Data_Sheet_1_Disease burden and long-term trends of urinary tract infections: A worldwide report.PDF

BackgroundUrinary tract infections (UTIs) are one of the most common infections worldwide, but little is known about their global scale and long-term trends. We aimed to estimate the spatiotemporal patterns of UTIs' burden along with its attributable risk factors at a global level, as well as the variations of the burdens according to socio-demographic status, regions, nations, sexes, and ages, which may be helpful in guiding targeted prevention and treatment programs.MethodsData from the Global Burden of Disease Study 2019 were analyzed to depict the incidence, mortality, and disability-adjusted life years (DALYs) of UTIs in 204 countries and territories from 1990 to 2019 by socio-demographic status, nations, region, sex, and age.ResultsGlobally, 404.61 million cases, 236,790 deaths, and 520,200 DALYs were estimated in 2019. In particular, 2.4 times growth in deaths from 1990 to 2019 was observed, along with an increasing age-standardized mortality rate (ASMR) from 2.77/100,000 to 3.13/100,000. Age-standardized incidence rate (ASIR) was consistently pronounced in regions with higher socio-demographic index (SDI), which presented remarkable upward trends in ASMR and age-standardized DALY rate (ASDR). In contrast, countries with a low SDI or high baseline burden achieved a notable decline in burden rates over the past three decades. Although the ASIR was 3.6-fold higher in females than males, there was no sex-based difference in ASMR and ASDR. The burden rate typically increased with age, and the annual increasing trend was more obvious for people over 60 years, especially in higher SDI regions.ConclusionsThe burden of UTIs showed variations according to socio-demographic status, nation, region, sex, and age in the last three decades. The overall increasing burden intimates that proper prevention and treatment efforts should be strengthened, especially in high-income regions and aging societies.</p

Table1_Overexpression of CHAF1A is associated with poor prognosis, tumor immunosuppressive microenvironment and treatment resistance.DOCX

Background: As distinct marker of proliferating cells, chromatin assembly factor-1 (CAF-1) was critical in DNA replication. However, there is paucity information about the clinical significance, functions and co-expressed gene network of CHAF1A, the major subunit in CAF-1, in cancer.Methods: Bioinformatic analysis of CHAF1A and its co-expression gene network were performed using various public databases. Functional validation of CHAF1A was applied in breast cancer.Results: Overexpression of CHAF1A was found in 20 types of cancer tissues. Elevated expression of CHAF1A was positively correlated with breast cancer progression and poor patients’ outcome. The analysis of co-expression gene network demonstrated CHAF1A was associated with not only cell proliferation, DNA repair, apoptosis, but cancer metabolism, immune system, and drug resistance. More importantly, higher expression of CHAF1A was positively correlated with immunosuppressive microenvironment and resistance to endocrine therapy and chemotherapy. Elevated expression of CHAF1A was confirmed in breast cancer tissues. Silencing of CHAF1A can significantly inhibit cell proliferation in MDA-MB-231 cells.Conclusion: The current work suggested that overexpression of CHAF1A can be used as diagnostic and poor prognostic biomarker of breast cancer. Higher expression of CHAF1A induced fast resistance to endocrine therapy and chemotherapy, it may be a promising therapeutic target and a biomarker to predict the sensitivity of immunotherapy in breast cancer.</p

Table_1_Construction and Comparison of Different Models in Detecting Prostate Cancer and Clinically Significant Prostate Cancer.docx

BackgroundWith the widespread adoption of prostatic-specific antigen (PSA) screening, the detection rates of prostate cancer (PCa) have increased. Due to the low specificity and high false-positive rate of serum PSA levels, it was difficult to diagnose PCa accurately. To improve the diagnosis of PCa and clinically significant prostate cancer (CSPCa), we established novel models on the basis of the prostate health index (PHI) and multiparametric magnetic resonance imaging (mpMRI) in the Asian population.MethodsWe retrospectively collected the clinical indicators of patients with TPSA at 4–20 ng/ml. Furthermore, mpMRI was performed using a 3.0-T scanner and reported in the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS). Univariable and multivariable logistic analyses were performed to construct the models. The performance of different models based on PSA derivatives, PHI derivatives, PI-RADS, and a combination of PHI derivatives and PI-RADS was evaluated.ResultsAmong the 128 patients, 47 (36.72%) patients were diagnosed with CSPCa and 81 (63.28%) patients were diagnosed with non-CSPCa. Of the 81 (63.28%) patients, 8 (6.25%) patients were diagnosed with Gleason Grade 1 PCa and 73 (57.03%) patients were diagnosed with non-PCa. In the analysis of the receiver operator characteristic (ROC) curves in TPSA 4–20 ng/ml, the multivariable model for PCa was significantly larger than that for the model based on the PI-RADS (p = 0.004) and that for the model based on the PHI derivatives (p = 0.031) in diagnostic accuracy. The multivariable model for CSPCa was significantly larger than that for the model based on the PI-RADS (p = 0.003) and was non-significantly larger than that for the model based on the PHI derivatives (p = 0.061) in diagnostic accuracy. For PCa in TPSA 4–20 ng/ml, a multivariable model achieved the optimal diagnostic value at four levels of predictive variables. For CSPCa in TPSA 4–20 ng/ml, the multivariable model achieved the optimal diagnostic value at a sensitivity close to 90% and 80%.ConclusionsThe models combining PHI derivatives and PI-RADS performed better in detecting PCa and CSPCa than the models based on either PHI or PI-RADS.</p

Table_2_Construction and Comparison of Different Models in Detecting Prostate Cancer and Clinically Significant Prostate Cancer.docx

BackgroundWith the widespread adoption of prostatic-specific antigen (PSA) screening, the detection rates of prostate cancer (PCa) have increased. Due to the low specificity and high false-positive rate of serum PSA levels, it was difficult to diagnose PCa accurately. To improve the diagnosis of PCa and clinically significant prostate cancer (CSPCa), we established novel models on the basis of the prostate health index (PHI) and multiparametric magnetic resonance imaging (mpMRI) in the Asian population.MethodsWe retrospectively collected the clinical indicators of patients with TPSA at 4–20 ng/ml. Furthermore, mpMRI was performed using a 3.0-T scanner and reported in the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS). Univariable and multivariable logistic analyses were performed to construct the models. The performance of different models based on PSA derivatives, PHI derivatives, PI-RADS, and a combination of PHI derivatives and PI-RADS was evaluated.ResultsAmong the 128 patients, 47 (36.72%) patients were diagnosed with CSPCa and 81 (63.28%) patients were diagnosed with non-CSPCa. Of the 81 (63.28%) patients, 8 (6.25%) patients were diagnosed with Gleason Grade 1 PCa and 73 (57.03%) patients were diagnosed with non-PCa. In the analysis of the receiver operator characteristic (ROC) curves in TPSA 4–20 ng/ml, the multivariable model for PCa was significantly larger than that for the model based on the PI-RADS (p = 0.004) and that for the model based on the PHI derivatives (p = 0.031) in diagnostic accuracy. The multivariable model for CSPCa was significantly larger than that for the model based on the PI-RADS (p = 0.003) and was non-significantly larger than that for the model based on the PHI derivatives (p = 0.061) in diagnostic accuracy. For PCa in TPSA 4–20 ng/ml, a multivariable model achieved the optimal diagnostic value at four levels of predictive variables. For CSPCa in TPSA 4–20 ng/ml, the multivariable model achieved the optimal diagnostic value at a sensitivity close to 90% and 80%.ConclusionsThe models combining PHI derivatives and PI-RADS performed better in detecting PCa and CSPCa than the models based on either PHI or PI-RADS.</p

Image_1_Construction and Comparison of Different Models in Detecting Prostate Cancer and Clinically Significant Prostate Cancer.tif

BackgroundWith the widespread adoption of prostatic-specific antigen (PSA) screening, the detection rates of prostate cancer (PCa) have increased. Due to the low specificity and high false-positive rate of serum PSA levels, it was difficult to diagnose PCa accurately. To improve the diagnosis of PCa and clinically significant prostate cancer (CSPCa), we established novel models on the basis of the prostate health index (PHI) and multiparametric magnetic resonance imaging (mpMRI) in the Asian population.MethodsWe retrospectively collected the clinical indicators of patients with TPSA at 4–20 ng/ml. Furthermore, mpMRI was performed using a 3.0-T scanner and reported in the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS). Univariable and multivariable logistic analyses were performed to construct the models. The performance of different models based on PSA derivatives, PHI derivatives, PI-RADS, and a combination of PHI derivatives and PI-RADS was evaluated.ResultsAmong the 128 patients, 47 (36.72%) patients were diagnosed with CSPCa and 81 (63.28%) patients were diagnosed with non-CSPCa. Of the 81 (63.28%) patients, 8 (6.25%) patients were diagnosed with Gleason Grade 1 PCa and 73 (57.03%) patients were diagnosed with non-PCa. In the analysis of the receiver operator characteristic (ROC) curves in TPSA 4–20 ng/ml, the multivariable model for PCa was significantly larger than that for the model based on the PI-RADS (p = 0.004) and that for the model based on the PHI derivatives (p = 0.031) in diagnostic accuracy. The multivariable model for CSPCa was significantly larger than that for the model based on the PI-RADS (p = 0.003) and was non-significantly larger than that for the model based on the PHI derivatives (p = 0.061) in diagnostic accuracy. For PCa in TPSA 4–20 ng/ml, a multivariable model achieved the optimal diagnostic value at four levels of predictive variables. For CSPCa in TPSA 4–20 ng/ml, the multivariable model achieved the optimal diagnostic value at a sensitivity close to 90% and 80%.ConclusionsThe models combining PHI derivatives and PI-RADS performed better in detecting PCa and CSPCa than the models based on either PHI or PI-RADS.</p

Supplementary Data from KIF15-Mediated Stabilization of AR and AR-V7 Contributes to Enzalutamide Resistance in Prostate Cancer

Supplemental Materials and Methods</p

Supplementary Data from KIF15-Mediated Stabilization of AR and AR-V7 Contributes to Enzalutamide Resistance in Prostate Cancer

Supplementary Tables</p

Supplementary Data from KIF15-Mediated Stabilization of AR and AR-V7 Contributes to Enzalutamide Resistance in Prostate Cancer

supplemary figures and figure legend</p

Supplementary Information from Heterochromatin Protein 1α Mediates Development and Aggressiveness of Neuroendocrine Prostate Cancer

Supplementary materials and methods used in this study. Supplementary table with primer sequences. Figure S1, Histological feature of PDX models and heatmaps of PDX and clinical cohorts. Figure S2, IHC staining of HP1α in PDX models, and expression of Hp1α mRNA in NPp53 CRPC GEM models. Figure S3, Additional analyses of HP1α function in NEPC cells. Figure S4, Additional analyses of HP1α function in NE transdifferentiation. Figure S5, Additional analyses of the repressive function of HP1α on AR and REST expression. Figure S6, Additional analyses of the function of HP1α. Supplementary references.</p