Daily intake of cod or salmon for 2 weeks decreases the 18:1n-9/18:0 ratio and serum triacylglycerols in healthy subjects

Intake of fish and omega-3 (n-3) fatty acids is associated with a reduced concentration of plasma triacylglycerols (TAG) but the mechanisms are not fully clarified. Stearoyl-CoA desaturase-1 (SCD1) activity, governing TAG synthesis, is affected by n-3 fatty acids. Peripheral blood mononuclear cells (PBMC) display expression of genes involved in lipid metabolism. The aim of the present study was to estimate whether intake of lean and fatty fish would influence n-3 fatty acids composition in plasma phospholipids (PL), serum TAG, 18:1n-9/18:0 ratio in plasma PL, as well as PBMC gene expression of SCD1 and fatty acid synthase (FAS). Healthy males and females (n = 30), aged 20–40, consumed either 150 g of cod, salmon, or potato (control) daily for 15 days. During intervention docosahexaenoic acid (DHA, 22:6n-3) increased in the cod group (P\0.05), while TAG concentration decreased (P\0.05). In the salmon group both eicosapentaenoic acid (EPA, 20:5n-3) and DHA increased (P\0.05) whereas TAG concentration and the 18:1n-9/ 18:0 ratio decreased (P\0.05). Reduction of the 18:1n-9/ 18:0 ratio was associated with a corresponding lowering of TAG (P\0.05) and an increase in EPA and DHA (P\0.05). The mRNA levels of SCD1 and FAS in PBMC were not significantly altered after intake of cod or salmon when compared with the control group. In conclusion, both lean and fatty fish may lower TAG, possibly by reducing the 18:1n-9/18:0 ratio related to allosteric inhibition of SCD1 activity, rather than by influencing the synthesis of enzyme protei

Effect of MLC tracking latency on conformal volumetric modulated arc therapy (VMAT) plans in 4D stereotactic lung treatment

AbstractBackground and purposeThe latency of a multileaf collimator (MLC) tracking system used to overcome respiratory motion causes misalignment of the treatment beam with respect to the gross tumour volume, which may result in reduced target coverage. This study investigates the magnitude of this effect.Material and methodsSimulated superior–inferior breathing motion was used to construct histograms of isocentre offset with respect to the gross tumour volume (GTV) for a variety of tracking latencies. Dose distributions for conformal volumetric modulated arc therapy (VMAT) arcs were then calculated at a range of offsets and summed according to these displacement histograms. The results were verified by delivering the plans to a Delta4 phantom on a motion platform.ResultsIn the absence of an internal target margin, a tracking latency of 150ms reduces the GTV D95% by approximately 2%. With a margin of 2mm, the same drop in dose occurs for a tracking latency of 450ms. Lung V13Gy is unaffected by a range of latencies. These results are supported by the phantom measurements.ConclusionsAssuming that internal motion can be modelled by a rigid translation of the patient, MLC tracking of conformal VMAT can be effectively accomplished in the absence of an internal target margin for substantial breathing motion (4s period and 20mm peak–peak amplitude) so long as the system latency is less than 150ms

Prospective Study Delivering Simultaneous Integrated High-dose Tumor Boost (≤70 Gy) With Image Guided Adaptive Radiation Therapy for Radical Treatment of Localized Muscle-Invasive Bladder Cancer

PurposeImage guided adaptive radiation therapy offers individualized solutions to improve target coverage and reduce normal tissue irradiation, allowing the opportunity to increase the radiation tumor dose and spare normal bladder tissue.Methods and MaterialsA library of 3 intensity modulated radiation therapy plans were created (small, medium, and large) from planning computed tomography (CT) scans performed at 30 and 60 minutes; treating the whole bladder to 52 Gy and the tumor to 70 Gy in 32 fractions. A “plan of the day” approach was used for treatment delivery. A post-treatment cone beam CT (CBCT) scan was acquired weekly to assess intrafraction filling and coverage.ResultsA total of 18 patients completed treatment to 70 Gy. The plan and treatment for 1 patient was to 68 Gy. Also, 1 patient's plan was to 70 Gy but the patient was treated to a total dose of 65.6 Gy because dose-limiting toxicity occurred before dose escalation. A total of 734 CBCT scans were evaluated. Small, medium, and large plans were used in 36%, 48%, and 16% of cases, respectively. The mean ± standard deviation rate of intrafraction filling at the start of treatment (ie, week 1) was 4.0 ± 4.8 mL/min (range 0.1-19.4) and at end of radiation therapy (ie, week 5 or 6) was 1.1 ± 1.6 mL/min (range 0.01-7.5; P=.002). The mean D98 (dose received by 98% volume) of the tumor boost and bladder as assessed on the post-treatment CBCT scan was 97.07% ± 2.10% (range 89.0%-104%) and 99.97% ± 2.62% (range 96.4%-112.0%). At a median follow-up period of 19 months (range 4-33), no muscle-invasive recurrences had developed. Two patients experienced late toxicity (both grade 3 cystitis) at 5.3 months (now resolved) and 18 months after radiation therapy.ConclusionsImage guided adaptive radiation therapy using intensity modulated radiation therapy to deliver a simultaneous integrated tumor boost to 70 Gy is feasible, with acceptable toxicity, and will be evaluated in a randomized trial

Insulin resistance, adiponectin and adverse outcomes following elective cardiac surgery: a prospective follow-up study

<p>Abstract</p> <p>Background</p> <p>Insulin resistance and adiponectin are markers of cardio-metabolic disease and associated with adverse cardiovascular outcomes. The present study examined whether preoperative insulin resistance or adiponectin were associated with short- and long-term adverse outcomes in non-diabetic patients undergoing elective cardiac surgery.</p> <p>Methods</p> <p>In a prospective study, we assessed insulin resistance and adiponectin levels from preoperative fasting blood samples in 836 patients undergoing cardiac surgery. Population-based medical registries were used for postoperative follow-up. Outcomes included all-cause death, myocardial infarction or percutaneous coronary intervention, stroke, re-exploration, renal failure, and infections. The ability of insulin resistance and adiponectin to predict clinical adverse outcomes was examined using receiver operating characteristics.</p> <p>Results</p> <p>Neither insulin resistance nor adiponectin were statistically significantly associated with 30-day mortality, but adiponectin was associated with an increased 31-365-day mortality (adjusted odds ratio 2.9 [95% confidence interval 1.3-6.4]) comparing the upper quartile with the three lower quartiles. Insulin resistance was a poor predictor of adverse outcomes. In contrast, the predictive accuracy of adiponectin (area under curve 0.75 [95% confidence interval 0.65-0.85]) was similar to that of the EuroSCORE (area under curve 0.75 [95% confidence interval 0.67-0.83]) and a model including adiponectin and the EuroSCORE had an area under curve of 0.78 [95% confidence interval 0.68-0.88] concerning 31-365-day mortality.</p> <p>Conclusions</p> <p>Elevated adiponectin levels, but not insulin resistance, were associated with increased mortality and appear to be a strong predictor of long-term mortality. Additional studies are warranted to further clarify the possible clinical role of adiponectin assessment in cardiac surgery.</p> <p>Trial Registration</p> <p>The Danish Data Protection Agency; reference no. 2007-41-1514.</p

The multidrug resistance 1 (MDR1) gene polymorphism G-rs3789243-A is not associated with disease susceptibility in Norwegian patients with colorectal adenoma and colorectal cancer; a case control study

<p>Abstract</p> <p>Background</p> <p>Smoking, dietary factors, and alcohol consumption are known life style factors contributing to gastrointestinal carcinogenesis. Genetic variations in carcinogen handling may affect cancer risk. The multidrug resistance 1(<it>MDR1/ABCB1</it>) gene encodes the transport protein P-glycoprotein (a phase III xenobiotic transporter). P-glycoprotein is present in the intestinal mucosal lining and restricts absorption of certain carcinogens, among these polycyclic aromatic hydrocarbons. Moreover, P-glycoprotein transports various endogenous substrates such as cytokines and chemokines involved in inflammation, and may thereby affect the risk of malignity. Hence, genetic variations that modify the function of P-glycoprotein may be associated with the risk of colorectal cancer (CRC). We have previously found an association between the <it>MDR1 </it>intron 3 G-rs3789243-A polymorphism and the risk of CRC in a Danish study population. The aim of this study was to investigate if this <it>MDR1 </it>polymorphism was associated with risk of colorectal adenoma (CA) and CRC in the Norwegian population.</p> <p>Methods</p> <p>Using a case-control design, the association between the <it>MDR1 </it>intron 3 G-rs3789243-A polymorphism and the risk of colorectal carcinomas and adenomas in the Norwegian population was assessed in 167 carcinomas, 990 adenomas, and 400 controls. Genotypes were determined by allelic discrimination. Odds ratio (OR) and 95 confidence interval (95% CI) were estimated by binary logistic regression.</p> <p>Results</p> <p>No association was found between the <it>MDR1 </it>polymorphism (G-rs3789243-A) and colorectal adenomas or cancer. Carriers of the variant allele of MDR1 intron 3 had odds ratios (95% CI) of 0.97 (0.72–1.29) for developing adenomas, and 0.70 (0.41–1.21) for colorectal cancer, respectively, compared to homozygous wild type carriers.</p> <p>Conclusion</p> <p>The <it>MDR1 </it>intron 3 (G-rs3789243-A) polymorphism was not associated with a risk of colorectal adenomas or carcinomas in the present Norwegian study group. Thus, this <it>MDR1 </it>polymorphism does not seem to play an important role in colorectal carcinogenesis in this population.</p

Pitfalls in machine learning‐based assessment of tumor‐infiltrating lymphocytes in breast cancer: a report of the international immuno‐oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer (BC) has been well established, and tumor-infiltrating lymphocytes (TILs) have emerged as a predictive and prognostic biomarker for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2 negative) breast cancer (TNBC) and HER2-positive breast cancer. How computational assessment of TILs can complement manual TIL-assessment in trial- and daily practices is currently debated and still unclear. Recent efforts to use machine learning (ML) for the automated evaluation of TILs show promising results. We review state-of-the-art approaches and identify pitfalls and challenges by studying the root cause of ML discordances in comparison to manual TILs quantification. We categorize our findings into four main topics; (i) technical slide issues, (ii) ML and image analysis aspects, (iii) data challenges, and (iv) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns, or design choices in the computational implementation. To aid the adoption of ML in TILs assessment, we provide an in-depth discussion of ML and image analysis including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial- and routine clinical management of patients with TNBC

Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer