Publications by authors named "Yun-Ru He"

In vitro transcription (IVT) based on single-subunit RNA polymerase (ssRNAP) has enhanced the widespread application of RNA drugs in the biomedical field, showcasing unprecedented potential for disease prevention and treatment. While the classical enzyme T7 RNA polymerase (T7 RNAP) has driven significant progress in RNA production, several challenges persist. These challenges include the selectivity of the initiation nucleotide, low incorporation efficiency of modified nucleotides, limited processivity on certain templates, heterogeneity at the 3' end of RNA products, and high level of double-stranded RNA (dsRNA) byproducts.

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Triple-negative breast cancer (TNBC) has been considered a huge clinical unmet need due to its aggressive progression and highly frequent metastasis. mRNA therapeutics supply a potential and versatile immunotherapy of oncology treatment. Here, we developed α-lactalbumin mRNA-lipid nanoparticles (α-LNP) as a potential therapeutical strategy for TNBC.

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Prostate cancer (PCa) is characterized as a "cold tumor" with limited immune responses, rendering the tumor resistant to immune checkpoint inhibitors (ICI). Therapeutic messenger RNA (mRNA) vaccines have emerged as a promising strategy to overcome this challenge by enhancing immune reactivity and significantly boosting anti-tumor efficacy. In our study, we synthesized Tetra, an mRNA vaccine mixed with multiple tumor-associated antigens, and ImmunER, an immune-enhancing adjuvant, aiming to induce potent anti-tumor immunity.

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In response to the human Mpox (hMPX) epidemic that began in 2022, there is an urgent need for a monkeypox vaccine. Here, we have developed a series of mRNA-lipid nanoparticle (mRNA-LNP)-based vaccine candidates that encode a collection of four highly conserved Mpox virus (MPXV) surface proteins involved in virus attachment, entry, and transmission, namely A29L, A35R, B6R, and M1R, which are homologs to Vaccinia virus (VACV) A27, A33, B5, and L1, respectively. Despite possible differences in immunogenicity among the four antigenic mRNA-LNPs, administering these antigenic mRNA-LNPs individually (5 μg each) or an average mixture of these mRNA-LNPs at a low dose (0.

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Article Synopsis
  • Deep machine learning can significantly streamline the process of calculating radiographic bone loss (RBL), making it easier to diagnose and plan treatment for periodontitis.
  • In a study involving 236 patients, a multitasking InceptionV3 model achieved an accuracy of 87% in classifying RBL, with solid performance metrics such as sensitivity and specificity around 86-88%.
  • The study concludes that while the results are promising, increasing the amount of clinical data will improve the model's performance and enhance its clinical utility.
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