Renewable butene production through dehydration reactions over nano-HZSM-5/γ-Al2O3 hybrid catalysts

The development of new, improved zeolitic materials is of prime importance to progress heterogeneous catalysis and adsorption technologies. The zeolite HZSM-5 and metal oxide γ-Al2O3 are key materials for processing bio-alcohols, but both have some limitations, i.e., HZSM-5 has a high activity but low catalytic stability, and vice versa for γ-Al2O3. To combine their advantages and suppress their disadvantages, this study reports the synthesis, characterization, and catalytic results of a hybrid nano-HZSM-5/γ-Al2O3 catalyst for the dehydration of n-butanol to butenes. The hybrid catalyst is prepared by the in-situ hydrothermal synthesis of nano-HZSM-5 onto γ-Al2O3. This catalyst combines mesoporosity, related to the γ-Al2O3 support, and microporosity due to the nano-HZSM-5 crystals dispersed on the γ-Al2O3. HZSM-5 and γ-Al2O3 being in one hybrid catalyst leads to a different acid strength distribution and outperforms both single materials as it shows increased activity (compared to γ-Al2O3) and a high selectivity to olefins, even at low conversion and a higher stability (compared to HZSM-5). The hybrid catalyst also outperforms a physical mixture of nano-HZSM-5 and γ-Al2O3, indicating a truly synergistic effect in the hybrid catalyst

Prediction of electroconvulsive therapy response and remission in major depression: Meta-analysis

Electroconvulsive therapy (ECT) is considered to be the most effective treatment in severe major depression. The identification of reliable predictors of ECT response could contribute to a more targeted patient selection and consequently increased ECT response rates. Aims To investigate the predictive value of age, depression severity, psychotic and melancholic features for ECT response and remission in major depression. Method A meta-analysis was conducted according to the PRISMA statement. A literature search identified recent studies that reported on at least one of the potential predictors. Results Of the 2193 articles screened, 34 have been included for metaanalysis. Presence of psychotic features is a predictor of ECT remission (odds ratio (OR) = 1.47, P = 0.001) and response (OR = 1.69, P < 0.001), as is older age (standardised mean difference (SMD) = 0.26 for remission and 0.35 for response (P < 0.001)). The severity of depression predicts response (SMD = 0.19, P = 0.001), but not remission. Data on melancholic symptoms were inconclusive. Conclusions ECT is particularly effective in patients with depression with psychotic features and in elderly people with depression. More research on both biological and clinical predictors is needed to further evaluate the position of ECT in treatment protocols for major depression

Prediction of electroconvulsive therapy response and remission in major depression: meta-analysis

Abstract: Background Electroconvulsive therapy (ECT) is considered to be the most effective treatment in severe major depression. The identification of reliable predictors of ECT response could contribute to a more targeted patient selection and consequently increased ECT response rates. Aims To investigate the predictive value of age, depression severity, psychotic and melancholic features for ECT response and remission in major depression. Method A meta-analysis was conducted according to the PRISMA statement. A literature search identified recent studies that reported on at least one of the potential predictors. Results Of the 2193 articles screened, 34 have been included for metaanalysis. Presence of psychotic features is a predictor of ECT remission (odds ratio (OR) = 1.47, P = 0.001) and response (OR = 1.69, P < 0.001), as is older age (standardised mean difference (SMD) = 0.26 for remission and 0.35 for response (P < 0.001)). The severity of depression predicts response (SMD = 0.19, P = 0.001), but not remission. Data on melancholic symptoms were inconclusive. Conclusions ECT is particularly effective in patients with depression with psychotic features and in elderly people with depression. More research on both biological and clinical predictors is needed to further evaluate the position of ECT in treatment protocols for major depression

Understanding personalized dynamics to inform precision medicine: a dynamic time warp analysis of 255 depressed inpatients

Background: Major depressive disorder (MDD) shows large heterogeneity of symptoms between patients, but within patients, particular symptom clusters may show similar trajectories. While symptom clusters and networks have mostly been studied using cross-sectional designs, temporal dynamics of symptoms within patients may yield information that facilitates personalized medicine. Here, we aim to cluster depressive symptom dynamics through dynamic time warping (DTW) analysis. Methods: The 17-item Hamilton Rating Scale for Depression (HRSD-17) was administered every 2 weeks for a median of 11 weeks in 255 depressed inpatients. The DTW analysis modeled the temporal dynamics of each pair of individual HRSD-17 items within each patient (i.e., 69,360 calculated “DTW distances”). Subsequently, hierarchical clustering and network models were estimated based on similarities in symptom dynamics both within each patient and at the group level. Results: The sample had a mean age of 51 (SD 15.4), and 64.7% were female. Clusters and networks based on symptom dynamics markedly differed across patients. At the group level, five dynamic symptom clusters emerged, which differed from a previously published cross-sectional network. Patients who showed treatment response or remission had the shortest average DTW distance, indicating denser networks with more synchronous symptom trajectories. Conclusions: Symptom dynamics over time can be clustered and visualized using DTW. DTW represents a promising new approach for studying symptom dynamics with the potential to facilitate personalized psychiatric care