Population-based Model Research Articles

Single seeds exhibit transcriptional heterogeneity during secondary dormancy induction.

Seeds are highly resilient to the external environment, which allows plants to persist in unpredictable and unfavorable conditions. Some plant species have adopted a bet-hedging strategy to germinate a variable fraction of seeds in any given condition, and this could be explained by population-based threshold models. Here, in the model plant Arabidopsis (Arabidopsis thaliana), we induced secondary dormancy (SD) to address the transcriptional heterogeneity among seeds that leads to binary germination/nongermination outcomes. We developed a single-seed RNA-seq strategy that allowed us to observe a reduction in seed transcriptional heterogeneity as seeds enter stress conditions, followed by an increase during recovery. We identified groups of genes whose expression showed a specific pattern through a time course and used these groups to position the individual seeds along the transcriptional gradient of germination competence. In agreement, transcriptomes of dormancy-deficient seeds (mutant of DELAY OF GERMINATION 1) showed a shift toward higher values of the germination competence index. Interestingly, a significant fraction of genes with variable expression encoded translation-related factors. In summary, interrogating hundreds of single-seed transcriptomes during SD-inducing treatment revealed variability among the transcriptomes that could result from the distribution of population-based sensitivity thresholds. Our results also showed that single-seed RNA-seq is the method of choice for analyzing seed bet-hedging-related phenomena.

Development and validation of a multivariable mortality risk prediction model for COPD in primary care

Risk stratification of chronic obstructive pulmonary disease (COPD) patients is important to enable targeted management. Existing disease severity classification systems, such as GOLD staging, do not take co-morbidities into account despite their high prevalence in COPD patients. We sought to develop and validate a prognostic model to predict 10-year mortality in patients with diagnosed COPD. We constructed a longitudinal cohort of 37,485 COPD patients (149,196 person-years) from a UK-wide primary care database. The risk factors included in the model pertained to demographic and behavioural characteristics, co-morbidities, and COPD severity. The outcome of interest was all-cause mortality. We fitted an extended Cox-regression model to estimate hazard ratios (HR) with 95% confidence intervals (CI), used machine learning-based data modelling approaches including k-fold cross-validation to validate the prognostic model, and assessed model fitting and discrimination. The inter-quartile ranges of the three metrics on the validation set suggested good performance: 0.90–1.06 for model fit, 0.80–0.83 for Harrel’s c-index, and 0.40–0.46 for Royston and Saurebrei’s R_D^2 with a strong overlap of these metrics on the training dataset. According to the validated prognostic model, the two most important risk factors of mortality were heart failure (HR 1.92; 95% CI 1.87–1.96) and current smoking (HR 1.68; 95% CI 1.66–1.71). We have developed and validated a national, population-based prognostic model to predict 10-year mortality of patients diagnosed with COPD. This model could be used to detect high-risk patients and modify risk factors such as optimising heart failure management and offering effective smoking cessation interventions.

Parity-Adjusted Term Neonatal Growth Chart Modifies Neonatal Morbidity and Mortality Risk Stratification.

Objective: To investigate the impact of parity-customized versus population-based birth weight charts on the identification of neonatal risk for adverse outcomes in small (SGA) or large for gestational age (LGA) infants compared to appropriate for gestational age (AGA) infants. Study design: Observational, retrospective, cohort study based on electronic medical birth records at a single center between 2006 and 2017. Neonates were categorized by birth weight (BW) as SGA, LGA, or AGA, with the 10th and 90th centiles as boundaries for AGA in a standard population-based model adjusted for gestational age and gender only (POP) and a customized model adjusted for gestational age, gender, and parity (CUST). Neonates defined as SGA or LGA by one standard and not overlapping the other, are SGA/LGA CUST/POP ONLY. Analyses used a reference group of BW between the 25th and 75th centile for the population. Results: Overall 132,815 singleton, live, term neonates born to mothers with uncomplicated pregnancies were included. The customized model identified 53% more neonates as SGA-CUST ONLY who had significantly higher rates of morbidity and mortality compared to the reference group (OR = 1.33 95% CI [1.16–1.53]; p < 0.0001). Neonates defined as LGA by the customized model (LGA-CUST) and AGA by the population-based model LGA-CUST ONLY had a significantly higher risk for morbidity compared to the reference (OR = 1.36 95% CI [1.09–1.71]; p = 0.007) or the LGA POP group. Neonatal mortality only occurred in the SGA and AGA groups. Conclusions: The application of a parity-customized only birth weight chart in a population of singleton, term neonates is a simple platform to better identify birth weight related neonatal risk for morbidity and mortality.

Enduring undetectable MRD and updated outcomes in relapsed/refractory CLL after fixed-duration venetoclax-rituximab

AbstractThe MURANO trial (A Study to Evaluate the Benefit of Venetoclax Plus Rituximab Compared With Bendamustine Plus Rituximab in Participants With Relapsed or Refractory Chronic Lymphocytic Leukemia [CLL]; ClinicalTrials.gov identifier #NCT02005471) reported superior progression-free survival (PFS) and overall survival (OS) with venetoclax-rituximab (VenR) vs bendamustine-rituximab (BR) in relapsed/refractory (R/R) CLL. Patients were randomized to 2 years of VenR (n = 194; rituximab for the first 6 months) or 6 months of BR (n = 195). Although undetectable minimal residual disease (uMRD) was achieved more often with VenR, the long-term implications of uMRD with this fixed-duration, chemotherapy-free regimen have not been explored. We report MRD kinetics and updated outcomes with 5 years' follow-up. Survival benefits with VenR vs BR were sustained (median PFS [95% confidence interval]: 53.6 [48.4, 57.0] vs 17.0 [15.5, 21.7] months, respectively, P < .0001; 5-year OS [95% confidence interval]: 82.1% [76.4, 87.8] vs 62.2% [54.8, 69.6], P < .0001). VenR was superior to BR, regardless of cytogenetic category. VenR-treated patients with uMRD at end of treatment (EOT; n = 83) had superior OS vs those with high-MRD+ (n = 12): 3-year post-EOT survival rates were 95.3% vs 72.9% (P = .039). In those with uMRD at EOT, median time to MRD conversion was 19.4 months. Of 47 patients with documented MRD conversion, 19 developed progressive disease (PD); median time from conversion to PD was 25.2 months. A population-based logistic growth model indicated slower MRD median doubling time post-EOT with VenR (93 days) vs BR (53 days; P = 1.2 × 10−7). No new safety signals were identified. Sustained survival, uMRD benefits, and durable responses support 2-year fixed-duration VenR treatment in R/R CLL.

A sensitivity analysis of a human exposure model using the Sobol method.

The Air Pollutants Exposure Model (APEX) is a stochastic population-based inhalation exposure model which (along with its earlier version called pNEM) has been used by the U.S. Environmental Protection Agency (EPA) for over 30 years for assessment of human exposure to airborne pollutants. This study describes the application of a variance decomposition-based sensitivity analysis using the Sobol method to elucidate the key APEX inputs and processes that affect variability in exposure and dose for the simulated population. Understanding APEX's sensitivities to these inputs helps not only the model user but also the EPA in prioritizing limited resources towards data-collection and analysis efforts for the most influential variables, in order to maintain the quality and defensibility of the simulation results. This analysis examines exposure to ozone of children ages 5-18 years. The results show that selection of activity diaries and microenvironmental parameters (including air-exchange rate and decay rate) are the most influential to estimated exposure and dose, their aggregate main-effect indices (MEIs) equaling 0.818 (out of a maximum of 1.0) for daily-average ozone exposure and 0.469 for daily-average inhaled ozone dose. The modeled person's home location, sampled from national Census data, has a modest influence on exposure (MEI = 0.079 for daily averages), while age, sex, and body mass, also sampled from Census and other survey data, have modest influences on inhaled dose (aggregate MEI = 0.307). The sensitivity analysis also plays a quality-assurance role by evaluating the sensitivities against our knowledge of the physical properties of the model.

OC-0939 Development and validation of a population-based colorectal model for radiation therapy dosimetry

OC-0939 Development and validation of a population-based colorectal model for radiation therapy dosimetry

Behavioral Predictive Analytics Towards Personalization for Self-management: a Use Case on Linking Health-Related Social Needs.

The objective of this research is to investigate the feasibility of applying behavioral predictive analytics to optimize patient engagement in diabetes self-management, and to gain insights on the potential of infusing a chatbot with NLP technology for discovering health-related social needs. In the U.S., less than 25% of patients actively engage in self-health management, even though self-health management has been reported to associate with improved health outcomes and reduced healthcare costs. The proposed behavioral predictive analytics relies on manifold clustering to identify subpopulations segmented by behavior readiness characteristics that exhibit non-linear properties. For each subpopulation, an individualized auto-regression model and a population-based model were developed to support self-management personalization in three areas: glucose self-monitoring, diet management, and exercise. The goal is to predict personalized activities that are most likely to achieve optimal engagement. In addition to actionable self-health management, this research also investigates the feasibility of detecting health-related social needs through unstructured conversational dialog. This paper reports the result of manifold clusters based on 148 subjects with type 2 diabetes and shows the preliminary result of personalization for 22 subjects under different scenarios, and the preliminary results on applying Latent Dirichlet Allocation to the conversational dialog of ten subjects for discovering social needs in five areas: food security, health (insurance coverage), transportation, employment, and housing.

A population-based predictive model to identify patients with signet ring cell carcinoma of the stomach who are most suitable for primary tumor resection

BackgroundThough the survival benefit of primary tumor operation for patients with signet ring cell carcinoma of the stomach is known, the specific characteristics of those patients who would profit from the operation are yet to be determined. To this end, a predictive model was developed to identify the conjecture that the survival profit from primary tumor operation would only be obtained by patients.MethodThe clinical data of the patients with signet ring cell carcinoma of the stomach were obtained from the Surveillance, Epidemiology, and End Results database, and then divided into operation and no-operation groups based on whether the patients underwent the primary tumor operation. To remove the confounding factors, propensity score matching was employed, and it was hypothesized that the patients who had been operated on and lived a longer life than the median cancer-specific survival time of those who hadn’t must have profited from the surgery. To discuss the independent factors of cancer-specific survival time in the beneficial group and the non-beneficial group, the Cox model was used, and based on the various vital predictive factors, a nomogram was drawn using logistic regression.ResultThe number of eligible patients was 12,484, with 43.9% (5483) of them having received surgery. After employing propensity score matching, the cancer-specific survival time of the operation group was found to be apparently longer (median: 21 vs. 5 months; p < 0.001) than the no-operation group. In the operation group, 4757 (86.7%) of the patients lived longer than five months (beneficial group). The six indexes (beneficial and non-beneficial group) included gender, age, Tumor Node Metastasis stage, histologic type, differentiation grade, and tumor position, and were used as predictors to draw the nomogram. The nomogram was used to divide the patients who had taken operations into two groups: the beneficial operation group and the non-beneficial operation group. The beneficial operation group, it was found, survived longer than the non-beneficial operation group (median cancer-specific survival time: 28 vs. 3 months, p < 0.001). Moreover, there was we could tell little difference in survival between the two groups (median cancer-specific survival time: 3 vs. 5 months).ConclusionsThe predictive model created to select suitable candidates for surgical treatment from patients with signet ring carcinoma of the stomach could be adopted to identify certain patients benefiting from the primary tumor operation.

Modeling apoptosis resistance in CHO cells with CRISPR-mediated knockouts of Bak1, Bax, and Bok.

Chinese hamster ovary (CHO) cells are the primary platform for the production of biopharmaceuticals. To increase yields, many CHO cell lines have been genetically engineered to resist cell death. However, the kinetics that governs cell fate in bioreactors are confounded by many variables associated with batch processes. Here, we used CRISPR‐Cas9 to create combinatorial knockouts of the three known BCL‐2 family effector proteins: Bak1, Bax, and Bok. To assess the response to apoptotic stimuli, cell lines were cultured in the presence of four cytotoxic compounds with different mechanisms of action. A population‐based model was developed to describe the behavior of the resulting viable cell dynamics as a function of genotype and treatment. Our results validated the synergistic antiapoptotic nature of Bak1 and Bax, while the deletion of Bok had no significant impact. Importantly, the uniform application of apoptotic stresses permitted direct observation and quantification of a delay in the onset of cell death through Bayesian inference of meaningful model parameters. In addition to the classical death rate, a delay function was found to be essential in the accurate modeling of the cell death response. These findings represent an important bridge between cell line engineering strategies and biological modeling in a bioprocess context.

A Horse Herd Optimization Algorithm (HOA)-Based MPPT Technique under Partial and Complex Partial Shading Conditions

The inconsistent irradiance, temperature, and unexpected behavior of the weather affect the output of photovoltaic (PV) systems, classified as partial or complex partial shading conditions. Under these circumstances, obtaining the maximum output power from PV systems becomes problematic. This paper proposes a population-based optimization model, the horse herd optimization algorithm (HOA), inspired by natural behavior, to solicit the maximum power under partial or complex partial shading conditions. It is an intelligent strategy inspired by the surprise pounce-chasing style of the horse herd model. The proposed technique outperforms the standard in different weather conditions, needs less computational time, and has a fast convergence speed and zero oscillations after reaching a power point’s maximum limit. A performance comparison of the HOA is achieved with conventional techniques, such as “perturb and observe” (P&amp;O), the bio-inspired adaptive cuckoo search optimization (ACS), particle swarm optimization (PSO), and the dragonfly algorithm (DA). The following comparison of the presented scheme with the other techniques shows its better performance with respect to fast tracking and efficiency, as well as stability under disparate weather conditions and the ability to obtain maximum power with negligible oscillation under partial and complex shading.

Abstract EP68: Can A Single Model For General Population Predict Cardiovascular Events In Cancer Survivors?

Introduction: No specific guideline exists for the primary prevention of cardiovascular disease (CVD) in cancer survivors. We hypothesized that CVD risk and its association with risk factors are comparable between cancer survivors and the general population, such that they may share currently available risk scores (e.g., Pooled Cohort Equation; PCE) for initial risk discussion. Methods: We studied 59 445 cancer survivors (≥5 years from diagnosis) and 59 445 age/sex-matched non-cancer controls aged 40-79 years (51.6% female) and free of CVD among participants of 2009 Korean National Health Insurance health screening. We calculated 10-year cumulative incidence of CVD events (composite of myocardial infarction, stroke, or cardiovascular death) and hazard ratios for traditional CVD risk factors in cancer survivors vs. non-cancer controls. We then evaluated discrimination and calibration of a previously-derived, general Korean population-based CVD risk model (having similar predictors as the PCE) applied to cancer survivors. Results: During a median follow-up of 10.3 years, 6,809 CVD events occurred. Multivariable-adjusted 10-year cumulative CVD incidence was 2.8% (2.7-3.0) among cancer survivors and 3.0% (2.0-3.2) among non-cancer controls (Figure A). Associations of traditional risk factors with CVD events were similar between cancer survivors and controls (Figure B). Results were consistent when stratified by sex and age. A CVD risk model, previously derived from the general Korean population similarly as the PCE, showed good discrimination (C-index, 0.747) and calibration (Figure C) when applied to cancer survivors. By cancer subtype, some cancers were associated with higher or lower CVD risk than controls (Figure D). Conclusions: CVD risk and associations with predictors were similar between cancer survivors and non-cancer individuals. Pre-existing models for general population may perform well in overall cancer survivors, although subtype-specific recalibration may be desirable for some cancers.

Dynamic 68Ga-DOTATATE PET/MRI in the Diagnosis and Management of Intracranial Meningiomas.

Purpose To evaluate dynamic gallium 68 (68Ga) tetraazacyclododecane tetraacetic acid octreotate (DOTATATE) brain PET/MRI as an adjunct modality in meningioma, enabling multiparametric standardized uptake value (SUV) and Patlak net binding rate constant (Ki) imaging, and to optimize static acquisition period. Materials and Methods In this prospective study (ClinicalTrials.gov no. NCT04081701, DOMINO-START), 68Ga-DOTATATE PET/MRI-derived time-activity curves (TACs) were measured in 84 volumes of interest in 19 participants (mean age, 63 years; range, 36-89 years; 13 women; 2019-2021) with meningiomas. Region- and voxel-specific Ki were determined using Patlak analysis with a validated population-based reference tissue TAC model built from an independent data set of nine participants. Mean and maximum absolute and relative-to-superior-sagittal-sinus SUVs were extracted from the entire 50 minutes (SUV50) and last 10 minutes (SUV10) of acquisition. SUV versus Ki Spearman correlation, SUV and Ki meningioma versus posttreatment-change Mann-Whitney U tests, and SUV50 versus SUV10 Wilcoxon matched-pairs signed rank tests were performed. Results Absolute and relative maximum SUV50 demonstrated a strong positive correlation with Patlak Ki in meningioma (r = 0.82, P < .001 and r = 0.85, P < .001, respectively) and posttreatment-change lesions (r = 0.88, P = .007 and r = 0.83, P = .02, respectively). Patlak Ki images yielded higher lesion contrast by mitigating nonspecific background signal. All SUV50 and SUV10 metrics differed between meningioma and posttreatment-change regions (P < .001). Within the meningioma group, SUV10 attained higher mean scores than SUV50 (P < .001). Conclusion Combined SUV and Patlak Ki68Ga-DOTATATE PET/MRI enabled multiparametric evaluation of meningioma, offering the potential to enhance lesion contrast with Ki imaging and optimize the SUV measurement postinjection window. Keywords: Molecular Imaging-Clinical Translation, Neuro-Oncology, PET/MRI, Dynamic, Patlak ClinicalTrials.gov registration no. NCT04081701 © RSNA, 2022.

Comparison of Two Types of Amino Acid Solutions on 177Lu-Dotatate Pharmacokinetics and Pharmacodynamics in Patients with Metastatic Gastroenteropancreatic Neuroendocrine Tumors.

177Lu-Dotatate is used in the treatment of somatostatin-receptor-positive inoperable progressive gastroenteropancreatic neuroendocrine tumors. A co-infusion of amino acids (AAs) is administered to prevent renal toxicity. This study aimed to quantify the impact of two types of AA cocktails on the pharmacokinetics and toxicity of 177Lu-Dotatate. Four injections of 7400 MBq 177Lu-Dotatate were given per patient with administration of either Primene® 10% (containing a cocktail of 20 AAs with 22g of Lysine and 16.8 g of Arginine) or Lysakare® (containing 25 g of Lysine and 25 g of Arginine). Nine blood samples were collected at each cycle. Radioactivity-time data were analyzed according to a population-based model using NONMEM (version 7.4.1). Renal and hematological toxicity was evaluated after each cycle. 1,678 177Lu-Dotatate plasma concentrations versus time were analyzed from 83 consecutive patients with Primene® (n= 45 pts) or Lysakare® (n= 36 pts). Population pharmacokinetic analysis showed that Primene® significantly increased the elimination rate constant of 177Lu-Dotatate as opposed to Lysakare®. Primene® also significantly lowered Lutathera® plasma exposure (AUC) by 34%, whereas Lysakare® increased AUC by 7%. There was no renal toxicity in either case. Lymphopenia significantly correlated with AUC (p=0.021) with a trend towards higher toxicity with Lysakare®. Unlike Primene®, Lysakare® does not increase 177Lu-Dotatate elimination. This difference is associated with a significant impact on AUC. The latter parameter has a high interpatient variability but a low intrapatient variability, which could have important clinical implications for treatment tailoring.

Development and validation of a population-based risk stratification model for severe COVID-19 in the general population

The shortage of recently approved vaccines against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has highlighted the need for evidence-based tools to prioritize healthcare resources for people at higher risk of severe coronavirus disease (COVID-19). Although age has been identified as the most important risk factor (particularly for mortality), the contribution of underlying comorbidities is often assessed using a pre-defined list of chronic conditions. Furthermore, the count of individual risk factors has limited applicability to population-based “stratify-and-shield” strategies. We aimed to develop and validate a COVID-19 risk stratification system that allows allocating individuals of the general population into four mutually-exclusive risk categories based on multivariate models for severe COVID-19, a composite of hospital admission, transfer to intensive care unit (ICU), and mortality among the general population. The model was developed using clinical, hospital, and epidemiological data from all individuals among the entire population of Catalonia (North-East Spain; 7.5 million people) who experienced a COVID-19 event (i.e., hospitalization, ICU admission, or death due to COVID-19) between March 1 and September 15, 2020, and validated using an independent dataset of 218,329 individuals with COVID-19 confirmed by reverse transcription—polymerase chain reaction (RT-PCR), who were infected after developing the model. No exclusion criteria were defined. The final model included age, sex, a summary measure of the comorbidity burden, the socioeconomic status, and the presence of specific diagnoses potentially associated with severe COVID-19. The validation showed high discrimination capacity, with an area under the curve of the receiving operating characteristics of 0.85 (95% CI 0.85–0.85) for hospital admissions, 0.86 (0.86–0.97) for ICU transfers, and 0.96 (0.96–0.96) for deaths. Our results provide clinicians and policymakers with an evidence-based tool for prioritizing COVID-19 healthcare resources in other population groups aside from those with higher exposure to SARS-CoV-2 and frontline workers.

A modeling framework for computational simulations of thoracic endovascular aortic repair.

Thoracic endovascular aortic repair (TEVAR) is a minimally invasive treatment for thoracic aortic conditions including aneurysms and is associated with a number of postoperative stent graft related complications. Computational simulations of TEVAR have the potential to predict surgical outcomes and complications preoperatively. When using simulations for stent graft design and prediction of complications in a population, it is difficult to generalize patient-specific TEVAR computational models due to patient variability. This study proposes a novel modeling framework for creating realistic population-based computational models of TEVAR focused on aneurysms that allow for developing various clinically relevant geometric configurations and scenarios that are not easily attainable with limited patient data. The framework includes a methodology for developing population-based thoracic aortic geometries and defining age-dependent aortic tissue material models, as well as detailed steps and boundary conditions for finite element modeling of stent graft deployment during TEVAR. The simulation framework is illustrated for predicting the formation of a bird-beak configuration, a wedge-shaped gap at the proximal end of the deployed stent graft in TEVAR that leads to incomplete seal. A baseline TEVAR simulation model was developed along with three simulations in which the value of aortic curvature, aortic arch angle, or aortic tissue properties varied from the baseline model. Analyzing the length and angle of the bird-beak configuration in each case shows that the bird-beak size is sensitive to different values of the aortic geometry highlighting the importance of using realistic parameter values.

Morphological variation in paediatric lower limb bones

Available methods for generating paediatric musculoskeletal geometry are to scale generic adult geometry, which is widely accessible but can be inaccurate, or to obtain geometry from medical imaging, which is accurate but time-consuming and costly. A population-based shape model is required to generate accurate and accessible musculoskeletal geometry in a paediatric population. The pelvis, femur, and tibia/fibula were segmented from 333 CT scans of children aged 4–18 years. Bone morphology variation was captured using principal component analysis (PCA). Subsequently, a shape model was developed to predict bone geometry from demographic and linear bone measurements and validated using a leave one out analysis. The shape model was compared to linear scaling of adult and paediatric bone geometry. The PCA captured growth-related changes in bone geometry. The shape model predicted bone geometry with root mean squared error (RMSE) of 2.91 ± 0.99 mm in the pelvis, 2.01 ± 0.62 mm in the femur, and 1.85 ± 0.54 mm in the tibia/fibula. Linear scaling of an adult mesh produced RMSE of 4.79 ± 1.39 mm in the pelvis, 4.38 ± 0.72 mm in the femur, and 4.39 ± 0.86 mm in the tibia/fibula. We have developed a method for capturing and predicting lower limb bone shape variation in a paediatric population more accurately than linear scaling without using medical imaging.

Optimal chemotherapy counteracts cancer adaptive resistance in a cell-based, spatially-extended, evolutionary model

Most aggressive cancers are incurable due to their fast evolution of drug resistance. We model cancer growth and adaptive response in a simplified cell-based (CB) setting, assuming a genetic resistance to two chemotherapeutic drugs. We show that optimal administration protocols can steer cells resistance and turned it into a weakness for the disease. Our work extends the population-based model proposed by Orlando et al (2012 Phys. Biol.), in which a homogeneous population of cancer cells evolves according to a fitness landscape. The landscape models three types of trade-offs, differing on whether the cells are more, less, or equal effective when generalizing resistance to two drugs as opposed to specializing to a single one. The CB framework allows us to include genetic heterogeneity, spatial competition, and drugs diffusion, as well as realistic administration protocols. By calibrating our model on Orlando et al’s assumptions, we show that dynamical protocols that alternate the two drugs minimize the cancer size at the end of (or at mid-points during) treatment. These results significantly differ from those obtained with the homogeneous model—suggesting static protocols under the pro-generalizing and neutral allocation trade-offs—highlighting the important role of spatial and genetic heterogeneities. Our work is the first attempt to search for optimal treatments in a CB setting, a step forward toward realistic clinical applications.

FDA Should Re-evaluate All mRNA Vaccines and Revoke Their Use Authorizations (The Short Version).

Drug industry, controlling medical publishers and large media promote flawed medicine for their revenues by systematically laundering medical knowledge in decades. They maintain and promote flawed research models and suppress disruptive discoveries, thereby precluding reform of medicine. In this study, I will deeply explore how the wrong life model, population-based research model, misused clinical trials, flawed statistical models, the symptom based research methods, binary disease classification, failure to address the massive vital organ capacities, failure to correct biases caused by expected delay in realizing side effects, and failure to address the interference effects of non-controllable factors affect the conclusions of “effectiveness and safety” for mRNA vaccines. I will directly analyze three studies that have been relied upon by FDA in approving mNRA use authorizations: one BNT162b2 effectiveness study published in NEJM, one booster shot study published in NEJM and a Seven Integrated Health Care Organizations study published by CDC. I will expose fatal flaws in the frequency risk concept, effectiveness rate, and hazard reduction ratios, and show why 3% death rate, 95% effectiveness rate and 90% mortality reduction are all meaningless and misleading, and should never have been used as treatment guidance. I will also examine common biases that can be easily practiced by sponsors’ researchers to alter conclusions in favor of approval. By relying on laundered medical “knowledge”, FDA has consistently failed to predict latent drug side effects for any drugs and vaccines in its history. FDA approved disastrous DES in 1941, Swine Flu vaccine in 1976, and mRNA vaccines in 2020. The vaccines are used to deliver short-term benefits on a small percent of persons at the costs of damaging health, causing deaths that could be avoided, and shortening lifespans for all people in the population. I thus urge FDA to reevaluate all mRNA vaccines and revoke their use authorizations.

The Sport Participation Pathway Model (SPPM): a conceptual model for participation and retention in community sport

ABSTRACT National sport policy has a dual focus on population-based participation, and elite performance. There are various models depicting the pathway to elite, however, there has not been a population-based sport participation pathway model to track, develop and facilitate holistic participation policies in sport. The aim of this study is to review sport participation trends, including new data on retention and drop-out across age groups in community club-based sport, and combine this data with evidence from literature about people moving in and out of sport, to develop the Sport Participation Pathway Model (SPPM), which provides a holistic view of sport participation. We conducted a 3-year analysis of sport participation for eight major sports from 2015–2017 for children and young people aged 4–29 years. The total number of participants was 579,696. Only half 50.8% played continuously for the three years, 44.7% dropped out, and 4.5% played discontinuously. Drop-out was highest for those aged 4 (57.0%), and lowest for those aged 10–14 years (39.3%). The SPPM demonstrates the movements of participation in sport and the drop-out at various stages out of competitive club-based sport and for those who are the main participants. The model is a standalone sport participation model that is a representation and integration of empirical sport participation. This model therefore is most useful for sports organisations to recognise issues around retention and drop-out. In developing sport policies, government can also use the model to make decisions on target groups and funding support.

Fractional stochastic modelling of COVID-19 under wide spread of vaccinations: Egyptian case study

This work predicts the dynamics of the COVID-19 under widespread vaccination to anticipate the virus's current and future waves. We focused on establishing two population-based models for predictions: the fractional-order model and the fractional-order stochastic model. Based on dose efficacy, which is one of the main imposed assumptions in our study, some vaccinated people will probably be exposed to infection by the same viral wave. We validated the generated models by applying them to the current viral wave in Egypt. We assumed that the Egyptian current wave began on 10th September 2021. Using current actual data and varying our models’ fractional orders, we generate different predicted wave scenarios. The numerical solution of our models is obtained using the fractional Euler method and the fractional Euler Maruyama method. At the end, we compared the current predicted wave under a high vaccination rate with the previous viral wave. Through this comparison, the vaccination control effect is quantified.