency of a positive AI-ECG was 20% in myocardial injury, 38% in type 1 MI, and 20% in type 2 MI. At 2-years follow-up, as compared to patients with a negative AI-ECG, those with a positive AI-ECG had a higher risk for MACE (48% vs. 21%, p&lt;0.0001, adjusted HR 1.39, 95% CI 1.11–1.75) (Figure 1), mainly because of more deaths (43% vs. 30%, p=0.004) and HF hospitalizations (36% vs. 13%, p&lt;0.0001). A positive AI-ECG was associated with a higher risk for MACE (60% vs. 41%, p&lt;0.0001, adjusted HR 1.30, 95% CI 1.02–1.64) in those with hs-cTnT increases &gt;99th percentile, but not in those without hs-cTnT increases. Among patients with an echocardiogram during index presentation or within 30-days (n=452), the diagnostic accuracy of AI-ECG for LVEF ≤35% was 81.4% (95% CI 77.5, 84.9) with a negative predictive value of 96.5% (95% CI 94.0, 98.2). A normal LVEF (&gt;50%) was observed in 87% of those with a negative AI ECG, whereas in those with a positive AI-ECG LVEF was reduced (&lt;50%) in 60%.

Conclusions

Among ED patients evaluated with hs-cTnT, a positive AI-ECG screen for LVSD identifies patients at high risk of MACE. These findings are largely because of more deaths and HF hospitalizations in those with hs-cTnT increases &gt;sex-specific 99th percentiles.

Funding Acknowledgement

Type of funding sources: None.

Optimization of the BCLC Staging System for Locoregional Therapy for Hepatocellular Carcinoma by Using Quantitative Tumor Burden Imaging Biomarkers at MRI

Background Patients with intermediate- and advanced-stage hepatocellular carcinoma (HCC) represent a highly heterogeneous patient collective with substantial differences in overall survival. Purpose To evaluate enhancing tumor volume (ETV) and enhancing tumor burden (ETB) as new criteria within the Barcelona Clinic Liver Cancer (BCLC) staging system for optimized allocation of patients with intermediate- and advanced-stage HCC to undergo transarterial chemoembolization (TACE). Materials and Methods In this retrospective study, 682 patients with HCC who underwent conventional TACE or TACE with drug-eluting beads from January 2000 to December 2014 were evaluated. Quantitative three-dimensional analysis of contrast-enhanced MRI was performed to determine thresholds of ETV and ETB (ratio of ETV to normal liver volume). Patients with ETV below 65 cm³ or ETB below 4% were reassigned to BCLC B_n, whereas patients with ETV or ETB above the determined cutoffs were restratified or remained in BCLC C_n by means of stepwise verification of the median overall survival (mOS). Results This study included 494 patients (median age, 62 years [IQR, 56-71 years]; 401 men). Originally, 123 patients were classified as BCLC B with mOS of 24.3 months (95% CI: 21.4, 32.9) and 371 patients as BCLC C with mOS of 11.9 months (95% CI: 10.5, 14.8). The mOS of all included patients (including the BCLC B and C groups) was 15 months (95% CI: 12.3, 17.2). A total of 152 patients with BCLC C tumors were restratified into a new BCLC B_n class, in which the mOS was then 25.1 months (95% CI: 21.8, 29.7; P < .001). The mOS of the remaining patients (ie, BCLC C_n group) (n = 222; ETV ≥65 cm³ or ETB ≥4%) was 8.4 months (95% CI: 6.1, 11.2). Conclusion Substratification of patients with intermediate- and advanced-stage hepatocellular carcinoma according to three-dimensional quantitative tumor burden identified patients with a survival benefit from transarterial chemoembolization before therapy. © RSNA, 2022 Online supplemental material is available for this article.

Super-resolution multi-reference alignment

We study super-resolution multi-reference alignment, the problem of estimating a signal from many circularly shifted, down-sampled and noisy observations. We focus on the low SNR regime, and show that a signal in ℝ M is uniquely determined when the number L of samples per observation is of the order of the square root of the signal's length ( L = O ( M ) ). Phrased more informally, one can square the resolution. This result holds if the number of observations is proportional to 1/SNR³. In contrast, with fewer observations recovery is impossible even when the observations are not down-sampled (L = M). The analysis combines tools from statistical signal processing and invariant theory. We design an expectation-maximization algorithm and demonstrate that it can super-resolve the signal in challenging SNR regimes.

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.

Spectral neighbor joining for reconstruction of latent tree Models

A common assumption in multiple scientific applications is that the distribution of observed data can be modeled by a latent tree graphical model. An important example is phylogenetics, where the tree models the evolutionary lineages of a set of observed organisms. Given a set of independent realizations of the random variables at the leaves of the tree, a key challenge is to infer the underlying tree topology. In this work we develop Spectral Neighbor Joining (SNJ), a novel method to recover the structure of latent tree graphical models. Given a matrix that contains a measure of similarity between all pairs of observed variables, SNJ computes a spectral measure of cohesion between groups of observed variables. We prove that SNJ is consistent, and derive a sufficient condition for correct tree recovery from an estimated similarity matrix. Combining this condition with a concentration of measure result on the similarity matrix, we bound the number of samples required to recover the tree with high probability. We illustrate via extensive simulations that in comparison to several other reconstruction methods, SNJ requires fewer samples to accurately recover trees with a large number of leaves or long edges.

The Spectral Underpinning of word2vec

Word2vec introduced by Mikolov et al. is a word embedding method that is widely used in natural language processing. Despite its success and frequent use, a strong theoretical justification is still lacking. The main contribution of our paper is to propose a rigorous analysis of the highly nonlinear functional of word2vec. Our results suggest that word2vec may be primarily driven by an underlying spectral method. This insight may open the door to obtaining provable guarantees for word2vec. We support these findings by numerical simulations. One fascinating open question is whether the nonlinear properties of word2vec that are not captured by the spectral method are beneficial and, if so, by what mechanism.

Health related quality of life in chronic liver diseases

BACKGROUND & AIMS

The impact of chronic liver diseases (CLDs) on health-related quality of life (HRQoL) is relevant to understand the burden of these conditions and inform decision-making processes related to their care. Studies simultaneously comparing the HRQoL of patients affected by the major CLDs to that of the general population are still lacking and are the subject of this study.

METHODS

Using the EQ-5D-3L questionnaire, we analysed and compared HRQoL data from 2962 Italian patients affected by CLDs and forming a representative sample of the general Italian population (6800 individuals). Exploratory analyses were conducted to investigate the effects of each CLD on HRQoL, using the general population as reference and adjusting for possible confounders.

RESULTS

Patients with CLDs (HBV, HCV, PSC, PBC, AIH, NAFLD/NASH) in the chronic hepatitis stage and with compensated cirrhosis (CC) showed HRQoL similar to the general population. However, AIH were more likely to report problems in self-care and lower EQ-5D VAS score, while NAFLD/NASH and HCV showed an increased risk of anxiety/depression. On the other hand, with progression to more advanced stages of liver disease (DC or HCC), HRQoL decreased significantly with higher risk of reporting problems in the physical domains, and significant reductions in the VAS and utility index scores.

CONCLUSIONS

Different subtypes of CLD affected different QoL domains. This study therefore provides a real estimate of the impact of CLDs on patients' HRQoL, and represents a much needed tool to inform decision-making while assessing the effectiveness and cost-effectiveness of the care of these patients.

Liver Transplantation in the Time of COVID19: Barriers and Ethical considerations for Management and Next Steps

The recent outbreak of the novel virus severe acute respiratory syndrome coronavirus‐2 (SARS‐CoV‐2), which causes the corona virus disease of 2019 (COVID19), has spread globally and affects millions of people. This pandemic has taxed our health care system and disrupted normal operations, even life‐saving procedures, such as liver transplants. During these unprecedented times, providers and patients are imperiled and resources for diagnosis and care may be limited. Continuing to perform resource‐intense advanced procedures is challenging, as is caring for patients with end‐stage liver disease or patients with urgent needs for liver tumor control. Liver transplantation, in particular, requires critical resources, like blood products and critical care beds, which are fairly limited in the COVID19 pandemic. The potential of COVID19 infections in posttransplant recipients on immunosuppression and staff contacts further adds to the complexity. Therefore, transplant programs must reevaluate the ethicality, feasibility, and safety of performing liver transplants during this pandemic. Herein, we discuss the clinical and ethical challenges posed by performing liver transplants and offer guidance for managing patients with end‐stage liver disease during the COVID19 pandemic.

Randomized near-neighbor graphs, giant components and applications in data science

If we pick n random points uniformly in [0, 1] d and connect each point to its c d log n-nearest neighbors, where d ≥ 2 is the dimension and c d is a constant depending on the dimension, then it is well known that the graph is connected with high probability. We prove that it suffices to connect every point to c d,1 log log n points chosen randomly among its c d,2 log n-nearest neighbors to ensure a giant component of size n - o(n) with high probability. This construction yields a much sparser random graph with ~ n log log n instead of ~ n log n edges that has comparable connectivity properties. This result has nontrivial implications for problems in data science where an affinity matrix is constructed: instead of connecting each point to its k nearest neighbors, one can often pick k' ≪ k random points out of the k nearest neighbors and only connect to those without sacrificing quality of results. This approach can simplify and accelerate computation; we illustrate this with experimental results in spectral clustering of large-scale datasets.

Pathophysiology of Hepatic Encephalopathy

Hepatic encephalopathy (HE) is one of the major clinical decompensations of cirrhosis, with a high impact on health care resource utilization and cost. For an effective and comprehensive management of HE, the clinicians need to understand the pathophysiologic mechanisms of HE. This review describes the multiorgan processes involved in HE and how several HE precipitants and treatment strategies act on ammonia production, excretion, and neurotoxicity, including the impact of diabetes and use of cannabinoids. The authors also discuss the current and future role of gut microbiome, systemic/central inflammation, and various neurotransmitters for the pathogenesis and treatment of HE.

NEWTON CORRECTION METHODS FOR COMPUTING REAL EIGENPAIRS OF SYMMETRIC TENSORS

Real eigenpairs of symmetric tensors play an important role in multiple applications. In this paper we propose and analyze a fast iterative Newton-based method to compute real eigenpairs of symmetric tensors. We derive sufficient conditions for a real eigenpair to be a stable fixed point for our method and prove that given a sufficiently close initial guess, the convergence rate is quadratic. Empirically, our method converges to a significantly larger number of eigenpairs compared with previously proposed iterative methods, and with enough random initializations typically finds all real eigenpairs. In particular, for a generic symmetric tensor, the sufficient conditions for local convergence of our Newton-based method hold simultaneously for all its real eigenpairs.

Multiparameter cell-tracking intrinsic cytometry for single-cell characterization

An abundance of label-free microfluidic techniques for measuring cell intrinsic markers exists, yet these techniques are seldom combined because of integration complexity such as restricted physical space and incompatible modes of operation. We introduce a multiparameter intrinsic cytometry approach for the characterization of single cells that combines ≥2 label-free measurement techniques onto the same platform and uses cell tracking to associate the measured properties to cells. Our proof-of-concept implementation can measure up to five intrinsic properties including size, deformability, and polarizability at three frequencies. Each measurement module along with the integrated platform were validated and evaluated in the context of chemically induced changes in the actin cytoskeleton of cells. viSNE and machine learning classification were used to determine the orthogonality between and the contribution of the measured intrinsic markers for cell classification.

The impact of vitamin D status and parameters of calcium metabolism in patients with primary hyperparathyroidism

BACKGROUND

There is ample evidence associating vitamin D deficiency in primary hyperparathyroidism (PHP) patients with more severe disease manifestations and increased risk of postoperative hypocalcemia. Yet, there is limited data regarding the safety of vitamin D repletion in these patients.

AIM

To assess the safety of vitamin D repletion in PHP patients in a real-world setting.

DESIGN

We included patients with asymptomatic PHP and few symptomatic patients who declined surgery, followed in our clinic, and treated on a routine basis with 2000 IU/day of vitamin D3.

METHODS

Serum calcium (sCa), PTH, 25-hydroxyvitamin D, and 24 h urinary calcium (uCa) and creatinine collections were compared between the lowest and the highest vitamin D time points.

RESULTS

There were 40 patients of a mean age was 63 ± 10 years. 25(OH)D at lowest and highest vitamin D time points was 15.5 ± 6.2 ng/ml and 33.2 ± 8, respectively (P < 0.001). Serum calcium was not affected by the changes in vitamin D levels. In none of the patients did sCa exceed 11.5 mg/dL. uCa was 220 ± 110 mg/24 h at the lowest vitamin D time point and 260 ± 140 at the highest vitamin D time point (P = 0.14). uCa exceeded 400 mg/24 h in two vs. five patients (P = 0.23) at the lowest and highest vitamin D time points, respectively. PTH was not significantly different between the different vitamin D time points.

DISCUSSION/CONCLUSION

Vitamin D repletion in PHP seems safe. Considering the documented adverse influence of vitamin D deficiency in PHP, particularly on skeletal manifestations and on the postoperative course, vitamin D repletion is warranted.

Effectiveness and response predictors of omalizumab in a severe allergic asthma population with a high prevalence of comorbidities: the Australian Xolair Registry

BACKGROUND

Severe asthma is a high impact disease. Omalizumab targets the allergic inflammatory pathway; however, effectiveness data in a population with significant comorbidities are limited.

AIMS

To describe severe allergic asthma, omalizumab treatment outcomes and predictors of response among the Australian Xolair Registry participants.

METHODS

A web-based post-marketing surveillance registry was established to characterise the use, effectiveness and adverse effects of omalizumab (Xolair) for severe allergic asthma.

RESULTS

Participants (n = 192) (mean age 51 years, 118 female) with severe allergic asthma from 21 clinics in Australia were assessed, and 180 received omalizumab therapy. They had poor asthma control (Asthma Control Questionnaire, ACQ-5, mean score 3.56) and significant quality of life impairment (Asthma-related Quality of Life Questionnaire score 3.57), and 52% were using daily oral corticosteroid (OCS). Overall, 95% had one or more comorbidities (rhinitis 48%, obesity 45%, cardiovascular disease 23%). The omalizumab responder rate, assessed by an improvement of at least 0.5 in ACQ-5, was high at 83%. OCS use was significantly reduced. The response in participants with comorbid obesity and cardiovascular disease was similar to those without these conditions. Baseline ACQ-5 ≥ 2.0 (P = 0.002) and older age (P = 0.05) predicted the magnitude of change in ACQ-5 in response to omalizumab. Drug-related adverse events included anaphylactoid reactions (n = 4), headache (n = 2) and chest pains (n = 1).

CONCLUSION

Australian patients with severe allergic asthma report a high disease burden and have extensive comorbidity. Symptomatic response to omalizumab was high despite significant comorbid disease. Omalizumab is an effective targeted therapy for severe allergic asthma with comorbidity in a real-life setting.

A Review of Antiviral Use for the Treatment of Chronic Hepatitis B Virus Infection in Pregnant Women

Mother-to-child transmission (MTCT) of hepatitis B virus (HBV) remains high even with the proper use of active-passive immunoprophylaxis in newborns. Mothers with significant viremia are at a much higher risk of MTCT; therefore, treatments aimed at lowering HBV DNA levels during pregnancy may ultimately decrease global disease burden. The exact threshold for treatment remains controversial; however, most studies have accepted levels greater than 2 × 5 log₁₀ IU/mL as significant viremia. We reviewed the most recent literature on antiviral efficacy, maternal and fetal safety, and viral resistance patterns when used for short-duration therapy in pregnancy. The literature review shows that antiviral therapy during pregnancy significantly reduces maternal HBV DNA levels with subsequent reductions in infant HBV infections. Tenofovir disoproxil fumarate (TDF) is associated with mild gastrointestinal distress and may cause decreased fetal bone growth (although long-term studies are needed to evaluate the clinical significance of this finding), and the impact of this drug is likely limited when use is restricted to the third trimester. Lamivudine and telbivudine remain inferior to TDF in regard to resistance profiles. Overall, TDF, lamivudine, and telbivudine in conjunction with standard immunoprophylaxis are recommended for use in pregnant women with significant HBV viremia (>2 × 5 log₁₀ IU/mL) to prevent MTCT and appear reassuring in regard to their maternal and fetal safety profiles.

Dextroamphetamine sulfate provided quick relief of severe post-partum depression that was recalcitrant to standard antidepressants and psychotherapy

PURPOSE

To determine if dextroamphetamine sulfate could improve symptoms of post-partum depression.

MATERIALS AND METHODS

A woman with severe post-partum depression that was resistant to standard antidepressant therapy and psychotherapy was treated with dextroamphetamine sulfate extended release capsules 15 mg/day.

RESULTS

A quick and complete abrogation of the depression ensued along with improvement of migraine headaches, insomnia, and chronic fatigue.

CONCLUSIONS

Dextr6amphetamine sulfate should be considered as a treatment modality for post-partum depression.

Real-life effectiveness of omalizumab in severe allergic asthma above the recommended dosing range criteria

BACKGROUND

Omalizumab (Xolair) dosing in severe allergic asthma is based on serum IgE and bodyweight. In Australia, patients eligible for omalizumab but exceeding recommended ranges for IgE (30-1500 IU/mL) and bodyweight (30-150 kg) may still receive a ceiling dose of 750 mg/4 weeks. About 62% of patients receiving government-subsidized omalizumab are enrolled in the Australian Xolair Registry (AXR).

OBJECTIVES

To determine whether AXR participants above the recommended dosing ranges benefit from omalizumab and to compare their response to within-range participants.

METHODS

Data were stratified according to dose range status (above-range or within-range). Further sub-analyses were conducted according to the reason for being above the dosing range (IgE only vs. IgE and weight).

RESULTS

Data for 179 participants were analysed. About 55 (31%) were above recommended dosing criteria; other characteristics were similar to within-range participants. Above-range participants had higher baseline IgE [812 (IQR 632, 1747) IU/mL vs. 209 (IQR 134, 306) IU/mL] and received higher doses of omalizumab [750 (IQR 650, 750) mg] compared to within-range participants [450 (IQR, 300, 600) mg]. At 6 months, improvements in Juniper 5-item Asthma Control Questionnaire (ACQ-5, 3.61 down to 2.01 for above-range, 3.47 down to 1.93 for within-range, P < 0.0001 for both) and Asthma Quality of Life Questionnaire (AQLQ mean score (3.22 up to 4.41 for above-range, 3.71 up to 4.88 for within-range, P < 0.0001) were observed in both groups. Forced expiratory volume in one second (FEV₁ ) improved among above-range participants. There was no difference in response between above-range and within-range participants. Above-range participants due to either IgE alone or IgE and weight had similar improvements in ACQ-5, AQLQ and FEV₁ .

CONCLUSIONS AND CLINICAL RELEVANCE

Patients with severe allergic asthma above recommended dosing criteria for omalizumab have significantly improved symptom control, quality of life and lung function to a similar degree to within-range participants, achieved without dose escalation above 750 mg.

Assessing the feasibility of a web-based registry for multiple orphan lung diseases: the Australasian Registry Network for Orphan Lung Disease (ARNOLD) experience

Background

We investigated the feasibility of using an online registry to provide prevalence data for multiple orphan lung diseases in Australia and New Zealand.

Methods

A web-based registry, The Australasian Registry Network of Orphan Lung Diseases (ARNOLD) was developed based on the existing British Paediatric Orphan Lung Disease Registry. All adult and paediatric respiratory physicians who were members of the Thoracic Society of Australia and New Zealand in Australia and New Zealand were sent regular emails between July 2009 and June 2014 requesting information on patients they had seen with any of 30 rare lung diseases. Prevalence rates were calculated using population statistics.

Results

Emails were sent to 649 Australian respiratory physicians and 65 in New Zealand. 231 (32.4 %) physicians responded to emails a total of 1554 times (average 7.6 responses per physician). Prevalence rates of 30 rare lung diseases are reported.

Conclusions

A multi-disease rare lung disease registry was implemented in the Australian and New Zealand health care settings that provided prevalence data on orphan lung diseases in this region but was limited by under reporting.

Resolution of pelvic pain related to adenomyosis following treatment with dextroamphetamine sulfate

PURPOSE

To determine if treatment with dextroamphetamine sulfate can reduce pelvic pain that was attributed to adenomyosis.

MATERIALS AND METHODS

Dextroamphetamine sulfate was given to a 32-year-old woman who suffered on a daily basis from severe chronic pelvic pain that was not relieved by laparoscopic removal of endometriosis by oral contraceptive and ibuprofen. The adenomyosis was diagnosed by magnetic resonance imaging.

RESULTS

Within three months the pain was completely gone and has remained absent for six months.

CONCLUSIONS

Dextroamphetamine sulfate relieved pain from adenomyosis similar to its effect on endometriosis.

Measurement of the cosmic microwave background polarization lensing power spectrum with the POLARBEAR experiment

Gravitational lensing due to the large-scale distribution of matter in the cosmos distorts the primordial cosmic microwave background (CMB) and thereby induces new, small-scale B-mode polarization. This signal carries detailed information about the distribution of all the gravitating matter between the observer and CMB last scattering surface. We report the first direct evidence for polarization lensing based on purely CMB information, from using the four-point correlations of even- and odd-parity E- and B-mode polarization mapped over ∼30 square degrees of the sky measured by the POLARBEAR experiment. These data were analyzed using a blind analysis framework and checked for spurious systematic contamination using null tests and simulations. Evidence for the signal of polarization lensing and lensing B modes is found at 4.2σ (stat+sys) significance. The amplitude of matter fluctuations is measured with a precision of 27%, and is found to be consistent with the Lambda cold dark matter cosmological model. This measurement demonstrates a new technique, capable of mapping all gravitating matter in the Universe, sensitive to the sum of neutrino masses, and essential for cleaning the lensing B-mode signal in searches for primordial gravitational waves.

Evidence for gravitational lensing of the cosmic microwave background polarization from cross-correlation with the cosmic infrared background

We reconstruct the gravitational lensing convergence signal from cosmic microwave background (CMB) polarization data taken by the Polarbear experiment and cross-correlate it with cosmic infrared background maps from the Herschel satellite. From the cross spectra, we obtain evidence for gravitational lensing of the CMB polarization at a statistical significance of 4.0σ and indication of the presence of a lensing B-mode signal at a significance of 2.3σ. We demonstrate that our results are not biased by instrumental and astrophysical systematic errors by performing null tests, checks with simulated and real data, and analytical calculations. This measurement of polarization lensing, made via the robust cross-correlation channel, not only reinforces POLARBEAR auto-correlation measurements, but also represents one of the early steps towards establishing CMB polarization lensing as a powerful new probe of cosmology and astrophysics.

Exhaled breath condensate in pediatric asthma: promising new advance or pouring cold water on a lot of hot air? a systematic review

BACKGROUND

Exhaled breath condensate (EBC) analysis is a simple non-invasive technique that allows repeated collection of breath samples with a minimum of inconvenience for the subject. These breath samples can potentially indicate lung disease activity and given the ease of collection, EBC is becoming a useful research tool in the study of respiratory diseases. It has the potential to be used in both population-based studies and in the context of pediatric asthma it may prove useful in diagnosis and monitoring.

METHODS

A systematic review was conducted to identify studies of EBC markers in childhood asthma.

RESULTS

Most of the studies were cross-sectional in design, and the results suggest that simple chemical entities such as hydrogen ions (as pH), hydrogen peroxide, and oxides of nitrogen are associated with pediatric allergic asthma and exacerbations. In addition, more complex molecules including leukotrienes, prostaglandins, and cytokines such as the interleukins IL-4 and IL-5 are also elevated in the breath of those with asthma.

CONCLUSION

EBC has the potential to aid diagnosis, and to evaluate the inflammatory status of asthmatic children. Future studies may be able to refine further how best to collect EBC samples, to interpret them, and the technique has the potential to allow repeated sampling which will allow studies of natural history, pathogenesis and response to treatment to be undertaken.

NID2 and HOXA9 promoter hypermethylation as biomarkers for prevention and early detection in oral cavity squamous cell carcinoma tissues and saliva

Differentially methylated oral squamous cell carcinoma (OSCC) biomarkers, identified in vitro and validated in well-characterized surgical specimens, have shown poor clinical correlation in cohorts with different risk profiles. To overcome this lack of relevance, we used the HumanMethylation27 BeadChip, publicly available methylation and expression array data, and quantitative methylation specific PCR to uncover differential methylation in OSCC clinical samples with heterogeneous risk profiles. A two stage design consisting of discovery and prevalence screens was used to identify differential promoter methylation and deregulated pathways in patients diagnosed with OSCC and head and neck squamous cell carcinoma. Promoter methylation of KIF1A (κ = 0.64), HOXA9 (κ = 0.60), NID2 (κ = 0.60), and EDNRB (κ = 0.60) had a moderate to substantial agreement with clinical diagnosis in the discovery screen. HOXA9 had 68% sensitivity, 100% specificity, and a 0.81 Area Under the Curve (AUC). NID2 had 71% sensitivity, 100% specificity, and a 0.79 AUC. In the prevalence screen, HOXA9 (κ = 0.82) and NID2 (κ = 0.80) had an almost perfect agreement with histologic diagnosis. HOXA9 had 85% sensitivity, 97% specificity, and a 0.95 AUC. NID2 had 87% sensitivity, 95% specificity, and a 0.91 AUC. A HOXA9 and NID2 gene panel had 94% sensitivity, 97% specificity, and a 0.97 AUC. In saliva, from OSCC cases and controls, HOXA9 had 75% sensitivity, 53% specificity, and a 0.75 AUC. NID2 had 87% sensitivity, 21% specificity, and a 0.73 AUC. This phase I Biomarker Development Trial identified a panel of differentially methylated genes in normal and OSCC clinical samples from patients with heterogeneous risk profiles. This panel may be useful for early detection and cancer prevention studies.