A Randomized Clinical Trial Comparing Two Treatment Strategies, Evaluating the Meaningfulness of HAM-D Rating Scale in Patients With Major Depressive Disorder

Neurosteroids as emerging therapeutics for treatment-resistant depression: Mechanisms and clinical potential

Treatment-resistant depression (TRD) is a severe and persistent subset of major depressive disorder (MDD) that remains unresponsive to at least two different classes of antidepressants. Given the limitations of conventional treatments, neurosteroids have emerged as promising alternatives due to their rapid and multi-faceted mechanisms of action. Neurosteroids such as allopregnanolone, pregnenolone, and dehydroepiandrosterone (DHEA) modulate key neurotransmitter systems, including gamma-aminobutyric acid (GABA_A) and N-methyl-D-aspartate (NMDA) receptors, enhancing inhibitory transmission and promoting synaptic plasticity. They regulate the hypothalamic-pituitary-adrenal (HPA) axis, mitigating stress-related neurotoxicity and restoring neurochemical balance. Preclinical studies have demonstrated the efficacy of neurosteroids in reversing depressive-like behaviors in rodent models of chronic stress, while clinical trials highlight their potential for rapid and sustained antidepressant effects. Notably, the FDA approval of brexanolone for postpartum depression underscores the translational potential of neurosteroid-based therapies. However, challenges such as limited bioavailability, long-term safety concerns, and regulatory hurdles must be addressed to optimize their clinical application. This review explores the therapeutic potential of neurosteroids in TRD, discussing their mechanisms, clinical evidence, and future directions. The findings support the integration of neurosteroid-based treatments into TRD management, offering new hope for patients unresponsive to conventional antidepressants. This review uniquely highlights the paradigm shift offered by neurosteroids, moving beyond the traditional monoamine hypothesis, and positions them as novel, multi-target therapeutics capable of addressing the complex neurobiology of TRD.

Fremanezumab for the Treatment of Patients With Migraine and Comorbid Major Depressive Disorder: The UNITE Randomized Clinical Trial

Importance

Migraine and major depressive disorder are frequently comorbid; however, evidence evaluating the efficacy of preventive migraine therapy in patients with both diseases is limited.

Objective

To evaluate the efficacy and safety of fremanezumab in adults with migraine and comorbid major depressive disorder.

Design, Setting, and Participants

The UNITE study was a double-blind, placebo-controlled, parallel-group, randomized clinical trial consisting of a 4-week screening period, 12-week double-blind period, and 12-week open-label extension (OLE), conducted between July 9, 2020, and August 31, 2022. The trial was conducted at 55 centers across 12 countries. Eligible patients were adults with episodic migraine (EM) or chronic migraine (CM), history of major depressive disorder according to Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) criteria for 12 or more months before screening, and active symptoms of depression (9-item Patient Health Questionnaire score of 10 or more) at screening.

Interventions

Patients were randomized 1:1 to receive monthly fremanezumab (225 mg) or matched placebo. All patients in the OLE received quarterly fremanezumab (675 mg).

Main Outcomes and Measures

The primary end point was the mean change from baseline in monthly migraine days during the 12-week double-blind period.

Results

Of the 540 patients screened for the study, 353 patients (mean [SD] age, 42.9 [12.3] years; 310 female [88%]; EM, 48%; CM, 52%) were eligible and randomized to receive fremanezumab (n = 175) or placebo (n = 178). Mean (SE) change from baseline in monthly migraine days during the 12-week double-blind period was -5.1 (0.50; 95% CI, -6.09 to -4.13) for fremanezumab and -2.9 (0.49; 95% CI, -3.89 to -1.96) for placebo (P <.001). Mean (SE) change from baseline in the Hamilton Depression Rating Scale-17 Items score at week 8 was -6.0 (0.55; 95% CI, -7.10 to -4.95) for fremanezumab and -4.6 (0.54; 95% CI, -5.66 to -3.55) for placebo (least squares mean [SE] difference: -1.4 [0.61]; 95% CI, -2.61 to -0.22; P = .02). Adverse events were consistent with other fremanezumab trials. Results were maintained throughout the OLE.

Conclusions and Relevance

Treatment with fremanezumab compared with placebo resulted in significant reductions in monthly migraine days and depressive symptoms. No new safety concerns were observed. To the authors' knowledge, this was the first placebo-controlled, randomized clinical trial, specifically designed to assess patients with migraine and comorbid depressive disorder, to demonstrate significant improvements in migraine and depressive symptoms with a single pharmacological intervention.

Trial Registration

ClinicalTrials.gov Identifier: NCT04041284.

Development of Composite Index in Psychiatry Clinical Trial

In psychiatry clinical trials, a validated instrument (or questionnaire) which consists of a number of questions (or items) is often used for evaluation of the safety and efficacy of a test treatment under investigation. This approach based on rating scales for evaluation of safety and efficacy of a test treatment under study, however, has been criticized of being subjective. To overcome the problem, the use of a composite index which combines the subjective rating scales and objective functional magnetic resonance imaging is proposed. For this purpose, statistical methods for development of composite index are derived. The proposed composite index is evaluated both theoretically and via extensive clinical simulation studies.

Early detection of autism spectrum disorder using explainable AI and optimized teaching strategies

Autism spectrum disorder (ASD) is defined by the deficits of social relating, language, object use and understanding, intelligence and learning, and verbal and nonverbal communication. Most of the individuals with ASD have genetic conditions; however, early identification and intervention reduce the use of health services and other diagnostic procedures. The varied nature of ASD is widely acknowledged, with each affected individual displaying distinct traits. The variability among autistic children underscores the challenge of identifying effective teaching strategies, as what works for one child may not be suitable for another. In this study, we merge two ASD screening datasets focusing on toddlers. We employ three feature engineering techniques to extract significant features from the dataset to enhance model performance. This study presents an innovative two-phase method where initially, we employ diverse machine learning models, such as a combination of logistic regression and support vector machine classifiers. The focus of the second phase is on identifying tailored educational methods for children with ASD through the assessment of their behavioral, verbal, and physical responses. The main goal of this study is to develop personalized educational strategies for individuals with ASD. This will be achieved by employing machine learning techniques to enhance precision and better meet their unique needs. Experimental results achieve a classification accuracy of 94% in ASD identification using Chi-square extracted features. Concerning the choice of the best teaching approach for ASD children, the proposed approach shows 99.29% accuracy. Performance comparison with existing studies shows the superior performance of the proposed LR-SVM ensemble coupled with Chi-square features. In conclusion, the proposed approach provides a two-phase strategy for identifying ASD children and offering a suitable teaching strategy with respect to the severity of the ASD, thereby potentially contributing to the development of tailored solutions for children with varying needs.

Data-centric automated approach to predict autism spectrum disorder based on selective features and explainable artificial intelligence

Autism spectrum disorder (ASD) is a neurodevelopmental condition marked by notable challenges in cognitive function, understanding language, recognizing objects, interacting with others, and communicating effectively. Its origins are mainly genetic, and identifying it early and intervening promptly can reduce the necessity for extensive medical treatments and lengthy diagnostic procedures for those impacted by ASD. This research is designed with two types of experimentation for ASD analysis. In the first set of experiments, authors utilized three feature engineering techniques (Chi-square, backward feature elimination, and PCA) with multiple machine learning models for autism presence prediction in toddlers. The proposed XGBoost 2.0 obtained 99% accuracy, F1 score, and recall with 98% precision with chi-square significant features. In the second scenario, main focus shifts to identifying tailored educational methods for children with ASD through the assessment of their behavioral, verbal, and physical responses. Again, the proposed approach performs well with 99% accuracy, F1 score, recall, and precision. In this research, cross-validation technique is also implemented to check the stability of the proposed model along with the comparison of previously published research works to show the significance of the proposed model. This study aims to develop personalized educational strategies for individuals with ASD using machine learning techniques to meet their specific needs better.

An Evaluation of the Effectiveness of Repetitive Transcranial Magnetic Stimulation (rTMS) for the Management of Treatment-Resistant Depression with Somatic Attributes: A Hospital-Based Study in Oman

Depressive illnesses in non-Western societies are often masked by somatic attributes that are sometimes impervious to pharmacological agents. This study explores the effectiveness of repetitive transcranial magnetic stimulation (rTMS) for people experiencing treatment-resistant depression (TRD) accompanied by physical symptoms. Data were obtained from a prospective study conducted among patients with TRD and some somatic manifestations who underwent 20 sessions of rTMS intervention from January to June 2020. The Hamilton Rating Scale for Depression (HAMD) was used for clinical evaluation. Data were analysed using descriptive and inferential techniques (multiple logistic regression) in SPSS. Among the 49 participants (mean age: 42.5 ± 13.3), there was a significant reduction in posttreatment HAMD scores compared to baseline (t = 10.819, p < 0.0001, and 95% CI = 8.574-12.488), indicating a clinical response. Approximately 37% of the patients responded to treatment, with higher response rates among men and those who remained in urban areas, had a history of alcohol use, and were subjected to the standard 10 HZ protocol. After adjusting for all extraneous variables, the rTMS protocol emerged as the only significant predictor of response to the rTMS intervention. To our knowledge, this is the first study to examine the effectiveness of rTMS in the treatment of somatic depression.

A Novel Framework on Security and Energy Enhancement Based on Internet of Medical Things for Healthcare 5.0

Background: The Internet of Medical Things, often known as IoMT, is a revolutionary method of connecting medical equipment and the software that operates on it to the computer networks that are used in healthcare 5.0. The rapid development of smart medical devices on IoMT platforms has led to the adoption of major technologies in the modernization of healthcare procedures, the administration of diseases, and the improvement in patient treatment standards. The IoMT offers a variety of cloud-based applications, including data exchange, data screening, patient surveillance, information collection and analysis, and hygienic hospital attention. Wireless sensor networks (WSNs) are responsible for both the gathering and delivery of data. Method: The safety of patients and their right to privacy are the top priorities in the healthcare sector. Anyone may see and modify the patient’s health information because the data from these smart gadgets are sent wirelessly through the airways. Hence, we developed a unique elliptic curve cryptography-based energy-efficient routing protocol (ECC-EERP) to provide a high level of security and energy efficient system for healthcare 5.0. Data can be encrypted using the key-based method ECC-EERP. It employs pairs of public and private keys to decrypt and encrypts web traffic and reducse the amount of energy needed by a WSN in aggregate. Result and Discussion: The efficiency of the suggested method was evaluated in comparison with that of a variety of existing methods. The suggested method was evaluated with the use of many parameters such as security, encryption throughput, energy efficiency, network lifetime, communication overload, computation time, and implementation cost. The results showed that the proposed technique provides enhanced security and energy efficiency.