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Monitoring the effect of belinostat in solid tumors by H4 acetylation

Histone deacetylase (HDAC) inhibition is a novel entity in medical oncology, and several HDAC inhibitors are in clinical trials. One of them is the hydroxamic acid belinostat (PXD101) that has demonstrated therapeutic efficacy for several clinical indications. Acetylation of histones is a key event after treatment with HDAC inhibitors, and could thus be used as a marker for monitoring cellular response to HDAC inhibitor treatment. Here we describe the utility of a newly described monoclonal antibody against acetylated H4 for immunohistochemistry on paraffin-embedded fine needle biopsies from nude mice carrying A2780 human ovarian cancer xenografts. Acetylated H4 was monitored in vivo by immunohistochemistry during treatment with belinostat, and compared with pharmacokinetics in plasma and tumor tissue. We found an increased level of acetylated H4 15 min after a single treatment (200 mg/kg i.v.) with maximum level reached after 1 h. H4 acetylation intensity reflected the belinostat concentration in plasma and tumor tissue. The threshold level for belinostat activity, indicated by acetylated H4, correlated with belinostat plasma concentrations above 1,000 ng/ml. In conclusion, examination of H4 acetylation in fine needle biopsies using the T25 antibody may prove useful in monitoring HDAC inhibitor efficacy in clinical trials involving humans with solid tumors

Comparative evaluation of the treatment efficacy of suberoylanilide hydroxamic acid (SAHA) and paclitaxel in ovarian cancer cell lines and primary ovarian cancer cells from patients

BACKGROUND: In most patients with ovarian cancer, diagnosis occurs after the tumour has disseminated beyond the ovaries. In these cases, post-surgical taxane/platinum combination chemotherapy is the "gold standard". However, most of the patients experience disease relapse and eventually die due to the emergence of chemotherapy resistance. Histone deacetylase inhibitors are novel anticancer agents that hold promise to improve patient outcome. METHODS: We compared a prototypic histone deacetylase inhibitor, suberoylanilide hydroxamic acid (SAHA), and paclitaxel for their treatment efficacy in ovarian cancer cell lines and in primary patient-derived ovarian cancer cells. The primary cancer cells were isolated from malignant ascites collected from five patients with stage III ovarian carcinomas. Cytotoxic activities were evaluated by Alamar Blue assay and by caspase-3 activation. The ability of SAHA to kill drug-resistant 2780AD cells was also assessed. RESULTS: By employing the cell lines OVCAR-3, SK-OV-3, and A2780, we established SAHA at concentrations of 1 to 20 μM to be as efficient in inducing cell death as paclitaxel at concentrations of 3 to 300 nM. Consequently, we treated the patient-derived cancer cells with these doses of the drugs. All five isolates were sensitive to SAHA, with cell killing ranging from 21% to 63% after a 72-h exposure to 20 μM SAHA, while four of them were resistant to paclitaxel (i.e., <10% cell death at 300 nM paclitaxel for 72 hours). Likewise, treatment with SAHA led to an increase in caspase-3 activity in all five isolates, whereas treatment with paclitaxel had no effect on caspase-3 activity in three of them. 2780AD cells were responsive to SAHA but resistant to paclitaxel. CONCLUSION: These ex vivo findings raise the possibility that SAHA may prove effective in the treatment of paclitaxel-resistant ovarian cancer in vivo

Development and evaluation of machine learning in whole-body magnetic resonance imaging for detecting metastases in patients with lung or colon cancer: a diagnostic test accuracy study.

OBJECTIVES: Whole-body magnetic resonance imaging (WB-MRI) has been demonstrated to be efficient and cost-effective for cancer staging. The study aim was to develop a machine learning (ML) algorithm to improve radiologists' sensitivity and specificity for metastasis detection and reduce reading times. MATERIALS AND METHODS: A retrospective analysis of 438 prospectively collected WB-MRI scans from multicenter Streamline studies (February 2013-September 2016) was undertaken. Disease sites were manually labeled using Streamline reference standard. Whole-body MRI scans were randomly allocated to training and testing sets. A model for malignant lesion detection was developed based on convolutional neural networks and a 2-stage training strategy. The final algorithm generated lesion probability heat maps. Using a concurrent reader paradigm, 25 radiologists (18 experienced, 7 inexperienced in WB-/MRI) were randomly allocated WB-MRI scans with or without ML support to detect malignant lesions over 2 or 3 reading rounds. Reads were undertaken in the setting of a diagnostic radiology reading room between November 2019 and March 2020. Reading times were recorded by a scribe. Prespecified analysis included sensitivity, specificity, interobserver agreement, and reading time of radiology readers to detect metastases with or without ML support. Reader performance for detection of the primary tumor was also evaluated. RESULTS: Four hundred thirty-three evaluable WB-MRI scans were allocated to algorithm training (245) or radiology testing (50 patients with metastases, from primary 117 colon [n = 117] or lung [n = 71] cancer). Among a total 562 reads by experienced radiologists over 2 reading rounds, per-patient specificity was 86.2% (ML) and 87.7% (non-ML) (-1.5% difference; 95% confidence interval [CI], -6.4%, 3.5%; P = 0.39). Sensitivity was 66.0% (ML) and 70.0% (non-ML) (-4.0% difference; 95% CI, -13.5%, 5.5%; P = 0.344). Among 161 reads by inexperienced readers, per-patient specificity in both groups was 76.3% (0% difference; 95% CI, -15.0%, 15.0%; P = 0.613), with sensitivity of 73.3% (ML) and 60.0% (non-ML) (13.3% difference; 95% CI, -7.9%, 34.5%; P = 0.313). Per-site specificity was high (>90%) for all metastatic sites and experience levels. There was high sensitivity for the detection of primary tumors (lung cancer detection rate of 98.6% with and without ML [0.0% difference; 95% CI, -2.0%, 2.0%; P = 1.00], colon cancer detection rate of 89.0% with and 90.6% without ML [-1.7% difference; 95% CI, -5.6%, 2.2%; P = 0.65]). When combining all reads from rounds 1 and 2, reading times fell by 6.2% (95% CI, -22.8%, 10.0%) when using ML. Round 2 read-times fell by 32% (95% CI, 20.8%, 42.8%) compared with round 1. Within round 2, there was a significant decrease in read-time when using ML support, estimated as 286 seconds (or 11%) quicker (P = 0.0281), using regression analysis to account for reader experience, read round, and tumor type. Interobserver variance suggests moderate agreement, Cohen κ = 0.64; 95% CI, 0.47, 0.81 (with ML), and Cohen κ = 0.66; 95% CI, 0.47, 0.81 (without ML). CONCLUSIONS: There was no evidence of a significant difference in per-patient sensitivity and specificity for detecting metastases or the primary tumor using concurrent ML compared with standard WB-MRI. Radiology read-times with or without ML support fell for round 2 reads compared with round 1, suggesting that readers familiarized themselves with the study reading method. During the second reading round, there was a significant reduction in reading time when using ML support

Development and Evaluation of Machine Learning in Whole-Body Magnetic Resonance Imaging for Detecting Metastases in Patients With Lung or Colon Cancer: A Diagnostic Test Accuracy Study

OBJECTIVES: Whole-body magnetic resonance imaging (WB-MRI) has been demonstrated to be efficient and cost-effective for cancer staging. The study aim was to develop a machine learning (ML) algorithm to improve radiologists' sensitivity and specificity for metastasis detection and reduce reading times. MATERIALS AND METHODS: A retrospective analysis of 438 prospectively collected WB-MRI scans from multicenter Streamline studies (February 2013-September 2016) was undertaken. Disease sites were manually labeled using Streamline reference standard. Whole-body MRI scans were randomly allocated to training and testing sets. A model for malignant lesion detection was developed based on convolutional neural networks and a 2-stage training strategy. The final algorithm generated lesion probability heat maps. Using a concurrent reader paradigm, 25 radiologists (18 experienced, 7 inexperienced in WB-/MRI) were randomly allocated WB-MRI scans with or without ML support to detect malignant lesions over 2 or 3 reading rounds. Reads were undertaken in the setting of a diagnostic radiology reading room between November 2019 and March 2020. Reading times were recorded by a scribe. Prespecified analysis included sensitivity, specificity, interobserver agreement, and reading time of radiology readers to detect metastases with or without ML support. Reader performance for detection of the primary tumor was also evaluated. RESULTS: Four hundred thirty-three evaluable WB-MRI scans were allocated to algorithm training (245) or radiology testing (50 patients with metastases, from primary 117 colon [n = 117] or lung [n = 71] cancer). Among a total 562 reads by experienced radiologists over 2 reading rounds, per-patient specificity was 86.2% (ML) and 87.7% (non-ML) (-1.5% difference; 95% confidence interval [CI], -6.4%, 3.5%; P = 0.39). Sensitivity was 66.0% (ML) and 70.0% (non-ML) (-4.0% difference; 95% CI, -13.5%, 5.5%; P = 0.344). Among 161 reads by inexperienced readers, per-patient specificity in both groups was 76.3% (0% difference; 95% CI, -15.0%, 15.0%; P = 0.613), with sensitivity of 73.3% (ML) and 60.0% (non-ML) (13.3% difference; 95% CI, -7.9%, 34.5%; P = 0.313). Per-site specificity was high (>90%) for all metastatic sites and experience levels. There was high sensitivity for the detection of primary tumors (lung cancer detection rate of 98.6% with and without ML [0.0% difference; 95% CI, -2.0%, 2.0%; P = 1.00], colon cancer detection rate of 89.0% with and 90.6% without ML [-1.7% difference; 95% CI, -5.6%, 2.2%; P = 0.65]). When combining all reads from rounds 1 and 2, reading times fell by 6.2% (95% CI, -22.8%, 10.0%) when using ML. Round 2 read-times fell by 32% (95% CI, 20.8%, 42.8%) compared with round 1. Within round 2, there was a significant decrease in read-time when using ML support, estimated as 286 seconds (or 11%) quicker (P = 0.0281), using regression analysis to account for reader experience, read round, and tumor type. Interobserver variance suggests moderate agreement, Cohen κ = 0.64; 95% CI, 0.47, 0.81 (with ML), and Cohen κ = 0.66; 95% CI, 0.47, 0.81 (without ML). CONCLUSIONS: There was no evidence of a significant difference in per-patient sensitivity and specificity for detecting metastases or the primary tumor using concurrent ML compared with standard WB-MRI. Radiology read-times with or without ML support fell for round 2 reads compared with round 1, suggesting that readers familiarized themselves with the study reading method. During the second reading round, there was a significant reduction in reading time when using ML support

The extratropical upper troposphere and lower stratosphere

The extratropical upper troposphere and lower stratosphere (Ex-UTLS) is a transition region between the stratosphere and the troposphere. The Ex-UTLS includes the tropopause, a strong static stability gradient and dynamic barrier to transport. The barrier is reflected in tracer profiles. This region exhibits complex dynamical, radiative, and chemical characteristics that place stringent spatial and temporal requirements on observing and modeling systems. The Ex-UTLS couples the stratosphere to the troposphere through chemical constituent transport (of, e.g., ozone), by dynamically linking the stratospheric circulation with tropospheric wave patterns, and via radiative processes tied to optically thick clouds and clear-sky gradients of radiatively active gases. A comprehensive picture of the Ex-UTLS is presented that brings together different definitions of the tropopause, focusing on observed dynamical and chemical structure and their coupling. This integral view recognizes that thermal gradients and dynamic barriers are necessarily linked, that these barriers inhibit mixing and give rise to specific trace gas distributions, and that there are radiative feedbacks that help maintain this structure. The impacts of 21st century anthropogenic changes to the atmosphere due to ozone recovery and climate change will be felt in the Ex-UTLS, and recent simulations of these effects are summarized and placed in context

How and why are communities of practice established in the healthcare sector? A systematic review of the literature

Background: Communities of Practice (CoPs) are promoted in the healthcare sector as a means of generating and sharing knowledge and improving organisational performance. However CoPs vary considerably in the way they are structured and operate in the sector. If CoPs are to be cultivated to benefit healthcare organisations, there is a need to examine and understand their application to date. To this end, a systematic review of the literature on CoPs was conducted, to examine how and why CoPs have been established and whether they have been shown to improve healthcare practice. Methods. Peer-reviewed empirical research papers on CoPs in the healthcare sector were identified by searching electronic health-databases. Information on the purpose of establishing CoPs, their composition, methods by which members communicate and share information or knowledge, and research methods used to examine effectiveness was extracted and reviewed. Also examined was evidence of whether or not CoPs led to a change in healthcare practice. Results: Thirty-one primary research papers and two systematic reviews were identified and reviewed in detail. There was a trend from descriptive to evaluative research. The focus of CoPs in earlier publications was on learning and exchanging information and knowledge, whereas in more recently published research, CoPs were used more as a tool to improve clinical practice and to facilitate the implementation of evidence-based practice. Means by which members communicated with each other varied, but in none of the primary research studies was the method of communication examined in terms of the CoP achieving its objectives. Researchers are increasing their efforts to assess the effectiveness of CoPs in healthcare, however the interventions have been complex and multifaceted, making it difficult to directly attribute the change to the CoP. Conclusions: In keeping with Wenger and colleagues' description, CoPs in the healthcare sector vary in form and purpose. While researchers are increasing their efforts to examine the impact of CoPs in healthcare, cultivating CoPs to improve healthcare performance requires a greater understanding of how to establish and support CoPs to maximise their potential to improve healthcare