Sensor orientation and other factors which increase the blast overpressure reporting errors

Repetitive Low-Level Blast Exposure via Akt/NF-κB Signaling Pathway Mediates the M1 Polarization of Mouse Alveolar Macrophage MH-S Cells

Repetitive low-level blast (rLLB) exposure is a potential risk factor for the health of soldiers or workers who are exposed to it as an occupational characteristic. Alveolar macrophages (AMs) are susceptible to external blast waves and produce pro-inflammatory or anti-inflammatory effects. However, the effect of rLLB exposure on AMs is still unclear. Here, we generated rLLB waves through a miniature manual Reddy-tube and explored their effects on MH-S cell morphology, phenotype transformation, oxidative stress status, and apoptosis by immunofluorescence, real-time quantitative PCR (qPCR), western blotting (WB) and flow cytometry. Ipatasertib (GDC-0068) or PDTC was used to verify the role of the Akt/NF-κB signaling pathway in these processes. Results showed that rLLB treatment could cause morphological irregularities and cytoskeletal disorders in MH-S cells and promote their polarization to the M1 phenotype by increasing iNOS, CD86 and IL-6 expression. The molecular mechanism is through the Akt/NF-κB signaling pathway. Moreover, we found reactive oxygen species (ROS) burst, Ca²⁺ accumulation, mitochondrial membrane potential reduction, and early apoptosis of MH-S cells. Taken together, our findings suggest rLLB exposure may cause M1 polarization and early apoptosis of AMs. Fortunately, it is blocked by specific inhibitors GDC-0068 or PDTC. This study provides a new treatment strategy for preventing and alleviating health damage in the occupational population caused by rLLB exposure.

A sensor-enabled cloud-based computing platform for computational brain biomechanics

BACKGROUND AND OBJECTIVES

Driven by the risk of repetitive head trauma, sensors have been integrated into mouthguards to measure head impacts in contact sports and military activities. These wearable devices, referred to as "instrumented" or "smart" mouthguards are being actively developed by various research groups and organizations. These instrumented mouthguards provide an opportunity to further study and understand the brain biomechanics due to impact. In this study, we present a brain modeling service that can use information from these sensors to predict brain injury metrics in an automated fashion.

METHODS

We have built a brain modeling platform using several of Amazon's Web Services (AWS) to enable cloud computing and scalability. We use a custom-built cloud-based finite element modeling code to compute the physics-based nonlinear response of the intracranial brain tissue and provide a frontend web application and an application programming interface for groups working on head impact sensor technology to include simulated injury predictions into their research pipeline.

RESULTS

The platform results have been validated against experimental data available in literature for brain-skull relative displacements, brain strains and intracranial pressure. The parallel processing capability of the platform has also been tested and verified. We also studied the accuracy of the custom head surfaces generated by Avatar 3D.

CONCLUSION

We present a validated cloud-based computational brain modeling platform that uses sensor data as input for numerical brain models and outputs a quantitative description of brain tissue strains and injury metrics. The platform is expected to generate transparent, reproducible, and traceable brain computing results.

Evolution and Applications of Recent Sensing Technology for Occupational Risk Assessment: A Rapid Review of the Literature

Over the last decade, technological advancements have been made available and applied in a wide range of applications in several work fields, ranging from personal to industrial enforcements. One of the emerging issues concerns occupational safety and health in the Fourth Industrial Revolution and, in more detail, it deals with how industrial hygienists could improve the risk-assessment process. A possible way to achieve these aims is the adoption of new exposure-monitoring tools. In this study, a systematic review of the up-to-date scientific literature has been performed to identify and discuss the most-used sensors that could be useful for occupational risk assessment, with the intent of highlighting their pros and cons. A total of 40 papers have been included in this manuscript. The results show that sensors able to investigate airborne pollutants (i.e., gaseous pollutants and particulate matter), environmental conditions, physical agents, and workers' postures could be usefully adopted in the risk-assessment process, since they could report significant data without significantly interfering with the job activities of the investigated subjects. To date, there are only few "next-generation" monitors and sensors (NGMSs) that could be effectively used on the workplace to preserve human health. Due to this fact, the development and the validation of new NGMSs will be crucial in the upcoming years, to adopt these technologies in occupational-risk assessment.

Biomechanical Analysis of Head Subjected to Blast Waves and the Role of Combat Protective Headgear Under Blast Loading: A Review

Blast-induced traumatic brain injury (bTBI) is a rising health concern of soldiers deployed in modern-day military conflicts. For bTBI, blast wave loading is a cause, and damage incurred to brain tissue is the effect. There are several proposed mechanisms for the bTBI, such as direct cranial entry, skull flexure, thoracic compression, blast-induced acceleration, and cavitation that are not mutually exclusive. So the cause-effect relationship is not straightforward. The efficiency of protective headgears against blast waves is relatively unknown as compared with other threats. Proper knowledge about standard problem space, underlying mechanisms, blast reconstruction techniques, and biomechanical models are essential for protective headgear design and evaluation. Various researchers from cross disciplines analyze bTBI from different perspectives. From the biomedical perspective, the physiological response, neuropathology, injury scales, and even the molecular level and cellular level changes incurred during injury are essential. From a combat protective gear designer perspective, the spatial and temporal variation of mechanical correlates of brain injury such as surface overpressure, acceleration, tissue-level stresses, and strains are essential. This paper outlines the key inferences from bTBI studies that are essential in the protective headgear design context.