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The serrated pathway to colorectal cancers (CRCs) is a significant pathway encompassing five distinct types of lesions, namely hyperplastic polyps (HPs), sessile serrated lesions (SSLs), sessile serrated lesions with dysplasia (SSL-Ds), traditional serrated adenomas (TSAs), and serrated adenoma unclassified. In contrast to the conventional adenoma-carcinoma pathway, the serrated pathway primarily involves two mechanisms: BRAF/KRAS mutations and CpG island methylator phenotype (CIMP). HPs are the most prevalent non-malignant lesions, while SSLs play a crucial role as precursors to CRCs, On the other hand, traditional serrated adenomas (TSAs) are the least frequently encountered subtype, also serving as precursors to CRCs. It is crucial to differentiate these lesions based on their unique morphological characteristics observed in histology and colonoscopy, as the identification and management of these serrated lesions significantly impact colorectal cancer screening programs. The management of these lesions necessitates the crucial steps of removing premalignant lesions and implementing regular surveillance. This article provides a comprehensive summary of the epidemiology, histologic features, molecular features, and detection methods for various serrated polyps, along with recommendations for their management and surveillance.
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Background: We aimed to establish and validate a prognostic nomogram model for improving the prediction of 30-day mortality of gastrointestinal bleeding (GIB) in critically ill patients with severe sepsis. Methods: In this retrospective study, the current retrospective cohort study extracted data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, then partitioned the cohort randomly into training and validation subsets. The cohort was partitioned into training and validation subsets randomly. Our primary endpoint was 30-day all-cause mortality. To reduce data dimensionality and identify predictive variables, the least absolute shrinkage and selection operator (LASSO) regression was employed. A prediction model was constructed by multivariate logistic regression. Model performance was evaluated using the concordance index (C-index), receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). Results: The analysis included 1435 total patients, comprising 1005 in the training cohort and 430 in the validation cohort. We found that age, smoking status, glucose, (BUN), lactate, Sequential Organ Failure Assessment (SOFA) score, mechanical ventilation≥48h (MV), parenteral nutrition (PN), and chronic obstructive pulmonary disease (COPD) independently influenced mortality in sepsis patients with concomitant GIB. The C-indices were 0.746 (0.700-0.792) and 0.716 (0.663-0.769) in the training and validation sets, respectively. Based on the area under the curve (AUC) and DCA, the nomogram exhibited good discrimination for 30-day all-cause mortality in sepsis with GIB. Conclusions: For sepsis patients complicated with GIB, we created a unique nomogram model to predict the 30-day all-cause mortality. This model could be a significant therapeutic tool for clinicians in terms of personalized treatment and prognosis prediction.
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Abscisic acid (ABA) plays a crucial role in promoting plant stress resistance and seed dormancy. However, how ABA regulates rice quality remains unclear. This study identifies a key transcription factor SLR1-like2 (SLRL2), which mediates the ABA-regulated amylose content (AC) of rice. Mechanistically, SLRL2 interacts with NF-YB1 to co-regulate Wx, a determinant of AC and rice quality. In contrast to SLR1, SLRL2 is ABA inducible but insensitive to GA. In addition, SLRL2 exhibits DNA-binding activity and directly regulates the expression of Wx, bHLH144 and MFT2. SLRL2 competes with NF-YC12 for interaction with NF-YB1. NF-YB1 also directly represses SLRL2 transcription. Genetic validation supports that SLRL2 functions downstream of NF-YB1 and bHLH144 in regulating rice AC. Thus, an NF-YB1-SLRL2-bHLH144 regulatory module is successfully revealed. Furthermore, SLRL2 regulates rice dormancy by modulating the expression of MFT2. In conclusion, this study revealed an ABA-responsive regulatory cascade that functions in both rice quality and seed dormancy.