Pneumonia, the leading reason underlying childhood deaths, may be triggered or exacerbated by air pollution. To date, only a few studies have examined the association of air pollution with emergency department (ED) visits for pediatric pneumonia, with inconsistent results. Therefore, we aimed to elucidate the impact of short-term exposure to particulate matter (PM) and other air pollutants on the incidence of ED visits for pediatric pneumonia. PM2.5, PM10, and other air pollutant levels were measured at 11 air quality-monitoring stations in Kaohsiung City, Taiwan, between 2008 and 2014. Further, we extracted the medical records of non-trauma patients aged ≤17 years and who had visited an ED with the principal diagnosis of pneumonia. A time-stratified case–crossover study design was employed to determine the hazard effect of air pollution in a total of 4024 patients. The single-pollutant model suggested that per interquartile range increment in PM2.5, PM10, nitrogen dioxide (NO2), and sulfur dioxide (SO2) on 3 days before the event increased the odds of pediatric pneumonia by 14.0% [95% confidence interval (CI), 5.1–23.8%], 10.9% (95% CI, 2.4–20.0%), 14.1% (95% CI, 5.0–24.1%), and 4.5% (95% CI, 0.8–8.4%), respectively. In two-pollutant models, PM2.5 and NO2 were significant after adjusting for PM10 and SO2. Subgroup analyses showed that older children (aged ≥4 years) were more susceptible to PM2.5 (interaction p = 0.024) and children were more susceptible to NO2 during warm days (≥26.5 °C, interaction p = 0.011). Short-term exposure to PM2.5 and NO2 possibly plays an important role in pediatric pneumonia in Kaohsiung, Taiwan. Older children are more susceptible to PM2.5, and all children are more susceptible to NO2 during warm days.

Exposure to fine particulate matter with an aerodynamic diameter of ≤2.5 μm (PM_2.5) is associated with adverse health effects. This study aimed to evaluate the toxic effects of the constituents of PM_2.5 on mouse kidneys. We collected PM_2.5 near an industrial complex located in southern Kaohsiung, Taiwan, that was divided into water extract and insoluble particles. Male C57BL/6 mice were divided into five groups: control, low- and high-dose insoluble particle exposure, and low- and high-dose water extract exposure. Biochemical analysis, Western blot analysis, histological examination, and immunohistochemistry were performed to evaluate the impact of PM_2.5 constituents on mice kidneys. PM_2.5 was collected from January 1, 2021, to February 8, 2021, from an industrial complex in Kaohsiung, Taiwan. Metallic element analysis showed that Pb, Ni, V, and Ti were non-essential metals with enrichment factors >10. Polycyclic aromatic hydrocarbon and nitrate polycyclic aromatic hydrocarbon analyses revealed that the toxic equivalents are, in the order, benzo(a)pyrene (BaP), indeno(1,2,3-cd) pyrene (IP), dibenzo(a,h)anthracene (DBA), and benzo(b)fluoranthene (BbF), which are potential carcinogens. Both water extract and insoluble particle exposure induced inflammatory cytokine upregulation, inflammatory cell infiltration, antioxidant activity downregulation, and elevation of kidney injury molecule 1 (KIM-1) level in mouse kidneys. A dose-dependent effect of PM_2.5 water extract and insoluble particle exposure on angiotensin converter enzyme 2 downregulation in mouse kidneys was observed. We found that water-soluble extract and insoluble particles of PM_2.5 could induce oxidative stress and inflammatory reactions, influence the regulation of renin-angiotensin system (RAS), and lead to kidney injury marker level elevation in mouse kidneys. The lowest-observed-adverse-effect level for renal toxicity in mice was 40 μg water-soluble extract/insoluble particle inhalation per week, which was approximately equal to the ambient PM_2.5 concentration of 44 μg/m³ for mice.

Background: The prognosis of out-of-hospital cardiac arrest (OHCA) is very poor. While several prehospital factors are known to be associated with improved survival, the impact of prehospital factors on different age groups is unclear. The objective of the study was to access the impact of prehospital factors and pre-existing comorbidities on OHCA outcomes in different age groups. Methods: A retrospective observational analysis was conducted using the emergency medical service (EMS) database from January 2015 to December 2019. We collected information on prehospital factors, underlying diseases, and outcome of OHCAs in different age groups. Kaplan-Meier type survival curves and multivariable logistic regression were used to analyze the association between modifiable pre-hospital factors and outcomes. Results: A total of 4188 witnessed adult OHCAs were analyzed. For the age group 1 (age ≦75 years old), after adjustment for confounding factors, EMS response time (odds ratio [OR] = 0.860, 95% confidence interval [CI]: 0.811–0.909, p < 0.001), public location (OR = 1.843, 95% CI: 1.179–1.761, p < 0.001), bystander CPR (OR = 1.329, 95% CI: 1.007–1.750, p = 0.045), attendance by an EMT-Paramedic (OR = 1.666, 95% CI: 1.277–2.168, p < 0.001), and prehospital defibrillation by automated external defibrillator (AED)(OR = 1.666, 95% CI: 1.277–2.168, p < 0.001) were prognostic factors for survival to hospital discharge in OHCA patients. For the age group 2 (age > 75 years old), age (OR = 0.924, CI:0.880–0.966, p = 0.001), EMS response time (OR = 0.833, 95% CI: 0.742–0.928, p = 0.001), public location (OR = 4.290, 95% CI: 2.450–7.343, p < 0.001), and attendance by an EMT-Paramedic (OR = 2.702, 95% CI: 1.704–4.279, p < 0.001) were independent prognostic factors for survival to hospital discharge in OHCA patients. Conclusions: There were variations between younger and older OHCA patients. We found that bystander CPR and prehospital defibrillation by AED were independent prognostic factors for younger OHCA patients but not for the older group.

Background: Hip fracture is the most common type of fracture in elderly individuals. Numerous deep learning (DL) algorithms for plain pelvic radiographs (PXRs) have been applied to improve the accuracy of hip fracture diagnosis. However, their efficacy is still undetermined. Objective: The objective of this study is to develop and validate a human-algorithm integration (HAI) system to improve the accuracy of hip fracture diagnosis in a real clinical environment. Methods: The HAI system with hip fracture detection ability was developed using a deep learning algorithm trained on trauma registry data and 3605 PXRs from August 2008 to December 2016. To compare their diagnostic performance before and after HAI system assistance using an independent testing dataset, 34 physicians were recruited. We analyzed the physicians’ accuracy, sensitivity, specificity, and agreement with the algorithm; we also performed subgroup analyses according to physician specialty and experience. Furthermore, we applied the HAI system in the emergency departments of different hospitals to validate its value in the real world. Results: With the support of the algorithm, which achieved 91% accuracy, the diagnostic performance of physicians was significantly improved in the independent testing dataset, as was revealed by the sensitivity (physician alone, median 95%; HAI, median 99%; P<.001), specificity (physician alone, median 90%; HAI, median 95%; P<.001), accuracy (physician alone, median 90%; HAI, median 96%; P<.001), and human-algorithm agreement [physician alone κ, median 0.69 (IQR 0.63-0.74); HAI κ, median 0.80 (IQR 0.76-0.82); P<.001. With the help of the HAI system, the primary physicians showed significant improvement in their diagnostic performance to levels comparable to those of consulting physicians, and both the experienced and less-experienced physicians benefited from the HAI system. After the HAI system had been applied in 3 departments for 5 months, 587 images were examined. The sensitivity, specificity, and accuracy of the HAI system for detecting hip fractures were 97%, 95.7%, and 96.08%, respectively. Conclusions: HAI currently impacts health care, and integrating this technology into emergency departments is feasible. The developed HAI system can enhance physicians’ hip fracture diagnostic performance.

Background: Air pollution exposure is associated with greater risk for cardiovascular events. This study aims to examine the effects of increased exposure to short-term air pollutants on ST-segment elevation myocardial infarction (STEMI) and determine the susceptible groups. Methods: Data on particulate matter PM2.5 and PM10 and other air pollutants, measured at each of the 11 air-quality monitoring stations in Kaohsiung City, were collected between 2011 and 2016. The medical records of non-trauma adult (>17 years) patients who had visited the emergency department (ED) with a typical electrocardiogram change of STEMI were extracted. A time-stratified and case-crossover study design was used to examine the relationship between air pollutants and daily ED visits for STEMI. Results: An interquartile range increment in PM2.5 on lag 0 was associated with an increment of 25.5% (95% confidence interval, 2.6%–53.4%) in the risk of STEMI ED visits. Men and persons with ≥3 risk factors (male sex, age, hypertension, diabetes, current smoker, dyslipidemia, history of myocardial infarction, and high body mass index) for myocardial infarction (MI) were more sensitive to the hazardous effects of PM2.5 (interaction: p = 0.039 and p = 0.018, respectively). The associations between PM10, NO2, and O3 and STEMI did not achieve statistical significance. Conclusion: PM2.5 may play an important role in STEMI events on the day of exposure in Kaohsiung. Men and persons with ≥3 risk factors of MI are more susceptible to the adverse effects of PM2.5 on STEMI.

Emergency care is a critical area of medicine whose outcomes are influenced by the time, availability, and accuracy of contextual information. The success of critical or emergency care is determined by the quality and accuracy of the information received during the emergency call and the data collected during emergency transportation. The Internet of Things (IoT) consists of many smart devices and components that communicate via their connection to the Internet, which is used to collect data with sensors that obtain personal health parameters. In the past, most health measurement systems were based on a single dedicated orientation, and few systems had multiple devices on the same platform. In addition to traditional health measurement technologies, most such systems use centralized data transmission, which means that health measurement data have become the exclusive intellectual asset of the system developer. Therefore, this study develops an IoT-based message-broker system that is deployed and demonstrated for five health devices: blood oxygen, blood pressure, forehead temperature, body temperature, and body weight sensors. A central controller accessed by radio-frequency identification (RFID) collects clients’ health profiles on the cloud platform. All collected data can be quickly shared, analyzed, and visualized, and the health devices can be changed, added to, and removed reliably when the requirements change. Additionally, following the message queuing telemetry transport (MQTT) protocol, all devices can communicate with each other and be integrated into a higher-level health measurement standard (such as blood pressure plus weight or body temperature plus blood oxygen). We implement a smart healthcare monitoring system (SHMS) and verify its reliability. We use MQTT to establish an open communication format that other organizations can follow to perform individual patient vital sign monitoring in potential applications. The robustness and flexibility of this research can be verified through the addition of other systems. Through this structure, more large-scale health detection devices can be integrated into the method proposed in this research in the future. Personal RFID or health insurance cards can be used for personal services or in medical institutions, and the data can easily be shared through the mechanism of this research. Such information sharing will enable the utilization of medical resources to be maximized.