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Nanomaterials (Basel, Switzerland) Jun 2024Solid lipid nanoparticles (SLNs) represent promising nanostructures for drug delivery systems. This study successfully synthesized SLNs containing different proportions...
Solid lipid nanoparticles (SLNs) represent promising nanostructures for drug delivery systems. This study successfully synthesized SLNs containing different proportions of babassu oil (BBS) and copaiba oleoresin (COPA) via the emulsification-ultrasonication method. Before SLN synthesis, the identification and quantification of methyl esters, such as lauric acid and β-caryophyllene, were performed via GC-MS analysis. These methyl esters were used as chemical markers and assisted in encapsulation efficiency experiments. A 2 factorial design with a center point was employed to assess the impact of stearic acid and Tween 80 on particle hydrodynamic diameter (HD) and polydispersity index (PDI). Additionally, the effects of temperature (8 ± 0.5 °C and 25 ± 1.0 °C) and time (0, 7, 15, 30, 40, and 60 days) on HD and PDI values were investigated. Zeta potential (ZP) measurements were utilized to evaluate nanoparticle stability, while transmission electron microscopy provided insights into the morphology and nanometric dimensions of the SLNs. The in vitro cytotoxic activity of the SLNs (10 µg/mL, 30 µg/mL, 40 µg/mL, and 80 µg/mL) was evaluated using the MTT assay with PC-3 and DU-145 prostate cancer cell lines. Results demonstrated that SLNs containing BBS and COPA in a 1:1 ratio exhibited a promising cytotoxic effect against prostate cancer cells, with a percentage of viable cells of 68.5% for PC-3 at a concentration of 30 µg/mL and 48% for DU-145 at a concentration of 80 µg/mL. These findings underscore the potential therapeutic applications of SLNs loaded with BBS and COPA for prostate cancer treatment.
PubMed: 38921890
DOI: 10.3390/nano14121014 -
Current Issues in Molecular Biology May 2024Hyperlipidemia is a prevalent chronic metabolic disease that severely affects human health. Currently, commonly used clinical therapeutic drugs are prone to drug...
Identification of Key Hypolipidemic Components and Exploration of the Potential Mechanism of Total Flavonoids from Based on Network Pharmacology, Molecular Docking, and Zebrafish Experiment.
Hyperlipidemia is a prevalent chronic metabolic disease that severely affects human health. Currently, commonly used clinical therapeutic drugs are prone to drug dependence and toxic side effects. Dietary intervention for treating chronic metabolic diseases has received widespread attention. is a characteristic fruit tree in China whose fruits are rich in flavonoids, which have been shown to have a therapeutic effect on hyperlipidemia; however, their exact molecular mechanism of action remains unclear. Therefore, this study aimed to investigate the therapeutic effects of total flavonoid extract (RS) on hyperlipidemia and its possible mechanisms. A hyperlipidemic zebrafish model was established using egg yolk powder and then treated with RS to observe changes in the integral optical density in the tail vessels. Network pharmacology and molecular docking were used to investigate the potential mechanism of action of RS for the treatment of hyperlipidemia. The results showed that RS exhibited favorable hypolipidemic effects on zebrafish in the concentration range of 3.0-30.0 μg/mL in a dose-dependent manner. Topological and molecular docking analyses identified HSP90AA1, PPARA, and MMP9 as key targets for hypolipidemic effects, which were exerted mainly through lipolytic regulation of adipocytes and lipids; pathway analysis revealed enrichment in atherosclerosis, chemical carcinogenic-receptor activation pathways in cancers, and proteoglycans in prostate cancer and other cancers. Mover, chinensinaphthol possessed higher content and better target binding ability, which suggested that chinensinaphthol might be an important component of RS with hypolipidemic active function. These findings provide a direction for further research on RS interventions for the treatment of hyperlipidemia.
PubMed: 38920980
DOI: 10.3390/cimb46060308 -
Current Oncology (Toronto, Ont.) Jun 2024Interest in the oligometastatic prostate cancer (OMPC) is increasing, and various clinical studies have reported the benefits of metastasis-directed radiation therapy...
BACKGROUND
Interest in the oligometastatic prostate cancer (OMPC) is increasing, and various clinical studies have reported the benefits of metastasis-directed radiation therapy (MDRT) in OMPC. However, the recognition regarding the adopted definitions, methodologies of assessment, and therapeutic approaches is diverse among radiation oncologists. This study aims to evaluate the level of agreement for issues in OMPC among radiation oncologists.
METHODS
We generated 15 key questions (KQs) for OMPC relevant to definition, diagnosis, local therapies, and endpoints. Additionally, three clinical scenarios representing synchronous metastatic prostate cancer (mPC) (case 1), metachronous mPC with visceral metastasis (case 2), and metachronous mPC with castration-resistance and history of polymetastasis (case 3) were developed. The 15 KQs were adapted according to each scenario and transformed into 23 questions with 6-9 per scenario. The survey was distributed to 80 radiation oncologists throughout the Republic of Korea. Answer options with 0.0-29.9%, 30-49.9%, 50-69.9%, 70-79.9%, 80-89.9%, and 90-100% agreements were considered as no, minimal, weak, moderate, strong, and near perfect agreement, respectively.
RESULTS
Forty-five candidates voluntarily participated in this study. Among 23 questions, near perfect ( = 4), strong ( = 3), or moderate ( = 2) agreements were shown in nine. For the case recognized as OMPC with agreements of 93% (case 1), near perfect agreements on the application of definitive radiation therapy (RT) for whole metastatic lesions were achieved. While ≥70% agreements regarding optimal dose-fractionation for metastasis-directed RT (MDRT) has not been achieved, stereotactic body RT (SBRT) is favored by clinicians with higher clinical volume.
CONCLUSION
For the case recognized as OMPC, near perfect agreement for the application of definitive RT for whole metastatic lesions was reached. SBRT was more favored as a MDRT by clinicians with a higher clinical volume.
Topics: Male; Humans; Prostatic Neoplasms; Radiation Oncologists; Republic of Korea; Neoplasm Metastasis; Surveys and Questionnaires; Middle Aged
PubMed: 38920729
DOI: 10.3390/curroncol31060245 -
Cells Jun 2024Prostate cancer (PCa) remains a leading cause of mortality among American men, with metastatic and recurrent disease posing significant therapeutic challenges due to a... (Review)
Review
Prostate cancer (PCa) remains a leading cause of mortality among American men, with metastatic and recurrent disease posing significant therapeutic challenges due to a limited comprehension of the underlying biological processes governing disease initiation, dormancy, and progression. The conventional use of PCa cell lines has proven inadequate in elucidating the intricate molecular mechanisms driving PCa carcinogenesis, hindering the development of effective treatments. To address this gap, patient-derived primary cell cultures have been developed and play a pivotal role in unraveling the pathophysiological intricacies unique to PCa in each individual, offering valuable insights for translational research. This review explores the applications of the conditional reprogramming (CR) cell culture approach, showcasing its capability to rapidly and effectively cultivate patient-derived normal and tumor cells. The CR strategy facilitates the acquisition of stem cell properties by primary cells, precisely recapitulating the human pathophysiology of PCa. This nuanced understanding enables the identification of novel therapeutics. Specifically, our discussion encompasses the utility of CR cells in elucidating PCa initiation and progression, unraveling the molecular pathogenesis of metastatic PCa, addressing health disparities, and advancing personalized medicine. Coupled with the tumor organoid approach and patient-derived xenografts (PDXs), CR cells present a promising avenue for comprehending cancer biology, exploring new treatment modalities, and advancing precision medicine in the context of PCa. These approaches have been used for two NCI initiatives (PDMR: patient-derived model repositories; HCMI: human cancer models initiatives).
Topics: Humans; Prostatic Neoplasms; Male; Cellular Reprogramming; Animals
PubMed: 38920635
DOI: 10.3390/cells13121005 -
The Oncologist Jun 2024Prostate cancer is one of the most prevalent malignancies in men. In the United States, 1 in 8 men will be diagnosed with prostate cancer in their lifetime....
Prostate cancer is one of the most prevalent malignancies in men. In the United States, 1 in 8 men will be diagnosed with prostate cancer in their lifetime. Specifically, studies have delved into male subgroups that present a heightened risk for prostate cancer. Despite such high prevalence, prostate cancer can be heterogeneous and carry complexities that manifest differently between individuals. Metastatic hormone-sensitive prostate cancer (mHSPC) often has an abbreviated, aggressive disease course, and can have varying presentations with different molecular profiles that determine response/resistance to the approved treatments targeting the androgen-receptor pathway (eg, enzalutamide, apalutamide, darolutamide, and abiraterone acetate). We present a case of mHSPC quickly progressing to mCRPC, found to have microsatellite instability in mCRPC and excellent response to pembrolizumab, which raises the critical issues of early molecular testing and treatments personalized for the individual patient.
PubMed: 38920278
DOI: 10.1093/oncolo/oyae156 -
International Journal of General... 2024To explore the predictive factors and predictive model construction for the progression of prostate cancer bone metastasis to castration resistance.
Development and Validation of a Clinic Machine Learning Classifier for the Prediction of Risk Stratifications of Prostate Cancer Bone Metastasis Progression to Castration Resistance.
OBJECTIVE
To explore the predictive factors and predictive model construction for the progression of prostate cancer bone metastasis to castration resistance.
METHODS
Clinical data of 286 patients diagnosed with prostate cancer with bone metastasis, initially treated with endocrine therapy, and progressing to metastatic castration resistant prostate cancer (mCRPC) were collected. By comparing the differences in various factors between different groups with fast and slow occurrence of castration-resistant prostate cancer (CRPC). Kaplan-Meier survival analysis and COX multivariate risk proportional regression model were used to compare the differences in the time to progression to CRPC in different groups. The COX multivariate risk proportional regression model was used to evaluate the impact of candidate factors on the time to progression to CRPC and establish a predictive model. The accuracy of the model was then tested using receiver operating characteristic (ROC) curves and decision curve analysis (DCA).
RESULTS
The median time for 286 mCRPC patients to progress to CRPC was 17 (9.5-28.0) months. Multivariate analysis showed that the lowest value of PSA (PSA nadir), the time when PSA dropped to its lowest value (timePSA), and the number of BM, and LDH were independent risk factors for rapid progression to CRPC. Based on the four independent risk factors mentioned above, a prediction model was established, with the optimal prediction model being a random forest with area under curve (AUC) of 0.946[95% CI: 0.901-0.991] and 0.927[95% CI: 0.864-0.990] in the training and validation cohort, respectively.
CONCLUSION
After endocrine therapy, the PSA nadir, timePSA, the number of BM, and LDH are the main risk factors for rapid progression to mCRPC in patients with prostate cancer bone metastases. Establishing a CRPC prediction model is helpful for early clinical intervention decision-making.
PubMed: 38919704
DOI: 10.2147/IJGM.S465031 -
Frontiers in Oncology 2024To develop a semi-automatic model integrating radiomics, deep learning, and clinical features for Bone Metastasis (BM) prediction in prostate cancer (PCa) patients using...
OBJECTIVE
To develop a semi-automatic model integrating radiomics, deep learning, and clinical features for Bone Metastasis (BM) prediction in prostate cancer (PCa) patients using Biparametric MRI (bpMRI) images.
METHODS
A retrospective study included 414 PCa patients (BM, n=136; NO-BM, n=278) from two institutions (Center 1, n=318; Center 2, n=96) between January 2016 and December 2022. MRI scans were confirmed with BM status via PET-CT or ECT pre-treatment. Tumor areas on bpMRI images were delineated as tumor's region of interest (ROI) using auto-delineation tumor models, evaluated with Dice similarity coefficient (DSC). Samples were auto-sketched, refined, and used to train the ResNet BM prediction model. Clinical, radiomics, and deep learning data were synthesized into the ResNet-C model, evaluated using receiver operating characteristic (ROC).
RESULTS
The auto-segmentation model achieved a DSC of 0.607. Clinical BM prediction's internal validation had an accuracy (ACC) of 0.650 and area under the curve (AUC) of 0.713; external cohort had an ACC of 0.668 and AUC of 0.757. The deep learning model yielded an ACC of 0.875 and AUC of 0.907 for the internal, and ACC of 0.833 and AUC of 0.862 for the external cohort. The Radiomics model registered an ACC of 0.819 and AUC of 0.852 internally, and ACC of 0.885 and AUC of 0.903 externally. ResNet-C demonstrated the highest ACC of 0.902 and AUC of 0.934 for the internal, and ACC of 0.885 and AUC of 0.903 for the external cohort.
CONCLUSION
The ResNet-C model, utilizing bpMRI scanning strategy, accurately assesses bone metastasis (BM) status in newly diagnosed prostate cancer (PCa) patients, facilitating precise treatment planning and improving patient prognoses.
PubMed: 38919538
DOI: 10.3389/fonc.2024.1298516 -
Iranian Journal of Public Health Mar 2024Cadmium, a toxic heavy metal, experienced a surge in production during the 20th century due to the rise of nickel-cadmium batteries, metal plating, and plastic... (Review)
Review
BACKGROUND
Cadmium, a toxic heavy metal, experienced a surge in production during the 20th century due to the rise of nickel-cadmium batteries, metal plating, and plastic stabilizers. Exposure to cadmium primarily occurs through the consumption of contaminated food, such as vegetables and grains, as well as drinking water or inhaling polluted air. The objective of this study was to investigate the relationship between cadmium exposure and the incidence of prostate cancer using a systematic review and meta-analysis approach.
METHODS
This research involved searching and retrieving observational and experimental studies conducted until May 2022 from various databases, including ISI Web of Science, Cochrane, Science Direct, Scopus, Pub-Med, and Google Scholar. Data analysis was performed using Stata 15 statistical software.
RESULTS
The initial search yielded 794 articles, which were subsequently reduced to 427 articles after eliminating duplicates. Following the application of inclusion and exclusion criteria, a total of 16 studies were included in the meta-analysis. The odds ratio of prostate cancer compared to the first quartile of exposure in the second quartile was 1.03 (0.95-1.12), in the third quartile it was 1.12 (0.99-1.26) and in the fourth quartile of exposure was equal to 1.16 (0.79-1.70). Regarding the investigation of the probability of the occurrence of publication bias, the results of Begg's and Egger's tests were not statistically significant.
CONCLUSION
Although exposure to cadmium leads to an increase in the chance of prostate cancer, this chance increase was not statistically significant.
PubMed: 38919294
DOI: 10.18502/ijph.v53i3.15136 -
Frontiers in Pharmacology 2024Cancer refers to a group of diseases characterized by the uncontrolled growth and spread of abnormal cells in the body. Due to its complexity, it has been hard to find...
Cancer refers to a group of diseases characterized by the uncontrolled growth and spread of abnormal cells in the body. Due to its complexity, it has been hard to find an ideal medicine to treat all cancer types, although there is an urgent need for it. However, the cost of developing a new drug is high and time-consuming. In this sense, drug repurposing (DR) can hasten drug discovery by giving existing drugs new disease indications. Many computational methods have been applied to achieve DR, but just a few have succeeded. Therefore, this review aims to show DR approaches and the gap between these strategies and their ultimate application in oncology. The scoping review was conducted according to the Arksey and O'Malley framework and the Joanna Briggs Institute recommendations. Relevant studies were identified through electronic searching of PubMed/MEDLINE, Embase, Scopus, and Web of Science databases, as well as the grey literature. We included peer-reviewed research articles involving strategies applied to drug repurposing in oncology, published between 1 January 2003, and 31 December 2021. We identified 238 studies for inclusion in the review. Most studies revealed that the United States, India, China, South Korea, and Italy are top publishers. Regarding cancer types, breast cancer, lymphomas and leukemias, lung, colorectal, and prostate cancer are the top investigated. Additionally, most studies solely used computational methods, and just a few assessed more complex scientific models. Lastly, molecular modeling, which includes molecular docking and molecular dynamics simulations, was the most frequently used method, followed by signature-, Machine Learning-, and network-based strategies. DR is a trending opportunity but still demands extensive testing to ensure its safety and efficacy for the new indications. Finally, implementing DR can be challenging due to various factors, including lack of quality data, patient populations, cost, intellectual property issues, market considerations, and regulatory requirements. Despite all the hurdles, DR remains an exciting strategy for identifying new treatments for numerous diseases, including cancer types, and giving patients faster access to new medications.
PubMed: 38919258
DOI: 10.3389/fphar.2024.1400029 -
Biology Direct Jun 2024Prostate cancer (PCa) is the second leading cause of tumor-related mortality in men. Metastasis from advanced tumors is the primary cause of death among patients....
BACKGROUND
Prostate cancer (PCa) is the second leading cause of tumor-related mortality in men. Metastasis from advanced tumors is the primary cause of death among patients. Identifying novel and effective biomarkers is essential for understanding the mechanisms of metastasis in PCa patients and developing successful interventions.
METHODS
Using the GSE8511 and GSE27616 data sets, 21 metastasis-related genes were identified through the weighted gene co-expression network analysis (WGCNA) method. Subsequent functional analysis of these genes was conducted on the gene set cancer analysis (GSCA) website. Cluster analysis was utilized to explore the relationship between these genes, immune infiltration in PCa, and the efficacy of targeted drug IC50 scores. Machine learning algorithms were then employed to construct diagnostic and prognostic models, assessing their predictive accuracy. Additionally, multivariate COX regression analysis highlighted the significant role of POLD1 and examined its association with DNA methylation. Finally, molecular docking and immunohistochemistry experiments were carried out to assess the binding affinity of POLD1 to PCa drugs and its impact on PCa prognosis.
RESULTS
The study identified 21 metastasis-related genes using the WGCNA method, which were found to be associated with DNA damage, hormone AR activation, and inhibition of the RTK pathway. Cluster analysis confirmed a significant correlation between these genes and PCa metastasis, particularly in the context of immunotherapy and targeted therapy drugs. A diagnostic model combining multiple machine learning algorithms showed strong predictive capabilities for PCa diagnosis, while a transfer model using the LASSO algorithm also yielded promising results. POLD1 emerged as a key prognostic gene among the metastatic genes, showing associations with DNA methylation. Molecular docking experiments supported its high affinity with PCa-targeted drugs. Immunohistochemistry experiments further validated that increased POLD1 expression is linked to poor prognosis in PCa patients.
CONCLUSIONS
The developed diagnostic and metastasis models provide substantial value for patients with prostate cancer. The discovery of POLD1 as a novel biomarker related to prostate cancer metastasis offers a promising avenue for enhancing treatment of prostate cancer metastasis.
Topics: Humans; Male; Prostatic Neoplasms; Machine Learning; Immunotherapy; Neoplasm Metastasis; Biomarkers, Tumor; Prognosis; Molecular Docking Simulation; Gene Expression Regulation, Neoplastic
PubMed: 38918844
DOI: 10.1186/s13062-024-00494-x