Actas espanolas de psiquiatria最新文献

Background: Major depressive disorder (MDD) is a prevalent and debilitating mental health condition, necessitating early detection and effective treatment strategies. Near-infrared spectroscopy (NIRS) is a promising neuroimaging technique for monitoring cerebral hemodynamics and may serve as an objective biomarker for MDD diagnosis and treatment efficacy. This study aimed to investigate the utility of NIRS in the early detection and longitudinal monitoring of antidepressant treatment efficacy in MDD patients.

Methods: This longitudinal study, conducted from May 2022 to May 2024, included 138 participants. After propensity score matching analyses, 80 were included, including 40 MDD patients and 40 healthy controls matched for age, gender, race, education, height, weight, and body mass index (BMI). Participants underwent NIRS measurements during cognitive tasks, including verbal fluency, sustained attention (e-primer), and one-back memory tests. Clinical assessments were conducted using the Hamilton Depression Scale (HAMD), Hamilton Anxiety Scale (HAMA), Clinical Global Impression (CGI), Continuous Performance Test (CPT), and one-back tests at baseline and after treatment at 4 weeks and 24 weeks. Statistical analyses were performed to evaluate changes in oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) levels and their correlation with clinical outcomes.

Results: At baseline, MDD patients had significantly lower HbO and higher HbR levels compared to controls (p < 0.01). After treatment, HbO increased (4.77 ± 1.23 to 5.37 ± 1.21 µmol/L, p < 0.05) while HbR decreased (3.46 ± 0.98 to 2.91 ± 0.96 µmol/L, p < 0.05) in the MDD group. However, these levels differed significantly from controls at 4 weeks (p < 0.01). By 24 weeks, HbO further increased (6.01 ± 1.08 µmol/L, p < 0.05), and HbR further decreased (2.19 ± 0.71 µmol/L, p < 0.05), with no significant differences from controls (p > 0.05). Clinically, MDD patients showed significant improvements in HAMD, HAMA, CGI, CPT, and one-back scores over 24 weeks (all p < 0.05). At 4 weeks, HAMD, HAMA, and CGI scores were higher, and CPT and one-back responses were lower than controls (p < 0.01). By 24 weeks, HAMD, HAMA, and CGI scores remained higher (p < 0.01), and CPT and one-back responses were lower than controls (p < 0.01).

Conclusion: This study underscores the potential of NIRS as a non-invasive, objective tool for early detection and monitoring of treatment efficacy in MDD. The significant correlations between NIRS findings and clinical improvements highlight its utility in personalized treatment strategies, paving the way for more effective management of MDD.

Objective: This study aimed to explore the factors influencing sleep disorders in patients with functional dyspepsia.

Methods: A total of 100 patients with functional dyspepsia admitted to Gong An County People's Hospital from 2020 to 2021 were selected. According to the Pittsburgh Sleep Quality Index (PSQI), those with a score ≥8 were classified as the occurrence group, whereas those with a score <8 were classified as the non-occurrence group. Clinical and disease characteristics of patients were collected. Logistic regression analysis was used to identify influencing factors. The emotional distress and quality of life of patients with different severities of sleep disorders were compared. Pearson's correlation was used to identify the relationship between the degree of sleep disorders and various indicators.

Results: Out of 100 patients with functional dyspepsia, 58 (58.00%) had varying degrees of sleep disturbance. Logistic regression analysis showed that factors measured by the Self-Rating Anxiety Scale (SAS) (odds ratio [OR] = 3.088, p = 0.007), Self-Rating Depression Scale (SDS) (OR = 3.268, p = 0.005), Perceived Stress Scale (PSS) (OR = 2.659, p = 0.019), and Functional Digestive Disorders Quality of Life (FDDQL) questionnaire (OR = 2.591, p = 0.022) were the main factors influencing sleep disturbance. Pearson correlation analysis suggested that SAS (r = 0.677, p < 0.001), SDS (r = 0.623, p < 0.001), and PSS (r = 0.550, p < 0.001) scores were positively correlated with the severity of sleep disturbance, whereas FDDQL (r = -0.623, p < 0.001) score was negatively correlated with the severity of sleep disturbance.

Conclusion: Functional dyspepsia patients are prone to varying degrees of sleep disorders, which are closely related to emotional distress and quality of life. Clinical interventions can be developed in advance to stabilize patient emotions and improve their quality of life.

Background: Schizophrenia and psychotic disorders are disabling, complex and severe psychiatric conditions, which may pose a significant therapeutic challenge. Integrating current psychopharmacological treatment with psychosocial interventions demonstrated a higher efficacy in terms of prognosis. However, most schizophrenia or psychotic patients may have restricted or no access to evidence-based psychosocial interventions, mainly due to poor dissemination of specialized interventions or stigma. Therefore, we aim to systematically review all studies about the current evidence on the feasibility, acceptability, efficacy, effectiveness, and benefits of digitally-delivered psychoeducational and psychosocial interventions for individuals suffering from schizophrenia or psychotic disorders.

Methods: A systematic literature review was conducted of the literature from 2000 to 2024 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, by using PubMed-MEDLINE, Scopus and OVID databases and combining the search approach using both free text terms and Medical Subject Headings (MESH) headings for the topics "psychoeducation", "psychosocial intervention" and "psychosis" and "schizophrenia".

Results: Out of a total of 3042 reviewed papers, 69 studies were included here. The interventions included web-based family and individual psychoeducation, integrated web-based therapy, social networking, peer and expert moderation, virtual reality-assisted and mobile-based psychosocial interventions. Results showed that digitally-delivered interventions have a positive effect in ensuring the continuity and maintenance of the effectiveness of psychosocial treatments, by providing personalized, flexible, and evidence-based interventions to patients with psychosis and/or schizophrenia. At the same time, the studies included demonstrated the acceptability and feasibility of this kind of intervention in clinical practice.

Conclusions: Digital interventions have the potential to deliver non-stigmatizing, constantly available psychosocial and psychoeducational interventions in psychosis and schizophrenia by increasing access to mental health care and not costly interventions. However, further randomized controlled trials (RCTs) and observational studies should compare and evaluate the effectiveness and feasibility of web-based vs. face-to-face psychosocial interventions amongst schizophrenia and psychosis individuals.

Background: Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and limited behavior. Despite the association of numerous synaptic gene mutations with ASD, the presence of behavioral abnormalities in mice expressing autism-associated R617W mutation in synaptic adhesion protein neuroligin-3 (NL3) has not been established. This work focuses on establishing a mouse model of ASD caused by NL3 R617W missense mutation (NL3R617W) and characterizing and profiling the molecular as well as behavioral features of the animal model.

Methods: The expression and distribution of NL3R617W mutant protein in the 293T cell membrane and intracellular NL3 was detected by using immunofluorescence approach. Meanwhile, synaptic markers (Synapsin I, vesicular glutamate transporter (VGluT) I and vesicular γ-aminobutyric acid transporter (VGAT)) and synapse number were detected with a confocal fluorescence microscope. Thereafter, the effect on NL3R617W was verified. The expression of synaptic proteins, postsynaptic density protein-95 (PSD95) and Src homology domain and multiple ankyrin repeat domains protein 3 (SHANK3), was verified by Western blot. The interaction between NL3 and neurexin 1 (NRXN1) was studied by means of co-immunoprecipitation. The behavior of autistic mice induced by NL3R617W mutation was examined using the Morris water maze and the Y maze. NL3R617W mutant mice were assessed in the open field, and three-chamber test was conducted to assess and observe the presence of hyperactivity, repetitive behavior, friendliness, and social novelty.

Results: The results indicated that the NL3 mutation could influence the interaction between NL3 and NRXN1, and inhibit the expression of VGluT I. Nevertheless, NL3 mutation would not influence the expression of NL3 on cell membrane, the intracellular distribution of NL3, or the endoplasmic reticulum retention. The outcomes of animal studies demonstrated that the ASD mice with NL3R617W exhibited a significant decrease in the capacity for spatial memory and exploration, as well as the expression levels of the postsynaptic scaffolding proteins, PSD95 and SHANK3 (p < 0.05). The number of excitatory synapses in hippocampal cornu ammonis (CA)1 and CA3 and the sensory cortex was also significantly reduced (p < 0.01). Compared to the control mice, the NL3R617W mutant mice were less active in the open field (p < 0.001), a finding consistent with the three-chamber test result showing reduced degree of activity. Furthermore, compared to the control mice, the NL3R617W mutant animals spent less time with stranger mice (p < 0.05).

Conclusions: NL3R617W mutation may inhibit the expression of postsynaptic scaffolding proteins by influencing the interaction with NRXN1, thus inhibiting synapse formation and reducing the number of excitatory synapses.

The introduction of ChatGPT3 in 2023 disrupted the field of artificial intelligence (AI). ChatGPT uses large language models (LLMs) but has no access to copyrighted material including scientific articles and books. This review is limited by the lack of access to: (1) prior peer-reviewed articles and (2) proprietary information owned by the companies. Despite these limitations, the article reviews the use of LLMs in the publishing of scientific articles. The first use was plagiarism software. The second use by the American Psychological Association and Elsevier helped their journal editors to screen articles before their review. These two publishers have in common a large number of copyrighted journals and textbooks but, more importantly, a database of article abstracts. Elsevier is the largest of the five large publishing houses and the only one with a database of article abstracts developed to compete with the bibliometric experts of the Web of Science. The third use and most relevant, Scopus AI, was announced on 16 January 2024, by Elsevier; a version of ChatGPT-3.5 was trained using Elsevier copyrighted material written since 2013. Elsevier's description suggests to the authors that Scopus AI can write review articles or the introductions of original research articles with no human intervention. The editors of non-Elsevier journals not willing to approve the use of Scopus AI for writing scientific articles have a problem on their hands; they will need to trust that the authors who have submitted articles have not lied and have not used Scopus AI at all.

Background: Stroke is a leading cause of long-term disability globally, with post-stroke depression and physical fatigue recognized as prominent complications affecting recovery and rehabilitation. This study aims to comprehensively investigate the impact of post-stroke depression and physical fatigue on the functional outcomes of individuals who have experienced stroke.

Methods: This research involved a retrospective analysis of clinical data from patients with stroke admitted to Taihe County People's Hospital between January 2022 and May 2023. Patients were categorized into two groups based on their prognostic functional status: good and poor. The impact of post-stroke depression and physical fatigue on functional outcomes was assessed using standardized assessment tools. Specifically, the Patient Health Questionnaire-9 (PHQ-9) was employed to measure depression severity, while the Fatigue Severity Scale (FSS) was utilized to quantify physical fatigue.

Results: Post-stroke depression and physical fatigue were significantly associated with functional status. The post-stroke depression scores were notably higher in the poor functional status group (10.58 ± 3.82) compared to the good functional status group (7.81 ± 2.12) (t = 4.482, p < 0.001). Similarly, post-stroke physical fatigue scores were significantly elevated in the poor functional status group (56.87 ± 2.53) compared to the good functional status group (43.26 ± 1.58) (t = 32.264, p < 0.001). Correlation analysis revealed a minimal correlation between depression scores and functional status (rho = 0.043, p = 0.674) after 3 months, as well as between physical fatigue scores and functional status (rho = -0.168, p = 0.094). At the six-month follow-up, a statistically significant correlation was observed between depression scores and functional status (rho = 0.398, p < 0.001). Moreover, a strong and significant correlation was identified between physical fatigue scores and functional status (rho = 0.761, p < 0.001). Multivariate logistic regression analysis revealed that at three months post-stroke, depression did not significantly affect functional status (odds ratio (OR) = 3.328, p = 0.079). However, at six months post-stroke, depression demonstrated a statistically significant effect (OR = 1.436, p = 0.030). Physical fatigue showed no significant impact on functional status at three months (OR = 1.010, p = 0.927), whereas at six months, it showed a statistically significant effect (OR = 1.581, p < 0.001).

Conclusions: These findings underscore the critical importance of integrated care models and early intervention strategies addressing post-stroke depression and physical fatigue to optimize functional outcomes and enhance the overall quality of life for stroke survivors.

Background: Alzheimer's Disease (AD), a complex clinical condition, relies on neuropsychological assessments for early diagnosis. Recently, the gut-brain axis has been recognized as crucial in AD development, with dysbiosis in gut microbiota implicated in disease progression. Utilizing 16S rRNA analysis provides comprehensive monitoring of gut microbiota, potentially revealing biological markers for Early Alzheimer's Disease (EAD). Therefore, this study aimed to investigate the diagnostic impact of 16S ribosomal RNA (rRNA) on changes in intestinal flora among EAD patients.

Methods: This study analyzed stool samples from 50 AD patients and 50 healthy controls between June 2022 and June 2023. Based on the disease stage, patients were categorized into EAD (n = 14) and Late Alzheimer's Disease (LAD) groups (n = 36). The V3-V4 region was sequenced using 16S rRNA quantitative Polymerase Chain Reaction (qPCR) to compare the composition of gut microbiota and differences in abundance among the three experimental groups.

Results: The abundance and diversity of gut microbiota significantly increased in EAD patients compared to the healthy control group. Furthermore, 39 genera showed considerable variations between EAD and LAD patients and healthy controls, with notable increases in the abundance of Bryantella, Gemmiger, Desulfovibrio, Collinsella, and Odoribacter among EAD patients. Additionally, significant differences were observed across the Desulfovibrioales and Verrucomicrobiales, which could help distinguish EAD patients (Area Under the Curve (AUC) range 0.854, 0.966, p < 0.05).

Conclusion: 16S rRNA technology can be used to identify EAD patients, with the Desulfovibrioales and Verrucomicrobiales indicators serving as potential biological markers.

Background: Depression is a widely recognized neuropsychiatric condition that often occurs as a comorbidity with various medical illnesses, including neurodegenerative disorders like Parkinson's disease (PD). This study aimed to identify the age of onset and underlying disease characteristics associated with patients exhibiting mild to moderate depression comorbid with PD.

Methods: This retrospective case-control study included 114 elderly patients (age ≥65 years) diagnosed with Parkinson's disease. The patients were divided into two groups: the non-depressed group (n = 65) and the mild to moderate depression group (n = 49). Patients' emotional and affective symptoms, cognitive function, and clinical characteristics were assessed using standardized scales. Statistical analyses, including chi-square tests, Wilcoxon rank-sum tests, and logistic regression analysis, were performed to evaluate associations and correlations between the variables of interest.

Results: Our findings revealed that patients in the mild to moderate depression group exhibited a significantly lower onset age of PD (52.33 ± 3.87 years) compared to the non-depressed group (59.27 ± 3.62 years, p < 0.001). Furthermore, patients with mild to moderate depression showed significantly higher scores in mood and affective symptoms measures, including the Hamilton Anxiety Scale (HAM-A) (p < 0.001) and Apathy Scale (p < 0.001). Additionally, the duration of Parkinson's disease was significantly longer in the mild to moderate depression group (6.78 ± 2.01 years) compared to the non-depressed group (3.45 ± 1.52 years, p < 0.001). Similarly, patients in the mild to moderate depression group exhibited significantly poorer performance on the Mini-Mental State Examination (MMSE) (p < 0.001), Montreal Cognitive Assessment (MoCA) (p = 0.025), verbal fluency (p < 0.001), and Trail Making Test (p = 0.005). Additionally, correlation and logistic regression analysis revealed associations and predictive value of these variables with the presence of mild to moderate depression in Parkinson's disease.

Conclusion: The study highlights the complex interaction of age and underlying disease characteristics in patients with mild to moderate depression comorbid with Parkinson's disease. Early recognition and tailored management of depressive symptoms, mood and affective disturbances, cognitive impairment, and disease-specific characteristics are crucial for optimizing patient care and improving outcomes in individuals with Parkinson's disease. These findings underscore the need for a comprehensive, patient-centered approach that considers the diverse interaction of demographic, clinical, and cognitive variables.