Automated contouring, treatment planning, and quality assurance for VMAT craniospinal irradiation (VMAT-CSI).

Purpose: Create a comprehensive automated solution for pediatric and adult VMAT-CSI including contouring, planning, and plan check to reduce planning time and improve plan quality. Methods: Seventy-seven previously treated CSI patients (age, 2-67 years) were used for creation of an auto-contouring model to segment 25 organs at risk (OARs). The auto-contoured OARs were evaluated using the Dice Similarity Coefficient (DSC), 95% Hausdorff Distance (HD95), and a qualitative ranking by one physician and one physicist (scale: 1-acceptable, 2-minor edits, 3-major edits). The auto-planning script was developed using the Varian Eclipse Scripting API and tested with 20 patients previously treated with either low-dose VMAT-CSI (12 Gy) or high-dose VMAT-CSI (36 Gy + 18 Gy boost). Clinically relevant metrics, planning time, and blinded physician review were used to evaluate significance of differences between the auto and manual plans. Finally, the plan preparation for treatment and plan check processes were automated to improve efficiency and safety of VMAT-CSI. Results: The auto-contours achieved an average DSC of 0.71 ± 0.15, HD95 of 4.81 ± 4.68, and reviewers' ranking of 1.22 ± 0.39, indicating close to "acceptable-as-is" contours. Compared to the manual CSI plans, the auto-plans for both dose regimens achieved statistically significant reductions in body V50% and Dmean for parotids, submandibular, and thyroid glands. The variance in the dosimetric parameters decreased for the auto-plans as compared to the manual plans indicating better plan consistency. From the blinded review, the auto-plans were marked as equivalent or superior to the manual-plans 88.3% of the time. The required time for the auto-contouring and planning was consistently between 1-2 hours compared to an estimated 5-6 hours for manual contouring and planning. Conclusions: Reductions in contouring and planning time without sacrificing plan quality were obtained using the developed auto-planning process. The auto-planning scripts and documentation will be made freely available to other institutions and clinics.

Adaptive Region-Specific Loss for Improved Medical Image Segmentation.

Defining the loss function is an important part of neural network design and critically determines the success of deep learning modeling. A significant shortcoming of the conventional loss functions is that they weight all regions in the input image volume equally, despite the fact that the system is known to be heterogeneous (i.e., some regions can achieve high prediction performance more easily than others). Here, we introduce a region-specific loss to lift the implicit assumption of homogeneous weighting for better learning. We divide the entire volume into multiple sub-regions, each with an individualized loss constructed for optimal local performance. Effectively, this scheme imposes higher weightings on the sub-regions that are more difficult to segment, and vice versa. Furthermore, the regional false positive and false negative errors are computed for each input image during a training step and the regional penalty is adjusted accordingly to enhance the overall accuracy of the prediction. Using different public and in-house medical image datasets, we demonstrate that the proposed regionally adaptive loss paradigm outperforms conventional methods in the multi-organ segmentations, without any modification to the neural network architecture or additional data preparation.

Patient-Specific Auto-segmentation on Daily kVCT Images for Adaptive Radiation Therapy.

PURPOSE: This study explored deep-learning-based patient-specific auto-segmentation using transfer learning on daily RefleXion kilovoltage computed tomography (kVCT) images to facilitate adaptive radiation therapy, based on data from the first group of patients treated with the innovative RefleXion system. METHODS AND MATERIALS: For head and neck (HaN) and pelvic cancers, a deep convolutional segmentation network was initially trained on a population data set that contained 67 and 56 patient cases, respectively. Then the pretrained population network was adapted to the specific RefleXion patient by fine-tuning the network weights with a transfer learning method. For each of the 6 collected RefleXion HaN cases and 4 pelvic cases, initial planning computed tomography (CT) scans and 5 to 26 sets of daily kVCT images were used for the patient-specific learning and evaluation separately. The performance of the patient-specific network was compared with the population network and the clinical rigid registration method and evaluated by the Dice similarity coefficient (DSC) with manual contours being the reference. The corresponding dosimetric effects resulting from different auto-segmentation and registration methods were also investigated. RESULTS: The proposed patient-specific network achieved mean DSC results of 0.88 for 3 HaN organs at risk (OARs) of interest and 0.90 for 8 pelvic target and OARs, outperforming the population network (0.70 and 0.63) and the registration method (0.72 and 0.72). The DSC of the patient-specific network gradually increased with the increment of longitudinal training cases and approached saturation with more than 6 training cases. Compared with using the registration contour, the target and OAR mean doses and dose-volume histograms obtained using the patient-specific auto-segmentation were closer to the results using the manual contour. CONCLUSIONS: Auto-segmentation of RefleXion kVCT images based on the patient-specific transfer learning could achieve higher accuracy, outperforming a common population network and clinical registration-based method. This approach shows promise in improving dose evaluation accuracy in RefleXion adaptive radiation therapy.

MR to ultrasound image registration with segmentation-based learning for HDR prostate brachytherapy.

PURPOSE: Propagation of contours from high-quality magnetic resonance (MR) images to treatment planning ultrasound (US) images with severe needle artifacts is a challenging task, which can greatly aid the organ contouring in high dose rate (HDR) prostate brachytherapy. In this study, a deep learning approach was developed to automatize this registration procedure for HDR brachytherapy practice. METHODS: Because of the lack of training labels and difficulty of accurate registration from inferior image quality, a new segmentation-based registration framework was proposed for this multi-modality image registration problem. The framework consisted of two segmentation networks and a deformable registration network, based on the weakly -supervised registration strategy. Specifically, two 3D V-Nets were trained for the prostate segmentation on the MR and US images separately, to generate the weak supervision labels for the registration network training. Besides the image pair, the corresponding prostate probability maps from the segmentation were further fed to the registration network to predict the deformation matrix, and an augmentation method was designed to randomly scale the input and label probability maps during the registration network training. The overlap between the deformed and fixed prostate contours was analyzed to evaluate the registration accuracy. Three datasets were collected from our institution for the MR and US image segmentation networks, and the registration network learning, which contained 121, 104, and 63 patient cases, respectively. RESULTS: The mean Dice similarity coefficient (DSC) results of the two prostate segmentation networks are 0.86 ± 0.05 and 0.90 ± 0.03, for MR images and the US images after the needle insertion, respectively. The mean DSC, center-of-mass (COM) distance, Hausdorff distance (HD), and averaged symmetric surface distance (ASSD) results for the registration of manual prostate contours were 0.87 ± 0.05, 1.70 ± 0.89 mm, 7.21 ± 2.07 mm, 1.61 ± 0.64 mm, respectively. By providing the prostate probability map from the segmentation to the registration network, as well as applying the random map augmentation method, the evaluation results of the four metrics were all improved, such as an increase in DSC from 0.83 ± 0.08 to 0.86 ± 0.06 and from 0.86 ± 0.06 to 0.87 ± 0.05, respectively. CONCLUSIONS: A novel segmentation-based registration framework was proposed to automatically register prostate MR images to the treatment planning US images with metal artifacts, which not only largely saved the labor work on the data preparation, but also improved the registration accuracy. The evaluation results showed the potential of this approach in HDR prostate brachytherapy practice.

Automated contour propagation of the prostate from pCT to CBCT images via deep unsupervised learning.

PURPOSE: To develop and evaluate a deep unsupervised learning (DUL) framework based on a regional deformable model for automated prostate contour propagation from planning computed tomography (pCT) to cone-beam CT (CBCT). METHODS: We introduce a DUL model to map the prostate contour from pCT to on-treatment CBCT. The DUL framework used a regional deformable model via narrow-band mapping to augment the conventional strategy. Two hundred and fifty-one anonymized CBCT images from prostate cancer patients were retrospectively selected and divided into three sets: 180 were used for training, 12 for validation, and 59 for testing. The testing dataset was divided into two groups. Group 1 contained 50 CBCT volumes, with one physician-generated prostate contour on CBCT image. Group 2 contained nine CBCT images, each including prostate contours delineated by four independent physicians and a consensus contour generated using the STAPLE method. Results were compared between the proposed DUL and physician-generated contours through the Dice similarity coefficients (DSCs), the Hausdorff distances, and the distances of the center-of-mass. RESULTS: The average DSCs between DUL-based prostate contours and reference contours for test data in group 1 and group 2 consensus were 0.83 ± 0.04, and 0.85 ± 0.04, respectively. Correspondingly, the mean center-of-mass distances were 3.52 mm ± 1.15 mm, and 2.98 mm ± 1.42 mm, respectively. CONCLUSIONS: This novel DUL technique can automatically propagate the contour of the prostate from pCT to CBCT. The proposed method shows that highly accurate contour propagation for CBCT-guided adaptive radiotherapy is achievable via the deep learning technique.

Automatic intraprostatic lesion segmentation in multiparametric magnetic resonance images with proposed multiple branch UNet.

PURPOSE: Contouring intraprostatic lesions is a prerequisite for dose-escalating these lesions in radiotherapy to improve the local cancer control. In this study, a deep learning-based approach was developed for automatic intraprostatic lesion segmentation in multiparametric magnetic resonance imaging (mpMRI) images contributing to clinical practice. METHODS: Multiparametric magnetic resonance imaging images from 136 patient cases were collected from our institution, and all these cases contained suspicious lesions with Prostate Imaging Reporting and Data System (PI-RADS) score ≥ 4. The contours of the lesion and prostate were manually created on axial T2-weighted (T2W), apparent diffusion coefficient (ADC) and high b-value diffusion-weighted imaging (DWI) images to provide the ground truth data. Then a multiple branch UNet (MB-UNet) was proposed for the segmentation of an indistinct target in multi-modality MRI images. An encoder module was designed with three branches for the three MRI modalities separately, to fully extract the high-level features provided by different MRI modalities; an input module was added by using three sub-branches for three consecutive image slices, to consider the contour consistency among different image slices; deep supervision strategy was also integrated into the network to speed up the convergency of the network and improve the performance. The probability maps of the background, normal prostate and lesion were output by the network to generate the segmentation of the lesion, and the performance was evaluated using the dice similarity coefficient (DSC) as the main metric. RESULTS: A total of 162 lesions were contoured on 652 image slices, with 119 lesions in the peripheral zone, 38 in the transition zone, four in the central zone and one in the anterior fibromuscular stroma. All prostates were also contoured on 1,264 image slices. As for the segmentation of lesions in the testing set, MB-UNet achieved a per case DSC of 0.6333, specificity of 0.9993, sensitivity of 0.7056; and global DSC of 0.7205, specificity of 0.9993, sensitivity of 0.7409. All the three deep learning strategies adopted in this study contributed to the performance promotion of the MB-UNet. Missing the DWI modality would degrade the segmentation performance more markedly compared with the other two modalities. CONCLUSIONS: A deep learning-based approach with proposed MB-UNet was developed to automatically segment suspicious lesions in mpMRI images. This study makes it feasible to adopt boosting intraprostatic lesions in clinical practice to achieve better outcomes.

Glass 3D printing of microfluidic pressure sensor interrogated by fiber-optic refractometry.

This letter reports a novel fused silica microfluidic device with pressure sensing capability that is fabricated by integrated additive and subtractive manufacturing (IASM) method. The sensor consists of a capillary and a 3D printed glass reservoir, where the reservoir volume change under pressure manifests liquid level deviation inside the capillary, thus realizing the conversion between small pressure change into large liquid level variation. Thanks to the design flexibility of this unique IASM method, the proposed microfluidic device is fabricated with liquid-in-glass thermometer configuration, where the reservoir is sealed following a novel 3D printing assisted glass bonding process. And liquid level is interrogated by a fiber-optic sensor based on multimode interference (MMI) effect. This proposed microfluidic device is attractive for chemical and biomedical sensing because it is flexible in design, and maintains good chemical and mechanical stability, and adjustable sensitivity and range.

One-dimensional sensor learns to sense three-dimensional space.

A sensor system with ultra-high sensitivity, high resolution, rapid response time, and a high signal-to-noise ratio can produce raw data that is exceedingly rich in information, including signals that have the appearances of "noise". The "noise" feature directly correlates to measurands in orthogonal dimensions, and are simply manifestations of the off-diagonal elements of 2nd-order tensors that describe the spatial anisotropy of matter in physical structures and spaces. The use of machine learning techniques to extract useful meanings from the rich information afforded by ultra-sensitive one-dimensional sensors may offer the potential for probing mundane events for novel embedded phenomena. Inspired by our very recent invention of ultra-sensitive optical-based inclinometers, this work aims to answer a transformative question for the first time: can a single-dimension point sensor with ultra-high sensitivity, fidelity, and signal-to-noise ratio identify an arbitrary mechanical impact event in three-dimensional space? This work is expected to inspire researchers in the fields of sensing and measurement to promote the development of a new generation of powerful sensors or sensor networks with expanded functionalities and enhanced intelligence, which may provide rich n-dimensional information, and subsequently, data-driven insights into significant problems.

Information integrated glass module fabricated by integrated additive and subtractive manufacturing.

In this Letter, we report a novel integrated additive and subtractive manufacturing (IASM) method to fabricate an information integrated glass module. After a certain number of glass layers are 3D printed and sintered by direct ${{\rm CO}_2}$CO2 laser irradiation, a microchannel will be fabricated on top of the printed glass by integrated picosecond laser, for intrinsic Fabry-Perot interferometer (IFPI) optical fiber sensor embedment. Then, the glass 3D printing process continues for the realization of bonding between optical fiber and printed glass. Temperature sensing up to 1000°C was demonstrated using the fabricated information integrated module. In addition, the long-term stability of the glass module at 1000°C was conducted. Enhanced sensor structure robustness and harsh temperature sensing capability make this glass module attractive for harsh environment structural health monitoring.

The microdosimetric extension in TOPAS: development and comparison with published data.

Microdosimetric energy depositions have been suggested as a key variable for the modeling of the relative biological effectiveness (RBE) in proton and ion radiation therapy. However, microdosimetry has been underutilized in radiation therapy. Recent advances in detector technology allow the design of new mico- and nano-dosimeters. At the same time Monte Carlo (MC) simulations have become more widely used in radiation therapy. In order to address the growing interest in the field, a microdosimetric extension was developed in TOPAS. The extension provides users with the functionality to simulate microdosimetric spectra as well as the contribution of secondary particles to the spectra, calculate microdosimetric parameters, and determine RBE with a biological weighting function approach or with the microdosimetric kinetic (MK) model. Simulations were conducted with the extension and the results were compared with published experimental data and other simulation results for three types of microdosimeters, a spherical tissue equivalent proportional counter (TEPC), a cylindrical TEPC and a solid state microdosimeter. The corresponding microdosimetric spectra obtained with TOPAS from the plateau region to the distal tail of the Bragg curve generally show good agreement with the published data.

Characterization of DNPH-coated microreactor chip for analysis of trace carbonyls with application for breath analysis.

The analysis of trace carbonyls including aldehydes and ketones is important for monitoring environmental air quality, determining toxicity of aerosol of electronic cigarette, and detecting diseases by breath analysis. This work reports investigation of a single microreactor chip with HClO4-acidified DNPH coating for capture and analysis of carbonyls in air and exhaled breath. Three aldehydes and three ketones were spiked into one liter synthetic air in Tedlar bags serving as gaseous carbonyl standard for characterization of capture efficiency (CE). The HClO4-acidified DNPH showed higher CE of carbonyls than conventionally-used acid including H3PO4 and H2SO4 acidified DNPH under the microreactor conditions. The microreactor conditions including HClO4 to DNPH molar ratio, DNPH to carbonyls molar ratio, and gaseous sample flow rate through the microreactor were studied in detail and thereby optimized. Under the optimized conditions, 100% of CEs for aldehydes and above 80% for ketones were obtained. The microreactor chips were applied to determine acetone concentration in exhaled breath.

Continuous flow synthesis of a pharmaceutical intermediate: a computational fluid dynamics approach.

Continuous flow chemistry has the potential to greatly improve efficiency in the synthesis of active pharmaceutical ingredients (APIs); however, the optimization of these processes can be complicated by a large number of variables affecting reaction success. In this work, a screening design of experiments was used to compare computational fluid dynamics (CFD) simulations with experimental results. CFD simulations and experimental results both identified the reactor residence time and reactor temperature as the most significant factors affecting product yield for this reaction within the studied design space. A point-to-point comparison of the results showed absolute differences in product yield as low as 2.4% yield at low residence times and up to 19.1% yield at high residence times with strong correlation between predicted and experimental percent yields. CFD was found to underestimate the product yields at low residence times and overestimate at higher residence times. The correlation in predicted product yield and the agreement in identifying significant factors in reaction performance reveals the utility of CFD as a valuable tool in the design of continuous flow tube reactors with significantly reduced experimentation.

3D printing of all-glass fiber-optic pressure sensor for high temperature applications.

In this paper, we report a fiber-optic pressure sensor fabricated by three-dimensional (3D) printing of glass using direct laser melting method. An all-glass fiber-housing structure is 3D printed on top of a fused silica substrate, which also serves as the pressure sensing diaphragm. And an optical fiber can be inserted inside the fiber housing structure and brought in close proximity to the diaphragm to form a Fabry-Perot interferometer. The theoretical analysis and experimental verification of the pressure sensing capability are presented.

Modelling of Cellular Survival Following Radiation-Induced DNA Double-Strand Breaks.

A mechanistic model of cellular survival following radiation-induced DNA double-strand breaks (DSBs) was proposed in this study. DSBs were assumed as the initial lesions in the DNA of the cell nucleus induced by ionizing radiation. The non-homologous end-joining (NHEJ) pathway was considered as the domain pathway of DSB repair in mammalian cells. The model was proposed to predict the relationship between radiation-induced DSBs in nucleus and probability of cell survival, which was quantitatively described by two input parameters and six fitting parameters. One input parameter was the average number of primary particles which caused DSB, the other input parameter was the average number of DSBs yielded by each primary particle that caused DSB. The fitting parameters were used to describe the biological characteristics of the irradiated cells. By determining the fitting parameters of the model with experimental data, the model is able to estimate surviving fractions for the same type of cells exposed to particles with different physical parameters. The model further revealed the mechanism of cell death induced by the DSB effect. Relative biological effectiveness (RBE) of charged particles at different survival could be calculated with the model, which would provide reference for clinical treatment.

Impact of potentially variable RBE in liver proton therapy.

Currently, the relative biological effectiveness (RBE) is assumed to be constant with a value of 1.1 in proton therapy. Although trends of RBE variations are well known, absolute values in patients are associated with considerable uncertainties. This study aims to evaluate the impact of a variable proton RBE in proton therapy liver trials using different fractionation schemes. Sixteen liver cancer cases were evaluated assuming two clinical schedules of 40 Gy/5 fractions and 58.05 Gy/15 fractions. The linear energy transfer (LET) and physical dose distribution in patients were simulated using Monte Carlo. The variable RBE distribution was calculated using a phenomenological model, considering the influence of the LET, fraction size and α/ß value. Further, models to predict normal tissue complication probability (NTCP) and tumor control probability (TCP) were used to investigate potential RBE effects on outcome predictions. Applying the variable RBE model to the 5 and 15 fractions schedules results in an increase in mean fraction-size equivalent dose (FED) to the normal liver of 5.0% and 9.6% respectively. For patients with a mean FED to the normal liver larger than 29.8 Gy, this results in a non-negligible increase in the predicted NTCP of the normal liver averaging 11.6%, ranging from 2.7% to 25.6%. On the other hand, decrease in TCP was less than 5% for both fractionation regimens for all patients when assuming a variable RBE instead of constant. Consequently, the difference in TCP between the two fractionation schedules did not change significantly assuming a variable RBE while the impact on the NTCP difference was highly case specific. In addition, both the NTCP and TCP decrease with increasing α/ß value for both fractionation schemes, with the decreases being more pronounced when using a variable RBE compared to using RBE = 1.1. Assuming a constant RBE of 1.1 most likely overestimates the therapeutic ratio in proton therapy for liver cancer, predominantly due to underestimation of the RBE-weighted dose to the normal liver. The impact of applying a variable RBE (as compared to RBE = 1.1) on the NTCP difference of the two fractionation regimens is case dependent. A variable RBE results in a slight increase in TCP difference. Variations in patient radiosensitivity increase when using a variable RBE.

Field Validation of a Magnetic Sensor to Monitor Borehole Deviation during Tunnel Excavation.

In this article, a magnetic sensor is proposed to monitor borehole deviation during tunnel excavation. It is made by piling four super-strong N42 NdFeB cylinder magnets and then encasing them in an aluminum alloy hollow cylinder. The distribution of the magnetic field produced by the magnetic sensor and its summation with the geomagnetic field (GMF) in a global coordinate system are derived based on the theory of magnetic fields. An algorithm is developed to localize the position of the magnetic sensor. The effect of the GMF variation on the effective monitoring range of the magnetic sensor is also studied numerically. Field validation tests are conducted at Jinzhai Pumped-Storage hydroelectric power station, during the excavation of an inclined tunnel in Anhui Province of China. Test results show that the algorithm and the magnetic sensor are used successfully to detect the deviation of the borehole with an estimated error of approximately 0.5 m. The errors are mainly from the measurement errors of the coordinates, of both the test and the measurement points. The effective monitoring range of the magnetic sensor is dependent on the direction of the magnetic sensor as well as the variation of the GMF.

A hollow coaxial cable Fabry-Pérot resonator for liquid dielectric constant measurement.

We report, for the first time, a low-cost and robust homemade hollow coaxial cable Fabry-Pérot resonator (HCC-FPR) for measuring liquid dielectric constant. In the HCC design, the traditional dielectric insulating layer is replaced by air. A metal disk is welded onto the end of the HCC serving as a highly reflective reflector, and an open cavity is engineered on the HCC. After the open cavity is filled with the liquid analyte (e.g., water), the air-liquid interface acts as a highly reflective reflector due to large impedance mismatch. As a result, an HCC-FPR is formed by the two highly reflective reflectors, i.e., the air-liquid interface and the metal disk. We measured the room temperature dielectric constant for ethanol/water mixtures with different concentrations using this homemade HCC-FPR. Monitoring the evaporation of ethanol in ethanol/water mixtures was also conducted to demonstrate the ability of the sensor for continuously monitoring the change in dielectric constant. The results revealed that the HCC-FPR could be a promising evaporation rate detection platform with high performance. Due to its great advantages, such as high robustness, simple configuration, and ease of fabrication, the novel HCC-FPR based liquid dielectric constant sensor is believed to be of high interest in various fields.

Displacement and Strain Measurement up to 1000 °C Using a Hollow Coaxial Cable Fabry-Perot Resonator.

We present a hollow coaxial cable Fabry-Perot resonator for displacement and strain measurement up to 1000 °C. By employing a novel homemade hollow coaxial cable made of stainless steel as a sensing platform, the high-temperature tolerance of the sensor is dramatically improved. A Fabry-Perot resonator is implemented on this hollow coaxial cable by introducing two highly-reflective reflectors along the cable. Based on a nested structure design, the external displacement and strain can be directly correlated to the cavity length of the resonator. By tracking the shift of the amplitude reflection spectrum of the microwave resonator, the applied displacement and strain can be determined. The displacement measurement experiment showed that the sensor could function properly up to 1000 °C. The sensor was also employed to measure the thermal strain of a steel plate during the heating process. The stability of the novel sensor was also investigated. The developed sensing platform and sensing configurations are robust, cost-effective, easy to manufacture, and can be flexibly designed for many other measurement applications in harsh high-temperature environments.

Probing changes in tilt angle with 20 nanoradian resolution using an extrinsic Fabry-Perot interferometer-based optical fiber inclinometer.

In this paper, we introduce and demonstrate a novel optical fiber extrinsic Fabry-Perot interferometer (EFPI) for tilt measurements with 20 nrad resolution. Compared with in-line optical fiber inclinometers, an extrinsic sensing structure is used in the inclinometer reported herein. Our design greatly improves on the tilt angle resolution, the temperature stability, and the mechanical robustness of inclinometers with advanced designs. An EFPI cavity, which is formed between endfaces of a suspended rectangular mass block and a fixed optical fiber, is packaged inside a rectangular container box with an oscillation dampening mechanism. Importantly, the two reflectors of the EFPI sensor remain parallel while the cavity length of the EFPI sensor meters a change in tilt. According to the Fabry-Perot principle, the change in the cavity length can be determined, and the tilt angle of the inclinometer can be calculated. The sensor design and the measurement principle are discussed. An experiment based on measuring the tilt angle of a simply-supported 70-cm beam induced by a small load is presented to verify the resolution of our prototype inclinometer. The experimental results demonstrate significantly higher resolution (ca. 20 nrad) compared to commercial devices. The temperature cross-talk of the inclinometer was also investigated in a separate experiment and found to be 0.0041 µrad /°C. Our inclinometer was also employed for monitoring the daily periodic variations in the tilt angle of a windowsill in a cement building caused by local temperature changes during a five-day period. The multi-day study demonstrated excellent stability and practicability for the novel device. The significant inclinometer improvements in differential tilt angle resolution, temperature compensation, and mechanical robustness also provide unique opportunities for investigating spatial-temporal modulations of gravitational fields.

Electronic cigarette-generated aldehydes: The contribution of e-liquid components to their formation and the use of urinary aldehyde metabolites as biomarkers of exposure.

Electronic cigarettes (e-cigarette) have emerged as a popular electronic nicotine delivery system (ENDS) in the last decade. Despite the absence of combustion products and toxins such as carbon monoxide (CO) and tobacco-specific nitrosamines (TSNA), carbonyls including short-chain, toxic aldehydes have been detected in e-cigarette-derived aerosols up to levels found in tobacco smoke. Given the health concerns regarding exposures to toxic aldehydes, understanding both aldehyde generation in e-cigarette and e-cigarette exposure is critical. Thus, we measured aldehydes generated in aerosols derived from propylene glycol (PG):vegetable glycerin (VG) mixtures and from commercial e-liquids with flavorants using a state-of-the-art carbonyl trap and mass spectrometry. To track e-cigarette exposure in mice, we measured urinary metabolites of 4 aldehydes using ULPC-MS/MS or GC-MS. Aldehyde levels, regardless of abundance (saturated: formaldehyde, acetaldehyde >> unsaturated: acrolein, crotonaldehyde), were dependent on the PG:VG ratio and the presence of flavorants. The metabolites of 3 aldehydes - formate, acetate and 3-hydroxypropyl mercapturic acid (3-HPMA; acrolein metabolite) -- were increased in urine after e-cigarette aerosol and mainstream cigarette smoke (MCS) exposures, but the crotonaldehyde metabolite (3-hydroxy-1-methylpropylmercapturic acid, HPMMA) was increased only after MCS exposure. Interestingly, exposure to menthol-flavored e-cigarette aerosol increased the levels of urinary 3-HPMA and sum of nicotine exposure (nicotine, cotinine, trans-3'-hydroxycotinine) relative to exposure to a Classic Tobacco-flavored e-cigarette aerosol. Comparing these findings with aerosols of other ENDS and by measuring aldehyde-derived metabolites in human urine following exposure to e-cigarette aerosols will further our understanding of the relationship between ENDS use, aldehyde exposure and health risk.