Common Exposures Research Articles

Population-based study of environmental heavy metal exposure and hearing loss: A systematic review and meta-analysis.

Previous studies have shown an association between environmental exposure to heavy metals and hearing loss. However, the findings regarding the relationship between exposure to different metals and hearing loss development are inconsistent. To address this, we conducted a meta-analysis to explore the link between common heavy metal exposures and hearing loss. This study examined the effects of lead (Pb), cadmium (Cd), and mercury (Hg) pollution on hearing loss at various levels, and systematically reviewed the literature on manganese (Mn), barium (Ba), arsenic (As), and hearing loss. We conducted systematic searches in five major databases, including PubMed, Web of Science, Embase, Cochrane Library, and Scopus. In addition, we searched three Chinese digital libraries: CNKI, Wanfang Data, and Wipu. From an initial pool of 649 articles, we carefully screened and selected 15 articles for further analysis. The effect sizes from these selected studies were synthesized through a meta-analysis to calculate the overall effect size. Our findings showed that: (1) There was a significant association between Pb and Cd exposure and hearing loss; (2) There is a proportional relationship between the increase of metal index detected in blood and hearing loss; (3) In the PTA measurement of hearing loss at different frequencies, the 4 kHz high frequency range had a stronger correlation with hearing loss than the low frequency, with OR 1.44 (1.22, 1.71); and (4) There was a more significant correlation between Barium (Ba) levels in nails and hair than in urine. The study presented evidence of a significant association between human hearing loss and exposure to lead (Pb) and cadmium (Cd). It not only revealed a positive correlation between blood heavy metal concentrations and the incidence of hearing loss but also highlighted that long-term exposure indicators of heavy metals were more indicative of the correlation with hearing loss. Lastly, the study recommends utilizing high frequency 4 kHz for the effective assessment and diagnosis of hearing loss caused by exposure to heavy metals.

Health problems and exposure to infectious risks in returning humanitarian aid workers.

Humanitarian aid workers are exposed to deployment-related health threats. Identifying subgroups at a higher risk of infection in this diverse population could help optimize prevention. We carried out a retrospective study based on anonymized data of humanitarian aid workers that visited our clinic for a post-deployment visit between 1 January 2018 and 31 December 2021. We conducted a descriptive analysis of basic demographic data, self-reported risk exposure and health problems encountered during deployment extracted from a standard questionnaire. The questionnaire was administered to 1238 aid workers during 1529 post-deployment medical consultations. The median age was 37.2years (IQR 31.7-44.3), and 718/1529 (47.0%) were female aid workers. The median duration of deployment was 6months (IQR 3-12months). Most deployments (1321/1529 (86.4%)) were for a medical organization and in Sub-Saharan Africa (73.2%). The most common risk exposures were contact with freshwater in schistosomiasis endemic regions (187/1308 (14.3%)), unprotected sexual contact with a person other than a regular partner (138/1529 (9.0%)), suspected rabies exposure (56/1529 (3.7%)) and accidental exposure to blood (44/1529 (2.9%)). Gastrointestinal problems (487/1529 (31.9%)), malaria (237/1529 (15.5%)) and respiratory tract infections (94/1529 (6,2%)) were the most encountered health problems. Fifteen volunteers (1%) were hospitalized during deployment and 19 (1.2%) repatriated due to health problems. Adherence to malaria chemoprophylaxis was poor, only taken according to the prescription in 355 out of 1225 (29.0%) of aid workers for whom prophylaxis was indicated. Humanitarian aid workers deployed abroad encounter significant rates of health problems and report a high level of risk exposure during their deployment, with the risks being greatest among younger people, those deployed to rural areas, and those working for non-medical organizations. These findings help guide future pre-deployment consultations, to increase awareness and reduce risk behaviour during deployment, as well as focus on adherence to medical advice such as malaria chemoprophylaxis.

Cancer incidence trends in New York State and associations with common population-level exposures 2010–2018: an ecological study

The impact of common environmental exposures in combinations with socioeconomic and lifestyle factors on cancer development, particularly for young adults, remains understudied. Here, we leveraged environmental and cancer incidence data collected in New York State at the county level to examine the association between 31 exposures and 10 common cancers (i.e., lung and bronchus, thyroid, colorectal, kidney and renal pelvis, melanoma, non-Hodgkin lymphoma, and leukemia for both sexes; corpus uteri and female breast cancer; prostate cancer), for three age groups (25–49, 50–69, and 70–84 year-olds). For each cancer, we stratified by age group and sex, and applied regression models to examine the associations with multiple exposures simultaneously. The models included 642,013 incident cancer cases during 2010–2018 and found risk factors consistent with previous reports (e.g., smoking and physical inactivity). Models also found positive associations between ambient air pollutants (ozone and PM2.5) and prostate cancer, female breast cancer, and melanoma of the skin across multiple population strata. Additionally, the models were able to better explain the variation in cancer incidence data among 25–49 year-olds than the two older age groups. These findings support the impact of common environmental exposures on cancer development, particularly for younger age groups.

Prioritization of Application Security Vulnerability Remediation Using Metrics, Correlation Analysis, and Threat Model

As part of a continuing research for evaluating threats posed for exposed attack surface, this study will provide a consolidated view of exploitability of vulnerable applications presenting a web attack surface of an organization exposed to an attacker. While testing and scanning technologies like Static Analysis Security Testing (SAST), Dynamic Analysis Security Testing (DAST), Application Ethical Hack (Penetration Testing), a monitoring technology like the Web Application Firewall (WAF) provides web traffic information of the number of transaction requests for every application under study. To ensure validity, reliability, and completeness of observation multiple applications must be observed. Research from a prior study is referenced that shows correlation between incoming WAF requests and existing vulnerabilities. Using correlation analysis, vulnerabilities metrics, and a threat model analysis help identify pathways to an attack. A vulnerability map-based attack tree can be developed using Common Weakness Enumeration (CWE) and Common Vulnerabilities and Exposures (CVE) information. The threat model analysis and vulnerability-based attack tree can help in simulation studies of possible attacks. This attack tree will show the linkages between vulnerabilities and a lineage pointing to how an attack could travel from the incoming WAF requests to deep down into the application code of exposed and existing, open vulnerabilities travelling laterally to create a more expanded attack crossing trust boundaries using application data flow.

Analyzing and Discovering Spatial Algorithm Complexity Vulnerabilities in Recursion

The algorithmic complexity vulnerability (ACV) that may lead to denial of service attacks greatly disrupts the security and availability of applications, and due to the widespread use of third-party libraries, its impact may be amplified through the software supply chain. The existing work in the field is dedicated to abstract loop or iterative patterns and fuzzing the entire application to discover algorithm complexity vulnerabilities, but they still face efficiency and effectiveness issues. Our research focuses on: (1) proposing a representation and extraction method for code features related to algorithmic complexity vulnerabilities, helping analysts quickly understand program logic; (2) providing a new ACV detecting model, focusing on the spatial complexity anomalies caused by deep recursion structures, and proposing a new filtering method; and (3) aiming at the difficulty of efficiently generating complex-data-type-related payloads using existing symbol execution techniques, a call-chain-guided payload construction method is proposed. We tested third-party components in the open-source Java Maven Repository, identified many unexposed vulnerabilities, and eight of them received Common Vulnerabilities and Exposures (CVE) identifiers, and demonstrated that our method can discover more algorithmic complexity vulnerabilities compared to existing tools with better performance.

Potentially modifiable risk and protective factors affecting mental and emotional wellness in pregnancy.

Impaired mental and emotional wellness often co-occurs with prenatal substance use, and both affect infant socio-emotional, cognitive, language, motor, and adaptive behavioral outcomes. Guided by the modified biopsychosocial framework, this study examined the role of common substance exposures during pregnancy (i.e., alcohol and cannabis), socio-cultural factors (social support during pregnancy, adverse childhood experiences), and reproductive health factors on maternal mental health (MMH). Data were obtained from a prospective cohort study-Ethanol, Neurodevelopment, Infant, and Child Health (ENRICH-2), and included 202 pregnant persons. Alcohol and cannabis exposures were assessed through repeated prospective interviews and a comprehensive battery of drug and ethanol biomarkers. MMH outcomes were evaluated during the third trimester through the Perceived Stress Scale, Edinburgh Depression Scale, Generalized Anxiety Disorders-7, and Post-traumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders. Univariate and multivariable linear regression models evaluated significant predictors of MMH. Results of multivariable analysis indicate that both maternal adverse childhood experiences and alcohol exposure, even at low-to-moderate levels, during pregnancy were associated with poorer scores for most MMH measures, while higher level of social support and Spanish as the primary language at home (as a proxy of enculturation) had protective effects (all p's < 0.05). These findings highlight the importance of assessing substance use, including periconceptional alcohol exposure, and mental health in pregnant persons as closely related risk factors which cannot be addressed in isolation. Our findings also emphasize a strong protective effect of socio-cultural factors on maternal mental and emotional wellbeing-a strong precursor to maternal-infant bonding and infant neurodevelopment.

NuMoM2b Study Insights: Primary Exposures, Outcomes, and Directions for Future Research.

To summarize the publications to date from a large obstetric cohort of nulliparous individuals. We summarized all of the publications from the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-be (nuMoM2b). We descriptively summarized the most common outcomes and exposures reported in current publications. Fifty-six publications to date are discussed. The most common primary exposures reported are participant baseline characteristics such as body mass index (24%), sociodemographic characteristics (22%), and sleep factors (16%). These exposures were most commonly measured in the first trimester (77%). The most commonly reported primary outcomes were related to adverse pregnancy outcomes (APOs, 51.6%), with 25% using a composite of multiple APOs as the primary outcome. At least 8,000 participants were used in the analyses of over half of the publications. The nuMoM2b study has generated a diverse array of publications and conclusions on factors associated with APOs. The publicly available data set from the nuMoM2b study continues to hold potential for considerable advances, new insights, and future research opportunities to optimize pregnancy and pregnancy-related health. · The nuMoM2b pregnancy cohort has generated 56 publications thus far.. · The main findings of these publications are summarized and categorized in this work.. · The data and specimens from this cohort are available and can answer many clinical questions..

Practitioners' expectations on automated release note generation techniques

AbstractThe software development life cycle relies heavily on the software release note, a crucial document. Various practitioners, including project managers and clients, benefit from release notes as they provide an overview of the latest software release. However, the manual generation of release notes is a time‐consuming and stressful task. Researchers have recently proposed automated techniques to generate release notes, saving developers' time and enhancing their understanding of software projects. Unfortunately, the adoption of these tools in practice remains limited. To address this gap, we have taken steps to understand the expectations and requirements of practitioners regarding release note generation techniques before implementing new automated approaches. Consequently, our approach involves two main stages: First, we conduct a comprehensive review of the relevant literature and analyze existing release notes from GitHub repositories to gain insights into the current practices. Second, we conduct an online survey study to gather input from practitioners and understand their expectations regarding release notes. We have reviewed 16 papers related to release notes and explored 3347 release notes from 21 GitHub repositories. Our analysis revealed key artifacts present in release note contents, including issues (29%), pull requests (32%), commits (19%), and common vulnerabilities and exposures (CVE) issues (6%). Additionally, we conducted a survey study involving 32 professionals to understand the essential information that should be included in release notes based on users' roles. For instance, project managers were more interested in learning about new features rather than less critical bug fixes. Furthermore, we identified gaps in existing systems and essential factors to consider when implementing release notes techniques in software engineering. The insights gained from our study can guide future research directions and assist practitioners in generating release notes with relevant content, thus improving the overall quality of documentation in software development.

Towards System-Level Security Analysis of IoT Using Attack Graphs

Most IoT systems involve IoT devices, communication protocols, remote cloud, IoT applications, mobile apps, and the physical environment. However, existing IoT security analyses only focus on a subset of all the essential components, such as device firmware or communication protocols, and ignore IoT systems' interactive nature, resulting in limited attack detection capabilities. In this work, we propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Iota</small> , a logic programming-based framework to perform system-level security analysis for IoT systems. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Iota</small> generates attack graphs for IoT systems, showing all of the system resources that can be compromised and enumerating potential attack traces. In building <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Iota</small> , we design novel techniques to scan IoT systems for individual vulnerabilities and further create generic exploit models for IoT vulnerabilities. We also identify and model physical dependencies between different devices as they are unique to IoT systems and are employed by adversaries to launch complicated attacks. In addition, we utilize NLP techniques to extract IoT app semantics based on app descriptions. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Iota</small> automatically translates vulnerabilities, exploits, and device dependencies to Prolog clauses and invokes MulVAL to construct attack graphs. To evaluate vulnerabilities' system-wide impact, we propose three metrics based on the attack graph, which provide guidance on hardening IoT systems. Evaluation on 127 IoT CVEs (Common Vulnerabilities and Exposures) shows that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Iota</small> 's exploit modeling module achieves over 80% accuracy in predicting vulnerabilities' preconditions and effects. We apply <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Iota</small> to 37 synthetic smart home IoT systems based on real-world IoT apps and devices. Experimental results show that our framework is effective and highly efficient. Among 27 shortest attack traces revealed by the attack graphs, 62.8% are not anticipated by the system administrator. It only takes 1.2 seconds to generate and analyze the attack graph for an IoT system consisting of 50 devices.

Nationwide Aquatic Envenomations Reported to US Poison Control Centers from 2011 to 2020.

Aquatic envenomations are common injuries along the coastal United States that pose a public health risk and can cause significant morbidity. We examined aquatic envenomation exposures that were called in to poison control centers (PCC) in the United States from 2011 to 2020. The Association of Poison Control Center's (AAPCC) National Poison Data System was queried for all aquatic envenomations reported during the 10 y period from January 1, 2011, to December 31, 2020. Data collected included date, exposure and geographic location, patient age and sex, signs and symptoms, management setting, treatments, and clinical outcome. Duplicated records, confirmed nonexposure, and reports not originating within the United States were excluded. There were 8517 human aquatic envenomations reported during the study period, 62% (5243) of whom were male; 56% (4264) of patients were 30 y or younger. There were an average of 852 calls per year, with 46% of calls occurring during June to August. California, Texas, and Florida had the highest number of envenomations during the study period. Fish (61%; 5159) and Cnidaria (30%; 2519) envenomations were the most common exposures. Overall, 37% (3151) of exposures were treated in healthcare facilities, with no deaths reported. The highest proportion of aquatic envenomations occurred among younger males (≤30 y) during the summer months. While rarely leading to major adverse events, aquatic envenomations were commonly reported injuries to PCC and occurred in all 50 states. Poison control centers continue to be real-time sources of information and data regarding aquatic envenomation trends.

Contrasted life trajectories: reconstituting the main population exposomes in French Guiana.

In French Guiana, life expectancy is between 2 and 3 years below that of France, reflecting differences in mortality rates that are largely sensitive to primary healthcare and thus preventable. However, because poverty affects half of the population in French Guiana, global measurements of life expectancy presumably conflate at least two distinct situations: persons who have similar life expectancies as in mainland France and persons living in precariousness who have far greater mortality rates than their wealthier counterparts. We thus aimed to synthesize what is known about statistical regularities regarding exposures and sketch typical French Guiana exposomes in relation to health outcomes. We conducted a narrative review on common exposures in French Guiana and made comparisons between French Guiana and mainland France, between rich and poor in French Guiana, and between urban and rural areas within French Guiana. The most striking fact this panorama shows is that being a fetus or a young child in French Guiana is fraught with multiple threats. In French Guiana, poverty and poor pregnancy follow-up; renouncing healthcare; wide variety of infectious diseases; very high prevalence of food insecurity; psychosocial stress; micronutrient deficiencies; obesity and metabolic problems; and frequent exposure to lead and mercury in rural areas constitute a stunningly challenging exposome for a new human being to develop into. A substantial part of the population's health is hence affected by poverty and its sources of nutrition.

Decomposing Systemic Risk: The Roles of Contagion and Common Exposures

Decomposing Systemic Risk: The Roles of Contagion and Common Exposures

The developmental origins of health and disease and intergenerational inheritance: a scoping review of multigenerational cohort studies.

Epidemiologic research has increasingly acknowledged the importance of developmental origins of health and disease (DOHaD) and suggests that prior exposures can be transferred across generations. Multigenerational cohorts are crucial to verify the intergenerational inheritance among human subjects. We carried out this scoping review aims to summarize multigenerational cohort studies' characteristics, issues, and implications and hence provide evidence to the DOHaD and intergenerational inheritance. We adopted a comprehensive search strategy to identify multigenerational cohorts, searching PubMed, EMBASE, and Web of Science databases from the inception of each dataset to June 20th, 2022, to retrieve relevant articles. After screening, 28 unique multigenerational cohort studies were identified. We classified all studies into four types: population-based cohort extended three-generation cohort, birth cohort extended three-generation cohort, three-generation cohort, and integrated birth and three-generation cohort. Most cohorts (n = 15, 53%) were categorized as birth cohort extended three-generation studies. The sample size of included cohorts varied from 41 to 167,729. The study duration ranged from two years to 31 years. Most cohorts had common exposures, including socioeconomic factors, lifestyle, and grandparents' and parents' health and risk behaviors over the life course. These studies usually investigated intergenerational inheritance of diseases as the outcomes, most frequently, obesity, child health, and cardiovascular diseases. We also found that most multigenerational studies aim to disentangle genetic, lifestyle, and environmental contributions to the DOHaD across generations. We call for more research on large multigenerational well-characterized cohorts, up to four or even more generations, and more studies from low- and middle-income countries.

Lead exposure sources and public health investigations for children with elevated blood lead in England, 2014 to 2022.

Lead exposure at any concentration can adversely impact health, with children being more vulnerable to its effects. In England, children with an elevated blood lead concentration (BLC) are reported to Health Protection Teams (HPTs) for public health investigation. A detailed review of these cases has not yet been conducted. The objectives of this study were to describe the demographics, likely setting and sources of lead exposure, risk behaviours, public health investigations and outcomes for children aged <16 years with a BLC requiring public health action reported to HPTs between 2014-2022 in England. Data were collected via a lookback questionnaire and a live enhanced surveillance questionnaire. Data were deduplicated, cleaned and results summarised as numbers and percentages using R studio. A thematic analysis was conducted on qualitative responses to a question relating to problems experienced during case investigation. There were 340 cases in our study: the majority were aged 1-4 years old (53%) and male (69%). Ethnicity data was poorly recorded. A higher than expected proportion (31%) lived in the most deprived areas. Pica (76%) and learning difficulties (60%) were often present. Cases were primarily exposed to lead in the domestic setting (92%) with paint (43%) and soil (29%) the most common exposures. Most cases lived in rented accommodation (63%), with a higher proportion in social rentals (48%) than privately rented (37%). Case investigations were resource intensive and poor stakeholder engagement/response was most frequently identified as challenging by HPTs. Lead exposure is harmful to children and requires public health and clinical management, which can be complex and challenging. Prevention of lead exposure in children should be the focus of intervention efforts. Outreach, engagement and preventative work should focus on both renters and homeowners. Collecting ethnicity data consistently may enable identification of more specific groups at increased risk of lead exposure in England.

CVE Severity Prediction From Vulnerability Description - A Deep Learning Approach

The Common Vulnerabilities and Exposures (CVE) system is a widely used standard for identifying and tracking known vulnerabilities in software systems. The severity of these vulnerabilities must be determined in order to prioritize mitigation efforts. However, assigning severity to a vulnerability is a challenging task that requires careful analysis of its characteristics and potential impact. Considering the vast number of vulnerabilities identified every year, it is vital to automate the severity assignment, thereby reducing manual effort. This paper proposes a novel approach for predicting the severity of vulnerabilities based on their CVE description using GPT-2, a state-of-the-art language model. The CVSS severity values distribution imbalance is addressed using oversampling and contextual data augmentation techniques. This approach leverages the large-scale language modeling capabilities of GPT-2 to automatically extract relevant features from CVE descriptions and predict the severity level of the vulnerability. The model is evaluated on a test data set of 7,765 CVEs and achieves a high accuracy of 84.2% and an F1 score of 0.82 in predicting the severity of the vulnerabilities on the test data. A comparative analysis of this approach was done against state-of-the-art methods, demonstrating the superior performance of the proposed approach. Based on the results, the proposed approach could be considered a valuable tool for quickly and accurately identifying high-severity vulnerabilities, facilitating more efficient and effective vulnerability management practices. Furthermore, this approach could be extended to other natural language processing tasks related to vulnerability analysis and management.

A Deep Learning Approach to Discover Router Firmware Vulnerabilities

An attack on Industrial IoT systems can cause severe damage to connected devices and their owners. Therefore, detecting router firmware vulnerabilities has become a critical issue. However, collecting a dataset of firmware samples is challenging as no open-source datasets are available online. A manual effort was required to verify the states of samples in both the Common Vulnerabilities and Exposures (CVE) and The National Vulnerability Database (NVD) databases as either vulnerable or benign. After verification, 1450 samples were collected. This paper investigates the effectiveness of using convolutional neural networks (CNNs) and computer vision techniques to analyze home router firmware. The collected firmware samples were read as an array of byte strings, divided into sub-arrays based on the image's dimensions, then layered on top of one another to produce the firmware images. The images were divided by manufacturer and used as inputs for various CNN models to test their accuracy. Three statistical filtering algorithms were used on each manufacturer's set to produce multiple versions of each set, totaling 24 datasets across four manufacturers, with six datasets per manufacturer (4 filtered images and two grayscale and RGB images). The image filter algorithms used include local binary pattern (LBP), histogram-oriented gradients (HOG), and Gabor filter used on the LBP and HOG sets. After testing all combinations of the filtered/normal datasets with the CNN training model, the HOG filter was the most accurate, with an average accuracy of 85.81% across all tests and models, with results as high as 97.94% when used with the appropriate CNN model.

Overview of Occupational Injuries Among Registered Nurses in Washington State, 2007 to 2019.

Registered nurses (RNs) represent the largest segment of the health care workforce and have unique job demands and occupational health considerations. The purpose of this study was to describe the incidence, cost, and causes of occupational injuries among RNs in Washington State and to quantify the cumulative cost and burden of each type of injury, relative to all injuries among RNs. Annual injury claims data covered under Washington State workers' compensation (WC) fund were analyzed over a 13-year period (2007-2019). Annual mean incidence and cost of injuries were calculated and stratified by nature, source, and event/exposure. Negative binomial regression models were used to examine trends in injury incidence over time, for injury incidence overall, and by the most common injury classifications. Between 2007 and 2019, 10,839 WC claims were filed and accepted for Washington State RNs (annual M = 834), totaling more than US$65 million. No significant trend in overall injury incidence was observed (incidence rate ratio [IRR]: 0.99, 95% confidence interval [CI] = [0.94, 1.05]). The most common injury exposures were bodily reaction and exertion, contact with objects and equipment, falls, and assaults and violent acts. To our knowledge, this is the first broad study of the incidence and costs of occupational injuries among RNs across all workplace settings. We identified high-cost, high-frequency incidence rates of musculoskeletal, sharp, and violence-related occupational injury claims, highlighting intervention targets. Implications for Occupational Health Practice: Policy makers, health systems, and occupational health nurse leaders can use this information to identify priority areas where evidence-based occupational health and prevention programs are most needed.

Modified graph-based algorithm to analyze security threats in IoT.

In recent years, the growing and widespread usage of Internet of Things (IoT) systems has led to the emergence of customized structures dependent on these systems. Industrial IoT (IIoT) is a subset of IoT in terms of applications and usage areas. IIoT presents many participants in various domains, such as healthcare, transportation, agriculture, and manufacturing. Besides the daily life benefits, IIoT technology provides major contributions via the Industrial Control System (ICS) and intelligent systems. The convergence of IoT and IIoT systems brings some integration and interoperability problems. In IIoT systems, devices interact with each other using information technologies (IT) and network space. However, these common usages and interoperability led to some security risks. To avoid security risks and vulnerabilities, different systems and protocols have been designed and published. Various public databases and programs identify and provide some of the security threats to make it easier for system administrators' missions. However, effective and long-term security detection mechanisms are needed. In the literature, there are numerous approaches to detecting security threats in IoT-based systems. This article presents two major contributions: First, a graph-based threat detection approach for IoT-based network systems is proposed. Threat path detection is one of the most critical steps in the security of IoT-based systems. To represent vulnerabilities, a directed acyclic graph (DAG) structure is constructed using threat weights. General threats are identified using Common Vulnerabilities and Exposures (CVE). The proposed threat pathfinding algorithm uses the depth first search (DFS) idea and discovers threat paths from the root to all leaf nodes. Therefore, all possible threat paths are detected in the threat graph. Second, threat path-reducing algorithms are proposed considering the total threat weight, hop length, and hot spot thresholds. In terms of available threat pathfinding and hot spot detecting procedures, the proposed reducing algorithms provide better running times. Therefore, all possible threat paths are founded and reduced by the constructed IoT-based DAG structure. Finally, simulation results are compared, and remarkable complexity performances are obtained.

Common teratogenic medication exposures—a population-based study of pregnancies in the United States

BackgroundRisk mitigation for most teratogenic medications relies on risk communication via drug label, and prenatal exposures remain common. Information on the types of and risk factors for prenatal exposures to medications with teratogenic risk can guide strategies to reduce exposures. ObjectivesTo identify medications with known or potential teratogenic risk commonly used during pregnancy among privately insured persons. Study DesignWe used MarketScan® Commercial Claims and Encounters Data to identify pregnancy episodes with live or non-live (ectopic pregnancies, spontaneous and elective abortions, stillbirths) outcomes among persons aged 12 to 55 years during 2011-2018. Start/end dates of medication exposure and pregnancy outcomes were identified via an adapted algorithm based on validation studies. We required continuous health plan enrollment from 90 days before conception until 30 days after pregnancy end date. Medications with known or potential teratogenic risk were selected from the Teratogen Information System (TERIS) and drug monographs based on the level of risk and quality of evidence (138 with known and 60 with potential risk). We defined prenatal exposure based on ≥1 outpatient pharmacy claim or medical encounter for drug administration during target pregnancy periods considering drug risk profiles (e.g., risk only in the first trimester or at a certain dose threshold). Sex hormones and hormone analogs, abortion and post-partum/abortion hemorrhage treatments were not considered as teratogenic medications because of challenges to separate pregnancy-related indications, nor were opioids (due to complex risk-benefit) or anti-obesity drugs if their only teratogenic mechanism was weight loss. ResultsAmong all pregnancies, the 10 medications with known teratogenic risk and with the highest prenatal exposures were sulfamethoxazole/trimethoprim (SMX/TMP) (1,988 per 100,000 pregnancy years), high-dose fluconazole (1248), topiramate (351), lisinopril (144), warfarin (57), losartan (56), carbamazepine (50), valproate (49), vedolizumab (28 since 2015), and valsartan (25). Exposure prevalence to SMX/TMP decreased from 2,346 to 1,453 per 100,000 pregnancy years during 2011 – 2018, but exposure prevalence to vedolizumab increased sixfold since its approval in 2015. Prenatal exposures in the first trimester among non-live pregnancies were higher compared to live-birth pregnancies, with the largest difference for warfarin (non-live 370 vs. live-birth 78), followed by valproate (258 vs. 86) and topiramate (1,728 vs. 674). Prenatal exposures to medications with potential teratogenic risk were most prevalent for low-dose fluconazole (6,495), metoprolol (1,325) and atenolol (448). The largest first-trimester exposure differences between non-live and live-birth pregnancies were for lithum (242 vs. 89), gabapentin (1,639 vs. 653), and duloxetine (1,914 vs. 860). Steady increases in hydralazine and gabapentin exposures were seen during study years, while atenolol exposures decreased (561 to 280). ConclusionsSeveral medications with teratogenic risk for which there are potentially safer alternatives continue to be used during pregnancy. The fluctuating rates of prenatal exposure observed in select teratogenic medications suggest regular reevaluation of risk mitigation strategies is needed. Future research focusing on understanding the clinical context of medication use is necessary to develop effective strategies for reducing exposures to medications with teratogenic risk during pregnancy.

The Impacts of Cybersecurity and AI on Businesses and Individuals

In today’s ever growing technological world, the protection of systems is needed and cybersecurity does just that. Cyber security is the field of prohibiting unauthorized users from accessing data, networks, and devices and keeping data safeguarded. AI or artificial intelligence plays a major part in this field since they can enhance the activities of humans more efficiently. Therefore, this paper deeply explores the specific positive and negative effects of cyber security and AI on businesses and individuals. This paper also dives deep into some tools of the field: CVE or common vulnerability and exposures and CVSS or common vulnerability scoring system. This leads the paper to discuss two major cyber incidents from JP Morgan and Chase and Microsoft Exchange Data Breach and the attacks used. These incidents displayed the need for a more vigorous cyber security. A few of the attacks these companies encountered and more types of attacks are explained in this paper as well. In addition, this paper informs about the specific effects cyber breaches can have on companies. This paper revealed that cybersecurity can positively affect businesses and individuals through mitigating attacks, securing data, making data accessible to only those who are allowed, and better quality of life and negative effects include high costs, more management, user inconvenience, constant new threats, and human error. The negative effects of AI include analyzing personal information, target vulnerabilities, mislead individuals, impersonate humans, and provide false information and positives include network intrusion detection products, threat hunting, vulnerability management, and data centers.