Classical integer-order models provide useful insights into alcoholism dynamics but cannot adequately capture the memory and hereditary effects associated with addiction, relapse, and recovery. In this work, the Caputo fractional derivative is used to study an alcoholism model describing susceptible to alcohol consumption ([Formula: see text]), moderate drinkers ([Formula: see text]), heavy drinkers ([Formula: see text]), affluent heavy drinkers receiving care in private treatment facilities ([Formula: see text]), disadvantaged heavy drinkers attending public treatment centers ([Formula: see text]), and those who have stopped drinking ([Formula: see text]). Using the concepts of the fixed point theory and nonlinear analysis, the existence, uniqueness, and stability of solutions of the considered fractional alcoholism model are rigorously proved. Using the Newton interpolation polynomial, the numerical analysis and simulations of the fractional alcoholism model are carried out to visualize the behavior of the considered system and to examine the effects of various crucial parameters on its dynamics. Optimal control analysis for the fractional order model is demonstrated to show its dynamics with and without control. A deep neural network surrogate is also developed to approximate the numerical trajectories generated by the fractional-order model.

Preterm infants have a higher risk for developing neurocognitive deficits and behavioral abnormalities across the lifespan, but the individual long-term neurobiological heterogeneity presents a challenge for developmental risk assessment and outcome prediction. Thus, we took advantage of the large sample size of the Adolescent Brain Cognitive Development dataset to investigate neuroanatomical subtypes that were previously unobservable in typical case-control designs, and explored potential neurodevelopmental mechanisms by analyzing the brain maturation of preterm subtypes. Our findings revealed two markedly distinct and reproducible brain structural and behavioral features that differentiated subtypes of preterm adolescents: 1) the "at-risk" subtype had an "average preterm brain pattern" of widespread lower cortical volume/area and subcortical volume and higher cortical thickness, and showed cognitive deficiencies and psychopathological risk; 2) the "resilient" subtype demonstrated more complex brain structural differences including both higher and lower cortical and subcortical volumes in different regions simultaneously, as well as widespread higher cortical area and lower cortical thickness. In addition, the "resilient: subtype showed accelerated brain maturation that may contribute to their normal cognitive function though they still had psychopathological risk. These neurodevelopmental alterations had significant associations with gestational age, birth weight, puberty development levels, psychopathological risk and cognitive deficits. The findings provide mechanistic insights into neurobiological heterogeneities in long-term neurodevelopmental trajectories in preterm adolescents, aiming to guide risk stratification and support healthy development in preterm adolescents.

Relationship distress is a prevalent issue with significant economic and societal consequences, including increased mental and physical health problems, decreased workplace productivity, and increased absences. Although couple therapy has been shown to improve relationship and individual well-being, its high costs limit accessibility. Digital relationship interventions provide a cost-effective alternative. However, limited research has assessed the cost-benefit of such programs using experimental designs. This study conducted a cost-benefit analysis of the OurRelationship program using data from two randomized controlled trials (RCTs). The Washington State Institute for Public Policy (WSIPP) cost-benefit model was applied to quantify economic benefits across four domains-depression, anxiety, alcohol misuse, and insomnia-relative to a waitlist control. Program costs were estimated at $280 per individual, and benefits were assessed in terms of healthcare savings, productivity gains, and labor market outcomes. The primary analysis found that, for every dollar spent on the OurRelationship program, $6.60 was saved in societal costs. Sensitivity analyses suggested benefit-cost ratios ranging from $1.88 to $22.06 depending on assumptions regarding the number of participants, duration of effects, and magnitude of program impact. Findings suggest that the OurRelationship program yields substantial economic benefits, making it a viable investment for policymakers, employers, and healthcare systems. The study supports broader implementation of efficacious digital relationship interventions and highlights the importance of economic evaluations in guiding funding decisions for relationship support programs. Further research should explore the long-term benefits and applicability across diverse populations.

Cerebrovascular disease is a leading cause of death and long-term disability worldwide, most commonly manifesting as ischemic stroke. The pathogenesis of ischemic stroke is largely driven by atherosclerotic diseases of the cerebral and extracranial vasculature, where lipid accumulation and vascular inflammation play central roles. Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a key mediator in this process. Elevated circulating PCSK9 contributes to stroke risk and severity across multiple stages of disease progression, promoting atherogenesis, plaque instability, and thrombosis. It exacerbates acute ischemic injury by elevating circulating low-density lipoprotein cholesterol (LDL-C) and promoting vascular inflammation via upregulation of monocyte adhesion, foam cell formation, and cytokine release. Following cerebral ischemia, upregulated PCSK9 also contributes to neuroinflammation within the brain parenchyma, thereby driving secondary neuronal injury and poor neurological outcomes. Preclinical stroke models have shown that PCSK9 inhibition (PCSK9i) is effective within this neurovascular context, attenuating both vascular and neuroinflammatory signaling and thereby limiting ischemic injury. Recent clinical trials have focused on the potential of PCSK9i for broader cardiometabolic indications and stroke prevention, drawing on evidence that treatment can reduce the risk of these events and improve neurological outcomes for patients.

Chronic alcohol and other psychoactive substance use is accompanied not only by disturbances in classical neurotransmitter systems but also by persistent activation of innate and adaptive immunity, leading to neuroinflammation. This review summarizes experimental and clinical data on how microglia and astrocytes act as central mediators at the intersection of immune, metabolic, and neuronal processes in alcohol-related disorders. We discuss Toll-like receptor 4 (TLR4)-dependent pathways, activation of the NLRP3 inflammasome, impaired glutamate clearance, metabolic "reprogramming" of glia, and mitochondrial dysfunction. These changes lead to energy deficiency, oxidative stress, and persistent remodeling of reward, stress, and cognitive control networks. Particular attention is given to the impact of neuroinflammation on dopaminergic, glutamatergic, GABAergic, and serotonergic neurotransmission, including the shift of tryptophan metabolism toward the kynurenine pathway. We also consider the role of the gut-liver-brain axis, dysbiosis, endotoxemia, systemic inflammation, and impaired production of short-chain fatty acids in maintaining neuroimmune-metabolic stress. Contribution of hepatic and adipose tissue to the formation of a chronic inflammatory milieu and its effect on blood-brain barrier (BBB) permeability is discussed. Based on the combined data, the authors propose an integrative model of dependence as a state arising at the intersection of disrupted neural signaling, disordered energy metabolism, and altered inter-organ communication. Promising therapeutic targets are outlined, including normalization of glial function, modulation of the gut microbiota, reduction of systemic inflammation, and targeting energy metabolism. The need to develop biomarker panels to identify subgroups of patients with the pronounced neuroinflammatory burden is emphasized.

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 = 12,503 $$ n=\mathrm{12,503} $$). 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 ten predictors. We externally validate the model on two independent datasets. The areas under the receiver operating characteristic curves (and 95% CIs) for AUD and CUD, respectively, are: (a) 0.72 (0.70, 0.73) and 0.69 (0.66, 0.70) based on 5-fold cross-validation, (b) 0.75 (0.68, 0.81) and 0.71 (0.62, 0.79) based on validation dataset 1, and (c) 0.65 (0.60, 0.71) and 0.75 (0.70, 0.79) 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 outcomes in terms of predictive accuracy. Our model may help in identifying adolescent substance users at high risk of developing SUD in adulthood, who can then be helped with appropriate intervention.