Thus, these results indicated that miR-29a may serve as a potential target of HCC treatment.

Background Septic shock comprises a heterogeneous population, and individualized resuscitation strategy is of vital importance. The study aimed to identify subclasses of septic shock with non-supervised learning algorithms, so as to tailor resuscitation strategy for each class. Methods Patients with septic shock in 25 tertiary care teaching hospitals in China from January 2016 to December 2017 were enrolled in the study. Clinical and laboratory variables were collected on days 0, 1, 2, 3 and 7 after ICU admission. Subclasses of septic shock were identified by both finite mixture modeling and K-means clustering. Individualized fluid volume and norepinephrine dose were estimated using dynamic treatment regime (DTR) model to optimize the final mortality outcome. DTR models were validated in the eICU Collaborative Research Database (eICU-CRD) dataset. Results A total of 1437 patients with a mortality rate of 29% were included for analysis. The finite mixture modeling and K-means clustering robustly identified five classes of septic shock. Class 1 (baseline class) accounted for the majority of patients over all days; class 2 (critical class) had the highest severity of illness; class 3 (renal dysfunction) was characterized by renal dysfunction; class 4 (respiratory failure class) was characterized by respiratory failure; and class 5 (mild class) was characterized by the lowest mortality rate (21%). The optimal fluid infusion followed the resuscitation/de-resuscitation phases with initial large volume infusion and late restricted volume infusion. While class 1 transitioned to de-resuscitation phase on day 3, class 3 transitioned on day 1. Classes 1 and 3 might benefit from early use of norepinephrine, and class 2 can benefit from delayed use of norepinephrine while waiting for adequate fluid infusion. Conclusions Septic shock comprises a heterogeneous population that can be robustly classified into five phenotypes. These classes can be easily identified with routine clinical variables and can help to tailor resuscitation strategy in the context of precise medicine.

These data support a role for CCN2 in the growth and metastasis of HCC and highlight CCN2 as a potential novel therapeutic target.

Pancreatic stellate cells (PSCs) play a critical role in fibrogenesis during alcoholic chronic pancreatitis (ACP). Transforming growth factor‐beta1 (TGF‐β1) is a key regulator of extracellular matrix production and PSC activation. Endotoxin lipopolysaccharide (LPS) has been recognized as a trigger factor in the pathogenesis of ACP. This study aimed to investigate the mechanisms by which LPS modulates TGF‐β1 signalling and pancreatic fibrosis. Sprague‐Dawley rats fed with a Lieber‐DeCarli alcohol (ALC) liquid diet for 10 weeks with or without LPS challenge during the last 3 weeks. In vitro studies were performed using rat macrophages (Mφs) and PSCs (RP‐2 cell line). The results showed that repeated LPS challenge resulted in significantly more collagen production and PSC activation compared to rats fed with ALC alone. LPS administration caused overexpression of pancreatic TLR4 or TGF‐β1 which was paralleled by an increased number of TLR4‐positive or TGF‐β1‐positive Mφs or PSCs in ALC‐fed rats. In vitro, TLR4 or TGF‐β1 production in Mφs or RP‐2 cells was up‐regulated by LPS. LPS alone or in combination with TGF‐β1 significantly increased type I collagen and α‐SMA production and Smad2 and 3 phosphorylation in serum‐starved RP‐2 cells. TGF‐β pseudoreceptor BAMBI production was repressed by LPS, which was antagonized by Si‐TLR4 RNA or by inhibitors of MyD88/NF‐kB. Additionally, knockdown of Bambi with Si‐Bambi RNA significantly increased TGF‐β1 signalling in RP‐2 cells. These findings indicate that LPS increases TGF‐β1 production through paracrine and autocrine mechanisms and that LPS enhances TGF‐β1 signalling in PSCs by repressing BAMBI via TLR4/MyD88/NF‐kB activation.