The hepatic low-density lipoprotein receptor (LDLR) pathway is essential for clearing circulating LDL-cholesterol (LDL-C). While the transcriptional regulation of LDLR is well-characterized, the post-transcriptional mechanisms which govern LDLR expression are just beginning to emerge. Here, we developed a high-throughput genome-wide screening assay to systematically identify microRNAs (miRNAs) that regulate LDLR activity in human hepatic cells. From this screen, we characterize miR-148a as a negative regulator of LDLR expression and activity, and define a novel SREBP1-mediated pathway by which miR-148a regulates LDL-C uptake. Importantly, inhibition of miR-148a increases hepatic LDLR expression and decreases plasma LDL-C in vivo. We also provide evidence that miR-148a regulates hepatic ABCA1 expression and circulating HDL-C levels. Collectively, these studies uncover miR-148a as an important regulator of hepatic LDL-C clearance through direct regulation of LDLR expression, and demonstrate the therapeutic potential of inhibiting miR-148a to ameliorate the elevated LDL-C/HDL-C ratio, a prominent risk factor for cardiovascular disease.

T he scientific, academic, medical and data science communities have come together in the face of the COVID-19 pandemic crisis to rapidly assess novel paradigms in artificial intelligence (AI) that are rapid and secure, and potentially incentivize data sharing and model training and testing without the usual privacy and data ownership hurdles of conventional collaborations 1,2 . Healthcare providers, researchers and industry have pivoted their focus to address unmet and critical clinical needs created by the crisis, with remarkable results [3][4][5][6][7][8][9] . Clinical trial recruitment has been expedited and facilitated by national regulatory bodies and an international cooperative spirit 10-12 . The data analytics and AI disciplines have always fostered open

Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New Background Previous research has shown different effects of anesthetics on cancer cell growth. Here, the authors investigated the association between type of anesthetic and patient survival after elective colon cancer surgery. Methods A retrospective cohort study included patients who received elective colon cancer surgery between January 2005 and December 2014. Patients were grouped according to anesthesia received: propofol or desflurane. After exclusion of those who received combined propofol anesthesia with inhalation anesthesia or epidural anesthesia, survival curves were constructed from the date of surgery to death. After propensity matching, univariable and multivariable Cox regression models were used to compare hazard ratios for death. Subgroup analyses were performed for tumor–node–metastasis staging and postoperative metastasis. Results A total of 706 patients (307 deaths, 43.5%) with desflurane anesthesia and 657 (88 deaths, 13.4%) with propofol anesthesia were eligible for analysis. After propensity matching, 579 patients remained in each group (189 deaths, 32.6%, in the desflurane group vs. 87, 15.0%, in the propofol group). In the matched analyses, the propofol-treated group had a better survival, irrespective of lower tumor–node–metastasis stage (hazard ratio, 0.22; 95% CI, 0.11 to 0.42; P < 0.001) or higher tumor–node–metastasis stage (hazard ratio, 0.42; 95% CI, 0.32 to 0.55; P < 0.001) and presence of metastases (hazard ratio, 0.67; 95% CI, 0.51 to 0.86; P = 0.002) or absence of metastases (hazard ratio, 0.08; 95% CI, 0.01 to 0.62; P = 0.016). Simple propensity score adjustment produced similar findings. Conclusions Propofol anesthesia for colon cancer surgery is associated with better survival irrespective of tumor–node–metastasis stage.