Implementing a psychosocial intervention DIALOG+ for patients with psychotic disorders in low and middle income countries in South Eastern Europe: protocol for a hybrid effectiveness-implementation cluster randomized clinical trial (IMPULSE)

Psychotic disorders have large treatment gap in low- and middle-income countries (LMICs) in South-Eastern Europe, where up to 45% of affected people do not receive care for their condition. This study will assess the implementation of a generic psychosocial intervention called DIALOG+ in mental health care services and its effectiveness at improving patients’ clinical and social outcomes.This is a protocol for a multi-country, pragmatic, hybrid effectiveness–implementation, cluster-randomised, clinical trial. The trial aims to recruit 80 clinicians and 400 patients across 5 South-Eastern European LMICs: Bosnia and Herzegovina, Kosovo*, Montenegro, Republic of North Macedonia and Serbia. Clusters are clinicians working with patients with psychosis, and each clinician will deliver the intervention to five patients. After patient baseline assessments, clinicians will be randomly assigned to either the DIALOG+ intervention or treatment as usual, with an allocation ratio of 1:1. The intervention will be delivered six times over 12 months during routine clinical meetings. TThe primary outcome measure is the quality of life at 12 months [Manchester Short Assessment of Quality of Life (MANSA)]; the secondary outcomes include mental health symptoms [Brief Psychiatric Rating Scale (BPRS), Clinical Assessment Interview for Negative Symptoms (CAINS), Brief Symptom Inventory (BSI)], satisfaction with services [Client Satisfaction Questionnaire (CSQ-8)] and economic costs at 12 months [based on Client Service Receipt Inventory (CSRI), EQ-5D-5L and Recovering Quality of Life (ReQOL-10)]. The study will assess the intervention fidelity and the experience of clinicians and patients’ about implementing DIALOG+ in real-life mental health care settings. In the health economic assessment, the incremental cost-effectiveness ratio is calculated with effectiveness measured by quality-adjusted life year. Data will also be collected on sustainability and reach to inform guidelines for potentially scaling up and implementing the intervention widely. Conclusion: The study is expected to generate new scientific knowledge on the treatment of people with psychosis in health care systems with limited resources. The learning from LMICs could potentially help other countries to expand the access to care and alleviate the suffering of patients with psychosis and their families. Trial registration: ISRCTN 11913964This study has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 779334

Using random forest and decision tree models for a new vehicle prediction approach in computational toxicology

yesDrug vehicles are chemical carriers that provide beneficial aid to the drugs they bear. Taking advantage of their favourable properties can potentially allow the safer use of drugs that are considered highly toxic. A means for vehicle selection without experimental trial would therefore be of benefit in saving time and money for the industry. Although machine learning is increasingly used in predictive toxicology, to our knowledge there is no reported work in using machine learning techniques to model drug-vehicle relationships for vehicle selection to minimise toxicity. In this paper we demonstrate the use of data mining and machine learning techniques to process, extract and build models based on classifiers (decision trees and random forests) that allow us to predict which vehicle would be most suited to reduce a drug’s toxicity. Using data acquired from the National Institute of Health’s (NIH) Developmental Therapeutics Program (DTP) we propose a methodology using an area under a curve (AUC) approach that allows us to distinguish which vehicle provides the best toxicity profile for a drug and build classification models based on this knowledge. Our results show that we can achieve prediction accuracies of 80 % using random forest models whilst the decision tree models produce accuracies in the 70 % region. We consider our methodology widely applicable within the scientific domain and beyond for comprehensively building classification models for the comparison of functional relationships between two variables

Improving treatment of patients with psychosis in low-and-middle-income countries in Southeast Europe: Results from a hybrid effectiveness-implementation, pragmatic, cluster-randomized clinical trial (IMPULSE)

Background In Southeast Europe (SEE) standard treatment of patients with psychosis is largely based on pharmacotherapy with psychosocial interventions rarely available. DIALOG+ is a digital psychosocial intervention designed to make routine care therapeutically effective. This trial simultaneously examined effectiveness of DIALOG+ versus standard care on clinical and social outcomes (Aim 1) and explored intervention fidelity (Aim 2). Methods A hybrid type II effectiveness–implementation, cluster-randomized trial was conducted in five SEE countries: Bosnia and Herzegovina, Kosovo*, Montenegro, North Macedonia, and Serbia. The intervention was offered to patients six times across 12 months instead of routine care. The outcomes were subjective quality of life (primary), clinical symptoms, satisfaction with services, and economic costs. Intervention fidelity was operationalized as adherence to the protocol in terms of frequency, duration, content, and coverage. Data were analyzed using multilevel regression. Results A total of 81 clinicians and 468 patients with psychosis were randomized to DIALOG+ or standard care. The intervention was delivered with high fidelity. The average number of delivered sessions was 5.5 (SD = 2.3) across 12 months. Patients in the intervention arm had better quality of life (MANSA) at 6 months (p = 0.03). No difference was found for other outcomes at 6 months. Due to disruptions caused by the COVID-19 pandemic, 12-month data were not interpretable. Conclusions DIALOG+ improved subjective quality of life of individuals with psychosis at 6 months (after four sessions), albeit with small effect size. The intervention has the potential to contribute to holistic care of patients with psychosis

Acatalasemic mice are mildly susceptible to adriamycin nephropathy and exhibit increased albuminuria and glomerulosclerosis

Background: Catalase is an important antioxidant enzyme that regulates the level of intracellular hydrogen peroxide and hydroxyl radicals. The effects of catalase deficiency on albuminuria and progressive glomerulosclerosis have not yet been fully elucidated. The adriamycin (ADR) nephropathy model is considered to be an experimental model of focal segmental glomerulosclerosis. A functional catalase deficiency was hypothesized to exacerbate albuminuria and the progression of glomerulosclerosis in this model. Methods: ADR was intravenously administered to both homozygous acatalasemic mutant mice (C3H/AnLCs(b)Cs(b)) and control wild-type mice (C3H/AnLCs(a)Cs(a)). The functional and morphological alterations of the kidneys, including albuminuria, renal function, podocytic, glomerular and tubulointerstitial injuries, and the activities of catalase were then compared between the two groups up to 8 weeks after disease induction. Moreover, the presence of a mutation of the toll-like receptor 4 (tlr4) gene, which was previously reported in the C3H/HeJ strain, was investigated in both groups. Results: The ADR-treated mice developed significant albuminuria and glomerulosclerosis, and the degree of these conditions in the ADR-treated acatalasemic mice was higher than that in the wild-type mice. ADR induced progressive renal fibrosis, renal atrophy and lipid peroxide accumulation only in the acatalasemic mice. In addition, the level of catalase activity was significantly lower in the kidneys of the acatalasemic mice than in the wild-type mice during the experimental period. The catalase activity increased after ADR injection in wild-type mice, but the acatalasemic mice did not have the ability to increase their catalase activity under oxidative stress. The C3H/AnL strain was found to be negative for the tlr4 gene mutation. Conclusions: These data indicate that catalase deficiency plays an important role in the progression of renal injury in the ADR nephropathy model

Carbon-Nanotube-Embedded Hydrogel Sheets for Engineering Cardiac Constructs and Bioactuators

We engineered functional cardiac patches by seeding neonatal rat cardiomyocytes onto carbon nanotube (CNT)-incorporated photo-cross-linkable gelatin methacrylate (GelMA) hydrogels. The resulting cardiac constructs showed excellent mechanical integrity and advanced electrophysiological functions. Specifically, myocardial tissues cultured on 50 μm thick CNT-GelMA showed 3 times higher spontaneous synchronous beating rates and 85% lower excitation threshold, compared to those cultured on pristine GelMA hydrogels. Our results indicate that the electrically conductive and nanofibrous networks formed by CNTs within a porous gelatin framework are the key characteristics of CNT-GelMA leading to improved cardiac cell adhesion, organization, and cell–cell coupling. Centimeter-scale patches were released from glass substrates to form 3D biohybrid actuators, which showed controllable linear cyclic contraction/extension, pumping, and swimming actuations. In addition, we demonstrate for the first time that cardiac tissues cultured on CNT-GelMA resist damage by a model cardiac inhibitor as well as a cytotoxic compound. Therefore, incorporation of CNTs into gelatin, and potentially other biomaterials, could be useful in creating multifunctional cardiac scaffolds for both therapeutic purposes and in vitro studies. These hybrid materials could also be used for neuron and other muscle cells to create tissue constructs with improved organization, electroactivity, and mechanical integrity.United States. Army Research Office. Institute for Soldier NanotechnologiesNational Institutes of Health (U.S.) (HL092836)National Institutes of Health (U.S.) (EB02597)National Institutes of Health (U.S.) (AR057837)National Institutes of Health (U.S.) (HL099073)National Science Foundation (U.S.) (DMR0847287)United States. Office of Naval Research (ONR PECASE Award)United States. Office of Naval Research (Young Investigator award)National Research Foundation of Korea (grant (NRF-2010-220-D00014)

Drought Impact Is Alleviated in Sugar Beets (Beta vulgaris L.) by Foliar Application of Fullerenol Nanoparticles

Over the past few years, significant efforts have been made to decrease the effects of drought stress on plant productivity and quality. We propose that fullerenol nanoparticles (FNPs, molecular formula C-60(OH)(24)) may help alleviate drought stress by serving as an additional intercellular water supply. Specifically, FNPs are able to penetrate plant leaf and root tissues, where they bind water in various cell compartments. This hydroscopic activity suggests that FNPs could be beneficial in plants. The aim of the present study was to analyse the influence of FNPs on sugar beet plants exposed to drought stress. Our results indicate that intracellular water metabolism can be modified by foliar application of FNPs in drought exposed plants. Drought stress induced a significant increase in the compatible osmolyte proline in both the leaves and roots of control plants, but not in FNP treated plants. These results indicate that FNPs could act as intracellular binders of water, creating an additional water reserve, and enabling adaptation to drought stress. Moreover, analysis of plant antioxidant enzyme activities (CAT, APx and GPx), MDA and GSH content indicate that fullerenol foliar application could have some beneficial effect on alleviating oxidative effects of drought stress, depending on the concentration of nanoparticles applied. Although further studies are necessary to elucidate the biochemical impact of FNPs on plants; the present results could directly impact agricultural practice, where available water supplies are often a limiting factor in plant bioproductivity

Thermal expansion in BaRuO3 perovskites–an unusual case of bond strengthening at high temperatures

The temperature dependences of the structures of three polytypes of BaRuO3 have been investigated between room temperature and 1000 °C using high resolution synchrotron X-ray diffraction. The structural studies reveal a systematic decrease of the Ru–Ru distance as the pressure required to prepare the polytype increases. The O–O distance across the shared face increases as the Ru–Ru separation decreases. The 9R and 4H polytypes undergo unexceptional changes with increasing temperature. In 6H-BaRuO3 there is an apparent increase in the Ru–Ru interaction at around 650 °C and a concurrent reduction in the O–O distance, indicating an anomalous strengthening of the Ru–Ru interactions upon heating.Depto. de Química InorgánicaFac. de Ciencias QuímicasTRUEpu