Dose-response Parameters Research Articles

A power-law distribution of infectious quanta for the top 30% of SARS-CoV-2-infected individuals

Minimising airborne infection with respiratory viruses, such as SARS-CoV-2, requires knowledge of the infectious quanta generation rate for determining the minimum dilution requirement. The two existing methods for estimating quanta generation rates are the viral load method and outbreak method. The former method is challenged by significant uncertainty in input data, including dose-response parameters and infectious viral loads. The latter method, based on the Wells–Riley equation, is challenged by significant individual heterogeneity in quanta generation rates and lack of outbreak data. In this study, the two methods are integrated for studying the quanta generation profile of all individuals infected with an ancestral SARS-CoV-2 strain, based on four reported outbreaks of infection. The airborne transmission droplet size ranges in the four outbreaks, which were determined in previous studies, are used to estimate the hourly volume of expired droplets for the viral load method. Various viral load datasets and conversion factors from RNA copies to infectious quanta are tested. Two criteria are used to identify the probable quanta generation profile, i.e. 70% of infected individuals do not infect others, and the estimated quanta generation rates estimated using the outbreak method should be within the top 80%–99% of infected individuals. The predicted quanta generation profile of all individuals infected with SARS-CoV-2 follows a log-normal distribution, whereas that of the top 30% of infected individuals approximately follows a power-law distribution.Practical significance: A major obstacle in defining dilution requirements for minimising airborne infection is the lack of infectious quanta generation rates for the general population. Our approach integrates two existing quanta estimation methods and paves the way to obtaining reliable quanta generation rate profiles at the population level.

Benchmarking proton RBE models

Objective. To biologically optimise proton therapy, models which can accurately predict variations in proton relative biological effectiveness (RBE) are essential. Current phenomenological models show large disagreements in RBE predictions, due to different model assumptions and differences in the data to which they were fit. In this work, thirteen RBE models were benchmarked against a comprehensive proton RBE dataset to evaluate predictions when all models are fit using the same data and fitting techniques, and to assess the statistical robustness of the models. Approach. Model performance was initially evaluated by fitting to the full dataset, and then a cross-validation approach was applied to assess model generalisability and robustness. The impact of weighting the fit and the choice of biological endpoint (either single or multiple survival levels) was also evaluated. Main results. Fitting the models to a common dataset reduced differences between their predictions, however significant disagreements remained due to different underlying assumptions. All models performed poorly under cross-validation in the weighted fits, suggesting that some uncertainties on the experimental data were significantly underestimated, resulting in over-fitting and poor performance on unseen data. The simplest model, which depends linearly on the LET but has no tissue or dose dependence, performed best for a single survival level. However, when fitting to multiple survival levels simultaneously, more complex models with tissue dependence performed better. All models had significant residual uncertainty in their predictions compared to experimental data. Significance. This analysis highlights that poor quality of error estimation on the dose response parameters introduces substantial uncertainty in model fitting. The significant residual error present in all approaches illustrates the challenges inherent in fitting to large, heterogeneous datasets and the importance of robust statistical validation of RBE models.

Dose-response modeling and treatment plan assessment with a python software toolkit

This software assistant aims at calculating the dose-response relations of tumors and normal tissues, or clinically assessing already determined values by other researchers. It can also indicate the optimal dose prescription by optimizing the expected treatment outcome. The software is developed solely in python programming language, and it employs PSFL license for its Graphical User Interface (GUI), NUMPY, MATPLOTLIB, and SCIPY libraries. It comprises of two components. The first is the Dose-response relations derivation component, which takes as input the dose volume histograms (DVHs) of patients and their recorded responses regarding a given clinical endpoint to determine the parameters of different tumor control probability (TCP) or normal tissue complication probability (NTCP) models. The second is the Treatment Plan Assessment component, which uses the DVHs of a plan and the dose-response parameters values of the involved tumors and organs at risk (OARs) to calculate their expected responses. Additionally, the overall probabilities of benefit (PB), injury (PI) and complication-free tumor control (P+) are calculated. The software calculates rapidly the corresponding generalized equivalent uniform doses (gEUD) and biologically effective uniform doses (D‾‾) for the Lyman-Kutcher-Burman (LKB), parallel volume (PV) and relative seriality (RS) models respectively, determining the model parameters. In the Dose-Response Relations Derivation component, the software plots the dose-response curves of the irradiated organ with the relevant confidence internals along with the data of the patients with and without toxicity. It also calculates the odds ratio (OR) and the area under the curve (AUC) of different dose metrics or model parameter values against the individual patient outcomes to determine their discrimination capacity. It also performs a goodness-of-fit evaluation of any model parameter set. The user has the option of viewing plots like Scatter, 3D surfaces, and Bootstrap plots. In the Treatment Plan Assessment part, the software calculates the TCP and NTCP values of the involved tumors and OARs, respectively. Furthermore, it plots the dose-response curves of the TCPs, NTCPs, PB, PI, and P+ for a range of prescription doses for different treatment plans. The presented software is ideal for efficiently conducting studies of radiobiological modeling. Furthermore, it is ideal for performing treatment plan assessment, comparison, and optimization studies.

Anomalous heating rate effect in GdAl3(BO3)4:Dy3+ under beta radiation stimulation: Analysis of dose response and kinetic parameters

This study presents a comprehensive investigation into the thermoluminescence (TL) properties of Dy3+-activated gadolinium aluminate garnet (Dy3+:GdAl3(BO3)4 or Dy3+-GAB) phosphor materials. The research aims to unravel the intricate interplay among heating rate, radiation dose, and TL glow curve responses to optimize dosimetry applications. The TL response of the material is scrutinized across diverse heating rates (HR) and dose levels, while accounting for temperature lag correction. Concentration quenching effects are explored through Dy3+ concentrations spanning from 0.5 to 7 wt%, revealing the optimal doping concentration to be 3 wt%. The study underscores the critical role of choosing an appropriate band-pass filter, revealing the effectiveness of the “IRSL-TL wideband blue” filter's in capturing TL signals. Furthermore, the study examines kinetic parameter estimated using different approaches and shedding light on how heating rate and radiation dose affect activation energy values. Intriguingly, the study observes an anomalous heating rate effect, resulting in elevated TL peak intensities at higher HR. This effect is attributed to non-radiative transitions and the semi-localized transition model. The reusability of Dy3+-doped GAB is also examined, confirming its consistency and reproducibility across multiple uses. This study significantly contributes to the advancement of TL dosimetry methodologies and enhances our understanding of luminescent material behaviours. We utilized both the Tm-Tstop technique in conjunction with the Initial Rise (IR) method and Computerized Glow Curve Deconvolution (CGCD) techniques, revealing the presence of seven overlapping glow peaks alongside the main ones. Both methods appear to provide excellent agreement in terms of activation energy values, ranging from 0.70 to 1.50 eV for each peak. Furthermore, the findings strongly indicate the effective utilization of TL signals in radiation dosimetry applications.

Unravelling the impact of unusual heating rate, dose-response and trap parameters on the thermoluminescence of Sm3+ activated GdAl3(BO3)4 phosphors exposed to beta particle irradiation

The thermoluminescence of GdAl3(BO3)4 (GAB) doped with various concentrations of Sm3+ (i.e. from 0.5 to 7 wt%), prepared by gel combustion, was studied. TL glow peaks at 78 °C and 225 °C are observed. The intensity of the glow peak at 225 °C increased with a faster heating rate. To gain insight into the trap activation energies, the methods of Hoogenstraaten and Booth-Bohun-Parfianovitch were used, where the calculated activation energies are 0.57 eV and 0.60 eV for Peak I and 1.69 eV and 1.71 eV for Peak II respectively. The dose-response of GAB:0.5 wt%Sm3+ demonstrates robust linearity up to 40 Gy, with a strong correlation coefficient of 0.999. Both TM-Tstop combined with the Initial Rise (IR) and Computerized Glow Curve Deconvolution (CGCD) techniques were employed, which revealed six overlapping glow peaks beneath the main peaks. Additionally, the results suggest that the TL signal can be efficiently exploited for radiation dosimetry applications.

An investigation of non-informative priors for Bayesian dose-response modeling

Toxicology analyses are built around dose-response modeling, and increasingly these methodologies utilize Bayesian estimation techniques. Bayesian estimation is unique because it includes prior distributional information in the analysis, which may impact the dose-response estimate meaningfully. As such analyses are often used for human health risk assessment, the practitioner must understand the impact of adding prior information to the dose-response study. One proposal in the literature is the use of the flat uniform prior distribution, which places a uniform prior probability over the dose-response model's parameters for a chosen range of values. Though the motivation of such a prior distribution is laudable in that it is most like maximum likelihood estimation seeking unbiased estimates of the dose-response, one can show that such priors add information and may introduce unexpected biases into the analysis. This manuscript shows through numerous empirical examples why prior distributions that are non-informative across all endpoints of interest do not exist for dose-response models; that is, other quantities of interest will be informed by choosing one inferential quantity not informed.

Listeriosis Risk Model for Cancer Patients Who Consume Ready-to-Eat Salad

The foodborne pathogen Listeria monocytogenes generally infects immunocompromised individuals, such as cancer patients, more frequently and with higher morbidity and mortality than the general population. Because of the anticipated risk associated with L. monocytogenes and other pathogens in produce, immunocompromised individuals are often placed on neutropenic diets that exclude fresh produce, though these risks have not been quantified. Therefore, this study developed a data-driven risk model for listeriosis in cancer patients who consume ready-to-eat (RTE) salads, consisting of leafy greens, cucumbers, and tomatoes, as influenced by kitchen-scale treatments and storage practices. Monte Carlo simulations were used to model the risk of invasive listeriosis during one chemotherapy cycle. Refrigerating all salad components decreased the median risk by approximately one-half log. For refrigerated salads with no treatment, the predicted median risk was ≤ 4.3 × 10−08. When salad ingredients were surface blanched with greens rinsed, the predicted risk decreased to 5.4 × 10−10. Predicted risk was lowest (1.4 × 10−13) for a blanched “salad” consisting of solely cucumbers and tomatoes. Interestingly, rinsing, as recommended by FDA, only decreased the median risk by 1 log. A sensitivity analysis revealed that the highly variable dose-response parameter k strongly influenced risk, indicating that reducing uncertainty in this variable may improve model accuracy. Overall, this study demonstrates that kitchen-scale pathogen reduction approaches have high risk reduction efficacy and could be considered as an alternative to diets that exclude produce when making risk management decisions.

Evaluation of the clinical impact of the differences between planned and delivered dose in prostate cancer radiotherapy based on CT-on-rails IGRT and patient-reported outcome scores.

To estimate the clinical impact of differences between delivered and planned dose using dose metrics and normal tissue complication probability (NTCP) modeling. Forty-six consecutive patients with prostate adenocarcinoma between 2010 and 2015 treated with intensity-modulated radiation therapy (IMRT) and who had undergone computed tomography on rails imaging were included. Delivered doses to bladder and rectum were estimated using a contour-based deformable image registration method. The bladder and rectum NTCP were calculated using dose-response parameters applied to planned and delivered dose distributions. Seven urinary and gastrointestinal symptoms were prospectively collected using the validated prostate cancer symptom indices patient reported outcome (PRO) at pre-treatment, weekly treatment, and post-treatment follow-up visits. Correlations between planned and delivered doses against PRO were evaluated in this study. Planned mean doses to bladder and rectum were 44.9 ± 13.6 Gy and 42.8 ± 7.3 Gy, while delivered doses were 46.1 ± 13.4 Gy and 41.3 ± 8.7 Gy, respectively. D10cc for rectum was 64.1 ± 7.6 Gy for planned and 60.1 ± 9.3 Gy for delivered doses. NTCP values of treatment plan were 22.3% ± 8.4% and 12.6% ± 5.9%, while those for delivered doses were 23.2% ± 8.4% and 9.9% ± 8.3% for bladder and rectum, respectively. Seven of 25 patients with follow-up data showed urinary complications (28%) and three had rectal complications (12%). Correlations of NTCP values of planned and delivered doses with PRO follow-up data were random for bladder and moderate for rectum (0.68 and 0.67, respectively). Sensitivity of bladder to clinical variations of dose accumulation indicates that an automated solution based on a DIR that considers inter-fractional organ deformation could recommend intervention. This is intended to achieve additional rectum sparing in cases that indicate higher than expected dose accumulation early during patient treatment in order to prevent acute severity of bowel symptoms.

Dosimetric characteristics of Gd-doped silica glass subjected to neutron and gamma irradiations

The dosimetric characteristics of newly developed gadolinium (Gd) glass dosimeter produced via sol-gel method are reported. Irradiation were made using a 750 kW neutron flux thermal power and 1.25 MeV 60Co gamma rays with entrance doses from 2 to 10 Gy. Investigation has been done on various Gd dopant concentrations, ranging from 1 to 10 mol%. The Gd-doped silica glass have been characterised for thermoluminescence (TL) dose response, sensitivity, linearity index, glow curve and kinetic parameter analysis. For particular dopant concentration obtained in 6 mol% Gd, the least squares fit shows the change in TL yield, correlation coefficient (r2) of better than 0.980 (at 95% confidence level), with neutron and gamma exposure to be 8 and 4 times greater than that of 1 mol% Gd, respectively. Broad peaks in the absence of any sharp peak observed in the glow curve confirms the amorphous nature of the prepared glass. A glow curve of Gd-doped SiO2 sample is observed with a single prominent peak (Tm) within 200–250 °C (peak shifting appears with respect to the increment of dopant concentration) and 350 °C (for all respective Gd dopants) for neutron and gamma irradiations, respectively. Deconvolution shows the glow curves of the Gd-doped SiO2 glass to be formed of seven and five overlapping peaks, with figures of merit below 2% (FOM) of between 1.38-1.79 and 1.30–1.97 for the particular neutron and gamma irradiations, respectively. Through use of Glowfit deconvolution software, the key trapping parameters of activation energy, E and frequency factor, s−1 were calculated for the Gd-doped SiO2 glass. The mechanism of TL yield with the gradual increase in Gd concentrations and doses is explained upon the incorporation of Gd and radiation damage that change the structure of the electron traps in the glass matrix. These early results indicate that selectively screened Gd-SiO2 glass can be developed into a promising TL system towards dosimetric applications.

An application for relating Legionella shower water monitoring results to estimated health outcomes

Exposure models are useful tools for relating environmental monitoring data to expected health outcomes. The objective of this study was to (1) compare two Legionella shower exposure models, and (2) develop a risk calculator tool for relating environmental monitoring data to estimated Legionella infection risks and Legionnaires’ Disease (LD) illness risks. Legionella infection risks for a single shower event were compared using two shower Legionella exposure models. These models varied in their description of partitioning of Legionella in aerosols and aerosol deposition in the lung, where Model 1 had larger and fewer aerosol ranges than Model 2. Model 2 described conventional vs. water efficient showers separately, while Model 1 described exposure for an unspecified shower type (did not describe it as conventional or water efficient). A Monte Carlo approach was used to account for variability and uncertainty in these aerosolization and deposition parameters, Legionella concentrations, and the dose-response parameter. Methods for relating infection risks to illness risks accounting for demographic differences were used to inform the risk calculator web application (“app”). Model 2 consistently estimated higher infection risks than Model 1 for the same Legionella concentration in water and estimated deposited doses with less variability. For a 7.8-min shower with a Legionella concentration of 0.1 CFU/mL, the average infection risks estimated using Model 2 were 4.8 × 10−6 (SD=3.0 × 10−6) (conventional shower) and 2.3 × 10−6 (SD=1.7 × 10−6) (water efficient). Average infection risk estimated by Model 1 was 1.1 × 10−6 (SD=9.7 × 10−7). Model 2 was used for app development due to more conservative risk estimates and less variability in estimated dose. While multiple Legionella shower models are available for quantitative microbial risk assessments (QMRAs), they may yield notably different infection risks for the same environmental microbial concentration. Model comparisons will inform decisions regarding their integration with risk assessment tools. The development of risk calculator tools for relating environmental microbiology data to infection risks will increase the impact of exposure models for informing water treatment decisions and achieving risk targets.

Ecological risk thresholds for Zn in Chinese soils

The environmental risk threshold of a pollutant is a yardstick to measure soil environmental quality. The derivation of ecological risk thresholds of the heavy metal zinc (Zn) in soil environments based on up-to-date ecological risk assessments plays an important role in soil protection policy. According to regional soil classification, different representative soils with various degrees of acidity and alkalinity were selected, and a data set comprising ecotoxicities of Zn to 21 different test endpoints (plants, soil fauna, microorganisms, etc.) found in representative farmland soils of China was compiled based on new and published data to determine toxicological limits of Zn effects on endpoints. These limits were derived from fitted dose-response model parameters and indicated by EC10 values (the effective concentrations of Zn that inhibit 10% of endpoint bioactivity and also represents the toxicity threshold of Zn in this study) ranging from 36 mg·kg−1 to 682 mg·kg−1. The hormesis effect appeared in the dose-response curve of Zn, for example, the relative Chinese cabbage growth reached more than 120% at most. Zn concentrations added in toxicity tests were also corrected for aging and leaching effects in order to more accurately reflect field conditions. The hazardous concentrations for 5% of the species affected (HC5) were derived by the species sensitivity distribution (SSD) approach for four major types of Chinese soils: acidic (38 mg·kg−1), neutral (106 mg·kg−1), alkaline (217 mg·kg−1), and alkaline calcareous soils (155 mg·kg−1). Prediction models of ecological risk thresholds for Zn based on soil properties were generated, such as logHC5 = 0.564 + 0.218pH + 0.097OC (R2 = 0.790,p < 0.001). The predicted models based on lab test data were verified in the field, and the measured field data fell within two-fold of the prediction intervals. This work provides a scientific framework for developing soil-specific guidance on Zn toxicity thresholds.

Thermoluminescence characteristics of a novel Li2MoO4 phosphor: Heating rate, dose response and kinetic parameters

Lithium molybdate (Li2MoO4) phosphor was synthesized by a gel combustion method and its thermoluminescence properties were studied with the irradiation of beta. Various Heating Rate (VHR), Initial Rise (IR), and Computerized Glow Curve Deconvolution (CGCD) methods were used to determine the kinetic parameters (activation energy E (eV), frequency factor s (s−1), and kinetic order b) of the visible glow peaks. According to the kinetic study, the TL glow curve is made up of seven separate peaks with activation energies of 1.05, 0.76, 0.40, 0.60, 0.78, 1.81 and 1.25 eV and these peaks follow general-order kinetics. The results clearly showed that undoped Li2MoO4 has a potential to be considered in dosimetric applications where high doses have to be monitored as in the case of clinical dosimetry.

Dose-response models to guide site-specific nutrient management and lessons for fertiliser trial design in sub-Saharan Africa

Summary Optimisation of fertiliser use and site-specific nutrient management are increasingly becoming critical because of the growing need to balance agricultural productivity with the growing demand for food and environmental concerns. Trials to determine responses of crops to fertilisers have been widely conducted in sub-Saharan Africa (SSA) with increasing emphasis on the development of economically optimum rates (EORs). Computation of EORs depends on accurate estimation of both the optimum nutrient rate and the agronomic maximum yield response; however, estimation of nutrient-response parameters and EORs is beset by a number of problems. Therefore, the objectives of this paper were to (1) point out common problems in the development and use of nutrient dose-response models and (2) provide corrective measures to facilitate future trial design and data analysis. This review outlines the underlying assumptions, strengths and limitations of the various response functions in order to facilitate informed choices by practitioners. Using specific examples, it also shows that (1) the commonly used trial designs do not allow examination of interactions between two or more nutrients and (2) trial designs with ≤5 nutrient levels and wide spacing between the levels result in large uncertainty in dose-response parameters. The key recommendations emerging from the review are as follows: (1) factorial designs and response surface models should be used more widely to address interactions between nutrients; (2) a minimum of six carefully spaced nutrient levels should be used to correctly estimate dose-response parameters; and (3) when locating field trials, Reference Soil Groups and cropping history should be carefully considered to produce site-specific EORs.

Evaluation of estrogenic and antiestrogenic activity in sludge and explanation of individual compound contributions

Mixture toxicity, including agonistic and antagonistic effects, is an unrevealed environmental problem. Estrogenic endocrine disruptors are known to cause adverse effects for aquatic biota, but causative chemicals and their contributions to the total activity in sewage sludge remain unknown. Therefore, advanced analytical methods, a yeast bioassay and mixture toxicity models were concurrently applied for the characterization of 8 selected sludges with delectable estrogenic activity (and 3 sludges with no activity as blanks) out of 25 samples from wastewater treatment plants (WWTPs). The first applied full logistic model adequately explained total activity by considering the concentrations of the monitored compounds. The results showed that the activity was primarily caused by natural estrogens in municipal WWTP sludge. Nevertheless, activity in a sample originating from a car-wash facility was dominantly caused by partial agonists – nonylphenols – and only a model enabling prediction of all dose-response curve parameters of the final mixture curve explained these results. Antiestrogenic effects were negligible, and effect-directed analysis identified the causative chemicals.

Identifying treatment effect heterogeneity in dose-finding trials using Bayesian hierarchical models: Identifying treatment effect heterogeneity in dose-finding trials.

An important task in drug development is to identify patients, which respond better or worse to an experimental treatment. Identifying predictive covariates, which influence the treatment effect and can be used to define subgroups of patients, is a key aspect of this task. Analyses of treatment effect heterogeneity are however known to be challenging, since the number of possible covariates or subgroups is often large, while samples sizes in earlier phases of drug development are often small. In addition, distinguishing predictive covariates from prognostic covariates, which influence the response independent of the given treatment, can often be difficult. While many approaches for these types of problems have been proposed, most of them focus on the two-arm clinical trial setting, where patients are given either the treatment or a control. In this article we consider parallel groups dose-finding trials, in which patients are administered different doses of the same treatment. To investigate treatment effect heterogeneity in this setting we propose a Bayesian hierarchical dose-response model with covariate effects on dose-response parameters. We make use of shrinkage priors to prevent overfitting, which can easily occur, when the number of considered covariates is large and sample sizes are small. We compare several such priors in simulations and also investigate dependent modeling of prognostic and predictive effects to better distinguish these two types of effects. We illustrate the use of our proposed approach using a Phase II dose-finding trial and show how it can be used to identify predictive covariates and subgroups of patients with increased treatment effects.

Commentary on "Intensive Bimanual Intervention for Children Who Have Undergone Hemispherectomy: A Pilot Study".

“How should I apply this information?” Sensorimotor deficits that result from hemispherectomy are more severe and debilitating than other forms of hemiplegia. For families with a child who requires this surgical approach and the clinicians who support them during their postoperative rehabilitation, little information is available regarding motor changes in response to specific interventions. This preliminary work offers hope. Regardless of the subject's interhemispheric lateralization, type of hemiplegia, or length of time from surgery, clinically meaningful changes in bimanual function were reported with Hand-Arm Bimanual Intensive Training (HABIT). Given this approach received a “green-light” rating by Novak et al1 in a 2019 systematic review and it is grounded in key principles associated with experience-dependent neuroplasticity, this finding is not surprising. These key principles are focused on: task-specific practice, goal-directed tasks and activities, high-intensity doses of meaningful practice, and self-generated, active movement. From a family's perspective, HABIT represents an escape from “appointment fatigue” and an opportunity to intensively practice real-life activities. “Our daughter became more aware and intentional about using her affected side, and ... activities carried over to home!” “What should I be mindful of when applying this information?” Ninety hours of intervention within a 2-week period requires a high level of commitment from families, children, clinicians, health care organizations that employ clinicians, and third-party payers. Cost-effectiveness analysis is not considered nor are sustainable funding mechanisms discussed. For therapists providing intervention in school-based settings, this intensity of service and the one-on-one support it requires is not feasible; for those in outpatient settings, the time required for organizing and implementing HABIT, the materials needed to engage children across a broad range of ages for 6 hours of daily therapy, and the limited reimbursement options for therapists' one-on-one involvement require a high level of creativity and pose unique challenges. Future research should explore family perspectives, cost-effectiveness, and dose-response parameters that might further individualize the length of necessary intervention. Sandra L. Willett, PT, PhD, PCS Emily Drew, PT, PCS Munroe-Meyer Institute, University of Nebraska Medical Center, Omaha, Nebraska Jennifer Dibbern Parent, Omaha, Nebraska

Dose-response relationships of intestinal organs and excessive mucus discharge after gynaecological radiotherapy.

BackgroundThe study aims to determine possible dose-volume response relationships between the rectum, sigmoid colon and small intestine and the ‘excessive mucus discharge’ syndrome after pelvic radiotherapy for gynaecological cancer.Methods and materialsFrom a larger cohort, 98 gynaecological cancer survivors were included in this study. These survivors, who were followed for 2 to 14 years, received external beam radiation therapy but not brachytherapy and not did not have stoma. Thirteen of the 98 developed excessive mucus discharge syndrome. Three self-assessed symptoms were weighted together to produce a score interpreted as ‘excessive mucus discharge’ syndrome based on the factor loadings from factor analysis. The dose-volume histograms (DVHs) for rectum, sigmoid colon, small intestine for each survivor were exported from the treatment planning systems. The dose-volume response relationships for excessive mucus discharge and each organ at risk were estimated by fitting the data to the Probit, RS, LKB and gEUD models.ResultsThe small intestine was found to have steep dose-response curves, having estimated dose-response parameters: γ50: 1.28, 1.23, 1.32, D50: 61.6, 63.1, 60.2 for Probit, RS and LKB respectively. The sigmoid colon (AUC: 0.68) and the small intestine (AUC: 0.65) had the highest AUC values. For the small intestine, the DVHs for survivors with and without excessive mucus discharge were well separated for low to intermediate doses; this was not true for the sigmoid colon. Based on all results, we interpret the results for the small intestine to reflect a relevant link.ConclusionAn association was found between the mean dose to the small intestine and the occurrence of ‘excessive mucus discharge’. When trying to reduce and even eliminate the incidence of ‘excessive mucus discharge’, it would be useful and important to separately delineate the small intestine and implement the dose-response estimations reported in the study.

Alpha entrainment drives pain relief using visual stimulation in a sample of chronic pain patients: a proof-of-concept controlled study.

One-third of the population in the UK and worldwide struggle with chronic pain. Entraining brain alpha activity through noninvasive visual stimulation has been shown to reduce experimental pain in healthy volunteers. Neural oscillations entrainment offers a potential noninvasive and nonpharmacological intervention for patients with chronic pain, which can be delivered in the home setting and has the potential to reduce use of medications. However, evidence supporting its use in patients with chronic pain is lacking. This study explores whether (a) alpha entrainment increase alpha power in patients and (b) whether this increase in alpha correlates with analgesia. In total, 28 patients with chronic pain sat in a comfortable position and underwent 4-min visual stimulation using customised goggles at 10 Hz (alpha) and 7 Hz (control) frequency blocks in a randomised cross-over design. 64-channel electroencephalography and 11-point numeric rating scale pain intensity and pain unpleasantness scores were recorded before and after stimulation. Electroencephalography analysis revealed frontal alpha power was significantly higher when stimulating at 10 Hz when compared to 7 Hz. There was a significant positive correlation between increased frontal alpha and reduction in pain intensity (r = 0.33; P < 0.05) and pain unpleasantness (r = 0.40; P < 0.05) in the 10 Hz block. This study provides the first proof of concept that changes in alpha power resulting from entrainment correlate with an analgesic response in patients with chronic pain. Further studies are warranted to investigate dose-response parameters and equivalence to analgesia provided by medications.

The Effects of Exercise in Patients with Sarcopenia.

The aim of this study was to assess the effects of different types of exercise interventions for treating sarcopenia compared to no specific treatment, a minimal intervention (e.g., education), or another active treatment (nutritional supplements). A review was conducted of the recent English literature searching PubMed and ScienceDirect databases. Ten randomized controlled trials (RCTs) were included in this review, presenting the results of 671 sarcopenic patients. The exercise interventions were resistance training (four studies), a multimodal program (five studies, encompassing resistance training and additional exercises such as aerobic exercises, flexibility, balance and strength training), and a whole body vibration program (one study). Results show that exercise interventions could have beneficial effects in improving muscle mass, muscle strength, and physical performance in 3months of intervention. Resistance training, added to an adequate nutrition and aerobic exercise, appeared to deliver the most positive outcome after 3months of intervention. Types of exercise and dose-response parameters of exercise eliciting improvement warrant further investigation. Due to the significant heterogeneity in clinical trials, the current evidence provides limited guidance. Well-designed studies evaluating exercise interventions are needed before treatment guidelines can be developed.

Early stopping in seamless phase I/II clinical trials.

In recent years, seamless phase I/II clinical trials have drawn much attention, as they consider both toxicity and efficacy endpoints in finding an optimal dose (OD). Engaging an appropriate number of patients in a trial is a challenging task. This paper attempts a dynamic stopping rule to save resources in phase I/II trials. That is, the stopping rule aims to save patients from unnecessary toxic or subtherapeutic doses. We allow a trial to stop early when widths of the confidence intervals for the dose-response parameters become narrower or when the sample size is equal to a predefined size, whichever comes first. The simulation study of dose-response scenarios in various settings demonstrates that the proposed stopping rule can engage an appropriate number of patients. Therefore, we suggest its use in clinical trials.