Risk Assessment Metrics Research Articles

Online Screening and Virtual Patient Education for Hereditary Cancer Risk Assessment and Testing.

To use online screening and virtual patient education tools to improve the provision of hereditary cancer risk assessment. We conducted a prospective, single-arm study in which clinicians at five U.S. community obstetrics and gynecology practices underwent an 8-week observation followed by 3-4 weeks of training on online patient screening and virtual patient education (prerecorded video with or without a genetic counselor phone call) for genetic testing-eligible patients. After a 4-week practice period, hereditary cancer risk assessment and patient education metrics were collected at 8 weeks and compared with preintervention metrics using univariate conditional logistic regression models stratified by site. The primary outcome was the change in genetic testing completion rate. Clinicians and patients were invited to complete a satisfaction survey. A total of 5,795 and 5,135 patients were seen before and after the intervention, respectively. The proportion of screened patients meeting testing guidelines increased from 21.6% before the intervention to 28.2% after the intervention (odds ratio [OR] 1.36, 95% CI, 1.26-1.47, P<.001). Guideline-eligible patients were significantly more likely to be offered genetic testing (59.1% vs 89.1%, OR 2.06, 95% CI, 1.87-2.27, P<.001), to submit a sample (32.9% vs 45.0%, OR 1.49, 95% CI, 1.27-1.74, P<.001), and to complete testing (16.0% vs 34.2%, OR 2.38, 95% CI, 2.00-2.83, P<.001). Most clinicians agreed or strongly agreed that the screening tool improved the identification of patients meeting hereditary cancer risk assessment guidelines (92.1%), saved time (64.9%), and was easy to incorporate (68.4%) and that patient education improved their ability to deliver hereditary cancer risk assessment standard of care (84.2%). Most patients agreed or strongly agreed that virtual education helped them understand the purpose (91.7%) and implications (92.6%) of genetic testing. A guideline-based online patient screening tool and virtual patient education were well received. The online tool enabled identification of significantly more guideline-eligible candidates for hereditary cancer risk assessment, and education improved patients' genetic literacy. Together, these tools ultimately improved the genetic testing completion rate.

Multidimensional Financial Metrics for Corporate Financial Risk Assessment and Early Warning Mechanisms

By constructing a comprehensive multi-dimensional financial index evaluation system, this study effectively identifies, evaluates, and forewarns the financial risks of enterprises. Utilizing public financial data from S&amp;P Global and Alpha Vantage, a BP neural network-based enterprise financial risk assessment framework is built, and the KMO method analyzes different risk factors. This comprehensive evaluation model, paired with a risk warning mechanism, assesses financial situations through financial leverage, profitability, solvency, operating efficiency, growth indicators, and cash flow indicators. The example analysis shows: 1) The RMSE value ranges from 46.71 to 64.94, indicating high accuracy and stability in predicting financial risk; 2) The R value is very close to 1, demonstrating high stability.

Ideal cardiovascular health and risk of death in a large Swedish cohort

BackgroundIdeal cardiovascular health (CVH) can be assessed by 7 metrics: smoking, body mass index, physical activity, diet, hypertension, dyslipidemia and diabetes, proposed by the American Heart Association. We examined the association of ideal CVH metrics with risk of all-cause, CVD and non-CVD death in a large cohort.MethodsA total of 29,557 participants in the Swedish National March Cohort were included in this study. We ascertained 3,799 deaths during a median follow-up of 19 years. Cox regression models were used to estimate hazard ratios with 95% confidence intervals (95% CIs) of the association between CVH metrics with risk of death. Laplace regression was used to estimate 25th, 50th and 75th percentiles of age at death.ResultsCompared with those having 6–7 ideal CVH metrics, participants with 0–2 ideal metrics had 107% (95% CI = 46-192%) excess risk of all-cause, 224% (95% CI = 72-509%) excess risk of CVD and 108% (31-231%) excess risk of non-CVD death. The median age at death among those with 6–7 vs. 0–2 ideal metrics was extended by 4.2 years for all-causes, 5.8 years for CVD and 2.9 years for non-CVD, respectively. The observed associations were stronger among females than males.ConclusionsThe strong inverse association between number of ideal CVH metrics and risk of death supports the application of the proposed seven metrics for individual risk assessment and general health promotion.

&lt;i&gt;AssessITS&lt;/i&gt;: Integrating Procedural Guidelines and Practical Evaluation Metrics for Organizational IT and Cybersecurity Risk Assessment

&lt;i&gt;AssessITS&lt;/i&gt;: Integrating Procedural Guidelines and Practical Evaluation Metrics for Organizational IT and Cybersecurity Risk Assessment

The impact of aging and atrial fibrillation on thrombus formation in-silico

Abstract Background Atrial fibrillation (AF) is an age-related cardiac arrythmia that underlies one third of all strokes, with thromboemboli commonly originating from the left atrial appendage (LAA). Personalised in-silico modelling of Virchow’s triad (blood stasis, hypercoagulability and endothelial damage) can improve stroke risk assessment metrics, such as the CHA2DS2-VASc score, by providing quantitative mechanistic information [1]. The ability to quantify the impact of aging may play a critical role in patient stratification and therapy selection to reduce the risk of AF-related stroke. Aim We aim to perform in-silico modelling to quantify the effect of aging and AF on thrombus formation in the LAA by using advanced computational tools to capture blood flow, clotting mechanisms and endothelial damage [2]. Methods A virtual physiological LA model was derived from cardiac Cine MRI from an adult AF patient imaged during sinus rhythm (SR). Biomarkers for a geriatric patient were obtained from literature and applied as: i) elevated plasma fibrinogen concentration and ii) longer thrombin time-to-peak (ttP) [3]. Blood flow was simulated in SR and AF conditions for 5 cardiac cycles in the LA for both ages (totalling 4 test cases). The catalytic conversion of fibrinogen to a fibrin clot by thrombin was modelled to mimic hypercoagulability [2]. The site of endothelial injury was prescribed in the middle of the LAA. The area under curve for the fibrin concentration (Fn AUC) was measured to quantify thrombus formation. Results In the two SR cases, LA blood flow velocity increased to a maximum of 1.8 m/s after 0.6 s with a mean of 0.63 m/s during the cardiac cycle, while the two AF cases showed 1.3 m/s and 0.39 m/s, respectively, corresponding to a 38% decrease in AF. Peak velocity in the LAA was 0.35 m/s during SR and 0.18 m/s in AF. The fibrin concentration in the adult-SR case rose to 1.1 mmol/m³ over the first cardiac cycle, but both thrombin and fibrin were washed out of the LA after 1.5 s, with a Fn AUC of 0.045mmol s/m³ (Fig. 1a). The geriatric-SR case sustained fibrin generation throughout the 5 cardiac cycles reaching 3.1 mmol/m³, an Fn AUC of 2.4 mmol s/m³, and the formation of an LAA thrombus (Fig. 1b). Similarly, the adult-AF and geriatric-AF cases also saw continuous growth to 5.0 and 7.4 mmol/m³, with Fn AUCs of 6.4 and 12.6 mmol s/m³, respectively, with formation of a thrombus in the LAA in both (Fig. 1c and d). The thrombus formed in the adult-AF case was 25% larger than the geriatric-SR case, while thrombus in the geriatric-AF case was 80% larger. Conclusions This investigation quantifies the increased risk of LAA thrombus formation in older patients, with an Fn AUC increase of 55% between adult and geriatric in SR and a 100% increase when combining geriatric biomarkers with AF conditions. With further validation on larger patient cohorts, this novel in-silico tool may improve our understanding of the risks in AF-related stroke.

Shifting quality chronic pain treatment measures from processes to outcomes.

Misapplication of the 2016 Centers for Disease Control (CDC) opioid prescribing guidelines has led to overem-phasis of morphineequivalent daily dose (MEDD) as a "metric of success" in chronic noncancer pain (CNCP), resulting in unintentional harms to patients. This article reviews CNCP-related guidelines and patient preferences in order to identify pragmatic, patient-centered metrics to assess treatment response and safety in opioid-treated CNCP. We reviewed the clinical (CDC), research (Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials), and implementability-related guidelines (GuideLine Implementability Appraisal), along with relevant patient-identified treatment goals. From these, we summarize a guideline-concordant, patient-centered, implementable set of measures to aid the clinical management of opioid-treated CNCP. We identify metrics across three domains of care: (1) treatment response metrics, which align with the CNCP care goals (pain intensity, pain interference including function and quality of life, and global impression of change); (2) risk assessment ("safety") metrics, eg, MEDD, benzodiazepine-opioid or naloxone-opioid coprescribing, and severity of mental health disorders, which evaluate the risk-benefit profile of opioid therapy; and (3) adherence ("process") metrics, which assess clinician/patient adherence to the guideline-recommended opioid therapy monitoring practices, eg, the presence of completed treatment agreement or urine toxicology testing. All metrics should be informed by implementability principles, eg, be decidable, executable, and measurable. This article summarizes guideline-concordant, patient-centered, implementable metrics for assessing treatment response, safety, and adherence in opioid-treated CNCP. Regardless of which specific treatment guidelines are applied, this approach could help conceptualize and standardize the collection and reporting of CNCP-relevant metrics, compare them across health systems, and optimize care and treatment outcomes in opioid-treated CNCP.

Assessing the smart city: A review of metrics for performance assessment, risk assessment and construction ability assessment

Assessing the smart city: A review of metrics for performance assessment, risk assessment and construction ability assessment

Risk prioritization using a modified FMEA analysis in industry 4.0

To assess the reliability of production systems, a Failure Mode and Effects Analysis (FMEA) is usually performed to determine how reliable a system is in its entirety. In order to ensure smooth production operations and timely delivery of orders, it is necessary to ensure continuity of operations without failure and interruption. Industry 4.0 (I4.0) can provide so many benefits, but it also introduces new challenges because of its technical components such as sensors and actuators. In this paper, we propose an FMEA model and structure quantitative risk assessment metrics for a yogurt-filling system in an I4.0 environment. We use the risk priority number (RPN), which is usually a product of three factors: severity (S), occurrence (O), and detection (D) ratings. In this study, we use also a fourth factor, namely, dependency (D2). We compare RPN scores for traditional components and I4.0 components. The application method of the FMEA model was presented in detail, and its practicality was verified and demonstrated. To get valid results, a team of experts was utilized. Then Pareto method was used together with the FEMA approach to prioritize the risks that need more attention. After that, risk prevention steps were introduced. Results showed that four components are the most important ones. Results also showed that some of the components of I4.0, such as proximity sensors and color detection sensors were found with the largest priority. A better performance was achieved following the enhancements suggested by the experts' team. A reduction in RPN of 55% has been achieved by implementing the upgrades, indicating an increase in system reliability.

The impacts of ambient ozone pollution on China's wheat yield and forest production from 2010 to 2021

Near-surface ozone causes damages on both crop and forest but their long-term spatiotemporal changes in China have been insufficiently explored, preventing comprehensive policy making with food security and climate targets. Moreover, limitation exists in the current metrics for long-term regional ozone risk assessment, AOT40 (the accumulated hourly ozone over a threshold of 40 ppbv) and PODY (phytotoxic ozone dose over a threshold of Y nmol ozone m−2 PLA s−1), with ignorance of meteorological influence for the former and complicated data collection and calculation procedures for the latter. Here, we developed a new metric for ozone-induced risk on winter wheat, O3MET, which can be easily derived based on ozone concentrations and meteorological variables, and is suitable for long-term assessment of ozone-induced wheat loss at the regional scale. Combining with existing metric for forest (O3RH), we comprehensively quantified the ozone damages on winter wheat yield and forest gross primary production (GPP) for mainland China during 2010–2021, the period with fast growth of ozone level across the country. The annual average losses of wheat yield and forest GPP were estimated at 26.5 Mt and 552.6 TgC, accounting for 17% and 4% of the total yield and GPP without ozone impact, respectively. Heavy dual ozone-induced damages on both wheat and forest were presented in East and South China. The ozone-induced wheat yield loss and forest GPP loss were estimated to increase at a rate of 1.8 Mt/yr and 13.9 TgC/yr for the entire country, respectively, driven mainly by the enhanced ambient ozone level within the research period. Besides ecological impact, the ozone pollution in the developed eastern China resulted in serious health burden as well, thus effective actions on ozone pollution alleviation in the region is crucial for reducing its ecological and health risks simultaneously.

Seismic risk prioritisation schemes for reinforced concrete bridge portfolios

A significant portion of the existing bridge inventory in Italy is decades old, requiring continuous maintenance and safety assessment approaches. Recent collapses of existing reinforced concrete bridges have piqued public interest, placing pressure on management agencies to define methodologies with which to prioritise asset maintenance and to effectively utilise their limited resources. When looking for decision variables to perform this prioritisation, seismic risk assessment metrics, such as average annual losses (AAL), are an appealing choice. However, obtaining this metric for a large bridge inventory is technically challenging and requires large amounts of information that are seldom available, promoting the development of practical approaches that can predict the relative priority of assets within a portfolio, based on processing simple indicators with acceptable accuracy. In this research, a case study of 617 bridges from the Italian road network was assessed considering state-of-the-art approaches to calculate total losses. The results were explored with data science techniques, identifying the main features that drive the relative importance of bridges in terms of AAL and using them as guidance to calibrate a simplified methodology, based on the recent Italian Guidelines for Bridge Safety Assessment. The proposed AAL-based modifications demonstrate a notable improvement in the definition of bridge assessment priorities, as well as providing further resolution in the classification for more efficient decision making.

Safety Risk Assessment of Overturning Construction of Towering Structure Based on Cloud Matter–Element Coupled Model

Rapid urbanization has led to a surge in the number of towering structures, and overturning is widely used because it can better accommodate the construction of shaped structures such as variable sections. The complexity of the construction process makes the construction risk have certain randomness, so this paper proposes a cloud-based coupled matter-element model to address the ambiguity and randomness in the safety risk assessment of overturning construction of towering structures. In the pretended model, the digital eigenvalues of the cloud model are used to replace the eigenvalues in the matter–element basic element, and calculate the cloud correlation of the risk assessment metrics through the correlation algorithm of the cloud model to build the computational model. Meanwhile, the improved hierarchical analysis method based on the cloud model is used to determine the weight of the index. The comprehensive evaluation scores of the evaluation event are then obtained through the weighted average method, and the safety risk level is determined accordingly. Through empirical analysis, (1) the improved hierarchical analysis method based on the cloud model can incorporate the data of multiple decision-makers into the calculation formula to determine the weights, which makes the assessment results more credible; (2) the evaluation results of the cloud-based matter-element coupled model method are basically consistent with those of the other two commonly used methods, and the confidence factor is less than 0.05, indicating that the cloud-based physical element coupled model method is reasonable and practical for towering structure overturning; (3) the cloud-based coupled element model method, which confirms the reliability of risk level by performing Spearman correlation on comprehensive assessment scores, can provide more comprehensive information of instances compared with other methods, and more comprehensively reflects the fuzzy uncertainty relationship between assessment indexes, which makes the assessment results more realistic, scientific and reliable.

Environmental health and risk assessment metrics with special mention to biotransfer, bioaccumulation and biomagnification of environmental pollutants.

The environment pollutants, which are landed up in environment because of human activities like urbanization, mining and industrializations, affects human health, plants and animals. The living organisms present in environment are constantly affected by the toxic pollutants through direct contact or bioaccumulation of chemicals from the environment. The toxic and hazardous pollutants are easily transferred to different environmental matrices like land, air and water bodies such as surface and ground waters. This comprehensive review deeply discusses the routes and causes of different environmental pollutants along with their toxicity, impact, occurrences and fate in the environment. Environment health and risk assessment tools that are used to evaluate the harmfulness, exposure of living organisms to pollutants and the amount of pollutant accumulated are explained with help of bio-kinetic models. Biotransfer, toxicity factor, biomagnification and bioaccumulation of different pollutants in the air, water and marine ecosystems are critically addressed. Thus, the presented survey would be collection of correlations those addresses the factors involved in assessing the environmental health and risk impacts of distinct environmental pollutants.

Dynamic risk assessment for geologic CO2 sequestration

At a geologic CO2 sequestration (GCS) site, geologic uncertainty usually leads to large uncertainty in the predictions of properties that influence metrics for leakage risk assessment, such as CO2 saturations and pressures in potentially leaky wellbores, CO2/brine leakage rates, and leakage consequences such as changes in drinking water quality in groundwater aquifers. The large uncertainty in these risk-related system properties and risk metrics can lead to over-conservative risk management decisions to ensure safe operations of GCS sites. The objective of this work is to develop a novel approach based on dynamic risk assessment to effectively reduce the uncertainty in the predicted risk-related system properties and risk metrics. We demonstrate our framework for dynamic risk assessment on two case studies: a 3D synthetic example and a synthetic field example based on the Rock Springs Uplift (RSU) storage site in Wyoming, USA. Results show that the U.S. National Risk Assessment Partnership’s Open Source Integrated Assessment Model (NRAP-Open-IAM) coupled with a conformance evaluation can be used to effectively quantify and reduce the uncertainty in the predictions of risk-related system properties and risk metrics in GCS.

Development of Data-Driven Metrics for Balance Impairment and Fall Risk Assessment in Older Adults.

Ageing incurs a natural decline of postural control which has been linked to an increased risk of falling. Accurate balance assessment is important in identifying postural instability and informing targeted interventions to prevent falls in older adults. Inertial sensor (IMU) technology offers a low-cost means for objective quantification of human movement. This paper describes two studies carried out to advance the use of IMU-based balance assessments in older adults. Study 1 (N = 39) presents the development of two new IMU-derived balance measures. Study 2 (N = 248) reports a reliability analysis of IMU postural stability measures and validates the novel balance measures through comparison with clinical scales. We also report a statistical fall risk estimation algorithm based on IMU data captured during static balance assessments alongside a method of improving this fall risk estimate by incorporating standard clinical fall risk factor data. Results suggest that both new balance measures are sensitive to balance deficits captured by the Berg Balance Scale (BBS) and Timed Up and Go test. Results obtained from the fall risk classifier models suggest they are more accurate (67.9%) at estimating fall risk status than a model based on BBS (59.2%). While the accuracies of the reported models are lower than others reported in the literature, the simplicity of the assessment makes it a potentially useful screening tool for balance impairments and falls risk. The algorithms presented in this paper may be suitable for implementation on a smartphone and could facilitate unsupervised assessment in the home.

Relations Between Collision Probability, Mahalanobis Distance, and Confidence Intervals for Conjunction Assessment

Some of the most common metrics for collision risk assessment are the probability of collision, miss distance in Mahalanobis space, and confidence intervals. Sometimes they are used in combination; for example, the miss distance is used as a prescreening method to identify potentially hazardous conjunctions; other times they are used as alternative means; that is, covariance ellipse overlapping checks are employed instead of computing the probability of collision. In this work, we show that the three risk indexes are intimately connected once a suitable distance is defined. We argue that Mahalanobis miss distance is a proper metric only when the sigma-normalized hard-body size is negligible; we thus investigate the minimum Mahalanobis distance between the hard-body circle and the combined position covariance as an alternative. Its computation is fully analytic, as the most complex operation is finding the roots of a quartic polynomial. When multiplied by the sigma-normalized hard-body radius, such distance provides an upper bound to the collision probability. When used to scale the covariance matrix, it provides the largest confidence interval supporting a noncollision event. Finally, when adopted as an actionable threshold, analytical bounds on the probability of miss detection and of false alarms can be computed.

Young drivers’ pedestrian anti-collision braking operation data modelling for ADAS development

Smart cities and smart mobility come from intelligent systems designed by humans. Artificial Intelligence (AI) is contributing significantly to the development of these systems, and the automotive industry is the most prominent example of "smart" technology entering the market: there are Advanced Driver Assistance System (ADAS), Radar/LIDAR detection units and camera-based Computer Vision systems that can assess driving conditions. Actually, these technologies have become consumer goods and services in mass-produced vehicles to provide human drivers with tools for a more comfortable and safer driving. Nevertheless, they need to be further improved for progress in the transition to fully automated driving or simply to increase vehicle automation levels. To this end, it becomes imperative to accurately predict driver’s decisions, model human driving behaviors, and introduce more accurate risk assessment metrics. This paper presents a system that can learn to predict the future braking behavior of a driver in a typically urban vehicle-pedestrian conflict, i.e., when a pedestrian enters a zebra crossing from the curb and a vehicle is approaching. The algorithm proposes a sequential prediction of relevant operational indicators that continuously describe the encounter process. A car driving simulator was used to collect reliable data on braking behaviours of a cohort of 68 licensed university students, who faced the same urban scenario. The vehicle speed, steering wheel angle, and pedal activity were recorded as the participants approached the crosswalk, along with the azimuth angle of the pedestrian and the relative longitudinal distance between the vehicle and the pedestrian: the proposed system employs the vehicle information as human driving decisions and the pedestrian information as explanatory variables of the environmental state. In fact, the pedestrian’s polar coordinates are usually calculated by an on-board millimeter-wave radar which is typically used to perceive the environment around a vehicle. All mentioned information is represented in the form of time series data and is used to train a recurrent neural network in a supervised machine learning process. The main purpose of this research is to define a system of behavioral profiles in non-collision conditions that could be used for enhancing the existing intelligent driving systems, e.g., to reduce the number of warnings when the driver is not on a collision course with a pedestrian. Preliminary experiments reveal the feasibility of the proposed system.

Large eddy simulation of cough jet dynamics, droplet transport, and inhalability over a ten minute exposure.

High fidelity simulations of expiratory events such as coughing provide the opportunity to predict the fate of the droplets from the turbulent jet cloud produced from a cough. It is well established that droplets carrying infectious pathogens with diameters of remain suspended in the air for several hours and transported by the air currents over considerable distances (e.g., in meters). This study used a highly resolved mesh to capture the multiphase turbulent buoyant cloud with suspended droplets produced by a cough. The cough droplets' dispersion was subjected to thermal gradients and evaporation and allowed to disperse between two humans standing 2 m apart. A nasal cavity anatomy was included inside the second human to determine the inhaled droplets. Three diameter ranges characterized the droplet cloud, , which made up 93% of all droplets by number; 5 to 100 μm comprised 3%, and m comprising 4%. The results demonstrated the temporal evolution of the cough event, where a jet is first formed, followed by a thermally driven puff cloud with the latter primarily composed of droplets under 5 μm diameter, moving with a vortex string structure. After the initial cough, the data were interpolated onto a more coarse mesh to allow the simulation to cover ten minutes, equivalent to 150 breathing cycles. We observe that the critical diameter size susceptible to inhalation was , although most inhaled droplets after 10 min by the second human were approximately . These observations offer insight into the risk of airborne transmission and numerical metrics for modeling and risk assessment.

Case Study of Expected Loss Failure Mode and Effect Analysis Model Based on Maintenance Data

Failure mode and effect analysis (FMEA) is one of the most widely employed pre-evaluation techniques to avoid risks during the product design and manufacturing phases. Risk priority number (RPN), a risk assessment indicator used in FMEA, is widely used in the field due to its simple calculation process, but its limitations as an absolute risk assessment indicator have been pointed out. There has also been criticism of the unstructured nature and lack of systematicity in the FMEA procedures. This work proposes an expected loss-FMEA (EL-FMEA) model that organizes FMEA procedures and structures quantitative risk assessment metrics. In the EL-FMEA model, collectible maintenance record data is defined and based on this, the failure rate of components and systems and downtime and uptime of the system are calculated. Moreover, based on these calculated values, the expected economic loss is computed considering the failure detection time. It also provides an alternative coefficient to evaluate whether or not a detection system is installed to improve the expected loss of failure. Finally, a case study was conducted based on the maintenance record data, and the application procedure of the EL-FMEA model was presented in detail, and the practicality of this model was verified through the results.

Experimental investigation of indoor aerosol dispersion and accumulation in the context of COVID-19: Effects of masks and ventilation.

The ongoing COVID-19 pandemic has highlighted the importance of aerosol dispersion in disease transmission in indoor environments. The present study experimentally investigates the dispersion and build-up of an exhaled aerosol modeled with polydisperse microscopic particles (approximately 1 μm mean diameter) by a seated manikin in a relatively large indoor environment. The aims are to offer quantitative insight into the effect of common face masks and ventilation/air purification, and to provide relevant experimental metrics for modeling and risk assessment. Measurements demonstrate that all tested masks provide protection in the immediate vicinity of the host primarily through the redirection and reduction of expiratory momentum. However, leakages are observed to result in notable decreases in mask efficiency relative to the ideal filtration efficiency of the mask material, even in the case of high-efficiency masks, such as the R95 or KN95. Tests conducted in the far field ( distance from the subject) capture significant aerosol build-up in the indoor space over a long duration (). A quantitative measure of apparent exhalation filtration efficiency is provided based on experimental data assimilation to a simplified model. The results demonstrate that the apparent exhalation filtration efficiency is significantly lower than the ideal filtration efficiency of the mask material. Nevertheless, high-efficiency masks, such as the KN95, still offer substantially higher apparent filtration efficiencies (60% and 46% for R95 and KN95 masks, respectively) than the more commonly used cloth (10%) and surgical masks (12%), and therefore are still the recommended choice in mitigating airborne disease transmission indoors. The results also suggest that, while higher ventilation capacities are required to fully mitigate aerosol build-up, even relatively low air-change rates () lead to lower aerosol build-up compared to the best performing mask in an unventilated space.

Identification of Circulating Gene Expression Signatures of Intracranial Aneurysm in Peripheral Blood Mononuclear Cells.

Peripheral blood mononuclear cells (PBMCs) play an important role in the inflammation that accompanies intracranial aneurysm (IA) pathophysiology. We hypothesized that PBMCs have different transcriptional profiles in patients harboring IAs as compared to IA-free controls, which could be the basis for potential blood-based biomarkers for the disease. To test this, we isolated PBMC RNA from whole blood of 52 subjects (24 with IA, 28 without) and performed next-generation RNA sequencing to obtain their transcriptomes. In a randomly assigned discovery cohort of n = 39 patients, we performed differential expression analysis to define an IA-associated signature of 54 genes (q < 0.05 and an absolute fold-change ≥ 1.3). In the withheld validation dataset, these genes could delineate patients with IAs from controls, as the majority of them still had the same direction of expression difference. Bioinformatics analyses by gene ontology enrichment analysis and Ingenuity Pathway Analysis (IPA) demonstrated enrichment of structural regulation processes, intracellular signaling function, regulation of ion transport, and cell adhesion. IPA analysis showed that these processes were likely coordinated through NF-kB, cytokine signaling, growth factors, and TNF activity. Correlation analysis with aneurysm size and risk assessment metrics showed that 4/54 genes were associated with rupture risk. These findings highlight the potential to develop predictive biomarkers from PBMCs to identify patients harboring IAs.