Breast cancer (BC) has a complex etiology and pathogenesis, and is the most common malignant tumor type in females, in USA in 2018, yet its relevant molecular mechanisms remain largely unknown. The ...collagen type V α‑1 chain (COL5A1) gene is differentially expressed in renal and ovarian cancer. Using bioinformatics methods, COL5A1 was determined to also be a significant gene in BC, but its association with BC has not been sufficiently reported. COL5A1 microarray and relevant clinical data were collected from the Gene Expression Omnibus, The Cancer Genome Atlas and other databases to summarize COL5A1 expression in BC and its subtypes at the mRNA and protein levels. All associated information was comprehensively analyzed by various software. The clinical significance of the mutation was obtained via the cBioPortal. Furthermore, Gene Ontology functional annotation and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were also performed to investigate the mechanism of COL5A1 in BC. Immunohistochemistry was also conducted to detect and confirm COL5A1 expression. It was determined that COL5A1 was highly expressed in BC tissues, compared with normal tissues at the mRNA level standard mean difference, 0.84; 95% confidence interval (CI), 0.60‑1.07; P=0.108. The area under the summary receiver operator characteristic curve for COL5A1 was 0.87 (95% CI, 0.84‑0.90). COL5A1 expression was altered in 32/817 (4%) sequenced samples. KEGG analysis confirmed the most notable pathways, including focal adhesion, extracellular matrix‑receptor interaction and regulation of the actin cytoskeleton. Immunohistochemical detection was used to verify the expression of COL5A1 in 136 selected cases of invasive BC tissues and 55 cases of adjacent normal tissues, while the rate of high expression of COL5A1 in BC was up to 90.4%. These results indicated that COL5A1 is highly expressed at the mRNA and protein levels in BC, and the prognosis of patients with BC with high COL5A1 expression may be reduced; therefore, COL5A1 may be used independently or combined with other detection factors in BC diagnosis.
Chronic liver disease(CLD) is a slow-developing and long-term disease that can cause serious damage to the liver. Thus far, it has been associated with viral hepatitis, non-alcoholic fatty liver ...disease(NAFLD), alcoholic liver disease(ALD), hepatic fibrosis(HF), liver cirrhosis (LC), and liver cancer. Qinghao Biejia Decoction (QBD) is a classic ancient Chinese herbal prescription with strong immune-enhancing, anti-inflammatory, and anti-tumor effects. In this study, we used a network pharmacology approach to investigate the molecular mechanisms of QBD in the inflammation-carcinoma transformation process of chronic liver disease. Two key drug targets, MAPK1 and PIK3CA, were screened using network pharmacology and molecular docking techniques, revealing dihydroartemisinin, artesunate, 12-O-Nicotinoylisolineolone, caffeic acid, and diincarvilone A as active ingredients involved in QBD mechanisms. The main signaling pathways involved were the PI3K-AKT signaling pathway and MAPK signaling pathway. In summary, our results indicated that QBD affects the inflammatory transformation of chronic liver disease through MAPK1 and PIK3CA and signaling pathways MAPK and PI3K/AKT. These data provide research direction for investigating the mechanisms underlying the inflammation-carcinoma transformation process in QBD for chronic liver disease.
Aims:
To explore the interactive influence of glucocorticoids and cytochrome P450 (CYP450) polymorphisms on voriconazole (VRC) plasma trough concentrations (C
min
) and provide a reliable basis for ...reasonable application of VRC.
Methods:
A total of 918 VRC C
min
from 231 patients was collected and quantified using high-performance liquid chromatography in this study. The genotypes of
CYP2C19
,
CYP3A4
, and
CYP3A5
were detected by DNA sequencing assay. The effects of different genotypes and the coadministration of glucocorticoids on VRC C
min
were investigated. Furthermore, the interactive effects of glucocorticoids with CYP450s on VRC C
min
were also analyzed.
Results:
The median C
min
of oral administration was lower than that of intravenous administration (1.51 vs. 4.0 mg l
−1
). Coadministration of glucocorticoids (including dexamethasone, prednisone, prednisolone, and methylprednisolone) reduced the VRC C
min
/dose, respectively, among which dexamethasone make the median of the VRC C
min
/dose ratio lower. As a result, when VRC was coadministrated with glucocorticoids, the proportion of VRC C
min
/dose in the subtherapeutic window was increased. Different CYP450 genotypes have different effects on the C
min
/dose of VRC. Mutations of
CYP2C19*2
and
*3
increased C
min
/dose of VRC, while
CYP2C19*17
and
CYP3A4
rs4646437 polymorphisms decreased C
min
/dose of VRC. The mutation of
CYP3A5
has no significant effect. Furthermore,
CYP2C19*17
mutants could strengthen the effects of glucocorticoids and decrease VRC C
min
/dose to a larger extent.
Conclusion:
Our study revealed that glucocorticoids reduced the C
min
/dose levels of VRC and different SNPs of CYP450 have different effects on the C
min
/dose ratio of VRC. Glucocorticoids and
CYP2C19*17
mutants had a synergistic effect on reducing VRC C
min
/dose. The present results suggested that when VRC is combined with glucocorticoids, we should pay more attention to the clinical efficacy of VRC, especially when
CYP2C19*17
mutants exist.
The inventory level has a significant influence on the cost of process scheduling. The stochastic cutting stock problem (SCSP) is a complicated inventory-level scheduling problem due to the existence ...of random variables. In this study, we applied a model-free on-policy reinforcement learning (RL) approach based on a well-known RL method, called the Advantage Actor-Critic, to solve a SCSP example. To achieve the two goals of our RL model, namely, avoiding violating the constraints and minimizing cost, we proposed a two-stage discount factor algorithm to balance these goals during different training stages and adopted the game concept of an episode ending when an action violates any constraint. Experimental results demonstrate that our proposed method obtains solutions with low costs and is good at continuously generating actions that satisfy the constraints. Additionally, the two-stage discount factor algorithm trained the model faster while maintaining a good balance between the two aforementioned goals.
DNA hypomethylation plays an important role in the pathogenesis of systemic lupus erythematosus (SLE). Here we investigated whether 3-hydroxy butyrate dehydrogenase 2 (BDH2), a modulator of ...intracellular iron homeostasis, was involved in regulating DNA hypomethylation and hyper-hydroxymethylation in lupus CD4+ T cells. Our results showed that BDH2 expression was decreased, intracellular iron was increased, global DNA hydroxymethylation level was elevated, while methylation level was reduced in lupus CD4+ T cells compared with healthy controls. The decreased BDH2 contributed to DNA hyper-hydroxymethylation and hypomethylation via increasing intracellular iron in CD4+ T cells, which led to overexpression of immune related genes. Moreover, we showed that BDH2 was the target gene of miR-21. miR-21 promoted DNA demethylation in CD4+ T cells through inhibiting BDH2 expression. Our data demonstrated that the dysregulation of iron homeostasis in CD4+ T cells induced by BDH2 deficiency contributes to DNA demethylation and self-reactive T cells in SLE.
HER2-positive (HER2
) metastatic breast cancer (mBC) is highly aggressive and a major threat to human health. Despite the significant improvement in patients' prognosis given the drug development ...efforts during the past several decades, many clinical questions still remain to be addressed such as efficacy when combining different therapeutic modalities, best treatment sequences, interindividual variability as well as resistance and potential coping strategies. To better answer these questions, we developed a mechanistic quantitative systems pharmacology model of the pathophysiology of HER2
mBC that was extensively calibrated and validated against multiscale data to quantitatively predict and characterize the signal transduction and preclinical tumor growth kinetics under different therapeutic interventions. Focusing on the second-line treatment for HER2
mBC, e.g., antibody-drug conjugates (ADC), small molecule inhibitors/TKI and chemotherapy, the model accurately predicted the efficacy of various drug combinations and dosing regimens at the in vitro and in vivo levels. Sensitivity analyses and subsequent heterogeneous phenotype simulations revealed important insights into the design of new drug combinations to effectively overcome various resistance scenarios in HER2
mBC treatments. In addition, the model predicted a better efficacy of the new TKI plus ADC combination which can potentially reduce drug dosage and toxicity, while it also shed light on the optimal treatment ordering of ADC versus TKI plus capecitabine regimens, and these findings were validated by new in vivo experiments. Our model is the first that mechanistically integrates multiple key drug modalities in HER2
mBC research and it can serve as a high-throughput computational platform to guide future model-informed drug development and clinical translation.
We report a case of Listeria meningitis related to mantle cell lymphoma. A clinical pharmacist adjusted repeatedly the patient’s anti-infective therapeutic regimen by analyzing the pharmacologic and ...pharmacokinetic characteristics of antibacterial drugs (such as cefotaxime, meropenem, etc.) due to the patient’s repeated fever during hospitalization. To the best of our knowledge, this is the first case of Listeria meningitis related to mantle cell lymphoma treated successfully with meropenem reported in China. This case aims to optimize the anti-infection treatment regimen of Listeria meningitis and to provide a reference for clinicians and clinical pharmacists to use drugs rationally.
The present study focused on exploring the inhibitory mechanism of microRNA (miR)-23a in endometrial cancer. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) was used to ...investigate miR-23a expression in endometrial tissues and endometrial cancer cells. A colony formation assay using crystal violet staining was performed to compare cell proliferation, while wound-healing and Transwell assays were performed to compare cell migration and invasion. Subsequently, bioinformatics and a luciferase reporter gene assay were used to investigate the effect of miR-23a on sine oculis homeobox homolog 1 (SIX1) expression, and the biological function of SIX1 was analyzed. Additionally, a nude mouse tumorigenicity assay was performed to test the inhibitory effect of miR-23a and TaxolR therapy in endome-trial cancer. Finally, immunohistochemistry and RT-qPCR were used to explore the association between miR-23a and SIX1 expression in endometrial cancer tissues. miR-23a was underexpressed in endometrial cancer tissues compared with in para-carcinoma tissues, and the overexpression of miR-23a inhibited proliferation and invasion of endometrial cancer cells. Furthermore, SIX1 was demonstrated to be a downstream target of miR-23a, and miR-23a reduced SIX1 expression. Additionally, SIX1 inversely promoted cell proliferation, migration and invasion. In addition, the effects of reduced cell proliferation and increased cell invasion following miR-23a overexpression could be reversed by adding SIX1 to in vitro culture. Furthermore, the inhibitory effect of miR-23a and Taxol therapy, which reduced SIX1 expression in endometrial cancer, was demonstrated in vivo. Finally, a negative association between miR-23a and SIX1 expression was demonstrated in endometrial cancer tissues. The results of the present study revealed that miR-23a may inhibit endometrial cancer development by targeting SIX1.
Existing semi-empirical formulas for predicting punching shear capacity in FRP bar reinforced concrete flat slabs without shear reinforcement often prove inaccurate and unstable. This is primarily ...due to limited modeling data, inadequate consideration of key variables and neglect of complex nonlinear relationships. To address these challenges, this study delves into the utilization of advanced machine learning (ML) algorithms to offer precise and dependable estimates of punching shear capacity in such structural components. The study initially compiled a comprehensive database comprising 165 sets of test data, integrating eight crucial variables for model development. Subsequently, four data-driven models including back propagation artificial neural network (BPANN), support vector regression (SVR), random forest (RF) and gradient boosting regression tree (GBRT) were formulated to estimate the shear capacity. The efficacy of these models was assessed in comparison to existing prediction formulas. To interpret the models, this study also introduced shapley additive explanation (SHAP) and partial dependence plot (PDP) to quantitatively evaluate the influence of variables on predicted results. Research findings suggest that: (a) Among 25 existing formulas, Ju et al.’s approach performs notably well, with R2, Pre/Exp, MAPE and RMSE values at 0.76, 1.02, 22.2 % and 142.8 kN, respectively. (b) ML models surpass traditional formulas in predictive accuracy, with R2, Pre/Exp, MAPE and RMSE values ranging from 0.89 to 0.93, 1.03–1.09, 4.8–9.5 % and 55.4–69.0 kN, respectively. The GBRT model demonstrates the highest precision. (c) SHAP analysis of the GBRT model reveals that effective slab height and column section aspect ratio are pivotal variables influencing punching shear capacity. (d) PDP analysis quantitatively illustrates how punching shear capacity varies with each key variable.
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