Human-mouse comparison of the multistage nature of radiation carcinogenesis in a mathematical model.

Mouse models are vital for assessing risk from environmental carcinogens, including ionizing radiation, yet the interspecies difference in the dose response precludes direct application of experimental evidence to humans. Herein, we take a mathematical approach to delineate the mechanism underlying the human-mouse difference in radiation-related cancer risk. We used a multistage carcinogenesis model assuming a mutational action of radiation to analyze previous data on cancer mortality in the Japanese atomic bomb survivors and in lifespan mouse experiments. Theoretically, the model predicted that exposure will chronologically shift the age-related increase in cancer risk forward by a period corresponding to the time in which the spontaneous mutational process generates the same mutational burden as that the exposure generates. This model appropriately fitted both human and mouse data and suggested a linear dose response for the time shift. The effect per dose decreased with increasing age at exposure similarly between humans and mice on a per-lifespan basis (0.72- and 0.71-fold, respectively, for every tenth lifetime). The time shift per dose was larger by two orders of magnitude in humans (7.8 and 0.046 years per Gy for humans and mice, respectively, when exposed at ~35% of their lifetime). The difference was mostly explained by the two orders of magnitude difference in spontaneous somatic mutation rates between the species plus the species-independent radiation-induced mutation rate. Thus, the findings delineate the mechanism underlying the interspecies difference in radiation-associated cancer mortality and may lead to the use of experimental evidence for risk prediction in humans.

Semisupervised Calibration of Risk with Noisy Event Times (SCORNET) using electronic health record data.

Leveraging large-scale electronic health record (EHR) data to estimate survival curves for clinical events can enable more powerful risk estimation and comparative effectiveness research. However, use of EHR data is hindered by a lack of direct event time observations. Occurrence times of relevant diagnostic codes or target disease mentions in clinical notes are at best a good approximation of the true disease onset time. On the other hand, extracting precise information on the exact event time requires laborious manual chart review and is sometimes altogether infeasible due to a lack of detailed documentation. Current status labels-binary indicators of phenotype status during follow-up-are significantly more efficient and feasible to compile, enabling more precise survival curve estimation given limited resources. Existing survival analysis methods using current status labels focus almost entirely on supervised estimation, and naive incorporation of unlabeled data into these methods may lead to biased estimates. In this article, we propose Semisupervised Calibration of Risk with Noisy Event Times (SCORNET), which yields a consistent and efficient survival function estimator by leveraging a small set of current status labels and a large set of informative features. In addition to providing theoretical justification of SCORNET, we demonstrate in both simulation and real-world EHR settings that SCORNET achieves efficiency akin to the parametric Weibull regression model, while also exhibiting semi-nonparametric flexibility and relatively low empirical bias in a variety of generative settings.

European Lipid Guidelines and Cardiovascular Risk Estimation: Current Status and Future Challenges.

PURPOSE OF REVIEW: Genetic, experimental, epidemiologic, and clinical data support the causal role of elevated levels of low-density lipoprotein cholesterol (LDL-C) in atherosclerosis and cardiovascular disease (CVD). The recommendations of the 2019 European guidelines are based on the concept of differential CV risk, which in turn defines the LDL-C goals that should be achieved. RECENT FINDINGS: The 2019 ESC/EAS guidelines for dyslipidaemia use the Systematic COronary Risk Evaluation (SCORE) model to assess CV risk, which provides a 10-year risk of fatal CV event. The SCORE model has recently been updated to reflect current rates of cardiovascular disease in Europe. The new SCORE2 model provides estimates of the 10-year risk of fatal and non-fatal CVD events in people aged 40-69 years, thus improving the identification of individuals at higher risk of a CVD event. However, as in the SCORE age is the main determinant of risk, young people have a relatively low estimated 10-year risk of a CV event even with high levels of one or more causal risk factors. Individuals with familial hypercholesterolaemia, who have elevated LDL-C levels from birth and have a high risk of premature CVD, are one example. The concept of cumulative LDL exposure is thus becoming increasingly important. This is also supported by Mendelian randomisation studies showing that carrying genetic variants associated with lower LDL-C levels reduces CV risk. These observations have introduced the concept of "cholesterol-years", which takes into account both LDL-C levels and time of exposure. It is crucial that future European guidelines pay more attention to this point.

The Biggest Unmet Needs in Cardiovascular Disease Prevention in 2023.

BACKGROUND: While the major causes of atherosclerotic cardiovascular disease (CVD) are known, clinical audits continue to show inadequate risk factor control, even in the highest risk subjects. More effective risk estimation methods may help, and advances in this field are outlined. There exist excellent guidelines on CVD prevention, but their very length and complexity may limit their use. Other factors inhibiting guideline implementation are explored. SUMMARY: While new medications continue to be developed, the real challenges to effective CVD prevention are societal and political. Both nationally and at European levels, cohesive, integrated strategies with defined responsibilities and accountability are needed, together with empowerment of people to understand the concept of risk and what they can do about it. KEY MESSAGES: There are profound health inequalities between and within countries that need to be addressed.

Cholesterol associated genetic risk score and acute coronary syndrome in Czech males.

BACKGROUND: Despite a general decline in mean levels across populations, LDL-cholesterol levels remain a major risk factor for acute coronary syndrome (ACS). The APOB, LDL-R, CILP, and SORT-1 genes have been shown to contain variants that have significant effects on plasma cholesterol levels. METHODS AND RESULTS: We examined polymorphisms within these genes in 1191 controls and 929 patients with ACS. Only rs646776 within SORT-1 was significantly associated with a risk of ACS (P < 0.05, AA vs. + G comparison; OR 1.21; 95% CI 1.01-1.45). With regard to genetic risk score (GRS), the presence of at least 7 alleles associated with elevated cholesterol levels was connected with increased risk (P < 0.01) of ACS (OR 1.26; 95% CI 1.06-1.52). Neither total mortality nor CVD mortality in ACS subjects (follow up-9.84 ± 3.82 years) was associated with the SNPs analysed or cholesterol-associated GRS. CONCLUSIONS: We conclude that, based on only a few potent SNPs known to affect plasma cholesterol, GRS has the potential to predict ACS risk, but not ACS associated mortality.

Prototyping a Secure and Usable User Authentication Mechanism for Mobile Passenger ID Devices for Land/Sea Border Control.

As the number of European Union (EU) visitors grows, implementing novel border control solutions, such as mobile devices for passenger identification for land and sea border control, becomes paramount to ensure the convenience and safety of passengers and officers. However, these devices, handling sensitive personal data, become attractive targets for malicious actors seeking to misuse or steal such data. Therefore, to increase the level of security of such devices without interrupting border control activities, robust user authentication mechanisms are essential. Toward this direction, we propose a risk-based adaptive user authentication mechanism for mobile passenger identification devices for land and sea border control, aiming to enhance device security without hindering usability. In this work, we present a comprehensive assessment of novelty and outlier detection algorithms and discern OneClassSVM, Local Outlier Factor (LOF), and Bayesian_GaussianMixtureModel (B_GMM) novelty detection algorithms as the most effective ones for risk estimation in the proposed mechanism. Furthermore, in this work, we develop the proposed risk-based adaptive user authentication mechanism as an application on a Raspberry Pi 4 Model B device (i.e., playing the role of the mobile device for passenger identification), where we evaluate the detection performance of the three best performing novelty detection algorithms (i.e., OneClassSVM, LOF, and B_GMM), with B_GMM surpassing the others in performance when deployed on the Raspberry Pi 4 device. Finally, we evaluate the risk estimation overhead of the proposed mechanism when the best performing B_GMM novelty detection algorithm is used for risk estimation, indicating efficient operation with minimal additional latency.

Personalized medicine with germline pathogenic variants: Importance of population- and region-wide evidence.

Rare germline pathogenic variants in cancer-predisposing genes have a high impact and potential for clinical utility. In the last 30 years, based on evidence of cancer risk associated with germline pathogenic variants, several measures have been suggested for personalized medicine, including the development of novel treatments, treatment stratification, risk reduction by surgical measures, chemoprevention, removal of environmental factors, and surveillance for early detection among specific high-risk individuals. However, this evidence is mainly based on evaluations of European populations. Our large-scale analyses of more than 100,000 individuals, including 14 disease cases and non-cancer controls in the Japanese population, suggest some discrepancies in the associations between cancer-predisposing genes and diseases, expansion of the targeted diseases of BRCA1 and BRCA2, and a potential novel risk-reduction measure for gastric cancer. They are likely to be explained by population and region variations; therefore, more population-wide and region-wide research could provide improved personalized medicine as well as a better understanding of disease mechanisms. This review summarizes current personalized medicine and discusses the potential use of germline pathogenic variants.

Evaluating the performance of machine learning methods for risk estimation of delirium in patients hospitalized from the emergency department.

INTRODUCTION: Delirium is a cerebral dysfunction seen commonly in the acute care setting. It is associated with increased mortality and morbidity and is frequently missed in the emergency department (ED) and inpatient care by clinical gestalt alone. Identifying those at risk of delirium may help prioritize screening and interventions in the hospital setting. OBJECTIVE: Our objective was to leverage electronic health records to identify a clinically valuable risk estimation model for prevalent delirium in patients being transferred from the ED to inpatient units. METHODS: This was a retrospective cohort study to develop and validate a risk model to detect delirium using patient data available from prior visits and ED encounter. Electronic health records were extracted for patients hospitalized from the ED between January 1, 2014, and December 31, 2020. Eligible patients were aged 65 or older, admitted to an inpatient unit from the emergency department, and had at least one DOSS assessment or CAM-ICU recorded within 72 h of hospitalization. Six machine learning models were developed to estimate the risk of delirium using clinical variables including demographic features, physiological measurements, medications administered, lab results, and diagnoses. RESULTS: A total of 28,531 patients met the inclusion criteria with 8057 (28.4%) having a positive delirium screening within the outcome observation period. Machine learning models were compared using the area under the receiver operating curve (AUC). The gradient boosted machine achieved the best performance with an AUC of 0.839 (95% CI, 0.837-0.841). At a 90% sensitivity threshold, this model achieved a specificity of 53.5% (95% CI 53.0%-54.0%) a positive predictive value of 43.5% (95% CI 43.2%-43.9%), and a negative predictive value of 93.1% (95% CI 93.1%-93.2%). A random forest model and L1-penalized logistic regression also demonstrated notable performance with AUCs of 0.837 (95% CI, 0.835-0.838) and 0.831 (95% CI, 0.830-0.833) respectively. CONCLUSION: This study demonstrated the use of machine learning algorithms to identify a combination of variables that enables an estimation of risk of positive delirium screens early in hospitalization to develop prevention or management protocols.

Accumulation and dietary risks of perfluoroalkyl substances in fish and shellfish: A market-based study in Barcelona.

Since the 1940s, per- and polyfluoroalkyl substances (PFAS) have been widely produced and used in various applications due to their unique properties. Consequently, the principal exposure routes of PFAS have been broadly studied, leading to the conclusion that dietary exposure (more specifically, the consumption of fish and seafood) was one of their main contributors. Thus, developing an analytical method that determines the level of PFAS in fish and seafood has become a relevant subject. In this work, a previous analytical method has been optimized to determine 12 PFAS in fish muscle from salmon, tuna, cod, hake, sardine, anchovy, and sole, as well as in seven different seafood species (i.e., cuttlefish, octopus, squid, shrimp, Norway lobster, prawn, and mussel) by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). Subsequently, the PFAS profile of the different species was studied to determine if it was consistent with that previously reviewed in the literature and to know the most relevant contribution of PFAS for each species. Finally, human exposure to PFAS through their consumption was estimated by the daily intake for seven different age/gender groups. PFAS were obtained from 0.014 to 0.818 ng g-1 wet weight in fish samples. Sardines, anchovies, and soles presented the highest PFAS levels. However, cod samples also showed some PFAS traces. Regarding seafood, PFAS levels range from 0.03 to 36.7 ng g-1 dry weight for the studied species. A higher concentration of PFAS has been found in the cephalopods' spleens and the crustaceans' heads. PFOS and PFBS were the predominant compounds in each seafood species, respectively. On the other hand, in the case of mussels, which are the less polluted species of the study, contamination by longer-chained PFAS was also observed. Finally, the total intake of PFAS due to fish and shellfish consumption for the Spanish adult population was estimated at 17.82 ng day-1. Nevertheless, none of the analyzed samples exceeded the European Food Safety Authority (EFSA) risk value for the supervised PFAS in any age/gender group reviewed.

Biomarkers of Hepatic Dysfunction and Cardiovascular Risk.

PURPOSE OF REVIEW: The objective of this manuscript is to examine the current literature on non-alcoholic fatty liver disease (NAFLD) biomarkers and their correlation with cardiovascular disease (CVD) outcomes and cardiovascular risk scores. RECENT FINDINGS: There has been a growing appreciation for an independent link between NAFLD and CVD, culminating in a scientific statement by the American Heart Association in 2022. More recently, studies have begun to identify biomarkers of the three NAFLD phases as potent predictors of cardiovascular risk. Despite the body of evidence supporting a connection between hepatic biomarkers and CVD, more research is certainly needed, as some studies find no significant relationship. If this relationship continues to be robust and readily reproducible, NAFLD and its biomarkers may have an exciting role in the future of cardiovascular risk prediction, possibly as risk-enhancing factors or as components of novel cardiovascular risk prediction models.

Hazard and cumulative incidence of umbilical cord metabolic acidemia at birth in fetuses experiencing the second stage of labor and pathologic intrapartum fetal heart rate requiring expedited delivery.

PURPOSE: The aim of the study was to determine the cause-specific hazard (CSH) and the cumulative incidence function (CIF) for umbilical cord metabolic acidemia at birth (MA; pH < 7.0 and/or BE [Formula: see text] - 12 mmol/L) at delivery in patients experiencing the 2nd stage of labor (2STG), stratified for both FIGO-2015 pathologic intrapartum cardiotocography requiring expedited delivery (CTG_RED) and duration of 2nd stage of labor. METHODS: 3459 pregnancies experiencing the 2nd stage of labor and delivering at the Division of Obstetrics and Prenatal Medicine, IRCCS Sant'Orsola-Malpighi Hospital, Bologna (Italy), were identified between 2018 and 2019. Survival analysis was used to assess CSH and CIF for MA, stratified for FIGO-2015 pathologic CTG and relevant covariates. RESULTS: FIGO-2015 pathological CTG with expedited operative delivery or urgent cesarean section within 10 or 20 min from diagnosis, respectively occurred in 282/3459 (8.20%). The rate of MA at delivery was 3.32% (115/3459). The spline of CSH for MA showed a direct correlation with the duration of 2STG always presenting higher values and greater slope in the presence of pathologic CTG, with plateau between 60 and 120 min and rapid increase after 120 min. The CIF at 180 min in the 2STG was 2.67% for nonpathological and 10.63% for pathological CTG_RED. Nulliparity, pathological CTG, and meconium-stained amniotic fluid resulted significant predictors of MA in our multivariable model. CONCLUSION: The risk for MA increases moderately across the 2STG with nonpathological CTG and quadruples with pathological CTG_RED. Adjustment for other predictors of MA including meconium-stained amniotic fluid and nulliparity reveals a significant hazard increase for MA associated with pathologic CTG_RED.

Does changing the Functional Movement Screen composite score threshold influence injury risk estimation in junior Australian football players?

This study aimed to identify whether a revised lower Functional Movement Screen (FMS) composite score threshold would be associated with a greater injury risk for junior athletes than the common threshold of≤14. This prospective cohort study included tracking of 809 elite junior male Australian football players for injuries that resulted in a missed game. All athletes completed pre-season FMS testing and a 12-month self-reported retrospective injury questionnaire. Analyses examined the relationship between composite score thresholds of≤14, ≤13, and≤12 and the risk of injury. The relationship between prospective injury and the common composite threshold score of ≤ 14 was dependent on the presence of a recent injury history (relative risk [RR] = 1.45, p = 0.004) in comparison to no recent injury history (RR = 0.98, p = 0.887). Scoring≤12 in the presence of a recent injury history had the greatest diagnostic accuracy but only a trivial increase in injury risk (RR = 1.59, p = 0.001, sensitivity = 0.35, specificity = 0.80, negative and positive likelihood ratios = 0.81 and 1.75). Whilst some small statistical relationships existed between prospective injury and the FMS composite score thresholds, all three thresholds were not associated with a clinically meaningful relationship with prospective injury and were no more effective than retrospective injury for determining athletes at risk of injury.

Design of Fire Risk Estimation Method Based on Facility Data for Thermal Power Plants.

In this paper, we propose a data classification and analysis method to estimate fire risk using facility data of thermal power plants. To estimate fire risk based on facility data, we divided facilities into three states-Steady, Transient, and Anomaly-categorized by their purposes and operational conditions. This method is designed to satisfy three requirements of fire protection systems for thermal power plants. For example, areas with fire risk must be identified, and fire risks should be classified and integrated into existing systems. We classified thermal power plants into turbine, boiler, and indoor coal shed zones. Each zone was subdivided into small pieces of equipment. The turbine, generator, oil-related equipment, hydrogen (H2), and boiler feed pump (BFP) were selected for the turbine zone, while the pulverizer and ignition oil were chosen for the boiler zone. We selected fire-related tags from Supervisory Control and Data Acquisition (SCADA) data and acquired sample data during a specific period for two thermal power plants based on inspection of fire and explosion scenarios in thermal power plants over many years. We focused on crucial fire cases such as pool fires, 3D fires, and jet fires and organized three fire hazard levels for each zone. Experimental analysis was conducted with these data set by the proposed method for 500 MW and 100 MW thermal power plants. The data classification and analysis methods presented in this paper can provide indirect experience for data analysts who do not have domain knowledge about power plant fires and can also offer good inspiration for data analysts who need to understand power plant facilities.

Real-Time Estimation and Monitoring of COVID-19 Aerosol Transmission Risk in Office Buildings.

A healthy and safe indoor environment is an important part of containing the coronavirus disease 2019 (COVID-19) pandemic. Therefore, this work presents a real-time Internet of things (IoT) software architecture to automatically calculate and visualize a COVID-19 aerosol transmission risk estimation. This risk estimation is based on indoor climate sensor data, such as carbon dioxide (CO2) and temperature, which is fed into Streaming MASSIF, a semantic stream processing platform, to perform the computations. The results are visualized on a dynamic dashboard that automatically suggests appropriate visualizations based on the semantics of the data. To evaluate the complete architecture, the indoor climate during the student examination periods of January 2020 (pre-COVID) and January 2021 (mid-COVID) was analyzed. When compared to each other, we observe that the COVID-19 measures in 2021 resulted in a safer indoor environment.

A Survey on Quantitative Risk Estimation Approaches for Secure and Usable User Authentication on Smartphones.

Mobile user authentication acts as the first line of defense, establishing confidence in the claimed identity of a mobile user, which it typically does as a precondition to allowing access to resources in a mobile device. NIST states that password schemes and/or biometrics comprise the most conventional user authentication mechanisms for mobile devices. Nevertheless, recent studies point out that nowadays password-based user authentication is imposing several limitations in terms of security and usability; thus, it is no longer considered secure and convenient for the mobile users. These limitations stress the need for the development and implementation of more secure and usable user authentication methods. Alternatively, biometric-based user authentication has gained attention as a promising solution for enhancing mobile security without sacrificing usability. This category encompasses methods that utilize human physical traits (physiological biometrics) or unconscious behaviors (behavioral biometrics). In particular, risk-based continuous user authentication, relying on behavioral biometrics, appears to have the potential to increase the reliability of authentication without sacrificing usability. In this context, we firstly present fundamentals on risk-based continuous user authentication, relying on behavioral biometrics on mobile devices. Additionally, we present an extensive overview of existing quantitative risk estimation approaches (QREA) found in the literature. We do so not only for risk-based user authentication on mobile devices, but also for other security applications such as user authentication in web/cloud services, intrusion detection systems, etc., that could be possibly adopted in risk-based continuous user authentication solutions for smartphones. The target of this study is to provide a foundation for organizing research efforts toward the design and development of proper quantitative risk estimation approaches for the development of risk-based continuous user authentication solutions for smartphones. The reviewed quantitative risk estimation approaches have been divided into the following five main categories: (i) probabilistic approaches, (ii) machine learning-based approaches, (iii) fuzzy logic models, (iv) non-graph-based models, and (v) Monte Carlo simulation models. Our main findings are summarized in the table in the end of the manuscript.

A flexible modeling approach for biomarker-based computation of absolute risk of Alzheimer's disease dementia.

INTRODUCTION: As Alzheimer's disease (AD) biomarkers rapidly develop, tools are needed that accurately and effectively communicate risk of AD dementia. METHODS: We analyzed longitudinal data from >10,000 cognitively unimpaired older adults. Five-year risk of AD dementia was modeled using survival analysis. RESULTS: A demographic model was developed and validated on independent data with area under the receiver operating characteristic curve (AUC) for 5-year prediction of AD dementia of 0.79. Clinical and cognitive variables (AUC = 0.79), and apolipoprotein E genotype (AUC = 0.76) were added to the demographic model. We then incorporated the risk computed from the demographic model with hazard ratios computed from independent data for amyloid positron emission tomography status and magnetic resonance imaging hippocampal volume (AUC = 0.84), and for plasma amyloid beta (Aß)42/Aß40 (AUC = 0.82). DISCUSSION: An adaptive tool was developed and validated to compute absolute risks of AD dementia. This approach allows for improved accuracy and communication of AD risk among cognitively unimpaired older adults.

Bias-reduced estimators of conditional odds ratios in matched case-control studies with unmatched confounding.

We study bias-reduced estimators of exponentially transformed parameters in general linear models (GLMs) and show how they can be used to obtain bias-reduced conditional (or unconditional) odds ratios in matched case-control studies. Two options are considered and compared: the explicit approach and the implicit approach. The implicit approach is based on the modified score function where bias-reduced estimates are obtained by using iterative procedures to solve the modified score equations. The explicit approach is shown to be a one-step approximation of this iterative procedure. To apply these approaches for the conditional analysis of matched case-control studies, with potentially unmatched confounding and with several exposures, we utilize the relation between the conditional likelihood and the likelihood of the unconditional logit binomial GLM for matched pairs and Cox partial likelihood for matched sets with appropriately setup data. The properties of the estimators are evaluated by using a large Monte Carlo simulation study and an illustration of a real dataset is shown. Researchers reporting the results on the exponentiated scale should use bias-reduced estimators since otherwise the effects can be under or overestimated, where the magnitude of the bias is especially large in studies with smaller sample sizes.

Analysis and asymptotic theory for nested case-control designs under highly stratified proportional hazards models.

Nested case-control sampled event time data under a highly stratified proportional hazards model, in which the number of strata increases proportional to sample size, is described and analyzed. The data can be characterized as stratified sampling from the event time risk sets and the analysis approach of Borgan et al. (Ann Stat 23:1749-1778, 1995) is adapted to accommodate both the stratification and case-control sampling from the stratified risk sets. Conditions for the consistency and asymptotic normality of the maximum partial likelihood estimator are provided and the results are used to compare the efficiency of the stratified analysis to an unstratified analysis when the baseline hazards can be semi-parametrically modeled in two special cases. Using the stratified sampling representation of the stratified analysis, methods for absolute risk estimation described by Borgan et al. (1995) for nested case-control data are used to develop methods for absolute risk estimation under the stratified model. The methods are illustrated by a year of birth stratified analysis of radon exposure and lung cancer mortality in a cohort of uranium miners from the Colorado Plateau.

Generalizing the Wells-Riley Infection Probability: A Superstatistical Scheme for Indoor Infection Risk Estimation.

Recent evidence supports that air is the main transmission pathway of the recently identified SARS-CoV-2 coronavirus that causes COVID-19 disease. Estimating the infection risk associated with an indoor space remains an open problem due to insufficient data concerning COVID-19 outbreaks, as well as, methodological challenges arising from cases where environmental (i.e., out-of-host) and immunological (i.e., within-host) heterogeneities cannot be neglected. This work addresses these issues by introducing a generalization of the elementary Wells-Riley infection probability model. To this end, we adopted a superstatistical approach where the exposure rate parameter is gamma-distributed across subvolumes of the indoor space. This enabled us to construct a susceptible (S)-exposed (E)-infected (I) dynamics model where the Tsallis entropic index q quantifies the degree of departure from a well-mixed (i.e., homogeneous) indoor-air-environment state. A cumulative-dose mechanism is employed to describe infection activation in relation to a host's immunological profile. We corroborate that the six-foot rule cannot guarantee the biosafety of susceptible occupants, even for exposure times as short as 15 min. Overall, our work seeks to provide a minimal (in terms of the size of the parameter space) framework for more realistic indoor SEI dynamics explorations while highlighting their Tsallisian entropic origin and the crucial yet elusive role that the innate immune system can play in shaping them. This may be useful for scientists and decision makers interested in probing different indoor biosafety protocols more thoroughly and comprehensively, thus motivating the use of nonadditive entropies in the emerging field of indoor space epidemiology.

Comprehensive evaluation of abattoirs with no Hazard Analysis Critical Control Point plan in Tucumán, Argentina.

This work focused on the comprehensive study of two provincial transit abattoirs in Tucumán, Argentina, with no Hazard Analysis Critical Control Point (HACCP) plan. Visits (n=20) were conducted between 2016 and 2018 during the operational and post-operational processes. Risk was estimated and the bacteriological analysis of carcass and environmental samples was performed. Risk estimation showed the predominance of high risk in both abattoirs. The main deviations from the HACCP plan were: deficient building conditions, deficient workflow, lack of sectorization of changing rooms and bathrooms, lack of implementation of Standardized Sanitary Operational Procedures, and no food safety training of workers. The counts of indicator microorganisms from both abattoirs were not significant. Salmonella spp. was isolated from 7.5% carcass and 7.3% environmental samples. The Salmonella serovars identified were Cerro, Corvallis, Havana and Agona. Shiga toxin (stx) genes were detected in 24.4% carcass and 30.9% environmental samples. The isolates were characterized as Escherichia coli O8:H7/stx1, O116:H49/stx2 and O136:H40/stx2. Based on these results, it would be possible to implement an improvement plan in Tucumán abattoirs together with the local health authorities. Still, the need to work jointly with the sanitary authority in search of a unique sanitary standard for Argentina remains unaddressed.