73 research outputs found
Cost-effectiveness of implementing a digital psychosocial intervention for patients with psychotic spectrum disorders in low- and middle-income countries in Southeast Europe: Economic evaluation alongside a cluster randomised trial
BACKGROUND: DIALOG+ is a digital psychosocial intervention aimed at making routine meetings between patients and clinicians therapeutically effective. This study aimed to evaluate the cost-effectiveness of implementing DIALOG+ treatment for patients with psychotic disorders in five low- and middle-income countries in Southeast Europe alongside a cluster randomised trial. METHODS: Resource use and quality of life data were collected alongside the multi-country cluster randomised trial of 468 participants with psychotic disorders. Due to COVID-19 interruptions of the trial’s original 12-month intervention period, adjusted costs and quality-adjusted life years (QALYs) were estimated at the participant level using a mixed-effects model over the first 6 months only. We estimated the incremental cost-effectiveness ratio (ICER) with uncertainty presented using a cost-effectiveness plane and a cost-effectiveness acceptability curve. Seven sensitivity analyses were conducted to check the robustness of the findings. RESULTS: The average cost of delivering DIALOG+ was €91.11 per participant. DIALOG+ was associated with an incremental health gain of 0.0032 QALYs (95% CI –0.0015, 0.0079), incremental costs of €84.17 (95% CI –8.18, 176.52), and an estimated ICER of €26,347.61. The probability of DIALOG+ being cost-effective against three times the weighted gross domestic product (GDP) per capita for the five participating countries was 18.9%. CONCLUSION: Evidence from the cost-effectiveness analyses in this study suggested that DIALOG+ involved relatively low costs. However, it is not likely to be cost-effective in the five participating countries compared with standard care against a willingness-to-pay threshold of three times the weighted GDP per capita per QALY gained
Evaluation of multiple protein docking structures using correctly predicted pairwise subunits
<p>Abstract</p> <p>Background</p> <p>Many functionally important proteins in a cell form complexes with multiple chains. Therefore, computational prediction of multiple protein complexes is an important task in bioinformatics. In the development of multiple protein docking methods, it is important to establish a metric for evaluating prediction results in a reasonable and practical fashion. However, since there are only few works done in developing methods for multiple protein docking, there is no study that investigates how accurate structural models of multiple protein complexes should be to allow scientists to gain biological insights.</p> <p>Methods</p> <p>We generated a series of predicted models (decoys) of various accuracies by our multiple protein docking pipeline, Multi-LZerD, for three multi-chain complexes with 3, 4, and 6 chains. We analyzed the decoys in terms of the number of correctly predicted pair conformations in the decoys.</p> <p>Results and conclusion</p> <p>We found that pairs of chains with the correct mutual orientation exist even in the decoys with a large overall root mean square deviation (RMSD) to the native. Therefore, in addition to a global structure similarity measure, such as the global RMSD, the quality of models for multiple chain complexes can be better evaluated by using the local measurement, the number of chain pairs with correct mutual orientation. We termed the fraction of correctly predicted pairs (RMSD at the interface of less than 4.0Å) as <it>fpair </it>and propose to use it for evaluation of the accuracy of multiple protein docking.</p
Recommended from our members
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
The 2022 symposium on dementia and brain aging in low- and middle-income countries: Highlights on research, diagnosis, care, and impact
\ua9 2024 The Authors. Alzheimer\u27s & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer\u27s Association.Two of every three persons living with dementia reside in low- and middle-income countries (LMICs). The projected increase in global dementia rates is expected to affect LMICs disproportionately. However, the majority of global dementia care costs occur in high-income countries (HICs), with dementia research predominantly focusing on HICs. This imbalance necessitates LMIC-focused research to ensure that characterization of dementia accurately reflects the involvement and specificities of diverse populations. Development of effective preventive, diagnostic, and therapeutic approaches for dementia in LMICs requires targeted, personalized, and harmonized efforts. Our article represents timely discussions at the 2022 Symposium on Dementia and Brain Aging in LMICs that identified the foremost opportunities to advance dementia research, differential diagnosis, use of neuropsychometric tools, awareness, and treatment options. We highlight key topics discussed at the meeting and provide future recommendations to foster a more equitable landscape for dementia prevention, diagnosis, care, policy, and management in LMICs. Highlights: Two-thirds of persons with dementia live in LMICs, yet research and costs are skewed toward HICs. LMICs expect dementia prevalence to more than double, accompanied by socioeconomic disparities. The 2022 Symposium on Dementia in LMICs addressed advances in research, diagnosis, prevention, and policy. The Nairobi Declaration urges global action to enhance dementia outcomes in LMICs
Application of 3D Zernike descriptors to shape-based ligand similarity searching
Background: The identification of promising drug leads from a large database of compounds is an important step in the preliminary stages of drug design. Although shape is known to play a key role in the molecular recognition process, its application to virtual screening poses significant hurdles both in terms of the encoding scheme and speed. Results: In this study, we have examined the efficacy of the alignment independent three-dimensional Zernike descriptor (3DZD) for fast shape based similarity searching. Performance of this approach was compared with several other methods including the statistical moments based ultrafast shape recognition scheme (USR) and SIMCOMP, a graph matching algorithm that compares atom environments. Three benchmark datasets are used to thoroughly test the methods in terms of their ability for molecular classification, retrieval rate, and performance under the situation that simulates actual virtual screening tasks over a large pharmaceutical database. The 3DZD performed better than or comparable to the other methods examined, depending on the datasets and evaluation metrics used. Reasons for the success and the failure of the shape based methods for specific cases are investigated. Based on the results for the three datasets, general conclusions are drawn with regard to their efficiency and applicability
- …