OBJECTIVE: Trauma complications have been one of the most serious public health concerns worldwide. In most reports, urogenital injuries (UGIs) are seen in approximately 10% of adult traumatic patients and less than 3% of children with multiple/severe trauma to the abdomen or pelvis. Traffic accidents are the most common cause of UGIs. The purpose of this study is to systematically determine the prevalence and types of UGIs after car accidents.
METHODS: The search strategy was aimed at finding relevant studies in October 2023. No restrictions on language or date were applied. The following criteria were considered eligibility criteria: reporting at least one epidemiological aspect of UGIs in people with road traffic injury (RTI) and a separate epidemiological analysis of RTIs in UGI (we also included those articles that pointed out all RTIs but separately mentioned UGIs). Two experts assessed the reporting quality of articles using standardized critical appraisal instruments from the Joanna Briggs Institute. Statistical analysis for this study was conducted using the CMA statistical software version 3.2.0.
RESULTS: A total of 1,466,024 cases following RTIs through 107 studies were included in our review. Of these, 29 studies were related to children (20,036), and the others reported RTIs in adults (1,445,988). The total prevalence was 4.7%, and car accidents were responsible in 36 studies, followed by motorcycle accidents in 25, bicycles in 17 studies, and automobile-pedestrian collisions in 23 related studies. In subgroup analysis based on the damaged organ, the rate of bladder injury was 3.5%. This rate was 5.3% for kidneys.
CONCLUSIONS: This systematic review and meta-analysis found that the prevalence of UGI following RTIs was 4.7%, with car accidents being the most common cause. UGIs were more prevalent in adults than in children, and bladder and kidney injuries were the most commonly reported types. The prevalence of UGI varied by country and study design.

TRPM4 is a calcium activated non-selective cation channel, impermeable to Ca2+, in neurons it has been implicated in the regulation of the excitability and in the persistent firing. Cholinergic stimulation is also implicated in changes in excitability that leads neurons to an increased firing frequency, however it is not clear whether TRPM4 is involved in the cholinergic-induced increase in firing frequency. Here using a combination of patch clamp electrophysiology, Ca2+ imaging, immunofluorescence, fluorescence recovery after photobleaching (FRAP) and pharmacological approach, we demonstrate that carbachol (Cch) increases firing frequency, intracellular Ca2+ and that TRPM4 inhibition using 9-Ph and CBA reduces firing frequency and decreases the peak in intracellular Ca2+ induced by Cch in cortical pyramidal neurons in culture. Moreover, we determined that cholinergic stimulation reduces TRPM4 recycling and stabilizes TRPM4 in the plasma membrane. Together our results indicate that cholinergic stimulation increases firing in a TRPM4 dependent manner, and also increases the TRPM4 stability in the membrane, suggesting that TRPM4 is locked in microdomains in the membrane, possibly signaling or cytoskeleton proteins complexes.

OBJECTIVE: Road traffic injuries (RTIs) pose a significant public health burden, and more than half of these fatalities are attributed to vulnerable road users (VRUs). This study aimed to evaluate the epidemiology and outcomes of severe RTIs in Korea by focusing on different types of road users.
METHODS: This is nationwide retrospective observational study. Using data from the Korean Nationwide Severe Trauma Registry, this study analyzed severe RTI cases from 2016 to 2020. The study included EMS-treated severe trauma patients, defining severe RTI as cases with an injury severity score (ISS) ≥16 or out-of-hospital cardiac arrest (OHCA). The main variable of interest was the road user type, classified as motor vehicle occupants (MVOs), pedestrians, motorcyclists, and bicyclists. Trends and injury characteristics by road user type were analyzed, and multivariate logistic regression was conducted to calculate the adjusted odds ratios (AORs) and 95 % confidence intervals (CIs) of road user type for in-hospital mortality.
RESULTS: Of the 143,021 EMS-treated severe trauma cases, 24,464 were included in this study. Pedestrians represented the largest group (n = 8,782; 35.9 %). More than half of the patients died (n = 12,620, 51.6 %), and a high proportion of patients had OHCA (n = 10,048, 41.1 %). There was no significant change in the overall severe RTI numbers from 2016 to 2020, but a decrease in pedestrian cases and an increase in motorcyclist cases were noted (both p for trend<0.05). Low usage of safety devices was observed (28.2 % of motor vehicle occupants used seat belts, 35.9 % of motorcyclists used helmets, and 9.6 % of bicyclists used helmets). Head injuries were most common, particularly among bicyclists (77.0 %) and motorcyclists (69.8 %). Compared to motor vehicle occupants, pedestrians (AOR [95 % CI] 1.12 [1.04-1.20]) and others (AOR [95 % CI] 1.30 [1.02-1.65]) had higher odds of mortality, while motorcyclists (AOR [95 % CI] 0.64 [0.59-0.69]) and bicyclists (AOR [95 % CI] 0.68 [0.60-0.76]) had lower odds of mortality.
CONCLUSIONS: We found varying trends and injury characteristics in severe RTIs according to road user type. Adapting prevention strategies for evolving road user patterns, with particular attention to increasing safety device usage and addressing the high mortality associated with severe RTIs are warranted.

Vision-language models are of interest in various domains, including automated driving, where computer vision techniques can accurately detect road users, but where the vehicle sometimes fails to understand context. This study examined the effectiveness of GPT-4V in predicting the level of \'risk\' in traffic images as assessed by humans. We used 210 static images taken from a moving vehicle, each previously rated by approximately 650 people. Based on psychometric construct theory and using insights from the self-consistency prompting method, we formulated three hypotheses: (i) repeating the prompt under effectively identical conditions increases validity, (ii) varying the prompt text and extracting a total score increases validity compared to using a single prompt, and (iii) in a multiple regression analysis, the incorporation of object detection features, alongside the GPT-4V-based risk rating, significantly contributes to improving the model\'s validity. Validity was quantified by the correlation coefficient with human risk scores, across the 210 images. The results confirmed the three hypotheses. The eventual validity coefficient was r = 0.83, indicating that population-level human risk can be predicted using AI with a high degree of accuracy. The findings suggest that GPT-4V must be prompted in a way equivalent to how humans fill out a multi-item questionnaire.

BACKGROUND: Trauma is a significant public health concern in Iran, with high mortality and morbidity rates. This study aimed to assess trauma patients\' profiles in Shahroud, Iran. Study Design: A cross-sectional study.
METHODS: The study involved trauma patients who met specific criteria at Imam Hossein hospital in Shahroud, Iran, between 2016 and 2023, using the National Trauma Registry of Iran (NTRI). The relationship between injury characteristics and the cause of injury was analyzed using chi-square test and post hoc analysis. Quintile regression models assessed the association of demographic and clinical variables with length of stay.
RESULTS: Among 3513 trauma patients, road traffic crashes (RTCs) had a higher percentage of injuries with the Glasgow Coma Scale (GCS) between 9 and 12 (1.7%) compared to falls (0.3%) (P<0.001). Falls caused more moderate cases with injury severity scores (ISS) ranging from 9 to 15 (22.7%) than RTCs (17.1%) (P<0.001). RTC-related injuries required more ventilation (2.7%) and intensive care unit (ICU) admissions (11.1%) than falls (P<0.001). After adjusting for age, GCS, ISS, and body region, fall had a median length of stay nine hours shorter than RTCs (95% CI = -16.2, -1.8).
CONCLUSIONS: Significant injury pattern differences were observed between RTCs and falls. RTCs had higher frequencies of injuries resulting in GCS scores between 9 and 12, while falls had higher frequencies of moderate ISS scores. In addition, patients with RTC-related injuries required more mechanical ventilation and ICU admissions. Moreover, after adjusting for various factors, patients with RTC-related injuries had a significantly longer hospital stay compared to those with fall-related injuries.

BACKGROUND: People living with asthma are disproportionately affected by air pollution, with increased symptoms, medication usage, hospital admissions, and the risk of death. To date, there has been a focus on exhaust emissions, but traffic-related air pollution (TRAP) can also arise from the mechanical abrasion of tyres, brakes, and road surfaces. We therefore created a study with the aim of investigating the acute impacts of non-exhaust emissions (NEEs) on the lung function and airway immune status of asthmatic adults.
METHODS: A randomised three-condition crossover panel design will expose adults with asthma using a 2.5 h intermittent cycling protocol in a random order at three locations in London, selected to provide the greatest contrast in the NEE components within TRAP. Lung function will be monitored using oscillometry, fractional exhaled nitric oxide, and spirometry (the primary outcome is the forced expiratory volume in one second). Biomarkers of inflammation and airborne metal exposure will be measured in the upper airway using nasal lavage. Symptom responses will be monitored using questionnaires. Sources of exhaust and non-exhaust concentrations will be established using source apportionment via the positive matrix factorisation of high-time resolution chemical measures conducted at the exposure sites.
CONCLUSIONS: Collectively, this study will provide us with valuable information on the health effects of NEE components within ambient PM2.5 and PM10, whilst establishing a biological mechanism to help contextualise current epidemiological observations.

The aim of the study was an assessment of the pollution level and identification of the antimony sources in soils in areas subjected to industrial anthropopressure from: transport, metallurgy and electrical waste recycling. The combination of soil magnetometry, chemical analyzes using atomic spectrometry (ICP-OES and ICP-MS), Sb fractionation analysis, statistical analysis (Pearson\'s correlation matrix, factor analysis) as well as Geoaccumulation Index, Pollution Load Index, and Sb/As factor allowed not only the assessment of soil contamination degree, but also comprehensive identification of different Sb sources. The results indicate that the soil in the vicinity of the studied objects was characterized by high values of magnetic susceptibility and thus, high contents of potentially toxic elements. The most polluted area was in the vicinity of electrical waste processing plants. Research has shown that the impact of road traffic and wearing off brake blocks, i.e. traffic anthropopression in general, has little effect on the surrounding soil in terms of antimony content. Large amounts of Pb, Zn, As and Cd were found in the soil collected in the vicinity of the heap after the processing of zinc-lead ores, the average antimony (11.31 mg kg-1) content was lower in the vicinity of the heap than in the area around the electrical and electronic waste processing plant, but still very high. Antimony in the studied soils was demobilized and associated mainly with the residual fraction.

The one of the leading causes of adolescent morbidity and mortality worldwide is motor vehicle accidents (MVA). The use of seat belts significantly lowers MVA fatalities and injuries. The aim of this study is to investigate adolescent seat belt usage patterns and relation with risky behaviors. The study conducted at two medical institutions with non-immigrant and literate adolescents aged 12-18. Demographics, seat belt use, and risk-taking behavior were collected through questionnaires. 726 teenagers (422 girls and 304 boys) with an average age of 176.7 ± 23.37 months participated in the study. Parents\' educational levels and front-seat belt use have been found to be correlated. Comparatively to non-users, seat belt users demonstrated lower risk scores (total, traffic, substance, and social). The use of seat belts was significantly predicted by traffic risk, according to logistic regression. The frequency of seatbelt use was higher among participants from cities with higher socioeconomic status. As a result, it was found that adolescents who exhibited more risky behaviors had a lower frequency of seat belt use and seat belt use was associated with socioeconomic level and parental education level. It is thought that population-based studies to be conducted on this subject are important.

Traffic related air pollution is a major concern for perinatal health. Determining causal associations, however, is difficult since high-traffic areas tend to correspond with lower socioeconomic neighborhoods and other environmental exposures. To overcome confounding, we compared pregnant individuals living downwind and upwind of the same high-traffic road. We leveraged vital statistics data for Texas from 2007-2016 (n=3,570,272 births) and computed hourly wind estimates for residential addresses within 500 m of high-traffic roads (i.e., annual average daily traffic greater than 25,000) (10.9% of births). We matched pregnant individuals predominantly upwind to pregnant neighbors downwind of the same road segment (n=37,631 pairs). Living downwind was associated with an 11.6 gram (95% CI: -18.01, -5.21) decrease in term birth weight. No associations were observed with low term birth weight, preterm birth, or very preterm birth. In distance-stratified models, living downwind within 50 m was associated with a -36.3 gram (95% CI: -67.74, -4.93) decrease in term birth weight and living 51-100m downwind was associated with an odds ratio of 3.68 (95% CI: 1.71, 7.90) for very preterm birth. These results suggest traffic air pollution is associated with adverse birth outcomes, with steep distance decay gradients around major roads.

Air pollution is a major environmental problem and its monitoring is essential for regulatory purposes, policy making, and protecting public health. However, dense networks of air quality monitoring equipment are prohibitively expensive due to equipment costs, labor requirements, and infrastructure needs. As a result, alternative lower-cost methods that reliably determine air quality levels near potent pollution sources such as freeways are desirable. We present an approach that couples noise frequency measurements with machine learning to estimate near-roadway particulate matter (PM2.5), nitrogen dioxide (NO2), and black carbon (BC) at 1-min temporal resolution. The models were based on data collected by co-located noise and air quality instruments near a busy freeway in Long Beach, California. Model performance was excellent for all three pollutants, e.g., NO2 predictions yielded Pearson\'s R = 0.87 with a root mean square error of 7.2 ppb; this error represents about 10 % of total morning rush hour concentrations. Among the best air pollutant predictors were noise frequencies at 40 Hz, 500 Hz, and 800 Hz, and meteorology, particularly wind direction. Overall, our method potentially provides a cost-effective and efficient approach to estimating and/or supplementing near-road air pollutant concentrations in urban areas at high temporal resolution.