Prediction of declarative memory profile in panic disorder patients: a machine learning-based approach

Objective: To develop a classification framework based on random forest (RF) modeling to outline the declarative memory profile of patients with panic disorder (PD) compared to a healthy control sample. Methods: We developed RF models to classify the declarative memory profile of PD patients in comparison to a healthy control sample using the Rey Auditory Verbal Learning Test (RAVLT). For this study, a total of 299 patients with PD living in the city of Rio de Janeiro (70.9% females, age 39.9 ± 7.3 years old) were recruited through clinician referrals or self/family referrals. Results: Our RF models successfully predicted declarative memory profiles in patients with PD based on RAVLT scores (lowest area under the curve [AUC] of 0.979, for classification; highest root mean squared percentage [RMSPE] of 17.2%, for regression) using relatively bias-free clinical data, such as sex, age, and body mass index (BMI). Conclusions: Our findings also suggested that BMI, used as a proxy for diet and exercises habits, plays an important role in declarative memory. Our framework can be extended and used as a prospective tool to classify and examine associations between clinical features and declarative memory in PD patients.

Prediction of declarative memory profile in panic disorder patients: a machine learning-based approach.

OBJECTIVE: To develop a classification framework based on random forest (RF) modeling to outline the declarative memory profile of patients with panic disorder (PD) compared to a healthy control sample. METHODS: We developed RF models to classify the declarative memory profile of PD patients in comparison to a healthy control sample using the Rey Auditory Verbal Learning Test (RAVLT). For this study, a total of 299 patients with PD living in the city of Rio de Janeiro (70.9% females, age 39.9 ± 7.3 years old) were recruited through clinician referrals or self/family referrals. RESULTS: Our RF models successfully predicted declarative memory profiles in patients with PD based on RAVLT scores (lowest area under the curve [AUC] of 0.979, for classification; highest root mean squared percentage [RMSPE] of 17.2%, for regression) using relatively bias-free clinical data, such as sex, age, and body mass index (BMI). CONCLUSIONS: Our findings also suggested that BMI, used as a proxy for diet and exercises habits, plays an important role in declarative memory. Our framework can be extended and used as a prospective tool to classify and examine associations between clinical features and declarative memory in PD patients.

A model of dopamine and serotonin-kynurenine metabolism in cortisolemia: Implications for depression.

A major factor contributing to the etiology of depression is a neurochemical imbalance of the dopaminergic and serotonergic systems, which is caused by persistently high levels of circulating stress hormones. Here, a computational model is proposed to investigate the interplay between dopaminergic and serotonergic-kynurenine metabolism under cortisolemia and its consequences for the onset of depression. The model was formulated as a set of nonlinear ordinary differential equations represented with power-law functions. Parameter values were obtained from experimental data reported in the literature, biological databases, and other general information, and subsequently fine-tuned through optimization. Model simulations predict that changes in the kynurenine pathway, caused by elevated levels of cortisol, can increase the risk of neurotoxicity and lead to increased levels of 3,4-dihydroxyphenylaceltahyde (DOPAL) and 5-hydroxyindoleacetaldehyde (5-HIAL). These aldehydes contribute to alpha-synuclein aggregation and may cause mitochondrial fragmentation. Further model analysis demonstrated that the inhibition of both serotonin transport and kynurenine-3-monooxygenase decreased the levels of DOPAL and 5-HIAL and the neurotoxic risk often associated with depression. The mathematical model was also able to predict a novel role of the dopamine and serotonin metabolites DOPAL and 5-HIAL in the ethiology of depression, which is facilitated through increased cortisol levels. Finally, the model analysis suggests treatment with a combination of inhibitors of serotonin transport and kynurenine-3-monooxygenase as a potentially effective pharmacological strategy to revert the slow-down in monoamine neurotransmission that is often triggered by inflammation.

Corticosteroid therapy in borderline tuberculoid leprosy patients co-infected with HIV undergoing reversal reaction: a clinical study.

Background: Mycobacterium leprae and HIV cause infectious diseases of great concern for the public health care sector worldwide. Both are especially worrisome diseases when patients become co-infected and exhibit the expected clinical exuberance. The objective of this study was to evaluate episodes of reversal reaction (RR) and the effect of the use of corticosteroids on the treatment of borderline tuberculoid leprosy patients co-infected with the human immunodeficiency virus (HIV). Methods: This is a retrospective cohort study in which the clinical manifestations of the patients and their responses to corticosteroid therapy were observed. Variables were analysed during and after multidrug therapy between the first and last days of prednisone, which occurred up to a maximum of 6 months after initiating corticosteroid therapy. Results: A total of 22 HIV-positive and 28 HIV-negative cases were included. Loss of sensitivity and neural thickening were statistically significant while clinically ulcerated lesions were only observed in the co-infected group. Most patients were diagnosed with leprosy in the presence of RR and six patients manifested RR as an immune reconstitution inflammatory syndrome. On average, both groups received similar doses of corticosteroids (difference of 0·1 mg/kg/day).

Algoritmo de reconstrução de imagens para um sistema de Tomografia por Impedância Elétrica (TIE) baseado em configuração multiterminais / Image reconstruction algorithm for a multiterminal-based Electrical Impedance Tomography (EIT) system

INTRODUÇÃO: A Tomografia por Impedância Elétrica (TIE) é um método de imageamento de baixo custo, não invasivo, portátil e livre de radiacão ionizante, que é capaz de mapear a distribuicão das propriedades elétricas de tecidos biológicos. Apesar da baixa resolucão das imagens reconstruídas, quando comparadas a outras técnicas de imageamento tomográfico, tem-se observado uma série de aplicacões clínicas nas quais a EIT é utilizada para monitorar a atividade de determinados tecidos do corpo humano. A maioria dos sistemas de EIT comerciais tem sido pautada no paradigma originalmente proposto de uma configuracão multiportas. Este trabalho apresenta um algoritmo de reconstrucão de imagem para um sistema alternativo de EIT que usa uma configuracão multiterminais. MÉTODOS: O algoritmo é baseado na associacão entre o plano tomográfico do volume condutor e um sistema eletrostático análogo. Para avaliar o algoritmo, imagens de EIT foram reconstruídas a partir de dados obtidos experimentalmente em tanque com solucão salina, sendo as mesmas quantificadas por meio de métricas estabelecidas para este propósito. RESULTADOS: Quantitativamente, as imagens reconstruídas com o algoritmo proposto apresentaram um erro de posicão de ±10%, resolucão e deformacão decrescentes do centro para a borda e oscilacão em torno de 60% do meio. CONCLUSÃO: Uma vez que o algoritmo não possui nenhum processo de filtragem, os resultados foram considerados satisfatórios e encorajadores para trabalhos futuros visando melhorias.

INTRODUCTION: Electrical Impedance Tomography (EIT) is a low cost, non-invasive, portable and free of ionizing radiation method for imaging the distribution of the electric properties of biological tissues. Despite the low resolution of the reconstructed images when compared with other tomographic techniques, there is a range of clinical applications in which the EIT has been used to monitor the activity of certain body tissues. Most of commercial EIT systems are based on the original paradigm defined as a multiport configuration. This paper presents an algorithm developed to reconstruct images for an alternative multiterminal-based EIT system. METHODS: The algorithm is based on the association between the tomographic plane of the conductor volume and an analogous electrostatic system. To evaluate the algorithm, EIT images were reconstructed using data obtained experimentally in a saline solution tank, which were quantified by some metrics established for this purpose. RESULTS: Quantitatively, EIT images reconstructed by the proposed algorithm presented a position error around ±10%, resolution and shape deformation decreasing from center to the edge, and ringing around 60% of the medium. CONCLUSION: Taking into account that no filtering was used in the algorithm, the results can be considered satisfactory and encourage future works aiming further improvement.