Drug-disease association prediction using semantic graph and function similarity representation learning over heterogeneous information networks.

Discovering new indications for existing drugs is a promising development strategy at various stages of drug research and development. However, most of them complete their tasks by constructing a variety of heterogeneous networks without considering available higher-order connectivity patterns in heterogeneous biological information networks, which are believed to be useful for improving the accuracy of new drug discovering. To this end, we propose a computational-based model, called SFRLDDA, for drug-disease association prediction by using semantic graph and function similarity representation learning. Specifically, SFRLDDA first integrates a heterogeneous information network (HIN) by drug-disease, drug-protein, protein-disease associations, and their biological knowledge. Second, different representation learning strategies are applied to obtain the feature representations of drugs and diseases from different perspectives over semantic graph and function similarity graphs constructed, respectively. At last, a Random Forest classifier is incorporated by SFRLDDA to discover potential drug-disease associations (DDAs). Experimental results demonstrate that SFRLDDA yields a best performance when compared with other state-of-the-art models on three benchmark datasets. Moreover, case studies also indicate that the simultaneous consideration of semantic graph and function similarity of drugs and diseases in the HIN allows SFRLDDA to precisely predict DDAs in a more comprehensive manner.

Improvement in Liver Stiffness in Pediatric Patients with Hepatitis C Virus after Treatment with Direct Acting Antivirals.

OBJECTIVES: To assess the degree of liver stiffness using transient elastography in Egyptian children infected with hepatitis C virus (HCV) at baseline and 1 year after achievement of sustained virologic response (SVR) with direct acting antivirals. STUDY DESIGN: This prospective study included children infected with HCV who received treatment with sofosbuvir/ledipasvir and achieved SVR. At baseline and 1 year after achievement of SVR, the extent of hepatic fibrosis was assessed by transient elastography using FibroScan to measure liver stiffness, in addition to noninvasive markers including aspartate aminotransferase/platelet ratio index (APRI) and fibrosis-4 (FIB-4) index. RESULTS: The study included 23 cases that had variable degrees of fibrosis at baseline; their ages ranged between 10 and 18 years. At baseline, 13 patients had F1; 3 patients had F1-F2; 1 patient had F2; 3 patients had F3; 2 had F3-F4; and 2 patients with F4. One year after achievement of SVR, there was a statistically significant improvement in liver stiffness, APRI, and FIB-4 index (P = .03, <.001, .02, respectively). In 13 patients (56.5%), the liver stiffness improved; in 7 patients, it was stationary; and the remaining 3 patients showed mild increase in liver stiffness that was, however, associated with improvement in APRI and FIB-4 index. Comorbid conditions and previous treatment with interferon were not associated with increased liver stiffness 1 year after SVR. CONCLUSIONS: Egyptian children infected with HCV genotype 4 achieved significant regression in liver stiffness after treatment with direct acting antivirals.

SLFN11 is a general target for enhancing the sensitivity of cancer to chemotherapy (DNA-damaging agents).

In patients with cancer, drug tolerance often occurs during the use of chemotherapy drugs, seriously affecting patient prognosis and survival. Therefore, scientists began to study the factors that affect chemotherapy drug sensitivity, and the high correlation between Schlafen-11 (SLFN11) and sensitivity to chemical drugs (mainly DNA-damaging agents, DDAs) has received increasing attention since it was discovered through bioinformatics analyses. Regarding the mechanism, SLFN11 may sensitise cells to chemotherapy drugs by preventing DNA damage repair. In recent years, SLFN11 has gradually become a hot research topic, and the results are enriching our understanding of this molecule. Indeed, the biological functions of SLFN11 under normal physiological conditions and in cancer, changes in its expression levels and mechanisms promoting apoptosis within the context of chemotherapeutic interventions have gradually been uncovered. Studies to date provide knowledge and the experimental and theoretical bases underlying SLFN11 and its effects on sensitivity to chemotherapy drugs. This review summarises the existing research on SLFN11 with the aim of achieving a more comprehensive understanding and furthering the development of strategies to target SLFN11 in the treatment of cancer.

Piecewise Structural Equation Model (SEM) Disentangles the Environmental Conditions Favoring Diatom Diazotroph Associations (DDAs) in the Western Tropical North Atlantic (WTNA).

Diatom diazotroph associations (DDAs) are important components in the world's oceans, especially in the western tropical north Atlantic (WTNA), where blooms have a significant impact on carbon and nitrogen cycling. However, drivers of their abundances and distribution patterns remain unknown. Here, we examined abundance and distribution patterns for two DDA populations in relation to the Amazon River (AR) plume in the WTNA. Quantitative PCR assays, targeting two DDAs (het-1 and het-2) by their symbiont's nifH gene, served as input in a piecewise structural equation model (SEM). Collections were made during high (spring 2010) and low (fall 2011) flow discharges of the AR. The distributions of dissolved nutrients, chlorophyll-a, and DDAs showed coherent patterns indicative of areas influenced by the AR. A symbiotic Hemiaulus hauckii-Richelia (het-2) bloom (>106 cells L-1) occurred during higher discharge of the AR and was coincident with mesohaline to oceanic (30-35) sea surface salinities (SSS), and regions devoid of dissolved inorganic nitrogen (DIN), low concentrations of both DIP (>0.1 µmol L-1) and Si (>1.0 µmol L-1). The Richelia (het-1) associated with Rhizosolenia was only present in 2010 and at lower densities (10-1.76 × 105nifH copies L-1) than het-2 and limited to regions of oceanic SSS (>36). The het-2 symbiont detected in 2011 was associated with H. membranaceus (>103nifH copies L-1) and were restricted to regions with mesohaline SSS (31.8-34.3), immeasurable DIN, moderate DIP (0.1-0.60 µmol L-1) and higher Si (4.19-22.1 µmol L-1). The piecewise SEM identified a profound direct negative effect of turbidity on the het-2 abundance in spring 2010, while DIP and water turbidity had a more positive influence in fall 2011, corroborating our observations of DDAs at subsurface maximas. We also found a striking difference in the influence of salinity on DDA symbionts suggesting a niche differentiation and preferences in oceanic and mesohaline salinities by het-1 and het-2, respectively. The use of the piecewise SEM to disentangle the complex and concomitant hydrography of the WTNA acting on two biogeochemically relevant populations was novel and underscores its use to predict conditions favoring abundance and distributions of microbial populations.

A feasible, economical, and accurate analytical method for simultaneous determination of six alkaloid markers in Aconiti Lateralis Radix Praeparata from different manufacturing sources and processing ways.

Aconiti Lateralis Radix Praeparata (Fuzi) is a commonly used traditional Chinese medicine in clinic for its potency in restoring yang and rescuing from collapse. Aconiti alkaloids, mainly including monoester-diterpenoidaconitines (MDAs) and diester-diterpenoidaconitines (DDAs), are considered to act as both bioactive and toxic constituents. In the present study, a feasible, economical, and accurate HPLC method for simultaneous determination of six alkaloid markers using the Single Standard for Determination of Multi-Components (SSDMC) method was developed and fully validated. Benzoylmesaconine was used as the unique reference standard. This method was proven as accurate (recovery varying between 97.5%-101.8%, RSD < 3%), precise (RSD 0.63%-2.05%), and linear (R > 0.999 9) over the concentration ranges, and subsequently applied to quantitative evaluation of 62 batches of samples, among which 45 batches were from good manufacturing practice (GMP) facilities and 17 batches from the drug market. The contents were then analyzed by principal component analysis (PCA) and homogeneity test. The present study provided valuable information for improving the quality standard of Aconiti Lateralis Radix Praeparata. The developed method also has the potential in analysis of other Aconitum species, such as Aconitum carmichaelii (prepared parent root) and Aconitum kusnezoffii (prepared root).