Clinical drug-drug interactions (DDIs) have been a major cause for not only medical error but also adverse drug events (ADEs). The published literature on DDI clinical toxicity continues to grow significantly, and high-performance DDI information retrieval (IR) text mining methods are in high demand. The effectiveness of IR and its machine learning (ML) algorithm depends on the availability of a large amount of training and validation data that have been manually reviewed and annotated. In this study, we investigated how active learning (AL) might improve ML performance in clinical safety DDI IR analysis. We recognized that a direct application of AL would not address several primary challenges in DDI IR from the literature. For instance, the vast majority of abstracts in PubMed will be negative, existing positive and negative labeled samples do not represent the general sample distributions, and potentially biased samples may arise during uncertainty sampling in an AL algorithm. Therefore, we developed several novel sampling and ML schemes to improve AL performance in DDI IR analysis. In particular, random negative sampling was added as a part of AL since it has no expanse in the manual data label. We also used two ML algorithms in an AL process to differentiate random negative samples from manually labeled negative samples, and updated both the training and validation samples during the AL process to avoid or reduce biased sampling. Two supervised ML algorithms, support vector machine (SVM) and logistic regression (LR), were used to investigate the consistency of our proposed AL algorithm. Because the ultimate goal of clinical safety DDI IR is to retrieve all DDI toxicity-relevant abstracts, a recall rate of 0.99 was set in developing the AL methods. When we used our newly proposed AL method with SVM, the precision in differentiating the positive samples from manually labeled negative samples improved from 0.45 in the first round to 0.83 in the second round, and the precision in differentiating the positive samples from random negative samples improved from 0.70 to 0.82 in the first and second rounds, respectively. When our proposed AL method was used with LR, the improvements in precision followed a similar trend. However, the other AL algorithms tested did not show improved precision largely because of biased samples caused by the uncertainty sampling or differences between training and validation data sets.

Jian-pi-bu-xue-formula (JPBXF), a TCM formula composed of twelve Chinese medicinal herbs, has been used in clinic to ease patients' state of weakness and fatigue especially after receiving anti-tumor chemotherapy in China. The lack of the phytochemical characterization, detail therapeutic evaluation and mechanism of JPBXF remains the main limitation for its spreading. In this study, we systematically evaluated the effectiveness and underline mechanism of JPBXF on cyclophosphamide (CTX)-induced myelosuppression and identified the main constituents of JPBXF aqueous extract. JPBXF treatments reversed CTX-induced myelosuppression through increasing the number of haematopoietic stem cells (HSCs) and expression of C-kit in bone marrow cells. Simultaneously, JPBXF treatments alleviated CTX-induced blood cells reduction by increasing numbers of RBCs and WBCs and levels of GM-CSF, TPO and EPO in plasma. JPBXF treatments reduced CTX-induced immunosuppression by increasing expressions of CD3, CD4, and CD8a in PBMCs, and recovering structure damages of thymus and spleen. Moreover, JPBXF notably increased the expression of NRF2 compared with CTX group, and subsequently up-regulated HO1 and NQO1 both in mRNA and protein levels. In addition, eighteen compounds were recognized from JPBXF aqueous extract and the potential targets of the identified compounds were predicted. Overall, JPBXF can greatly reverse CTX-induced myelosuppression in C57BL/6 mice, especially in improving the blood and immune function through activating NRF2/HO1/NQO1 signaling pathway, which provides a reliable reference for JPBXF application in clinical. By recognizing eighteen compounds in JPBXF aqueous extract and predicting the underline mechanisms of the identified compounds, our study would provide theoretical guidance for further research of JPBXF.

Drugs targeting intestinal bacteria have shown great efficacy for alleviating symptoms of Alzheimer's disease (AD), and microbial metabolites are important messengers. Our previous work indicated that Rheum tanguticum effectively improved cognitive function and reshaped the gut microbial homeostasis in AD rats. However, its therapeutic mechanisms remain unclear. Herein, this study aimed to elaborate the mechanisms of rhubarb for the treatment of AD by identifying effective metabolites associated with rhubarb-responsive bacteria. The results found that rhubarb reduced hippocampal inflammation and neuronal damage in APP/PS1 transgenic (Tg) mice. 16S rRNA sequencing and metabolomic analysis revealed that gut microbiota and their metabolism in Tg mice were disturbed in an age-dependent manner. Rhubarb-responsive bacteria were further identified by real-time polymerase chain reaction (RT-PCR) sequencing. Four different metabolites reversed by rhubarb were found in the position of the important nodes on rhubarb-responsive bacteria and their corresponding metabolites combined with pathological indicators co-network. Furthermore, in vitro experiments demonstrated o-tyrosine not only inhibited the viabilities of primary neurons as well as BV-2 cells, but also increased the levels of intracellular reactive oxygen species and nitric oxide. In the end, the results suggest that rhubarb ameliorates cognitive impairment in Tg mice through decreasing the abundance of o-tyrosine in the gut owing to the regulation of rhubarb-responsive bacteria. Our study provides a promising strategy for elaborating therapeutic mechanisms of bacteria-targeted drugs for AD.

Network pharmacology is considered as the next paradigm in drug discovery. In an era when obesity has become global epidemic, network pharmacology becomes an ideal tool to discover novel herbal-based therapeutics with effective anti-obesity effects. Zanthoxylum bungeanum Maxim (ZBM) is a medicinal herb. The mature pericarp of ZBM is used for disease treatments and as spice for cooking. Here, we used the network pharmacology approach to investigate whether ZBM possesses anti-obesity effects and reveal the underlying mechanism of action. We first built up drug-ingredient-gene symbol-disease network and protein-protein interaction network of the ZBM-related obesity targets, followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. The results highlight apoptosis as a promising signaling pathway that mediates the anti-obesity effects of ZBM. Molecular docking also reveals quercetin, a compound in ZBM has the highest degree of connections in the compound-target network and has direct bindings with the apoptotic markers. Furthermore, the apoptotic effects of ZBM are further validated in 3T3-L1 adipocytes and in the high-fat diet-induced obesity mouse model. These findings not only suggest ZBM can be developed as potential anti-obesity therapeutics but also demonstrate the application of network pharmacology for the discovery of herbal-based therapeutics for disease treatments.

Introduction: During the coronavirus disease 2019 (COVID-19) pandemic, a large number of critically ill and severe COVID-19 patients meet the diagnostic criteria for sepsis and even septic shock. The treatments for COVID-19 patients with sepsis are still very limited. For sepsis, improving ventilation is one of the main treatments. Nitric oxide (NO) and almitrine have been reported to improve oxygenation in patients with "classical" sepsis. Here, we conducted a systematic review and meta-analysis to evaluate the efficacy and safety of NO, almitrine, and the combination of both for COVID-19 (at the edge of sepsis) patients. Method: A systematic search was performed on Embase, PubMed, the Cochrane Library, the Web of Science, Wanfang Data, and China National Knowledge Infrastructure. Randomized clinical trials, cohort studies, cross-sectional studies, case-control studies, case series, and case reports in COVID-19 patients with suspected or confirmed sepsis were performed. Study characteristics, patient demographics, interventions, and outcomes were extracted from eligible articles. Results: A total of 35 studies representing 1,701 patients met eligibility criteria. Inhaled NO did not affect the mortality (OR 0.96, 95% CI 0.33-2.8, I2 = 81%, very low certainty), hospital length of stay (SMD 0.62, 95% CI 0.04-1.17, I2 = 83%, very low certainty), and intubation needs (OR 0.82, 95% CI 0.34-1.93, I2 = 56%, very low certainty) of patients with COVID-19 (at the edge of sepsis). Meanwhile, almitrine did not affect the mortality (OR 0.44, 95% CI 0.17-1.13, low certainty), hospital length of stay (SMD 0.00, 95% CI -0.29-0.29, low certainty), intubation needs (OR 0.94, 95% CI 0.5-1.79, low certainty), and SAEs (OR 1.16, 95% CI 0.63-2.15, low certainty). Compared with pre-administration, the PaO2/FiO2 of patients with NO (SMD-0.87, 95% CI -1.08-0.66, I2 = 0%, very low certainty), almitrine (SMD-0.73, 95% CI-1.06-0.4, I2 = 1%, very low certainty), and the combination of both (SMD-0.94, 95% CI-1.71-0.16, I2 = 47%, very low certainty) increased significantly. Conclusion: Inhaled NO, almitrine, and the combination of the two drugs improved oxygenation significantly, but did not affect the patients' mortality, hospitalization duration, and intubation needs. Almitrine did not significantly increase the patients' SAEs. Well-designed high-quality studies are needed for establishing a stronger quality of evidence. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=367667, identifier CRD42022367667.

Lung adenocarcinoma (LAC), predominant subclassfication of lung cancer, leads high incidence and mortality annually worldwide. During the premalignant transition from lung adenomas to LAC, cellular senescence is regard as a critical physiological barrier against tumor progression. Nevertheless, the role of senescence in tumorigenesis is controversial and few senescence inducers are extensively determined. In this study, we used two classical cell lines A549 and H1299 and two NSCLC xenograft models on Balb/c-nude mice to reveal the pro-senescence effects of shikonin and the corresponding underlying mechanism in LAC. Shikonin, a pure compound isolated from the herbal medicine Lithospermum erythrorhizon, remarkably stimulated cellular senescence including increased SAHF formation, enlarged cellular morphology, and induced SA-β-Gal positive staining. Further mechanism study revealed that the pro-senescence effect of shikonin was dependent on the increased intercellular ROS generation, which subsequently triggered DNA damage-p53/p21waf axis without activating oncogenes such as Ras and MEK-1. Meanwhile, Kdm2b, an H3K36me2-specific demethylase effectively suppressed ROS generation, was also notably suppressed by shikonin treatment. Moreover, shikonin at 10 mg/kg significantly inhibited tumor weights by 55.84% and 50.98% in A549 and H1299 xenograft model, respectively (P < 0.05) through activating cellular senescence. Our study suggested that shikonin, a ROS-dependent senescence inducer, could serve as a promising agent for further lung cancer treatment.

Lung cancer is a leading cause of cancer-related deaths worldwide. NOTCH3 signaling is mainly expressed in non-small cell lung carcinoma (NSCLC), and has been proposed as a therapeutic target of NSCLC. While, few agents for preventing or treating NSCLC via targeting NOTCH3 signaling are used in modern clinical practice. Evodiamine (EVO), an alkaloid derived from Euodiae Fructus, possesses low toxicity and has long been shown to exert anti-lung cancer activity. However, the underlying anti-lung cancer mechanisms of EVO are not yet fully understood. In this study, we explored the involvement of NOTCH3 signaling in the anti-lung cancer effects of EVO. Urethane-induced lung cancer mouse model and two NSCLC cell models, A549 and H1299, were used to evaluate the in vivo and in vitro anti-lung cancer action of EVO. A DNA methyltransferase inhibitor was employed to investigate the role of NOTCH3 signaling in the anti-lung cancer effects of EVO. Results showed that EVO potently reduced tumor size and tumor numbers in mice, and inhibited NOTCH3 in the tumors. EVO also dramatically reduced cell viability, induced G2/M cell cycle arrest, inhibited cell migration and reduced stemness in cultured NSCLC cells. Mechanistic studies showed that EVO potently inhibited NOTCH3 signaling by activation of DNMTs-induced NOTCH3 methylation. Importantly, inhibition of NOTCH3 methylation in NSCLC cells diminished EVO's anti-NSCLC effects. Collectively, EVO, a novel NOTCH3 methylation stimulator, exerted potent anti-lung cancer effects partially by inhibiting NOTCH3 signaling. These findings provide new insight into the EVO's anti-NSCLC action, and suggest a potential role of EVO in lung cancer prevention and treatment.

Developing therapeutic approaches that target neuronal differentiation will be greatly beneficial for the regeneration of neurons and synaptic networks in neurological diseases. Protein synthesis (mRNA translation) has recently been shown to regulate neurogenesis of neural stem/progenitor cells (NSPCs). However, it has remained unknown whether engineering translational machinery is a valid approach for manipulating neuronal differentiation. The present study identifies that a bivalent securinine compound SN3-L6, previously designed and synthesized by our group, induces potent neuronal differentiation through a novel translation-dependent mechanism. An isobaric tag for relative and absolute quantitation (iTRAQ)-based proteomic analysis in Neuro-2a progenitor cells revealed that SN3-L6 upregulated a group of neurogenic transcription regulators, and also upregulated proteins involved in RNA processing, translation, and protein metabolism. Notably, puromycylation and metabolic labeling of newly synthesized proteins demonstrated that SN3-L6 induced rapid and robust activation of general mRNA translation. Importantly, mRNAs of the proneural transcription factors Foxp1, Foxp4, Hsf1, and Erf were among the targets that were translationally upregulated by SN3-L6. Either inhibition of translation or knockdown of these transcription factors blocked SN3-L6 activity. We finally confirmed that protein synthesis of a same set of transcription factors was upregulated in primary cortical NPCs. These findings together identify a new compound for translational activation and neuronal differentiation, and provide compelling evidence that reprogramming transcriptional regulation network at translational levels is a promising strategy for engineering NSPCs.