搜索结果：Alcohol and alcoholism (Oxford, Oxfordshire)

Photonics/light localization techniques are important in understanding the structural changes in biological tissues at the nano- to sub-micron scale. It is now known that structural alteration starts at the nanoscale at the beginning of cancer progression. This study examines the molecular-specific nano-structural alterations of chronic alcoholism and probiotic effects on colon cancer using a mouse model of colon cancer. Confocal microscopy and mesoscopic light-scattering analysis are applied to quantify structural changes in DNA (chromatin), cytoskeleton, and ki-67 protein cells with appropriate staining dyes. We assessed alcohol-treated and azoxymethane (AOM) with dextran sulfate sodium (DSS)-induced colitis models, including ethanol (EtOH) and probiotic (L.Casei) treatments separately and together. The inverse participation ratio (IPR) technique was employed to quantify the degree of light localization to access the molecular-specific spatial structural disorder as a biomarker for cancer progression detection. Significant enhancement of cancer progression was observed in the alcohol-treated group, and probiotics treatment with alcohol showed partial reversal of these changes in

Alcoholism is one of the most common diseases in the world. This type of substance abuse leads to mental and physical dependence on ethanol-containing drinks. Alcoholism is accompanied by progressive degradation of the personality and damage to the internal organs. Today still not exists a quick diagnosis method to detect this disease. This article presents the method for the quick and anonymous alcoholism diagnosis by neural networks. For this method, don't need any private information about the subject. For the implementation, we considered various algorithms of machine learning and deep neural networks. In detail analyzed the correlation of the signals from electrodes by neural networks. The wavelet transforms and the fast Fourier transform was considered. The manuscript demonstrates that the deep neural network which operates only with a dataset of EEG correlation signals can anonymously classify the alcoholic and control groups with high accuracy. On the one hand, this method will allow subjects to be tested for alcoholism without any personal data, which will not cause inconvenience or shame in the subject, and on the other hand, the subject will not be able to deceive specia

Electroencephalography provides a non-invasive and cost-effective approach for analyzing neural patterns associated with alcohol dependence. However, reported classification performance in EEG-based alcoholism studies varies considerably, often due to differences in validation strategies rather than intrinsic model capability. This study presents a validation-aware machine learning framework to assess the impact of evaluation methodology on classification performance. A balanced multi-channel EEG dataset of 300 trials (150 alcoholic, 150 control) was analyzed using a structured feature representation combining statistical descriptors and spectral band interactions. Five classifiers, including support vector machines (linear and radial basis function kernels), random forest, k-nearest neighbors, and AdaBoost, were evaluated under standard and nested cross-validation protocols. Results show that conventional validation with global hyperparameter tuning introduces optimistic bias. In particular, SVM with radial basis function kernel exhibited a performance decrease of approximately 5\% under nested cross-validation, indicating overestimation. Ensemble-based methods showed more stable

Prolonged alcohol use results in neuroadaptations that mark more severe and treatment-resistant alcohol use. The goal of this study was to identify functional connectivity brain patterns underlying Alcohol Use Disorder (AUD)-related characteristics in fifty-five adults (31 female) who endorsed heavy alcohol use. We hypothesized that resting-state functional connectivity (rsFC) of the Salience (SN), Frontoparietal (FPN), and Default Mode (DMN) networks would reflect self-reported recent and lifetime alcohol use, laboratory-based alcohol seeking, urgency, and sociodemographic characteristics related to AUD. To test our hypothesis, we combined the triple network model (TNM) of psychopathology with a multivariate data-driven approach, regularized partial least squares (rPLS), to unfold concurrent functional connectivity (FC) patterns and their association with AUD-related characteristics. We observed three concurrent associations of interest: i) drinking and age-related cross communication between the SN and both the FPN and DMN; ii) family history density of AUD and urgency anticorrelations between the SN and FPN; and iii) alcohol seeking and sex-associated SN and DMN interactions. Th

Exploring the impact of alcohol additives on combustion and pyrolysis of ammonia/methane is of great importance in the pursuit of sustainable energy technologies. This work employs Reactive Force Field (ReaxFF) molecular dynamics (MD) simulations to investigate the underlying mechanism of how ethanol and methanol additives affect reaction pathways, NOx emissions and bond energy characteristics in ammonia-methane pyrolysis and combustion processes. It shows that adding alcohols altered NOx formation pathways, reducing the diversity of NOx and shifting the equilibrium toward simpler NOx such as NO and NO2. At 2,000 K, alcohol blends, particularly methanol, demonstrated a notable reduction in NO2 formation. At 3,000 K, both ethanol and methanol suppressed NO production, but the influence of methanol was stronger. Nitric acid production, HNO3, was present at lower temperatures but became negligible at higher temperatures because of the thermal breakdown of the higher-order NOx. These trends confirm that alcohol additives realize a probable role in moderating NOx emissions and stabilizing reaction pathways. The pyrolysis in modified reaction pathways, which facilitated the decomposition

Two dimensional site interaction models of water and alcohols are mixed in various proportions and studied by Monte Carlo simulations, with the purpose to clarify problems related to simulation of real micro-heterogeneous systems. Three alcohols are considered, methanol, pentanol and octanol. The main finding is that, while real alcohols demix with water from butanol onward, their 2D analogs are always fully miscible, while developing increasingly pronounced micro-segregation as the alcohol tail length increases. This is not a consequence of the intrinsically higher fluctuations in 2D, but rather a reorganization of these fluctuations under the charge ordering mechanism. The second finding is that water drives the micro-segregation through strong self-aggregation, but this is not enough to achieve full phase separation because of the water-alcohol contact at the outer rim of the water domains. In this work we examine how this local heterogeneity develops with increasing alcohol alkyl tails, monitored with the study of pair correlation functions, structure factors and Kirkwood-Buff integrals. The absence of clear local self-averaging of the latter provides an illustration of the ten

Substance use disorders (SUDs) are a serious public health concern in the United States. Alcohol and cannabis are two of the most widely used substances. For adolescent/youth users of alcohol or cannabis, we propose a joint Bayesian learning model to predict their risks of developing alcohol use disorder (AUD) and cannabis use disorder (CUD) in adulthood based on their personal risk factors. The model is trained on nationally representative longitudinal data from Add Health (n = 12503). It consists of sub-models that predict the two SUDs for three groups of users-those who use alcohol only, cannabis only, and both substances - based on shared as well as unique risk factors. The model comprises of ten predictors. We externally validate the model on two independent datasets. The areas under the receiver operating characteristic curves for AUD and CUD, respectively, are: (a) 0.719 and 0.690 based on 5-fold cross-validation, (b) 0.748 and 0.710 based on validation dataset 1, and (c) 0.650 and 0.750 based on validation dataset 2. A simulation study shows that the proposed joint modeling approach generally performs better than separate univariate modeling of the corresponding dependent o

Reforming alcohol price regulations in wine-producing countries is challenging, as current price regulations reflect the alignment of cultural preferences with economic interests rather than public health concerns. We evaluate and compare the impact of counterfactual alcohol pricing policies on consumer behaviors, firms, and markets in France. We develop a micro-founded partial equilibrium model that accounts for consumer preferences over purchase volumes across alcohol categories and over product quality within categories, and for firms' strategic price-setting. After calibration on household scanner data, we compare the impacts of replacing current taxes by ethanol-based volumetric taxes with a minimum unit price (MUP) policy of 0.50 Euro per standard drink. The results show that the MUP in addition to the current tax outperforms a tax reform in reducing ethanol purchases (-15% vs. -10% for progressive taxation), especially among heavy drinking households (-17%). The MUP increases the profits of small and medium wine firms (+39%) while decreasing the profits of large manufacturers and retailers (-39%) and maintaining tax revenues stable. The results support the MUP as a targeted

Combined studies on fluorescence quantum yield in coenzymes NADH and FAD in water-methanol, water-ethanol and water-propylene glycol mixtures and on time-resolved fluorescence of the same molecules under excitation at 450 and 355~nm by means of the TCSPC method have been carried out. The dependence of quantum yield in NADH on alcohol concentration was found to be similar in methanol and ethanol and different from that in propylene glycol. The behavior of quantum yield in FAD was found to be almost independent of the type of alcohol and exhibited a dramatic 5--6 times increase with alcohol concentration. A model describing molecular excited state relaxation dynamics in pico- and nanosecond time domain was developed on the basis on the quantum mechanical theory and used for analysis of the experimental results. In particular, the model provided a new insight into the nature of the Decay Associated Spectra (DAS) phenomenon. The analysis of the role of pico- and nanosecond quenching in NADH suggested that the picosecond decay in methanol and ethanol likely does not occur through electron transfer in the stacking configuration of the nicotinamide and adenine moieties, but through other

Mesoscopic physics-based dual spectroscopic imaging techniques, partial wave spectroscopy (PWS) and inverse participation ratio (IPR), are used to quantify the nano to submicron scales structural alterations in postnatal pups brain cells and tissues due to fetal alcoholism. Chronic alcoholism during pregnancy, being teratogenic, results in fetal alcohol syndrome and neurological disorder. Results of PWS studies of brain tissues show a higher degree of structural alterations. Furthermore, the IPR analyses of cell nuclei show that spatial molecular mass density structural disorder increases in DNA while decreases for histone. This study characterize the brain spatial structures from molecular to tissue level in fetal alcoholism.

Alcohol Use Disorder (AUD) is a major concern for public health organizations worldwide, especially as regards the adolescent population. The consumption of alcohol in adolescents is known to be influenced by seeing friends and even parents drinking alcohol. Building on this fact, a number of studies into alcohol consumption among adolescents have made use of Social Network Analysis (SNA) techniques to study the different social networks (peers, friends, family, etc.) with whom the adolescent is involved. These kinds of studies need an initial phase of data gathering by means of questionnaires and a subsequent analysis phase using the SNA techniques. The process involves a number of manual data handling stages that are time consuming and error-prone. The use of knowledge engineering techniques (including the construction of a domain ontology) to represent the information, allows the automation of all the activities, from the initial data collection to the results of the SNA study. This paper shows how a knowledge model is constructed, and compares the results obtained using the traditional method with this, fully automated model, detailing the main advantages of the latter. In the

The paper proposes a novel approach towards identification of alcohol and drug induced people, through the use of a wearable bracelet.As alcohol and drug induced human people are in an unconscious state of mind, they need external help from the surroundings.With proposed Bracelet system we can identify the alcohol and drug indused people and warning trigger message is sent to their care takers. There is a definite relationship between an individual's Blood Alcohol Content (BAC) and Pulse Rate to identify the alcohol or drug consumed person .This relationship of pulse rate with BAC is sensed by piezoelectric sensor and warning system is developed as a Bracelet device . The viability of the Bracelet is verified by Simulating a Database of 199 People's BAC and Pulse Rate Features and classification is done among the Alcohol Induced and Normal People. For classification,Ensemble Boosted Tree Algorithm is used which is having 81.9% accuracy in decision.

Molecular specific photonic localization is a sensitive technique to probe the structural alterations or abnormalities in a cell such as abnormalities due to alcohol or other drugs. Alcohol consumption during pregnancy by mother, or fetal alcoholism, is one of the major factors of mental retardation in children. Fetal alcohol syndrome and alcohol related neurodevelopmental disorder are awful outcomes of the maternal alcohol consumption linked with notable cognitive and behavioral defects. Alcohol consumed by the pregnant mother, being teratogenic, interferes with the fetal health resulting brain damage and other birth defects. This might affect the brain cells at the very nanolevel which cannot be predicted by the present histopathological procedures. We perform quantification of nanoscale spatial structural alterations in two different spatial molecular components, DNA and histone molecular mass densities, in brain cell nuclei of fetal alcohol effected (FAE) pups at postnatal day 60. Confocal images of the brain cells are collected and the degree of morphological alterations in DNA and histone, in terms of mass density fluctuations are obtained using the recently developed molecul

Non-invasive continuous alcohol monitoring has potential applications in both population research and in clinical management of acute alcohol intoxication or chronic alcoholism. Current wearable monitors based on transdermal alcohol content (TAC) sensing are relatively bulky and have limited quantification accuracy. Here we describe the development of a discreet wearable transdermal alcohol (TAC) sensor in the form of a wristband or armband. This novel sensor can detect vapor-phase alcohol in perspiration from 0.09 ppm (equivalent to 0.09 mg/dL sweat alcohol concentration at 25 °C under Henry's Law equilibrium) to over 500 ppm at one-minute time resolution. The TAC sensor is powered by a 110 mAh lithium battery that lasts for over 7 days. In addition, the sensor can function as a medical "internet-of-things" (IoT) device by connecting to an Android smartphone gateway via Bluetooth Low Energy (BLE) and upload data to a cloud informatics system. Such wearable IoT sensors may enable large-scale alcohol-related research and personalized management. We also present evidence suggesting a hypothesis that perspiration rate is the dominant factor leading to TAC measurement variabilities, wh

Determination of the volume content of ethanol in the alcohol products in practice is usually determined by pycnometry, electronic densimetry, or densimetry using a hydrostatic balance in accordance with Commission Regulation No 2870/2000. However, these methods determine directly only density of the tested liquid sample and does not take into account the effects of other volatile components such as aldehydes, esters and higher alcohols. So they are appropriate only for binary water-ethanol solutions in accordance with international table adopted by the International Legal Metrology Organization in its Recommendation No 22. Availability notable concentrations of the higher alcohols and ethers in different alcohol-based products, e. g. in whisky, cognac, brandy, wine as well as in waste alcohol and alcohol beverage production, leads to the significant contribution of these compounds in the value of the density of tested alcohol-containing sample. As a result, determination of the volume of ethanol content for such alcohol products in gives the value of the strength, which may significantly differ from the true one. Using incorrectly calculated volume content of ethyl alcohol leads t

About 30% of all traffic crash fatalities in the United States involve drunk drivers, making the prevention of drunk driving paramount to vehicle safety in the US and other locations which have a high prevalence of driving while under the influence of alcohol. Driving impairment can be monitored through active use of sensors (when drivers are asked to engage in providing breath samples to a vehicle instrument or when pulled over by a police officer), but a more passive and robust mechanism of sensing may allow for wider adoption and benefit of intelligent systems that reduce drunk driving accidents. This could assist in identifying impaired drivers before they drive, or early in the driving process (before a crash or detection by law enforcement). In this research, we introduce a study which adopts a multi-modal ensemble of visual, thermal, audio, and chemical sensors to (1) examine the impact of acute alcohol administration on driving performance in a driving simulator, and (2) identify data-driven methods for detecting driving under the influence of alcohol. We describe computer vision and machine learning models for analyzing the driver's face in thermal imagery, and introduce a

Alcohol Use Disorder (AUD) is a prevalent addictive disorder affecting an estimated 29.5 million Americans. It is characterized by impaired control over alcohol consumption despite negative consequences. The number of diagnostic criteria met by an individual typically determines the severity of AUD. Research into AUD focuses on understanding individual susceptibility differences and developing preventive strategies. Alcohol vapor inhalation has emerged as a promising method for pathophysiological investigations in animals, allowing researchers to control the dose and duration of alcohol exposure. This approach is crucial for studying the escalation of voluntary alcohol-drinking behavior. Current commercial systems for alcohol vapor generation have limitations, including combustion risks and the need to adjust multiple parameters. Other methods, like bubbling or blow-over evaporation, face challenges in maintaining equilibrium and avoiding aerosolization. To address these issues, a new type of ethanol vapor generating system is proposed that relies solely on temperature control, creating a vacuum into which ethanol evaporates under thermodynamic control. This approach eliminates the

Using modified Arrhenius approximations, the activation energies of water, alcohols, and hexane structure rearrangement reactions responsible for temperature dependences of their dynamic and dielectric characteristics were determined. The interactions of van der Waals and charged centers of water and alcohol molecules regulate translational and rotational motion of molecules, ensuring coordination and balance of thermal effects of exothermic and endothermic reactions of changes in local structure of liquid. The long range action of fluctuating dipoles of hydrogen bonds and their resonant excitation by thermal energy underlies the anomalies in temperature dependences of water properties and initiates its phase transitions at points 273 K and 298 K. The deviation of the molecular dynamics of water from Arrhenius and Stokes Einstein equations in range from 273 to 298 K was associated with a high contribution of collective dynamics of ice like phase of water consisting of a network of hydrogen bonds structured by hexagonal clusters of Ih ice.

In a clinical trial of a treatment for alcoholism, a common response variable of interest is the number of alcoholic drinks consumed by each subject each day, or an ordinal version of this response, with levels corresponding to abstinence, light drinking and heavy drinking. In these trials, within-subject drinking patterns are often characterized by alternating periods of heavy drinking and abstinence. For this reason, many statistical models for time series that assume steady behavior over time and white noise errors do not fit alcohol data well. In this paper we propose to describe subjects' drinking behavior using Markov models and hidden Markov models (HMMs), which are better suited to describe processes that make sudden, rather than gradual, changes over time. We incorporate random effects into these models using a hierarchical Bayes structure to account for correlated responses within subjects over time, and we estimate the effects of covariates, including a randomized treatment, on the outcome in a novel way. We illustrate the models by fitting them to a large data set from a clinical trial of the drug Naltrexone. The HMM, in particular, fits this data well and also contains