Elevated triglyceride-glucose-body mass index associated with lower probability of future regression to normoglycemia in Chinese adults with prediabetes: a 5-year cohort study.

Objective: Despite the clear association of TyG-BMI with prediabetes and the progression of diabetes, no study to date has examined the relationship between TyG-BMI and the reversal of prediabetes to normoglycemia. Methods: 25,279 participants with prediabetes who had physical examinations between 2010 and 2016 were enrolled in this retrospective cohort study. The relationship between baseline TyG-BMI and regression to normoglycemia from prediabetes was examined using the Cox proportional hazards regression model in this study. Additionally, the nonlinear association between TyG-BMI and the likelihood of regression to normoglycemia was investigated using the Cox proportional hazards regression with cubic spline function. Competing risk multivariate Cox regression analysis was conducted, with progression to diabetes as a competing risk for prediabetes reversal to normoglycemia. Furthermore, subgroup analyses and a series of sensitivity analyses were performed. Results: After adjusting for covariates, the results showed that TyG-BMI was negatively associated with the probability of returning to normoglycemia (per 10 units, HR=0.970, 95% CI: 0.965, 0.976). They were also nonlinearly related, with an inflection point for TyG-BMI of 196.46. The effect size (HR) for TyG-BMI to the right of the inflection point (TyG-BMI ≥ 196.46) and the probability of return of normoglycemia was 0.962 (95% CI: 0.954, 0.970, per 10 units). In addition, the competing risks model found a negative correlation between TyG-BMI and return to normoglycemia (SHR=0.97, 95% CI: 0.96-0.98). Sensitivity analyses demonstrated the robustness of our results. Conclusion: This study demonstrated a negative and nonlinear relationship between TyG-BMI and return to normoglycemia in Chinese adults with prediabetes. Through active intervention, the combined reduction of BMI and TG levels to bring TyG-BMI down to 196.46 could significantly increase the probability of returning to normoglycemia.

ZLDI-8 suppresses angiogenesis and vasculogenic mimicry in drug-resistant NSCLC in vitro and in vivo.

AIMS: The chemotherapy drugs for NSCLC often face the consequences of treatment failure due to acquired drug resistance. Tumor chemotherapy resistance is often accompanied by angiogenesis. Here, we aimed to investigate the effect and underlying mechanisms of ADAM-17 inhibitor ZLDI-8 we found before on angiogenesis and vasculogenic mimicry(VM) in drug-resistant NSCLC. MAIN METHODS: The tube formation assay was used to evaluate angiogenesis and VM. Migration and invasion were assessed with transwell assays in the co-culture condition. To explore the underlying mechanisms of how ZLDI-8 inhibited tubes formation, ELISA assay and western blot assay were preformed. The effects of ZLDI-8 on angiogenesis in vivo were investigated in Matrigel plug, CAM and Rat aortic ring assays. KEY FINDINGS: In the present study, ZLDI-8 significantly inhibited the tube formation of human umbilical vein endothelial cells (HUVECs) in either normal medium or in tumor supernatants. Furthermore, ZLDI-8 also inhibited VM tubes formation of A549/Taxol cells. In the co-culture assay, the interaction between lung cancer cells and HUVECs promotes increased cell migration and invasion, while ZLDI-8 eliminates this promotion. Moreover, the VEGF secretion were decreased by ZLDI-8 and the expression of Notch1, Dll4, HIF1α and VEGF were inhibited by ZLDI-8. In addition, ZLDI-8 can inhibit blood vessel formation in the Matrigel plug, CAM and Rat aortic ring assays. SIGNIFICANCE: ZLDI-8 inhibits angiogenesis and VM in drug-resistant NSCLC through suppressing Notch1-HIF1α-VEGF signaling pathway. This study lays the foundation for the discovery of drugs that inhibit angiogenesis and VM in drug resistant NSCLC.

Allergic bronchopulmonary aspergillosis with marked peripheral blood eosinophilia and pulmonary eosinophilia: A case report.

BACKGROUND: Allergic bronchopulmonary aspergillosis (ABPA) is an immune-related pulmonary disease caused by sensitization of airway by Aspergillus fumigatus. The disease manifests as bronchial asthma and recurring pulmonary shadows, which may be associated with bronchiectasis. The diagnosis of ABPA mainly depends on serological, immunological, and imaging findings. Pathological examination is not necessary but may be required in atypical cases to exclude pulmonary tuberculosis, tumor, and other diseases through lung biopsy. CASE SUMMARY: An 18-year-old man presented with recurrent wheezing, cough, and peripheral blood eosinophilia. Chest computed tomography showed pulmonary infiltration. There was a significant increase in eosinophils in bronchoalveolar lavage fluid. There was no history of residing in a parasite-endemic area or any evidence of parasitic infection. Pathologic examination of bronchoalveolar lavage fluid excluded fungal and mycobacterial infections. The patient was receiving medication for comorbid diseases, but there was no temporal correlation between medication use and clinical manifestations, which excluded drug-induced etiology. Histopathological examination of lung biopsy specimen showed no signs of eosinophilic granulomatosis with polyangiitis, IgG4-related diseases, or tumors. The diagnosis of ABPA was considered based on the history of asthma and the significant increase in serum Aspergillus fumigatus-specific immunoglobulin (Ig)E. Eosinophil-related diseases were excluded through pathological biopsy, which showed typical pathological manifestations of ABPA. CONCLUSION: The possibility of ABPA should be considered in patients with poorly controlled asthma, especially those with eosinophilia, lung infiltration shadows, or bronchiectasis. Screening for serum IgE, Aspergillus fumigatus-specific IgE and IgG, and alveolar lavage can help avoid misdiagnosis.

Springer: An R package for bi-level variable selection of high-dimensional longitudinal data.

In high-dimensional data analysis, the bi-level (or the sparse group) variable selection can simultaneously conduct penalization on the group level and within groups, which has been developed for continuous, binary, and survival responses in the literature. Zhou et al. (2022) (PMID: 35766061) has further extended it under the longitudinal response by proposing a quadratic inference function-based penalization method in gene-environment interaction studies. This study introduces "springer," an R package implementing the bi-level variable selection within the QIF framework developed in Zhou et al. (2022). In addition, R package "springer" has also implemented the generalized estimating equation-based sparse group penalization method. Alternative methods focusing only on the group level or individual level have also been provided by the package. In this study, we have systematically introduced the longitudinal penalization methods implemented in the "springer" package. We demonstrate the usage of the core and supporting functions, which is followed by the numerical examples and discussions. R package "springer" is available at https://cran.r-project.org/package=springer.

Robust Bayesian variable selection for gene-environment interactions.

Gene-environment (G× E) interactions have important implications to elucidate the etiology of complex diseases beyond the main genetic and environmental effects. Outliers and data contamination in disease phenotypes of G× E studies have been commonly encountered, leading to the development of a broad spectrum of robust regularization methods. Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing studies. We develop a fully Bayesian robust variable selection method for G× E interaction studies. The proposed Bayesian method can effectively accommodate heavy-tailed errors and outliers in the response variable while conducting variable selection by accounting for structural sparsity. In particular, for the robust sparse group selection, the spike-and-slab priors have been imposed on both individual and group levels to identify important main and interaction effects robustly. An efficient Gibbs sampler has been developed to facilitate fast computation. Extensive simulation studies, analysis of diabetes data with single-nucleotide polymorphism measurements from the Nurses' Health Study, and The Cancer Genome Atlas melanoma data with gene expression measurements demonstrate the superior performance of the proposed method over multiple competing alternatives.

Formation of monodomain polymer-stabilized blue phase liquid crystals using surface acoustic waves.

This work uses surface acoustic waves (SAWs) that are generated by a piezoelectric substrate containing an interdigital transducer (IDT) to which a low voltage of 2 mV was applied at a frequency of 1 kHz to fabricate a polymer-stabilized blue phase liquid crystal (PS-BPLC) layer. The PS-BPLC layer has a more uniform optical microscope (OM) image at a voltage of 2 mV than at zero voltage, and its reflective spectrum exhibits a smaller full width at half maximum (FWHM) at the former than at the latter. The uniform OM image and small FWHM reveal that the lattices in the PS-BPLC layer have monodomain structure. The monodomain PS-BPLC layer is formed because the SAWs cause longitudinal and transverse vibrations of the PS-BPLC lattices in the vertical plane along their traveling direction. The proposed method for fabricating the monodomain PS-BPLC layer using the SAWs has potential for the development of reflective optical devices that consume low power during their fabrication.

The Bayesian Regularized Quantile Varying Coefficient Model.

The quantile varying coefficient (VC) model can flexibly capture dynamical patterns of regression coefficients. In addition, due to the quantile check loss function, it is robust against outliers and heavy-tailed distributions of the response variable, and can provide a more comprehensive picture of modeling via exploring the conditional quantiles of the response variable. Although extensive studies have been conducted to examine variable selection for the high-dimensional quantile varying coefficient models, the Bayesian analysis has been rarely developed. The Bayesian regularized quantile varying coefficient model has been proposed to incorporate robustness against data heterogeneity while accommodating the non-linear interactions between the effect modifier and predictors. Selecting important varying coefficients can be achieved through Bayesian variable selection. Incorporating the multivariate spike-and-slab priors further improves performance by inducing exact sparsity. The Gibbs sampler has been derived to conduct efficient posterior inference of the sparse Bayesian quantile VC model through Markov chain Monte Carlo (MCMC). The merit of the proposed model in selection and estimation accuracy over the alternatives has been systematically investigated in simulation under specific quantile levels and multiple heavy-tailed model errors. In the case study, the proposed model leads to identification of biologically sensible markers in a non-linear gene-environment interaction study using the NHS data.

Circuit quantum electrodynamics with dressed states of a superconducting artificial atom.

A dynamical control of the coupling strengths between dressed states and probe photon states is demonstrated with a transmon-like artificial atom coupled to two closely spaced resonant modes. When the atom is driven with one mode, the atom state and driving photon states form the so-called dressed states. Dressed states with sideband index up to 3 were prepared and probed via the strong coupling to the other resonant mode. Spectroscopy reveals that the coupling strengths are "dressed" and can be modulated by the power and sideband index of the driving. The transmission of the probe tone is modulated by the driving microwave amplitude with a Bessel behavior, displaying multi-photon process associated with the inter-atomic level transitions.

PSME2 identifies immune-hot tumors in breast cancer and associates with well therapeutic response to immunotherapy.

Breast cancer (BrCa) is a heterogeneous disease, which leads to unsatisfactory prognosis in females worldwide. Previous studies have proved that tumor immune microenvironment (TIME) plays crucial roles in oncogenesis, progression, and therapeutic resistance in Breast cancer. However, biomarkers related to TIME features have not been fully discovered. Proteasome activator complex subunit 2 (PSME2) is a member of proteasome activator subunit gene family, which is critical to protein degradation mediated by the proteasome. In the current research, we comprehensively analyzed the expression and immuno-correlations of Proteasome activator complex subunit 2 in Breast cancer. Proteasome activator complex subunit 2 was significantly upregulated in tumor tissues but associated with well prognosis. In addition, Proteasome activator complex subunit 2 was overexpressed in HER2-positive Breast cancer but not related to other clinicopathological features. Interestingly, Proteasome activator complex subunit 2 was positively related to immune-related processes and identified immuno-hot TIME in Breast cancer. Specifically, Proteasome activator complex subunit 2 was positively correlated with immunomodulators, tumor-infiltrating immune cells (TIICs), immune checkpoints, and tumor mutation burden (TMB) levels. Moreover, the positive correlation between Proteasome activator complex subunit 2 and PD-L1 expression was confirmed in a tissue microarray (TMA) cohort. Furthermore, in an immunotherapy cohort of Breast cancer, patients with pathological complete response (pCR) expressed higher Proteasome activator complex subunit 2 compared with those with non-pathological complete response. In conclusion, Proteasome activator complex subunit 2 is upregulated in tumor tissues and correlated with the immuno-hot tumor immune microenvironment, which can be a novel biomarker for the recognition of tumor immune microenvironment features and immunotherapeutic response in Breast cancer.

Construction of a novel prognostic signature based on the composition of tumor-infiltrating immune cells in clear cell renal cell carcinoma.

Emerging evidence has uncovered that tumor-infiltrating immune cells (TIICs) play significant roles in regulating the tumorigenesis and progression of clear cell renal cell carcinoma (ccRCC). However, the exact composition of TIICs and their prognostic values in ccRCC have not been well defined. A total of 534 ccRCC samples with survival information and TIIC data from The Cancer Genome Atlas (TCGA) dataset were included in our research. The ImmuCellAI tool was employed to estimate the abundance of 24 TIICs and further survival analysis explored the prognostic values of TIICs in ccRCC. In addition, the expression levels of immunosuppressive molecules (PDL1, PD1, LAG3, and CTLA4) in the high- and low-risk groups were explored. Various subtypes of TIICs had distinct infiltrating features and most TIICs exhibited dysregulated abundance between normal and tumor tissues. Moreover, specific kinds of TIICs had encouraging prognostic values in ccRCC. Further analysis constructed a 4-TIICs signature to evaluate the prognosis of ccRCC patients. Cox regression analyses confirmed the independent prognostic role of the signature in ccRCC. Moreover, immunosuppressive molecules, including PD1, LAG3, and CTLA4, were significantly upregulated in the high-risk group and predicted poor prognosis. However, PDL1 was not changed between high- and low-risk groups and could not predict poor prognosis. To sum up, our research explored the landscape of TIICs in ccRCC and established a novel 4-TIIC prognostic signature, which could effectively predict the prognosis for patients with ccRCC. Based on this signature, we also concluded that PDL1 may not predict prognosis in ccRCC.

Sparse group variable selection for gene-environment interactions in the longitudinal study.

Penalized variable selection for high-dimensional longitudinal data has received much attention as it can account for the correlation among repeated measurements while providing additional and essential information for improved identification and prediction performance. Despite the success, in longitudinal studies, the potential of penalization methods is far from fully understood for accommodating structured sparsity. In this article, we develop a sparse group penalization method to conduct the bi-level gene-environment (G × $\times $ E) interaction study under the repeatedly measured phenotype. Within the quadratic inference function framework, the proposed method can achieve simultaneous identification of main and interaction effects on both the group and individual levels. Simulation studies have shown that the proposed method outperforms major competitors. In the case study of asthma data from the Childhood Asthma Management Program, we conduct G × $\times $ E study by using high-dimensional single nucleotide polymorphism data as genetic factors and the longitudinal trait, forced expiratory volume in 1 s, as the phenotype. Our method leads to improved prediction and identification of main and interaction effects with important implications.

Interep: An R Package for High-Dimensional Interaction Analysis of the Repeated Measurement Data.

We introduce interep, an R package for interaction analysis of repeated measurement data with high-dimensional main and interaction effects. In G × E interaction studies, the forms of environmental factors play a critical role in determining how structured sparsity should be imposed in the high-dimensional scenario to identify important effects. Zhou et al. (2019) (PMID: 31816972) proposed a longitudinal penalization method to select main and interaction effects corresponding to the individual and group structure, respectively, which requires a mixture of individual and group level penalties. The R package interep implements generalized estimating equation (GEE)-based penalization methods with this sparsity assumption. Moreover, alternative methods have also been implemented in the package. These alternative methods merely select effects on an individual level and ignore the group-level interaction structure. In this software article, we first introduce the statistical methodology corresponding to the penalized GEE methods implemented in the package. Next, we present the usage of the core and supporting functions, which is followed by a simulation example with R codes and annotations. The R package interep is available at The Comprehensive R Archive Network (CRAN).

Identifying Gene-Environment Interactions With Robust Marginal Bayesian Variable Selection.

In high-throughput genetics studies, an important aim is to identify gene-environment interactions associated with the clinical outcomes. Recently, multiple marginal penalization methods have been developed and shown to be effective in G×E studies. However, within the Bayesian framework, marginal variable selection has not received much attention. In this study, we propose a novel marginal Bayesian variable selection method for G×E studies. In particular, our marginal Bayesian method is robust to data contamination and outliers in the outcome variables. With the incorporation of spike-and-slab priors, we have implemented the Gibbs sampler based on Markov Chain Monte Carlo (MCMC). The proposed method outperforms a number of alternatives in extensive simulation studies. The utility of the marginal robust Bayesian variable selection method has been further demonstrated in the case studies using data from the Nurse Health Study (NHS). Some of the identified main and interaction effects from the real data analysis have important biological implications.

Cervical Cancer Development: Implications of HPV16 E6E7-NFX1-123 Regulated Genes.

High-risk human papillomavirus (HR HPV) causes nearly all cervical cancers, half of which are due to HPV type 16 (HPV16). HPV16 oncoprotein E6 (16E6) binds to NFX1-123, and dysregulates gene expression, but their clinical implications are unknown. Additionally, HPV16 E7's role has not been studied in concert with NFX1-123 and 16E6. HR HPVs express both oncogenes, and transformation requires their expression, so we sought to investigate the effect of E7 on gene expression. This study's goal was to define gene expression profiles across cervical precancer and cancer stages, identify genes correlating with disease progression, assess patient survival, and validate findings in cell models. We analyzed NCBI GEO datasets containing transcriptomic data linked with cervical cancer stage and utilized LASSO analysis to identify cancer-driving genes. Keratinocytes expressing 16E6 and 16E7 (16E6E7) and exogenous NFX1-123 were tested for LASSO-identified gene expression. Ten out of nineteen genes correlated with disease progression, including CEBPD, NOTCH1, and KRT16, and affected survival. 16E6E7 in keratinocytes increased CEBPD, KRT16, and SLPI, and decreased NOTCH1. Exogenous NFX1-123 in 16E6E7 keratinocytes resulted in significantly increased CEBPD and NOTCH1, and reduced SLPI. This work demonstrates the clinical relevance of CEBPD, NOTCH1, KRT16, and SLPI, and shows the regulatory effects of 16E6E7 and NFX1-123.

Case Report: Management of Primary Tracheobronchial Light Chain Amyloidosis in a Patient With Biclonal Gammopathy Using a Systemic Bortezomib-Based Regimen.

Primary tracheobronchial light chain (AL) amyloidosis is a rare and heterogeneous disease characterized by the buildup of amyloid deposits in the airway mucosa. Although its treatment remains challenging, the current view is that the localized form can be treated conservatively due to its slow progression. While radiotherapy has proven effective in treating localized form of the disease, some patients do not respond to local treatment and continue to experience poor quality of life, highlighting the need to explore additional treatment strategies. In this report, we discuss a case of primary tracheobronchial AL amyloidosis with biclonal gammopathy (IgA κ and IgG κ) in a 46-year-old man who was transferred to our hospital due to dyspnea progression over the preceding 3 years. Chest computed tomography revealed irregular tracheobronchial stenosis with wall thickening, and histological examination of the bronchial biopsies confirmed the diagnosis of endobronchial AL amyloidosis. Owing to the poor effect of radiation therapy and treatments for improving airway patency, he was treated with a systemic chemotherapy regimen [cyclophosphamide-bortezomib-dexamethasone (CyBorD)]. We observed substantial improvements in his dyspnea, highlighting the potential of systemic therapy to improve quality of life of patients with tracheobronchial AL amyloidosis. However, the long-term pathological changes associated with local bronchial lesions require further investigation.

Development and Validation of a Nomogram for Predicting the Disease Progression of Nonsevere Coronavirus Disease 2019.

BACKGROUND AND OBJECTIVES: The majority of coronavirus disease 2019 (COVID-19) cases are nonsevere, but severe cases have high mortality and need early detection and treatment. We aimed to develop a nomogram to predict the disease progression of nonsevere COVID-19 based on simple data that can be easily obtained even in primary medical institutions. METHODS: In this retrospective, multicenter cohort study, we extracted data from initial simple medical evaluations of 495 COVID-19 patients randomized (2:1) into a development cohort and a validation cohort. The progression of nonsevere COVID-19 was recorded as the primary outcome. We built a nomogram with the development cohort and tested its performance in the validation cohort. RESULTS: The nomogram was developed with the nine factors included in the final model. The area under the curve (AUC) of the nomogram scoring system for predicting the progression of nonsevere COVID-19 into severe COVID-19 was 0.875 and 0.821 in the development cohort and validation cohort, respectively. The nomogram achieved a good concordance index for predicting the progression of nonsevere COVID-19 cases in the development and validation cohorts (concordance index of 0.875 in the development cohort and 0.821 in the validation cohort) and had well-fitted calibration curves showing good agreement between the estimates and the actual endpoint events. CONCLUSIONS: The proposed nomogram built with a simplified index might help to predict the progression of nonsevere COVID-19; thus, COVID-19 with a high risk of disease progression could be identified in time, allowing an appropriate therapeutic choice according to the potential disease severity.

Recent advances in convex probe endobronchial ultrasound: a narrative review.

Convex probe endobronchial ultrasound (CP-EBUS) has been widely used in the lymph node staging and restaging of lung tumors and the diagnosis of mediastinal diseases. Recent years have seen continuous progress in this technology. For diagnosis, elastography technology can preliminarily distinguish between benign and malignant lesions, so that reduce the number of punctures. CP-EBUS can also be used as an endoscopic ultrasound (EUS) to guide needle aspirations of liver lesions, retroperitoneal lymph nodes and left adrenal gland (LAG) lesions sometimes. Some advances help diagnosing more accurately and effectively, such as the intranodal forceps biopsy (IFB), the new type of 22G needle, the rapid on-site evaluation (ROSE) and the cancer gene methylation, etc. In addition, special advances are being made in diagnosis using artificial intelligence (AI). For treatment, CP-EBUS has yielded novel research results when applied to transbronchial needle injection (TBNI) and radioactive seed implantation in clinical cases, and blocking of the cardiac plexus in animal studies. The next-generation CP-EBUS is also ready for use in the clinic and the technology will be improving continuously. Through this review, we hope to educate clinicians on the latest uses of CP-EBUS and open up further research ideas for readers interested in this technology.

Eltrombopag-related renal vein thromboembolism in a patient with immune thrombocytopenia: A case report.

BACKGROUND: Eltrombopag is an orally administered thrombopoietin receptor agonist linked to a heightened risk of treatment-related thromboembolism. Both venous and arterial thromboses have been documented in the medical literature. CASE SUMMARY: In the absence of nephropathy, a 48-year-old patient receiving eltrombopag for immune thrombocytopenia (ITP) developed renal vein thrombosis and pulmonary embolism. The renal vein thrombus spontaneously resolved during subsequent anticoagulant treatment, restoring venous circulation. CONCLUSION: A rapid upsurge in platelets, rather than their absolute number, may trigger thrombotic events in this setting. For patients at high thrombotic risk, individualized eltrombopag dosing and vigilance in platelet monitoring are perhaps needed during treatment of ITP.

Gene-Environment Interaction: A Variable Selection Perspective.

Gene-environment interactions have important implications for elucidating the genetic basis of complex diseases beyond the joint function of multiple genetic factors and their interactions (or epistasis). In the past, G × E interactions have been mainly conducted within the framework of genetic association studies. The high dimensionality of G × E interactions, due to the complicated form of environmental effects and the presence of a large number of genetic factors including gene expressions and SNPs, has motivated the recent development of penalized variable selection methods for dissecting G × E interactions, which has been ignored in the majority of published reviews on genetic interaction studies. In this article, we first survey existing studies on both gene-environment and gene-gene interactions. Then, after a brief introduction to the variable selection methods, we review penalization and relevant variable selection methods in marginal and joint paradigms, respectively, under a variety of conceptual models. Discussions on strengths and limitations, as well as computational aspects of the variable selection methods tailored for G × E studies, have also been provided.

Integrating Multi-Omics Data for Gene-Environment Interactions.

Gene-environment (G×E) interaction is critical for understanding the genetic basis of complex disease beyond genetic and environment main effects. In addition to existing tools for interaction studies, penalized variable selection emerges as a promising alternative for dissecting G×E interactions. Despite the success, variable selection is limited in terms of accounting for multidimensional measurements. Published variable selection methods cannot accommodate structured sparsity in the framework of integrating multiomics data for disease outcomes. In this paper, we have developed a novel variable selection method in order to integrate multi-omics measurements in G×E interaction studies. Extensive studies have already revealed that analyzing omics data across multi-platforms is not only sensible biologically, but also resulting in improved identification and prediction performance. Our integrative model can efficiently pinpoint important regulators of gene expressions through sparse dimensionality reduction, and link the disease outcomes to multiple effects in the integrative G×E studies through accommodating a sparse bi-level structure. The simulation studies show the integrative model leads to better identification of G×E interactions and regulators than alternative methods. In two G×E lung cancer studies with high dimensional multi-omics data, the integrative model leads to an improved prediction and findings with important biological implications.