Search Results (31)

This study aims to decrease the number of drunk drivers, a significant social problem. Traditional methods to measure alcohol intake include blood alcohol concentration (BAC) and breath alcohol concentration (BrAC) tests. While BAC testing requires blood samples and is impractical, BrAC testing is commonly used in drunk driving enforcement. In this study, the multiple biological signals of electrocardiogram (ECG), photoplethysmogram (PPG), and electrodermal activity (EDA) were collected non-invasively and with minimal driver restraint in a driving simulator. Data were collected from 10 participants for approximately 10 min at BrAC levels of 0.00%, 0.03%, and 0.08%, which align with the latest Korean drunk driving standards. The collected data underwent frequency filtering and were segmented into 30 s intervals with a 10 s overlap to extract heart rate variability (HRV) and pulse arrival time (PAT). Using more than 10 machine learning algorithms, the classification accuracy reached 88%. The results indicate that it is possible to classify a driver’s level of intoxication using only non-invasive biological signals within a short period of about 30 s, potentially aiding in the prevention of drunk driving. Full article

Illicit drug use is common among attendees of electronic dance music (EDM) festivals, but is often significantly underreported by participants. The current study aimed to compare the prevalence and over- and under-reporting of illicit drug use among attendees at EDM festivals in two European countries with distinct drug laws and cultures. Self-reported data regarding recent drug use were collected through interviews. Participants’ blood alcohol concentrations were measured using a breathalyzer. Recent illicit drug use was assessed through sampling microparticles in the breath and consequent off-site analysis through liquid chromatography and tandem mass spectroscopy. Illicit drug use was higher in Belgium than in Sweden as indicated by self-reports (56.8 vs. 4.3%) and drug testing (37.2 vs. 12.5%). Underreporting was higher in Sweden than in Belgium; in Sweden, only 2.6% reported taking an illicit drug other than cannabis, whereas 11.6% tested positive, while the corresponding figures in Belgium were 36.5% and 36.9%. In both countries, results from self-reporting and drug testing for specific drugs matched poorly at the individual level, indicating unwitting consumption of substances. This study indicates that the drug use prevalence and the likelihood of disclosure may differ between countries or cultures, which should be considered when choosing methods to investigate drug use prevalence. Full article

Microplastics (MPs) are prevalent in our environment, being present in the air we breathe and in the food we consume. Due to the widespread use of plastic materials in everyday life, the amount of microplastics being released into the biosphere has become increasingly apparent in recent years. This study provides the first documentation of the presence of microplastics in Chinese baijiu, a popular alcoholic beverage in China, bottled in plastic. It is essential to note the significance of this discovery and potential implications for human health. Analysis of samples collected from the Chinese market showed the presence of microplastics in all six Chinese baijiu brands tested. Concentrations ranged from 172 MPs/500 mL to 944 MPs/500 mL with an average concentration of 436 MPs/500 mL. Cellulose and PA accounted for 70.4% and 17.8% of all microplastics detected, respectively. These were the most commonly detected types. Other types of microplastics were also found, including PET, PP, PVC, and PE. PET accounted for 2.7%, PP for 2.2%, PVC for 1.2%, and PE for 0.1% of all microplastics. The possible sources of contamination include raw materials, ambient air, and equipment and vessels that shed microplastics. Therefore, this study emphasizes the requirement for further research to mitigate the potential hazards associated with human exposure to microplastics. Additionally, it presents significant findings on the presence of microplastics in Chinese baijiu sold in plastic bottles. Full article

Auto-brewery syndrome (ABS), also called gut fermentation syndrome, is an extremely infrequent but also underrecognized disorder where ethanol is produced endogenously, similar to a typical bioreactor. The reliability of forensic alcohol analysis results is frequently challenged as the ethanol concentration in the breath, blood, and/or urine constitutes important evidence for prosecuting drivers under the influence of the alcohol. This further emphasizes the need to understand ABS, as in legal proceedings it is often presented as grounds for acquittal due to the concept that the findings could have corresponded to endogenously produced ethanol. However, this rare and underdiagnosed medical condition should not be considered as purely a lawyer’s favorite argument. Manifestations of ABS can have a severe impact on a patient’s life and pose social consequences as well. Unfortunately, barely anything has been unearthed, and aspects such as genetic susceptibility, gut-mucus-eating microorganisms, and fecal microbiome transplantation were reviewed for the first time in this context. The framework of this review was not limited to the gut microbiota exclusively; moreover, the overgrowth of microorganisms is linked to the use of antibiotics. Studies have indicated that carbohydrate fermentation occurs in locations other than in intra-intestinal flora. Accordingly, the literature was searched for cases of patients with ABS with yeast infections in their genitourinary or oral systems. Full article

With the increases in work environment regulations restricting alcohol to 1000 ppm, and in drink-driving laws, testing for alcohol with a simple method is a crucial issue. Conventional alcohol sensors based on sulfide, metal oxide, boron nitride or graphene oxide have a detection limit in the range of 50–1000 ppm but have disadvantages of complicated manufacture and longer processing times. A recent portable alcohol meter based on semiconductor material using conductivity or chemistry measurements still has the problem of a complex and lengthy manufacturing process. In this paper, a simple and effective resistive-type alcohol vapor sensor using one-step anodic aluminum oxide (AAO) is proposed. The nanoporous AAO was produced in one-step by anodizing low-purity AA1050 at room temperature of 25 °C, which overcame the traditional high-cost and lengthy process at low temperature of anodization and etching from high-purity aluminum. The highly specific surface area of AAO has benefits for good sensing performance, especially as a humidity or alcohol vapor sensor. With the resistance measurement method, alcohol vapor concentration of 0, 100, 300, 500, 700 and 1000 ppm correspond to mean resistances of 8524 Ω, 8672 Ω, 9121 Ω, 9568 Ω, 10,243 Ω, and 11,045 Ω, respectively, in a linear relationship. Compared with other materials for detecting alcohol vapor, the AAO resistive sensor has advantages of fast and simple manufacturing with good detection limits for practical applications. The resistive-type alcohol vapor-sensing mechanism is described with respect to the resistivity of the test substance and the pore morphology of AAO. In a human breath test, the AAO sensor can quickly distinguish whether the subject is drinking, with normal breath response of −30% to −40% and −20% to −30% response after drinking 50 mL of wine of 25% alcohol. Full article

Although alcohol consumption may produce effects that can be beneficial or harmful, alcohol consumption prevails among communities around the globe. Additionally, alcohol consumption patterns may be associated with several factors among communities and individuals. Numerous technologies and methods are implemented to enhance the detection and tracking of alcohol consumption, such as vehicle-integrated and wearable devices. In this paper, we present a cellular-based Internet of Things (IoT) implementation in a breath analyzer to enable data collection from multiple users via a single device. Cellular technology using hypertext transfer protocol (HTTP) was implemented as an IoT gateway. IoT integration enabled the direct retrieval of information from a database relative to the device and direct upload of data from the device onto the database. A manually developed threshold algorithm was implemented to quantify alcohol concentrations within a range from 0 to 200 mcg/100 mL breath alcohol content using electrochemical reactions in a fuel-cell sensor. Two data collections were performed: one was used for the development of the model and was split into two sets for model development and on-machine validation, and another was used as an experimental verification test. An overall accuracy of 98.16% was achieved, and relative standard deviations within the range from 1.41% to 2.69% were achieved, indicating the reliable repeatability of the results. The implication of this paper is that the developed device (an IoT-integrated breath analyzer) may provide practical assistance for healthcare representatives and researchers when conducting studies involving the detection and data collection of alcohol consumption patterns. Full article

Background: Experiences of unwanted sexual attention (UWSA) are commonplace within nightlife environments. While typically associated with aggression perpetration, literature has suggested that a history of childhood corporal punishment (CCP) may also be related to experiences of victimisation in nightlife environments. The current exploratory study aims to examine the associations between experiences of UWSA victimisation and a history of CCP, trait aggression, and conformity to masculine norms (Playboy and Winning), for males and females separately. Method: Street intercept interviews in the Brisbane inner-city entertainment precincts were used to measure demographic details and participants’ breath alcohol concentration. Online follow-up surveys were used to record participants’ experiences of UWSA on the night of interview, history of CCP, and self-reported rates of trait aggression and conformity to masculine norms. The final sample consisted of 288 females, as there were not sufficient male UWSA experiences for analysis. Results: Approximately 20% of female nightlife patrons experienced some form of UWSA victimisation. Logistic regression analyses identified that after controlling for age and intoxication, a history of CCP, trait aggression and masculine norm conformity were unrelated to experiences of UWSA for female respondents. Conclusions: The current study found that individual factors were unrelated to experiences of UWSA, indicating that simply being in the nightlife environment, especially as a female, increases the risk of UWSA victimisation. Understanding and exploring social and environmental risk factors, rather than individual factors, is needed to prevent victimisation in nightlife environments. Full article

Psychological stress exposure is associated with long-lasting health effects including memory problems, depression, aches and pains, eating disorders, and alcohol or drug use. Thus, there is a need to develop effective stress management strategies that are easy to learn and practice. Respiratory biofeedback is an evidence-based stress management technique presenting breathing-related information to help subjects learn specific breathing skills for relaxation. It is suggested that the use of biofeedback techniques in conjunction with virtual reality makes biofeedback training an even more effective tool for stress management. The current study aimed to investigate dynamics of distinct stress indicators before, after, as well as during one brief virtual reality-based respiratory biofeedback session. Thirty-nine healthy volunteers participated in the study. Individuals provided their saliva samples and evaluated their mood status, fatigue, and strain level before and after the session. The subjects’ heart and respiratory rate, heart rate variability, and galvanic skin response measures were recorded during the session. The results showed that after single 12 min relaxation session, there was a significant decrease in salivary cortisol concentration, heart and respiratory rate, as well as decrease in skin conductance values. Self-reported strain, fatigue level, and mood status also significantly improved. VR-based respiratory-biofeedback-assisted relaxation sessions might serve as an effective stress management strategy, as even single session had positive effects on subjects’ autonomic nervous system (ANS) and hypothalamic-pituitary–adrenal (HPA) axis activity, as well as self-reported fatigue, strain level, and mood status. Full article

Volatile organic compounds (VOCs) are a differentiated class of molecules, continuously generated in the human body and released as products of metabolic pathways. Their concentrations vary depending on pathophysiological conditions. They are detectable in a wide variety of biological samples, such as exhaled breath, faeces, and urine. In particular, urine represents an easily accessible specimen widely used in clinics. The most used techniques for VOCs detections are expensive and time-consuming, thus not allowing for rapid clinical analysis. In this perspective, the aim of this study is a comprehensive characterisation of the urine volatilome by the development of an alternative rapid analytical method. Briefly, 115 urine samples are collected; sample treatment is not needed. VOCs are detected in the urine headspace using gas chromatography coupled to ion mobility spectrometry (GC–IMS) by an extremely fast analysis (10 min). The method is analytically validated; the analysis is sensitive and robust with results comparable to those reported with other techniques. Twenty-three molecules are identified, including ketones, aldehydes, alcohols, and sulphur compounds, whose concentration is altered in several pathological states such as cancer and metabolic disorders. Therefore, it opens new perspectives for fast diagnosis and screening, showing great potential for clinical applications. Full article

Breathalyzer is a common approach to measuring blood alcohol concentration (BAC) levels of individuals suspected of drunk driving. Nevertheless, this device is relatively high-cost, inconvenient for people with limited breathing capacity, and risky for COVID-19 exposure. Here, we designed and developed a smart wristband integrating a real-time noninvasive sweat alcohol metal oxide (MOX) gas sensor with a Drunk Mate, an Internet of Thing (IoT)-based alarming system. A MOX sensor acquired transdermal alcohol concentration (TAC) which was converted to BAC and sent via the IoT network to the Blynk application platform on a smartphone, triggering alarming messages on LINE Notify. A user would receive an immediate alarming message when his BAC level reached an illegal alcohol concentration limit (BAC 50 mg%; TAC 0.70 mg/mL). The sensor readings showed a high linear correlation with TAC (R² = 0.9815; limit of detection = 0.045 mg/mL) in the range of 0.10–1.05 mg/mL alcohol concentration in artificial sweat, achieving an accuracy of 94.66%. The sensor readings of ethanol in water were not statistically significantly different (p > 0.05) from the measurements in artificial sweat and other sweat-related solutions, suggesting that the device responded specifically to ethanol and was not affected by other electrolytes in the artificial sweat. Moreover, the device could continuously monitor TAC levels simulated in real-time in an artificial sweat testing system. With the integration of an IoT-based alarming system, the smart wristband developed from a commercial gas sensor presented here offers a promising low-cost MOX gas sensor monitoring technology for noninvasive and real-time sweat alcohol measurement and monitoring. Full article

Introduction and aims: Associations between bar trading hours, a government lever for controlling alcohol availability, nightlife-goer intoxication levels and their likelihood of alcohol use disorder (AUD) have not been explored. We investigated whether: (i) participant AUD was associated with blood alcohol concentration (BAC); and, (ii) any association between AUD and BAC was moderated by participant preferred bar (i.e., venue spent most time at) closing time. Design and methods: A cross-sectional observational study using a sample of nightlife-goers who went out drinking in Perth, Western Australia, on weekends in 2015-16. Participants who reported alcohol use that night and spent most time in a bar (n = 667) completed street intercept surveys including AUDIT-C (n = 459) and provided a breath sample to estimate BAC (n = 651). We used gender-specific multinomial logistic regression models to explore associations between participant AUDIT-C score (1–4, lower risk; 5–7, hazardous; 8–12, active AUD), preferred bar type (standard vs. late closing time based on absence or presence of an extended trading permit) and BAC (male: 0–0.049, 0.05–0.099, ≥0.1 g/100 mL; female: 0–0.049, 0.05–0.079, ≥0.08 g/100 mL). Results: Males with active AUD (RR = 3.31; 95% CI 1.30–8.42; p = 0.01) and females with hazardous/active AUD (RR = 9.75; 95% CI 2.78–34.21; p < 0.001) were both more likely to have high-range BAC than their counterparts typically drinking at lower risk. We also found preferred bar type moderated the association between AUDIT-C score and BAC for some males but no females. Males with active AUD and high-range BAC were less likely to prefer late closing bars than males usually drinking at lower risk and high-range BAC (RR = 0.12; 95% CI 0.02–0.96; p = 0.046). Discussion and conclusions: Our study provides evidence of positive associations between AUD and acute intoxication among nightlife-goers and on the moderating effect of bar closing times among males. Full article

Alcohol hangover (AH) has been associated with poor sleep due to the negative effects of alcohol intoxication on sleep quantity and sleep quality. The aim of the current study was to further explore the relationship between AH severity and sleep using a naturalistic study design. A further aim was to determine whether quantitative aspects of sleep were a mediating influence on the relationship between AH severity and cognitive performance. As part of the naturalistic study design, 99 drinkers were recruited following a night of drinking in an Australian state capital, with breath alcohol concentration (BrAC) measured as participants were leaving the entertainment district. The following morning at home, participants answered online questions regarding their drinking behaviour on the previous evening, current AH symptoms and sleep quality. Participants also completed an online version of the Trail-Making Test B (TMT-B) to assess cognitive performance. The findings reveal the duration of nightly awakenings to be negatively related to six individual AH symptoms as well as overall AH severity. The number of nightly awakenings, sleep quality and total sleep time correlated with four AH symptoms including overall AH severity. Total AH severity accounted for a moderate amount of variance (11%) in the time to complete the TMT-B. These findings confirm that alcohol consumption negatively affects sleep, which is related to higher next-day hangover severity ratings and poorer cognitive performance. Full article

In this paper, we report the fabrication and characterization of a portable transdermal alcohol sensing device via a human finger, using tin dioxide (SnO₂) chemoresistive gas sensors. Compared to conventional detectors, this non-invasive technique allowed us the continuous monitoring of alcohol with low cost and simple fabrication process. The sensing layers used in this work were fabricated by using the reactive radio frequency (RF) magnetron sputtering technique. Their structure and morphology were investigated by means of X-ray spectroscopy (XRD) and scanning electron microscopy (SEM), respectively. The results indicated that the annealing time has an important impact on the sensor sensitivity. Before performing the transdermal measurements, the sensors were exposed to a wide range of ethanol concentrations and the results displayed good responses with high sensitivity, stability, and a rapid detection time. Moreover, against high relative humidity (50% and 70%), the sensors remained resistant by showing a slight change in their gas sensing performances. A volunteer (an adult researcher from our volunteer group) drank 50 mL of tequila in order to realize the transdermal alcohol monitoring. Fifteen minutes later, the volunteer’s skin started to evacuate alcohol and the sensor resistance began to decline. Simultaneously, breath alcohol measurements were attained using a DRAGER 6820 certified breathalyzer. The results demonstrated a clear correlation between the alcohol concentration in the blood, breath, and via perspiration, which validated the embedded transdermal alcohol device reported in this work. Full article

Auto-brewery syndrome (ABS) is a rare, unstudied, unknown, and underreported phenomenon in modern medicine. Patients with this syndrome become inebriated and may suffer the medical and social implications of alcoholism, including arrest for inebriated driving. The pathophysiology of ABS is reportedly due to a fungal type dysbiosis of the gut that ferments some carbohydrates into ethanol and may mimic a food allergy or intolerance. This syndrome should be considered in patients with chronic obstruction or hypomotility presenting with elevated breath and blood alcohol concentrations, especially after a high carbohydrate intake. A glucose challenge test should be performed as the confirmatory test. Treatment typically includes antifungal drugs combined with changes in lifestyle and nutrition. Additional studies are particularly needed on the human microbiome to shed light on how imbalances of commensal bacteria in the gut allow yeast to colonize on a pathological level. Full article

Background: Alcohol-related road-traffic injury is the leading cause of premature death in middle- and lower-income countries, including Thailand. Applying machine-learning algorithms can improve the effectiveness of driver-impairment screening strategies by legal limits. Methods: Using 4794 RTI drivers from secondary cross-sectional data from the Thai Governmental Road Safety Evaluation project in 2002–2004, the machine-learning models (Gradient Boosting Classifier: GBC, Multi-Layers Perceptrons: MLP, Random Forest: RF, K-Nearest Neighbor: KNN) and a parsimonious logistic regression (Logit) were developed for predicting the mortality risk from road-traffic injury in drunk drivers. The predictors included alcohol concentration level in blood or breath, driver characteristics and environmental factors. Results: Of 4974 drivers in the derived dataset, 4365 (92%) were surviving drivers and 429 (8%) were dead drivers. The class imbalance was rebalanced by the Synthetic Minority Oversampling Technique (SMOTE) into a 1:1 ratio. All models obtained good-to-excellent discrimination performance. The AUC of GBC, RF, KNN, MLP, and Logit models were 0.95 (95% CI 0.90 to 1.00), 0.92 (95% CI 0.87 to 0.97), 0.86 (95% CI 0.83 to 0.89), 0.83 (95% CI 0.78 to 0.88), and 0.81 (95% CI 0.75 to 0.87), respectively. MLP and GBC also had a good model calibration, visualized by the calibration plot. Conclusions: Our machine-learning models can predict road-traffic mortality risk with good model discrimination and calibration. External validation using current data is recommended for future implementation. Full article