Automatic literature screening using the PAJO deep-learning model for clinical practice guidelines.

BACKGROUND: Clinical practice guidelines (CPGs) are designed to assist doctors in clinical decision making. High-quality research articles are important for the development of good CPGs. Commonly used manual screening processes are time-consuming and labor-intensive. Artificial intelligence (AI)-based techniques have been widely used to analyze unstructured data, including texts and images. Currently, there are no effective/efficient AI-based systems for screening literature. Therefore, developing an effective method for automatic literature screening can provide significant advantages. METHODS: Using advanced AI techniques, we propose the Paper title, Abstract, and Journal (PAJO) model, which treats article screening as a classification problem. For training, articles appearing in the current CPGs are treated as positive samples. The others are treated as negative samples. Then, the features of the texts (e.g., titles and abstracts) and journal characteristics are fully utilized by the PAJO model using the pretrained bidirectional-encoder-representations-from-transformers (BERT) model. The resulting text and journal encoders, along with the attention mechanism, are integrated in the PAJO model to complete the task. RESULTS: We collected 89,940 articles from PubMed to construct a dataset related to neck pain. Extensive experiments show that the PAJO model surpasses the state-of-the-art baseline by 1.91% (F1 score) and 2.25% (area under the receiver operating characteristic curve). Its prediction performance was also evaluated with respect to subject-matter experts, proving that PAJO can successfully screen high-quality articles. CONCLUSIONS: The PAJO model provides an effective solution for automatic literature screening. It can screen high-quality articles on neck pain and significantly improve the efficiency of CPG development. The methodology of PAJO can also be easily extended to other diseases for literature screening.

Inhibition of castration-resistant prostate cancer growth by genistein through suppression of AKR1C3.

Background: Prostate cancer is the second leading cause of cancer-related death among males in America. The patients' survival time is significantly reduced after prostate cancer develops into castration-resistant prostate cancer (CRPC). It has been reported that AKR1C3 is involved in this progression, and that its abnormal expression is directly correlated with the degree of CRPC malignancy. Genistein is one of the active components of soy isoflavones, and many studies have suggested that it has a better inhibitory effect on CRPC. Objective: This study aimed to investigate the antitumor effect of genistein on CRPC and the potential mechanism of action. Design: A xenograft tumor mouse model established with 22RV1 cells was divided into the experimental group and the control group, and the former was given 100 mg/kg.bw/day of genistein, with 22RV1, VCaP, and RWPE-1 cells cultured in a hormone-free serum environment and treated with different concentrations of genistein (0, 12.5, 25, 50, and 100 µmol/L) for 48 h. Molecular docking was used to elucidate the molecular interactions between genistein and AKR1C3. Results: Genistein inhibits CRPC cell proliferation and in vivo tumorigenesis. The western blot analysis confirmed that the genistein significantly inhibited prostate-specific antigen production in a dose-dependent manner. In further results, AKR1C3 expression was decreased in both the xenograft tumor tissues and the CRPC cell lines following genistein gavage feeding compared to the control group, with the reduction becoming more obvious as the concentration of genistein was increased. When the genistein was combined with AKR1C3 small interfering ribonucleic acid and an AKR1C3 inhibitor (ASP-9521), the inhibitory effect on the AKR1C3 was more pronounced. In addition, the molecular docking results suggested that the genistein had a strong affinity with the AKR1C3, and that it could be a promising AKR1C3 inhibitor. Conclusion: Genistein inhibits the progression of CRPC via the suppression of AKR1C3.

Modulation of the gut microbiota and lipidomic profiles by black chokeberry (Aronia melanocarpa L.) polyphenols via the glycerophospholipid metabolism signaling pathway.

Black chokeberry (Aronia melanocarpa L.) is rich in polyphenols with various physiological and pharmacological activities. However, the relationship between the modulation effect of black chokeberry polyphenols on obesity and the alteration of lipid metabolism is not clearly understood. This study aimed to investigate the beneficial effects of the black chokeberry polyphenols (BCPs) treatment on the structure of gut microbiota, lipid metabolism, and associated mechanisms in high-fat diet (HFD)-induced obese rats. Here, we found that a high-fat diet promoted body weight gain and lipid accumulation in rats, while oral BCPs supplementation reduced body weight, liver, and white adipose tissue weight and alleviated dyslipidemia and hepatic steatosis in HFD-induced obese rats. In addition, BCPs supplementation prevented gut microbiota dysbiosis by increasing the relative abundance of Bacteroides, Prevotella, Romboutsia, and Akkermansia and decreasing the relative abundance of Desulfovibrio and Clostridium. Furthermore, 64 lipids were identified as potential lipid biomarkers through lipidomics analysis after BCPs supplementation, especially PE (16:0/22:6), PE (18:0/22:6), PC (20:3/19:0), LysoPE (24:0), LysoPE (24:1), and LysoPC (20:0). Moreover, our studies provided new evidence that composition of gut microbiota was closely related to the alteration of lipid profiles after BCPs supplementation. Additionally, BCPs treatment could ameliorate the disorder of lipid metabolism by regulating the mRNA and protein expression of genes related to the glycerophospholipid metabolism signaling pathway in HFD-induced obese rats. The mRNA and protein expression of PPARα, CPT1α, EPT1, and LCAT were significantly altered after BCPs treatment. In conclusion, the results of this study indicated that BCPs treatment alleviated HFD-induced obesity by modulating the composition and function of gut microbiota and improving the lipid metabolism disorder via the glycerophospholipid metabolism signaling pathway.

Cloning and functional analysis of three aphid alarm pheromone genes from German chamomile (Matricaria chamomilla L.).

German chamomile (Matricaria chamomilla L.) is one of the most ancient medicinal species in the world and terpenoids from their flowers have important medicinal value. We cloned three sesquiterpene synthase genes, McGDS1, McGDS2 and McGDS3, and performed sequence alignment and phylogenetic analysis. The encoded proteins possess three conserved structural features: an RRxxxxxxxxW motif, an RxR motif, and a DDxxD motif. McGDS1, McGDS2 and McGDS3 were confirmed to be (E)-farnesene synthase, germacrene D synthase, and germacrene A synthase, respectively. Subcellular localization revealed diffuse GFP reporter-gene signals in the cytoplasm and nucleus. qPCR indicated that McGDS1, McGDS2 and McGDS3, were more highly expressed in young flowers than in old flowers and the expression was highly correlated with amounts of the end-product essential oils ((E)-ß-farnesene, germacrene D and ß-elemene), with coefficients of 0.76, 0.83 and 0.68, respectively. We also established a transformation system for chamomile hairy roots. The overexpression of McGDS1, McGDS2 and McGDS3 resulted in γ-muurolene accumulation in hairy roots. The activity of three aphid alarm pheromones here forms the molecular basis for the study of the biosynthesis and regulation of volatile terpenes. Transformation of chamomile hairy roots provides a simple system in which to study terpene biosynthesis in chamomile.

Analysis of terpenoid biosynthesis pathways in German chamomile (Matricaria recutita) and Roman chamomile (Chamaemelum nobile) based on co-expression networks.

German chamomile and Roman chamomile are the two most widely known chamomile species due to the medicinal properties of volatile compounds from their flowers. We determined the volatile compound content of different organs of these two chamomiles, and found that main volatile compounds in German chamomile were terpenoids and those in Roman chamomile were esters. Furthermore, 24 tissues from two chamomiles were sequenced and analyzed by gene co-expression network. The results showed higher terpene synthase expression levels and more modules correlated with sesquiterpenoids in German chamomile, which may explain its high sesquiterpenoid content. In both chamomiles, unigenes in volatile compound-correlated modules were significantly enriched in pathways related to plant-pathogen interactions and circadian rhythm, demonstrating that volatile compounds of chamomiles are influenced by these factors. There were ten times more unigenes related to plant-pathogen interactions in German chamomile than in Roman chamomile, which indicates German chamomile has higher resistance to pathogens.