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Background: Despite progress made towards SDG 3, sub-Saharan Africa lags behind the rest of the world, accounting for over 50% of global neonatal deaths. The increased number of hospital births in the region has not reciprocated the reduction in neonatal mortality rates. Sick newborns face uncertain journeys from peripheral facilities to specialized centres arriving in suboptimal conditions, which impacts their outcomes, due partly to the scarcity of dedicated neonatal transport services.

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Importance: Pediatric cancer care services in high-income nations are mainly centralized in metropolitan cities. To allow treatments closer to home, patients across Ontario, Canada, a geographically large province, are offered decentralized care via satellite clinics; however, it is unclear whether the utilization of these pediatric oncology satellite clinics differs by area-level sociodemographic factors.

Objective: To examine whether sociodemographic factors, such as area-level income and rurality, are independently associated with the odds of satellite clinic visit and the hazards of time to first visit among pediatric oncology patients receiving cancer treatment.

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Shenxian-Shengmai (SXSM) is a Chinese patent medicine used in the treatment of sick sinus syndrome (SSS). However, its active chemical compounds and the underlying molecular mechanisms remain unclear. In this study, we researched the underlying mechanisms of SXSM in treating SSS.

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Active commuting can be beneficial for health. We examined whether active commuting by walking or cycling was associated with a lower risk of sickness absence in a Finnish public sector cohort of 28 485 employees. We used negative binomial regression to test associations of weekly active commuting in kilometers (no, low, moderate, and high dose) with all-cause sickness absence.

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Delivering medical agents to diseased tissues has been challenging, leading researchers to study the in vivo transport process in the body for improving delivery. Many imaging techniques exist for mapping the distribution of medical agent-carrying nanoparticles in tissues, but they cannot capture the three-dimensional context of tissues with single nanoparticle resolution. Here, we developed 3DEM-NPD, a three-dimensional electron microscopy (3D EM) machine learning strategy to image and map single nanoparticle distributions (NPD) in tissues.

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