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Road traffic collisions are a major cause of traumatic brain injury. However, the relationship between road traffic collision dynamics and traumatic brain injury risk for different road users is unknown. We investigated 2,065 collisions from Great Britain’s Road Accident In-depth Studies collision database involving 5,374 subjects (2013-20). 595 subjects sustained a traumatic brain injury (20.2% of 2,940 casualties), including 315 moderate-severe and 133 mild-probable. Key pathologies included skull fracture (179, 31.9%), subarachnoid haemorrhage (171, 30.5%), focal brain injury (168, 29.9%) and subdural haematoma (96, 17.1%). These results were extended nationally using >1,000,000 police-reported collision casualties. Extrapolating from the in-depth data we estimate that there are ∼20,000 traumatic brain injury casualties (∼5,000 moderate-severe) annually on Great Britain’s roads, accounting for severity differences. Detailed collision investigation allows vehicle collision dynamics to be understood and the change-in-velocity (known as delta-V) to be estimated for a subset of in-depth collision data. Higher delta-V increased the risk of moderate-severe brain injury for all road users. The four key pathologies were not observed below 8km/h delta-V for pedestrians/cyclists and 19km/h delta-V for car occupants (higher delta-V threshold for focal injury in both groups). Traumatic brain injury risk depended on road user type, delta-V and impact direction. Accounting for delta-V, pedestrians/cyclists had a 6-times higher likelihood of moderate-severe brain injury than car occupants. Wearing a cycle helmet was protective against overall and mild-to-moderate-severe brain injury, particularly skull fracture and subdural haematoma. Cycle helmet protection was not due to travel or impact speed differences between helmeted and non-helmeted cyclist groups. We additionally examined the influence of delta-V direction. Car occupants exposed to a higher lateral delta-V component had a greater prevalence of moderate-severe brain injury, particularly subarachnoid haemorrhage. Multivariate logistic regression models created using total delta-V value and whether lateral delta-V was dominant had the best prediction capabilities (area under the receiver operator curve as high as 0.95). Collision notification systems are routinely fitted in new cars. These record delta-V and automatically alert emergency services to a collision in real-time. These risk relationships could therefore inform how routinely fitted automatic collision notification systems alert the emergency services to collisions with a high brain injury risk. Early notification of high-risk scenarios would enable quicker activation of the highest level of emergency service response. Identifying those that require neurosurgical care and ensuring they are transported directly to a centre with neuro-specialist provisions could improve patient outcomes.

Gravitational lensing provides a means to measure mass that does not rely on detecting and analysing light from the lens itself. Compact objects are ideal gravitational lenses, because they have relatively large masses and are dim. In this paper we describe the prospects for predicting lensing events generated by the local population of compact objects, consisting of 250 neutron stars, 5 black holes, and ≈ 35, 000 white dwarfs. By focusing on a population of nearby compact objects with measured proper motions and known distances from us, we can measure their masses by studying the characteristics of any lensing event they generate. Here we concentrate on shifts in the position of a background source due to lensing by a foreground compact object. With HST, JWST, and Gaia, measurable centroid shifts caused by lensing are relatively frequent occurrences. We find that 30 − 50 detectable events per decade are expected for white dwarfs. Because relatively few neutron stars and black holes have measured distances and proper motions, it is more difficult to compute realistic rates for them. However, we show that at least one isolated neutron star has likely produced detectable events during the past several decades. This work is particularly relevant to the upcoming data releases by the Gaia mission and also to data that will be collected by JWST. Monitoring predicted microlensing events will not only help to determine the masses of compact objects, but will also potentially discover dim companions to these stellar remnants, including orbiting exoplanets.

Head injuries are common for cyclists involved in collisions. Such collision scenarios result in a range of injuries, with different head impact speeds, angles, locations, or surfaces. A clear understanding of these collision characteristics is vital to design high fidelity test methods for evaluating the performance of helmets. We review literature detailing real-world cyclist collision scenarios and report on these key characteristics. Our review shows that helmeted cyclists have a considerable reduction in skull fracture and focal brain pathologies compared to non-helmeted cyclists, as well as a reduction in all brain pathologies. The considerable reduction in focal head pathologies is likely to be due to helmet standards mandating thresholds of linear acceleration. The less considerable reduction in diffuse brain injuries is likely to be due to the lack of monitoring head rotation in test methods. We performed a novel meta-analysis of the location of 1809 head impacts from ten studies. Most studies showed that the side and front regions are frequently impacted, with one large, contemporary study highlighting a high proportion of occipital impacts. Helmets frequently had impact locations low down near the rim line. The face is not well protected by most conventional bicycle helmets. Several papers determine head impact speed and angle from in-depth reconstructions and computer simulations. They report head impact speeds from 5 to 16 m/s, with a concentration around 5 to 8 m/s and higher speeds when there was another vehicle involved in the collision. Reported angles range from 10° to 80° to the normal, and are concentrated around 30°–50°. Our review also shows that in nearly 80% of the cases, the head impact is reported to be against a flat surface. This review highlights current gaps in data, and calls for more research and data to better inform improvements in testing methods of standards and rating schemes and raise helmet safety.

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Instrumented mouthguards (iMG) measure head kinematics in sport, but their measurements have not been validated at high levels of accelerations observed in those sports like rugby. In addition, the effects of filter cut-off frequency on the measured kinematics are still unknown. To address these questions, a drop testing helmeted head-form of an anthropometric testing device (ATD) was used to produce a range of accelerations and accurately control them. Peak linear acceleration (PLA), rotational velocity (PRV), rotational acceleration (PRA) and maximum principal strain (MPS) values were computed. The influence of filter cut-off frequency on peak kinematics was also calculated. Comparison of the peak values across ATD and iMG indicated high levels of agreement, with a total concordance correlation coefficient of 0.97 and intraclass correlation coefficients of 0.990 for PLA, 0.970 for PRV, 0.945 for PRA, and 0.970 for MPS. Cut-off frequencies of 100-300Hz did not significantly attenuate peak kinematics, but frequencies lower than 100Hz did. This is the first study to test an iMG under impact conditions seen in sport. The method presented can be used for in-lab validation of iMGs under head accelerations seen in sport. Furthermore, these results can contribute towards defining standards for filtering iMG data.

Traumatic brain injury (TBI) is a common injury in the workplace. Trips and falls are the leading causes of TBI in the workplace. However, industrial safety helmets are not designed for protecting the head under these impact conditions. Instead, they are designed to pass the regulatory standards which test head protection against falling heavy and sharp objects. This is likely to be due to the limited understanding of head impact conditions from trips and falls in workplace. In this study, we used validated human multi-body models to predict the head impact location, speed and angle (measured from the ground) during trips, forward falls and backward falls. We studied the effects of worker size, initial posture, walking speed, width and height of the tripping barrier, bracing and falling height on the head impact conditions. Overall, we performed 1692 simulations. The head impact speed was over two folds larger in falls than trips, with backward falls producing highest impact speeds. However, the trips produced impacts with smaller impact angles to the ground. Increasing the walking speed increased the head impact speed but bracing reduced it. We found that 41% of backward falls and 19% of trips/forward falls produced head impacts located outside the region of helmet coverage. Next, we grouped all the data into three sub-groups based on the head impact angle: [0°, 30°], (30°, 60°] and (60°, 90°] and excluded groups with small number of cases. We found that most trips and forward falls lead to impact angles within the (30°, 60°] and (60°, 90°] groups while all backward falls produced impact angles within (60°, 90°] group. We therefore determined five representative head impact conditions from these groups by selecting the 75th percentile speed, mean value of angle intervals and median impact location (determined by elevation and azimuth angles) of each group. This led to two representative head impact conditions for trips: 2.7 m/s at 45° and 3.9 m/s at 75°, two for forward falls: 3.8 m/s at 45° and 5.5 m/s at 75° and one for backward falls: 9.4 m/s at 75°. These impact conditions can be used to improve industrial helmet standards.

Traumatic brain injury (TBI) is a prevalent injury among cyclists experiencing head collisions. In legal cases, reliable brain injury evaluation can be difficult and controversial as mild injuries cannot be diagnosed with conventional brain imaging methods. In such cases, accident reconstruction may be used to predict the risk of TBI. However, lack of collision details can render accident reconstruction nearly impossible. Here, we introduce a reconstruction method to evaluate the brain injury in a bicycle–vehicle collision using the crash helmet alone. Following a thorough inspection of the cyclist’s helmet, we identified a severe impact, a moderate impact and several scrapes, which helped us to determine the impact conditions. We used our helmet test rig and intact helmets identical to the cyclist’s helmet to replicate the damage seen on the cyclist’s helmet involved in the real-world collision. We performed both linear and oblique impacts, measured the translational and rotational kinematics of the head and predicted the strain and the strain rate across the brain using a computational head model. Our results proved the hypothesis that the cyclist sustained a severe impact followed by a moderate impact on the road surface. The estimated head accelerations and velocity (167 g, 40.7 rad/s and 13.2 krad/s2) and the brain strain and strain rate (0.541 and 415/s) confirmed that the severe impact was large enough to produce mild to moderate TBI. The method introduced in this study can guide future accident reconstructions, allowing for the evaluation of TBI using the crash helmet only.