-
Journal of Environmental Management Jun 2024The development of a natural pyrite/peroxymonosulfate (PMS) system for the removal of antibiotic contamination from water represented an economic and green sustainable...
The development of a natural pyrite/peroxymonosulfate (PMS) system for the removal of antibiotic contamination from water represented an economic and green sustainable strategy. Yet, a noteworthy knowledge gap remained considering the underlying reaction mechanism of the system, particularly in relation to its pH sensitivity. Herein, this paper investigated the impacts of critical reaction parameters and initial pH levels on the degradation of sulfadiazine (SDZ, 3 mg/L) in the pyrite/PMS system, and elucidated the pH dependence of the reaction mechanism. Results showed that under optimal conditions, SDZ could be completely degraded within 5 min at a broad pH range of 3.0-9.0, with a pseudo-first-order reaction rate of >1.0 min. The low or high PMS doses could lower degradation rates of SDZ through the decreased levels of active species, while the amount of pyrite was positively correlated with the removal rate of SDZ. The diminutive concentrations of anions exerted minor impacts on the decomposition of SDZ within the pyrite PMS system. Mechanistic results demonstrated that the augmentation of pH levels facilitated the transition from the non-radical to the radical pathway within the natural pyrite/PMS system, while concurrently amplifying the role of •OH in the degradation process of SDZ. This could be attributed to the change in interface electrostatic repulsion induced by pH fluctuations, as well as the mutual transformation between active species. The stable presence of the relative content of Fe(II) in the used pyrite was ensured owing to the reduced sulfur species acting as electron donors, providing the pyrite/PMS system excellent reusability. This paper sheds light on the mechanism regulation of efficient removal of organic pollutants through pyrite PMS systems, contributing to practical application.
PubMed: 38941847
DOI: 10.1016/j.jenvman.2024.121607 -
Science Advances Jun 2024Functional deficits in basal ganglia (BG) circuits contribute to cognitive and motor dysfunctions in alcohol use disorder. Chronic alcohol exposure alters synaptic...
Functional deficits in basal ganglia (BG) circuits contribute to cognitive and motor dysfunctions in alcohol use disorder. Chronic alcohol exposure alters synaptic function and neuronal excitability in the dorsal striatum, but it remains unclear how it affects BG output that is mediated by the substantia nigra pars reticulata (SNr). Here, we describe a neuronal subpopulation-specific synaptic organization of striatal and subthalamic (STN) inputs to the medial and lateral SNr. Chronic alcohol exposure (CIE) potentiated dorsolateral striatum (DLS) inputs but did not change dorsomedial striatum and STN inputs to the SNr. Chemogenetic inhibition of DLS direct pathway neurons revealed an enhanced role for DLS direct pathway neurons in execution of an instrumental lever-pressing task. Overall, we reveal a subregion-specific organization of striatal and subthalamic inputs onto the medial and lateral SNr and find that potentiated DLS-SNr inputs are accompanied by altered BG control of action execution following CIE.
Topics: Animals; Neuronal Plasticity; Basal Ganglia; Substantia Nigra; Ethanol; Corpus Striatum; Male; Mice; Neurons; Alcoholism; Neural Pathways
PubMed: 38941461
DOI: 10.1126/sciadv.adm6951 -
Journal of Addiction Medicine Jun 2024To prospectively assess rates of QT prolongation, arrhythmia, syncope, and sudden cardiac death (SCD) in a cohort of people with heroin dependence.
OBJECTIVES
To prospectively assess rates of QT prolongation, arrhythmia, syncope, and sudden cardiac death (SCD) in a cohort of people with heroin dependence.
METHODS
To estimate rates of QT prolongation, arrhythmia, and syncope, a subcohort (n = 130) from the Australian Treatment Outcomes Study, a prospective longitudinal cohort study of 615 people with heroin dependence, underwent medical history, venepuncture, and ECG at the 18- to 20-year follow-up.To estimate rates of SCD, probabilistic matching for the entire cohort was undertaken with the Australian Institute of Health and Welfare National Death Index. Deaths were classified into suicide, accidental overdose, trauma, unknown, and disease, which were then further subclassified by probability of SCD. SCD rate was the number of possible or probable SCDs divided by total patient years from the cohort.
RESULTS
From the subcohort, 4 participants (3%) met the criteria for QT prolongation; 3 were prescribed methadone. Seven participants (5%) reported history of arrhythmia, including 2 transferred from methadone to buprenorphine. Thirty participants (23%) reported a previous syncopal event-14 diagnosed as nonarrhythmic syncope and 13 not investigated. In the previous 12 months, 66 participants (51%) reported heroin use; 55 participants (42%) were prescribed methadone. No participant had QTc greater than 500 milliseconds.There were 3 possible SCDs, translating to an estimated SCD rate of 0.29 (CI: 0.05, 0.8) events per 1000 patient years. More cohort members died of overdose (n = 50), suicide (n = 11), and hepatitis C (n = 4).
CONCLUSIONS
Low rates of QT prolongation, arrhythmia, syncope, and SCD in the cohort despite high rates of heroin use and methadone treatment.
PubMed: 38941157
DOI: 10.1097/ADM.0000000000001317 -
JMIR Medical Informatics Jun 2024The pursuit of groundbreaking health care innovations has led to the convergence of artificial intelligence (AI) and traditional Chinese medicine (TCM), thus marking a...
The pursuit of groundbreaking health care innovations has led to the convergence of artificial intelligence (AI) and traditional Chinese medicine (TCM), thus marking a new frontier that demonstrates the promise of combining the advantages of ancient healing practices with cutting-edge advancements in modern technology. TCM, which is a holistic medical system with >2000 years of empirical support, uses unique diagnostic methods such as inspection, auscultation and olfaction, inquiry, and palpation. AI is the simulation of human intelligence processes by machines, especially via computer systems. TCM is experience oriented, holistic, and subjective, and its combination with AI has beneficial effects, which presumably arises from the perspectives of diagnostic accuracy, treatment efficacy, and prognostic veracity. The role of AI in TCM is highlighted by its use in diagnostics, with machine learning enhancing the precision of treatment through complex pattern recognition. This is exemplified by the greater accuracy of TCM syndrome differentiation via tongue images that are analyzed by AI. However, integrating AI into TCM also presents multifaceted challenges, such as data quality and ethical issues; thus, a unified strategy, such as the use of standardized data sets, is required to improve AI understanding and application of TCM principles. The evolution of TCM through the integration of AI is a key factor for elucidating new horizons in health care. As research continues to evolve, it is imperative that technologists and TCM practitioners collaborate to drive innovative solutions that push the boundaries of medical science and honor the profound legacy of TCM. We can chart a future course wherein AI-augmented TCM practices contribute to more systematic, effective, and accessible health care systems for all individuals.
PubMed: 38941141
DOI: 10.2196/58491 -
JAMA Network Open Jun 2024
Topics: Humans; Opioid-Related Disorders; Primary Health Care; Health Knowledge, Attitudes, Practice; Female; Male; Adult; Middle Aged; Analgesics, Opioid; Cross-Sectional Studies; Surveys and Questionnaires
PubMed: 38941101
DOI: 10.1001/jamanetworkopen.2024.19094 -
Clinical Psychology & Psychotherapy 2024Previous research has indicated that various factors, such as psychological distress, distress intolerance, anhedonia, impulsivity and smoking metacognitions, have been...
Previous research has indicated that various factors, such as psychological distress, distress intolerance, anhedonia, impulsivity and smoking metacognitions, have been individually linked to the urge to smoke, withdrawal symptoms and dependence. However, these factors have not been collectively examined to determine whether smoking metacognitions independently and significantly contribute to these outcomes. Therefore, the aim of this study was to investigate the impact of distress intolerance, anhedonia, impulsivity and smoking metacognitions on the urge to smoke, withdrawal symptoms and dependency in men who are dependent on smoking. A total of 300 smoking-dependent men completed psychological scales and smoking-related measures. The findings of the study indicated that positive metacognitions about emotion regulation significantly predicted the urge to smoke, even when accounting for other significant predictors such as the number of daily cigarettes smoked, psychological distress, anhedonia and impulsivity. Furthermore, positive metacognitions about cognitive regulation were found to be a significant predictor of withdrawal symptoms, independent of other significant predictors such as psychological distress and the urge to smoke. Smoking dependence was predicted by negative metacognitions about uncontrollability beyond other significant predictors, including the number of daily cigarettes smoked and distress intolerance. These results highlight the role of metacognitions about smoking in both short- and long-term clinical outcomes related to smoking. Consequently, addressing such beliefs during treatment for smoking dependence should be an important therapeutic goal.
Topics: Humans; Male; Substance Withdrawal Syndrome; Adult; Metacognition; Tobacco Use Disorder; Impulsive Behavior; Smoking; Middle Aged; Young Adult; Anhedonia
PubMed: 38940697
DOI: 10.1002/cpp.3024 -
The International Journal of Eating... Jun 2024Pediatric loss-of-control (LOC) eating is associated with high BMI and predicts binge-eating disorder and obesity onset with age. Research on the etiology of this common...
OBJECTIVE
Pediatric loss-of-control (LOC) eating is associated with high BMI and predicts binge-eating disorder and obesity onset with age. Research on the etiology of this common comorbidity has not explored the potential for shared genetic risk. This study examined genetic and environmental influences on LOC eating and its shared influence with BMI.
METHOD
Participants were 499 monozygotic and 398 same-sex dizygotic twins (age = 17.38 years ± 0.67, BMIz = 0.03 ± 1.03, 54% female) from the Colorado Center for Antisocial Drug Dependence Study. LOC eating was assessed dichotomously. Self-reported height and weight were converted to BMIz. Univariate and bivariate twin models estimated genetic and environmental influences on LOC eating and BMIz.
RESULTS
More girls (21%) than boys (9%, p < 0.001) reported LOC eating. The phenotypic correlation with BMIz was 0.03 in girls and 0.18 in boys. Due to the nonsignificant phenotypic correlation in girls, bivariate twin models were fit in boys only. Across all models, the best-fitting model included genetic and unique environmental effects. Genetic factors accounted for 0.51 (95% CI: 0.23, 0.73) of the variance of LOC eating in girls and 0.54 (0.18, 0.90) in boys. The genetic correlation between LOC eating and BMIz in boys was 0.45 (0.15, 0.75).
DISCUSSION
Findings indicate moderate heritability of LOC eating in adolescence, while emphasizing the role of unique environmental factors. In boys, LOC eating and BMIz share a proportion of their genetic influences.
PubMed: 38940253
DOI: 10.1002/eat.24245 -
Clinical Transplantation Jul 2024Alcohol-associated liver disease (ALD) is a leading indication for liver transplant (LT) in the United States. Rates of early liver transplant (ELT) with less than 6...
A Tailored Virtual Program for Alcohol Use Disorder Treatment Among Liver Transplant Candidates and Recipients Is Feasible and Associated With Lower Post-Transplant Relapse.
BACKGROUND
Alcohol-associated liver disease (ALD) is a leading indication for liver transplant (LT) in the United States. Rates of early liver transplant (ELT) with less than 6 months of sobriety have increased substantially. Patients who receive ELT commonly have alcohol-associated hepatitis (AH) and are often too ill to complete an intensive outpatient program (IOP) for alcohol use disorder (AUD) prior to LT. ELT recipients feel alienated from traditional IOPs.
METHODS
We implemented Total Recovery-LT, a tailored virtual outpatient IOP specific for patients under evaluation or waitlisted for LT who were too ill to attend community-based alcohol treatment programs. The 12-week program consisted of weekly group and individual counseling delivered by a master's level Certified Addiction Counselor trained in the basics of LT. Treatment consisted of 12-Step Facilitation, Motivational Interviewing, and Cognitive Behavioral Therapy. We report on program design, implementation, feasibility and early outcomes.
RESULTS
From March 2021 to September 2022, 42 patients (36% female, 23 in LT evaluation, 19 post-transplant) enrolled across five cohorts with 76% (32/42) completing the program. Alcohol relapse was more common among noncompleters versus those who completed the program (8/10, 80% vs. 7/32, 22%, p = 0.002). History of trauma or post-traumatic stress symptoms were associated with lower likelihood of completion. Patients' desire for continued engagement after completion led to the creation of a monthly alumni group.
CONCLUSIONS
Our integrated IOP model for patients with high-risk AUD in LT evaluation or post-transplant is well-received by patients and could be considered a model for LT programs.
Topics: Humans; Female; Liver Transplantation; Male; Middle Aged; Follow-Up Studies; Alcoholism; Feasibility Studies; Prognosis; Recurrence; Adult; Postoperative Complications; Telemedicine; Liver Diseases, Alcoholic
PubMed: 38940230
DOI: 10.1111/ctr.15381 -
Bioinformatics (Oxford, England) Jun 2024In drug discovery, it is crucial to assess the drug-target binding affinity (DTA). Although molecular docking is widely used, computational efficiency limits its...
MOTIVATION
In drug discovery, it is crucial to assess the drug-target binding affinity (DTA). Although molecular docking is widely used, computational efficiency limits its application in large-scale virtual screening. Deep learning-based methods learn virtual scoring functions from labeled datasets and can quickly predict affinity. However, there are three limitations. First, existing methods only consider the atom-bond graph or one-dimensional sequence representations of compounds, ignoring the information about functional groups (pharmacophores) with specific biological activities. Second, relying on limited labeled datasets fails to learn comprehensive embedding representations of compounds and proteins, resulting in poor generalization performance in complex scenarios. Third, existing feature fusion methods cannot adequately capture contextual interaction information.
RESULTS
Therefore, we propose a novel DTA prediction method named HeteroDTA. Specifically, a multi-view compound feature extraction module is constructed to model the atom-bond graph and pharmacophore graph. The residue concat graph and protein sequence are also utilized to model protein structure and function. Moreover, to enhance the generalization capability and reduce the dependence on task-specific labeled data, pre-trained models are utilized to initialize the atomic features of the compounds and the embedding representations of the protein sequence. A context-aware nonlinear feature fusion method is also proposed to learn interaction patterns between compounds and proteins. Experimental results on public benchmark datasets show that HeteroDTA significantly outperforms existing methods. In addition, HeteroDTA shows excellent generalization performance in cold-start experiments and superiority in the representation learning ability of drug-target pairs. Finally, the effectiveness of HeteroDTA is demonstrated in a real-world drug discovery study.
AVAILABILITY AND IMPLEMENTATION
The source code and data are available at https://github.com/daydayupzzl/HeteroDTA.
Topics: Drug Discovery; Molecular Docking Simulation; Proteins; Deep Learning; Pharmacophore
PubMed: 38940179
DOI: 10.1093/bioinformatics/btae240 -
Frontiers in Bioscience (Landmark... Jun 2024
Review
Topics: Humans; Cocaine-Related Disorders; Gastrointestinal Microbiome; Cocaine; Animals; Yin-Yang
PubMed: 38940056
DOI: 10.31083/j.fbl2906215