Ensuring food safety, particularly for vulnerable groups, like infants and young children, requires identifying and prioritizing potential hazards in food chains. We previously developed a web-based decision support system (DSS) to identify specific microbiological hazards (MHs) in infant and toddler foods through a structured five-step process. This study takes the framework further by introducing systematic risk ranking (RR) steps to rank MH risks with seven criteria: process survival, recontamination, growth opportunity, meal preparation, hazard-food association evidence, food consumption habits of infants and toddlers in the EU, and MH severity. Each criterion is given a semi-quantitative or quantitative score or risk value, contributing to the final MH risk calculation via three aggregation methods: semi-quantitative risk scoring, semi-quantitative risk value, and outranking multi-criteria decision analysis (MCDA). To validate the criteria and ranking approaches, we conducted a case study to rank MH risks in infant formula, compared the results of the three risk ranking methods, and additionally evaluated the ranking results against expert opinions to ensure their accuracy. The results showed strong agreement among the three methods, consistently ranking Salmonella non-Typhi and Cronobacter spp. and Shiga-toxin-producing Escherichia coli as the top MH risks in infant formulae, with minor deviations. When MHs were ranked after an initial hazard identification step, all three methods produced nearly identical MH rankings, reinforcing the reliability of the ranking steps and the selected criteria. Notably, the risk value and MCDA methods provided more informative MH rankings compared to the risk scoring method. The risk value and risk scoring methods were implemented into an online tool, called the MIcrobiological hazards risk RAnking decision support system (Mira-DSS), available at https://foodmicrobiologywur.shinyapps.io/MIcrobial_hazards_RAnking/. In conclusion, our framework enables the ranking of MH risks, facilitating intervention comparisons and resource allocations to mitigate MH risks in infant foods, with potential applicability to broader food categories.

Every work environment contains different types of risks and interactions between risks. Therefore, the method to be used when making a risk assessment is very important. When determining which risk assessment method (RAM) to use, there are many factors such as the types of risks in the work environment, the interactions of these risks with each other, and their distance from the employees. Although there are many RAMs available, there is no RAM that will suit all workplaces and which method to choose is the biggest question. There is no internationally accepted scale or trend on this subject. In the study, 26 sectors, 10 different RAMs and 10 criteria were determined. A hybrid approach has been designed to determine the most suitable RAMs for sectors by using k-means clustering and support vector machine (SVM) classification algorithms, which are machine learning (ML) algorithms. First, the data set was divided into subsets with the k-means algorithm. Then, the SVM algorithm was run on all subsets with different characteristics. Finally, the results of all subsets were combined to obtain the result of the entire dataset. Thus, instead of the threshold value determined for a single and large cluster affecting the entire cluster and being made mandatory for all of them, a flexible structure was created by determining separate threshold values for each sub-cluster according to their characteristics. In this way, machine support was provided by selecting the most suitable RAMs for the sectors and eliminating the administrative and software problems in the selection phase from the manpower. The first comparison result of the proposed method was found to be the hybrid method: 96.63%, k-means: 90.63 and SVM: 94.68%. In the second comparison made with five different ML algorithms, the results of the artificial neural networks (ANN): 87.44%, naive bayes (NB): 91.29%, decision trees (DT): 89.25%, random forest (RF): 81.23% and k-nearest neighbours (KNN): 85.43% were found.

UNASSIGNED: The purpose of this study was to evaluate the pharmacovigilance and clinical characteristics of psychiatric adverse events(AEs) related to Fluoroquinolones(FQs), and to determine the risk factors for timely management.
UNASSIGNED: Data about AE reporting comes from the FDA Adverse Event Reporting System (FAERS) database, which was used for pharmacovigilance assessments. In addition, we also analyzed the cases of psychiatric AEs related to FQs retrospectively.
UNASSIGNED: Both of the FAERS database analysis and literature reports show that the proportion of FQs-related psychiatric AEs reported in females were higher (51.11% VS 33.44% and 53.23% VS 46.77%). Both of them show that the proportion of psychiatric AEs caused by FQs was higher in the age groups of 19-44 (28.08% and 40.32%) and 45-64 (28.17% and 25.81%). Most psychiatric AEs occurred within 10 days after FQs administration. Literature shows that 67.74% of the psychiatric AEs disappeared within 3 days after drug withdrawal (some cases were accompanied by other drug).
UNASSIGNED: Psychiatric AEs caused by FQs are serious, and there are many important safety signals that have not been mentioned in the label or previous studies. It is very important to identify and manage psychiatric AEs in time for the safe use of FQs.

Mining is one of the most risky and dangerous sectors. It is impossible to ignore the losses of life and material experienced by occupational accidents, which take place in the field of mining. Risk analysis begins with a risk assessment to identify the probability and severity of workplace hazards. Hazards must be controlled by precautions according to the risk score levels. In this study, a fault tree analysis method was conducted to analyze spontaneous combustion hazards and to predict future risks in underground coal mines. Three main causes of the top event were defined and for each of these causes, risk scores were computed using a fault tree analysis. Finally, the causes of spontaneous combustion, which is an event that is frequently encountered in coal mines, were discussed, and the spontaneous combustion risk probability was calculated as 0.3012 in cases of air entry into the gob and failure to prevent coal-air contact in development drifts. As a result of the study, the fundamental causes of spontaneous combustion, the greatest hazard in underground coal mining worldwide, have been examined in detail. The innovative approach introduced by the study aims to increase the awareness and recognition of conditions that lead to spontaneous combustion among industry workers and engineers through detailed evaluation. By doing so, it seeks to minimize the occurrence of spontaneous combustion incidents.•This paper introduces a main flowchart and countermeasure algorithm to prevent spontaneous combustion.•This paper also analyzes events which trigger spontaneous combustion and mentioned preventive measures for this events.

The application of a One Health approach recognizes that human health, animal health, plant health and ecosystem health are intrinsically connected. Tackling complex challenges associated with foodborne zoonoses, antimicrobial resistance, and emerging threats is imperative. Therefore, the One Health European Joint Programme was established within the European Union research programme Horizon 2020. The One Health European Joint Programme activities were based on the development and harmonization of a One Health science-based framework in the European Union (EU) and involved public health, animal health and food safety institutes from almost all EU Member States, the UK and Norway, thus strengthening the cooperation between public, medical and veterinary organizations in Europe. Activities including 24 joint research projects, 6 joint integrative projects and 17 PhD projects, and a multicountry simulation exercise facilitated harmonization of laboratory methods and surveillance, and improved tools for risk assessment. The provision of sustainable solutions is integral to a One Health approach. To ensure the legacy of the work of the One Health European Joint Programme, focus was on strategic communication and dissemination of the outputs and engagement of stakeholders at the national, European and international levels.

Confronting the continuing risk of an attack, security systems have adopted target-hardening strategies through the allocation of security measures. Most previous work on defensive resource allocation considers the security system as a monolithic architecture. However, systems such as schools are typically characterized by multiple layers, where each layer is interconnected to help prevent single points of failure. In this paper, we study the defensive resource allocation problem in a multilayered system. We develop two new resource allocation models accounting for probabilistic and strategic risks, and provide analytical solutions and illustrative examples. We use real data for school shootings to illustrate the performance of the models, where the optimal investment strategies and sensitivity analysis are presented. We show that the defender would invest more in defending outer layers over inner layers in the face of probabilistic risks. While countering strategic risks, the defender would split resources in each layer to make the attacker feel indifferent between any individual layer. This paper provides new insights on resource allocation in layered systems to better enhance the overall security of the system.

UNASSIGNED: Absolute risk models estimate an individual\'s future disease risk over a specified time interval. Applications utilizing server-side risk tooling, the R-based iCARE (R-iCARE), to build, validate, and apply absolute risk models, face limitations in portability and privacy due to their need for circulating user data in remote servers for operation. We overcome this by porting iCARE to the web platform.
UNASSIGNED: We refactored R-iCARE into a Python package (Py-iCARE) and then compiled it to WebAssembly (Wasm-iCARE)-a portable web module, which operates within the privacy of the user\'s device.
UNASSIGNED: We showcase the portability and privacy of Wasm-iCARE through 2 applications: for researchers to statistically validate risk models and to deliver them to end-users. Both applications run entirely on the client side, requiring no downloads or installations, and keep user data on-device during risk calculation.
UNASSIGNED: Wasm-iCARE fosters accessible and privacy-preserving risk tools, accelerating their validation and delivery.

The suspension wet media milling manufacturing process is a complex multi-unit operation, resulting in drug substance comminution to a target particle size. As a result of this complexity, microbial contamination is of paramount concern, particularly for suspensions dosed for parenteral use. This perspective sought to review the influence of (4) critical manufacturing unit operations using a quality risk management approach to better identify and articulate impact of each unit operation on bioburden viability. The manufacturing unit operations in scope included slurry compounding, deaeration, milling, and filling. Bow tie risk analysis was used as a visual gap analysis tool to evaluate if conventional controls were appropriate to detect and mitigate potential for microbial contamination. A deep dive into these unit operations clarified that mechanisms such as turbohypobiosis, cavitation during deaeration, high energy milling, and inert overlay may have an appreciable influence on bioburden viability and proliferation. The resultant analysis also explicated that endotoxin oversight must be closely monitored through barriers (input material controls, water quality controls) to minimize impact to the product and patient. The identified manufacturing unit operations were not appropriate as mitigating controls for endotoxin. The output of this article relates risk intersections for microbial contamination during wet media milling and offers insights in critical areas for intervention.

BACKGROUND: The etiology of ischemic stroke is multifactorial. Several gene mutations have been identified as leading causes of cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), a hereditary disease that causes stroke and other neurological symptoms.
OBJECTIVE: We aimed to identify the variants of NOTCH3 and thrombophilia genes, and their complex interactions with other factors.
METHODS: We conducted a hierarchical cluster analysis (HCA) on the data of 100 patients diagnosed with ischemic stroke. The variants of NOTCH3 and thrombophilia genes were identified by polymerase chain reaction with confronting 2-pair primers and real-time polymerase chain reaction. The overall preclinical characteristics, cumulative cutpoint values, and factors associated with these somatic mutations were analyzed in unidimensional and multidimensional scaling models.
RESULTS: We identified the following optimal cutpoints: creatinine, 83.67 (SD 9.19) µmol/L; age, 54 (SD 5) years; prothrombin (PT) time, 13.25 (SD 0.17) seconds; and international normalized ratio (INR), 1.02 (SD 0.03). Using the Nagelkerke method, cutpoint 50% values of the Glasgow Coma Scale score; modified Rankin scale score; and National Institutes of Health Stroke Scale scores at admission, after 24 hours, and at discharge were 12.77, 2.86 (SD 1.21), 9.83 (SD 2.85), 7.29 (SD 2.04), and 6.85 (SD 2.90), respectively.
CONCLUSIONS: The variants of MTHFR (C677T and A1298C) and NOTCH3 p.R544C may influence the stroke severity under specific conditions of PT, creatinine, INR, and BMI, with risk ratios of 4.8 (95% CI 1.53-15.04) and 3.13 (95% CI 1.60-6.11), respectively (Pfisher<.05). It is interesting that although there are many genes linked to increased atrial fibrillation risk, not all of them are associated with ischemic stroke risk. With the detection of stroke risk loci, more information can be gained on their impacts and interconnections, especially in young patients.

Global pandemics restrict long-haul mobility and international trade. To restore air traffic, a policy named \"travel bubble\" was implemented during the recent COVID-19 pandemic, which seeks to re-establish air connections among specific countries by permitting unrestricted passenger travel without mandatory quarantine upon arrival. However, travel bubbles are prone to bursting for safety reasons, and how to develop an effective restoration plan through travel bubbles is under-explored. Thus, it is vital to learn from COVID-19 and develop a formal framework for implementing travel bubble therapy for future public health emergencies. This article conducts an analytical investigation of the air travel bubble problem from a network design standpoint. First, a link-based network design problem is established with the goal of minimizing the total infection risk during air travel. Then, based on the relationship between origin-destination pairs and international candidate links, the model is reformulated into a path-based one. A Lagrangian relaxation-based solution framework is proposed to determine the optimal restored international air routes and assign the traffic flow. Finally, computational experiments on both hypothetical data and real-world cases are conducted to examine the algorithm\'s performance. The results demonstrate the effectiveness and efficiency of the proposed model and algorithm. In addition, compared to a benchmark strategy, it is found that the infection risk under the proposed travel bubble strategy can be reduced by up to 45.2%. More importantly, this work provides practical insights into developing pandemic-induced air transport recovery schemes for both policymakers and aviation operations regulators.