Inter and intra-hemispheric structural imaging markers predict depression relapse after electroconvulsive therapy: a multisite study.

Relapse of depression following treatment is high. Biomarkers predictive of an individual's relapse risk could provide earlier opportunities for prevention. Since electroconvulsive therapy (ECT) elicits robust and rapidly acting antidepressant effects, but has a >50% relapse rate, ECT presents a valuable model for determining predictors of relapse-risk. Although previous studies have associated ECT-induced changes in brain morphometry with clinical response, longer-term outcomes have not been addressed. Using structural imaging data from 42 ECT-responsive patients obtained prior to and directly following an ECT treatment index series at two independent sites (UCLA: n = 17, age = 45.41±12.34 years; UNM: n = 25; age = 65.00±8.44), here we test relapse prediction within 6-months post-ECT. Random forests were used to predict subsequent relapse using singular and ratios of intra and inter-hemispheric structural imaging measures and clinical variables from pre-, post-, and pre-to-post ECT. Relapse risk was determined as a function of feature variation. Relapse was well-predicted both within site and when cohorts were pooled where top-performing models yielded balanced accuracies of 71-78%. Top predictors included cingulate isthmus asymmetry, pallidal asymmetry, the ratio of the paracentral to precentral cortical thickness and the ratio of lateral occipital to pericalcarine cortical thickness. Pooling cohorts and predicting relapse from post-treatment measures provided the best classification performances. However, classifiers trained on each age-disparate cohort were less informative for prediction in the held-out cohort. Post-treatment structural neuroimaging measures and the ratios of connected regions commonly implicated in depression pathophysiology are informative of relapse risk. Structural imaging measures may have utility for devising more personalized preventative medicine approaches

Preliminary prediction of individual response to electroconvulsive therapy using whole-brain functional magnetic resonance imaging data.

Electroconvulsive therapy (ECT) works rapidly and has been widely used to treat depressive disorders (DEP). However, identifying biomarkers predictive of response to ECT remains a priority to individually tailor treatment and understand treatment mechanisms. This study used a connectome-based predictive modeling (CPM) approach in 122 patients with DEP to determine if pre-ECT whole-brain functional connectivity (FC) predicts depressive rating changes and remission status after ECT (47 of 122 total subjects or 38.5% of sample), and whether pre-ECT and longitudinal changes (pre/post-ECT) in regional brain network biomarkers are associated with treatment-related changes in depression ratings. Results show the networks with the best predictive performance of ECT response were negative (anti-correlated) FC networks, which predict the post-ECT depression severity (continuous measure) with a 76.23% accuracy for remission prediction. FC networks with the greatest predictive power were concentrated in the prefrontal and temporal cortices and subcortical nuclei, and include the inferior frontal (IFG), superior frontal (SFG), superior temporal (STG), inferior temporal gyri (ITG), basal ganglia (BG), and thalamus (Tha). Several of these brain regions were also identified as nodes in the FC networks that show significant change pre-/post-ECT, but these networks were not related to treatment response. This study design has limitations regarding the longitudinal design and the absence of a control group that limit the causal inference regarding mechanism of post-treatment status. Though predictive biomarkers remained below the threshold of those recommended for potential translation, the analysis methods and results demonstrate the promise and generalizability of biomarkers for advancing personalized treatment strategies

Incidencia de reacciones adversas a medicamentos en la división de medicina del Hospital Nacional de la Policía Nacional del Perú "Luis N. Sáenz"

El presente estudio corresponde a un diseño observacional, descriptivo, transversal, ambispectivo sobre la Incidencia de Reacciones Adversas a Medicamentos (RAM) en pacientes hospitalizados en la División de Medicina del Hospital Nacional PNP “Luis N. Sáenz”, en el año 2013. La metodología aplicada fue la de Vigilancia Intensiva, recopilación de información clínica donde se registraron todos aquellos efectos adversos que pudieran ser interpretados como inducidos por medicamentos. La muestra consistió en analizar 329 casos, detectándose 48 casos de RAM, obteniéndose una incidencia de 14,59 %; con predominio en mujeres con 15,75 %, y según la edad, el grupo de 25 - 39 años con 22,58 %. Los principales grupos terapéuticos implicados fueron los del Sistema Nervioso (N: 33,93 %), y los Antiinfecciosos generales de uso sistémico (J: 21,43 %). Los principales órganos y sistemas comprometidos fueron el Gastrointestinal (0600: 35,71 %), y las afecciones en el Metabolismo y Nutrición (0800: 12,5 %). Por aplicación del Algoritmo de Decisión para la Evaluación de Causalidad de una RAM, se observa que las RAM son Probables en un 55,36 %; según Gravedad, las RAM resultaron siendo 80,36% del grado Serio; y según Tipo, se observó que el 73,21 % fueron de Tipo A.This study corresponds to an observational descriptive cross-sectional design ambispective about the Incidence of Adverse Drug Reaction (ADR) in hospitalized patients in the Medicine Divition of the Hospital Nacional de la Policía Nacional del Perú "Luis N. Saenz" in 2013. The methodology used was the Intensive Vigilance, collecting clinical information where it was recorded those adverse effects that could be interpreted as induced by drugs. The sample consisted of analyzing 329 cases , 48 cases of ADR was detected, yielding an incidence of 14.59 %, with a predominance in women with 15.75% , according to the age, the group between 25 - 39 years old with 22.58% . The main therapeutic groups involved were the Nervous System (N: 33.93 %), and General Anti-infectives for Systemic Use (J: 21.43 %). The main organs and systems compromised were Gastrointestinal (0600: 35.71%), and the conditions in Metabolism and Nutrition (0800: 12.5%). By application of Decision Algorithm for the Evaluation of Causality of RAM, it is observed that the ADR are Likely to 55.36 %, according to Gravity, the ARD were being 80.36 % of Serius degree and according to type, it was observed that 73.21% were of type A. Keywords: Incidence, Adverse Drug Reaction, Decision Algorithm, Pharmacovigilance.Tesi

Diseño de alcantarillado sanitario convencional de la ciudad de Dolores Carazo

Presenta el diseño de alcantarillado sanitario convencional de la ciudad de Dolores Carazo en el cual se realizó un estudio socioeconómico de la población y proyectarlo a 20 años así como un diseño de la red alcantarillado sanitario y planta de tratamiento de aguas residuales que cumpla con los parámetros y normas de diseño nacional

Elongation and fluctuations of semi-flexible polymers in a nematic solvent

We directly visualize single polymers with persistence lengths ranging from $\ell_p=0.05$ to 16 $\mu$m, dissolved in the nematic phase of rod-like {\it fd} virus. Polymers with sufficiently large persistence length undergo a coil-rod transition at the isotropic-nematic transition of the background solvent. We quantitatively analyze the transverse fluctuations of semi-flexible polymers and show that at long wavelengths they are driven by the fluctuating nematic background. We extract both the Odijk deflection length and the elastic constant of the background nematic phase from the data.Comment: 4 pages, 4 figures, submitted to PR

Dynamic Functional Connectivity Predicts Treatment Response to Electroconvulsive Therapy in Major Depressive Disorder

Background: Electroconvulsive therapy (ECT) is one of the most effective treatments for major depressive disorder. Recently, there has been increasing attention to evaluate the effect of ECT on resting-state functional magnetic resonance imaging (rs-fMRI). This study aims to compare rs-fMRI of depressive disorder (DEP) patients with healthy participants, investigate whether pre-ECT dynamic functional network connectivity network (dFNC) estimated from patients rs-fMRI is associated with an eventual ECT outcome, and explore the effect of ECT on brain network states. Method: Resting-state functional magnetic resonance imaging (fMRI) data were collected from 119 patients with depression or depressive disorder (DEP) (76 females), and 61 healthy (HC) participants (34 females), with an age mean of 52.25 (N = 180) years old. The pre-ECT and post-ECT Hamilton Depression Rating Scale (HDRS) were 25.59 ± 6.14 and 11.48 ± 9.07, respectively. Twenty-four independent components from default mode (DMN) and cognitive control network (CCN) were extracted, using group-independent component analysis from pre-ECT and post-ECT rs-fMRI. Then, the sliding window approach was used to estimate the pre-and post-ECT dFNC of each subject. Next, k-means clustering was separately applied to pre-ECT dFNC and post-ECT dFNC to assess three distinct states from each participant. We calculated the amount of time each subject spends in each state, which is called “occupancy rate” or OCR. Next, we compared OCR values between HC and DEP participants. We also calculated the partial correlation between pre-ECT OCRs and HDRS change while controlling for age, gender, and site. Finally, we evaluated the effectiveness of ECT by comparing pre- and post-ECT OCR of DEP and HC participants. Results: The main findings include (1) depressive disorder (DEP) patients had significantly lower OCR values than the HC group in state 2, where connectivity between cognitive control network (CCN) and default mode network (DMN) was relatively higher than other states (corrected p = 0.015), (2) Pre-ECT OCR of state, with more negative connectivity between CCN and DMN components, is linked with the HDRS changes (R = 0.23 corrected p = 0.03). This means that those DEP patients who spent less time in this state showed more HDRS change, and (3) The post-ECT OCR analysis suggested that ECT increased the amount of time DEP patients spent in state 2 (corrected p = 0.03). Conclusion: Our finding suggests that dynamic functional network connectivity (dFNC) features, estimated from CCN and DMN, show promise as a predictive biomarker of the ECT outcome of DEP patients. Also, this study identifies a possible underlying mechanism associated with the ECT effect on DEP patients