The Body Mass Index Paradox in Head and Neck Cancer: A Systematic Review and Meta-Analysis.

The body mass index (BMI) paradox describes that among patients with certain cancers, higher pretreatment BMI may be associated with improved survival. We examine the impact of BMI on overall survival (OS) in head and neck squamous cell carcinoma (HNSCC) patients. A literature search was performed, and articles using hazard ratios to describe the prognostic impact of BMI on OS in HNSCC were included. Random-effects DerSimonian and Laird methods were employed for meta-analysis. Meta-analysis of OS indicated a lower hazards of death in the overweight (BMI: 25 kg/m2-30 kg/m2) compared to the normal weight (BMI: 18.5 kg/m2-25 kg/m2). This protective relationship loses significance when BMI exceeds 30 kg/m2. Underweight patients (BMI < 18.5 kg/m2) demonstrate higher hazards of death compared to normal weight patients. Compared to HNSCC patients with normal weight, being overweight up to a BMI of 30 kg/m2 is a positive predictor of OS, while being underweight confers a prognostic disadvantage. Further studies are needed to determine the mechanisms by which increased body mass influences survival outcomes in HNSCC.

Clinical and radiographic characteristics of diffuse astrocytic glioma, IDH-wildtype, with molecular features of glioblastoma: a single institution review.

PURPOSE: Genetic analyses of gliomas have identified key molecular features that impact treatment paradigms beyond conventional histomorphology. Despite at-times lower grade histopathologic appearances, IDH-wildtype infiltrating gliomas expressing certain molecular markers behave like higher-grade tumors. For IDH-wildtype infiltrating gliomas lacking traditional features of glioblastoma, these markers form the basis for the novel diagnosis of diffuse astrocytic glioma, IDH-wildtype (wt), with molecular features of glioblastoma (GBM), WHO grade-IV (DAG-G). However, given the novelty of this approach to diagnosis, literature detailing the exact clinical, radiographic, and histopathologic findings associated with these tumors remain in development. METHODS: Data for 25 patients matching the DAG-G diagnosis were obtained from our institution's retrospective database. Information regarding patient demographics, treatment regimens, radiographic imaging, and genetic pathology were analyzed to determine association with clinical outcomes. RESULTS: The initial radiographic findings, histopathology, and symptomatology of patients with DAG-G were similar to lower-grade astrocytomas (WHO grade 2/3). Overall survival (OS) and progression free survival (PFS) associated with our cohort, however, were similar to that of IDH-wt GBM, indicating a more severe clinical course than expected from other associated features (15.1 and 5.39 months respectively). CONCLUSION: Despite multiple features similar to lower-grade gliomas, patients with DAG-G experience clinical courses similar to GBM. Such findings reinforce the need for biopsy and subsequent analysis of molecular features associated with any astrocytoma regardless of presenting characteristics.

Cluster analysis of urinary tobacco biomarkers among U.S. adults: Population Assessment of Tobacco and Health (PATH) biomarker study (2013-2014).

Tobacco use delivers nicotine, tobacco-specific nitrosamines (TSNAs), volatile organic compounds (VOCs), and polycyclic aromatic hydrocarbons (PAHs), which are metabolized and excreted in urine offering useful biomarkers of exposure. Previous studies compared individual toxicants across tobacco users. Based on a group of biomarkers, cluster analysis was used to define tobacco toxicant exposure profiles. Clusters with distinct exposure profiles, were determined and described, based on levels of urinary biomarkers of exposure to nicotine, TSNAs, VOCs, and PAHs among a national sample of current, established, adult tobacco users, and examine the association of use behavior and cluster membership. The PATH Biomarker Wave 1 data were analyzed. Current established tobacco users with complete urinary biomarker data were included (N = 6724). User groups included cigarette smokers, users of electronic cigarette (ECIG), smokeless tobacco (SLT), and dual and poly tobacco users. Cluster analysis, pairwise comparisons, and multinomial logistic regression were conducted. Cigarette smokers were primarily in clusters with high biomarker concentrations across all groups, but actual concentrations were associated with smoking quantity. A cluster with high TSNAs but low levels of PAHs and VOCs was heavily populated by SLT users. Exclusive ECIG users, depending on use frequency, were predominantly in clusters with low biomarker concentrations, except for one cluster that had relatively high TSNAs. Clusters heavily populated by dual and poly tobacco users were the same as those heavily populated by cigarette smokers. Ten exposure profiles (clusters) were determined and linked to tobacco use behavior. Findings could inform future research and policy initiatives.

Long-term efficacy and safety of cannabidiol (CBD) in children with treatment-resistant epilepsy: Results from a state-based expanded access program.

INTRODUCTION: An intermediate-sized, multicenter, expanded-access study was opened in 2015 through the support of the State of Georgia. This study provided children with treatment-resistant epilepsy (TRE) access to plant-derived highly purified cannabidiol (CBD; Epidiolex® in the US; Epidyolex® in the EU; 100 mg/mL oral solution). These children had failed to achieve seizure freedom with available treatment options and were ineligible to participate in randomized controlled trials that only included patients with Lennox-Gastaut and Dravet syndromes. METHODS: Cannabidiol safety, changes in seizure type, frequency, and seizure-free days were evaluated for children aged 1-18 years (at time of consent) as an adjunctive treatment for 36 months. The study consisted of a two-month baseline period, a titration period, treatment period, and optional titration period, which occurred after ≥26 weeks of treatment. Cannabidiol treatment was administered up to a targeted dose of 25 mg/kg/day, with an optional secondary treatment up to 50 mg/kg/day. Daily seizure type, seizure frequency, and seizure-free days were recorded in a Web-based diary, and changes in these outcomes were recorded and analyzed for the duration of the study. The occurrence of adverse events (AEs) was also recorded. RESULTS: The median percentage change in seizures for 45 patients in Months 3, 6, 12, 18, 24, and 36 showed a statistically significant (p < 0.001) reduction in major seizures (ranging from 54 to 72% at various time points) and all seizures (61-70%) compared with baseline. A mean increase in seizure-free days per 28 days was >5 in all treatment periods after Month 2, and an average increase of 7.52 (p < 0.001) seizure-free days per 28 days was observed at the end of follow-up compared with baseline. All patients experienced ≥1 AE. Children who transitioned to the optional secondary treatment (high-dose group) reported more AEs before increasing their dose to >25.0 mg/kg/day compared with the low-dose group. However, the average rate of AEs was significantly lower after moving to a high-dose regimen (p = 0.004). Twelve children reported 20 serious AEs, none of which were considered related to CBD. CONCLUSIONS: This study supports CBD as an adjunctive treatment for children with TRE. Treatment was well tolerated in doses up to 50 mg/kg/day. Patients who did not achieve desired results at a dose of ≤25.0 mg/kg/day reported more AEs when CBD dose increased to >25.0 mg/kg/day. Decreases in major seizure frequency and an increase in seizure-free days compared with baseline were reported during treatment. This supports the efficacy and tolerability of CBD for mixed seizure etiologies.

Muscle-strengthening and aerobic activities and mortality among 3+ year cancer survivors in the U.S.

PURPOSE: This study examined the association between adherence to American College of Sports Medicine and American Cancer Society guidelines on aerobic and muscle-strengthening activities and mortality risks among 3+ year cancer survivors in the U.S. METHODS: The observational study was based on 1999-2009 National Health Interview Survey Linked Mortality Files with follow-up through 2011. After applying exclusion criteria, there were 13,997 observations. The hazard ratios (HRs) for meeting recommendations on muscle-strengthening activities only, on aerobic activities only, and on both types of physical activity (i.e., adhering to complete guidelines) were calculated using a reference group of cancer survivors engaging in neither. Unadjusted and adjusted HRs of all-cause, cancer-specific, and cardiovascular disease-specific mortalities were estimated using Cox proportional hazards models. RESULTS: In all models, compared to the reference group, cancer survivors adhering to complete guidelines had significantly decreased all-cause, cancer-specific, and cardiovascular disease-specific mortalities (HRs ranged from 0.37 to 0.64, p's < 0.05). There were no statistically significant differences between hazard rates of cancer survivors engaging in recommended levels of muscle-strengthening activities only and the reference group (HRs ranged from 0.76 to 0.94, p's > 0.05). Wald test statistics suggested a significant dose-response relationship between levels of adherence to complete guidelines and cancer-specific mortality. CONCLUSIONS: While muscle-strengthening activities by themselves do not appear to reduce mortality risks, such activities may provide added cancer-specific survival benefits to 3+ year cancer survivors who are already aerobically active.

Improved nonparametric estimation of the optimal diagnostic cut-off point associated with the Youden index under different sampling schemes.

A diagnostic cut-off point of a biomarker measurement is needed for classifying a random subject to be either diseased or healthy. However, the cut-off point is usually unknown and needs to be estimated by some optimization criteria. One important criterion is the Youden index, which has been widely adopted in practice. The Youden index, which is defined as the maximum of (sensitivity + specificity -1), directly measures the largest total diagnostic accuracy a biomarker can achieve. Therefore, it is desirable to estimate the optimal cut-off point associated with the Youden index. Sometimes, taking the actual measurements of a biomarker is very difficult and expensive, while ranking them without the actual measurement can be relatively easy. In such cases, ranked set sampling can give more precise estimation than simple random sampling, as ranked set samples are more likely to span the full range of the population. In this study, kernel density estimation is utilized to numerically solve for an estimate of the optimal cut-off point. The asymptotic distributions of the kernel estimators based on two sampling schemes are derived analytically and we prove that the estimators based on ranked set sampling are relatively more efficient than that of simple random sampling and both estimators are asymptotically unbiased. Furthermore, the asymptotic confidence intervals are derived. Intensive simulations are carried out to compare the proposed method using ranked set sampling with simple random sampling, with the proposed method outperforming simple random sampling in all cases. A real data set is analyzed for illustrating the proposed method.

Impact of in-hospital statin use on mortality in COVID-19 patients from a majority African American population.

BACKGROUND: The COVID-19 pandemic has claimed over 6.4 million lives globally. Finding effective medications to reduce mortality in hospitalized COVID-19 patients remains critical. No previous study has been published on the effects of statin use in a majority African American COVID-19 patient population. OBJECTIVE: This study aims to assess the relationship between in-hospital statin use and mortality in this population. METHODS: A retrospective chart review of patients diagnosed with COVID-19 from March 2020 to June 2020 admitted to the Phoebe Putney Health System in Albany, Georgia, an early epicenter of the COVID-19 pandemic, was conducted. The outcomes of 735 hospitalized COVID-19 positive patients from over 40 counties in Georgia were analyzed. The primary outcome of interest was all-cause mortality, with secondary outcomes of interest of ICU care, length of ICU stay, need for mechanical ventilator, duration of intubation, and need for dialysis. Multivariate logistic regression and Cox proportional hazards analysis were conducted to examine the effect of in-hospital statin use and mortality. RESULTS: 186 of 735 total patients were prescribed statins in-hospital. 83.8% were African American. Multivariate logistic regression found in-hospital statin use was not significantly associated with the primary outcome - all-cause mortality (p=0.23). Similar findings were seen in need for ICU care, length of ICU stay, need for mechanical ventilator, duration of intubation, and need for dialysis (p>0.05). Additionally, results from a Cox proportional hazards model found in-hospital statin use was not associated with survival time. Sensitivity analysis conducted on only African American patients validated that in-hospital statin use was not associated with all-cause mortality in these patients. Of note, immunosuppression and severe disease presentation were associated with a six-fold increase in risk of mortality and the largest decreases in survival time. CONCLUSION: It is possible statins have no mortality benefit for this patient population, but further research beyond this association study would need to be conducted to determine this conclusively. From this study, the best clinical recommendation would be to continue statins for COVID-19 patients with pre-hospital statin use and to launch a randomized clinical trial to definitively determine the efficacy of statins in the treatment of hospitalized COVID-19 patients.

Sleep apnea in end-stage renal disease patients: risk factors and mortality.

Sleep apnea (SA) is highly prevalent in the end-stage renal disease (ESRD) population. However, the impact of SA on mortality in ESRD is unclear. This study investigates the relationship between SA and mortality in ESRD. The United States Renal Data System was queried in a retrospective cohort study to identify ESRD patients aged 18-100 years who initiated hemodialysis between 2005 and 2013. Diagnoses of SA and comorbidities were determined from International Classification of Disease-9 codes and demographic variables from Centers for Medicare and Medicaid Services Form-2728. Cox proportional hazards models were used to examine the association of SA with mortality controlling for multiple variables. Of 858,131 subjects meeting inclusion criteria, 587 were found to have central SA (CSA) and 22,724 obstructive SA (OSA). The SA cohort was younger and more likely to be male and Caucasian compared to the non-SA cohort, with more diagnoses of tobacco and alcohol use, hypertension, heart failure, and diabetes. Both CSA (adjusted hazard ratio (aHR) = 1.42, 95% confidence interval (CI): 1.29-1.56) and OSA (aHR = 1.35, 95% CI: 1.32-1.37) were associated with increased mortality. Other variables associated with increased mortality included age, dialysis initiation with a catheter or graft, alcohol use, hypertension, and cardiovascular disease. Factors associated with decreased mortality included female sex, black race, Hispanic ethnicity, diagnosis of heart failure or diabetes, and an ESRD etiology of glomerulonephritis or polycystic kidney disease. Since a diagnosis of either OSA or CSA increases mortality risk, early identification of SA and therapy in this ESRD population may improve survival.

Altered mental status is an independent predictor of mortality in hospitalized COVID-19 patients.

BACKGROUND/AIMS: Limited data exists on the outcomes of COVID-19 patients presenting with altered mental status (AMS). Hence, we studied the characteristics and outcomes of hospitalized COVID-19 patients who presented with AMS at our hospital in rural southwest Georgia. METHODS: Data from electronic medical records of all hospitalized COVID-19 patients from March 2, 2020, to June 17, 2020, were analyzed. Patients were divided in 2 groups, those presenting with and without AMS. Primary outcome of interest was in-hospital mortality. Secondary outcomes were needed for mechanical ventilation, need for intensive care unit (ICU) care, need for dialysis, and length of stay. All analyses were performed using SAS 9.4 and R 3.6.0. RESULTS: Out of 710 patients, 73 (10.3%) presented with AMS. Majority of the population was African American (83.4%). Patients with AMS were older and more likely to have hypertension, chronic kidney disease (CKD), cerebrovascular disease, and dementia. Patients with AMS were less likely to present with typical COVID-19 symptoms, including dyspnea, cough, fever, and gastrointestinal symptoms. Predictors of AMS included age ≥ 70 years, CKD, cerebrovascular disease, and dementia. After multivariable adjustment, patients with AMS had higher rates of in-hospital mortality (30.1% vs 14.8%, odds ratio (OR) 2.139, p = 0.019), ICU admission (43.8% vs 40.2%, OR 2.59, p < 0.001), and need for mechanical ventilation (27.4% vs 18.5%, OR 2.06, p = 0.023). Patients presenting with AMS had increased length of stay. CONCLUSIONS: Patients with COVID-19 presenting with AMS are less likely to have typical COVID-19 symptoms, and AMS is an independent predictor of in-hospital mortality, need for ICU admission, and need for mechanical ventilation.

Incidence and risk factors for mucormycosis in renal transplant patients.

BACKGROUND: Renal transplant patients are at increased risk for mucormycosis. Diabetes, neutropenia, deferoxamine therapy, and immunosuppressive medications have been associated with increased risk of mucormycosis in studies of solid organ transplant recipients. To focus on renal transplant patients, the US Renal Data System (USRDS) was queried to determine the incidence and risk factors for mucormycosis. METHODS: All renal transplant patients in the USRDS from 1988 to 2015 were queried for a diagnosis of mucormycosis after the first transplant date using ICD-9 and ICD-10 codes. The International Classification of Diseases (ICD) codes, which currently exist in the ninth and tenth revisions, are a global system of classification used to code diagnoses, procedures, and symptoms. We defined proven mucormycosis by a histopathologic or fungal stain procedure code within 7 days of the diagnosis code. Logistic regression controlling for person-years at risk was used to examine demographic and clinical diagnosis risk factors for mucormycosis. RESULTS: Of the 306,482 renal transplant patients, 222 (0.07%) had codes consistent with proven mucormycosis. The incidence of mucormycosis increased from 1990 to 2000 (peak 17.6 per 100,000 person-years) and subsequently demonstrated more variability. Hispanic ethnicity (OR=1.45), age 65 years or greater (OR=1.64), other or black race compared with white race (OR=1.96 and 1.74), cadaver or other donor type (OR=2.41), and receiving tacrolimus (OR=2.09) were associated with increased risk. Comorbidities associated with decreased risk of mucormycosis included female sex (OR=0.68), iron overload (OR=0.56), and receiving mycophenolate mofetil (OR=0.67) or azathioprine (OR=0.53). CONCLUSIONS: In renal transplant patients, age, deceased donor graft transplant, tacrolimus administration, race other than white, and Hispanic ethnicity were associated with increased risk of mucormycosis. Unexpectedly, iron overload was protective. Mucormycosis is a rare infection in renal transplant patients which should be considered in patients with the above risk factors after more common infections have been ruled out.

Identification of circulating microvesicle-encapsulated miR-223 as a potential novel biomarker for ARDS.

Acute respiratory distress syndrome (ARDS) is a lethal disease with severe forms conferring a mortality rate approaching 40%. The initial phase of ARDS results in acute lung injury (ALI) characterized by a severe inflammatory response and exudative alveolar flooding due to pulmonary capillary leak. Timely therapies to reduce ARDS mortality are limited by the lack of laboratory-guided diagnostic biomarkers for ARDS. The purpose of this study was to evaluate the prognostic role of circulating microvesicles (MVs)-containing miR-223 (MV-miR-223) if indicate more severe lung injury and worse outcomes in ARDS patients. Human plasma samples from one hundred ARDS patients enrolled in Albuterol to Treat Acute Lung Injury (ALTA) trial were compared to a control group of twenty normal human plasma specimens. The amount of MV-miR-223 was measured using absolute real-time polymerase chain reaction (PCR) with a standard curve. Mann-Whitney-Wilcoxon, Spearman correlation, Chi-squared tests, and Kaplan-Meier curves were computed to assess different variables and survival. Plasma levels of MV-miR-223 were significantly higher in ARDS patients compared to normal control subjects. Upon receiver operator characteristic (ROC) analysis of MV-miR-223 in relation to 30-day mortality, MV-miR-223 had an area under the curve (AUC) of 0.7021 with an optimal cut-off value of 2.413 pg/ml. Patients with high MV-miR-223 had higher 30-day mortality than subjects with low MV-miR-223 levels. MV-miR-223 was negatively correlated with ICU-free days, ventilator-free days, and organ failure-free days. Patients with high MV-miR-223 levels had higher 30 and 90-day mortality. MV-miR-223 was associated with 28-day clinical outcomes of ALTA trial including ICU-free days, ventilator-free days, and organ failure-free days. Thus, circulating MV-miR-223 may be a potential biomarker in prognosticating patient-centered outcomes and predicting mortality in ARDS.

Reverse engineering gene networks using global-local shrinkage rules.

Inferring gene regulatory networks from high-throughput 'omics' data has proven to be a computationally demanding task of critical importance. Frequently, the classical methods break down owing to the curse of dimensionality, and popular strategies to overcome this are typically based on regularized versions of the classical methods. However, these approaches rely on loss functions that may not be robust and usually do not allow for the incorporation of prior information in a straightforward way. Fully Bayesian methods are equipped to handle both of these shortcomings quite naturally, and they offer the potential for improvements in network structure learning. We propose a Bayesian hierarchical model to reconstruct gene regulatory networks from time-series gene expression data, such as those common in perturbation experiments of biological systems. The proposed methodology uses global-local shrinkage priors for posterior selection of regulatory edges and relaxes the common normal likelihood assumption in order to allow for heavy-tailed data, which were shown in several of the cited references to severely impact network inference. We provide a sufficient condition for posterior propriety and derive an efficient Markov chain Monte Carlo via Gibbs sampling in the electronic supplementary material. We describe a novel way to detect multiple scales based on the corresponding posterior quantities. Finally, we demonstrate the performance of our approach in a simulation study and compare it with existing methods on real data from a T-cell activation study.

Trends in the Use of Volunteers in US Hospices: 2000 to 2010.

PURPOSE: Using a longitudinal sample of freestanding Medicare-certified hospices in the 50 US states and the District of Columbia, this study sought to explore the factors associated with volunteer demand and describe how volunteer use has changed from 2000 to 2010. RESULTS: A temporal decline in the extent of use of volunteers in freestanding hospices was observed over the study period. Findings indicated that both organizational and environmental factors influence the use of volunteers in US freestanding hospices. CONCLUSION: Given the importance of volunteers, both in the preservation of hospices' philanthropic traditions and in reducing health care expenditure at the end of life, research is needed to further evaluate the factors associated with this decline. Emphasis should be placed on improving the retention of the existing hospice volunteer workforce.

Bootstrapping least-squares estimates in biochemical reaction networks.

The paper proposes new computational methods of computing confidence bounds for the least-squares estimates (LSEs) of rate constants in mass action biochemical reaction network and stochastic epidemic models. Such LSEs are obtained by fitting the set of deterministic ordinary differential equations (ODEs), corresponding to the large-volume limit of a reaction network, to network's partially observed trajectory treated as a continuous-time, pure jump Markov process. In the large-volume limit the LSEs are asymptotically Gaussian, but their limiting covariance structure is complicated since it is described by a set of nonlinear ODEs which are often ill-conditioned and numerically unstable. The current paper considers two bootstrap Monte-Carlo procedures, based on the diffusion and linear noise approximations for pure jump processes, which allow one to avoid solving the limiting covariance ODEs. The results are illustrated with both in-silico and real data examples from the LINE 1 gene retrotranscription model and compared with those obtained using other methods.

Algebraic Statistical Model for Biochemical Network Dynamics Inference.

With modern molecular quantification methods, like, for instance, high throughput sequencing, biologists may perform multiple complex experiments and collect longitudinal data on RNA and DNA concentrations. Such data may be then used to infer cellular level interactions between the molecular entities of interest. One method which formalizes such inference is the stoichiometric algebraic statistical model (SASM) of [2] which allows to analyze the so-called conic (or single source) networks. Despite its intuitive appeal, up until now the SASM has been only heuristically studied on few simple examples. The current paper provides a more formal mathematical treatment of the SASM, expanding the original model to a wider class of reaction systems decomposable into multiple conic subnetworks. In particular, it is proved here that on such networks the SASM enjoys the so-called sparsistency property, that is, it asymptotically (with the number of observed network trajectories) discards the false interactions by setting their reaction rates to zero. For illustration, we apply the extended SASM to in silico data from a generic decomposable network as well as to biological data from an experimental search for a possible transcription factor for the heat shock protein 70 (Hsp70) in the zebrafish retina.