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ObjectiveThis study aims to apply different machine learning (ML) methods to construct risk prediction models for pulmonary embolism (PE) in hospitalized patients, and to evaluate and compare the predictive efficacy and clinical benefit of each model.MethodsWe conducted a retrospective study involving 332 participants (172 PE positive cases and 160 PE negative cases) recruited from Guangdong Medical University. Participants were randomly divided into a training group (70%) and a validation group (30%). Baseline data were analyzed using univariate analysis, and potential independent risk factors associated with PE were further identified through univariate and multivariate logistic regression analysis. Six ML models, namely Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Support Vector Machine (SVM), and AdaBoost were developed. The predictive efficacy of each model was compared using the receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC). Clinical benefit was assessed using decision curve analysis (DCA).ResultsLogistic regression analysis identified lower extremity deep venous thrombosis, elevated D-dimer, shortened activated partial prothrombin time, and increased red blood cell distribution width as potential independent risk factors for PE. Among the six ML models, the RF model achieved the highest AUC of 0.778. Additionally, DCA consistently indicated that the RF model offered the greatest clinical benefit.ConclusionThis study developed six ML models, with the RF model exhibiting the highest predictive efficacy and clinical benefit in the identification and prediction of PE occurrence in hospitalized patients.

Introduction: As a valuable genetic resource, native birds can contribute to the sustainable development of animal production. Tibetan chickens, known for their special flavor, are one of the important local poultry breeds in the Qinghai–Tibet Plateau. However, Tibetan chickens have a slow growth rate and poor carcass traits compared with broilers. Although most of the research on Tibetan chickens focused on their hypoxic adaptation, there were fewer studies related to skeletal muscle development.Methods: Here, we performed the transcriptional sequencing of leg muscles from Tibetan chicken embryos at E (embryonic)10, E14, and E18.Results: In total, 1,600, 4,610, and 2,166 DE (differentially expressed) mRNAs, 210, 573, and 234 DE lncRNAs (long non-coding RNAs), and 52, 137, and 33 DE miRNAs (microRNAs) were detected between E10 and E14, E10 and E18, and E14 and E18, respectively. Functional prediction showed several DE mRNAs and the target mRNAs of DE lncRNAs and DE miRNAs were significantly enriched in sarcomere organization, actin cytoskeleton organization, myofibril, muscle fiber development, and other terms and pathways related to muscle growth and development. Finally, a lncRNA–miRNA–mRNA ceRNA (competing endogenous RNA) network associated with muscle growth and development, which contained 6 DE lncRNAs, 13 DE miRNAs, and 50 DE mRNAs, was constructed based on the screened DE RNAs by Gene Ontology (GO) enrichment. These DE RNAs may play a critical regulatory role in the skeletal muscle development of chickens.Discussion: The results provide a genomic resource for mRNAs, lncRNAs, and miRNAs potentially involved in the skeletal muscle development of chickens, which lay the foundation for further studies of the molecular mechanisms underlying skeletal muscle growth and development in Tibetan chickens.

ObjectiveThe incidence of thyroid dysfunction (TD) and major depressive disorder (MDD) is increasing year by year in the general population. However, the prevalence and correlates of TD in first-episode drug-naive (FEDN) MDD patients have not been explored. This study sought to fill this gap and examine the association between TD and MDD.MethodsWe recruited 1,289 FEDN MDD patients aged 18 ~ 45 years. A total of 1,289 FEDN MDD outpatients were recruited. Demographical and suicide data were collected for each patient, and lipid profiles, thyroid function, and fasting blood glucose (FBG) levels were measured. The Hamilton Depression Scale 17 (HAMD-17) was assessed for depression.ResultsThe prevalence of TD in young FEDN MDD patients was 64.86%. Compared with those without TD, patients with TD had longer duration of illness, greater HAMD score, higher BMI, TG, TC, and LDL-C levels, and higher suicide attempt rates, but lower HDL-C and FBG levels. Further logistic regression indicated that duration of illness, HAMD score, TC, HDL-C, BMI, and FBG levels were significantly associated with TD.LimitationsNo causal relationship can be drawn due to the cross-sectional design.ConclusionTD is common in young FEDN MDD patients. So clinicians should monitor thyroid function in patients with MDD.

In momentum space, the nodal loop is regarded as a ring-shaped band degeneracy and is classified into type-I and type-II configurations depending on the positive/negative dispersions of the degenerating bands. Here, we experimentally observe a new class of nodal loop in the photonic band structure, employing an artificially designed bilayer metasurface. Such degeneracy, termed type-III nodal loop, is formed by the crossing between a resonant flat band and a positively dispersive band and is protected by mirror symmetry Mz, which manifests in the metasurface’s bilayer architecture. Furthermore, the sequential topological transitions of band degeneracy from the nodal loop via Dirac point to gapped phase are demonstrated at the metasurfaces. Such transitions are enabled by the bilayer design and end with a pair of edge states on the domain wall of gapped systems. Our work reveals that properly engineered bilayer metasurfaces offer rich physics and vast flexibility in two-dimensional topological photonic research through assembling and tunning more symmetries and degrees of freedom along the stacking direction.

Background: Structural variations (SVs) are common genetic alterations in the human genome that could cause different phenotypes and diseases, including cancer. However, the detection of structural variations using the second-generation sequencing was limited by its short read length, which restrained our understanding of structural variations.Methods: In this study, we developed a 28-gene panel for long-read sequencing and employed it to Oxford Nanopore Technologies and Pacific Biosciences platforms. We analyzed structural variations in the 28 breast cancer-related genes through long-read genomic and transcriptomic sequencing of tumor, para-tumor, and blood samples in 19 breast cancer patients.Results: Our results showed that some somatic SVs were recurring among the selected genes, though the majority of them occurred in the non-exonic region. We found evidence supporting the existence of hotspot regions for SVs, which extended our previous understanding that they exist only for single nucleotide variations.Conclusion: In conclusion, we employed long-read genomic and transcriptomic sequencing to identify SVs from breast cancer patients and proved that this approach holds great potential in clinical application.