A novel de novo mutation in the serine-threonine kinase STK11 gene in a Korean patient with Peutz-Jeghers syndrome

BACKGROUND: Peutz-Jeghers syndrome (PJS) is an unusual autosomal dominant disorder characterized by mucocutaneous pigmentation and multiple gastrointestinal hamartomatous polyps. Patients with PJS are at an increased risk of developing multi-organ cancer, most frequently those involving the gastrointestinal tract. Germline mutation of the STK11 gene, which encodes a serine-threonine kinase, is responsible for PJS. METHODS: Using DNA samples obtained from the patient and his family members, we sequenced nine exons and flanking intron regions of the STK11 gene using polymerase chain reaction (PCR) and direct sequencing. RESULTS: Sequencing of the STK11 gene in the proband of the family revealed a novel 1-base pair deletion of guanine (G) in exon 6 (c.826delG; Gly276AlafsX11). This mutation resulted in a premature termination at codon 286, predicting a partial loss of the kinase domain and complete loss of the C-terminal domain. We did not observe this mutation in both parents of the PJS patient. Therefore, it is considered a novel de novo mutation. CONCLUSION: The results presented herein enlarge the spectrum of mutations of the STK11 gene by identifying a novel de novo mutation in a PJS patient and further support the hypothesis that STK11 mutations are disease-causing mutations for PJS with or without a positive family history

Image-Object-Specific Prompt Learning for Few-Shot Class-Incremental Learning

While many FSCIL studies have been undertaken, achieving satisfactory performance, especially during incremental sessions, has remained challenging. One prominent challenge is that the encoder, trained with an ample base session training set, often underperforms in incremental sessions. In this study, we introduce a novel training framework for FSCIL, capitalizing on the generalizability of the Contrastive Language-Image Pre-training (CLIP) model to unseen classes. We achieve this by formulating image-object-specific (IOS) classifiers for the input images. Here, an IOS classifier refers to one that targets specific attributes (like wings or wheels) of class objects rather than the image's background. To create these IOS classifiers, we encode a bias prompt into the classifiers using our specially designed module, which harnesses key-prompt pairs to pinpoint the IOS features of classes in each session. From an FSCIL standpoint, our framework is structured to retain previous knowledge and swiftly adapt to new sessions without forgetting or overfitting. This considers the updatability of modules in each session and some tricks empirically found for fast convergence. Our approach consistently demonstrates superior performance compared to state-of-the-art methods across the miniImageNet, CIFAR100, and CUB200 datasets. Further, we provide additional experiments to validate our learned model's ability to achieve IOS classifiers. We also conduct ablation studies to analyze the impact of each module within the architecture.Comment: 8 pages, 4 figures, 4 table

Anti-Inflammatory Effects of Inonotus obliquus in Colitis Induced by Dextran Sodium Sulfate

A total of 28 male BALB/c mice (average weight 20.7 ± 1.6 g) were divided into 4 treatment groups and fed a commercial diet (A), a commercial diet + induced colitis by dextran sodium sulfate (DSS) (B), Inonotus obliquus (IO) administration (C), and IO administration + induced colitis by DSS (D). IO treatment (C, D) decreased the expression of tumor necrosis factor (TNF)-α and signal transducers and activators of transcription (STAT)1 compared to those of the colitis induced group (B). The expressions of IL-4 and STAT6 were decreased in group D compared to the colitis induced group (B). The serum immunoglobulin (Ig)E level decreased in IO treatment groups (C, D) compared to no IO treatment groups (A and B) although there was no significant difference between the IO treatment groups. Extract from IO itself had a weak cytotoxic effect on murine macrophage cell line (RAW264.7 cells). Extract from IO inhibited lipopolysaccharide- (LPS-) induced, TNF-α, STAT1, pSTAT1, STAT6, and pSTAT6 production in RAW264.7 cells

Real-time data-driven and multi-scale model-guided system for bioproccess digital twin platform

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