Diagnostic performance of contrast-enhanced mammography for suspicious findings in dense breasts: A systematic review and meta-analysis.

PURPOSE: Contrast-enhanced spectral imaging (CEM) is a new mammography technique, but its diagnostic value in dense breasts is still inconclusive. We did a systematic review and meta-analysis of studies evaluating the diagnostic performance of CEM for suspicious findings in dense breasts. MATERIALS AND METHODS: The PubMed, Embase, and Cochrane Library databases were searched systematically until August 6, 2023. Prospective and retrospective studies were included to evaluate the diagnostic performance of CEM for suspicious findings in dense breasts. The QUADAS-2 tool was used to evaluate the quality and risk of bias of the included studies. STATA V.16.0 and Review Manager V.5.3 were used to meta-analyze the included studies. RESULTS: A total of 10 studies (827 patients, 958 lesions) were included. These 10 studies reported the diagnostic performance of CEM for the workup of suspicious lesions in patients with dense breasts. The summary sensitivity and summary specificity were 0.95 (95% CI, 0.92-0.97) and 0.81 (95% CI, 0.70-0.89), respectively. Enhanced lesions, circumscribed margins, and malignancy were statistically correlated. The relative malignancy OR value of the enhanced lesions was 28.11 (95% CI, 6.84-115.48). The relative malignancy OR value of circumscribed margins was 0.17 (95% CI, 0.07-0.45). CONCLUSION: CEM has high diagnostic performance in the workup of suspicious findings in dense breasts, and when lesions are enhanced and have irregular margins, they are often malignant.

Oyster (Ostrea Plicatula Gmelin) Peptides Improve Exercise Endurance Capacity via Activating AMPK and HO-1.

Objective: Previous studies have shown that oyster peptides (OPs) have antioxidant and anti-fatigue activities. This study aimed to investigate the effects of OPs on swimming endurance in mice and the underlying mechanisms.Methods: The mice were subjected to gavage with OPs and subjected to exercise training. After 14 days, various biochemical indicators in the blood and gastrocnemius muscle of mice were assessed, and real-time PCR was utilized to detect the level of signal pathway regulation by OPs in the gastrocnemius muscle. Molecular docking technology was employed to observe the potential active components in OPs that regulate signal pathways.Results: In this study, OPs supplementation combined with and without exercise significantly extended swimming time compared to the sedentary group. OPs supplementation with exercise also increased glycogen levels and decreased blood urea nitrogen, lactate dehydrogenase, and lactic acid levels. Additionally, mice in the exercise with OPs group exhibited higher activities of antioxidant enzymes. OPs can upregulate metabolic regulatory factors such as AMP-activated protein kinase, peroxisome proliferator-activated receptor gamma coactivator-1 alpha, peroxisome proliferator-activated receptor delta, and glucose transporter 4, thereby increasing energy supply during exercise. Additionally, OPs enhances the expression of heme oxygenase 1 and superoxide dismutase 2, thereby reducing oxidative stress during physical activity. Molecular docking analyses revealed that peptides found in OPs formed hydrogen bonds with AMPK and HO-1, indicating that they can exert bioactivity by activating target proteins such as AMPK and HO-1.Conclusions: OPs supplementation improved energy reserves, modulated energy metabolism pathways, and coordinated antioxidative stress responses, ultimately enhancing swimming endurance. These findings suggest that OPs have the potential to improve exercise levels by promoting metabolism and improving energy utilization efficiency.

Analysis of factors related to osteoporotic vertebral fracture in prostate cancer patients.

OBJECTIVE: This study was aimed at exploring the osteoporotic vertebral fracture rate and the related causal factors in prostate cancer patients before and after treatment. METHODS: One hundred prostate cancer patients were recruited in this study. One hundred men without prostate cancer history were selected as the control group. The study was approved by the Medical Ethics Committee under Ethics number B2021-373R and the requirement for the informed consent was waived. The T4-L1 vertebral body of the case group and the control group before and after treatment was evaluated according to Genant's semi-quantitative method. The difference in vertebral body fracture rate between the case group and the control group and the changes in vertebral body fracture rate before and after treatment among the case group were compared. They were grouped according to age, body mass index (BMI), prostate-specific antigen (PSA) levels, Gleason grade, and androgen deprivation therapy (ADT). Univariate and multivariate logistic regression models were used to determine the factors significantly associated with vertebral fracture rate in prostate cancer patients. RESULTS: The prevalence of vertebral fracture was 16% and 31% in prostate cancer patients before and after treatment, respectively, and 29% in the control group. The vertebral fracture rate of the patients before treatment significantly differed that of the control group and the patients after treatment. Univariate analysis showed that age, PSA levels, and treatment parameters were the significant influencing factors of vertebral fracture rates. Multivariate logistic regression analysis showed that age was the main influencing factor of vertebral fracture rates. CONCLUSION: Osteoporotic vertebral fractures in patients with prostate cancer was associated with many factors. And the incidence of vertebral fracture in prostate cancer patients after ADT was significantly higher than that before treatment.

Non-Invasive Diagnosis of Prostate Cancer and High-Grade Prostate Cancer Using Multiparametric Ultrasonography and Serological Examination.

OBJECTIVES: This study aimed to assess the efficacy of multiparametric ultrasonography (mpUS) combined with serological examination, as a non-invasive method, in detecting prostate cancer (PCa) or high-grade prostate cancer (HGPCa) respectively. METHODS: A cohort of 245 individuals with clinically suspected PCa were enrolled. All subjects underwent a comprehensive evaluation, including basic data collection, serological testing, mpUS and prostate biopsy. Random Forest (RF) models were developed, and the mean area under the curve (AUC) in 100 cross-validations was used to assess the performance in distinguishing PCa from HGPCa. RESULTS: mpUS features showed significant differences (p < 0.001) between the PCa and non-PCa groups, as well as between the HGPCa and low-grade prostate cancer (LGPCa) groups including prostate-specific antigen density (PSAD), transrectal real-time elastography (TRTE) and intensity difference (ID). The RF model, based on these features, demonstrated an excellent discriminative ability for PCa with a mean area under the curve (AUC) of 0.896. Additionally, another model incorporating free prostate-specific antigen (FPSA) and color Doppler flow imaging (CDFI) achieved a high accuracy in predicting HGPCa with a mean AUC of 0.830. The nomogram derived from these models exhibited excellent individualized prediction of PCa and HGPCa. CONCLUSION: The RF models incorporating mpUS and serological variables achieved satisfactory accuracies in predicting PCa and HGPCa.

Prediction of Receptor Status in Radiomics: Recent Advances in Breast Cancer Research.

Breast cancer is a multifactorial heterogeneous disease and the leading cause of cancer-related deaths in women; its diagnosis and treatment require clinical sensitivity and a comprehensive disciplinary research approach. The expression of different receptors on tumor cells not only provides the basis for molecular typing of breast cancer but also has a decisive role in the diagnosis, treatment, and prognosis of breast cancer. To date, immunohistochemistry (IHC), which uses invasive histological sampling, has been extensively used in clinical practice to analyze the status of receptors and to make an accurate diagnosis of breast cancer. As an invasive assay, IHC can provide important biological information on tumors at a single point in time, but cannot predict future changes (due to treatment or tumor mutations) without additional invasive procedures. These issues highlight the need to develop a non-invasive method for predicting receptor status. The emerging field of radiomics may offer a non-invasive approach to identification of receptor status without requiring biopsy. In this paper, we present a review of the latest research results in radiomics for predicting the status of breast cancer receptors, with potential important clinical applications.

Diagnostic value of mammography density of breast masses by using deep learning.

Objective: In order to explore the relationship between mammographic density of breast mass and its surrounding area and benign or malignant breast, this paper proposes a deep learning model based on C2FTrans to diagnose the breast mass using mammographic density. Methods: This retrospective study included patients who underwent mammographic and pathological examination. Two physicians manually depicted the lesion edges and used a computer to automatically extend and segment the peripheral areas of the lesion (0, 1, 3, and 5 mm, including the lesion). We then obtained the mammary glands' density and the different regions of interest (ROI). A diagnostic model for breast mass lesions based on C2FTrans was constructed based on a 7: 3 ratio between the training and testing sets. Finally, receiver operating characteristic (ROC) curves were plotted. Model performance was assessed using the area under the ROC curve (AUC) with 95% confidence intervals (CI), sensitivity, and specificity. Results: In total, 401 lesions (158 benign and 243 malignant) were included in this study. The probability of breast cancer in women was positively correlated with age and mass density and negatively correlated with breast gland classification. The largest correlation was observed for age (r = 0.47). Among all models, the single mass ROI model had the highest specificity (91.8%) with an AUC = 0.823 and the perifocal 5mm ROI model had the highest sensitivity (86.9%) with an AUC = 0.855. In addition, by combining the cephalocaudal and mediolateral oblique views of the perifocal 5 mm ROI model, we obtained the highest AUC (AUC = 0.877 P < 0.001). Conclusions: Deep learning model of mammographic density can better distinguish benign and malignant mass-type lesions in digital mammography images and may become an auxiliary diagnostic tool for radiologists in the future.

Modified Diacetylmonoxime-Thiosemicarbazide Detection Protocol for Accurate Quantification of Urea.

Renewable photo-/electrocatalytic coreduction of CO2 and nitrate to urea is a promising method for high-value utilization of CO2 . However, because of the low yields of the urea synthesis by photo-/electrocatalysis process, the accurate quantification of low concentration urea is challenging. The traditional diacetylmonoxime-thiosemicarbazide (DAMO-TSC) method for urea detection has a high limit of quantification and accuracy, but it is easily affected by NO2 - in the solution, which limits its application scope. Thus, the DAMO-TSC method urgently requires a more rigorous design to eliminate the effects of NO2 - and accurately quantify urea in nitrate systems. Herein, a modified DAMO-TSC method is reported, which consumes NO2 - in solution through a nitrogen release reaction; hence, the remaining products do not affect the accuracy of urea detection. The results of detecting urea solutions with different NO2 - concentrations (within 30 ppm) show that the improved method can effectively control the error of urea detection within 3%.

Analysis of Important Volatile Organic Compounds and Genes Produced by Aroma of Pepper Fruit by HS-SPME-GC/MS and RNA Sequencing.

Pepper is an important condiment, and its aroma affects its commercial value. In this study, transcriptome sequencing and combined headspace solid-phase microextraction and gas chromatography-mass spectrometry (HS-SPME-GC-MS) were used to analyze the differentially expressed genes and volatile organic compounds in spicy and non-spicy pepper fruits. Compared with non-spicy fruits, there were 27 up-regulated volatile organic compounds (VOCs) and 3353 up-regulated genes (Up-DEGs) in spicy fruits. The results of KEGG enrichment analysis of the Up-DEGs combined with differential VOCs analysis showed that fatty acid biosynthesis and terpenoid biosynthesis may be the main metabolic pathways for aroma differences between non-spicy and spicy pepper fruits. The expression levels of the fatty acid biosynthesis-related genes FAD, LOX1, LOX5, HPL, and ADH and the key terpene synthesis gene TPS in spicy pepper fruits were significantly higher than those in non-spicy pepper fruits. The differential expression of these genes may be the reason for the different aroma. The results can provide reference for the development and utilization of high-aroma pepper germplasm resources and the breeding of new varieties.

Family Matters More Than Friends on Problematic Social Media Use Among Adolescents: Mediating Roles of Resilience and Loneliness.

Problematic social media use (PSMU) among adolescents has raised global concern in the current digital age. Despite the important role of perceived social support in adolescents' PSMU has been examined, possible different influences between perceived support from family and friends are still unknown. To address the gap, the present study aimed to examine how perceived support from family and friends is associated differently with PSMU and the mediating roles of resilience and loneliness therein. A sample of 1056 adolescents was recruited to complete standard questionnaires. Mediation analysis showed that resilience and loneliness mediated this association partially between perceived support from family and PSMU but totally between perceived support from friends and PSMU. Further, ANOVA-based analysis showed that influences of perceived support from family and friends on PSMU were mutually independent, and there was no interaction between them. Our results not only highlight different and independent impacts of perceived support from family and friends on PSMU, but also clarify the mediating mechanisms linking perceived social support to adolescent PSMU.

De Novo transcriptome combined with physiological analyses revealed key genes for cadmium accumulation in Zhe-Maidong (Ophiopogon japonicus).

Introduction: Cadmium (Cd) is a toxic heavy metal that severely threatens safe food production. Zhe-Maidong, a well-known Chinese traditional herbal medicine, is susceptible to Cd stress. However, the characteristics of Cd transformation and migration, as well as the regulatory system for genes conferring Cd accumulation of Zhe-Maidong, remains an essential issue to be addressed. Methods: Zhe-Maidong seedling growth in Cd-contaminated and uncontaminated soil was conducted for 90 days. The Cd concentration was determined by inductively coupled plasma-mass spectrometry, and the Cd2+ fluorescence probe detected Cd distributions. The root transcriptome of Zhe-Maidong was then evaluated using various Cd stress hydroponic treatments designated Cd-0, Cd-M, and Cd-H. Results and discussion: The enrichment factor (EF) value in the root was four times that of the leaves, indicating that the root has a high ability to absorb and accumulate Cd. The Cd2+ were mainly distributed in the root hair and the epidermis in both roots and leaves, revealing that the epidermal cells of roots may collect Cd2+ and also have an outstanding role in Cd2+ uptake. A total of 50 DEGs involved in Cd translocation and accumulation were identified. Among these DEGs, ANN, ABCC2/4, HMA1- 5, and CCX gene expression were positively correlated with EF-root, EF-leaf, EF-total, Cd-leaf, Cd-root, and Cd-plant, indicating their role in Cd transport and accumulation under Cd-stress. These data could be helpful in uncovering the Cd accumulation characteristics in Zhe-Maidong, as well as provide a bioinformatic foundation for investigations on finding gene functions and the screening of candidate genes related to Cd accumulation.

Comprehensive analysis of TCGA data reveals correlation between DNA methylation and alternative splicing.

The effect of DNA methylation on the regulation of gene expression has been extensively discussed in the literature. However, the potential association between DNA methylation and alternative splicing is not understood well. In this study, we integrated multiple omics data types from The Cancer Genome Atlas (TCGA) and systematically examined the relationship between DNA methylation and alternative splicing. Using the methylation data and exon expression data, we identified many CpG sites significantly associated with exon expression in various types of cancers. We further observed that the direction and strength of significant CpG-exon correlation tended to be consistent across different cancer contexts, indicating that some CpG-exon correlation patterns reflect fundamental biological mechanisms that transcend tissue- and cancer- types. We also discovered that CpG sites correlated with exon expressions were more likely to be associated with patient survival outcomes compared to CpG sites that did not correlate with exon expressions. Furthermore, we found that CpG sites were more strongly correlated with exon expression than expression of isoforms harboring the corresponding exons. This observation suggests that a major effect of CpG methylation on alternative splicing may be related to the inclusion or exclusion of exons, which subsequently impacts the relative usage of various isoforms. Overall, our study revealed correlation patterns between DNA methylation and alternative splicing, which provides new insights into the role of methylation in the transcriptional process.

Methylation of CpG Sites as Biomarkers Predictive of Drug-Specific Patient Survival in Cancer.

Background: Though the development of targeted cancer drugs continues to accelerate, doctors still lack reliable methods for predicting patient response to standard-of-care therapies for most cancers. DNA methylation has been implicated in tumor drug response and is a promising source of predictive biomarkers of drug efficacy, yet the relationship between drug efficacy and DNA methylation remains largely unexplored. Method: In this analysis, we performed log-rank survival analyses on patients grouped by cancer and drug exposure to find CpG sites where binary methylation status is associated with differential survival in patients treated with a specific drug but not in patients with the same cancer who were not exposed to that drug. We also clustered these drug-specific CpG sites based on co-methylation among patients to identify broader methylation patterns that may be related to drug efficacy, which we investigated for transcription factor binding site enrichment using gene set enrichment analysis. Results: We identified CpG sites that were drug-specific predictors of survival in 38 cancer-drug patient groups across 15 cancers and 20 drugs. These included 11 CpG sites with similar drug-specific survival effects in multiple cancers. We also identified 76 clusters of CpG sites with stronger associations with patient drug response, many of which contained CpG sites in gene promoters containing transcription factor binding sites. Conclusion: These findings are promising biomarkers of drug response for a variety of drugs and contribute to our understanding of drug-methylation interactions in cancer. Investigation and validation of these results could lead to the development of targeted co-therapies aimed at manipulating methylation in order to improve efficacy of commonly used therapies and could improve patient survival and quality of life by furthering the effort toward drug response prediction.

An agaric-like covalent organic framework composite for efficient extraction of trace cytokinins in plant samples.

Covalent organic framework composites have received great interest and regarded as new-generation porous materials in application of sample preparation. In this work, an agaric-like graphene oxide @ covalent organic framework (AGO@COF) composite was first synthesized and used as an ideal adsorbent for enrichment of trace cytokinins (CKs) due to its large specific surface area, rich in micro-mesopore and organic functional groups. The contact areas between AGO@COF composite and CKs were greatly increased due to the special agaric-like morphology, which would further improve the extraction efficiency. Hence, a rapid, simple and efficient AGO@COF-based dispersive solid-phase extraction method for detecting four CKs (N6-(delta 2-isopentenyl)-adenine, kinetin, DL-dihydrozeatin and 6-benzylaminopurine) was successfully established. The proposed method had low limits of detection (0.1-2.0 pg mL-1), good reproducibility (RSDs≤3.9%, n=5) and high recoveries (84.4-107.7%). With this method, N6-(delta 2-isopentenyl)-adenine and 6-benzylaminopurine in plants were successfully detected and quantified. Results showed that the developed AGO@COF composite possessed superior extraction performance and potential application in enrichment of trace targets from complex plant samples.

Integrative analysis of TCGA data identifies miRNAs as drug-specific survival biomarkers.

Biomarkers predictive of drug-specific outcomes are important tools for personalized medicine. In this study, we present an integrative analysis to identify miRNAs that are predictive of drug-specific survival outcome in cancer. Using the clinical data from TCGA, we defined subsets of cancer patients who suffered from the same cancer and received the same drug treatment, which we call cancer-drug groups. We then used the miRNA expression data in TCGA to evaluate each miRNA's ability to predict the survival outcome of patients in each cancer-drug group. As a result, the identified miRNAs are predictive of survival outcomes in a cancer-specific and drug-specific manner. Notably, most of the drug-specific miRNA survival markers and their target genes showed consistency in terms of correlations in their expression and their correlations with survival. Some of the identified miRNAs were supported by published literature in contexts of various cancers. We explored several additional breast cancer datasets that provided miRNA expression and survival data, and showed that our drug-specific miRNA survival markers for breast cancer were able to effectively stratify the prognosis of patients in those additional datasets. Together, this analysis revealed drug-specific miRNA markers for cancer survival, which can be promising tools toward personalized medicine.

[Novel adsorption material for solid phase extraction in sample pretreatment of plant hormones].

Plant hormones (PHs) are of significance in plant growth, as they regulate the various processes related to plant growth, development, and resistance. Sensitive and precise quantitative analysis of PHs is a bottleneck in plant science research. Currently, liquid chromatography-tandem mass spectrometry is used for the accurate and efficient detection of PHs. Sample pretreatment is an indispensable step in the chromatography-mass spectrometry analysis of PHs because it directly affects the sensitivity and accuracy of subsequent detection methods. Among various pretreatment methods for PHs, solid phase extraction (SPE) is the most widely used. Various new types of SPE, such as dispersive SPE, magnetic SPE, and solid phase microextraction, have been developed by modifying the extraction cartridge. The choice of adsorption material is the key factor in the abovementioned SPE methods, which has a decisive effect on the extraction, purification, and enrichment effects of the target substance in the sample pretreatment process. Carbon-based materials, including carbon nanotubes, graphene, carbon and nitrogen compounds, as well as organic frameworks, including metal organic frameworks and covalent organic materials, are suitable adsorption materials because of their designable structure, large specific surface area, and good stability. Molecularly imprinted polymers and supramolecular compounds show specific molecular recognition based on host-guest interactions, which can significantly improve the selectivity of sample pretreatment methods. In this paper, SPE-related technology and the abovementioned types of functionalized adsorption materials in the pretreatment of PHs prevalent in the past five years have been reviewed. The related development trends are also summarized.

Update on the Role of Neuropeptide Y and Other Related Factors in Breast Cancer and Osteoporosis.

Breast cancer and osteoporosis are common diseases that affect the survival and quality of life in postmenopausal women. Women with breast cancer are more likely to develop osteoporosis than women without breast cancer due to certain factors that can affect both diseases simultaneously. For instance, estrogen and the receptor activator of nuclear factor-κB ligand (RANKL) play important roles in the occurrence and development of these two diseases. Moreover, chemotherapy and hormone therapy administered to breast cancer patients also increase the incidence of osteoporosis, and in recent years, neuropeptide Y (NPY) has also been found to impact breast cancer and osteoporosis.Y1 and Y5 receptors are highly expressed in breast cancer, and Y1 and Y2 receptors affect osteogenic response, thus potentially highlighting a potential new direction for treatment strategies. In this paper, the relationship between breast cancer and osteoporosis, the influence of NPY on both diseases, and the recent progress in the research and treatment of these diseases are reviewed.

New Scoring Method to Predict Risk of Hepatocellular Carcinoma in Patients with Chronic Hepatitis C After Pegylated Interferon and Ribavirin Therapy.

Antiviral therapy for chronic hepatitis C (CHC) infection using pegylated interferon and ribavirin (PR) therapy can reduce the risk of hepatocellular carcinoma (HCC). Our study developed a new scoring method for predicting HCC risk after PR therapy. Between 2002 and 2016, 743 PR-treated patients with CHC were enrolled. Significant predictors for HCC were identified using multiple Cox regression analysis in study cohort: treatment age ≥60 years (hazard ratio [HR]: 2.04, 95% confidence interval [CI] = 1.3-3.7), pretreatment bilirubin ≥1.1 mg/dL (HR: 1.99, 95% CI = 1.08-3.67), α-fetoprotein ≥7.9 ng/mL (HR: 2.44, 95% CI = 1.16-5.32), no sustained virological response (SVR; HR: 1.91, 95% CI = 1.05-3.45), and baseline cirrhosis (HR: 4.45, 95% CI = 2.07-9.73). These predictors form the new HCC prediction scoring method with an area under the receiver operating characteristic curve of 0.884, sensitivity of 86.2%, and specificity of 74%. In patients with CHC and SVR, the cumulative incidence of HCC at 5 and 10 years was 16.7% and 30.4%, respectively, in patients with high risk scores and 1.2% and 4.2%, respectively, in patients with low risk scores (P < 0.001). Patients with SVR and high risk scores after viral eradication should remain under an intensive surveillance program for HCC. [Figure: see text].

Effect of Oyster Meat Preload on Postmeal Glycemic Control in Healthy Young Adults.

Objective: Evidence suggests that food preload improves postmeal glycemic profiles, but the effects of marine food are poorly understood. Our study aims to verify the regulating effects of premeal oyster meat (OM) on postprandial blood glucose.Method: Edible parts of the flesh of oyster were prepared for a randomized crossover experiment. After overnight fasting, 20 healthy young men consumed 300 mL of preload drinks with 0 g/kg body weight (BW) (control), 0.1 g/kg BW, and 0.2 g/kg BW. Peripheral blood concentrations of glucose and gastrointestinal hormones were measured before preloading at baseline (0 minutes) and at intervals after the preload and after a preset rice meal. The nutrient composition of OM was analyzed.Results: Compared with other doses, 0.2 g/kg BW OM preload induced higher plasma premeal insulin (p < 0.05), C-peptide (p < 0.05), and glucagon-like peptide-1 (GLP-1; p < 0.05) without altering the glucose concentrations during premeal times. By contrast, 0.2 g/kg BW OM induced less secretion of glucose (p < 0.05) and gastric inhibitory peptide (GIP; p < 0.05), but higher secretion of GLP-1 (p < 0.05) than 0.1 g/kg BW of OM after a meal. During the entire experiment (0-170 minutes), OM reduced the blood glucose (p < 0.05) and GIP (p < 0.05), but increased GLP-1 (p < 0.05). OM was rich in protein (78.4%) and low in fat (6%). Glutamic acid, aspartic acids, glycine, and taurine are the amino acids with high content found in OM.Conclusions: OM preload reduces postmeal glycemia in healthy young people with associated changes in gastrointestinal hormone responses. This effect may be attributed to the rich contents of protein and amino acids of OM.

Transcription Factor Elf3 Modulates Vasopressin-Induced Aquaporin-2 Gene Expression in Kidney Collecting Duct Cells.

Aquaporin-2 (AQP2) is a molecular water channel protein responsible for water reabsorption by the kidney collecting ducts. Many water balance disorders are associated with defects in AQP2 gene expression regulated by the peptide hormone vasopressin. Here, we studied roles of Elf3 (E26 transformation-specific (Ets)-related transcription factor 3) in AQP2 gene expression in the collecting duct cells (mpkCCD). Vasopressin increased AQP2 mRNA and protein levels without affecting AQP2 mRNA degradation, indicative of transcriptional regulation. Elf3 knockdown and overexpression, respectively, reduced and increased AQP2 gene expression under basal and vasopressin-stimulated conditions. However, the vasopressin-to-basal ratios of AQP2 gene expression levels remained constant, indicating that Elf3 does not directly mediate vasopressin response but modulates the level of AQP2 gene expression inducible by vasopressin. The Elf3-modulated AQP2 gene expression was associated with AQP2 promoter activity, in line with Elf3's ability to bind an Ets element in the AQP2 promoter. Mutation in the Ets element reduced both basal and vasopressin-stimulated AQP2 promoter activity, again without affecting vasopressin-to-basal ratios of the AQP2 promoter activity. Lithium chloride reduced both Elf3 and AQP2 mRNA in the mpkCCD cells as well as in mouse kidney inner medulla. We conclude that Elf3 modulates AQP2 promoter activity thereby gauging vasopressin-inducible AQP2 gene expression levels. Our data provide a potential explanation to lithium-induced nephrogenic diabetes insipidus where lithium reduces Elf3 and hence AQP2 abundance.

Quantifying Risk Pathway Crosstalk Mediated by miRNA to Screen Precision drugs for Breast Cancer Patients.

Breast cancer has become the most common cancer that leads to women's death. Breast cancer is a complex, highly heterogeneous disease classified into various subtypes based on histological features, which determines the therapeutic options. System identification of effective drugs for each subtype remains challenging. In this work, we present a computational network biology approach to screen precision drugs for different breast cancer subtypes by considering the impact intensity of candidate drugs on the pathway crosstalk mediated by miRNAs. Firstly, we constructed and analyzed the subtype-specific risk pathway crosstalk networks mediated by miRNAs. Then, we evaluated 36 Food and Drug Administration (FDA)-approved anticancer drugs by quantifying their effects on these subtype-specific pathway crosstalk networks and combining with survival analysis. Finally, some first-line treatments of breast cancer, such as Paclitaxel and Vincristine, were optimized for each subtype. In particular, we performed precision screening of subtype-specific therapeutic drugs and also confirmed some novel drugs suitable for breast cancer treatment. For example, Sorafenib was applicable for the basal subtype treatment, Irinotecan was optimum for Her2 subtype treatment, Vemurafenib was suitable for the LumA subtype treatment, and Vorinostat could apply to LumB subtype treatment. In addition, the mechanism of these optimal therapeutic drugs in each subtype of breast cancer was further dissected. In summary, our study offers an effective way to screen precision drugs for various breast cancer subtype treatments. We also dissected the mechanism of optimal therapeutic drugs, which may provide novel insight into the precise treatment of cancer and promote researches on the mechanisms of action of drugs.