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Protocol to assess bottlenecks inhibiting the scaling up of evidence-based family planning practices in low-income and middle-income countries using mixed methods.
BMJ Open
December 2024
Department of Sexual and Reproductive Health and Research including UNDP/UNFPA/UNICEF/WHO/World Bank Special Programme of Research, World Health Organization, Geneva, Switzerland.
Introduction: Scaling up evidence-based practices (EBPs) in family planning (FP), as recommended by the WHO, has increasingly been accepted by global health actors as core to their mission, goals and activities. National policies, strategies, guidance, training materials, political commitment and donor support exist in many countries to adopt and scale up a range of EBPs, including postpregnancy FP, task sharing for FP and the promotion of social and behaviour change (SBC) for FP. While there has been some success in implementing these practices, coverage remains inadequate in many countries.
View Article and Find Full Text PDFA deep learning modular ECG approach for cardiologist assisted adjudication of atrial fibrillation and atrial flutter episodes.
Heart Rhythm O2
December 2024
Cardiology Department, Bichat Hospital, Paris, France.
Background: Detection of atrial tachyarrhythmias (ATA) on long-term electrocardiogram (ECG) recordings is a prerequisite to reduce ATA-related adverse events. However, the burden of editing massive ECG data is not sustainable. Deep learning (DL) algorithms provide improved performances on resting ECG databases.
View Article and Find Full Text PDFRegularly Arranged Heterogeneous Pores in Gas Separation Membranes Constructed by Cocrystallization of Porous Organic Molecules.
Angew Chem Int Ed Engl
January 2025
Zhejiang University, Chemistry, Department of Chemistry, ZhiJinGang Campus, 310058, Hangzhou, CHINA.
Integrating two or more materials to construct membranes with heterogeneous pore structures is an effective strategy for enhancing separation performance. Regularly arranging these heterogeneous pores can significantly optimize the combined effect of the introduced components. Porous Organic Cages (POCs), an emerging subclass of porous materials composed of discrete molecules, assemble to form interconnected pores and exhibit permanent porosity in the solid state.
View Article and Find Full Text PDFUnderstanding the complexity of near-infrared quantification of highly porous patient-tailored drug products by utilizing chemometrics and stimulated Raman imaging.
Int J Pharm
January 2025
Department of Pharmacy, Faculty of Health and Medical Sciences, University of Copenhagen 2100 Copenhagen, Denmark. Electronic address:
Additively manufactured drug products, typically produced using small-scale, on-demand batch mode, require rapid and non-destructive quantification methods. A tunable modular design (TMD) approach combining porous polymeric freeze-dried modules and an additive manufacturing method, inkjet printing, was proposed in an earlier study to fabricate accurate and patient-tailored doses of an antidepressant citalopram hydrobromide. This approach addresses the unmet medical needs associated with antidepressant tapering.
View Article and Find Full Text PDFMulti-Task Federated Split Learning Across Multi-Modal Data with Privacy Preservation.
Sensors (Basel)
January 2025
State Key Laboratory of Intelligent Vehicle Safety Technology, Chongqing 401133, China.
With the advancement of federated learning (FL), there is a growing demand for schemes that support multi-task learning on multi-modal data while ensuring robust privacy protection, especially in applications like intelligent connected vehicles. Traditional FL schemes often struggle with the complexities introduced by multi-modal data and diverse task requirements, such as increased communication overhead and computational burdens. In this paper, we propose a novel privacy-preserving scheme for multi-task federated split learning across multi-modal data (MTFSLaMM).
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