Objective: To implement a treatment algorithm to operationalize treatment-resistance and improve patient outcomes in youth with pediatric bipolar disorder (PBD). The term "treatment resistance" was operationally defined as significant persistent symptoms following the application of a treatment algorithm.
Method: Youth (6-17 years of age, n=120) with treatment-refractory bipolar I or II disorder, currently in a manic or mixed episode, were treated in accordance with the following 3 step algorithm: (1) removal of destabilizing agents (antidepressants, gamma aminobutyric acid [GABA]-agonists, and stimulants), (2) optimization of antimanic agents, and (3) use of a limited number (E 2) of mood stabilizers. The primary efficacy measure was change in scores on the Young Mania Rating Scale (YMRS) over the 6-month treatment course. Response was defined as repeated YMRS scores E 12.
Results: The sample was dichotomized into responders and non-responders. Both responders and non-responders improved significantly, with responders improving by a greater margin (d=3.2). At the end of 6 months, 75.8% of subjects demonstrated a significant and stable decrease in manic symptoms consistent with symptomatic remission (YMRS E 12). None of the subjects withdrew from the clinical process due to adverse events.
Conclusion: The application of this proposed treatment algorithm allows for more accurate identification of true treatment resistance and can significantly reduce manic symptoms in patients previously described as having treatment-refractory bipolar disorder.
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http://dx.doi.org/10.1097/01.pra.0000398411.59491.8c | DOI Listing |
Int J Bipolar Disord
December 2024
Department for Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt-Goethe University, Frankfurt am Main, Germany.
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Mental Health Research Center, Moscow, Russia.
Mental disorders are complex illnesses with multifactorial etiologies involving genetic and environmental components. This review focuses on cellular models derived from the olfactory epithelium as a promising tool to study the molecular mechanisms of some neuropsychiatric diseases. The authors consider cell lines allowing the identification of potential biomarkers and pathogenetic mechanisms of schizophrenia, bipolar disorder, and Alzheimer's disease.
View Article and Find Full Text PDFJ Control Release
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Department of Chemical Engineering, McMaster University, 1280 Main Street, West Hamilton, ON L8S 4L8, Canada. Electronic address:
While bipolar disorder patients can benefit from lithium therapy, high levels of lithium in the serum can induce undesirable systemic side effects. Intranasal (IN) lithium delivery offers a potential solution to this challenge given its potential to facilitate improved lithium transport to brain when delivered to the olfactory mucosa. Herein, a sprayable, in situ forming nanoparticle network hydrogel (NNH) based on Schiff base interactions between chelator-functionalized oxidized starch nanoparticles (SNPs) and carboxymethyl chitosan (CMCh) is reported that can be deployed within the nasal cavity to release ultra-small penetrative SNPs over time.
View Article and Find Full Text PDFEur Arch Psychiatry Clin Neurosci
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Department of Psychiatry, University of Muenster, Muenster, Germany.
Schizophrenia (SCZ), bipolar (BD) and major depression disorder (MDD) are severe psychiatric disorders that are challenging to treat, often leading to treatment resistance (TR). It is crucial to develop effective methods to identify and treat patients at risk of TR at an early stage in a personalized manner, considering their biological basis, their clinical and psychosocial characteristics. Effective translation of theoretical knowledge into clinical practice is essential for achieving this goal.
View Article and Find Full Text PDFCurr Issues Mol Biol
November 2024
Systems Biology Unit, Department of Experimental Biology, Faculty of Experimental Sciences, University of Jaén, 23071 Jaén, Spain.
Neurological disorders such as Autism Spectrum Disorder (ASD), Schizophrenia (SCH), Bipolar Disorder (BD), and Major Depressive Disorder (MDD) affect millions of people worldwide, yet their molecular mechanisms remain poorly understood. This study describes the application of the Comparative Analysis of Shapley values (CASh) to transcriptomic data from nine datasets associated with these complex disorders, demonstrating its effectiveness in identifying differentially expressed genes (DEGs). CASh, which combines Game Theory with Bootstrap resampling, offers a robust alternative to traditional statistical methods by assessing the contribution of each gene in the broader context of the complete dataset.
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