Joaquín García-Estrada

Anxiety symptomatology and excess weight are associated with chronic low-grade inflammation. Olive leaf extract (OLE) contains polyphenols with antioxidant and anti-inflammatory properties that have shown anxiolytic-like effects in experimental models; however, evidence in humans remains limited. This randomized double-blind placebo-controlled pilot trial evaluated the effects of OLE supplementation on anxiety symptomatology, inflammatory markers, and metabolic parameters in women with excess weight and mild to moderate anxiety symptoms. Participants received OLE (750 mg/day) or placebo for 12 weeks. Anxiety symptomatology was assessed using HAM-A, BAI, and STAI, while inflammatory and metabolic parameters were evaluated at baseline and post-intervention. Since the expected sample size was not achieved, our results are preliminary. No effect was observed from OLE supplementation. However, some differences within groups can direct future researchers interested in evaluating these parameters. OLE supplementation correlated with a particular reduction phenotype in anxious symptomatology (HAM-A, BAI and STAI-Trait) with inflammatory (hs-CRP) and metabolic parameters (c-HDL) that may open the possibility of an anti-inflammatory effect. In contrast, the observations in the Pb group seemed more associated with a pro-inflammatory state and a placebo effect related to somatic symptoms of anxiety. These preliminary observations need to be confirmed with larger randomized clinical trials that may help clarify these results and the possible underlying mechanisms.

Background: Substance Use Disorder (SUD) is characterized by recurrent craving episodes frequently associated with emotional dysregulation and altered reward processing. This study aimed to evaluate whether emotional states associated with craving episodes can be detected through automated facial emotion recognition during controlled emotional induction. Methods: Forty-one participants completed a 14-day ecological momentary assessment (EMA) monitoring anxiety and craving levels, followed by an emotional induction task using standardized stimuli from the EmoMadrid database and addiction-related images. Facial expressions were recorded and analyzed in real time using a computational facial emotion recognition model trained on the FER-2013 dataset. Results: Participants with SUD exhibited significantly reduced positive emotional valence and emotional activation in response to positive stimuli compared with healthy controls (HC), with large effect sizes observed for emotional valence (Hedges’ g = 1.76) and emotional activation (Hedges’ g = 1.33). Item-level analyses revealed that most between-group differences occurred in stimuli depicting social interactions. Individuals with SUD also showed higher frequencies of fear-related facial expressions and lower frequencies of disgust-related expressions compared with HC, with moderate effect sizes observed for both emotional dimensions (Hedges’ g = 0.72; p = 0.02). Conclusions: These results suggest that people with SUD have changes in how they process emotions, showing less response to positive things and unique facial expressions related to craving. However, given the relatively modest and clinically heterogeneous sample, the findings should be interpreted cautiously and require replication in larger and more homogeneous populations.

Anxiety symptomatology and excess weight are associated with chronic low-grade inflammation. Olive Leaf Extract (OLE) contains polyphenols with antioxidant and anti-inflammatory properties that have shown anxiolytic like effects in experimental models; however, evidence in humans remains limited. This randomized double-blind placebo-controlled pilot trial evaluated the effects of OLE supplementation on anxiety symptomatology, inflammatory markers, and metabolic parameters in women with excess weight and mild to moderate anxiety symptoms. Participants received OLE (750 mg/day) or placebo for 12 weeks. Anxiety symptomatology was assessed using HAM-A, BAI, and STAI, while inflammatory and metabolic parameters were evaluated at baseline and post intervention. OLE supplementation was associated with a significant reduction in HAM-A scores, particularly psychic anxiety symptoms, together with lower TNF-α levels compared with placebo at the end of the intervention. No significant differences were observed in body composition, caloric intake, IL-6, hs-CRP, cortisol, or most metabolic parameters. Correlation analyses revealed positive associations between inflammatory markers, fat mass, and anxiety related measures. These findings provide preliminary evidence suggesting that OLE supplementation may exert beneficial effects on psychic anxiety symptomatology and inflammatory activity in women with excess weight. However, larger randomized clinical trials are necessary to confirm these observations and clarify the underlying mechanisms.

Background: Substance Use Disorder (SUD) is characterized by recurrent craving episodes frequently associated with emotional dysregulation and altered reward processing. This study aimed to evaluate whether emotional states associated with craving episodes can be detected through automated facial emotion recognition during controlled emotional induction. Methods: Forty-one participants completed a 14-day ecological momentary assessment (EMA) monitoring anxiety and craving levels, followed by an emotional induction task using standardized stimuli from the EmoMadrid database and addiction-related images. Facial expressions were recorded and analyzed in real time using a computational facial emotion recognition model trained on the FER-2013 dataset. Results: Participants with SUD exhibited significantly reduced positive emotional valence and activation in response to positive stimuli compared with HC (p < 0.01). Item-level analyses revealed that most differences occurred in stimuli depicting social interactions. Positive emotions and energy were linked to less intense cravings and shorter substance use. People with SUD showed more fear and less disgust in their facial expressions than controls (p = 0.02). Conclusions: These results suggest that people with SUD have changes in how they process emotions, showing less response to positive things and unique facial expressions related to craving.

Background: Substance use disorder (SUD) involves excessive substance consumption and persistent reward-seeking behaviors, leading to serious physical, cognitive, and social consequences. This disorder is a global health crisis tied to increased mortality, unemployment, and reduced quality of life. Altered brain connectivity, circadian rhythms, and dopaminergic pathways contribute to sleep disorders, anxiety, and stress, which worsen SUD severity and relapse. Factors like trauma and socioeconomic disadvantages heighten risk. Digital health technologies, including wearables and machine learning, show promise for diagnosis, monitoring, and intervention, from relapse prediction to early detection of comorbidities. With high relapse rates and younger patient cases, these innovations could enhance the treatment outcomes of SUD. Objective: The objective of this study is to develop and validate a predictive model with machine learning for the duration of therapy and the rehabilitation or relapse in patients with SUD, using digital physiological measurements, psychological profiles, automatic facial emotion recognition, and the emotional state during craving. Methods: The study will be conducted with adult male patients with SUD at a rehabilitation center and control volunteers. Participants will undergo a self-reported demographic and psychological assessment, a clinician-administered craving and emotional reaction test, and will also be monitored using a smartwatch. SUD participants will be monitored for a total of 18 months (6 months during rehabilitation, an additional 12 months post discharge), and control participants for a total of 6 months. All participants will be reassessed at the sixth month of monitoring. The collected data will then be used to train models with a neural network, which will then be validated against other models and compared with other algorithms. Demographic, psychological, digital biomarkers, and craving profiles will be created, correlations will be analyzed, and they will be compared with controls to generate a digital phenotype of SUD. When the model achieves adequate validity (area under the curve of ≥0.80), a graphic user interface will then be designed for clinical use. Results: The study is supported by the Program for the Improvement of Working Conditions for Members of the SNII and SNCA (PROSNII U006EST), and APPAC-VII-CUCS-2025 for Article Publication Fees, from the University of Guadalajara. The research protocol was approved by the University of Guadalajara (reference CI-01225) in January 2025. Recruitment of patients with SUD and control participants will take place from January 2025 through January 2027. Conclusions: As shown in recent studies, accessible and affordable wearables, like commercial smartwatches, combined with psychological, demographic, and emotional state data, used with a machine learning predictive model, may be able to be used as tools to enhance SUD rehabilitation and prevent relapse. International Registered Report Identifier (IRRID): PRR1-10.2196/71374

Substance use disorders (SUDs) are widely prevalent in many countries, with the highest rates observed in nicotine and alcohol use, followed by opioid and cannabis use disorders. Within the field of SUDs, nutrition has become an increasingly important area of focus in both epidemiology and public health, as malnutrition is frequently observed among individuals affected by these disorders. Research indicates that people with SUDs are more likely to experience malnutrition than the general population; however, this issue remains an often-overlooked consequence that can impact disease progression and recovery outcomes. SUDs disrupt brain metabolism, leading to changes in brain function and disturbances in glucose, protein, and lipid metabolism. Evidence shows that individuals with certain SUDs often suffer from poor nutritional status, marked by high sugar consumption and insufficient intake of key micronutrients like iron, as well as vitamins D, C, A, and B—likely due to prioritizing drug use over adequate food intake. Importantly, diet can alter the metabolism and effects of drugs, potentially amplifying or diminishing their impact. While nutrition should play a central role in SUD treatment and rehabilitation, current research—both in animal models and human studies—on the role and benefits of specific nutrients in this context remains limited. This literature review aims to synthesize the available findings on the impact of malnutrition in human and murine models of SUDs, with the goal of identifying which nutrients may provide the most support for treatment and recovery.

Environmental noise has been repeatedly linked to negative effects on cognitive functioning among children and adolescents. This research sought to systematically assess studies investigating the relationship between noise exposure and cognitive outcomes in young individuals. Through a meta-analysis of eight primary studies published between 2001 and 2023, this study examined the effects of various noise types on cognitive performance across multiple domains in young populations. The findings reveal that noise exposure significantly impairs cognitive performance in children and adolescents, with a standardized mean difference (SMD) of –0.544 (95% CI: [−0.616, −0.472]), z = −14.85, p < 0.0001. These results underscore the profound impact of environmental noise on cognitive functioning in younger populations.

Environmental noise has been consistently associated with adverse effects on cognitive function in children and adolescents. This study aimed to systematically evaluate research examining the impact of noise exposure on young individuals on cognitive function. This meta-analysis systematically reviewed and synthesized findings on the effects of different types of noise from eight original studies published between 2001 and 2023, examining the impact of noise exposure on cognitive performance across various cognitive domains in young populations. Our results indicate that noise exposure exerts a detrimental effect on cognitive performance in children and adolescents, SMD= –0.544, 95% CI [-0.616 to -0.472], z= -14.85; p < 0.0001. These findings demonstrate the significant influence of noise on cognition function.

BACKGROUND Substance use disorder (SUD) involves excessive substance consumption and persistent reward-seeking behaviors, leading to serious physical, cognitive, and social consequences. This disorder is a global health crisis tied to increased mortality, unemployment, and reduced quality of life. Altered brain connectivity, circadian rhythms, and dopaminergic pathways contribute to sleep disorders, anxiety, and stress, which worsen SUD severity and relapse. Factors like trauma and socioeconomic disadvantages heighten risk. Digital health technologies, including wearables and machine learning, show promise for diagnosis, monitoring, and intervention, from relapse prediction to early detection of comorbidities. With high relapse rates and younger patient cases, these innovations could enhance the treatment outcomes of SUD. OBJECTIVE The objective of this study is to develop and validate a predictive model with machine learning for the duration of therapy and the rehabilitation or relapse in patients with SUD, using digital physiological measurements, psychological profiles, automatic facial emotion recognition, and the emotional state during craving. METHODS The study will be conducted with adult male patients with SUD at a rehabilitation center and control volunteers. Participants will undergo a self-reported demographic and psychological assessment, a clinician-administered craving and emotional reaction test, and will also be monitored using a smartwatch. SUD participants will be monitored for a total of 18 months (6 months during rehabilitation, an additional 12 months post discharge), and control participants for a total of 6 months. All participants will be reassessed at the sixth month of monitoring. The collected data will then be used to train models with a neural network, which will then be validated against other models and compared with other algorithms. Demographic, psychological, digital biomarkers, and craving profiles will be created, correlations will be analyzed, and they will be compared with controls to generate a digital phenotype of SUD. When the model achieves adequate validity (area under the curve of ≥0.80), a graphic user interface will then be designed for clinical use. RESULTS The study is supported by the Program for the Improvement of Working Conditions for Members of the SNII and SNCA (PROSNII U006EST), and APPAC-VII-CUCS-2025 for Article Publication Fees, from the University of Guadalajara. The research protocol was approved by the University of Guadalajara (reference CI-01225) in January 2025. Recruitment of patients with SUD and control participants will take place from January 2025 through January 2027. CONCLUSIONS As shown in recent studies, accessible and affordable wearables, like commercial smartwatches, combined with psychological, demographic, and emotional state data, used with a machine learning predictive model, may be able to be used as tools to enhance SUD rehabilitation and prevent relapse. CLINICALTRIAL INTERNATIONAL REGISTERED REPORT PRR1-10.2196/71374

Substance use disorder (SUD) significantly impacts public health, economics, and legal systems worldwide. Eye Movement Desensitization and Reprocessing (EMDR) was initially developed in the late 1980s as a therapeutic approach for post-traumatic stress disorder (PTSD), using bilateral stimulation to integrate traumatic memories with calming physiological responses. However, the effectiveness of EMDR in treating SUD remains unclear. This study aims to conduct a systematic review and meta-analysis of the impact of EMDR therapy on craving reduction in individuals with SUD. The search was conducted using databases such as PubMed and Web of Science, focusing on studies that measured craving and employed EMDR interventions. Both random and fixed effects models were used to pool effect sizes, utilizing an R software meta-package (R-4.4.1). The study adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The results indicated a significant reduction in cravings among patients undergoing EMDR therapy. Specifically, under the fixed effect model, the standardized mean difference (SMD) was −0.866 with a 95% confidence interval ranging from −1.121 to −0.611 (z = −6.66, p < 0.0001). These findings may demonstrate the significant efficacy of EMDR therapy in decreasing cravings in people with SUD.