As a complex physiological and psychological phenomenon, pain has a wide impact on the quality of life of patients. Chronic pain represents one of the most challenging public health issues, and ensuri Show more
As a complex physiological and psychological phenomenon, pain has a wide impact on the quality of life of patients. Chronic pain represents one of the most challenging public health issues, and ensuring effective pain management is not only a fundamental right of individuals but also a sacred duty of healthcare providers. This review focuses on recent advancements (within the past five years) in understanding how electroacupuncture (EA) alleviates pain-related affective disorders, such as anxiety and depression. By integrating findings from clinical trials and mechanistic studies, we highlight three key mechanisms: (1)Brain functional regulation: EA modulates brain regions (e.g., prefrontal cortex, insula, thalamus) and networks (default mode network, salience network) via functional magnetic resonance imaging (fMRI)-observed functional connectivity changes. (2)Neurotransmitter and receptor modulation: EA regulates pain and emotions by altering BDNF, β-endorphin, TRPV1, NMDARs, and P2Y12 receptor signaling, supported by studies on chronic pain and depression models. (3)Immune factor adjustment: EA reduces neuroinflammation by targeting TLR4/NF-κB pathways and pro-inflammatory cytokines (IL-1β, TNF-α), improving pain-related affective disorders. Clinical and preclinical evidence demonstrates EA's safety, efficacy, and multi-target effects, however, optimal treatment parameters and individualized strategies require further investigation. Future research should combine multi-omics, large-scale multi-center clinical studies , and precision medicine approaches to deepen understanding of EA's mechanisms and clinical applications. Show less
Diagnosis of affective disorders among adolescent population links with the high risk of suicide attempt. The use of clinical psychological scales and biological markers may help to understand the bac Show more
Diagnosis of affective disorders among adolescent population links with the high risk of suicide attempt. The use of clinical psychological scales and biological markers may help to understand the background of suicidal process. Here we present the exploratory data study on retrospective suicide attempt risk factors and classification model of diagnosis conversion from major depressive disorder to bipolar disorder among adolescent population. This retrospective classification study was conducted on 45 adolescent/early-adulthood patients with the diagnosis of major depressive disorders. The psychological profile of patients was assessed with the use of standard clinical scales, like: Defence Style Questionnaire, Barrat Impulsiveness Scale, Beck Depression Inventory, Family APGAR, Emotional Intelligence Questionnaire and Temperament and Character Inventory. We assessed also the baseline concentration of blood-serum proteins: brain-derived neurotrophic factor, proBDNF, epidermal growth factor, macrophage migration inhibitory protein, and Stem Cell Factor. Suicide attempt history was determined at baseline (lifetime occurrence). The machine learning were used to assess the classification of the risk of suicidal attempt as well as diagnosis conversion from major depression to bipolar disorder. The winning models of machine learning were logistic regression and random forest. Regarding the suicidal attempt risk classification, significant coefficient were found mainly in Hamilton Depression Rating Scale (both factor and item assessment) and Temperament and Character Inventory (AUC = 0.74 (95% CI: 0.53-0.91), permutation p = 0.003). Serum biomarkers showed no discriminative ability (AUC = 0.35-0.40, p > 0.5) for suicide attempts in the past. We found not reliable clinical and biological data on the diagnosis conversion prediction. Clinical psychological scales, not peripheral biomarkers, distinguished suicide attempters in this exploratory analysis. Show less
To identify associations of polymorphic variants of the genes of Two hundred thirty-five patients with AfD and 62 patients with AR and comorbid AlD aged 18 to 65 years were examined. The severity of A Show more
To identify associations of polymorphic variants of the genes of Two hundred thirty-five patients with AfD and 62 patients with AR and comorbid AlD aged 18 to 65 years were examined. The severity of AfD was assessed using the Structured Interview Guide for the Hamilton Depression Rating Scale, Seasonal Affective Disorder Version (SIGH-SAD) and the Clinical Global Impression (CGI), and the level of anxiety was assessed using the Hamilton Anxiety Rating Scale (HARS) at baseline and on Day 28 of psychopharmacotherapy. Polymorphic variants rs6265, rs7124442, rs11030104, and rs7103411 of the In AfD patients, rs3924999* The polymorphic variants rs3924999 of the Show less