The number of service visits of Alzheimer’s disease (AD) patients is different from each other and their visit time intervals are non-uniform. Although the literature has revealed many approaches in disease progression modeling, they fail to leverage these time-relevant part of patients’ medical records in predicting disease’s future status. This paper investigates how to predict the AD progression for a patient’s next medical visit through leveraging heterogeneous medical data. Data provided by the National Alzheimer’s Coordinating Center includes 5432 patients with probable AD from August 31, 2005 to May 25, 2017. Long short-term memory recurrent neural networks (RNN) are adopted. The approach relies on an enhanced “many-to-one” RNN architecture to support the shift of time steps. Hence, the approach can deal with patients’ various numbers of visits and uneven time intervals. The results show that the proposed approach can be utilized to predict patients’ AD progressions on their next visits with over 99% accuracy, significantly outperforming classic baseline methods. This study confirms that RNN can effectively solve the AD progression prediction problem by fully leveraging the inherent temporal and medical patterns derived from patients’ historical visits. More promisingly, the approach can be customarily applied to other chronic disease progression problems.

Aim: With the current coronavirus disease (COVID-19) pandemic and high endemic levels of chronic hepatitis B virus (HBV) infection worldwide, it is urgent to investigate liver function changes of COVID-19 patients with chronic HBV infection, and how severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in turn affects the course of chronic HBV infection. Method: We undertook a retrospective study based on 347 COVID-19 patients (21 vs. 326 with vs. without chronic HBV infection). With the propensity score matching (PSM) method, we yielded 20 and 51 matched patients for the HBV group and the non-HBV group, respectively. Results: At the end of follow-up, all of these 71 patients achieved SARS-CoV-2 clearance (P=0.1). During the follow-up, 30% versus 31.4% in the HBV group versus non-HBV group progressed to severe COVID-19 (P=0.97). After PSM, the longitudinal changes of median values for liver biochemistries were not significantly different between the two groups. In the HBV group versus non-HBV group, 35% (7/20) versus 37.25% (19/51) (P=0.86) had abnormal alanine aminotransferase at least once during hospitalization, 30% (6/20) versus 31.37% (16/51) had abnormal aspartate aminotransferase (P=0.91), 40% (8/20) versus 37.25% (19/51) had abnormal γ-glutamyltransferase (P=0.83), and 45% (9/20) versus 39.22% (20/51) had abnormal total bilirubin levels (P=0.91). Moreover, three patients in the HBV group had hepatitis B reactivation. Conclusions: Liver dysfunction presented in COVID-19 patients with/without chronic HBV. Moreover, those COVID-19 patients co-infected with chronic HBV could have a risk of hepatitis B reactivation. It is necessary to monitor liver function of COVID-19 patients, as well as HBV-DNA levels for those co-infected with HBV during the whole disease course.

The severity of acute liver failure (ALF) induced by bacterial lipopolysaccharide (LPS) is associated with the hepatic innate immune response. The core circadian molecular clock modulates the innate immune response by controlling rhythmic pathogen recognition by the innate immune system and daily variations in cytokine gene expression. However, the molecular link between circadian genes and the innate immune system has remained unclear. Here, we showed that mice lacking the clock gene Per1 (Period1) are more susceptible to LPS/d-galactosamine (LPS/GalN)-induced macrophage-dependent ALF compared with wild-type (WT) mice. Per1 deletion caused a remarkable increase in the number of Kupffer cells (KCs) in the liver, resulting in an elevation of the levels of pro-inflammatory cytokines after LPS treatment. Loss of Per1 had no effect on the proliferation or apoptosis of macrophages; however, it enhanced the recruitment of macrophages, which was associated with an increase in CC chemokine receptor 2 (Ccr2) expression levels in monocytes/macrophages. Deletion of Ccr2 rescued d-GalN/LPS-induced liver injury in Per1−/− mice. We demonstrated that the upregulation of Ccr2 expression by Per1 deletion could be reversed by the synthetic peroxisome proliferator-activated receptor gamma (PPAR-γ) antagonist GW9662. Further analysis indicated that PER1 binds to PPAR-γ on the Ccr2 promoter and enhanced the inhibitory effect of PPAR-γ on Ccr2 expression. These results reveal that Per1 reduces hepatic macrophage recruitment through interaction with PPAR-γ and prevents an excessive innate immune response in endotoxin-induced liver injury.