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Abstract Background The differential diagnosis of a neonate or fetus presenting with a bell-shaped or long narrow thorax includes a wide range of bony dysplasia syndromes. Where this is accompanied by respiratory distress, asphyxiating thoracic dystrophy (ATD, Jeune syndrome) is an important potential diagnosis. Shwachman-Diamond syndrome (SDS) is widely recognised as a cause of exocrine pancreatic dysfunction, short stature and bone marrow failure. It is not so well appreciated that rib and/or thoracic cage abnormalities occur in 30–50% of patients and that, in severe cases, these abnormalities may lead to thoracic dystrophy and respiratory failure in the newborn. There are, however, at least three previous case reports of children who were initially diagnosed with ATD who were subsequently shown to have SDS. Case presentation This report details the case history of a patient misdiagnosed as having ATD as a neonate following the neonatal asphyxial death of her brother. She subsequently developed progressive pancytopenia but was only diagnosed with SDS at 11 years of age after referral for haematopoietic stem cell transplantation for bone marrow failure accompanied by trilineage dysplasia and clonal cytogenetic abnormalities on bone marrow examination. Subsequent testing revealed the presence of fat globules in stools, reduced faecal chymotrypsin, fat-soluble vitamin deficiency, metaphyseal dysplasia on skeletal survey and heterozygous mutations of the SBDS gene. Conclusion This report highlights the potential for diagnostic confusion between ATD and SDS. It is important to include SDS in the differential diagnosis of newborns with thoracic dystrophy and to seek expert clinical and radiological assessment of such children.

We used in-line holography to create images of individual milk fat globules in diluted samples of milk. Analyzing these images with the exact Lorenz-Mie light scattering theory then yields the droplets' radii with nanometer resolution and their refractive indexes to within one part in a thousand. This procedure rapidly and directly characterizes both the quantity and quality of fat in milk. [ABSTRACT FROM AUTHOR]

The programmability of DNA oligonucleotides has led to sophisticated DNA nanotechnology and considerable research on DNA nanomachines powered by DNA hybridization. Here, we investigate an extension of this technology to the micrometer-colloidal scale, in which observations and measurements can be made in real time/space using optical microscopy and holographic optical tweezers. We use semirigid DNA origami structures, hinges with mechanical advantage, self-assembled into a nine-hinge, accordion-like chemomechanical device, with one end anchored to a substrate and a colloidal bead attached to the other end. Pulling the bead converts the mechanical energy into chemical energy stored by unzipping the DNA that bridges the hinge. Releasing the bead returns this energy in rapid (>20 μm/s) motion of the bead. Force-extension curves yield energy storage/retrieval in these devices that is very high. We also demonstrate remote activation and sensing-pulling the bead enables binding at a distant site. This work opens the door to easily designed and constructed micromechanical devices that bridge the molecular and colloidal/cellular scales., Competing Interests: The authors declare no competing interest., (Copyright © 2021 the Author(s). Published by PNAS.)

Objectives: There is an incomplete understanding of the host humoral immune response to severe acute respiratory syndrome (SARS)-coronavirus (CoV)-2, which underlies COVID-19, during acute infection. Host factors such as age and sex as well as the kinetics and functionality of antibody responses are important factors to consider as vaccine development proceeds. The receptor-binding domain of the CoV spike (RBD-S) protein mediates host cell binding and infection and is a major target for vaccine design to elicit neutralising antibodies., Methods: We assessed serum anti-SARS-CoV-2 RBD-S IgG, IgM and IgA antibodies by a two-step ELISA and neutralising antibodies in a cross-sectional study of hospitalised COVID-19 patients of varying disease severities. Anti-RBD-S IgG levels were also determined in asymptomatic seropositives., Results: We found equivalent levels of anti-RBD-S antibodies in male and female patients and no age-related deficiencies even out to 93 years of age. The anti-RBD-S response was evident as little as 6 days after onset of symptoms and for at least 5 weeks after symptom onset. Anti-RBD-S IgG, IgM and IgA responses were simultaneously induced within 10 days after onset, with anti-RBD-S IgG sustained over a 5-week period. Anti-RBD-S antibodies strongly correlated with neutralising activity. Lastly, anti-RBD-S IgG responses were higher in symptomatic COVID-19 patients during acute infection compared with asymptomatic seropositive donors., Conclusion: Our results suggest that anti-RBD-S IgG reflect functional immune responses to SARS-CoV-2, but do not completely explain age- and sex-related disparities in COVID-19 fatalities., Competing Interests: The authors declare no conflict of interest., (© 2020 The Authors. Clinical & Translational Immunology published by John Wiley & Sons Australia, Ltd on behalf of Australian and New Zealand Society for Immunology, Inc.)

Background: To initiate the development of a machine learning algorithm capable of comparing segments of pre and post pamidronate whole body MRI scans to assess treatment response and to compare the results of this algorithm with the analysis of a panel of paediatric radiologists., Methods: Whole body MRI of patients under the age of 16 diagnosed with CNO and treated with pamidronate at a tertiary referral paediatric hospital in United Kingdom between 2005 and 2017 were reviewed. Pre and post pamidronate images of the commonest sites of involvement (distal femur and proximal tibia) were manually selected (n = 45). A machine learning algorithm was developed and tested to assess treatment effectiveness by comparing pre and post pamidronate scans. The results of this algorithm were compared with the results of a panel of radiologists (ground truth)., Results: When tested initially the machine algorithm predicted 4/7 (57.1%) examples correctly in the multi class model, and 5/7 (71.4%) correctly in the binary group. However when compared to the ground truth, the machine model was able to classify only 33.3% of the samples correctly but had a sensitivity of 100% in detecting improvement or worsening of disease., Conclusion: The machine learning could detect new lesions or resolution of a lesion with good sensitivity but failed to classify stable disease accurately. However, further validation on larger datasets are required to improve the specificity and accuracy of the machine model.