Dose-response Model Research Articles

Analysis of SARS-CoV-2 transmission in a university classroom based on real human close contact behaviors

Due to high-population density, frequent close contact, possible poor ventilation, university classrooms are vulnerable for transmission of respiratory infectious diseases. Close contact and long-range airborne are possibly main routes for SARS-CoV-2 transmission. In this study, taking a university classroom in Beijing for example, close contact behaviors of students were collected through a depth-detection device, which could detect depth to each pixel of the image, based on semi-supervised learning. Finally, >23 h of video data were obtained. Using Computational Fluid Dynamics, the relationship between viral exposure and close contact behaviors (e.g. interpersonal distance, relative facial orientations, and relative positions) was established. A multi-route transmission model (short-range airborne, mucous deposition, and long-range airborne) of infectious diseases considering real close contact behaviors was developed. In the case of Omicron, the risk of infection in university classrooms and the efficacy of different interventions were assessed based on dose-response model. The average interpersonal distance in university classrooms is 0.9 m (95 % CI, 0.5 m–1.4 m), with the highest proportion of face-to-back contact at 87.0 %. The risk of infection of susceptible students per 45-min lesson was 1 %. The relative contributions of short-range airborne and long-range airborne transmission were 40.5 % and 59.5 %, respectively, and the mucous deposition was basically negligible. When all students are wearing N95 respirators, the infection risk could be reduced by 96 %, the relative contribution of long-range airborne transmission increases to 95.6 %. When the fresh air per capita in the classroom is 24 m3/h/person, the virus exposure could be decreased by 81.1 % compared to the real situation with 1.02 m3/h/person. In a classroom with an occupancy rate of 50 %, after optimized arrangement of student distribution, the infection risk could be decreased by 62 %.

Systematic update to the mammalian relative potency estimate database and development of best estimate toxic equivalency factors for dioxin-like compounds

The World Health Organization (WHO) assesses potential health risks of dioxin-like compounds using Toxic Equivalency Factors (TEFs). This study systematically updated the relative potency (REP) database underlying the 2005 WHO TEFs and applied advanced methods for quantitative integration of study quality and dose-response. Data obtained from fifty-one publications more than doubled the size of the previous REP database (∼1300 datasets). REP quality and relevance for these data was assessed via application of a consensus-based weighting framework. Using Bayesian dose-response modeling, available data were modeled to produce standardized dose/concentration-response Hill curves. Study quality and REP data were synthesized via Bayesian meta-analysis to integrate dose/concentration-response data, author-calculated REPs and benchmark ratios. The output is a prediction of the most likely relationship between each congener and its reference as model-predicted TEF uncertainty distributions, or the ‘best estimate TEF’ (BE-TEF). The resulting weighted BE-TEFs were similar to the 2005 TEFs, though provide more information to inform selection of TEF values as well as to provide risk assessors and managers with information needed to quantitatively characterize uncertainty around TEF values. Collectively, these efforts produce an updated REP database and an objective, reproducible approach to support development of TEF values based on all available data.

Exploring DNA repair deficient CHO cell response to low dose rate radiation

IntroductionDNA double-strand breaks (DSBs) induced by ionizing radiation pose a significant threat to genome integrity, necessitating robust repair mechanisms. This study explores the responses of repair-deficient cells to low dose rate (LDR) radiation. Non-homologous end joining (NHEJ) and homologous recombination (HR) repair pathways play pivotal roles in maintaining genomic stability. The hypothesis posits distinct cellular outcomes under LDR exposure compared to acute radiation, impacting DNA repair mechanisms and cell survival. Materials and methodsChinese hamster ovary (CHO) cells, featuring deficiencies in NHEJ, HR, Fanconi Anemia, and PARP pathways, were systematically studied. Clonogenic assays for acute and LDR gamma-ray exposures, cell growth inhibition analyses, and γ-H2AX foci assays were conducted, encompassing varied dose rates to comprehensively assess cellular responses. ResultsNHEJ mutants exhibited an unexpected inverse dose rate effect, challenging conventional expectations. HR mutants displayed unique radiosensitivity patterns, aligning with responses to major DNA-damaging agents. LDR exposure induced cell cycle alterations, growth delays, and giant cell formation, revealing context-dependent sensitivities. γ-H2AX foci assays indicated DSB accumulation during LDR exposure. DiscussionThese findings challenge established paradigms, emphasizing the intricate interplay between repair pathways and dose rates. The study offers comprehensive insights into repair-deficient cell responses, urging a reevaluation of conventional dose-response models and providing potential avenues for targeted therapeutic strategies in diverse radiation scenarios.

Quantitative microbial risk assessment of Salmonella in fresh chicken patties

Quantitative microbial risk assessment (QMRA) has witnessed rapid development within the context of food safety in recent years. As a means of contributing to these advancements, a QMRA for Salmonella spp. in fresh chicken patties for the general European Union (EU) population was developed. A two-dimensional (Second Order) Monte-Carlo simulation method was used for separating variability and uncertainty of model’s parameters. The stages of industrial processing, retail storage, domestic storage, and cooking in the domestic environment were considered in the exposure assessment. For hazard characterization, a dose-response model was developed by combining 8 published dose-response models using a Pert distribution for describing uncertainty. The QMRA model predicted a mean probability of illness of 1.19*10−4 (5.28*10−5 – 3.57*10−4 95 % C.I.), and a mean annual number of illnesses per 100,000 people of 2.13 (0.96 – 6.59 95 % C.I.). Moreover, sensitivity analysis was performed, and variability in cooking preferences was found to be the most influential model parameter (r = −0.39), followed by dose-response related variability (r = 0.22), and variability in the concentration of Salmonella spp. at the time of introduction at the processing facility (r = 0.11). Various mitigation strategy scenarios were tested, from which, “increasing the internal temperature of cooking” and “decreasing shelf life” were estimated to be the most effective in reducing the predicted risk of illness. Salmonella-related illnesses exhibit particularly high severity, making them some of the most prominent zoonotic diseases in the EU. Regular monitoring of this hazard in order to further highlight its related parameters and causes is a necessary procedure. This study not only provides an updated assessment of Salmonella spp. risk associated with chicken patties, but also facilitates the identification of crucial targets for scientific investigation and implementation of real-world intervention strategies.

Влияние облучения на заболевания крови среди участников ликвидации последствий аварии на Чернобыльской атомной электростанции с учётом мультиморбидности состояний пациентов

The statistical relationships between radiation doses and blood diseases with the account of the multimorbidity factor in the Russian cohort of workers involved in mitigation of the consequences of the accident at the Chernobyl NPP have been investigated. For analysis observation data accumulated from 1986 over 2022 kept in the National Radiation Epidemiological Register were used. The size of the liquidators cohort was 89,578 men. During the follow-up period 6,013 blood disorders (D50-D89 ICD-10) were detected for the first time. The average radiation dose in the cohort was 0.13 Gy, and the average age at the first exposure was 33.6 years. Common approaches to data mining (Data Mining) were used to identify statistical relationships between blood diseases and other diseases; for radiation risk assessment, logistic regression models were used to link disease probability to radiation dose. The effect of low-dose radiation (less than 0.3 Gy) on blood diseases is statistically significant and is characterized by a multiplicative linear no-threshold model, the dose-response model with ERR/Gy=0.59 at 95% CI (0.23; 0.99). When considering multimorbidity in patients, the blood diseases radiation risk change was not statistically significant. Radiation risks several times higher than risks of malignant neoplasms, were found in three groups of diseases: 1) blood and haemopoietic organs other than anaemia and hemorrhages (D70-D77), with ERR/Gy=2.25; 2) immunodeficiency states (D80-D89), with ERR/Gr=2.01; 3) aplastic anemias (D60-D61), with ERR/Gy=5.56. The inclusion of aplastic anaemia in the officially approved list of diseases caused by exposure to ionizing radiation in Russia should be considered as scientifically justified document. The list may need to be supplemented by diseases of blood and haemorrhagic organs other than anaemia and hemorrhage, as well as immunodeficiency conditions. The radiation risk coefficients (ERR/Gy) for the main groups of blood diseases estimated for the first time will make it possible to establish radiation dependence in exposed people with the use of a dose and radiation-epidemiological approach, estimating the attributive fraction of radiation in the etiology of each specific disease case.

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.

The Backfill i3+3 Design for Dose-Finding Trials in Oncology

We consider a formal statistical design that allows simultaneous enrollment of a main cohort and a backfill cohort of patients in a dose-finding trial. The goal is to accumulate more information at various doses to facilitate dose optimization. The proposed design, called Bi3+3, combines the simple dose-escalation algorithm in the i3+3 design and a model-based inference under the framework of probability of decisions (POD), both previously published. As a result, Bi3+3 provides a simple algorithm for backfilling patients to lower doses in a dose-finding trial once these doses exhibit safety profile in patients. The POD framework allows dosing decisions to be made when some backfill patients are still being followed with incomplete toxicity outcomes, thereby potentially expediting the clinical trial. At the end of the trial, Bi3+3 uses both toxicity and efficacy outcomes to estimate an optimal biological dose (OBD). The proposed inference is based on a dose-response model that takes into account either a monotone or plateau dose-efficacy relationship, which are frequently encountered in modern oncology drug development. Simulation studies show promising operating characteristics of the Bi3+3 design in comparison to existing designs.

Dose Selection for Clinical Development in the Treatment of Hypertension: The Aprocitentan Case and Lessons from the Past

Dose selection plays a critical role in the clinical development of a drug. This current review highlights the lessons learned from previous dose finding studies (DFSs) of antihypertensive drugs and from the recent example of aprocitentan, a novel endothelin receptor antagonist for the treatment of resistant hypertension. Based on these, the authors provide 10 key recommendations for an efficient DFS for a new antihypertensive medication. These recommendations respect critical comments repeatedly made by the U.S. Food and Drug Administration (FDA) Division of Cardiology and Nephrology over the last 5 decades and go beyond the more limited recommendations made in the 2016 Committee for Medicinal Products for Human Use (CHMP) guideline on the development of new antihypertensive medications. The added value of a dose-response modelling approach enriches prior regulatory advice on DFSs.

Alcohol consumption and its correlation with medical conditions: a UK Biobank study.

Alcohol consumption has been associated with the occurrence of many health conditions. We analyzed UK Biobank data to explore associations of various conditions to type and amount of alcohol consumed. UK Biobank is a large biomedical database providing information from UK participants, including lifestyle questionnaires and diagnosis data. Using UK Biobank, we examined the relationship between weekly alcohol consumption, alcohol type and the incidence of eight select conditions. We calculated counts of individuals consuming each type diagnosed with these conditions. To assess the effect of alcohol consumption on each condition's prevalence, we used log-logistic regression models to generate dose-response models for each alcohol type. The alcohol consumed included: red wine (228,439 participants), white wine (188811), beer (182648), spirits (129418), and fortified wine (34598). We observed increased condition prevalence with increasing amounts of alcohol. This was especially seen for chronic obstructive lung disease, cirrhosis of liver, hypertension, gastritis, and type 2 diabetes. Beer consumers showed higher prevalence for most conditions while fortified wine had the largest increases in incidence rates. Only white wine showed decreased incidence for acute myocardial infarction. In general, the prevalence of many conditions was higher among alcohol consumers, particularly for hypertension, 33.8%, compared to 28.6% for non-drinkers. Although many conditions were already prevalent among non-drinkers, participants consuming increasing amounts of alcohol had increased incidence rates for many of the studied conditions. This was especially true for consumers of beer and fortified wine, but also true to a lesser extent for consumers of spirits, red and white wine.

A Fixed-Bed Study on the Feedsorption of BSA Using PKC: Toward the Sustainable Agrosorption of Protein-rich Waste for Enhancing Low Nutritional-value Feed

Palm Kernel Cake (PKC), a by-product of Malaysia's palm oil industry, stands out as a sustainable and cost-efficient feed ingredient. However, its use is nutritionally limited for monogastric animals due to high fiber content and anti-nutritional factors. This groundbreaking study explores the innovative process of 'feedsorption'—a term we have coined under the broader concept of 'agrisorption'—to enhance the nutritional value of PKC. By adsorbing protein-rich agricultural, poultry, and farm animal wastes, represented by bovine serum albumin (BSA), we aim to elevate PKC's protein content. Through detailed experimentation involving varying bed depths (1 cm to 2 cm) and initial BSA concentrations (100 to 500 µg/mL) within fixed-bed columns, our findings reveal that increased bed depths significantly prolong breakthrough and exhaustion times, highlighting improved adsorption efficiency. Yet, depths beyond 1 cm pose a risk of clogging. Higher BSA concentrations were found to accelerate breakthrough, indicating a stronger driving force capable of overcoming mass transfer resistance. The Modified Dose Response (MDR) model outperformed the Thomas model in accurately predicting breakthrough curves across different conditions. This study not only confirms the feasibility of feedsorption to bolster the nutritional profile of low-quality feed using protein-rich waste but also introduces a promising avenue for enhancing sustainable livestock nutrition.

The role of genotypic and climatic variation at the range edge: A case study in winegrapes.

Changes in habitat suitability due to climate change are causing range shifts, with new habitat potentially available at cold range edges. We must predict these range shifts, but forecasters have limited knowledge of how genetic differences in plant physiological tolerances influence range shifts. Here, we focus on a major determinant of species ranges-physiological tolerance to extreme cold-to ask how warming over recent decades and genetic variation shape expansion across complex landscapes. We examined how genotypes vary in maximum cold tolerance from 9 years of cold hardiness data across 18 genotypes from 13 sites, using winegrapes (Vitis vinifera subsp. vinifera) as a case study. Combining a Bayesian hierarchical dose-response model with gridded climate data, we then project changes in climatic suitability near winegrapes' current cold range-edge between 1949 and 2016. Plants increased maximum cold hardiness non-linearly with decreasing air temperature (maximum cold hardiness: -23.6°C), but with substantial (by 2°C) variation across genotypes. Our results suggest, since the 1980s, decreasing freeze injury risk has made conditions more favorable for all genotypes at the cold range-edge, but conditions remained more favorable for more cold hardy genotypes and in warmer areas. There was substantial spatial variation in habitat suitability, with the majority of suitably warm habitat located in a narrow north-south oriented strip. We highlight the importance of genotypic differences in physiological tolerances when assessing range shift potential with climate change. Habitat improvements were unevenly distributed over the spatially complex landscape, though, emphasizing the importance of dispersal in range expansion.

Water and war: The effect of functioning chlorinated water stations in reducing waterborne diseases during conflict in northwest Syria, 2017-2021.

Since 2011, Syria has been engulfed in a complex conflict marked by both targeted and indiscriminate attacks on civilians and civilian infrastructure. Water infrastructure has been continuously targeted, exacerbating problems with contamination of and access to clean adequate drinking water, and increasing the risk of waterborne diseases. We aimed to determine whether having access to more functional and chlorinated water stations is associated with a reduced risk of waterborne disease in northwest Syria. We examined the effect of functioning chlorinated water stations on the incidence of waterborne disease in 10 districts of Northwest Syria between January 1, 2017, and June 30, 2021, using weekly reported disease surveillance data and data from a water, sanitation, and hygiene (WASH) system evaluation program of the Assistance Coordination Unit (ACU). We ran eight negative binomial models to examine the association between functioning chlorinated water stations and the incidence of four of the five waterborne diseases: acute bloody diarrhea (ABD), acute other diarrhea (AOD), acute jaundice syndrome (AJS), and severe typhoid fever (STF). Dose-response models were used to investigate how the incidence of disease can theoretically be reduced as functioning and chlorinated water stations strategically increase. Compared to areas with lower quintiles of functioning and chlorinated water stations, the rates of the four waterborne diseases were lower in areas with higher quintiles of functioning and chlorinated water stations. Exposure to functioning water stations had a stronger association with lower rates of waterborne diseases than exposure to chlorinated water stations. Dose-response models demonstrate a potential for curbing the incidence of acute diarrhea and acute jaundice syndrome. The results of this study provide an understanding of the effects of water station functionality and chlorination in conflict settings. These findings support greater prioritization of WASH activities in countries experiencing violence against civilian infrastructure.

Developing upflow filters operated in a cascade sequence to compare two green sorption media for phosphorus removal

Finding solutions to treat stormwater runoff and wastewater effluent that contain phosphorus is an important effort due to the potential harmful algal bloom impacts on both human health and the environment, such as red tide in coastal regions, eutrophication of waterbodies, and fish kills. However, many of the remaining phosphorus reserves are becoming depleted. Finding low-cost, energy-efficient, and low-maintenance solutions that incorporate green sorption media (GSM) mixed with natural and recycled materials to treat nutrient-laden water, with potential for recovery, is of critical importance for sustainable material development. Biochar and zero-valent iron (ZVI) have received significant attention as low-cost, low-impact adsorbents for the removal of heavy metals, pathogens, and nutrients due to their ability to aid in structural support and reasonable removal efficiencies in GSM. In this study, phosphorus removal efficiencies were studied using two new recipes of GSM within a unique passive cascade upflow filtration system. The two new GSM include zero-valent iron and perlite green environmental media (ZIPGEM) composed of 85% sand, 5% clay, 5% ZVI, and 5% perlite by volume, and biochar zero-valent iron and perlite green environmental media (BIPGEM) containing 80% sand, 5% biochar, 5% clay, 5% perlite, and 5% ZVI by volume. Results indicate that the ZIPGEM-based filtration system achieved an average removal of 78.9% to 97.9% in the initial 60-min to 300-min range, whereas BIPGEM exhibited an average 67.1% to 86.9% removal during this same time interval. At the time of shutdown as a system, BIPGEM showed an average 52.8% removal at the 5480-min mark, and ZIPGEM exhibited an average 69.1% removal at the 11,180-min mark. After examining three isotherm models, the dose-response model estimated the adsorption capacity of ZIPGEM as 6.5 mg·g−1. Further comparison of the material structures and compositions of both raw and spent media samples using X-ray diffraction, X-ray fluorescence spectrometry, and a scanning electron microscope helped confirm part of the removal mechanisms.

Enhancing manganese sorption: Batch and fixed-bed column studies on activated zeolite

Dissolved Mn2+ in groundwater is dangerous for human health since it may act as a neurotoxin. To address this issue, sorption has emerged as an environmentally-friendly technology for Mn2+ removal. This study aimed to assess the effect of activation of Chilean natural zeolite, by modification of its compensating cations with NaCl, on its capacity for Mn2+ removal using batch and fixed-bed column studies. The natural and activated zeolites were characterized by SEM-EDS, TXRF, XRD, and N2 adsorption assays. The results of batch studies demonstrate that Na+ enhances the Mn2+ removal capacity of these zeolites from 4.55 to 5.9−1, with negligible changes in the pore structure. The experimental data fitted well with the pseudo-second-order kinetic and Langmuir adsorption models. Furthermore, MnOx(s) formed during the Mn2+ removal was identified on the surface of the zeolite by XRD and SEM-EDS analysis. The fixed-bed column studies were conducted at two bed heights: H1 (5 cm) and H2 (10 cm). The breakthrough curves adjusted to models of Thomas and Dose-response. The dose-response model adequately described both experimental data with an R2 > 0.90. The Mn2+ adsorption capacity of the system (qmdr) was 2.38 and 5.13 mg g−1, for H1 and H2, respectively. The regeneration of the activated zeolite was achieved using NaCl solution improving the efficiency of Mn2+ removal after reuse. This work highlights natural zeolite potential as an effective material for Mn2+ removal and as a viable technology to improve the water quality affected by geogenic contamination.

Chemotaxonomy, antibacterial and antioxidant activities of selected aromatic plants from Tabuk region-KSA

Chemotaxonomy is a valuable tool for obtaining taxonomic insights, which are most effectively employed in combination with other forms of data to establish a system of classification that closely reflects natural connections. The utilization of plant secondary metabolites possessing diverse therapeutic qualities signifies the growing exploitation of natural products in the medical discipline. The objectives of the current study encompassed the identification of phytochemicals in the extracts of nine species of medicinal plants, the examination of their chemotaxonomic properties, and the assessment of the antibacterial and antioxidant capabilities exhibited by the extracts. GC-MS technology was employed for the identification of phytochemical compounds. The study utilized ClassyFire, an automated chemical classification system that incorporates an extensive and computable classification, to categorize chemicals. The chemical classification of plants was examined by the application of principal component analysis (PCA) and cluster analysis (CA). The bactericidal properties of plants were assessed against four harmful bacterial species using the disc diffusion technique. The antioxidant properties of plant extracts were assessed employing the DPPH free radical scavenging methodology, and the half maximal effective concentration (EC50) was determined using dose response models. The calculator being referred to is the Quest Graph™ EC50 Calculator. In the plant extracts, the analysis disclosed the occurrence of 160 phytochemicals, classified into 36 phytochemical classes. The results of CA and PCA demonstrated the proximity and associations among Asteraceae species, while indicating the divergence of the two Lamiaceae species. Achillea fragrantissima and Ducrosia flabellifolia demonstrated the most diversity in phytochemical classes, while Thymus vulgaris displayed the highest level of dominance. Pulicaria undulata and T. vulgaris had the most notable antibacterial activity. D. flabellifolia and P. incisa demonstrated the highest levels of antioxidant activity. Ethanol exhibited superior antibacterial efficacy compared to other solvents. The remarkable biological activities exhibited by these plant extracts can be ascribed to the copious presence of certain chemicals, predominantly sesquiterpenoids, monoterpenoids, benzene and its derivatives, naphthalenes, fatty acyls, and phenols. The susceptibility of Gram-positive bacterial species to plant extracts was shown to be higher in comparison to Gram-negative bacterial species.

Subgroup identification of targeted therapy effects on biomarker for time to event data.

The initiation biomarker-driven trials have revolutionized oncology drug development by challenging the traditional phased approach and introducing basket studies. Notable successes in non-small cell lung cancer (NSCLC) with ALK, ALK/ROS1, and EGFR inhibitors have prompted the need to expand this approach to other cancer sites. This study explores the use of dose response modeling and time-to-event algorithms on the biomarker molecular targeted agent (MTA). By simulating subgroup identification in MTA-related time-to-event data, the study aims to develop statistical methodology supporting biomarker-driven trials in oncology. A total of n patients are selected assigned for different doses. A dataset is prepared to mimic the situation on Subgroup Identification of MTA for time to event data analysis. The response is measured through MTA. The MTA value is also measured through ROC. The Markov Chain Monte Carlo (MCMC) techniques are prepared to perform the proposed algorithm. The analysis is carried out with a simulation study. The subset selection is performed through the Threshold Limit Value (TLV) by the Bayesian approach. The MTA is observed with range 12-16. It is expected that there is a marginal level shift of the MTA from pre to post-treatment. The Cox time-varying model can be adopted further as causal-effect relation to establishing the MTA on prolonging the survival duration. The proposed work in the statistical methodology to support the biomarker-driven trial for oncology research. This study extends the application of biomarker-driven trials beyond NSCLC, opening possibilities for implementation in other cancer sites. By demonstrating the feasibility and efficacy of utilizing MTA as a biomarker, the research lays the foundation for refining and validating biomarker use in clinical trials. These advancements aim to enhance the precision and effectiveness of cancer treatments, ultimately benefiting patients.

Bayesian model averaging of longitudinal dose-response models

ABSTRACT Selecting a safe and clinically beneficial dose can be difficult in drug development. Dose justification often relies on dose-response modeling where parametric assumptions are made in advance which may not adequately fit the data. This is especially problematic in longitudinal dose-response models, where additional parametric assumptions must be made. This paper proposes a class of longitudinal dose-response models to be used in the Bayesian model averaging paradigm which improve trial operating characteristics while maintaining flexibility a priori. A new longitudinal model for non-monotonic longitudinal profiles is proposed. The benefits and trade-offs of the proposed approach are demonstrated through a case study and simulation.

How do biomarkers dance? Specific moves of defense and damage biomarkers for biological interpretation of dose-response model trends

Omics studies are currently increasingly used in ecotoxicology to highlight the induction of known or novel biomarkers when organisms are exposed to contaminants. Although it is virtually impossible to identify all biomarkers from all organisms, biomarkers can be grouped as defense or damage biomarkers, exhibiting a limited number of response trends. Our working hypothesis is that defense and damage biomarkers follow different dose-response patterns. A meta-analysis of 156 articles and 2595 observations of dose-response curves of defense and damage biomarkers was carried out in order to characterize the response trends of these biological parameters in a large panel of living organisms (18 phyla) exposed to inorganic or organic contaminants (176 in total). Using multinomial logistic regression models, defense biomarkers were found to describe biphasic responses (bell- and U-shaped) to a greater extent (2.5 times) than damage biomarkers. In contrast, damage biomarkers varied mainly monotonically (decreasing or increasing), representing 85% of the observations. Neither the nature of the contaminant nor the type of organisms belonging to 4 kingdoms, influence these specific responses. This result suggests that cellular defense and damage mechanisms are not specific to stressors and are conserved throughout life. Trend analysis of dose-response models as a biological interpretation of biomarkers could thus be a valuable way to exploit large omics datasets.

Impact of Modern Low Dose Involved Site Radiation Therapy on Normal Tissue Toxicity in Cervicothoracic Non-Hodgkin Lymphomas: A Biophysical Study.

This biophysical study aimed to determine fitting parameters for the Lyman-Kutcher-Burman (LKB) dose-response model for normal tissue complication probability (NTCP) calculations of acute side effects and to investigate the impact of reduced radiation doses on the probability of their occurrence in supradiaphragmatic non-Hodgkin lymphoma (NHL) irradiation. A cohort of 114 patients with NHL in the cervicothoracic region, treated between 2015 and 2021 at the University Hospitals of Münster, Hamburg, and Essen, with involved site radiation therapy (ISRT), were included. Among them, 68 patients with aggressive NHL (a-NHL) received consolidative radiation therapy with 24-54 Gy following (R-)CHOP chemotherapy. Additionally, 46 patients with indolent NHL (i-NHL) underwent radiotherapy with 22.5-45.0 Gy. Two treatment plans were prospectively created for each patient (a-NHL: 30.0/40.0 Gy; i-NHL: 24.0/30.0 Gy). NTCP were then calculated using the optimized LKB model. The adapted dose-response models properly predicted the patient's probability of developing acute side effects when receiving doses ≤ 50 Gy. In addition, it was shown that reduced radiation doses can influence the NTCP of acute side effects depending on the aggressiveness of NHL significantly. This study provided a foundation to prospectively assess the probability of adverse side effects among today's reduced radiation doses in the treatment of NHL.

Dose-response Relationships Between Cigarette Smoking and Breast Cancer Risk: A Systematic Review and Meta-analysis.

The possible association between cigarette smoking and breast cancer risk has been quite controversial. We conducted a systematic review and meta-analysis of all available observational studies published on the issue up to January 2020. Random-effects models were used to compute pooled relative risks (RRs) for cigarette smoking status and dose-risk relationships were evaluated using one-stage random-effects dose-response models. A total of 169 studies were selected, providing a pooled RR for breast cancer of 1.07 (95% confidence interval [CI], 1.05-1.10) for current, 1.08 (95% CI, 1.06-1.10) for former, and 1.09 (95% CI, 1.07-1.11) for ever smokers, compared to never smokers. Results were consistent in case-control and cohort studies. No meaningful differences were observed across strata of most covariates considered, nor according to relevant genetic mutations and polymorphisms (ie, BRCA mutation, N-acetyltransferase and glutathione S-transferase genotypes, and P53). Breast cancer risk increased linearly with intensity of smoking (RR 1.12; 95% CI, 1.08-1.16 for 20 cigarettes/day and 1.26; 95% CI, 1.17-1.36 for 40 cigarettes/day), and with increasing duration of smoking (RR 1.05; 95% CI, 1.03-1.08 for 20 years of smoking and 1.11; 95% CI, 1.06-1.16 for 40 years of smoking). The present large and comprehensive meta-analysis-conducted using an innovative approach for study search-supports the evidence of a causal role of tobacco smoking on breast cancer risk.