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Diagnostics
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Advances in Diagnosis and Management of Neuropsychiatric Disorders
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Advances in Diagnosis and Management of Neuropsychiatric Disorders

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Pathology and Molecular Diagnostics".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 4675

Special Issue Editor

Dr. Jesús Ramírez-Bermúdez

E-Mail Website
Guest Editor
National Institute of Neurology and Neurosurgery, Ciudad de México, Mexico
Interests: neuropsychiatry; mania; MRI

Special Issue Information

Dear Colleagues,

With the ongoing changes in social pressure and our contemporary lifestyles, the incidence of neuropsychiatric disorders (including autism, depression, mania, etc.) is on the rise, becoming a global health issue. Due to these disorders’ complex and diverse symptoms and the fact that many diseases overlap, there are many difficulties in diagnosis and a high risk of misdiagnosis and missed diagnosis. This Special Issue aims to share the latest advancements in the diagnosis and management of neuropsychiatric disorders, explore pathogenic mechanisms, focus on the latest diagnostic technologies, and help patients receive timely and correct treatment. The scope includes but is not limited to the following:

  1. Pathogenesis of neuropsychiatric disorders;
  2. Imaging diagnosis of neuropsychiatric disorders;
  3. Biomarkers in the diagnosis and management of neuropsychiatric disorders;
  4. Prognostic evaluation of neuropsychiatric disorder treatment.

We welcome your contributions.

Dr. Jesús Ramírez-Bermúdez
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • neuropsychiatric disorders
  • autism
  • mania
  • depression
  • imaging
  • pathogenesis

Benefits of Publishing in a Special Issue

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

14 pages, 2824 KiB  
Article
Moderate Alcohol Consumption Increases the Risk of Clinical Relapse in Male Depressed Patients Treated with Serotonin-Norepinephrine Reuptake Inhibitors
by Mădălina Iuliana Mușat, Felicia Militaru, Victor Gheorman, Ion Udriștoiu, Smaranda Ioana Mitran and Bogdan Cătălin
Diagnostics 2024, 14(11), 1140; https://doi.org/10.3390/diagnostics14111140 - 30 May 2024
Viewed by 867
Abstract
Background: While depression can be associated with multiple comorbidities, the association between depression and liver injury significantly increases the mortality risk. The aim of this study was to evaluate if moderate alcohol intake affects the rate of clinical relapses in patients treated with [...] Read more.
Background: While depression can be associated with multiple comorbidities, the association between depression and liver injury significantly increases the mortality risk. The aim of this study was to evaluate if moderate alcohol intake affects the rate of clinical relapses in patients treated with antidepressants as monotherapy. Methods: We assessed, over a period of 30 months, the clinical records of 254 patients with depressive disorder, of either gender, without additional pathologies, receiving monotherapy treatment with antidepressants. Thirty-three patients with alcohol abuse, alcoholism or significant cognitive impairment were excluded. The medical and psychiatric history, medication and liver enzyme values were collected and analyzed. Results: Out of the 221 patients who met the inclusion criteria, 78 experienced relapses of depression. The rate of relapse did not correlate with the levels of liver enzymes. Alcohol consumption, as objectified based on GGT levels and the AST/ALT ratio, suggested that men had higher alcohol intake compared to women. Patients treated with serotonin-norepinephrine reuptake inhibitors (SNRIs) with elevated AST levels were approximately 9 times more likely to relapse, while the ones with elevated GGT had a 5.34 times higher risk. While GGT levels remained a marker for relapse in men with elevated GGT, ALT and not AST proved to be a better risk indicator for relapses in male patients. Conclusion: The use of SNRIs in depressed male patients with moderate alcohol intake should be carefully considered, as they might be susceptible to higher risks of relapse compared to alternative antidepressant therapies. Full article
(This article belongs to the Special Issue Advances in Diagnosis and Management of Neuropsychiatric Disorders)
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11 pages, 1376 KiB  
Article
Identifying Autism Gaze Patterns in Five-Second Data Records
by Pedro Lencastre, Maryam Lotfigolian and Pedro G. Lind
Diagnostics 2024, 14(10), 1047; https://doi.org/10.3390/diagnostics14101047 - 18 May 2024
Cited by 1 | Viewed by 1391
Abstract
One of the most challenging problems when diagnosing autism spectrum disorder (ASD) is the need for long sets of data. Collecting data during such long periods is challenging, particularly when dealing with children. This challenge motivates the investigation of possible classifiers of ASD [...] Read more.
One of the most challenging problems when diagnosing autism spectrum disorder (ASD) is the need for long sets of data. Collecting data during such long periods is challenging, particularly when dealing with children. This challenge motivates the investigation of possible classifiers of ASD that do not need such long data sets. In this paper, we use eye-tracking data sets covering only 5 s and introduce one metric able to distinguish between ASD and typically developed (TD) gaze patterns based on such short time-series and compare it with two benchmarks, one using the traditional eye-tracking metrics and one state-of-the-art AI classifier. Although the data can only track possible disorders in visual attention and our approach is not a substitute to medical diagnosis, we find that our newly introduced metric can achieve an accuracy of 93% in classifying eye gaze trajectories from children with ASD surpassing both benchmarks while needing fewer data. The classification accuracy of our method, using a 5 s data series, performs better than the standard metrics in eye-tracking and is at the level of the best AI benchmarks, even when these are trained with longer time series. We also discuss the advantages and limitations of our method in comparison with the state of the art: besides needing a low amount of data, this method is a simple, understandable, and straightforward criterion to apply, which often contrasts with “black box” AI methods. Full article
(This article belongs to the Special Issue Advances in Diagnosis and Management of Neuropsychiatric Disorders)
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14 pages, 2175 KiB  
Article
AI-Enhanced Predictive Modeling for Identifying Depression and Delirium in Cardiovascular Patients Scheduled for Cardiac Surgery
by Karina Nowakowska, Antonis Sakellarios, Jakub Kaźmierski, Dimitrios I. Fotiadis and Vasileios C. Pezoulas
Diagnostics 2024, 14(1), 67; https://doi.org/10.3390/diagnostics14010067 - 27 Dec 2023
Cited by 5 | Viewed by 1906
Abstract
Several studies have demonstrated a critical association between cardiovascular disease (CVD) and mental health, revealing that approximately one-third of individuals with CVD also experience depression. This comorbidity significantly increases the risk of cardiac complications and mortality, a risk that persists regardless of traditional [...] Read more.
Several studies have demonstrated a critical association between cardiovascular disease (CVD) and mental health, revealing that approximately one-third of individuals with CVD also experience depression. This comorbidity significantly increases the risk of cardiac complications and mortality, a risk that persists regardless of traditional factors. Addressing this issue, our study pioneers a straightforward, explainable, and data-driven pipeline for predicting depression in CVD patients. Methods: Our study was conducted at a cardiac surgical intensive care unit. A total of 224 participants who were scheduled for elective coronary artery bypass graft surgery (CABG) were enrolled in the study. Prior to surgery, each patient underwent psychiatric evaluation to identify major depressive disorder (MDD) based on the DSM-5 criteria. An advanced data curation workflow was applied to eliminate outliers and inconsistencies and improve data quality. An explainable AI-empowered pipeline was developed, where sophisticated machine learning techniques, including the AdaBoost, random forest, and XGBoost algorithms, were trained and tested on the curated data based on a stratified cross-validation approach. Results: Our findings identified a significant correlation between the biomarker “sRAGE” and depression (r = 0.32, p = 0.038). Among the applied models, the random forest classifier demonstrated superior accuracy in predicting depression, with notable scores in accuracy (0.62), sensitivity (0.71), specificity (0.53), and area under the curve (0.67). Conclusions: This study provides compelling evidence that depression in CVD patients, particularly those with elevated “sRAGE” levels, can be predicted with a 62% accuracy rate. Our AI-driven approach offers a promising way for early identification and intervention, potentially revolutionizing care strategies in this vulnerable population. Full article
(This article belongs to the Special Issue Advances in Diagnosis and Management of Neuropsychiatric Disorders)
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