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BackgroundMental illness during pregnancy has become a major public health problem in China over the recent years， and depression is the most common psychological symptom during pregnancy. Current research efforts are directed towards the therapy on prenatal depression， whereas the construction of prediction model for prenatal depression risk has been little studied.ObjectiveTo construct a simple model for predicting the risk of prenatal depression， thus providing a valuable reference for the prevention of maternal depression during pregnancy.MethodsA total of 803 pregnant women attending three hospitals in Nanchong city were consecutively recruited from May 2021 to February 2022. A self-administered questionnaire was developed for the assessment of social demographic variables， obstetrical and general medical indexes and psychological status of all participants， and Self-rating Depression Scale （SDS） was utilized to screen for the presence of maternal depression. Subjects were randomly assigned into modelling group （n=635） and validation group （n=168） at the ratio of 8∶2 under simple random sampling with replacement. The candidate risk factors of maternal depression during pregnancy were screened using binary Logistic regression analysis， and the predictive model was constructed. Then the performance of the predictive model was validated using receiver operating characteristics （ROC） curve.Results① Lack of companionship （β=-0.692， OR=0.501， 95% CI： 0.289~0.868）， low mood during the last menstrual period （β=-1.510， OR=0.221， 95% CI： 0.074~0.656）， emotional stress during the last menstrual period （β=-1.082， OR=0.339， 95% CI： 0.135~0.853）， unsatisfactory relationship between mother-in-law and daughter-in-law （β=-1.228， OR=0.293， 95% CI： 0.141~0.609）， and indifferent generally relationship between mother-in-law and daughter-in-law （β=-0.831， OR=0.436， 95% CI： 0.260~0.730） were risk factors for prenatal depression in pregnant women （P

Thyroid nodules are a common endocrine system disorder for which accurate ultrasound image segmentation is important for evaluation and diagnosis, as well as a critical step in computer‐aided diagnostic systems. However, the accuracy and consistency of segmentation remains a challenging task due to the presence of scattering noise, low contrast and resolution in ultrasound images. Therefore, we propose a deep learning‐based CAD (computer‐aided diagnosis) method, STU3Net in this paper, aiming at automatic segmentation of thyroid nodules. The method employs a modified Swin Transformer combined with a CNN encoder, which is capable of extracting morphological features and edge details of thyroid nodules in ultrasound images. In decoding through the features for image reconstruction, we introduce a modified three‐layer U‐Net network with cross‐layer connectivity to further enhance image reduction. This cross‐layer connectivity enhances the network's capture and representation of the contained image feature information by creating skip connections between different layers and merging the detailed information of the shallow network with the abstract information of the deeper network. Through comparison experiments with current mainstream deep learning methods on the TN3K and BUSI datasets, we validate the superiority of the STU3Net method in thyroid nodule segmentation performance. The experimental results show that STU3Net outperforms most of the mainstream models on the TN3K dataset, with Dice and IoU reaching 0.8368 and 0.7416, respectively, which are significantly better than other methods. The method demonstrates excellent performance on these datasets and provides radiologists with an effective auxiliary tool to accurately detect thyroid nodules in ultrasound images. [ABSTRACT FROM AUTHOR]

An epidemiological paradox surrounds Salmonella enterica serovar Enteritidis. In high-income settings, it has been responsible for an epidemic of poultry-associated, self-limiting enterocolitis, whereas in sub-Saharan Africa it is a major cause of invasive nontyphoidal Salmonella disease, associated with high case fatality. By whole-genome sequence analysis of 675 isolates of S. Enteritidis from 45 countries, we show the existence of a global epidemic clade and two new clades of S. Enteritidis that are geographically restricted to distinct regions of Africa. The African isolates display genomic degradation, a novel prophage repertoire, and an expanded multidrug resistance plasmid. S. Enteritidis is a further example of a Salmonella serotype that displays niche plasticity, with distinct clades that enable it to become a prominent cause of gastroenteritis in association with the industrial production of eggs and of multidrug-resistant, bloodstream-invasive infection in Africa.

Glycyl-tRNA synthetase (GlyRS) is the enzyme that covalently links glycine to cognate tRNA for translation. It is of great research interest because of its nonconserved quaternary structures, unique species-specific aminoacylation properties, and noncanonical functions in neurological diseases, but none of these is fully understood. We report two crystal structures of human GlyRS variants, in the free form and in complex with tRNA(Gly) respectively, and reveal new aspects of the glycylation mechanism. We discover that insertion 3 differs considerably in conformation in catalysis and that it acts like a "switch" and fully opens to allow tRNA to bind in a cross-subunit fashion. The flexibility of the protein is supported by molecular dynamics simulation, as well as enzymatic activity assays. The biophysical and biochemical studies suggest that human GlyRS may utilize its flexibility for both the traditional function (regulate tRNA binding) and alternative functions (roles in diseases).

Salmonella enterica serotype Enteritidis is one of the most commonly reported causes of human salmonellosis. Its low genetic diversity, measured by fingerprinting methods, has made subtyping a challenge. We used whole-genome sequencing to characterize 125 S. enterica Enteritidis and 3 S. enterica serotype Nitra strains. Single-nucleotide polymorphisms were filtered to identify 4,887 reliable loci that distinguished all isolates from each other. Our whole-genome single-nucleotide polymorphism typing approach was robust for S. enterica Enteritidis subtyping with combined data for different strains from 2 different sequencing platforms. Five major genetic lineages were recognized, which revealed possible patterns of geographic and epidemiologic distribution. Analyses on the population dynamics and evolutionary history estimated that major lineages emerged during the 17th-18th centuries and diversified during the 1920s and 1950s.