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Background: Disease progression within 24 months (POD24) significantly impacts overall survival (OS) in patients with follicular lymphoma (FL). This study aimed to develop a robust predictive model, FLIPI-C, using a machine learning approach to identify FL patients at high risk of POD24. Methods: A cohort of 1,938 FL patients (FL1-3a) from seventeen centers nationwide in China was randomly divided into training and internal validation sets (2:1 ratio). XGBoost was utilized to construct the POD24-predicting model, which was internally validated in the validation set and externally validated in the GALLIUM cohort. Key predictors of POD24 included lymphocyte-to-monocyte ratio (LMR), lactate dehydrogenase (LDH) > ULN, low hemoglobin (Hb), elevated beta-2 microglobulin (β2-MG), maximum standardized uptake value (SUVmax), and lymph node involvement. The FLIPI-C model assigned 2 points to LMR and 1 point to each of the other variables. Results: The FLIPI-C model demonstrated superior accuracy (AUC) for predicting POD24 and 3-year overall survival (OS) in both the internal (AUC POD24: 0.764, OS: 0.700) and external validation cohorts (AUC POD24: 0.703, OS: 0.653), compared to existing models (FLIPI, FLIPI-2, PRIMA-PI, FLEX). Decision curve analysis confirmed the superior net benefits of FLIPI-C. Conclusions: Developed using a machine learning approach, the FLIPI-C model offers superior predictive accuracy and utilizes simple, widely available markers. It holds promise for informing treatment decisions and prognostic assessments in clinical practice for FL patients at high risk of POD24. [ABSTRACT FROM AUTHOR]

Background: The Akita mutation (C96Y) in the insulin gene results in early onset diabetes in both humans and mice. Expression of mutant proinsulin (C96Y) causes endoplasmic reticulum (ER) stress in pancreatic β-cells and consequently the cell activates the unfolded protein response (UPR). Since the proinsulin is terminally misfolded ER stress is irremediable and chronic activation of the UPR eventually activates apoptosis in some cells. Here we analyzed the IRE1-dependent activation of genes in response to misfolded proinsulin production in an inducible mutant proinsulin (C96Y) insulinoma cell line. Results: The IRE1 endoribonuclease inhibitors 4μ8c and MKC-3946 prevented the splicing of the XBP1 mRNA in response to ER stress caused by mutant proinsulin production. Microarray expression analysis and qPCR validation of select genes revealed that maximal upregulation of many UPR genes in response to mutant proinsulin production required IRE1, although most were still increased above control. Interestingly, neither degradation of misfolded proinsulin via ER-associated degradation (ERAD), nor apoptosis induced by prolonged misfolded proinsulin expression were affected by inhibiting IRE1. Conclusions: Although maximal induction of most UPR genes requires IRE1, inhibition of IRE1 does not affect ERAD of misfolded proinsulin or predispose pancreatic β-cells expressing misfolded proinsulin to chronic ER stress-induced apoptosis. [ABSTRACT FROM AUTHOR]

Assigning bond orders is a necessary and essential step for characterizing a chemical structure correctly in force field based simulations. Several methods have been developed to do this. They all have advantages but with limitations too. Here, an automatic algorithm for assigning chemical connectivity and bond order regardless of hydrogen for organic molecules is provided, and only three dimensional coordinates and element identities are needed for our algorithm. The algorithm uses hard rules, length rules and conjugation rules to fix the structures. The hard rules determine bond orders based on the basic chemical rules; the length rules determine bond order by the length between two atoms based on a set of predefined values for different bond types; the conjugation rules determine bond orders by using the length information derived from the previous rule, the bond angles and some small structural patterns. The algorithm is extensively evaluated in three datasets, and achieves good accuracy of predictions for all the datasets. Finally, the limitation and future improvement of the algorithm are discussed.

Background: Cells respond to endoplasmic reticulum stress (ER) stress by activating the unfolded protein response. To study the ER stress response in pancreatic beta-cells we developed a model system that allows for pathophysiological ER stress based on the Akita mouse. This mouse strain expresses a mutant insulin 2 gene (C96Y), which prevents normal proinsulin folding causing ER stress and eventual beta-cell apoptosis. A double-stable pancreatic beta-cell line (pTet-ON INS-1) with inducible expression of insulin 2 (C96Y) fused to EGFP was generated to study the ER stress response., Results: Expression of Ins 2 (C96Y)-EGFP resulted in activation of the ER stress pathways (PERK, IRE1 and ATF6) and caused dilation of the ER. To identify gene expression changes resulting from mutant insulin expression we performed microarray expression profiling and real time PCR experiments. We observed an induction of various ER chaperone, co-chaperone and ER-associated degradation genes after 24 h and an increase in pro-apoptotic genes (Chop and Trib3) following 48 h of mutant insulin expression. The latter changes occurred at a time when general apoptosis was detected in the cell population, although the relative amount of cell death was low. Inhibiting the proteasome or depleting Herp protein expression increased mutant insulin levels and enhanced cell apoptosis, indicating that ER-associated degradation is maintaining cell survival., Conclusions: The inducible mutant insulin expressing cell model has allowed for the identification of the ER stress response in beta-cells and the repertoire of genes/proteins induced is unique to this cell type. ER-associated degradation is essential in maintaining cell survival in cells expressing mutant insulin. This cell model will be useful for the molecular characterization of ER stress-induced genes.