pH-mediated potentiation of gallium nitrate against Pseudomonas aeruginosa.

The emergence of multidrug-resistant Pseudomonas aeruginosa isolates is a growing concern for public health, necessitating new therapeutic strategies. Gallium nitrate [Ga(NO3)3], a medication for cancer-related hypercalcemia, has attracted great attention due to its ability to inhibit P. aeruginosa growth and biofilm formation by disrupting iron metabolism. However, the antibacterial efficacy of Ga(NO3)3 is not always satisfactory. It is imperative to investigate the factors that affect the bactericidal effects of Ga(NO3)3 and to identify new ways to enhance its efficacy. This study focused on the impact of pH on P. aeruginosa resistance to Ga(NO3)3, along with the underlying mechanism. The results indicate that acidic conditions could increase the effectiveness of Ga(NO3)3 against P. aeruginosa by promoting the production of pyochelin and gallium uptake. Subsequently, using glutamic acid, a clinically compatible acidic amino acid, the pH was significantly lowered and enhanced the bactericidal and inhibitory efficacy of Ga(NO3)3 against biofilm formation by P. aeruginosa, including a reference strain PA14 and several multidrug-resistant clinical isolates. Furthermore, we used an abscess mouse model to evaluate this combination in vivo; the results show that the combination of glutamic acid and Ga(NO3)3 significantly improved P. aeruginosa clearance. Overall, the present study demonstrates that acidic conditions can increase the sensitivity of P. aeruginosa to Ga(NO3)3. Combining glutamic acid and Ga(NO3)3 is a potential strategy for the treatment of P. aeruginosa infections.

A new pragmatic classification system for thymoma associated myasthenia gravis: a retrospective cohort study.

The management and outcomes of patients with thymoma associated with myasthenia gravis (TAMG) are heterogeneous. Here, we propose a novel classification system based on Masaoka stage and Myasthenia Gravis Foundation of America (MGFA) classification, aiming to guide surgical decisions and perioperative management for these patients. Considering both oncological and neurological factors, this novel TAMG classification provides valuable information on outcome stratification and clinical decision-making for TAMG.

The reporting quality of randomized controlled trials in pharmacotherapy for pituitary adenomas.

BACKGROUND: Medical therapy has become an increasingly important intervention owing to improvements in the multidisciplinary care for pituitary adenomas (PAs). This study aimed to assess the reporting quality of randomized controlled trials (RCTs) on PAs pharmacotherapy. METHODS: RCTs evaluating the efficacy of pharmacotherapy in PAs published in English between January 1, 1974, and December 31, 2022, were searched for and collected from PubMed and MEDLINE. The 2010 Consolidated Standards for Test Reports (CONSORT) statement-based 28 items overall quality score (OQS) was used to evaluate the overall quality of each report. RESULTS: Twenty-seven related RCTs including 1816 patients were retrieved. The median OQS score was 12 (range, 6-19) on a scale of 0 to 28. Important items, such as background, objectives, participants, interventions, and outcomes, were sufficiently reported in 100% (27/27) of the articles. Statistical methods were adequately described in 93% (25/27) of patients. However, RCTs underreported identification as randomized trials in the title (3/27, 11%), sample size, allocation concealment, implementation, ancillary analysis method, and Diagram and Ancillary analyses (1/27, 4%). The OQS of published RCTs has significantly increased since 2010 (P = .012). The multivariate final model showed significant associations between higher OQS and publication since 2010 and enrollment of more than 100 patients. CONCLUSIONS: The overall reporting quality of RCTs on pharmacotherapy in PAs was poor, based on the 2010 CONSORT statement. However, we noticed an improvement in the OQS over the years and identified the factors associated with a better report. Increased effort is necessary to raise awareness of these issues among writers, readers, reviewers, and editors.

Occipital bone defect caused by neurofibromatosis type I: A case report.

RATIONALE: The synergistic effect between nonmalignant lesions can also cause a serious impact on patient survival. This disease involves different organ systems and presents with a variety of clinical manifestations, such as schwannoma, depigmentation, low-grade glioma, and skeletal abnormalities. We report a case of type I neurofibromatosis with an occipital bone defect. PATIENT CONCERNS: We report a case of a 50-year-old man with type I neurofibromatosis with occipital bone defect. DIAGNOSIS: The patient was accepted by the local hospital due to sudden right upper limb weakness accompanied by jitter without recognizable cause or inducement. A computerized tomography scan at a local hospital suggested subcutaneous neurofibromatosis with a left occipital cranial defect with thinning bone. On admission physical examination, diffuse multiple masses could be seen throughout the body and head of different sizes and composed of soft and tough textures. The largest one was located in the posterior occipital bone, approximately 8*8 cm in size, with a child tumor and tough texture. Multiple café-au-lait spots could be found on the chest and back, and multiple freckles can be seen in the armpit. The patient underwent surgery. Postoperative pathology showed a spindle cell tumor, which was determined to be neurofibromatosis type I according to immunopathology and clinical data. INTERVENTIONS: The patient was admitted for surgical treatment. During the operation, the scalp mass was completely abducted and the tumor tissue at the skull defect was sharply separated. Postoperative pathology showed that the peripheral margin and the bottom margin were cleaned. OUTCOMES: Computerized tomography and magnetic resonance imaging showed that the tumor was completely. There were not any surgical complications. The patient recovered well, was cured and was dismissed from the hospital. LESSONS: The synergistic effect between nonmalignant lesions can also cause a serious impact on patient survival to encourage early medical intervention. The clinical presentation of neurofibromatosis type I am usually nonmalignant, and in this case, involvement of the skull with bone defect is very rare. Therefore, it is necessary to accumulate relevant cases, reveal the pathogenesis of the disease, predict the development and outcome, and provide more evidence for early therapeutic intervention of similar patients in the future.

Optimization of isocenter position for multiple targets with nonuniform-margin expansion.

PURPOSE: The single isocenter for multiple-target (SIMT) technique has become a popular treatment technique for multiple brain metastases. We have implemented a method to obtain a nonuniform margin for SIMT technique. In this study, we further propose a method to determine the isocenter position so that the total expanded margin volume is minimal. MATERIALS AND METHOD: Based on a statistical model, the relationship between nonuniform margin and the distance d (from isocenter to target point), setup uncertainties, and significance level was established. Due to the existence of rotational error, there is a nonlinear relationship between the margin volume and the isocenter position. Using numerical simulation, we study the relationship between optimal isocenter position and translational error, rotational error, and target size. In order to find the optimal isocenter position quickly, adaptive simulated annealing (ASA) algorithm was used. This method was implemented in the Pinnacle3 treatment planning system and compared with isocenter at center-of-geometric (COG), center-of-volume (COV), and center-of-surface (COS). Ten patients with multiple brain metastasis targets treated with the SIMT technique was selected for evaluation. RESULTS: When the size of tumors is equal, the optimal isocenter obtained by ASA and numerical simulation coincides with COG, COV, and COS. When the size of tumors is different, the optimal isocenter is close to the large tumor. The position of COS point is closer to the optimal point than the COV point for nearly all cases. Moreover, in some cases the COS point can be approximately selected as the optimal point. The ASA algorithm can reduce the calculating time from several hours to tens of seconds for three or more tumors. Using multiple brain metastases targets, a series of volume difference and calculating time were obtained for various tumor number, tumor size, and separation distances. Compared with the margin volume with isocenter at COG, the margin volume for optimal point can be reduced by up to 27.7%. CONCLUSION: Optimal treatment isocenter selection of multiple targets with large differences could reduce the total margin volume. ASA algorithm can significantly improve the speed of finding the optimal isocenter. This method can be used for clinical isocenter selection and is useful for the protection of normal tissue nearby.

Glioblastoma in the contralateral cerebral hemisphere with previous surgery for meningioma: A case report.

RATIONALE: Meningioma and glioblastoma (GBM) are 2 common intracranial tumors with different pathophysiologies and prognoses. It is rare for these 2 kinds of tumors to occur in the same patient. Most of the similar cases reported in the literature have been treated with radiotherapy, while cases without radiotherapy are rare. In particular, GBM in the contralateral cerebral hemisphere after resection of meningioma has not been reported. PATIENT CONCERNS: We present a case of a 66-years-old man with GBM in the right temporal lobe after previous resection of a benign meningioma of the left frontal lobe without radiotherapy. DIAGNOSES: The patient was admitted to our hospital for the first time because of right upper limb weakness. Brain magnetic resonance imaging indicated a space-occupying lesion in the left frontal area. Surgical treatment was performed, and postoperative pathology confirmed a meningioma. The patient was readmitted to the hospital 3 years after surgery of the meningioma due to a new lesion of the right temporal lobe and underwent reoperation. The postoperative pathological results showed GBM. INTERVENTIONS: The patient underwent 2 operations, and the postoperative pathologies were meningioma and GBM. In addition, the patient received concurrent chemoradiotherapy and 2 cycles of temozolomide adjuvant chemotherapy. OUTCOMES: During the last 4 months of follow-up, the patient was in good condition with no recurrence of the tumor. LESSONS: The development of GBM without radiotherapy after meningioma surgery is very rare, especially at different sites, and it is necessary to accumulate relevant cases to reveal the causes of the disease and provide more evidence for the treatment of similar patients in the future.

Deriving Pulmonary Ventilation Images From Clinical 4D-CBCT Using a Deep Learning-Based Model.

Purpose: The current algorithms for measuring ventilation images from 4D cone-beam computed tomography (CBCT) are affected by the accuracy of deformable image registration (DIR). This study proposes a new deep learning (DL) method that does not rely on DIR to derive ventilation images from 4D-CBCT (CBCT-VI), which was validated with the gold-standard single-photon emission-computed tomography ventilation image (SPECT-VI). Materials and Methods: This study consists of 4D-CBCT and 99mTc-Technegas SPECT/CT scans of 28 esophagus or lung cancer patients. The scans were rigidly registered for each patient. Using these data, CBCT-VI was derived using a deep learning-based model. Two types of model input data are studied, namely, (a) 10 phases of 4D-CBCT and (b) two phases of peak-exhalation and peak-inhalation of 4D-CBCT. A sevenfold cross-validation was applied to train and evaluate the model. The DIR-dependent methods (density-change-based and Jacobian-based methods) were used to measure the CBCT-VIs for comparison. The correlation was calculated between each CBCT-VI and SPECT-VI using voxel-wise Spearman's correlation. The ventilation images were divided into high, medium, and low functional lung regions. The similarity of different functional lung regions between SPECT-VI and each CBCT-VI was evaluated using the dice similarity coefficient (DSC). One-factor ANONA model was used for statistical analysis of the averaged DSC for the different methods of generating ventilation images. Results: The correlation values were 0.02 ± 0.10, 0.02 ± 0.09, and 0.65 ± 0.13/0.65 ± 0.15, and the averaged DSC values were 0.34 ± 0.04, 0.34 ± 0.03, and 0.59 ± 0.08/0.58 ± 0.09 for the density change, Jacobian, and deep learning methods, respectively. The strongest correlation and the highest similarity with SPECT-VI were observed for the deep learning method compared to the density change and Jacobian methods. Conclusion: The results showed that the deep learning method improved the accuracy of correlation and similarity significantly, and the derived CBCT-VIs have the potential to monitor the lung function dynamic changes during radiotherapy.

Association of the VEGFR2 single nucleotide polymorphism rs2305948 with glioma risk.

BACKGROUND: Many studies have reported a relationship between the vascular endothelial growth factor receptor 2 single nucleotide polymorphism (SNP) rs2305948 and glioma, but their conclusions have been controversial. A meta-analysis was performed to assess the association between rs2305948 and glioma susceptibility. METHODS: Inclusion criteria and a strategy for screening of original literature were created. Eligible articles on the correlation between the SNP rs2305948 and glioma were identified in the PubMed, Embase, Web of Science, Cochrane Library, CNKI and Wanfang databases. After extracting the data, Stata 12. 0 software was used to perform statistical analysis under 5 genetic models and to calculate the combined odds ratio (OR) value and its 95% confidence interval (CI). RESULTS: Four case-control studies including 1595 cases and 1657 controls were entered into the study. The overall analysis showed that no obvious association existed between rs2305948 and glioma risk (allele: OR = 1.20, 95% CI = 0.93-1.54, P = .162; dominant: OR = 1.17, 95% CI = 0.93-1.46, P = .174; recessive: OR = 1.72, 95% CI = 0.94-3.15, P = .076; heterozygous: OR = 1.11, 95% CI = 0.94-1.30, P = .226; homozygous: OR = 1.74, 95% CI = 0.92-3.29, P = .088). The subgroup analysis suggested that the SNP rs2305948 was related to glioma susceptibility under allele, dominant, recessive and homozygote models in the Asian population (allele: OR = 1.34, 95% CI = 1.16-1.55, P < .001; recessive: OR = 2.24, 95% CI = 1.49-3.36, P < .001; homozygous: OR = 2.32, 95% CI = 1.54-3.50, P < .001). CONCLUSION: The vascular endothelial growth factor receptor 2 rs2305948 gene polymorphism may be related to glioma susceptibility in the Asian population. However, the association is not clear in non-Asian populations, for which there has been less research.

Longitudinal Grouping of Target Volumes for Volumetric-Modulated Arc Therapy of Multiple Brain Metastases.

PURPOSE: Treatment of multiple brain metastases with single-isocenter volumetric modulated arc therapy causes unnecessary exposure to normal brain tissue. In this study, a longitudinal grouping method was developed to reduce such unnecessary exposure. MATERIALS AND METHODS: This method has two main aspects: grouping brain lesions longitudinally according to their longitudinal projection positions in beam's eye view, and rotating the collimator to 90° to make the multiple leaf collimator leaves conform to the targets longitudinally group by group. For 11 patients with multiple (5-30) brain metastases, two single-isocenter volumetric modulated arc therapy plans were generated using a longitudinal grouping strategy (LGS) and the conventional strategy (CVS). The prescription dose was 52 Gy for 13 fractions. Dose normalization to 100% of the prescription dose in 95% of the planning target volume was adopted. For plan quality comparison, Paddick conformity and the gradient index of the planning target volume, and the mean dose, the V100%, V50%, V25%, and V10% volumes of normal brain tissue were calculated. RESULTS: There were no significant differences between the LGS and CVS plans in Paddick conformity (p = 0.374) and the gradient index (p = 0.182) of the combined planning target volumes or for V100% (p = 0.266) and V50% (p = 0.155) of the normal brain. However, the V25% and V10% of the normal brain which represented the low-dose region were significantly reduced in the LGS plans (p = 0.004 and p = 0.003, respectively). Consistently, the mean dose of the entire normal brain was 12.04 and 11.17 Gy in the CVS and LGS plans, respectively, a significant reduction in the LGS plans (p = 0.003). CONCLUSIONS: The longitudinal grouping method can decrease unnecessary exposure and reduces the low-dose range in normal brain tissue.

Personalized setting of plan parameters using feasibility dose volume histogram for auto-planning in Pinnacle system.

PURPOSE: The personalized setting of plan parameters in the Auto-Planning module of the Pinnacle treatment planning system (TPS) using the PlanIQ feasibility tool was evaluated for lung cancer conventional fractionated radiotherapy (CFRT). MATERIALS AND METHOD: We reviewed the records of ten patients with lung cancer who were treated with volumetric modulated arc therapy (VMAT). Three plans were designed for each patient: the clinically accepted manual plan (MP) and two automatic plans including one generated using the generic plan parameters in technique script (AP1) and the other generated using personalized plan parameters derived based on feasibility dose volume histogram (FDVH) in PlanIQ (AP2). The plans were assessed according to the dosimetric parameters, monitor units, and planning time. A plan quality metric (PQM) was defined according to the clinical requirements for plan assessment. RESULTS: AP2 achieved better lung sparing than AP1 and MP. The PQM value of AP2 (52.5 ± 14.3) was higher than those of AP1 (49.2 ± 16.2) and MP (44.8 ± 16.9) with P < 0.05. The monitor units of AP2 (585.9 ± 142.9 MU) was higher than that of AP1 (511.1 ± 136.5 MU) and lower than that of MP (632.8 ± 143.8 MU) with p < 0.05. The planning time of AP2 (33.2 ± 4.8 min) was slightly higher than that of AP1 (28.2 ± 4.0 min) and substantially lower than that of MP (72.9 ± 28.5 min) with P < 0.05. CONCLUSIONS: The Auto-Planning module of the Pinnacle system using personalized plan parameters suggested by the PlanIQ Feasibility tool provides superior quality for lung cancer plans, especially in terms of lung sparing. The time consumption of Auto-Planning was slightly higher with the personalized parameters compared to that with the generic parameters, but significantly lower than that for the manual plan.

A practical method for predicting patient-specific collision in radiotherapy.

PURPOSE: To develop a practical method for predicting patient-specific collision during the treatment planning process. MATERIALS AND METHOD: Based on geometry information of the accelerator gantry and the location of plan isocenter, the collision-free space region could be determined. In this study, collision-free space region was simplified as a cylinder. Radius of cylinder was equal to the distance from isocenter to the collimator cover. The collision-free space was converted and imported into treatment planning system (TPS) in the form of region of interest (ROI) which was named as ROISS. Collision was viewed and evaluated on the fusion images of patient's CT and ROIs in TPS. If any points of patient's body or couch fell beyond the safety space, collision would occur. This method was implemented in the Pinnacle TPS. The impact of safety margin on accuracy was also discussed. Sixty-five plans of clinical patients were chosen for the clinical validation. RESULTS: When the angle of couch is zero, the ROISS displays as a series of circles on the cross section of the patient's CT. When the couch angle is not zero, ROISS is a series of ellipses in the transverse view of patient's CT. The ROISS can be generated quickly within five seconds after a single mouse click in TPS. Adding safety margin is an effective measure in preventing collisions from being undetected. Safety margin could increase negative predictive value (NPV) of test cases. Accuracy obtained was 96.3% with the 3 cm safety margin with 100% true positive collision detection. CONCLUSION: This study provides a reliable, accurate, and fast collision prediction during the treatment planning process. Potential collisions can be discovered and prevented early before delivering. This method can integrate with the current clinical workflow without any additional required resources, and contribute to improvement in the safety and efficiency of the clinic.

A longitudinal evaluation of improvements in treatment plan quality for lung cancer with volumetric modulated arc therapy.

PURPOSE: To investigate planning time and number of optimizations in routine clinical lung cancer plans based on the plan quality improvements following each optimization. MATERIALS AND METHOD: We selected 40 patients with lung cancer who were treated with conventional fractionated radiotherapy (CFRT). The 40 plans (divided into two groups with one or two target volumes) were completed by 9 planners using volumetric modulated arc therapy (VMAT). A planning strategy, including technique script for each group and a planning process for data collection, was introduced. The total planning time, number of optimizations, and dose-volume parameters of each plan were recorded and analyzed. A plan quality metric (PQM) was defined according to the clinical constraints. Statistical analysis of parameters of each plan following each optimization was performed for evaluating improvements in plan quality. RESULTS: According to the clinical plans generated by different planners, the median number of optimizations of each group was 4, and the median planning time was approximately 1 h (68.6 min and 62.0 min for plans with one or two target volumes, respectively). The dose deposited in organs at risk (OARs) gradually decreased, and the PQM values gradually improved following each optimization. The improvements were significant only between adjacent optimizations from the first optimization (Opt1) to the third optimization (Opt3). CONCLUSION: Increasing the number of optimizations was associated with significantly improved sparing of OARs with slight effects on the dose coverage and homogeneity of target volume. Generally, based on the designed planning strategy, there was no significant improvement of the plan quality for more than three optimizations.

Bovine Lactoferrin Quantification in Dairy Products by a Simple Immunoaffinity Magnetic Purification Method Coupled with High-Performance Liquid Chromatography with Fluorescence Detection.

This study described a simple, specific, and sensitive method using immunoaffinity magnetic purification coupled with high-performance liquid chromatography-fluorescence (HPLC-FL) detection for determination of bovine lactoferrin (bLf) in dairy products. BLf was selectively extracted from dairy products using immunoaffinity beads and then detected by HPLC-FL with its intrinsic fluorescence. During the analysis, standard solutions of bLf were pretreated with Tween 20, an anti-adsorptive agent, for blocking the nonspecific binding of bLf to polypropylene tubes. The calibration curve was linear over the range of 0.8-30 µg mL-1. The validated method was successfully applied to measure bLf at the intact level in dairy products.

A deep learning method for producing ventilation images from 4DCT: First comparison with technegas SPECT ventilation.

PURPOSE: The purpose of this study is to develop a deep learning (DL) method for producing four-dimensional computed tomography (4DCT) ventilation imaging and to evaluate the accuracy of the DL-based ventilation imaging against single-photon emission-computed tomography (SPECT) ventilation imaging (SPECT-VI). The performance of the DL-based method is assessed by comparing with density change- and Jacobian-based (HU and JAC) methods. MATERIALS AND METHODS: Fifty patients with esophagus or lung cancer who underwent thoracic radiotherapy were enrolled in this study. For each patient, 4DCT scans paired with 99mTc-Technegas SPECT/CT were acquired before the first radiotherapy treatment. 4DCT and SPECT/CT were first rigidly registered using MIMvista and converted to data matrix using MATLAB, and then transferred to a DL model based on U-net for correlating 4DCT features and SPECT-VI. Two forms of 4DCT dataset [(a) ten phases and (b) two phases of peak-exhalation and peak-inhalation] as input are studied. Tenfold cross-validation procedure was used to evaluate the performance of the DL model. For comparative evaluation, HU and JAC methodologies are used to calculate specific ventilation imaging based on 4DCT (CTVI) for each patient. The voxel-wise Spearman's correlation was evaluated over the whole lung between each of CTVI and corresponding SPECT-VI. The SPECT-VI and produced CTVIs were segmented into high, median, and low functional lung (HFL, MFL, and LFL) regions. The spatial overlap of corresponding HFL, MFL, and LFL for each CTVI against SPECT-VI was also evaluated using the dice similarity coefficient (DSC). The averaged DSC of functional lung regions was calculated and statistically analyzed with a one-factor ANONA model among different methods. RESULTS: The voxel-wise Spearman rs values were (0.22 ± 0.31), (-0.09 ± 0.18), and (0.73 ± 0.16)/(0.71 ± 0.17) for the CTVIHU , CTVIJAC , and CTVIDL(1) /CTVIDL(2) . These results showed the DL method yielded the strongest correlation with SPECT-VI. Using the DSC as the spatial overlap metric, we found that the CTVIHU , CTVIJAC , and CTVIDL(1) /CTVIDL(2) methods achieved averaged DSC values for all patients to be (0.45 ± 0.08), (0.33 ± 0.04), and (0.73 ± 0.09)/(0.71 ± 0.09), respectively. The results demonstrated that the DL method yielded the highest similarity with SPECT-VI with the prominently significant difference (P < 10-7 ). CONCLUSIONS: This study developed a DL method for producing CTVI and performed a validation against SPECT-VI. The results demonstrated that DL method can derive CTVI with greatly improved accuracy in comparison to HU and JAC methods. The produced ventilation images can be more accurate and useful for lung functional avoidance radiotherapy and treatment response modeling.

A study of nonuniform CTV to PTV margin expansion incorporating both rotational and translational uncertainties.

PURPOSE: In this work, we implemented a method to obtain a nonuniform clinical target volume (CTV) to planning target volume (PTV) margin caused by both rotational and translational uncertainties and evaluated it in the treatment planning system (TPS). MATERIALS AND METHOD: Based on a previously published statistical model, the relationship between a target margin and the distance d (from isocenter to target point), setup uncertainties, and significance level was established. For a single CTV, it can be thought as a combination of many small volume elements or target points. The margin of each point could be obtained using the suggested statistical model. The whole nonuniform CTV-PTV margin was determined by the union of all possible margins of the CTV boundary points. This method was implemented in the Pinnacle3 treatment planning system and compared with uniform margin algorithm. Ten vertebral metastases targets and multiple brain metastases targets were chosen for evaluation. RESULTS: The combined CTV-PTV margin as a function of d for various initial translational margin and rotational uncertainties was calculated. The combined margin increases as d, rotational uncertainties and translational margin increase. For the same rotational uncertainty, a smaller initial translational margin requires a larger rotational margin to compensate for the rotational error. Compared with the uniform margin algorithm, the advantage of this method is that it could minimize the PTVs volume for given CTVs to obtain same significance level. Using vertebral metastases targets and multiple brain metastases targets, a series of volume difference was obtained for various translational margins and rotational uncertainties. The volume difference of PTV could be more than 17% when translational margin is 2 mm and rotational uncertainty is 1.4°. CONCLUSION: Nonuniform margin algorithm could avoid excessive compensation for the CTV boundary points near isocenter. This method could be used for clinical margin determination and might be useful for the protection of risk organs.

Deep Learning Improved Clinical Target Volume Contouring Quality and Efficiency for Postoperative Radiation Therapy in Non-small Cell Lung Cancer.

Purpose: To investigate whether a deep learning-assisted contour (DLAC) could provide greater accuracy, inter-observer consistency, and efficiency compared with a manual contour (MC) of the clinical target volume (CTV) for non-small cell lung cancer (NSCLC) receiving postoperative radiotherapy (PORT). Materials and Methods: A deep dilated residual network was used to achieve the effective automatic contour of the CTV. Eleven junior physicians contoured CTVs on 19 patients by using both MC and DLAC methods independently. Compared with the ground truth, the accuracy of the contour was evaluated by using the Dice coefficient and mean distance to agreement (MDTA). The coefficient of variation (CV) and standard distance deviation (SDD) were rendered to measure the inter-observer variability or consistency. The time consumed for each of the two contouring methods was also compared. Results: A total of 418 CTV sets were generated. DLAC improved contour accuracy when compared with MC and was associated with a larger Dice coefficient (mean ± SD: 0.75 ± 0.06 vs. 0.72 ± 0.07, p < 0.001) and smaller MDTA (mean ± SD: 2.97 ± 0.91 mm vs. 3.07 ± 0.98 mm, p < 0.001). The DLAC was also associated with decreased inter-observer variability, with a smaller CV (mean ± SD: 0.129 ± 0.040 vs. 0.183 ± 0.043, p < 0.001) and SDD (mean ± SD: 0.47 ± 0.22 mm vs. 0.72 ± 0.41 mm, p < 0.001). In addition, a value of 35% of time saving was provided by the DLAC (median: 14.81 min vs. 9.59 min, p < 0.001). Conclusions: Compared with MC, the DLAC is a promising strategy to obtain superior accuracy, consistency, and efficiency for the PORT-CTV in NSCLC.

Availability of a simplified lung ventilation imaging algorithm based on four-dimensional computed tomography.

PURPOSE: It is still not conclusive which four-dimensional computed tomography (4DCT)-based ventilation imaging algorithm is most accurate and efficient. In this study, we proposed a simplified algorithm (VIAAVG) which only requires the average computed tomography (AVG CT) as input, and quantitatively compared its accuracy and efficiency with three other popular algorithms. MATERIAL AND METHODS: Fifty patients with lung or esophageal cancer who underwent radiotherapy were enrolled. Single photon emission computed tomography (SPECT) ventilation images (VI-SPECT) and 4DCT were acquired 1-3 days before the first treatment session. The end of exhalation and the end of inhalation CT were registered to derive deformable vector field (DVF) using MIMvista. 4DCT-based ventilation images (CTVI) were first calculated respectively by means of four algorithms (VIAJAC, VIAHU, VIAPRO and VIAAVG). The computation times were compared using paired t-test. The corresponding CTVIs (CTVIJAC, CTVIHU, CTVIPRO and CTVIAVG) and VI-SPECT were segmented into three equal sub-volumes (high, medium and low function lung, respectively) after smoothing and normalization. The Dice Similarity Coefficients (DSCs) were calculated for each sub-volume between each CTVI and VI-SPECT. The average DSCs for high, medium and low function lung in different CTVIs for each patient were compared using paired t-test. RESULTS: The mean DSCs for CTVIJAC, CTVIHU, CTVIPRO and CTVIAVG were 0.3255, 0.4465, 0.5865 and 0.5958, respectively. The average computation times for CTVIJAC, CTVIHU, CTVIPRO and CTVIAVG were 18.3 s, 24.2 s, 144.8 s and 15.0 s. CONCLUSION: VIAAVG is available for clinical use because of its high accuracy, improved efficiency and less input requirement compared to the other algorithms.

Polypyrrole-Modified Nylon 6 Nanofibers as Adsorbent for the Extraction of Two ß-Lactam Antibiotics in Water Followed by Determination with Capillary Electrophoresis.

A solid phase membrane adsorbent-a nylon 6 nanofibers membrane coated by polypyrrole (PPy-PA6-NFsM)-was firstly synthesized and used for extraction of two ß-lactam antibiotics (oxacillin and cloxacillin) in urban river water. Then the analytes were detected by capillary electrophoresis with a diode array detector (CE-DAD). The synthesized nanofibers membrane was characterized by scanning electron microscopy and a Fourier transform infrared spectrometer. The experimental conditions were optimized, including the amount used of PPy-PA6-NFsM, pH of the sample solutions, adsorption volume, and desorption conditions. Under the optimal extraction and separation conditions, the detection limits were found to be 2.0 ng/mL for both oxacillin and cloxacillin. The proposed method was applied to the determination of the two ß-lactams in water samples of an urban river. The recoveries of these two ß-lactams were found to be in the range 84.2-96.4%, demonstrating that PPy-PA6-NFsM has a high extraction capability for these two antibiotics. The relative standard deviations, ranging from 2.26% to 5.29% for intraday measurements and from 2.38% to 7.02% for inter-day determinations, were derived respectively.

A deep learning method for prediction of three-dimensional dose distribution of helical tomotherapy.

PURPOSE: To develop a deep learning method for prediction of three-dimensional (3D) voxel-by-voxel dose distributions of helical tomotherapy (HT). METHODS: Using previously treated HT plans as training data, a deep learning model named U-ResNet-D was trained to predict a 3D dose distribution. First, the contoured structures and dose volumes were converted from plan database to 3D matrix with a program based on a developed visualization toolkit (VTK), then transferred to U-ResNet-D for correlating anatomical features and dose distributions at voxel-level. One hundred and ninety nasopharyngeal cancer (NPC) patients treated by HT with multiple planning target volumes (PTVs) in different prescription patterns were studied. The model was typically trained from scratch with weights randomly initialized rather than using transfer-learning method, and used to predict new patient's 3D dose distributions. The predictive accuracy was evaluated with three methods: (a) The dose difference at the position r, δ(r, r) = Dc (r) - Dp (r), was calculated for each voxel. The mean (µÎ´(r,r) ) and standard deviation (σδ(r,r) ) of δ(r, r) were calculated to assess the prediction bias and precision; (b) The mean absolute differences of dosimetric indexes (DIs) including maximum and mean dose, homogeneity index, conformity index, and dose spillage for PTVs and organ at risks (OARs) were calculated and statistically analyzed with the paired-samples t test; (c) Dice similarity coefficients (DSC) between predicted and clinical isodose volumes were calculated. RESULTS: The U-ResNet-D model predicted 3D dose distribution accurately. For twenty tested patients, the prediction bias ranged from -2.0% to 2.3% and prediction error varied from 1.5% to 4.5% (relative to prescription) for 3D dose differences. The mean absolute dose differences for PTVs and OARs are within 2.0% and 4.2%, and nearly all the DIs for PTVs and OARs had no significant differences. The averaged DSC ranged from 0.95 to 1 for different isodose volumes. CONCLUSIONS: The study developed a new deep learning method for 3D voxel-by-voxel dose prediction, and shown to be able to produce accurately dose predictions for nasopharyngeal patients treated by HT. The predicted 3D dose map can be useful for improving radiotherapy planning design, ensuring plan quality and consistency, making clinical technique comparison, and guiding automatic treatment planning.

MAP30 promotes apoptosis of U251 and U87 cells by suppressing the LGR5 and Wnt/ß-catenin signaling pathway, and enhancing Smac expression.

Significant antitumor activity of Momordica anti-human immunodeficiency virus protein of 30 kDa (MAP30) purified from Momordica charantia has been the subject of previous research. However, the effective mechanism of MAP30 on malignant glioma cells has not yet been clarified. The aim of the present study was to investigate the effects and mechanism of MAP30 on U87 and U251 cell lines. A Cell Counting Kit-8 assay, wound healing assay and Transwell assay were used to detect the effects on U87 and U251 cells treated with different concentrations of MAP30 (0.5, 1, 2, 4, 8 and 16 µM) over different periods of time. Proliferation, migration and invasion of each cell line were markedly inhibited by MAP30 in a dose- and time-dependent manner. Flow cytometry and fluorescence staining demonstrated that apoptosis increased and the cell cycle was arrested in S-phase in the two investigated cell lines following MAP30 treatment. Western blot analysis demonstrated that leucine-rich-repeat-containing G-protein-coupled receptor 5 (LGR5) expression and key proteins in the Wnt/ß-catenin signaling pathway were apparently decreased, whereas second mitochondria-derived activator of caspase (Smac) protein expression significantly increased with MAP30 treatment in the same manner. These results suggest that MAP30 markedly induces apoptosis in U87 and U251 cell lines by suppressing LGR5 and the Wnt/ß-catenin signaling pathway, and enhancing Smac expression in a dose- and time-dependent manner.