Consecutive Segments Research Articles

Effects of Palliative Surgical Treatment for Spinal Metastases on the Patient's Quality of Life With a Focus on the Segment of the Metastasis: A Prospective Multicenter Study.

Prospective multicenter study. Palliative surgery is crucial for maintaining the quality of life (QOL) in patients with spinal metastases. This study aimed to compare the short-term outcomes of QOL after palliative surgery between patients with metastatic spinal tumors at different segments. We prospectively compared the data of 203 patients with spinal metastases at 2-3 consecutive segments who were divided into the following three groups: cervical, patients with cervical spine lesions; thoracic, patients with upper-middle thoracic spine lesions; and TL/L/S, patients with lesions at the thoracolumbar junction and lumbar and sacral regions. Preoperative and postoperative EuroQol 5-dimension (EQ5D) 5-level were compared. All groups exhibited improvement in the Frankel grade, performance status, pain, Barthel index, EQ5D health state utility value (HSUV), and EQ5D visual analog scale (VAS) postoperatively. Although preoperative EQ5D HSUVs did not significantly differ between the groups (cervical, 0.461 ± 0.291; thoracic, 0.321 ± 0.292; and TL/L/S, 0.376 ± 0.272), the thoracic group exhibited significantly lower postoperative EQ5D HSUVs than the other two groups (cervical, 0.653 ± 0.233; thoracic, 0.513 ± 0.252; and TL/L/S, 0.624 ± 0.232). However, postoperative EQ5D VAS was not significantly different between the groups (cervical, 63.4 ± 25.8; thoracic, 54.7 ± 24.5; and TL/L/S, 61.7 ± 21.9). Palliative surgery for metastatic spinal tumors provided comparable QOL improvement, irrespective of the spinal segment involved. Patients with upper and middle thoracic spinal metastases had poorer QOL outcomes than those with metastases in other segments; however, sufficient QOL improvement was achieved.

How Has the Paris Rail Public-Transportation Network Recovered After the COVID-19 Pandemic? Applying a Mixture of Regressions Model

Through a combination of regulations, fear of contagion, and changes in travelers’ habits, the COVID-19 pandemic affected the mobility of public-transit ridership worldwide. To understand the longer-term effects of the pandemic on public-transit ridership, we focus on the case of Paris, France, thanks to an open 5 year record of entries into more than 500 stations. To deal with the large volume of data, we use a statistical model that performs clustering and segmentation simultaneously while incorporating many exogenous variables, such as the day of the week or lockdowns, to account for their effect on the number of entries. We carry out an in-depth analysis of the results for the segments and clusters. Examining and comparing the regression coefficients across clusters and consecutive segments allows us to draw per-cluster and per-segment conclusions. We show that the number of weekday trips decreased in most clusters and that the reduction in weekly variations is proportional to the share of weekday trips in the volume of entries before the pandemic. In addition, we characterize the changes in the weekly profile: Thursday was replaced by Tuesday as the day with the highest ridership; because of teleworking, Friday became the least crowded weekday in clusters with strong differences between weekdays and weekends, while the lowest ridership weekday remains Monday in the other clusters.

Induction of Controllable Vortical Flow in a Dual-Stenosis Aorta Model: A Replication of Disordered Eddies Flow in Aneurysms.

This paper presents a two-stenosis aorta model mimicking vortical flow in vascular aneurysms. More specifically, we propose to virtually induce two adjacent stenoses in the abdominal aorta to develop various vortical flow zones post stenoses. Computational fluid dynamics (CFD) simulations were conducted for the virtual two-stenosis model based on physiological and anatomical data (i.e., diameters, flow rate waveforms) from adult rabbits. The virtual model includes adult rabbits' infra-renal portion of the aorta and iliac arteries. 3D CFD simulations in five different dual-stenosis configurations were performed using a commercial CFD package (FLUENT). In-house software assessed the evolution of flow vortices. Notably, spatial-temporally averaged wall shear stress (STA-WSS) and oscillatory shear index (OSI), the total volume of vortex flow, the number of vortices, and the phase-to-phase overlap of vortex flow within each region were evaluated. In all models, we found consistent patterns of the vortex flow parameters, indicating that the adjacent stenoses induced three different hemodynamic zones, namely, stable vortical flow (after the first stenosis), transient vortical flow (after the second stenosis), and unstable vortical flow (further distal to the second stenosis). Also, different degrees of flow disturbance can be achieved in these three zones. It is significant to note that, although the 'dual-stenosis' geometry is completely hypothetical, it allows us to create various vortical flows in consecutive vessel segments for the first time. As a result, if implemented as a pre-clinical model, the proposed two-stenosis model offers an attractive, tunable environment to investigate the interplays between subject-specific hemodynamics and vascular remodeling. This aspect remains in our future directions.

Legendre Polynomial Fitting-Based Permutation Entropy Offers New Insights into the Influence of Fatigue on Surface Electromyography (sEMG) Signal Complexity.

In a recently published work, we introduced local Legendre polynomial fitting-based permutation entropy (LPPE) as a new complexity measure for quantifying disorder or randomness in time series. LPPE benefits from the ordinal pattern (OP) concept and incorporates a natural, aliasing-free multiscaling effect by design. The current work extends our previous study by investigating LPPE's capability to assess fatigue levels using both synthetic and real surface electromyography (sEMG) signals. Real sEMG signals were recorded during biceps brachii fatiguing exercise maintained at 70% of maximal voluntary contraction (MVC) until exhaustion and were divided into four consecutive temporal segments reflecting sequential stages of exhaustion. As fatigue levels rise, LPPE values can increase or decrease significantly depending on the selection of embedding dimensions. Our analysis reveals two key insights. First, using LPPE with limited embedding dimensions shows consistency with the literature. Specifically, fatigue induces a decrease in sEMG complexity measures. This observation is supported by a comparison with the existing multiscale permutation entropy (MPE) variant, that is, the refined composite downsampling (rcDPE). Second, given a fixed OP length, higher embedding dimensions increase LPPE's sensitivity to low-frequency components, which are notably present under fatigue conditions. Consequently, specific higher embedding dimensions appear to enhance the discrimination of fatigue levels. Thus, LPPE, as the only MPE variant that allows a practical exploration of higher embedding dimensions, offers a new perspective on fatigue's impact on sEMG complexity, complementing existing MPE approaches.

Inter- and intra-subject similarity in network functional connectivity across a full narrative movie.

Naturalistic paradigms, such as watching movies during functional magnetic resonance imaging, are thought to prompt the emotional and cognitive processes typically elicited in real life situations. Therefore, naturalistic viewing (NV) holds great potential for studying individual differences. Previous studies have primarily focused on using shorter movie clips, geared toward eliciting specific and often isolated emotions, while the potential behind using full narratives depicted in commercial movies as a proxy for real-life experiences has barely been explored. Here, we offer preliminary evidence that a full narrative movie (FNM), that is, a movie covering a complete narrative arc, can capture complex socio-affective dynamics and their links to individual differences. Using the studyforrest dataset, we investigated inter- and intra-subject similarity in network functional connectivity (NFC) of 14 meta-analytically defined networks across a full narrative, audio-visual movie split into eight consecutive movie segments. We characterized the movie segments by valence and arousal portrayed within the sequences, before utilizing a linear mixed model to analyze which factors explain inter- and intra-subject similarity. Our results show that the model best explaining inter-subject similarity comprised network, movie segment, valence and a movie segment by valence interaction. Intra-subject similarity was influenced significantly by the same factors and an additional three-way interaction between movie segment, valence and arousal. Overall, inter- and intra-subject similarity in NFC were sensitive to the ongoing narrative and emotions in the movie. We conclude that FNMs offer complex content and dynamics that might be particularly valuable for studying individual differences. Further characterization of movie features, such as the overarching narratives, that enhance individual differences is needed for advancing the potential of NV research.

A fully automatic curve localization method for extracted spine

The automation of scoliosis positioning presents a challenging and often understated task, yet it holds fundamental significance for the automated analysis of spinal morphological anomalies. This paper introduces a novel spinal curve localization model for precisely differentiating the spinal curves and identifying their concave centers. The proposed model contains three components: i) custom spine central line model, to define the spine central line as a combination of several secant line sequences with different polarities; ii) custom curve model, to classify each spinal curve into one of 11 curves types and deduce each its concave centers by several custom formulas; and iii) adapted distance transform and quadratic line fitting algorithm coupled with custom secant line segment searching strategy (DTQL-LS), to search all line segments in the spine and group consecutive line segments with identical polarity into line sequence. Experimental results show that its positioning success rate is close to 99%. Furthermore, it exhibits significant time efficiency, with the average time to process a single image being less than 30 milliseconds. Moreover, even if some image boundaries are blurred, the center of the curve can still be accurately located.

Surgical techniques for optimal lens management of posterior segment metallic intraocular foreign body removal: a case series.

We describe three different approaches for lens management in cases of metallic IOFBs involving the posterior segment. All consecutive posterior segment metallic IOFB cases from 2012 to 2023 at a single institution were included in this retrospective case series. Surgical outcomes and surgical videos are presented. A total of 25 metallic IOFB cases were included. In 9 cases, there was no significant lens involvement, therefore not requiring lens extraction at the time of IOFB removal. In 8 cases with traumatic cataract, lens extraction sparing the anterior lens capsule was performed with IOFB removal. In 8 cases, both lens extraction and lens capsule removal were required at the time of IOFB removal. We demonstrate our algorithm to capsular management in patients who present with IOFB with good visual outcomes in follow-up.

A novel screw-type pyrolysis system for selectively producing value-added chemicals from lignocellulosic biomass

AbstractIn this study, a new unique screw-type (auger) reactor system was developed to selectively obtain valuable chemical-enriched liquid pyrolysis products. The new design has heating segments with increased temperature along the reactor tube and three-staged fractional condensing lines following pyrolysis gas ports at the end of each heating segment. To maximize liquid product yield, the effects of some parameters such as feeding rate, screw rate, and temperature profile were studied by the experiments designed with Response Surface Methodology (RSM). By fixing the maximum temperature at the last heating segment of a tubular reactor to 550 °C, the conditions for maximum liquid product yield were determined as feeder driver frequency of 21 Hz (13.6 rpm), auger driver frequency of 36 Hz (24.3 rpm), and ΔT (temperature difference between consecutive heating segments) of 66 °C. Biomass feeding rate and volumetric fullness ratio of the reactor at these conditions were 42 g min−1 and 0.58 (v/v), respectively. Under these conditions, total liquid, solid, and gas product yields were obtained as 57%, 19%, and 24%, respectively. In addition, analyses of the liquid fractions showed that valuable chemicals could be obtained selectively in a large extent by using the new design.

Improving the accuracy of predictions in multivariate time series using dynamic vine copulas

In this work, we deal with non-stationary multivariate time series, proposing a method which uses copulas to produce more accurate forecasting. The idea is to apply a copula-based approach to identify change points and then split the time series into consecutive segments based on these change points. In each segment, we define the best-fitting copula family and forecast values of the time series of each segment using the corresponding fitting copula. We apply our model to a financial data set to test the predictive power of our approach. A simulation study is also presented for a detailed illustration and assessment of our proposed methodology. Based on the results of numerical analysis, we observed that our proposed approach will help us to improve the accuracy of forecasting in comparison with other existing methods such as traditional time series forecasting as well as neural network forecasting.

MASH TL-3 Evaluation of the Hawaii Department of Transportation 34 in. Tall Aesthetic Bridge Rail with Pedestrian Rail and Sidewalk Options

Recommended guidelines were provided for the shape and size of aesthetic features added to roadside barriers in National Cooperative Highway Research Program Report No. 554, but limited full-scale crash testing has been performed on barriers with aesthetic features, especially in combination with sidewalks and pedestrian railings, each of which may contribute to increased propensity for vehicle snag, vehicle instabilities, and occupant risk metrics. A Hawaii Department of Transportation (HDOT) bridge rail design with vertical faces and aesthetic recessed panels was evaluated to Manual for Assessing Safety Hardware (MASH) TL-3 impact conditions in three configurations: stand-alone; in combination with a pedestrian handrail installed on the back side of the barrier system; and in combination with a pedestrian rail and 6 ft long x 6 in. tall sidewalk. The 34 in. tall aesthetic concrete bridge rail consisted of segments measuring 11 ft long at upstream and downstream ends and three 22 ft long, consecutive interior segments. MASH test designation Nos. 3-10 and 3-11 were performed on the stand-alone system and with the sidewalk and pedestrian rail, and test designation 3-11 was performed on the system on level terrain with handrail. Results indicated that the HDOT 34 in. tall aesthetic concrete bridge rail was crashworthy in all of the configurations evaluated.

Multi-scale 3D-CRU for EEG emotion recognition* *This research has been supported by the National Natural Science Foundation of China Joint Fund Key Project (U2003207), National Natural Science Foundation of China (6220100261902064), Natural Science Foundation of Anhui Province (No. 1908085MF209), Key Projects of Natural Science Foundation of Anhui Province Universities (No. KJ2018A0018).

In this paper, we propose a novel multi-scale 3D-CRU model, with the goal of extracting more discriminative emotion feature from EEG signals. By concurrently exploiting the relative electrode locations and different frequency subbands of EEG signals, a three-dimensional feature representation is reconstructed wherein the Delta (δ) frequency pattern is included. We employ a multi-scale approach, termed 3D-CRU, to concurrently extract frequency and spatial features at varying levels of granularity within each time segment. In the proposed 3D-CRU, we introduce a multi-scale 3D Convolutional Neural Network (3D-CNN) to effectively capture discriminative information embedded within the 3D feature representation. To model the temporal dynamics across consecutive time segments, we incorporate a Gated Recurrent Unit (GRU) module to extract temporal representations from the time series of combined frequency-spatial features. Ultimately, the 3D-CRU model yields a global feature representation, encompassing comprehensive information across time, frequency, and spatial domains. Numerous experimental assessments conducted on publicly available DEAP and SEED databases provide empirical evidence supporting the enhanced performance of our proposed model in the domain of emotion recognition. These findings underscore the efficacy of the features extracted by the proposed multi-scale 3D-GRU model, particularly with the incorporation of the Delta (δ) frequency pattern. Specifically, on the DEAP dataset, the accuracy of Valence and Arousal are 93.12% and 94.31%, respectively, while on the SEED dataset, the accuracy is 92.25%.

Estimate and compensate head motion in non-contrast head CT scans using partial angle reconstruction and deep learning.

Patient head motion is a common source of image artifacts in computed tomography (CT) of the head, leading to degraded image quality and potentially incorrect diagnoses. The partial angle reconstruction (PAR) means dividing the CT projection into several consecutive angular segments and reconstructing each segment individually. Although motion estimation and compensation using PAR has been developed and investigated in cardiac CT scans, its potential for reducing motion artifacts in head CT scans remains unexplored. To develop a deep learning (DL) model capable of directly estimating head motion from PAR images of head CT scans and to integrate the estimated motion into an iterative reconstruction process to compensate for the motion. Head motion is considered as a rigid transformation described by six time-variant variables, including the three variables for translation and three variables for rotation. Each motion variable is modeled using a B-spline defined by five control points (CP) along time. We split the full projections from 360° into 25 consecutive PARs and subsequently input them into a convolutional neural network (CNN) that outputs the estimated CPs for each motion variable. The estimated CPs are used to calculate the object motion in each projection, which are incorporated into the forward and backprojection of an iterative reconstruction algorithm to reconstruct the motion-compensated image. The performance of our DL model is evaluated through both simulation and phantom studies. The DL model achieved high accuracy in estimating head motion, as demonstrated in both the simulation study (mean absolute error (MAE) ranging from 0.28 to 0.45mm or degree across different motion variables) and the phantom study (MAE ranging from 0.40 to 0.48mm or degree). The resulting motion-corrected image, , exhibited a significant reduction in motion artifacts when compared to the traditional filtered back-projection reconstructions, which is evidenced both in the simulation study (image MAE drops from 178 33HU to 37 9HU, structural similarity index (SSIM) increases from 0.60 0.06 to 0.98 0.01) and the phantom study (image MAE drops from 117 17HU to 42 19HU, SSIM increases from 0.83 0.04 to 0.98 0.02). We demonstrate that using PAR and our proposed deep learning model enables accurate estimation of patient head motion and effectively reduces motion artifacts in the resulting head CT images.

Context-Aware Proposal-Boundary Network With Structural Consistency for Audiovisual Event Localization.

Audiovisual event localization aims to localize the event that is both visible and audible in a video. Previous works focus on segment-level audio and visual feature sequence encoding and neglect the event proposals and boundaries, which are crucial for this task. The event proposal features provide event internal consistency between several consecutive segments constructing one proposal, while the event boundary features offer event boundary consistency to make segments located at boundaries be aware of the event occurrence. In this article, we explore the proposal-level feature encoding and propose a novel context-aware proposal-boundary (CAPB) network to address audiovisual event localization. In particular, we design a local-global context encoder (LGCE) to aggregate local-global temporal context information for visual sequence, audio sequence, event proposals, and event boundaries, respectively. The local context from temporally adjacent segments or proposals contributes to event discrimination, while the global context from the entire video provides semantic guidance of temporal relationship. Furthermore, we enhance the structural consistency between segments by exploiting the above-encoded proposal and boundary representations. CAPB leverages the context information and structural consistency to obtain context-aware event-consistent cross-modal representation for accurate event localization. Extensive experiments conducted on the audiovisual event (AVE) dataset show that our approach outperforms the state-of-the-art methods by clear margins in both supervised event localization and cross-modality localization.

Dynamic functional connectivity analysis with temporal convolutional network for attention deficit/hyperactivity disorder identification.

Dynamic functional connectivity (dFC), which can capture the abnormality of brain activity over time in resting-state functional magnetic resonance imaging (rs-fMRI) data, has a natural advantage in revealing the abnormal mechanism of brain activity in patients with Attention Deficit/Hyperactivity Disorder (ADHD). Several deep learning methods have been proposed to learn dynamic changes from rs-fMRI for FC analysis, and achieved superior performance than those using static FC. However, most existing methods only consider dependencies of two adjacent timestamps, which is limited when the change is related to the course of many timestamps. In this paper, we propose a novel Temporal Dependence neural Network (TDNet) for FC representation learning and temporal-dependence relationship tracking from rs-fMRI time series for automated ADHD identification. Specifically, we first partition rs-fMRI time series into a sequence of consecutive and non-overlapping segments. For each segment, we design an FC generation module to learn more discriminative representations to construct dynamic FCs. Then, we employ the Temporal Convolutional Network (TCN) to efficiently capture long-range temporal patterns with dilated convolutions, followed by three fully connected layers for disease prediction. As the results, we found that considering the dynamic characteristics of rs-fMRI time series data is beneficial to obtain better diagnostic performance. In addition, dynamic FC networks generated in a data-driven manner are more informative than those constructed by Pearson correlation coefficients. We validate the effectiveness of the proposed approach through extensive experiments on the public ADHD-200 database, and the results demonstrate the superiority of the proposed model over state-of-the-art methods in ADHD identification.

VEAD: Variance profile Exploitation for Anomaly Detection in real-time IoT data streaming

The explosion of online streaming services in the Internet-of-Things (IoT) ecosystem poses new difficulties in detecting anomalies in real-time and continuous data. The IoT data anomalies are classified into long-term and short-term, with unique characteristics that make it difficult to develop a common detection mechanism for both. The existing techniques require excessive training data and suffer from high variability. This paper overcomes these challenges by proposing a Variance profile Exploitation for Anomaly Detection (VEAD) scheme using discrete wavelet transform and k-means clustering. VEAD is initialized by a fast training phase with a single data segment, from which a sensor variance profile is created. This variance profile reflects the degree of deviation in the collected data from different sensors at a specific time period and is continuously updated by integrating new data segments for effective anomaly detection. Overlapping data collection in the detection phase shows correlations among consecutive data segments, leading to improved detection accuracy (ACC). The Intel Berkeley Research Lab dataset with injected synthetic anomalies is used to perform numerical experiments. A comparative performance evaluation with state-of-the-art methods confirms the effectiveness of VEAD in achieving a higher ACC and a lower false-positive rate (FPR). Notably, 95% and 97% ACCs are achieved in detecting long-term and short-term anomalies, respectively. The high specificity of VEAD is also revealed by the low FPR of at most 2% in all cases. The low computational complexity and fast anomaly detection make VEAD suitable for deployment in live systems with massive data streams.

Abstract 137: Importance of Ventilation Rate When Assessing EtCO 2 Levels and Applying TOR Guidelines

Introduction: Ventilation is essential for the elimination of carbon dioxide (CO 2 ) from the body during CPR. However, variation in ventilation rate (VR) is an acknowledged confounder for the interpretation of EtCO 2 . VRs that are not according to guidelines alter measured EtCO 2 levels and make the decision of termination of resuscitation (TOR) based on EtCO 2 unreliable. Aim: To retrospectively analyze the effect of ventilation rate on levels of EtCO 2 for patients that do not achieve ROSC during prehospital resuscitation. Methods: Capnograms obtained from monitor-defibrillator recordings of non-ROSC adult OHCA cases, attended by TVF&R (OR, USA), were manually inspected to identify individual ventilations. Episodes were partitioned into consecutive 1-min segments, each characterized by its mean EtCO 2 and VR. We corrected the mean EtCO 2 values by adjusting the VRs to the recommended standard for patients with advanced airways (10 vpm), and obtained new EtCO 2,s values multiplying by the standardization factor 1.64 (1 - 0.91 VR ) published previously. Results: One-minute segments ( n = 3,791) from 244 cases were studied, for a total number of 27,312 ventilations. Of these, 206 cases had a duration equal or greater than 20-min, and 9.7% met the TOR criterium of EtCO 2 < 10 mmHg at 20-min. Median (IQR) VR, measured EtCO 2 and corrected EtCO 2,s were 8.1 (6.2-10.6) vpm, 27.7 (17.9-39.8) and 24.1 (16.0-33.7) mmHg, respectively, all at the 20-min mark. Median EtCO 2,s was at all times lower than measured EtCO 2 , with differences from 10 to 20 min ranging from 3.3 to 5.1 mmHg. Conclusion: TOR guidelines lack consensus, but the threshold of EtCO 2 < 10 mmHg at the 20-minute mark of resuscitation, as predictive of non-survivability, needs re-examination. Modern CPR introduces several confounders that produce higher measured EtCO 2 , including low VRs that are common but can be corrected. Based on a ventilation rate of 10 vpm, a new threshold should be considered as part of a multimodal approach in TOR guidelines.

Deep Reinforcement Learning-Based Approach for Video Streaming: Dynamic Adaptive Video Streaming over HTTP

Dynamic adaptive video streaming over HTTP (DASH) plays a crucial role in delivering video across networks. Traditional adaptive bitrate (ABR) algorithms adjust video segment quality based on network conditions and buffer occupancy. However, these algorithms rely on fixed rules, making it challenging to achieve optimal decisions considering the overall context. In this paper, we propose a novel deep-reinforcement-learning-based approach for DASH streaming, with the primary focus of maintaining consistent perceived video quality throughout the streaming session to enhance user experience. Our approach optimizes quality of experience (QoE) by dynamically controlling the quality distance factor between consecutive video segments. We evaluate our approach through a comprehensive simulation model encompassing diverse wireless network environments and various video sequences. We also conduct a comparative analysis with state-of-the-art methods. The experimental results demonstrate significant improvements in QoE, ensuring users enjoy stable, high-quality video streaming sessions.

Design of relocating sparse nested arrays for DOA estimation of non-circular signals

Recently, the ability of sparse arrays to achieve higher degrees of freedom (DOFs) and to be less sensitive to the mutual coupling (MC) effect has aroused increasing attention. Aiming at this, we investigate the sparse array design problem for direction of arrival (DOA) estimation of non-circular (NC) signals. Then, we propose two novel relocating sparse nested array (RSNA) structures, named RSNA-I and RSNA-II, respectively, and derive simple explicit expressions for their array locations and DOFs. Specifically, the RSNA-I is composed of two subarrays plus a separate sensor. By utilizing the separate sensor to enable the virtual sum and difference co-array (SDCA) with a longer consecutive segment and to alleviate the MC effect. In addition, to further make the array structure more robust to the MC effect, RSNA-II relocates the dense subarray of RSNA-I. Theoretical analysis and numerical simulation results demonstrate that the proposed RSNA-I and RSNA-II achieve a better balance between DOFs and the MC effect, which in turn effectively solves DOA estimation of NC signals and yields better DOA estimation performance.

Sex-dependent endozepinergic regulation of ventromedial hypothalamic nucleus glucose counter-regulatory neuron aromatase protein expression in the adult rat

The hypothalamic brain cell types that produce estradiol from testosterone remain unclear. Aromatase inhibition affects ventromedial hypothalamic nucleus (VMN) glucose-stimulatory nitric oxide (NO) and glucose-inhibitory γ-aminobutyric acid (GABA) transmission during insulin (INS)-induced hypoglycemia (IIH). Pure GABA and NO nerve cell samples acquired by laser-catapult-microdissection from consecutive rostro-caudal segments of the VMN were analyzed by Western blot to investigate whether regional subpopulations of each cell type contain machinery for neuro-estradiol synthesis. Astrocyte endozepinergic signaling governs brain steroidogenesis. Pharmacological tools were used here to determine if the glio-peptide octadecaneuropeptide (ODN) controls aromatase expression in GABA and NO neurons during eu- and/or hypoglycemia. Intracerebroventricular administration of the ODN G-protein coupled-receptor antagonist cyclo(1−8)[DLeu5]OP (LV-1075) decreased (male) or enhanced (female) VMN GABAergic neuron aromatase expression, but increased or reduced this profile in nitrergic neurons in a region-specific manner in each sex. IIH suppressed aromatase levels in GABA neurons located in the middle segment of the male VMN or distributed throughout this nucleus in the female. This inhibitory response was altered by the ODN isoactive surrogate octapeptide (OP) in female, but was refractory to OP in male. NO neuron aromatase protein in hypoglycemic male (middle and caudal VMN) and female (rostral and caudal VMN) rats, but was normalized in OP- plus INS-treated rats of both sexes. Results provide novel evidence that VMN glucose-regulatory neurons may produce neuro-estradiol, and that the astrocyte endozepine transmitter ODN may impose sex-specific control of baseline and/or hypoglycemic patterns of aromatase expression in distinct subsets of nitrergic and GABAergic neurons in this neural structure.

30 GY IN 4 STEREOTACTIC BODY RADIOTHERAPY (SBRT) FRACTIONS FOR COMPLEX SPINAL METASTASES: MATURE OUTCOMES SUPPORTING THIS NOVEL REGIMEN

Abstract We designed a 30 Gy in 4 fractions stereotactic body radiotherapy (SBRT) protocol, as an alternative option to our standard 2-fraction approach, for primarily large volume, multilevel, or previously radiated spinal metastases. We report imaging-based outcomes of this novel fractionation scheme. MATERIALS AND METHODS: Retrospective analysis of all patients who underwent 30 Gy/4 fractions from 2010-2021 identified from an institutional database. Primary outcomes were magnetic resonance-based vertebral compression fracture (VCF) and local failure (LF). RESULTS: We reviewed 245 treated segments in 116 patients. The median number of consecutive segments within the treatment volume was 2 (range, 1-6) and clinical target volume (CTV) was 126.2 cc (range, 10.4-863.5). Fifty-four percent had received at least one prior course of radiotherapy. The cumulative incidence of LF was 10.7% (95% CI 7.1-15.2) at 1 year and 16% (95% CI 11.5-21.2) at 2 years. The cumulative incidence of VCF was 7.3% (95% CI 4.4-11.2) at 1 and 11.2% (95% CI 7.5-15.8) at 2 years. On multivariable analysis, age ≥68 years (p=0.038), CTV volume ≥72 cc (p=0.021), and no prior surgery (p=0.021) predicted for VCF. The risk of VCF for CTV volumes <72 cc/≥72 cc was 1.8%/14.6% at 2 years. No case of radiation-induced myelopathy was observed. CONCLUSION: 30 Gy in 4 fractions was safe and efficacious despite a population at increased risk of toxicity. The lower risk of VCF in previously stabilized segments highlights the potential for a multi-modal treatment approach for complex metastases, especially for those with CTV volume ≥72 cc.