Integrating transcriptomics and proteomics to analyze the immune microenvironment of cytomegalovirus associated ulcerative colitis and identify relevant biomarkers.

BACKGROUND: In recent years, significant morbidity and mortality in patients with severe inflammatory bowel disease (IBD) and cytomegalovirus (CMV) have drawn considerable attention to the status of CMV infection in the intestinal mucosa of IBD patients and its role in disease progression. However, there is currently no high-throughput sequencing data for ulcerative colitis patients with CMV infection (CMV + UC), and the immune microenvironment in CMV + UC patients have yet to be explored. METHOD: The xCell algorithm was used for evaluate the immune microenvironment of CMV + UC patients. Then, WGCNA analysis was explored to obtain the co-expression modules between abnormal immune cells and gene level or protein level. Next, three machine learning approach include Random Forest, SVM-rfe, and Lasso were used to filter candidate biomarkers. Finally, Best Subset Selection algorithms was performed to construct the diagnostic model. RESULTS: In this study, we performed transcriptomic and proteomic sequencing on CMV + UC patients to establish a comprehensive immune microenvironment profile and found 11 specific abnormal immune cells in CMV + UC group. After using multi-omics integration algorithms, we identified seven co-expression gene modules and five co-expression protein modules. Subsequently, we utilized various machine learning algorithms to identify key biomarkers with diagnostic efficacy and constructed an early diagnostic model. We identified a total of eight biomarkers (PPP1R12B, CIRBP, CSNK2A2, DNAJB11, PIK3R4, RRBP1, STX5, TMEM214) that play crucial roles in the immune microenvironment of CMV + UC and exhibit superior diagnostic performance for CMV + UC. CONCLUSION: This 8 biomarkers model offers a new paradigm for the diagnosis and treatment of IBD patients post-CMV infection. Further research into this model will be significant for understanding the changes in the host immune microenvironment following CMV infection.

Giant dual-mode graphene-based terahertz modulator enabled by Fabry-Perot assisted multiple reflection.

We report a high-performance terahertz (THz) modulator with dual operation mode. For the pulse operation mode, the proposed THz modulator has the advantage of high modulation depth (MD) and can operate in a broadband frequency range. We have experimentally achieved a MD larger than 90% for the fifth-order pulse THz echo at 0.8 THz, and the MD stays larger than 75% in a broadband frequency range larger than 1 THz, whereas, for the coherent operation mode, the Fabry-Perot (F-P) interference effect has been taken into consideration and a MD larger than 75% at 0.76 THz has also been realized.

Ultrasensitive specific terahertz sensor based on tunable plasmon induced transparency of a graphene micro-ribbon array structure.

We proposed an ultrasensitive specific terahertz sensor consisting of two sets of graphene micro-ribbon with different widths. The interference between the plasmon resonances of the wide and narrow graphene micro-ribbons gives rise to the plasmon induced transparency (PIT) effect and enables ultrasensitive sensing in terahertz region. The performances of the PIT sensor have been analyzed in detail considering the thickness and refractive index sensing applications using full wave electromagnetic simulations. Taking advantage of the electrical tunability of graphene's Fermi level, we demonstrated the specific sensing of benzoic acid with detection limit smaller than 6.35 µg/cm2. The combination of specific identification and enhanced sensitivity of the PIT sensor opens exciting prospects for bio/chemical molecules sensing in the terahertz region.

Terahertz Spectroscopic Diagnosis of Myelin Deficit Brain in Mice and Rhesus Monkey with Chemometric Techniques.

While myelin deficit of the central nervous system leads to several severe diseases, the definitive diagnostic means are lacking. We proposed and performed terahertz time-domain spectroscopy (THz-TDS) combined with chemometric techniques to discriminate and evaluate the severity of myelin deficit in mouse and rhesus monkey brains. The THz refractive index and absorption coefficient of paraffin-embedded brain tissues from both normal and mutant dysmyelinating mice are shown. Principal component analysis of time-domain THz signal (PCA-tdTHz) and absorption-refractive index relation of THz spectrum identified myelin deficit without exogenous labeling or any pretreatment. Further, with the established PCA-tdTHz, we evaluated the severity of myelin deficit lesions in rhesus monkey brain induced by experimental autoimmune encephalomyelitis, which is the most-studied animal model of multiple sclerosis. The results well matched the pathological analysis, indicating that PCA-tdTHz is a quick, powerful, evolving tool for identification and evaluation myelin deficit in preclinical animals and potentially in para-clinical human biopsy.