Background Contrast Research Articles

The effect of contrast agents on the anticoagulant properties of oral factor Xa inhibitors.

Contrast agents may affect the anticoagulant properties of novel oral anticoagulants. To evaluate the effect of iohexol as a contrast agent on the anticoagulant activity of oral factor Xa inhibitors. The study included 65 individuals who underwent contrast computed tomography(CT). Group 1 comprised 20 patients using rivaroxaban, Group 2, 20 patients using apixaban, and Group 3, 20 patients using edoxaban. Group 4 was the control group of five healthy volunteers. Iohexol (60 mL) was used as a contrast agent. Blood samples of 2 mL were withdrawn into two tubes at 4 h after the drug dose and 1 h after the contrast CT (CT was performed 3 h after the drug was taken) from all the patients, and for the control group, at any time before and 1 h after contrast CT. The anticoagulant properties of rivaroxaban, apixaban, and edoxaban were evaluated using anti-factor Xa levels. The anti-factor Xa level was increased after using the contrast agent in the rivaroxaban group (0.66 ± 0.32 U/mL vs. 0.67 ± 0.32 U/mL; P = 0.01) and the edoxaban group (0.74 ± 0.35 U/mL vs. 0.76 ± 0.36 U/mL; P = 0.006). No significant difference was observed in the apixaban group (0.66 ± 0.33 U/mL vs. 0.66 ± 0.32 U/mL; P = 0.21) and control group (0.02 ± 0.01 U/mL vs. 0.03 ± 0.01 U/mL; P = 0.33). The anticoagulant properties of rivaroxaban and edoxaban tended to increase significantly, but there was no statistically significant difference in the anticoagulant properties of apixaban after the administration of contrast agent. To determine whether the small laboratory difference has a clinical effect, there is a need for larger clinical trials (NCT04611386).

Impact of COVID-19 on appendicitis presentations in children in Australia and New Zealand.

BackgroundIn contrast to many countries, the prevalence of COVID‐19 in Australia and New Zealand has been low. We hypothesised, however, that a potential secondary effect of the COVID‐19 pandemic would be delayed presentation of paediatric appendicitis, with resultant higher rates of complicated appendicitis. This study was an initiative of the Australian and New Zealand Surgery in Children Registrars' Association for Trials collaborative, a trainee‐led research group based in Australia and New Zealand.MethodsA binational multicentre, retrospective review was undertaken of paediatric patients with appendicitis early in the COVID‐19 pandemic (20 March–30 April 2020), compared with previous years (2018, 2019). Primary outcomes were the duration of symptoms prior to presentation and the severity of disease.ResultsA total of 400 patients from six centres were included. Duration of symptoms prior to presentation, sepsis at presentation, complicated disease and presence of complications did not differ significantly between time periods. Duration of intravenous antibiotic treatment and overall antibiotic treatment were both significantly shorter during 2020 (2.4 days versus 3.5 in 2018 and 3.0 in 2019 [P = 0.0038] and 3.7 days versus 5.2 in 2018 and 4.6 in 2019 [P = 0.04], respectively). Management approach did not differ, with the majority of patients managed operatively.ConclusionsWe did not demonstrate any difference in duration of symptoms prior to presentation or other markers of disease severity early in the pandemic. Duration of antibiotic treatment was shorter during this period compared with previous years. Management of children with appendicitis, both simple and complicated, did not appear to change as a result of COVID‐19.

Retrospective analysis of risk factors for postoperative perineal hernia after endoscopic abdominoperineal excision for rectal cancer

BackgroundIn contrast to open-surgery abdominoperineal excision (APE) for rectal cancer, postoperative perineal hernia (PPH) is reported to increase after extralevator APE and endoscopic surgery. In this study, therefore, we aimed to determine the risk factors for PPH after endoscopic APE.MethodsA total 73 patients who underwent endoscopic APE for rectal cancer were collected from January 2009 to March 2020, and the risk factors for PPH were analyzed retrospectively.ResultsNineteen patients (26%) developed PPH after endoscopic APE, and the diagnosis of PPH was made at 9–393 days (median: 183 days) after initial surgery. Logistic regression analysis showed that absence of pelvic peritoneal closure alone increased the incidence of PPH significantly (odds ratio; 13.76, 95% confidence interval; 1.48–1884.84, p = 0.004).ConclusionsThis preliminary study showed that pelvic peritoneal closure could prevent PPH after endoscopic APE.

Continuous Variation in an Aposematic Pattern Affects Background Contrast, but Is Not Associated With Differences in Microhabitat Use

Variation in aposematic signals was once predicted to be rare, yet in recent years it has become increasingly well documented. Despite increases in the frequency with which polytypism and polymorphism have been suggested to occur, population-wide variance is rarely quantified. We comprehensively sampled a subpopulation of the poison frog Oophaga sylvatica, a species which is polytypic across its distribution and also shows considerable within-population polymorphism. On one hand, color pattern polymorphism could be the result of multifarious selection acting to balance different signaling functions and leading to the evolution of discrete sub-morphs which occupy different fitness peaks. Alternatively, variance could simply be due to relaxed selection, where variation would be predicted to be continuous. We used visual modeling of conspecific and heterospecific observers to quantify the extent of within population phenotypic variation and assess whether this variation produced distinct signals. We found that, despite considerable color pattern variation, variance could not be partitioned into distinct groups, but rather all viewers would be likely to perceive variation as continuous. Similarly, we found no evidence that frog color pattern contrast was either enhanced or diminished in the frogs’ chosen microhabitats compared to alternative patches in which conspecifics were observed. Within population phenotypic variance therefore does not seem to be indicative of strong selection toward multiple signaling strategies, but rather pattern divergence has likely arisen due to weak purifying selection, or neutral processes, on a signal that is highly salient to both conspecifics and predators.

Validation of EncephalApp_Stroop as screening tool for the detection of minimal hepatic encephalopathy in German patients with liver cirrhosis

BackgroundIn contrast to overt hepatic encephalopathy (OHE), the diagnosis of minimal HE (MHE) is challenging in patients with cirrhosis requiring elaborate, specialized testing. The EncephalApp_Stroop is a smartphone-based application established as screening tool for the diagnosis of MHE but has not yet been validated in a German cohort and country specific cut-offs are currently missing. Methods93 patients with cirrhosis were enroled into this study. Psychometric hepatic encephalopathy score (PHES) was used to detect MHE, and a subset of the patients was tested with critical flicker frequency (CFF). All patients underwent testing with EncephalApp_Stroop. Cut-off thresholds for EncephalApp_Stroop were calculated according to Youden's Index and a separate cut-off was determined with focus on sensitivity. Results24 (26%) patients had MHE according to PHES. EncephalApp_Stroop had a strong correlation with PHES (r=-0.76, p<0.001), while there was only a modest correlation with CFF (r=-0.51, <0.001). On time as well as on+off time discriminated best between patients with and without MHE with AUROCS of 0.87 for both measures. According to Youden's index, a cut-off of >224.7 s (sec) (on+off time) discriminated best between patients with and without MHE with a sensitivity of 71% and a specificity of 88%. The adjusted cut-off value for on+off time with focus on sensitivity (sensitivity:specificity weighed 2:1) was 185.1 s, yielding an optimized sensitivity of 92% and a negative predictive value of 96%. By using this cut-off as a pre-screening test in a stepwise diagnosis algorithm, elaborate testing with PHES could have been avoided in 49% of all patients. ConclusionEncephalApp_Stroop may be useful in a stepwise diagnosis algorithm or even as a stand-alone screening tool to detect MHE in German patients with cirrhosis.

Robust plant segmentation of color images based on image contrast optimization

• A contrast-optimization approach was proposed for plant segmentation of color images. • Contrast-enhanced images were compared with index images using five image datasets. • The proposed method consistently enhanced image contrast and segmentation accuracy. • None of nine common color indices were robust enough to varying image conditions. Plant segmentation is a crucial task in computer vision applications for identification/classification and quantification of plant phenotypic features. Robust segmentation of plants is challenged by a variety of factors such as unstructured background, variable illumination, biological variations, and weak plant-background contrast. Existing color indices that are empirically developed in specific applications may not adapt robustly to varying imaging conditions. This study proposes a new method for robust, automatic segmentation of plants from background in color (red-green-blue, RGB) images. This method consists of unconstrained optimization of a linear combination of RGB component images to enhance the contrast between plant and background regions, followed by automatic thresholding of the contrast-enhanced images ( CEI s). The validity of this method was demonstrated using five plant image datasets acquired under different field or indoor conditions, with a total of 329 color images as well as ground-truth plant masks. The CEI s along with 10 common index images were evaluated in terms of image contrast and plant segmentation accuracy. The CEI s, based on the maximized foreground-background separability, achieved consistent, substantial improvements in image contrast over the index images, with an average segmentation accuracy of F1 = 95%, which is 4% better than the best accuracy obtained by the indices. The index images were found sensitive to imaging conditions and none of them performed robustly across the datasets. The proposed method is straightforward, easy to implement and can be potentially extended to nonlinear forms of color component combinations or other color spaces and generally useful in plant image analysis for precision agriculture and plant phenotyping.

Acoustic and Facial Features From Clinical Interviews for Machine Learning-Based Psychiatric Diagnosis: Algorithm Development.

BackgroundIn contrast to all other areas of medicine, psychiatry is still nearly entirely reliant on subjective assessments such as patient self-report and clinical observation. The lack of objective information on which to base clinical decisions can contribute to reduced quality of care. Behavioral health clinicians need objective and reliable patient data to support effective targeted interventions.ObjectiveWe aimed to investigate whether reliable inferences—psychiatric signs, symptoms, and diagnoses—can be extracted from audiovisual patterns in recorded evaluation interviews of participants with schizophrenia spectrum disorders and bipolar disorder.MethodsWe obtained audiovisual data from 89 participants (mean age 25.3 years; male: 48/89, 53.9%; female: 41/89, 46.1%): individuals with schizophrenia spectrum disorders (n=41), individuals with bipolar disorder (n=21), and healthy volunteers (n=27). We developed machine learning models based on acoustic and facial movement features extracted from participant interviews to predict diagnoses and detect clinician-coded neuropsychiatric symptoms, and we assessed model performance using area under the receiver operating characteristic curve (AUROC) in 5-fold cross-validation.ResultsThe model successfully differentiated between schizophrenia spectrum disorders and bipolar disorder (AUROC 0.73) when aggregating face and voice features. Facial action units including cheek-raising muscle (AUROC 0.64) and chin-raising muscle (AUROC 0.74) provided the strongest signal for men. Vocal features, such as energy in the frequency band 1 to 4 kHz (AUROC 0.80) and spectral harmonicity (AUROC 0.78), provided the strongest signal for women. Lip corner–pulling muscle signal discriminated between diagnoses for both men (AUROC 0.61) and women (AUROC 0.62). Several psychiatric signs and symptoms were successfully inferred: blunted affect (AUROC 0.81), avolition (AUROC 0.72), lack of vocal inflection (AUROC 0.71), asociality (AUROC 0.63), and worthlessness (AUROC 0.61).ConclusionsThis study represents advancement in efforts to capitalize on digital data to improve diagnostic assessment and supports the development of a new generation of innovative clinical tools by employing acoustic and facial data analysis.

A novel gradient echo data based vein segmentation algorithm and its application for the detection of regional cerebral differences in venous susceptibility

Accurate segmentation of cerebral venous vasculature from gradient echo data is of central importance in several areas of neuroimaging such as for the susceptibility-based assessment of brain oxygenation or planning of electrode placement in deep brain stimulation. In this study, a vein segmentation algorithm for single- and multi-echo gradient echo data is proposed. First, susceptibility maps, true susceptibility-weighted images, and, in the multi-echo case, R2* maps were generated from the gradient echo data. These maps were filtered with an inverted Hamming filter to suppress background contrast as well as artifacts from field inhomogeneities at the brain boundaries. A shearlet-based scale-wise representation was generated to calculate a vesselness function and to generate segmentations based on local thresholding. The accuracy of the proposed algorithm was evaluated for different echo times and image resolutions using a manually generated reference segmentation and two vein segmentation algorithms (Frangi vesselness-based, recursive vesselness filter) as a reference with the Dice and Cohen's coefficients as well as the modified Hausdorff distance. The Frangi-based and recursive vesselness filter methods were significantly outperformed with regard to all error metrics. Applying the algorithm, susceptibility differences likely related to differences in blood oxygenation between superficial and deep venous territories could be demonstrated.

A multi‐scale learning method with dilated convolutional network for concrete surface cracks detection

Concrete surface cracks detection is an important task to ensure the safety of infrastructure. Because of the complexity of background and low contrast of concrete surface, it is difficult to detect the cracks on the concrete surface accurately. To tackle this problem, a multi-scale dilated convolutional method for concrete surface crack detection is proposed to improve the accuracy of detection. The proposed network is based on the encoder-decoder structure of U-Net. Cascade multi-scale dilated convolutions in the centre of the network is used to get larger receptive field without additional parameters. In the decoder stage, the feature fusion module is used to integrated the multi-scale and multi-level side network feature for the final prediction. A large crack dataset is collected as a training set and other three smaller datasets are used for evaluation. Extensive experiments have been conducted on these three crack datasets, which achieves optimal dataset scale (ODS) F-score over 0.84, optimal image scale (OIS) F-score over 0.85 and average precision (AP) over 0.86. This algorithm performs better than the current crack detection, edge detection and semantic segmentation methods.

노화대응 주택개조를 위한 고령친화형 주택상태 진단도구 개발

The need for home modification for senior increases, but home condition diagnosis tools are insufficient so that senior can not check and diagnose their home conditions on their own. Therefore, it is necessary to develop a home condition diagnosis tool specialized for the age-friendly design to be used by senior before implementing home modification to reflect the characteristics and needs of elderly households. This study was intended to develop an age-friendly home condition diagnosis tool for home modification in response to aging, and the contents of the study were as follows. In order to extract home condition diagnosis items for senior citizens to respond to aging themselves, domestic and foreign research data and home condition diagnosis tools currently used were examined and the items used in 8 diagnostic tools were rearranged. In order to develop age-friendly diagnostic tools, The researchers of this study looked at 4 domestic and 4 overseas diagnostic tools designed for the elderly. After listing all of the age-friendly design elements used in these 8 diagnostic tools, similar items were rearranged. The subjects of this study included senior aged 60 or older. After the subjects were asked to use the self-checklist face-to-face, the usability evaluation was conducted using a questionnaire, and 5 preliminary surveys and 34 main surveys were conducted. Fianally, the 113 items in six areas at home were included in age-friendly home condition checklist. Design elements applied to elderly-friendly home condition checklist through tools designed to enhance the usability of the elderly are as follows: ① Shorten the length of sentences to enhance the readability of sentences, ② highlight important contents through the contrast of background and text colors, ③ the size of letters good for the elderly, ④ the font good for the elderly, ⑤ use pictograms and noticeable colors, and ⑥ use picture and painting to improve understanding of the contents.

Is conventional brain MRI useful for the diagnosis of cluster headache in patients who meet ICHD-3 criteria? Experience in three hospitals in Spain

ObjectiveTo assess the frequency of symptomatic structural lesions and the diagnostic yield of conventional brain MRI in cluster headache (CH). BackgroundIn contrast to migraine, brain MRI is recommended in patients with CH to exclude potential mimics. The prevalence of symptomatic CH is not known. MethodsWe retrospectively analysed in detail the brain MRIs of patients diagnosed as CH in 3 Neurology Services in Spain and reviewed their clinical history. Clinical diagnoses were reassessed based on the ICHD-3 criteria. ResultsWe included 130 patients: 113 (86.9%) were male; mean age at diagnosis being 41.4 years (range 7–82). Forty-nine (37.7%) showed some abnormal MRI finding. Only in two cases potential symptomatic lesions were found: one trigeminal schwannoma and one craneopharyngioma, but both presented atypical features (facial hypoesthesia on examination and episodes of prolonged duration that had progressed to continuous refractory pain without specific pattern, respectively) and therefore did not fulfil the ICHD-3 CH criteria. The remaining abnormal MRI findings were: white matter lesions (24 patients; 18.4%), sinus inflammatory changes (13; 10.0%), small arachnoid cysts (5; 3.8%), empty sella turca (3; 2.3%), and other unspecific findings (8; 6.2%). All of them were not symptomatic based on neuroimaging characteristics, clinical course and response to treatment. ConclusionsBrain MRI in patients who meet ICHD-3 CH criteria, with no atypical clinical features, does not show any clinically-relevant findings, suggesting that these criteria are highly predictive of its primary origin and that systematic MRI is not useful for the diagnosis of typical CH.

Epigenetic changes occur in placentas of spontaneous and recurrent miscarriages

BackgroundIn contrast to genetic abnormalities which are well known to be responsible for around 50 % of human miscarriages, there is very few data about epigenetic alterations in spontaneous and recurrent miscarriages (SM, RM). The aim of this study was to analyze the histone modification marks H3K9ac and H3K4me3 in SM and RM. MethodsThe abundance of histone modifications H3K4me3/H3K9ac was analyzed by western blot in frozen abortion material of SM and RM compared to a control group of legal pregnancy terminations. Further, to characterize placental tissue cells expressing H3K4me3/H3K9ac immunohistochemistry (IHC) and immunofluorescence was performed in 20 SM, 19 RM and 26 controls. ResultsThe western blot data showed a tendency to an overall reduction of H3K4me3/H3K9ac, in the placental tissue of particularly SM. Further we differentiated between syncytiotrophoblast, cytotrophoblast and decidual cells and found a significant decrease of H3K4me3 in SM in cytotrophoblast cells and syncytial stroma. In RM H3K4me3 was downregulated exclusively in the syncytiotrophoblast. H3K9ac was reduced in SM and RM in all evaluated compartments, except from the syncytiotrophoblast. ConclusionOur study showed an overall reduced histone modification of H3K4me3 and H3K9ac in the placental tissue of SM. Concerning RM, particularly the reduction of H3K9ac was detected in the placental tissue, indicating that RM group has distinct profile in epigenetic regulation. Whether these histone modifications are part of a possible pathophysiologic cascade during SM and RM or are merely indicating a defective placentation, cannot be concluded from this study.

Nivolumab First-Line Therapy for Elderly, Frail Hodgkin Lymphoma Patients: Niviniho, a Lysa Phase II Study

Nivolumab First-Line Therapy for Elderly, Frail Hodgkin Lymphoma Patients: Niviniho, a Lysa Phase II Study

A Data-Centric Approach to Design and Analysis of a Surface-Inspection System Based on Deep Learning in the Plastic Injection Molding Industry

Manufacturers are eager to replace the human inspector with automatic inspection systems to improve the competitive advantage by means of quality. However, some manufacturers have failed to apply the traditional vision system because of constraints in data acquisition and feature extraction. In this paper, we propose an inspection system based on deep learning for a tampon applicator producer that uses the applicator’s structural characteristics for data acquisition and uses state-of-the-art models for object detection and instance segmentation, YOLOv4 and YOLACT for feature extraction, respectively. During the on-site trial test, we experienced some False-Positive (FP) cases and found a possible Type I error. We used a data-centric approach to solve the problem by using two different data pre-processing methods, the Background Removal (BR) and Contrast Limited Adaptive Histogram Equalization (CLAHE). We have experimented with analyzing the effect of the methods on the inspection with the self-created dataset. We found that CLAHE increased Recall by 0.1 at the image level, and both CLAHE and BR improved Precision by 0.04–0.06 at the bounding box level. These results support that the data-centric approach might improve the detection rate. However, the data pre-processing techniques deteriorated the metrics used to measure the overall performance, such as F1-score and Average Precision (AP), even though we empirically confirmed that the malfunctions improved. With the detailed analysis of the result, we have found some cases that revealed the ambiguity of the decisions caused by the inconsistency in data annotation. Our research alerts AI practitioners that validating the model based only on the metrics may lead to a wrong conclusion.

Physical Confirmation of Biology-Guided Radiotherapy Directed at Static Targets With Varying Shapes and Background Contrast Environments

Biology-guided radiotherapy (BgRT), utilizes hardware that incorporates a PET detection system into a ring-gantry LINAC for real-time tracking delivery. For this study, we focus on the BgRT delivery performance in the case of static targets for dose accuracy measurement. During active beam delivery, the system operates by aiming beamlets of therapeutic radiation at malignant tumors in response to outgoing PET emissions with sub-second latency. Over a treatment fraction, these beamlets sum to the total intended dose prescribed by the physician. Physical demonstration of BgRT has not previously been reported. Here we report phantom experiments validating BgRT using static PET-avid targets of varying shapes and background PET environments.An FDG-fillable insert with different shaped targets was developed to mimic different potential radiotherapy targets. Spherical and C-shaped targets were filled with 18F-flurodeoxyglucose (FDG) to represent tumors and/or organs-at-risk. The background material in the insert was either a homogenous water medium or a water filled heterogeneous medium with a Styrofoam mesh simulating lung tissue surrounding the target. Targets and OARs were filled with FDG to achieve a target/OAR: background ratio of 8:1, while the background concentration varied from 4.5-10 kBq/ml to simulate typical patient background activity concentrations. For our set-ups were investigated: Spherical target in homogenous background, spherical target in heterogeneous background, C-shaped target in homogenous background, and spherical target in homogenous background with nearby PET-avid, C-shaped OAR. For each combination, SBRT and BgRT treatment plans were created on the RefleXion treatment planning system (TPS). Next, SBRT and BgRT plans were delivered on a pre-commercial version of the RefleXion system and dosimetric accuracy was measured using the AC phantom. Gamma criteria (3%/3mm) were used to compare delivered and calculated dose. Paired Student's t-test assessed differences in SBRT and BgRT gamma pass results.All BgRT deliveries met 3%/3mm gamma pass criteria greater than 95% (Range, 95.2% to 98.9%). Additionally, gamma passing rates were not significantly different than the companion SBRT plans performed on the same targets (P = 0.16).Dose accuracy results indicate that BgRT plans can be delivered accurately over a variety of test conditions (different target shapes, attenuating media and activity concentration levels) to FDG-avid targets, with performance similar to SBRT treatments.

Multisensor Image Fusion for Automated Detection of Defects in Printed Circuit Boards

This paper presents multisensor image fusion of polarization and infrared imaging to detect defects in printed circuit boards (PCBs). Many existing automated optical inspection techniques rely on visible imaging sensors. However, collected images suffer from uneven brightness levels due to the influence of lighting environment, which may significantly affect detection accuracies. Polarization information characterizes material types, surface roughness, and geometric shape of an object. Thermal infrared imaging reveals heat radiation difference between the defect region and the background. Polarization and infrared imaging are not sensitive to background illumination and contrast. In this paper, we utilize polarization information as well as infrared imaging to detect the defects in PCBs that conventional optical inspection techniques cannot easily detect. We design a multi-source image acquisition system to simultaneously acquire brightness intensity, polarization, and infrared intensity. Then a Multisensor Lightweight Detection Network (MLDN), trained on the PCB dataset collected, fuses polarization information and the brightness intensities in the visible and thermal infrared spectra to detect defects in challenging lighting conditions. Experiment results show that the proposed network outperforms the state-of-the-art automated optical inspection techniques in terms of mean average precision.

Extracting weak magnetic contrast from complex background contrast in plan-view FeGe thin films

The desire to design and build skyrmion-based devices has led to the need to characterize magnetic textures in thin films of functional materials. This can usually be achieved through the Lorentz transmission electron microscopy (LTEM) and the Lorentz scanning transmission electron microscopy (LSTEM) in thin film cross-section and single crystal specimens. However, direct imaging of the magnetic texture in plan-view samples of thin (< 50 nm) films has proved to be challenging due to the complex “background” contrast associated with the microstructure and defects, as well as contributions from bending of the specimens. Using a mechanically polished 35 nm plan-view FeGe thin film, we have explored three methods to extract magnetic contrast from the complex background contrast observed; (1) background subtraction in defocused LTEM images, (2) frequency filtered CoM-DPC reconstructed from LSTEM datasets and 3) registration of 4D-STEM datasets acquired at different tilt angles. Using these methods, we have successfully implemented real space imaging of both the helical phase and skyrmion phase. The ability to understand nanoscale magnetic behavior from plan-view thin films is a fundamental step towards development of highly integrated spin electronics.

Light-reflecting characteristics of optically rewritable electronic paper

Light-reflecting characteristics play a crucial role in optically rewritable liquid crystal display (electronic paper). By using a special optimizer software, light-reflecting characteristics of the twisted nematic cell were calculated. The achieved values of the normalized reflection coefficients range within ∼ 0.03 - 0.43, which is comparable or better than other commercial technologies. The calculated contrast ratio of the device is 10 : 1. A 2D image, which can be displayed on the electronic paper was obtained. Comparison of the simulated background color and contrast ratio is very similar with the results, which were obtained in experimental cells of other studies. Our calculations indicate that angular dependence of the reflectance coefficient is almost uniform within a wide range of viewing angles.

Prostate specific membrane antigen-radio guided surgery using Cerenkov luminescence imaging-utilization of a short-pass filter to reduce technical pitfalls.

BackgroundIntraoperative Cerenkov luminescence imaging (CLI) is a novel technique to assess surgical margins in patients undergoing nerve sparing radical prostatectomy (RP). Here, we analyze the efficacy of a 550-nm optical short-pass filter (OF) to improve its performance.MethodsIn this prospective single-center feasibility study ten patients with prostate cancer (PC) were included between December 2019 and April 2020, including three patients without tracer injection as a control group. After preoperative injection of 68-Ga-prostate-specific membrane antigen (PSMA)-11 followed by RP, CLI of the excised prostate and the incised index lesion was performed to visualize the primary tumor lesion. We compared the findings on intraoperative CLI to postoperative histopathology. Furthermore, CLI-intensities determined as tumor to background ratio (TBR) and contrast to noise ratio (CNR) were measured.ResultsHistopathology proved positive surgical margins (PSM) in 3 patients with corresponding findings in CLI. After magnetic resonance imaging (MRI)-informed incision above the index lesion 2 out of 3 prostates demonstrated elevated CLI signals with histopathological confirmation of PC cells. The use of the OF enabled a significant reduction of the area of the regions of interest from a median of 1.80 to 0.15 cm2 (reduction by 85%, P=0.005) leading to increased specificity. Signals due to PSMs were not suppressed by the 550-nm OF. The median TBR was reduced from 3.33 to 2.10. In all three patients of the control group elevated CLI intensities were detected at locations with diathermal energy deposition during surgery. After application of the 550-nm OF these were almost totally suppressed with a TBR of 1.10. Measurements of Cerenkov luminescence intensity with the 550-nm OF showed a significant Pearson’s correlation of 0.82 between PSM and the elevated TBR (P=0.003) and a significant Pearson’s correlation of 0.66 between PSM and elevated CNR (P=0.04). Measurements without the OF did not correlate significantly.ConclusionsIntraoperative 68-Ga-PSMA CLI in PC is a tool that warrants further investigation to visualize PSM especially in intermediate and high-risk PC. Intraoperative CLI benefits from usage of a 550-nm OF to reduce false-positive signals.

Regional heterogeneity in coral species richness and hue reveals novel global predictors of reef fish intra-family diversity

Habitat heterogeneity shapes biological communities, a well-known process in terrestrial ecosystems but substantially unresolved within coral reef ecosystems. We investigated the extent to which coral richness predicts intra-family fish richness, while simultaneously integrating a striking aspect of reef ecosystems—coral hue. To do so, we quantified the coral richness, coral hue diversity, and species richness within 25 fish families in 74 global ecoregions. We then expanded this to an analysis of all reef fishes (4465 species). Considering coral bleaching as a natural experiment, we subsequently examined hue's contribution to fish communities. Coral species and hue diversity significantly predict each family's fish richness, with the highest correlations (> 80%) occurring in damselfish, butterflyfish, emperors and rabbitfish, lower (60–80%) in substrate-bound and mid-water taxa such as blennies, seahorses, and parrotfish, and lowest (40–60%) in sharks, morays, grunts and triggerfish. The observed trends persisted globally. Coral bleaching's homogenization of reef colouration revealed hue’s contribution to maintaining fish richness, abundance, and recruit survivorship. We propose that each additional coral species and associated hue provide added ecological opportunities (e.g. camouflage, background contrast for intraspecific display), facilitating the evolution and co-existence of diverse fish assemblages.