Evaluating automatically generated normal tissue contours for safe use in head and neck and cervical cancer treatment planning.

PURPOSE: Volumetric-modulated arc therapy (VMAT) is a widely accepted treatment method for head and neck (HN) and cervical cancers; however, creating contours and plan optimization for VMAT plans is a time-consuming process. Our group has created an automated treatment planning tool, the Radiation Planning Assistant (RPA), that uses deep learning models to generate organs at risk (OARs), planning structures and automates plan optimization. This study quantitatively evaluates the quality of contours generated by the RPA tool. METHODS: For patients with HN (54) and cervical (39) cancers, we retrospectively generated autoplans using the RPA. Autoplans were generated using deep-learning and RapidPlan models developed in-house. The autoplans were, then, applied to the original, physician-drawn contours, which were used as a ground truth (GT) to compare with the autocontours (RPA). Using a "two one-sided tests" (TOST) procedure, we evaluated whether the autocontour normal tissue dose was equivalent to that of the ground truth by a margin, δ, that we determined based on clinical judgement. We also calculated the number of plans that met established clinically accepted dosimetric criteria. RESULTS: For HN plans, 91.8% and 91.7% of structures met dosimetric criteria for automatic and manual contours, respectively; for cervical plans, 95.6% and 95.7% of structures met dosimetric criteria for automatic and manual contours, respectively. Autocontours were equivalent to the ground truth for 71% and 75% of common DVH metrics for the HN and cervix, respectively. CONCLUSIONS: This study shows that dosimetrically equivalent normal tissue contours can be created for HN and cervical cancers using deep learning techniques. In general, differences between the contours did not affect the passing or failing of clinical dose tolerances.

Automatic end-to-end VMAT treatment planning for rectal cancers.

BACKGROUND: The treatment planning process from segmentation to producing a deliverable plan is time-consuming and labor-intensive. Existing solutions automate the segmentation and planning processes individually. The feasibility of combining auto-segmentation and auto-planning for volumetric modulated arc therapy (VMAT) for rectal cancers in an end-to-end process is not clear. PURPOSE: To create and clinically evaluate a complete end-to-end process for auto-segmentation and auto-planning of VMAT for rectal cancer requiring only the gross tumor volume contour and a CT scan as inputs. METHODS: Patient scans and data were retrospectively selected from our institutional records for patients treated for malignant neoplasm of the rectum. We trained, validated, and tested deep learning auto-segmentation models using nnU-Net architecture for clinical target volume (CTV), bowel bag, large bowel, small bowel, total bowel, femurs, bladder, bone marrow, and female and male genitalia. For the CTV, we identified 174 patients with clinically drawn CTVs. We used data for 18 patients for all structures other than the CTV. The structures were contoured under the guidance of and reviewed by a gastrointestinal (GI) radiation oncologist. The predicted results for CTV in 35 patients and organs at risk (OAR) in six patients were scored by the GI radiation oncologist using a five-point Likert scale. For auto-planning, a RapidPlan knowledge-based planning solution was modeled for VMAT delivery with a prescription of 25 Gy in five fractions. The model was trained and tested on 20 and 34 patients, respectively. The resulting plans were scored by two GI radiation oncologists using a five-point Likert scale. Finally, the end-to-end pipeline was evaluated on 16 patients, and the resulting plans were scored by two GI radiation oncologists. RESULTS: In 31 of 35 patients, CTV contours were clinically acceptable without necessary modifications. The CTV achieved a Dice similarity coefficient of 0.85 (±0.05) and 95% Hausdorff distance of 15.25 (±5.59) mm. All OAR contours were clinically acceptable without edits, except for large and small bowel which were challenging to differentiate. However, contours for total, large, and small bowel were clinically acceptable. The two physicians accepted 100% and 91% of the auto-plans. For the end-to-end pipeline, the two physicians accepted 88% and 62% of the auto-plans. CONCLUSIONS: This study demonstrated that the VMAT treatment planning technique for rectal cancer can be automated to generate clinically acceptable and safe plans with minimal human interventions.

Photosynthetic response mechanism to polybrominated diphenyl ether exposure in Chlorella pyrenoidosa.

Polybrominated diphenyl ether (PBDE) contamination is common in aquatic environments and can severely damage aquatic organisms. However, there is a lack of information on the response and self-adaptation mechanisms of these organisms. Chlorella pyrenoidosa was treated with 2,2',4,4'-tetrabromodiphenyl ether (BDE47), causing significant growth inhibition, pigment reduction, oxidative stress, and chloroplast atrophy. Photosynthetic damage contributed to inhibition, as indicated by Fv/Fm, Chl a fluorescence induction, photosynthetic oxygen evolution activity, and photosystem subunit stoichiometry. Here, Chl a fluorescence induction and quinone electron acceptor (QA-) reoxidation kinetics showed that the PSII donor and acceptor sides were insensitive to BDE47. Quantitative analyses of D1 and PsaD proteins illustrated that PSII and PSI complexes were the main primary targets of photosynthesis inhibition by BDE47. Significant modulation of PSII complex might have been caused by the potential binding of BDE47 on D1 protein, and molecular docking was performed to investigate this. Increased activation of antioxidant defense systems and photosystem repair as a function of exposure time indicated a positive resistance to BDE47. After a 5-day exposure, 23 % of BDE47 was metabolized. Our findings suggest that C. pyrenoidosa has potential as a bioremediator for wastewater-borne PBDEs and can improve our understanding of ecological risks to microalgae.

Customizable landmark-based field aperture design for automated whole-brain radiotherapy treatment planning.

PURPOSE: To develop and evaluate an automated whole-brain radiotherapy (WBRT) treatment planning pipeline with a deep learning-based auto-contouring and customizable landmark-based field aperture design. METHODS: The pipeline consisted of the following steps: (1) Auto-contour normal structures on computed tomography scans and digitally reconstructed radiographs using deep learning techniques, (2) locate the landmark structures using the beam's-eye-view, (3) generate field apertures based on eight different landmark rules addressing different clinical purposes and physician preferences. Two parallel approaches for generating field apertures were developed for quality control. The performance of the generated field shapes and dose distributions were compared with the original clinical plans. The clinical acceptability of the plans was assessed by five radiation oncologists from four hospitals. RESULTS: The performance of the generated field apertures was evaluated by the Hausdorff distance (HD) and mean surface distance (MSD) from 182 patients' field apertures used in the clinic. The average HD and MSD for the generated field apertures were 16 ± 7 and 7 ± 3 mm for the first approach, respectively, and 17 ± 7 and 7 ± 3 mm, respectively, for the second approach. The differences regarding HD and MSD between the first and the second approaches were 1 ± 2 and 1 ± 3 mm, respectively. A clinical review of the field aperture design, conducted using 30 patients, achieved a 100% acceptance rate for both the first and second approaches, and the plan review achieved a 100% acceptance rate for the first approach and a 93% acceptance rate for the second approach. The average acceptance rate for meeting lens dosimetric recommendations was 80% (left lens) and 77% (right lens) for the first approach, and 70% (both left and right lenses) for the second approach, compared with 50% (left lens) and 53% (right lens) for the clinical plans. CONCLUSION: This study provided an automated pipeline with two field aperture generation approaches to automatically generate WBRT treatment plans. Both quantitative and qualitative evaluations demonstrated that our novel pipeline was comparable with the original clinical plans.

Identifying the optimal deep learning architecture and parameters for automatic beam aperture definition in 3D radiotherapy.

PURPOSE: Two-dimensional radiotherapy is often used to treat cervical cancer in low- and middle-income countries, but treatment planning can be challenging and time-consuming. Neural networks offer the potential to greatly decrease planning time through automation, but the impact of the wide range of hyperparameters to be set during training on model accuracy has not been exhaustively investigated. In the current study, we evaluated the effect of several convolutional neural network architectures and hyperparameters on 2D radiotherapy treatment field delineation. METHODS: Six commonly used deep learning architectures were trained to delineate four-field box apertures on digitally reconstructed radiographs for cervical cancer radiotherapy. A comprehensive search of optimal hyperparameters for all models was conducted by varying the initial learning rate, image normalization methods, and (when appropriate) convolutional kernel size, the number of learnable parameters via network depth and the number of feature maps per convolution, and nonlinear activation functions. This yielded over 1700 unique models, which were all trained until performance converged and then tested on a separate dataset. RESULTS: Of all hyperparameters, the choice of initial learning rate was most consistently significant for improved performance on the test set, with all top-performing models using learning rates of 0.0001. The optimal image normalization was not consistent across architectures. High overlap (mean Dice similarity coefficient = 0.98) and surface distance agreement (mean surface distance < 2 mm) were achieved between the treatment field apertures for all architectures using the identified best hyperparameters. Overlap Dice similarity coefficient (DSC) and distance metrics (mean surface distance and Hausdorff distance) indicated that DeepLabv3+ and D-LinkNet architectures were least sensitive to initial hyperparameter selection. CONCLUSION: DeepLabv3+ and D-LinkNet are most robust to initial hyperparameter selection. Learning rate, nonlinear activation function, and kernel size are also important hyperparameters for improving performance.

Assessing the practicality of using a single knowledge-based planning model for multiple linac vendors.

PURPOSE: Knowledge-based planning (KBP) has been shown to be an effective tool in quality control for intensity-modulated radiation therapy treatment planning and generating high-quality plans. Previous studies have evaluated its ability to create consistent plans across institutions and between planners within the same institution as well as its use as teaching tool for inexperienced planners. This study evaluates whether planning quality is consistent when using a KBP model to plan across different treatment machines. MATERIALS AND METHODS: This study used a RapidPlan model (Varian Medical Systems) provided by the vendor, to which we added additional planning objectives, maximum dose limits, and planning structures, such that a clinically acceptable plan is achieved in a single optimization. This model was used to generate and optimize volumetric-modulated arc therapy plans for a cohort of 50 patients treated for head-neck cancer. Plans were generated using the following treatment machines: Varian 2100, Elekta Versa HD, and Varian Halcyon. A noninferiority testing methodology was used to evaluate the hypothesis that normal and target metrics in our autoplans were no worse than a set of clinically-acceptable baseline plans by a margin of 1.8 Gy or 3% dose-volume. The quality of these plans were also compared through the use of common clinical dose-volume histogram criteria. RESULTS: The Versa HD met our noninferiority criteria for 23 of 34 normal and target metrics; while the Halcyon and Varian 2100 machines met our criteria for 24 of 34 and 26 of 34 metrics, respectively. The experimental plans tended to have less volume coverage for prescription dose planning target volume and larger hotspot volumes. However, comparable plans were generated across different treatment machines. CONCLUSIONS: These results support the use of a head-neck RapidPlan models in centralized planning workflows that support clinics with different linac models/vendors, although some fine-tuning for targets may be necessary.

Development and validation of a checklist for use with automatically generated radiotherapy plans.

PURPOSE: To develop a checklist that improves the rate of error detection during the plan review of automatically generated radiotherapy plans. METHODS: A custom checklist was developed using guidance from American Association of Physicists in Medicine task groups 275 and 315 and the results of a failure modes and effects analysis of the Radiation Planning Assistant (RPA), an automated contouring and treatment planning tool. The preliminary checklist contained 90 review items for each automatically generated plan. In the first study, eight physicists were recruited from our institution who were familiar with the RPA. Each physicist reviewed 10 artificial intelligence-generated resident treatment plans from the RPA for safety and plan quality, five of which contained errors. Physicists performed plan checks, recorded errors, and rated each plan's clinical acceptability. Following a 2-week break, physicists reviewed 10 additional plans with a similar distribution of errors using our customized checklist. Participants then provided feedback on the usability of the checklist and it was modified accordingly. In a second study, this process was repeated with 14 senior medical physics residents who were randomly assigned to checklist or no checklist for their reviews. Each reviewed 10 plans, five of which contained errors, and completed the corresponding survey. RESULTS: In the first study, the checklist significantly improved the rate of error detection from 3.4 ± 1.1 to 4.4 ± 0.74 errors per participant without and with the checklist, respectively (p = 0.02). Error detection increased by 20% when the custom checklist was utilized. In the second study, 2.9 ± 0.84 and 3.5 ± 0.84 errors per participant were detected without and with the revised checklist, respectively (p = 0.08). Despite the lack of statistical significance for this cohort, error detection increased by 18% when the checklist was utilized. CONCLUSION: Our results indicate that the use of a customized checklist when reviewing automated treatment plans will result in improved patient safety.

Automation of radiation treatment planning for rectal cancer.

PURPOSE: To develop an automated workflow for rectal cancer three-dimensional conformal radiotherapy (3DCRT) treatment planning that combines deep learning (DL) aperture predictions and forward-planning algorithms. METHODS: We designed an algorithm to automate the clinical workflow for 3DCRT planning with field aperture creations and field-in-field (FIF) planning. DL models (DeepLabV3+ architecture) were trained, validated, and tested on 555 patients to automatically generate aperture shapes for primary (posterior-anterior [PA] and opposed laterals) and boost fields. Network inputs were digitally reconstructed radiographs, gross tumor volume (GTV), and nodal GTV. A physician scored each aperture for 20 patients on a 5-point scale (>3 is acceptable). A planning algorithm was then developed to create a homogeneous dose using a combination of wedges and subfields. The algorithm iteratively identifies a hotspot volume, creates a subfield, calculates dose, and optimizes beam weight all without user intervention. The algorithm was tested on 20 patients using clinical apertures with varying wedge angles and definitions of hotspots, and the resulting plans were scored by a physician. The end-to-end workflow was tested and scored by a physician on another 39 patients. RESULTS: The predicted apertures had Dice scores of 0.95, 0.94, and 0.90 for PA, laterals, and boost fields, respectively. Overall, 100%, 95%, and 87.5% of the PA, laterals, and boost apertures were scored as clinically acceptable, respectively. At least one auto-plan was clinically acceptable for all patients. Wedged and non-wedged plans were clinically acceptable for 85% and 50% of patients, respectively. The hotspot dose percentage was reduced from 121% (σ = 14%) to 109% (σ = 5%) of prescription dose for all plans. The integrated end-to-end workflow of automatically generated apertures and optimized FIF planning gave clinically acceptable plans for 38/39 (97%) of patients. CONCLUSION: We have successfully automated the clinical workflow for generating radiotherapy plans for rectal cancer for our institution.

Automatic contouring QA method using a deep learning-based autocontouring system.

PURPOSE: To determine the most accurate similarity metric when using an independent system to verify automatically generated contours. METHODS: A reference autocontouring system (primary system to create clinical contours) and a verification autocontouring system (secondary system to test the primary contours) were used to generate a pair of 6 female pelvic structures (UteroCervix [uterus + cervix], CTVn [nodal clinical target volume (CTV)], PAN [para-aortic lymph nodes], bladder, rectum, and kidneys) on 49 CT scans from our institution and 38 from other institutions. Additionally, clinically acceptable and unacceptable contours were manually generated using the 49 internal CT scans. Eleven similarity metrics (volumetric Dice similarity coefficient (DSC), Hausdorff distance, 95% Hausdorff distance, mean surface distance, and surface DSC with tolerances from 1 to 10 mm) were calculated between the reference and the verification autocontours, and between the manually generated and the verification autocontours. A support vector machine (SVM) was used to determine the threshold that separates clinically acceptable and unacceptable contours for each structure. The 11 metrics were investigated individually and in certain combinations. Linear, radial basis function, sigmoid, and polynomial kernels were tested using the combinations of metrics as inputs for the SVM. RESULTS: The highest contouring error detection accuracies were 0.91 for the UteroCervix, 0.90 for the CTVn, 0.89 for the PAN, 0.92 for the bladder, 0.95 for the rectum, and 0.97 for the kidneys and were achieved using surface DSCs with a thickness of 1, 2, or 3 mm. The linear kernel was the most accurate and consistent when a combination of metrics was used as an input for the SVM. However, the best model accuracy from the combinations of metrics was not better than the best model accuracy from a surface DSC as an input. CONCLUSIONS: We distinguished clinically acceptable contours from clinically unacceptable contours with an accuracy higher than 0.9 for the targets and critical structures in patients with cervical cancer; the most accurate similarity metric was surface DSC with a thickness of 1, 2, or 3 mm.

Adaptive Estimation Algorithm for Correcting Low-Cost MEMS-SINS Errors of Unmanned Vehicles under the Conditions of Abnormal Measurements.

In this paper, a low-cost small-sized strap-down inertial navigation system (SINS)-Gyrolab GL-VG 109-is studied. When the system is installed on an unmanned vehicle and works in autonomous mode, it is difficult to determine the navigation parameters of the unmanned vehicle. Correcting the SINS information from the Global Navigation Satellite System (GNSS) can significantly increase the determination accuracy of the navigation parameters. However, this is only available when the GNSS signals are stable. A new adaptive estimation algorithm that can automatically detect, evaluate, and process the abnormal measurements is proposed in the present work. The determination of the navigation parameters can reach the third accuracy class using the proposed method. The effectiveness of the algorithm is verified by the mathematical simulation and the experimental tests (with a real SINS GL-VG 109), which are conducted in urban environments with a GNSS signal containing 15% and 40% abnormal measurements. The results show that the proposed method can significantly reduce the impact of abnormal measurements and improve the estimation accuracy.

LINK-A lncRNA Promotes Proliferation and Inhibits Apoptosis of Mantle Cell Lymphoma Cell by Upregulating Survivin.

BACKGROUND LINK-A lncRNA acts as an oncogene in triple-negative breast cancer, but its involvement in other diseases is unknown. The present study was performed to investigate the involvement of LINK-A lncRNA in mantle cell lymphoma. MATERIAL AND METHODS Expressions of LINK-A lncRNA and survivin in plasma of patients with mantle cell lymphoma and healthy controls were detected by qRT-PCR and ELISA, respectively. ROC curve analysis was performed to investigate the diagnostic value of LINK-A lncRNA for mantle cell lymphoma. Correlations between plasma level of LINK-A lncRNA and survivin were analyzed by Pearson correlation coefficient. LINK-A lncRNA shRNA and expression vector were transfected into cells of human mantle cell lymphoma cell lines, followed by detection of cell proliferation, cell apoptosis, and survivin expression by cell proliferation assay, cell apoptosis assay, and Western blot analysis, respectively. RESULTS We found that, compared with healthy controls, plasma levels of LINK-A lncRNA and survivin were significantly increased in patients with mantle cell lymphoma. Upregulation of LINK-A lncRNA sensitively distinguished patients with mantle cell lymphoma from healthy controls. Plasma levels of LINK-A lncRNA and survivin were positively correlated in mantle cell lymphoma patients but not in healthy controls. CONCLUSIONS LINK-A lncRNA overexpression promoted cell proliferation, inhibited cell apoptosis, and upregulated survivin expression, while LINK-A lncRNA knockdown had the opposite effect.

Correction to: A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images.

The original version of this article, published on 24 November 2017, unfortunately contained a mistake.

A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images.

OBJECTIVES: To develop a model using radiomic features extracted from MR images to distinguish radiation necrosis from tumour progression in brain metastases after Gamma Knife radiosurgery. METHODS: We retrospectively identified 87 patients with pathologically confirmed necrosis (24 lesions) or progression (73 lesions) and calculated 285 radiomic features from four MR sequences (T1, T1 post-contrast, T2, and fluid-attenuated inversion recovery) obtained at two follow-up time points per lesion per patient. Reproducibility of each feature between the two time points was calculated within each group to identify a subset of features with distinct reproducible values between two groups. Changes in radiomic features from one time point to the next (delta radiomics) were used to build a model to classify necrosis and progression lesions. RESULTS: A combination of five radiomic features from both T1 post-contrast and T2 MR images were found to be useful in distinguishing necrosis from progression lesions. Delta radiomic features with a RUSBoost ensemble classifier had an overall predictive accuracy of 73.2% and an area under the curve value of 0.73 in leave-one-out cross-validation. CONCLUSIONS: Delta radiomic features extracted from MR images have potential for distinguishing radiation necrosis from tumour progression after radiosurgery for brain metastases. KEY POINTS: • Some radiomic features showed better reproducibility for progressive lesions than necrotic ones • Delta radiomic features can help to distinguish radiation necrosis from tumour progression • Delta radiomic features had better predictive value than did traditional radiomic features.

The value of 18F-FDG PET before and after induction chemotherapy for the early prediction of a poor pathologic response to subsequent preoperative chemoradiotherapy in oesophageal adenocarcinoma.

PURPOSE: The purpose of our study was to determine the value of 18F-FDG PET before and after induction chemotherapy in patients with oesophageal adenocarcinoma for the early prediction of a poor pathologic response to subsequent preoperative chemoradiotherapy (CRT). METHODS: In 70 consecutive patients receiving a three-step treatment strategy of induction chemotherapy and preoperative chemoradiotherapy for oesophageal adenocarcinoma, 18F-FDG PET scans were performed before and after induction chemotherapy (before preoperative CRT). SUVmax, SUVmean, metabolic tumour volume (MTV), and total lesion glycolysis (TLG) were determined at these two time points. The predictive potential of (the change in) these parameters for a poor pathologic response, progression-free survival (PFS) and overall survival (OS) was assessed. RESULTS: A poor pathologic response after induction chemotherapy and preoperative CRT was found in 27 patients (39 %). Patients with a poor pathologic response experienced less of a reduction in TLG after induction chemotherapy (p < 0.01). The change in TLG was predictive for a poor pathologic response at a threshold of -26 % (sensitivity 67 %, specificity 84 %, accuracy 77 %, PPV 72 %, NPV 80 %), yielding an area-under-the-curve of 0.74 in ROC analysis. Also, patients with a decrease in TLG lower than 26 % had a significantly worse PFS (p = 0.02), but not OS (p = 0.18). CONCLUSIONS: 18F-FDG PET appears useful to predict a poor pathologic response as well as PFS early after induction chemotherapy in patients with oesophageal adenocarcinoma undergoing a three-step treatment strategy. As such, the early 18F-FDG PET response after induction chemotherapy could aid in individualizing treatment by modification or withdrawal of subsequent preoperative CRT in poor responders.

Stage III Non-Small Cell Lung Cancer: Prognostic Value of FDG PET Quantitative Imaging Features Combined with Clinical Prognostic Factors.

PURPOSE: To determine whether quantitative imaging features from pretreatment positron emission tomography (PET) can enhance patient overall survival risk stratification beyond what can be achieved with conventional prognostic factors in patients with stage III non-small cell lung cancer (NSCLC). MATERIALS AND METHODS: The institutional review board approved this retrospective chart review study and waived the requirement to obtain informed consent. The authors retrospectively identified 195 patients with stage III NSCLC treated definitively with radiation therapy between January 2008 and January 2013. All patients underwent pretreatment PET/computed tomography before treatment. Conventional PET metrics, along with histogram, shape and volume, and co-occurrence matrix features, were extracted. Linear predictors of overall survival were developed from leave-one-out cross-validation. Predictive Kaplan-Meier curves were used to compare the linear predictors with both quantitative imaging features and conventional prognostic factors to those generated with conventional prognostic factors alone. The Harrell concordance index was used to quantify the discriminatory power of the linear predictors for survival differences of at least 0, 6, 12, 18, and 24 months. Models were generated with features present in more than 50% of the cross-validation folds. RESULTS: Linear predictors of overall survival generated with both quantitative imaging features and conventional prognostic factors demonstrated improved risk stratification compared with those generated with conventional prognostic factors alone in terms of log-rank statistic (P = .18 vs P = .0001, respectively) and concordance index (0.62 vs 0.58, respectively). The use of quantitative imaging features selected during cross-validation improved the model using conventional prognostic factors alone (P = .007). Disease solidity and primary tumor energy from the co-occurrence matrix were found to be selected in all folds of cross-validation. CONCLUSION: Pretreatment PET features were associated with overall survival when adjusting for conventional prognostic factors in patients with stage III NSCLC.

[A Simple but Effective Device to Avoid Objective Lens Overheating: An Air Blower Device].

The interior of the Earth is a high temperature and high pressure environment. High temperatures cause important changes in the physical and chemical properties of minerals. An increase in temperature leads to significant changes in the molecular and lattice vibrations, elasticity, and seismic velocity of minerals. The high temperature vibrational spectroscopy (infrared and Raman) used to study these changes can provide highly significant understanding of the Earth's interior. During high temperature spectroscopy, the heating device that is used to heat the sample can work at a very high temperature (e.g., 1 500 ℃) because it has a cooling device surrounding it that is used to prevent the temperature of its environments from getting too high. However, radiation from its heating elements is intense and this will shine on and heat the objective lens of the focusing system for the spectroscopic light source, and this would result in damage to the lens. Thus, to avoid damage to the objective lens, an upper limit is placed on the heater temperature. The significance of this work is that it presents a method to exceed the present instrument's temperature limit so that we can perform in situ spectroscopy on samples at higher temperatures. This work extended the temperature limit for the sample to a higher temperature by using an air blower around the objective lens to create a gas flow around it. The gas flow serves to remove heat from the objective lens by forced convection and its turbulent flow also served to increase the rate of heat transport from the lens to the moving gas stream, which together prevented overheating of the objective lens. Our results have shown that although this device is simple, it was highly effective: for a sample temperature of 1 000 ℃, the objective lens temperature was reduced from ～235 to ～68 ℃. Using this device, we performed in situ high temperature Raman spectroscopy of forsterite up to a sample temperature of 1 300 ℃. The results agreed well with previous studies and demonstrated that with our simple air blower device, we can perform in situ high temperature spectroscopy up to 1 300 ℃ without damaging the objective lens and without expensive components like a high temperature composite objective lens or a long focus objective lens.

Methylation of RASSF1A gene promoter and the correlation with DNMT1 expression that may contribute to esophageal squamous cell carcinoma.

BACKGROUND: Esophageal squamous cell carcinoma is one of the most common malignancies in the world. Studies have confirmed that there are many genes abnormally hypermethylated in esophageal squamous cell carcinoma. The objective is to detect methylation of the RASSF1A gene promoter and the expression of the DNA methyltransferase 1 (DNMT1) protein in esophageal cancer tissue and discuss their relationship with esophageal squamous cell carcinoma. METHODS: The CpG island methylation status of RASSF1A genes were analyzed in 100 cases of tumor specimens as well as their adjacent tissues which was used for methylation-specific polymerase chain reaction (MSP). The expression of DNMT1 protein was determined by immunohistochemistry. Difference between measurement data and categorical data was compared through analysis of t test and chi-square test. All the statistics were taken with a bilateral test. The difference was statistically significant (P < 0.05). RESULTS: The promoter methylation of the RASSF1A gene promoter has been detected in 45 out of 100 (45%) esophageal squamous carcinoma cases, while methylation of RASSF1A gene has been detected in 2 out of 100 adjacent normal tissues (2%). The RASSF1A gene promoter was highly methylated in cancer tissues, and there were significant differences between normal esophagus tissues and esophageal squamous carcinoma (P < 0.05). The expression of DNMT1 protein has been detected in 61 out of 100 (61%) esophageal squamous carcinoma cases, including 41 cases in the above 45 methylated samples of RASSF1A gene promoter, and none in adjacent tissues. DNMT1 proteins are highly expressed in cancer tissues, and there were significant differences (P < 0.05). In positive cases for methylation of RASSF1A, the DNMT1 protein had been detected in 41 out of 45 (91%), while in non-methylated cancer cases, 20 out of 55(36.3%), and the difference is significant (P < 0.05). CONCLUSIONS: Esophageal squamous carcinoma tumorigenesis may be related with hypermethylation of DNMT1 and RASSF1A promoter CpG island due to their high expression and also their hypermethylation.

Tree bark as a biomonitor for the determination of polychlorinated biphenyls and polybrominated diphenyl ethers from Southern Jiangsu, China: levels, distribution, and possible sources.

Tree bark was used as the passive air sampler to evaluate polychlorinated biphenyl (PCB) and polybrominated diphenyl ether (PBDE) pollution and possible sources in Southern Jiangsu. The concentrations of PCBs and Σ7PBDEs were in the range of 0.58-5.19 ng/g dry weight (dw; mean 1.79 ng/g dw) and 17.9-243 pg/g dw (mean 74.7 pg/g dw), respectively. Tri-PCBs were the major PCB homologs, and technical PCB product Ar1242 was identified as the main source. BDE209 concentrations (4.29-456 ng/g dw) were relatively high, indicating that BDE209 pollution was serious in this region. The deca-BDE commercial mixture was the predominant commercial PBDE product used in this region. A good correlation was found between tree bark and polyurethane foam (PUF) disks in Σ6PCB monitoring, suggesting that both of them respond well to the gas-phase PCB monitoring.

Levels and spatial distribution of polybrominated diphenyl ethers (PBDEs) in surface soil from the Yangtze River Delta, China.

Surface soil samples were collected from Suzhou, Wuxi and Nantong in the Yangtze River Delta (YRD), China. Eight BDE congeners (BDE-28, 47, 99, 100, 153, 154, 183 and 209) were measured to determine the levels and compositional profiles in the samples. The concentrations of Σ7PBDEs and BDE-209 ranged from 0.04 to 2.23 µg/kg dw and 1.48 to 41.7 µg/kg dw in the samples, respectively. BDE-209 was the predominant congener (contributing to 69.2 %-99.8 % of Σ8PBDEs) in all samples. It was found that small towns and rural economic development zones in this region had also become sources of polybrominated diphenyl ethers pollutants to surrounding areas. Investigation of the pattern of BDE congener profiles showed that deca- and octa- technical formulations as emission sources were identified in the samples collected from the YRD.

The modified cross-suture technique for loop stoma reversal.

TECHNIQUE: (1) A four-pointed star-shaped incision was made to separate the skin around the stoma intestine. (2) The stoma intestine was resected, and side-to-side or end-to-side anastomosis was performed to restore the continuity of the intestine. (3) The peritoneum and rectus sheath should be closed using continuous full-thickness sutures. (4) The subcutaneous fat layer and dermis layer should be sutured using purse-string sutures. Two holes should be made in the center of the sutured area. (5) The cross should be sutured intermittently on all four sides using 1-2 stitches. (6) A rubber strip should be placed in the center of the small hole. RESULTS: The presence of a small hole in the center of the incision and the use of a rubber strip for drainage facilitate early fluid drainage. The design of a cross-stitched skin incision helps reduce local tension. CONCLUSION: The modified cross-suture technique may reduce postoperative incision infections and associated pain, which is a suitable incision treatment method for loop stoma reversal.