Time-efficient Tool Research Articles

P0338 Early identification of pediatric inflammatory bowel disease based on machine learning with non-invasive data

Abstract Background Early identification of pediatric inflammatory bowel disease (IBD) can improve long-term prognosis, yet significant diagnostic delays persist. This study aims to develop an effective early identification tool using machine learning with non-invasive testing. Methods This retrospective study collected data from 314 pediatric patients (109 with IBD and 205 without IBD) hospitalized in Fudan University Children’s Hospital, to serve as the training dataset. We developed machine learning classifiers using support vector machine, artificial neural network, extreme gradient boosting, decision tree, random forest, k-nearest neighbors, logistic model, and gradient boosting machine methods, incorporating easily obtainable clinical examination tools. Participant data included age, sex, and three groups of features across different dimensions: IBD symptoms (diarrhea lasting ≥1 month; blood in stool for ≥1 week; recurrent perianal abscesses or fistulas; delayed growth; abdominal pain lasting ≥1 month; >10% weight loss; first-degree family history; arthritis, uveitis, erythema nodosum without definitive rheumatologic diagnosis; recurrent aphthous ulcers; unexplained fever), inflammatory biomarkers (elevated fecal calprotectin; elevated serum inflammatory markers; anemia; hypoalbuminemia; ANCA positivity), and transabdominal bowel ultrasound parameters (Limberg level >1 indicating bowel wall thickening with vascularity; mesenteric fat wrapping; bowel wall stratification disorder or loss; lymphadenopathy). We evaluated the performance of each feature group and their combinations, optimized hyperparameters, internal validation, and performed sequential validation with 66 prospectively recruited pediatric patients from the same center. The final model was selected based on optimal and stable performance in both the development and external validation cohorts, with Shapley values used to interpret feature importance. Results The support vector machine outperformed other algorithms, achieving an area under the curve of 0.95 in both internal and external validation datasets. The five most important features based on Shapley values were Limberg level >1, elevated ESR, elevated fecal calprotectin, recurrent perianal disease, and elevated CRP. Additionally, combining all three feature dimensions outperformed any single or paired combination. The probability of IBD classification showed moderate correlation with clinical, endoscopic, and histologic scores . Conclusion The machine learning model based on non-invasive data has the potential to become a low-cost and time-efficient tool for early identification of IBD. Ultrasound examination plays a significant role in distinguishing children with IBD from those without.

Exploring the Impact of Facebook's Reaction- Button on Interpersonal Communication: A Study on Immediacy and Social Acceleration

In recent years, the evolution of social media, particularly Facebook, has introduced tools that significantly accelerate social interactions. This study investigates the usage of Facebook’s reactionbutton and its impact on interpersonal communication, focusing on immediacy, media richness, and the societal drive for speed. This paper argues that, while the reaction-button enhances the speed and ease of communication, it may reduce the depth and clarity of interactions, potentially leading to miscommunication. This study employed a qualitative method involving in-depth interviews with 16 Facebook users. The participants provided insights into their experiences using the reaction-button and its effects on their social relationships. Results showed the widespread preference for the reaction-button over the traditional 'like' feature. Participants regard reactions as a timeefficient tool that enhances online engagement, though the immediacy of reactions sometimes undermines deeper communication. This suggests that, despite promoting faster communication, the reaction-button may undermine meaningful engagement. These findings have significant implications for understanding the evolving nature of digital interactions and highlight the need for social media platforms to balance speed with clarity.

Ultrasound acting as a guiding light in the diagnosis of posterior interosseous nerve entrapment at ligament of Frohse – A case report

Posterior interosseous nerve (PIN) entrapment syndrome is a relatively rare compression neuropathy which leads to weakness of the extensor muscles of hand. This becomes a cause of morbidity to the patient with resultant difficulty in conducting day-to-day activities. Thickening of proximal edge of the superficial head of the supinator muscle which forms the ligament of Frohse can lead to compression of the PIN as it enters the supinator canal. Although nerve conduction studies can indicate the diagnosis, they cannot identify the exact site and cause of compression which is crucial for deciding the further management. Magnetic resonance imaging can identify the cause of entrapment in some cases; however, this is extremely difficult given the small size of the PIN and arcade of Frohse. Ultrasound overcomes these limitations and provides a cost and time effective, efficient, and sensitive diagnostic tool for detecting nerve entrapment. Here, we would like to emphasize the emerging importance of ultrasound by describing a case of ligament of Frohse thickening causing PIN entrapment which was effectively diagnosed by ultrasound and confirmed on surgical exploration, treated with surgical ligament release with a full recovery.

Development and Validation of Fully Automated Measurement Framework to Quantify 3D Foot and Ankle Alignment Using Weight-Bearing CT

Category: Hindfoot; Ankle Introduction/Purpose: Traditionally, foot and ankle alignment is quantified on 2D standing radiographs. This imaging method present limitations in capturing the 3D nature of our foot and ankle alignment and the obtained manual measurements are prone to observer error. This study aims to integrate weight-bearing CT (WBCT) imaging and advanced deep learning techniques to automate and enhance quantification our 3D foot and ankle alignment. Methods: Thirty-two patients who underwent a WBCT of the foot and ankle were retrospectively included. Training and validation of a 3D nnU-Net model on 45 cases to automate the segmentation into bony models was assessed by Hausdorff distance (measures how far two 3D bone model scans are from each other) and dice coefficient (measures the similarity between 3D bone model scans). Afterwards, 35 clinically relevant 3D measurements were automatically computed using a custom-made tool. Automated measurements were assessed for accuracy against manual measurements, while the latter were analyzed for inter-observer reliability. Results: Deep learning segmentation results showed a mean Hausdorff distance of 1.41mm and a mean dice coefficient of 0.95. A good to excellent reliability and mean prediction error of under 2 degrees was found for all angles except the talonavicular coverage angle and distal metatarsal articular angle, which were under 2.5 degrees. Conclusion: In summary, this study introduces a fully automated framework (Fig. 1) to quantify foot and ankle alignment and showcases reliability comparable to current clinical practice measurements. This operator-friendly and time-efficient tool holds promise for implementation in a clinical setting, benefitting both radiologists and surgeons. Future studies are encouraged to assess the tool's impact on streamlining image assessment workflows in clinical practice.

Comprehensive analysis of glulam delamination through finite element modelling considering heat and mass transfer, plasticity and fracture mechanics: a case study using high density hardwood

With the ongoing emphasis on sustainable and eco-friendly construction, there is a rising demand for high-strength and high-stiffness engineered wood products. This trend presents both opportunities and challenges for the Australia’s hardwood industry, particularly concerning native forest-grown spotted gum (Corymbia citriodora). Glue laminated (glulam) spotted gum beams cannot be confidently commercialised due to the difficulty for its high-density to satisfy the bond integrity criteria (referred to as “delamination test”) for external products in accordance with the Australia and New Zealand Standard AS/NZS 1328.1. For in-depth understanding of the delamination process, an accurate numerical model represents a valuable and time-efficient tool. The aim of this study is to develop and detail such a model, considering heat and mass transfer, drying stresses, plasticity and fracture propagation models, using COMSOL Multiphysics 5.5. The model was validated against a series of wetting and drying experiments on spotted gum glulam, considering both moisture content variation and crack propagation along the gluelines. Results from the validated model showed that delamination is principally due to the tensile stress applied to the gluelines.

The First 100 Seconds of Sleep of rEEGs Can Be a Reliable Scoring Method for D/EE-SWAS.

Developmental/epileptic encephalopathy with spike wave activation with sleep, formerly known as electrical status epilepticus in sleep, is an electrographic pattern in which the interictal epileptiform activity is augmented by transition to sleep. Recent studies demonstrate the utility of the first 100 seconds of sleep of long-term monitoring (LTM) as a scoring method for electrical status epilepticus in sleep. Our aim was to measure the reliability of the spike-wave index (SWI) of the first 100 seconds of sleep of routine EEG (rEEG) as a tool for diagnosis of developmental/epileptic encephalopathy with spike wave activation with sleep. Approximately three hundred forty LTMs were reviewed, and 25 studies from 25 unique patients had comparable rEEGs. Two neurophysiologists calculated the SWI of the first 100 seconds of spontaneous stage II non-random eye movement sleep, the first 5-minute bin of sleep, and three separate 5-minute bins throughout sleep in LTM. This was compared to the SWI of the first 100 seconds of sleep in rEEG. Agreement was analyzed using Lin's concordance correlation coefficient (CCC). Using 50% as a diagnostic cut-off, we observed moderate agreement between the SWI of the first 100 seconds of sleep of rEEG and three bin LTM (CCC = 0.94, 95% CI: 0.88-0.97). Agreement was slightly higher for the comparison to first bin LTM SWI (CCC = 0.96, 95% CI: 0.92-0.98) and first 100 seconds LTM SWI (CCC = 0.96, 95% CI: 0.92-0.98). This study demonstrates the first 100 seconds of sleep of rEEG technique as a time efficient diagnostic tool for patients with concern for developmental/epileptic encephalopathy with spike wave activation with sleep.

Development and validity of the 'Modified Mediterranean Prime Screen' to assess dietary intake among women of reproductive age in Lebanon.

The Mediterranean Region registers an increasing prevalence of obesity. The region lacks a diet screener to assess obesogenic nutrients. This study aimed to evaluate the reproducibility and validity of the Modified Mediterranean Prime Screen (MMPS) in estimating obesogenic nutrients' intake among women of reproductive age, as compared with a culturally validated Food Frequency Questionnaire (FFQ), in Lebanon. We developed the MMPS consisting of thirty-two food/beverage items specific to the Lebanese Mediterranean culture. The MMPS and FFQ were administered in two visits (2 weeks-6 months apart), face to face and via telephone during the coronavirus disease 2019 pandemic. The reproducibility and validity of the MMPS were assessed using intraclass correlation coefficients (ICC) and Pearson's correlations, respectively. The study included 143 women, aged 31·5 (sd 4·6) years, BMI 24·2 (sd 4·0) kg/m2, 87 % with university education and 91 % food secure. The reproducibility of the MMPS was moderate for energy and all assessed nutrients except for SFA (ICC = 0·428). The agreement of the MMPS with the reference FFQ was adequate for energy and obesogenic nutrients. Yet, the Pearson correlations for energy-adjusted nutrient intake were low for trans-fatty acids (0·294) and PUFA (0·377). The MMPS can be a time-efficient tool for dietary assessment of energy and many obesogenic nutrients. Future studies should validate the MMPS across the lifespan and re-evaluate it after updating the fatty acid profiles in the culturally specific food composition tables.

Development and validation of a fully automated tool to quantify 3D foot and ankle alignment using weight-bearing CT

IntroductionFoot and ankle alignment plays a pivotal role in human gait and posture. Traditional assessment methods, relying on 2D standing radiographs, present limitations in capturing the dynamic 3D nature of foot alignment during weight-bearing and are prone to observer error. This study aims to integrate weight-bearing CT (WBCT) imaging and advanced deep learning (DL) techniques to automate and enhance quantification of the 3D foot and ankle alignment. MethodsThirty-two patients who underwent a WBCT of the foot and ankle were retrospectively included. After training and validation of a 3D nnU-Net model on 45 cases to automate the segmentation into bony models, 35 clinically relevant 3D measurements were automatically computed using a custom-made tool. Automated measurements were assessed for accuracy against manual measurements, while the latter were analyzed for inter-observer reliability. ResultsDL-segmentation results showed a mean dice coefficient of 0.95 and mean Hausdorff distance of 1.41 mm. A good to excellent reliability and mean prediction error of under 2 degrees was found for all angles except the talonavicular coverage angle and distal metatarsal articular angle. ConclusionIn summary, this study introduces a fully automated framework for quantifying foot and ankle alignment, showcasing reliability comparable to current clinical practice measurements. This operator-friendly and time-efficient tool holds promise for implementation in clinical settings, benefiting both radiologists and surgeons. Future studies are encouraged to assess the tool's impact on streamlining image assessment workflows in a clinical environment.

Improved motion correction in brain MRI using 3D radial trajectory and projection moment analysis.

To develop a generalized rigid body motion correction method in 3D radial brain MRI to deal with continuous motion pattern through projection moment analysis. An assumption was made that the multichannel coil moves with the head, which was achieved by using a flexible head coil. A two-step motion correction scheme was proposed to directly extract the motion parameters from the acquired k-space data using the analysis of center-of-mass with high noise robustness, which were used for retrospective motion correction. A recursive least-squares model was introduced to recursively estimate the motion parameters for every single spoke, which used the smoothness of motion and resulted in high temporal resolution and low computational cost. Five volunteers were scanned at 3 T using a 3D radial multidimensional golden-means trajectory with instructed motion patterns. The performance was tested through both simulation and in vivo experiments. Quantitative image quality metrics were calculated for comparison. The proposed method showed good accuracy and precision in both translation and rotation estimation. A better result was achieved using the proposed two-step correction compared to traditional one-step correction without significantly increasing computation time. Retrospective correction showed substantial improvements in image quality among all scans, even for stationary scans. The proposed method provides an easy, robust, and time-efficient tool for motion correction in brain MRI, which may benefit clinical diagnosis of uncooperative patients as well as scientific MRI researches.

Clinicians Address Gestational Weight Gain and Nutrition Using the International Federation of Gynecology and Obstetrics (FIGO) Nutrition Checklist.

The negative effects of excessive gestational weight gain (GWG) and obesity during pregnancy are well documented in the literature. However, lack of time, education, comfort, and confidence among health care providers often make it difficult to provide proper nutrition and weight gain guidance for pregnant persons. In response, the International Federation of Gynecology and Obstetrics (FIGO) has developed a nutrition checklist that can standardize recommendations for GWG, facilitate discussions with pregnant persons, and aid providers with nutrition education. The checklist is an innovative tool that can help reduce complications associated with excessive GWG. This article discusses the benefits of FIGO Nutrition Checklist and its implementation at a midwifery clinic that primarily serves Native American women. By using this quick, simple, guided, time-efficient tool, clinics can be successful in facilitating important conversations and education about nutrition and GWG during pregnancy.

Cashew (Anacardium occidentale L) bark extract for eco-safe dyeing of mordanted cotton fabric: Colorimetric and biomedical functional properties

Large volume of wastewater containing toxic chemicals often discharged into the environment by textile industry is adversely damaging the quality of the ecosystem. This study aimed to circumvent this unfortunate practice through replacement of toxic chemical-mordants with eco-safe herbal-mordants in the cashew bark (CB) naturally dyed cotton fabric. The high dye yield obtained within short period of time vindicated microwave (MW) radiator as a cost, time and labor efficient modern heating tool in dye extraction from natural source. The MW-assisted CB dye extract was chosen for dyeing cotton fabric based on its desired properties (like high yield, low moisture content and bulk density). It was discovered that dye exhaustion, dye-uptake and tint strength of cotton fabric were enhanced after 4 min MW treatment. Utilization of calcium (II) chloride (CaCl2) and iron (II) sulfate (FeSO4) as chemical mordants, Vernonia amygdalina and Sorghum bicolor leaf extracts as bio-mordants has given a set of attractive shades (yellow to chocolate). The mordanted CB dyed cotton fabrics have desired dye exhaustion, dye-uptake and tint strength. The good colorfastness and biomedical functional properties of MW induced CB dyed cotton fabric were enhanced to excellent after post-mordanted with the herbal-anchors. Overall, MW treatment and exploitation of herbal-anchors have showcased the cotton natural dyeing process as the potential industrial cleaner, ecological-friendly and sustainable practice.

Integrating plant morphological traits with remote-sensed multispectral imageries for accurate corn grain yield prediction.

Sustainable crop production requires adequate and efficient management practices to reduce the negative environmental impacts of excessive nitrogen (N) fertilization. Remote sensing has gained traction as a low-cost and time-efficient tool for monitoring and managing cropping systems. In this study, vegetation indices (VIs) obtained from an unmanned aerial vehicle (UAV) were used to detect corn (Zea mays L.) response to varying N rates (ranging from 0 to 208 kg N ha-1) and fertilizer application methods (liquid urea ammonium nitrate (UAN), urea side-dressing and slow-release fertilizer). Four VIs were evaluated at three different growth stages of corn (V6, R3, and physiological maturity) along with morphological traits including plant height and leaf chlorophyll content (SPAD) to determine their predictive capability for corn yield. Our results show no differences in grain yield (average 13.2 Mg ha-1) between furrow-applied slow-release fertilizer at ≥156 kg N ha-1 and 208 kg N ha-1 side-dressed urea. Early season remote-sensed VIs and morphological data collected at V6 were least effective for grain yield prediction. Moreover, multivariate grain yield prediction was more accurate than univariate. Late-season measurements at the R3 and mature growth stages using a combination of normalized difference vegetation index (NDVI) and green normalized difference vegetation index (GNDVI) in a multilinear regression model showed effective prediction for corn yield. Additionally, a combination of NDVI and normalized difference red edge index (NDRE) in a multi-exponential regression model also demonstrated good prediction capabilities.

A 1D CNN-Based Emulator of CMAQ: Predicting NO2 Concentration over the Most Populated Urban Regions in Texas

Abstract In this study, we developed an emulator of the Community Multiscale Air Quality (CMAQ) model by employing a one-dimensional (1D) convolutional neural network (CNN) algorithm to predict hourly surface nitrogen dioxide (NO2) concentrations over the most densely populated urban regions in Texas. The inputs for the emulator were the same as those for the CMAQ model, which includes emission, meteorological, and land-use/land-cover data. We trained the model over June, July, and August (JJA) of 2011 and 2014 and then tested it on JJA of 2017, achieving an index of agreement (IOA) of 0.95 and a correlation of 0.90. We also employed temporal threefold cross validation to evaluate the model’s performance, ensuring the robustness and generalizability of the results. To gain deeper insights and understand the factors influencing the model’s surface NO2 predictions, we conducted a Shapley additive explanations analysis. The results revealed solar radiation reaching the surface, planetary boundary layer height, and NOx (NO + NO2) emissions are key variables driving the model’s predictions. These findings highlight the emulator’s ability to capture the individual impact of each variable on the model’s NO2 predictions. Furthermore, our emulator outperformed the CMAQ model in terms of computational efficiency, being more than 900 times as fast in predicting NO2 concentrations, enabling the rapid assessment of various pollution management scenarios. This work offers a valuable resource for air pollution mitigation efforts, not just in Texas, but with appropriate regional data training, its utility could be extended to other regions and pollutants as well. Significance Statement This work develops an emulator of the Community Multiscale Air Quality model, using a one-dimensional convolutional neural network to predict hourly surface NO2 concentrations across densely populated regions in Texas. Our emulator is capable of providing rapid and highly accurate NO2 estimates, enabling it to model diverse scenarios and facilitating informed decision-making to improve public health outcomes. Notably, this model outperforms traditional methods in computational efficiency, making it a robust, time-efficient tool for air pollution mitigation efforts. The findings suggest that key variables like solar radiation, planetary boundary layer height, and NOx (NO + NO2) emissions significantly influence the model’s NO2 predictions. By adding appropriate training data, this work can be extended to other regions and other pollutants such as O3, PM2.5, and PM10, offering a powerful tool for pollution mitigation and public health improvement efforts worldwide.

Content-Based Text Extraction from Image using Deep Learning

Text extraction proves beneficial in various scenarios, as it allows the conversion of information stored in non-text formats, like images or documents, into machine-readable and searchable text. In contemporary times, this technique serves as a time-efficient tool across different sectors such as real estate, finance, law, food ordering and delivery, and e-commerce. Industries are increasingly adopting text extraction methods. Previously, numerous models centered around text extraction utilized OCR, CNN, and RNN. When it comes to extracting text from images using content-based approaches, CNNs play a crucial role in recognizing and locating text regions within the images. In situations where the identification and transcription of text from images are essential, RNNs prove valuable for content-based text extraction. While CNNs and RNNs independently yield accurate outcomes in content-driven text extraction from photos, the combined utilization of both methods surpasses the individual effectiveness of each. The proposed CRNN system stands out in various aspects compared to existing methodologies. It not only demonstrates heightened accuracy and efficiency but also exhibits superior performance overall. Our investigation's findings highlight that the CRNN methodology, when applied, outperforms previous approaches by recognizing text in images with a reduced latency and more precise recognition

A case study on the application of spore sampling for the monitoring of macrofungi.

Fungi play a vital role in ecosystem functioning, yet significant knowledge gaps persist in understanding their diversity and distribution leading to uncertainties about their threat status and extinction risk. This is partly owed to the difficulty of monitoring fungi using traditional fruiting body surveys. The present study evaluates airborne environmental DNA (eDNA) sampling as a monitoring tool with a focus on grassland macrofungi. We applied active and passive air sampling methods, complemented by extensive field surveys of waxcap and clavarioid fungi-species groups of high relevance for conservation. Twenty-nine species were recorded during the field surveys, 19 of which were also detectable by ITS2 metabarcoding of the collected samples. An additional 12 species from the studied genera were identified exclusively in air eDNA. We found that the patterns of species detection and read abundance in air samples reflected the abundance and occurrence of fruiting bodies on the field. Dispersal kernels fitted for the three dominant species predicted rapidly decreasing spore concentrations with increasing distance from fruitbodies. Airborne assemblages were dominated by a high diversity of common species, while rare and threatened red-listed species were under-represented, which underscores the difficulty in detecting rare species, not only in conventional surveys. Considering the benefits and drawbacks of air sampling and fruitbody surveys, we conclude that air sampling serves as a cost- and time-efficient tool to characterize local macrofungal communities, providing the potential to facilitate and improve future fungal monitoring efforts.

Microsoft Bing outperforms five other generative artificial intelligence chatbots in the Antwerp University multiple choice medical license exam.

Recently developed chatbots based on large language models (further called bots) have promising features which could facilitate medical education. Several bots are freely available, but their proficiency has been insufficiently evaluated. In this study the authors have tested the current performance on the multiple-choice medical licensing exam of University of Antwerp (Belgium) of six widely used bots: ChatGPT (OpenAI), Bard (Google), New Bing (Microsoft), Claude instant (Anthropic), Claude+ (Anthropic) and GPT-4 (OpenAI). The primary outcome was the performance on the exam expressed as a proportion of correct answers. Secondary analyses were done for a variety of features in the exam questions: easy versus difficult questions, grammatically positive versus negative questions, and clinical vignettes versus theoretical questions. Reasoning errors and untruthful statements (hallucinations) in the bots' answers were examined. All bots passed the exam; Bing and GPT-4 (both 76% correct answers) outperformed the other bots (62-67%, p = 0.03) and students (61%). Bots performed worse on difficult questions (62%, p = 0.06), but outperformed students (32%) on those questions even more (p<0.01). Hallucinations were found in 7% of Bing's and GPT4's answers, significantly lower than Bard (22%, p<0.01) and Claude Instant (19%, p = 0.02). Although the creators of all bots try to some extent to avoid their bots being used as a medical doctor, none of the tested bots succeeded as none refused to answer all clinical case questions.Bing was able to detect weak or ambiguous exam questions. Bots could be used as a time efficient tool to improve the quality of a multiple-choice exam.

Exploring reliable photogrammetry techniques for 3D modeling in anatomical research and education.

In anatomical research and education, three-dimensional visualization of anatomical structures is crucial for understanding spatial relationships in diagnostics, surgical planning, and teaching. While computed tomography (CT) and magnetic resonance imaging (MRI) offer valuable insights, they are often expensive and require specialized resources. This study explores photogrammetry as an affordable and accessible approach for 3D modeling in anatomical contexts. Two photogrammetry methods were compared: conventional open-source software (Colmap) and Apple's RealityKit Object Capture. Human C3 vertebrae were imaged with a 24 MP camera, with and without a cross-polarization filter. Reconstruction times, vertex distances, surface area, and volume measurements were compared to CT scans. Results revealed that the Object Capture method surpassed the conventional approach in reconstruction speed and user-friendliness. Both methods exhibited similar vertex distance from reference mesh and volume measurements, although the conventional approach produced larger surface areas compared to CT-based models. Cross-polarization filters eliminated the need for pre-processing and improved outcomes in challenging lighting conditions. This study demonstrates that photogrammetry, especially Object Capture, as a reliable and time-efficient tool for 3D modeling in anatomical research and education. It offers accessible alternatives to traditional techniques with advantages in texture mapping. While further validation of various anatomical structures is required, the accessibility and cost-effectiveness of photogrammetry make it a valuable asset for the field. In summary, photogrammetry would have the potential to revolutionize anatomical research and education by providing cost-effective, accessible, and accurate 3D modeling. The study underscores the promise of advancing anatomical research and education through the integration of photogrammetry with ongoing improvements in user-friendliness and accessibility.

Spatially encoded pure-shift diffusion-ordered NMR spectroscopy yielded by chirp excitation

Spatially-encoded diffusion-ordered NMR spectroscopy (SPEN-DOSY) has emerged as a new time-efficient tool for the analysis of mixtures of small molecules in solution. Time efficiency is achieved using the concept of spatial parallelization of the effective gradient area, instead of the sequential incrementation used in conventional diffusion experiments. The data acquired with such sequences are then usually processed to extract diffusion coefficients, but cases when peak overlap in the 1H spectrum are difficult to address. Such limitation in conventional diffusion experiments is addressed via using the Pure Shift Yielded by CHirp Excitation (PSYCHE)-iDOSY sequence. Here we have adapted the PSYCHE-iDOSY sequence by using echo planar spectroscopic imaging (EPSI) to acquire SPEN-DOSY data. The pure shift mode of PSYCHE separates the overlapped components and a modified Stejskal-Tanner equation is used to extract the corresponding diffusion coefficient. The primary results obtained with the above-mentioned mixtures seem to open the possibility of separating complex mixtures in less time than PSYCHE-iDOSY.

What is the role of screening instruments in the management of psychiatric comorbidities in epilepsy? Tools and practical tips for the most common comorbidities: Depression and anxiety

Depression and anxiety are the most common psychiatric comorbidities in epilepsy and are known to increase healthcare utilization, the risk of refractory epilepsy, and anti-seizure medication intolerability. Despite this, depression and anxiety continue to be underrecognized and undertreated in people with epilepsy (PWE). Several barriers to the identification of depression and anxiety in PWE exist, including reliance on unstructured interviews rather than standardized, validated instruments. Moreover, there is a dearth of behavioral health providers to manage these comorbidities once identified. The use of validated screening instruments in epilepsy clinics can assist with both the identification of psychiatric symptoms and monitoring of treatment response by the epilepsy clinician for PWE with comorbid depression and/or anxiety. While screening instruments can identify psychiatric symptoms occurring within a specified time, they are not definitively diagnostic. Screeners can be time efficient tools to identify patients requiring further evaluation for diagnostic confirmation.This article reviews recent literature on the utility of depression and anxiety screening instruments in epilepsy care, including commonly used screening instruments, and provides solutions for potential barriers to clinical implementation. Validated depression and anxiety screening instruments can increase identification of depression and anxiety and guide epilepsy clinician management of these comorbidities which has the potential to positively impact patient care.

Origin of extrinsic chirality in metasurfaces and nanoholes fabricated by nanosphere lithography

Nanosphere lithography is a cost- and time-efficient tool for the fabrication of various nanostructured materials. Multiple steps of metal layer deposition at different oblique angles were shown to produce complex asymmetric and chiral shapes. Here, we investigate samples in which polystyrene nanospheres are covered by Ag or combination of Ag and Au at a single step (under 45°). In this way, we obtain metasurfaces with asymmetric shells, with a nanohole array formed due to the shadowing effect. We investigate chiro-optical properties of four samples by exciting them in the 700-1000 nm range, at angles of incidence from -45° to +45°; we report on dissymmetry in the total extinction between left and right circularly polarized excitation gext, which follows the rules of extrinsic chirality. We then resolve the transmission of Ag metasurface in terms of hyperspectral Stokes parameters, and we connect the S3 parameter with gext. Finally, we characterize nanohole arrays obtained from the same samples when the nanospheres are removed; we further perform electromagnetic simulations to gain insight into the “egg” shaped nanohole.