d SIRT1, a highly conserved NAD؉ -dependent protein deacetylase, is a key metabolic sensor that directly links nutrient signals to animal metabolic homeostasis. Although SIRT1 has been implicated in a number of hepatic metabolic processes, the mechanisms by which hepatic SIRT1 modulates bile acid metabolism are still not well understood. Here we report that deletion of hepatic SIRT1 reduces the expression of farnesoid X receptor (FXR), a nuclear receptor that regulates bile acid homeostasis. We provide evidence that SIRT1 regulates the expression of FXR through hepatocyte nuclear factor 1␣ (HNF1␣). SIRT1 deficiency in hepatocytes leads to decreased binding of HNF1␣ to the FXR promoter. Furthermore, we show that hepatocyte-specific deletion of SIRT1 leads to derangements in bile acid metabolism, predisposing the mice to development of cholesterol gallstones on a lithogenic diet. Taken together, our findings indicate that SIRT1 plays a vital role in the regulation of hepatic bile acid homeostasis through the HNF1␣/FXR signaling pathway. SIRT1 is a mammalian member of the silent information regulator 2 (Sir2) family of proteins, also known as sirtuins (7). First identified in yeast as key components in gene silencing complexes (18), sirtuins have been increasingly recognized as crucial regulators for a variety of cellular processes, ranging from energy metabolism and stress response to tumorigenesis and aging (6). The mammalian genome encodes seven sirtuins, SIRT1 to SIRT7 (15). As the most conserved mammalian sirtuin, SIRT1 couples the deacetylation of numerous transcription factors and cofactors, including p53, E2F1, NF-B, FOXO, peroxisome proliferator-activated receptor gamma coactivator 1␣ (PGC-1␣), c-myc, hypoxia-inducible factor 1 (HIF-1), HIF-2␣, heat shock factor 1 (HSF1), liver X receptor (LXR), farnesoid X receptor (FXR), CLOCK and PER2, and TORC2 (2,9,13,21,26,28,29,32,34,35,42,49,55,58,59), to the hydrolysis of NAD ϩ . Therefore, SIRT1 has been considered as a metabolic sensor that directly links cellular metabolic status to gene expression regulation, playing an important role in a number of prosurvival and metabolic activities (19).In the liver, the central metabolic organ that controls key aspects of nutrient metabolism (48), SIRT1 has been shown to regulate metabolism of both glucose and lipids (45). For instance, SIRT1 inhibits TORC2, a key mediator of early phase gluconeogenesis, leading to decreased gluconeogenesis during the shortterm fasting phase (28). Prolonged fasting, on the other hand, increases SIRT1-mediated deacetylation and activation of PGC-1␣, an essential coactivator for a number of transcription factors, resulting in increased fatty acid oxidation and improved glucose homeostasis (41, 42). Consistently, adenoviral knockdown of SIRT1 reduces expression of fatty acid ␤-oxidation genes in the liver of fasted mice (43). Specific deletion of the exon 4 of the hepatic mouse SIRT1 gene, which results in a truncated, nonfunctional SIRT1 protein, impairs peroxisome proliferator-activated r...

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We investigate the effectiveness of a simple solution to the common problem of deep learning in medical image analysis with limited quantities of labeled training data. The underlying idea is to assign artificial labels to abundantly available unlabeled medical images and, through a process known as surrogate supervision, pre-train a deep neural network model for the target medical image analysis task lacking sufficient labeled training data. In particular, we employ 3 surrogate supervision schemes, namely rotation, reconstruction, and colorization, in 4 different medical imaging applications representing classification and segmentation for both 2D and 3D medical images. 3 key findings emerge from our research: 1) pre-training with surrogate supervision is effective for small training sets; 2) deep models trained from initial weights pre-trained through surrogate supervision outperform the same models when trained from scratch, suggesting that pretraining with surrogate supervision should be considered prior to training any deep 3D models; 3) pre-training models in the medical domain with surrogate supervision is more effective than transfer learning from an unrelated domain (e.g., natural images), indicating the practical value of abundant unlabeled medical image data.

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China experienced another widespread Coronavirus disease 2019 (COVID-19) outbreak recently caused by the Omicron variant, which is less severe but far more contagious than the other COVID-19 variants, leading local governments to focus efforts on eliminating the spread of the disease. Previous studies showed that after “recovering” from the virus, some patients could re-test positive for COVID-19 with nucleic acid tests, challenging the control of disease spread. In this study, we aimed to analyze the clinical and laboratory characteristics of re-positive COVID-19 patients in Northeast China. We retrospectively analyzed data from confirmed reverse transcription polymerase chain reaction (RT-PCR) re-positive COVID-19 patients who were admitted to the First Hospital of Jilin University, Jilin Province, China, from March to June 2022. Detailed clinical symptoms, medical history, anti-Corona Virus (CoV) IgG and IgM levels, and CoV nucleic acid cycle threshold (Ct) values during the re-positive period were collected and analyzed. A total of 180 patients were included in this study, including 62 asymptomatic cases and 118 mild cases. The cohort included 113 men and 67 women, with an average age of 45.73 years. The median time between recovery from the virus and re-positivity was 13 days. Our results showed that the proportion of re-positive patients with symptoms was lower, and the nucleic acid test-positive duration was shorter during the re-positive period. Furthermore, in patients with underlying disease, the proportion of patients with symptoms was higher, anti-CoV IgG levels were lower, and the total disease duration was longer. In conclusion, during the re-positive period, the symptoms were milder, and the CoV nucleic acid test-positive course was shorter. The concomitant underlying disease is an important factor associated with clinical symptoms, and the overall course of COVID-19 re-positive patients may be associated with lower anti-CoV IgG levels. Large-scale and multicenter studies are recommended to better understand the pathophysiology of recurrence in patients with COVID-19.