We report 4 children with encephalitis associated with human bocavirus (HBoV) 1 or 2. All children were severely underweight, and 2 died; 1 of them had a matching HBoV2 nucleotide sequence isolated from serum and bocavirus like particles in the cerebrospinal fluid that were observed with electron microscopy. No further pathogens were detected in the cerebrospinal fluid of these patients.

The H/M ratio with the ME collimator, after application of the TEW or IDW methods, was close to the theoretical value in the phantom study. However, the corrected H/M ratios with the LEHR collimator provided comparable H/M ratios to the uncorrected ME data in phantom and clinical studies.

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Due to the essential role that the three-dimensional conformation of a protein plays in regulating interactions with molecular partners, wet and dry laboratories seek biologically-active conformations of a protein to decode its function. Computational approaches are gaining prominence due to the labor and cost demands of wet laboratory investigations. Template-free methods can now compute thousands of conformations known as decoys, but selecting native conformations from the generated decoys remains challenging. Repeatedly, research has shown that the protein energy functions whose minima are sought in the generation of decoys are unreliable indicators of nativeness. The prevalent approach ignores energy altogether and clusters decoys by conformational similarity. Complementary recent efforts design protein-specific scoring functions or train machine learning models on labeled decoys. In this paper, we show that an informative consideration of energy can be carried out under the energy landscape view. Specifically, we leverage local structures known as basins in the energy landscape probed by a template-free method. We propose and compare various strategies of basin-based decoy selection that we demonstrate are superior to clustering-based strategies. The presented results point to further directions of research for improving decoy selection, including the ability to properly consider the multiplicity of native conformations of proteins.

Background The deployment of Community Health Workers (CHWs) is widely promoted as a strategy for reducing health inequities in low- and middle-income countries (LMIC). Yet there is limited evidence on whether and how CHW programmes achieve this. This systematic review aimed to synthesise research findings on the following questions: (1) How effective are CHW interventions at reaching the most disadvantaged groups in LMIC contexts? and (2) What evidence exists on whether and how these programmes reduce health inequities in the populations they serve? Methods We searched six academic databases for recent (2014–2020) studies reporting on CHW programme access, utilisation, quality, and effects on health outcomes/behaviours in relation to potential stratifiers of health opportunities and outcomes (e.g., gender, socioeconomic status, place of residence). Quantitative data were extracted, tabulated, and subjected to meta-analysis where appropriate. Qualitative findings were synthesised using thematic analysis. Results One hundred sixty-seven studies met the search criteria, reporting on CHW interventions in 33 LMIC. Quantitative synthesis showed that CHW programmes successfully reach many (although not all) marginalized groups, but that health inequalities often persist in the populations they serve. Qualitative findings suggest that disadvantaged groups experienced barriers to taking up CHW health advice and referrals and point to a range of strategies for improving the reach and impact of CHW programmes in these groups. Ensuring fair working conditions for CHWs and expanding opportunities for advocacy were also revealed as being important for bridging health equity gaps. Conclusion In order to optimise the equity impacts of CHW programmes, we need to move beyond seeing CHWs as a temporary sticking plaster, and instead build meaningful partnerships between CHWs, communities and policy-makers to confront and address the underlying structures of inequity. Trial registration PROSPERO registration number CRD42020177333.