Median Sensitivity Research Articles

Deep Learning and Automatic Detection of Pleomorphic Esophageal Lesions—A Necessary Step for Minimally Invasive Panendoscopy

Background: Capsule endoscopy (CE) improved the digestive tract assessment; yet, its reading burden is substantial. Deep-learning (DL) algorithms were developed for the detection of enteric and gastric lesions. Nonetheless, their application in the esophagus lacks evidence. The study aim was to develop a DL model for esophageal pleomorphic lesion (PL) detection. Methods: A bicentric retrospective study was conducted using 598 CE exams. Three different CE devices provided 7982 esophageal frames, including 2942 PL lesions. The data were divided into the training/validation and test groups, in a patient-split design. Three runs were conducted, each with unique patient sets. The sensitivity, specificity, accuracy, positive and negative predictive value (PPV and NPV), area under the conventional receiver operating characteristic curve (AUC-ROC), and precision–recall curve (AUC-PR) were calculated per run. The model’s diagnostic performance was assessed using the median and range values. Results: The median sensitivity, specificity, PPV, and NPV were 75.8% (63.6–82.1%), 95.8% (93.7–97.9%), 71.9% (50.0–90.1%), and 96.4% (94.2–97.6%), respectively. The median accuracy was 93.5% (91.8–93.8%). The median AUC-ROC and AUC-PR were 0.82 and 0.93. Conclusions: This study focused on the automatic detection of pleomorphic esophageal lesions, potentially enhancing the diagnostic yield of this type of lesion, compared to conventional methods. Specific esophageal DL algorithms may provide a significant contribution and bridge the gap for the implementation of minimally invasive CE-enhanced panendoscopy.

Comparable performance between automatic and manual laryngeal and hypopharyngeal GTV delineations validated with pathology.

Deep learning is a promising approach to increase reproducibility and time-efficiency of GTV delineation in head and neck cancer, but model evaluation primarily relies on manual GTV delineations as reference annotation, which are subjective and tend to overestimate tumor volume. This study aimed to validate a deep learning model for laryngeal and hypopharyngeal GTV segmentation with pathology and to compare its performance with clinicians' manual delineations. A retrospective dataset of 193 laryngeal and hypopharyngeal cancer patients was used to train a deep learning model with clinical GTV delineations as reference. For validation, a dataset comprising 18 patients who underwent imaging before total laryngectomy was used, with histopathology-based (n=16) tumor delineations as ground truth. The performance of the automatic segmentations was compared to that of clinicians' manual delineations, both quantitatively and qualitatively. Median sensitivity (0.90 and 0.91) and largest required CTV margin (6.4 and 6.6 mm) were comparable between automatic and manual GTV delineations. The positive predictive value yielded the only significant difference between automatic and manual GTV delineations, with medians of 0.52 and 0.61, respectively (P=.03). Clinical target volumes derived from automatic and manual GTVs exhibited similar sizes (median of 44.5 and 40.1 mL) and achieved a sensitivity of 1.00 in 13/16 and 14/16 tumors, respectively. Automatic segmentations were considered clinically acceptable in 67% of cases, compared to 63% of manual delineations. The proposed deep learning model for laryngeal and hypopharyngeal GTV segmentation achieved comparable results to clinicians' manual delineations, showing the potential for more consistency and efficiency in the radiotherapy workflow.

Retrospective evaluation of a novel ultrasound-based imaging analysis software for predicting radiofrequency ablation areas.

This study aimed to introduce and evaluate a novel software-based system, BioTrace, designed for real-time monitoring of thermal ablation tissue damage during image-guided radiofrequency ablation for hepatocellular carcinoma (HCC). BioTrace utilizes a proprietary algorithm to analyze the temporo-spatial behavior of thermal gas bubble activity during ablation, as seen in conventional B-mode ultrasound imaging. Its predictive accuracy was assessed by comparing the ablation zones it predicted with those annotated by radiologists using contrast-enhanced computed tomography (CECT) 24 hours post-treatment, considered the gold standard. The study included 20 liver tumors. The median tumor measurement along the major axis was 1.2 cm. The median Dice coefficient, Sensitivity, and Precision between BioTrace and CECT were 0.90, 0.91, and 0.91, respectively. The intraclass correlation showed excellent agreement in volume size between BioTrace and CECT findings (0.98). BioTrace effectively generates an ablation damage prediction map based on real-time ultrasound imaging, accurately predicting the ablation zone as confirmed by 24-hour post-procedural CECT. This system has the potential to enhance the safety and efficacy of ablation procedures in clinical settings.

How to Identify Priority Sites for Invasive Alien Species Policy and Management

ABSTRACTAimIdentifying priority species and introduction pathways has long been a goal of national and international policy for reducing and mitigating the impacts of invasive alien species (IAS). Although identifying priority sites for invasion management is included within Target 6 of the Kunming–Montreal Global Biodiversity Framework, methods for doing so that capture both site sensitivity (i.e., the level of biodiversity value) and susceptibility to invasion have received little attention. Here we describe and implement a data‐driven approach to priority site identification that integrates spatial conservation planning and biodiversity modelling techniques.LocationAustralia.MethodsWe use the modelled distributions of 5113 Australian native species and 12 invasive alien insect species as a case study for demonstrating a data‐driven approach for identifying priority sites for the purposes of IAS surveillance and management. The approach consists of three components, namely the identification of sensitive, susceptible and subsequently their overlap (i.e., priority sites). We also compare our approach with a proposed alternative for use as priority sites, Australia's key biodiversity area (KBA) network.ResultsNumerous sensitive sites were identified across Australia using a large and taxonomically diverse set of native species and areas of known conservation importance. Most IAS distributions had a high degree of overlap with sensitive sites, with 10 out 12 species having median site sensitivities above 0.70. We also demonstrate that, by comparison, using KBA's as priority sites can underestimate the potential threat of environmentally invasive alien insects.Main ConclusionsGiven that sites most susceptible to invasion may not be the most sensitive, implementing site‐based prioritisation approaches should account for both components of priority site identification to guide IAS management and most effectively mitigate their environmental impacts. The approach demonstrated here can be applied at multiple national and sub‐national scales and improve the efficiency of interventions for IAS.

A novel interpretable deep learning model for diagnosis in emergency department dyspnoea patients based on complete data from an entire health care system.

Dyspnoea is one of the emergency department's (ED) most common and deadly chief complaints, but frequently misdiagnosed and mistreated. We aimed to design a diagnostic decision support which classifies dyspnoeic ED visits into acute heart failure (AHF), exacerbation of chronic obstructive pulmonary disease (eCOPD), pneumonia and "other diagnoses" by using deep learning and complete, unselected data from an entire regional health care system. In this cross-sectional study, we included all dyspnoeic ED visits of patients ≥ 18 years of age at the two EDs in the region of Halland, Sweden, 07/01/2017-12/31/2019. Data from the complete regional health care system within five years prior to the ED visit were analysed. Gold standard diagnoses were defined as the subsequent in-hospital or ED discharge notes, and a subsample was manually reviewed by emergency medicine experts. A novel deep learning model, the clinical attention-based recurrent encoder network (CareNet), was developed. Cohort performance was compared to a simpler CatBoost model. A list of all variables and their importance for diagnosis was created. For each unique patient visit, the model selected the most important variables, analysed them and presented them to the clinician interpretably by taking event time and clinical context into account. AUROC, sensitivity and specificity were compared. The most prevalent diagnoses among the 10,315 dyspnoeic ED visits were AHF (15.5%), eCOPD (14.0%) and pneumonia (13.3%). Median number of unique events, i.e., registered clinical data with time stamps, per ED visit was 1,095 (IQR 459-2,310). CareNet median AUROC was 87.0%, substantially higher than the CatBoost model´s (81.4%). CareNet median sensitivity for AHF, eCOPD, and pneumonia was 74.5%, 92.6%, and 54.1%, respectively, with a specificity set above 75.0, slightly inferior to that of the CatBoost baseline model. The model assembled a list of 1,596 variables by importance for diagnosis, on top were prior diagnoses of heart failure or COPD, daily smoking, atrial fibrillation/flutter, life management difficulties and maternity care. Each patient visit received their own unique attention plot, graphically displaying important clinical events for the diagnosis. We designed a novel interpretable deep learning model for diagnosis in emergency department dyspnoea patients by analysing unselected data from a complete regional health care system.

Effect of patient-contextual skin images in human- and artificial intelligence-based diagnosis of melanoma: Results from the 2020 SIIM-ISIC melanoma classification challenge.

While the high accuracy of reported AI tools for melanoma detection is promising, the lack of holistic consideration of the patient is often criticized. Along with medical history, a dermatologist would also consider intra-patient nevi patterns, such that nevi that are different from others on a given patient are treated with suspicion. To evaluate whether patient-contextual lesion-images improves diagnostic accuracy for melanoma in a dermoscopic image-based AI competition and a human reader study. An international online AI competition was held in 2020. The task was to classify dermoscopy images as melanoma or benign lesions. A multi-source dataset of dermoscopy images grouped by patient were provided, and additional use of public datasets was permitted. Competitors were judged on area under the receiver operating characteristic (AUROC) on a private leaderboard. Concurrently, a human reader study was hosted using a subset of the test data. Participants gave their initial diagnosis of an index case (melanoma vs. benign) and were then presented with seven additional lesion-images of that patient before giving a second prediction of the index case. Outcome measures were sensitivity and specificity. The top 50 of 3308 AI competition entries achieved AUROC scores ranging from 0.943 to 0.949. Few algorithms considered intra-patient lesion patterns and instead most evaluated images independently. The median sensitivity and specificity of human readers before receiving contextual images were 60.0% and 86.7%, and after were 60.0% and 85.7%. Human and AI algorithm performance varied by image source. This study provided an open-source state-of-the-art algorithm for melanoma detection that has been evaluated at multiple centres. Patient-contextual images did not positively impact performance of AI algorithms or human readers. Providing seven contextual images and no total body image may have been insufficient to test the applicability of the intra-patient lesion patterns.

Effects of postoperative glucocorticoids on mitigation of organ dysfunction in patients with type A aortic dissection: a randomized controlled trial.

This study aims to evaluate the organ-protective efficacy of postoperative glucocorticoid in patients with type A aortic dissection. Postoperative type A aortic dissection patients were randomly allocated to receive either postoperative glucocorticoid or standard-of-care treatment. Intravenous methylprednisolone was administered for 3 days. The primary outcome was the reduction of Sequential Organ Failure Assessment score on postoperative day 4 compared to baseline (on postoperative day 1 before methylprednisolone administration). Two hundred twelve patients were included in the intention-to-treat analysis. The primary outcome was significantly different between groups: 3.16 ± 2.52 in the control group versus 4.36 ± 2.82 in the glucocorticoid group (absolute difference 1.20 [95% CI 0.52-1.93], P = 0.001). The glucocorticoid group showed markedly lower median high-sensitivity C-reactive protein levels compared to the control group (91.0 mg/l vs 182.0 mg/l; absolute difference: -91 (95% CI -122 to -57), P = 0.009) on postoperative day 4. Fewer patients in the glucocorticoid group required continuous renal replacement therapy (8.5% vs 19.8% in the control group; absolute difference: -10.4 [95% CI -19.1 to -1.3], P = 0.03). This trial demonstrates that postoperative glucocorticoid in patients with type A aortic dissection significantly reduces postoperative inflammation and improves recovery of early organ dysfunction. These findings advocate for the implementation of glucocorticoid in the early phase after surgery for enhanced organ protection.

Evaluating Imputation Methods to Improve Prediction Accuracy for an HIV Study in Uganda

Standard statistical analyses often exclude incomplete observations, which can be particularly problematic when predicting rare outcomes, such as HIV positivity. In the linkage to the HIV care dataset, there were initially 553 complete HIV positive cases, with an additional 554 cases added through imputation. Imputation methods amelia, hmisc, mice and missForest were evaluated. Simulations were conducted across various scenarios using the complete data to guide imputation for the full dataset. A random forest model was used to predict HIV status, assessing imputation precision, overall prediction accuracy, and sensitivity. While missForest produced imputed values closer to the observed ones, this did not translate into better predictive models. Hmisc and mice imputations led to higher prediction accuracy and sensitivity, with median accuracy increasing from 64% to 76% and median sensitivity rising from 0.4 to 0.75. Hmisc and amelia were the fastest imputation methods. Additionally, oversampling the minority class combined with undersampling the majority class did not improve predictions of new HIV positive cases using only the complete observations. However, increasing the minority class information through imputation enhanced sensitivity for predicting cases in this class.

Using signal detection test to assess the correlation between daily sleep duration, mental workload, and attention sensitivity in nurses

BackgroundIncreased attention among healthcare workers, particularly nurses, is crucial for preventing medical errors and patient harm. This study uses an objective approach that overcomes the limitations of the subjective self-report measures employed in prior research on nurses’ attention.MethodsThis cross-sectional study was conducted from January to March 2023 among 108 nurses at Besat Hospital in Tehran, Iran. The data collected included demographic information, the NASA Task Load Index (NASA-TLX) for mental workload assessment, and the CogLab signal detection test for attention sensitivity measurement.ResultsThe participants exhibited a median mental workload of 68.5 (IQR = 14.9) and a median attention sensitivity of 52.5 (IQR = 39.2). Daily sleep duration was positively correlated with attention sensitivity (r = 0.644, p < 0.001), whereas mental workload was negatively correlated with attention sensitivity (r = -0.655, p < 0.001). Men demonstrated greater attention sensitivity (p = 0.040), and women reported greater mental workload (p = 0.043).ConclusionReducing daily sleep duration and increasing mental workload can diminish nurses’ attention sensitivity. Prioritizing adequate sleep and implementing strategies to reduce mental workload are crucial for enhancing nurse performance and patient safety.

Added Value of Medical Subject Headings Terms in Search Strategies of Systematic Reviews: Comparative Study.

The massive increase in the number of published scientific articles enhances knowledge but makes it more complicated to summarize results. The Medical Subject Headings (MeSH) thesaurus was created in the mid-20th century with the aim of systematizing article indexing and facilitating their retrieval. Despite the advent of search engines, few studies have questioned the relevance of the MeSH thesaurus, and none have done so systematically. The objective of this study was to estimate the added value of using MeSH terms in PubMed queries for systematic reviews (SRs). SRs published in 4 high-impact medical journals in general medicine over the past 10 years were selected. Only SRs for which a PubMed query was provided were included. Each query was transformed to obtain 3 versions: the original query (V1), the query with free-text terms only (V2), and the query with MeSH terms only (V3). These 3 queries were compared with each other based on their sensitivity and positive predictive values. In total, 59 SRs were included. The suppression of MeSH terms had an impact on the number of relevant articles retrieved for 24 (41%) out of 59 SRs. The median (IQR) sensitivities of queries V1 and V2 were 77.8% (62.1%-95.2%) and 71.4% (42.6%-90%), respectively. V1 queries provided an average of 2.62 additional relevant papers per SR compared with V2 queries. However, an additional 820.29 papers had to be screened. The cost of screening an additional collected paper was therefore 313.09, which was slightly more than triple the mean reading cost associated with V2 queries (88.67). Our results revealed that removing MeSH terms from a query decreases sensitivity while slightly increasing the positive predictive value. Queries containing both MeSH and free-text terms yielded more relevant articles but required screening many additional papers. Despite this additional workload, MeSH terms remain indispensable for SRs.

Specific human gene expression in response to infection is an effective marker for diagnosis of latent and active tuberculosis

RNA sequencing and microarray analysis revealed transcriptional markers expressed in whole blood can differentiate active pulmonary TB (ATB) from other respiratory diseases (ORDs), and latent TB infection (LTBI) from healthy controls (HC). Here we describe a streamlined reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) assay that could be applied at near point-of-care for diagnosing and distinguishing ATB from ORDs and LTBI from HC. A literature review was undertaken to identify the most plausible host-gene markers (HGM) of TB infection. Primers, and dual labelled hydrolysis probes were designed and analytically evaluated for accuracy in an in-vitro model of infection using a lung fibroblast cell-line. Best performing genes were multiplexed into panels of 2–4 targets and taken forward for clinical evaluation. Mycobacteria Growth Indicator Tube and QuantiFERON-TB Gold Plus were used as reference tests for ATB and LTBI respectively. A total of 16 HGM were selected and incorporated into five multiplex RT-qPCR panels. PCR assay efficiency of all evaluated targets was ≥ 90% with a median analytical sensitivity of 292 copies/µl [IQR: 215.0-358.3 copies/µl], and a median limit of quantification of 61.7 copies/µl [IQR: 29.4-176.3 copies/µl]. Clinically, ATB was characterised by higher gene expression than ORDs, while LTBI was associated with lower gene expression than HC, Kruskal-Wallis p < 0.0001. Crucially, BATF2, CD64, GBP5, C1QB, GBP6, DUSP3, and GAS6 exhibited high differentiative ability for ATB from ORDs, LTBI or HC while KLF2, PTPRC, NEMF, ASUN, and ZNF296 independently discriminated LTBI from HC. Our results show that different HGM maybe required for ATB and LTBI differentiation from ORDs or HC respectively and demonstrate the feasibility of host gene-based RT-qPCR to diagnose ATB and LTBI at near point-of-care.

Automatic Segmentation of Abdominal Aortic Aneurysms from Time-Resolved 3D Ultrasound Images Using Deep Learning.

Abdominal aortic aneurysms (AAAs) are rupture-prone dilatations of the aorta. In current clinical practice, the maximal diameter of AAAs is monitored with 2D ultrasound to estimate their rupture risk. Recent studies have shown that 3-dimensional and mechanical AAA parameters might be better predictors for aneurysm growth and rupture than the diameter. These parameters can be obtained with time-resolved 3D ultrasound (3D+t US), which requires robust and automatic segmentation of AAAs from 3D+t US. This study proposes and validates a deep learning (DL) approach for automatic segmentation of AAAs. 500 AAA patients were included for follow-up 3D+t US imaging, resulting in 2495 3D+t US images. Segmentation masks for model training were obtained using a conventional automatic segmentation algorithm ('nonDL'). Four different DL models were trained and validated by (1) comparison to CT and (2) reader scoring. Performance of the nonDL and different DL segmentation strategies were evaluated by comparing Hausdorff distance, Dice scores, accuracy, sensitivity, and specificity with a sign test. All DL models had higher median Dice scores, accuracy, and sensitivity (all p < 0.003) compared to nonDL segmentation. The full image-resolution model without data augmentation showed the highest median Dice score and sensitivity (p < 0.001). Applying the DL model on an independent test group produced fewer poor segmentation scores of 1 to 2 on a five-point scale (8% for DL, 18% for nonDL). This demonstrates that a robust and automatic segmentation algorithm for segmenting abdominal aortic aneurysms from 3D+t US images was developed, showing improved performance compared to conventional segmentation.

Artificial intelligence-based quantification of lymphocytes in feline small intestinal biopsies.

Feline chronic enteropathy is a poorly defined condition of older cats that encompasses chronic enteritis to low-grade intestinal lymphoma. The histological evaluation of lymphocyte numbers and distribution in small intestinal biopsies is crucial for classification and grading. However, conventional histological methods for lymphocyte quantification have low interobserver agreement, resulting in low diagnostic reliability. This study aimed to develop and validate an artificial intelligence (AI) model to detect intraepithelial and lamina propria lymphocytes in hematoxylin and eosin-stained small intestinal biopsies from cats. The median sensitivity, positive predictive value, and F1 score of the AI model compared with the majority opinion of 11 veterinary anatomic pathologists, were 100% (interquartile range [IQR] 67%-100%), 57% (IQR 38%-83%), and 67% (IQR 43%-80%) for intraepithelial lymphocytes, and 89% (IQR 71%-100%), 67% (IQR 50%-82%), and 70% (IQR 43%-80%) for lamina propria lymphocytes, respectively. Errors included false negatives in whole-slide images with faded stain and false positives in misidentifying enterocyte nuclei. Semiquantitative grading at the whole-slide level showed low interobserver agreement among pathologists, underscoring the need for a reproducible quantitative approach. While semiquantitative grade and AI-derived lymphocyte counts correlated positively, the AI-derived lymphocyte counts overlapped between different grades. Our AI model, when supervised by a pathologist, offers a reproducible, objective, and quantitative assessment of feline intestinal lymphocytes at the whole-slide level, and has the potential to enhance diagnostic accuracy and consistency for feline chronic enteropathy.

Radiomics-based machine learning role in differential diagnosis between small renal oncocytoma and clear cells carcinoma on contrast-enhanced CT: A pilot study

PurposeTo investigate the potential role of radiomics-based machine learning in differentiating small renal oncocytoma (RO) from clear cells carcinoma (ccRCC) on contrast-enhanced CT (CECT). Material and methodsFifty-two patients with small renal masses who underwent CECT before surgery between January 2016 and December 2020 were retrospectively included in the study. At pathology examination 39 ccRCC and 13 RO were identified. All lesions were manually delineated unenhanced (B), arterial (A) and venous (V) phases. Radiomics features were extracted using three different fixed bin widths (bw) of 25 HU, 10 HU, and 5 HU from each phase (B, A, V), and with different combinations (B+A, B+V, B+A+V, A+V), leading to 21 different datasets. Montecarlo Cross Validation technique was used to quantify the estimator performance. The final model built using the hyperparameter selected with Optuna was trained again on the training set and the final performance evaluation was made on the test set. ResultsThe A+V bw 10 achieved the greater median (IQR) balanced accuracy considering all the models of 0.70 (0.64–0.75), while A bw 10 considering only the monophasic ones. The A bw 10 model achieved a median (IQR) sensitivity of 0.60 (0.40–0.60), specificity of 0.80 (0.73–0.87), AUC-ROC of 0.77 (0.66–0.84), accuracy of 0.75 (0.70–0.80), and a Phi Coefficient of 0.38 (0.20–0.47). None of the nine models with the lowest mean balanced accuracy values implemented features from A. ConclusionThe A bw 10 model was identified as the most efficient mono-phasic model in differentiating small RO from ccRCC.

The Local Group L-band Survey: The First Measurements of Localized Cold Neutral Medium Properties in the Low-metallicity Dwarf Galaxy NGC 6822

Abstract Measuring the properties of the cold neutral medium (CNM) in low-metallicity galaxies provides insights into heating and cooling mechanisms in early Universe-like environments. We report detections of two localized atomic neutral hydrogen (H i) absorption features in NGC 6822, a low-metallicity (0.2 Z ⊙) dwarf galaxy in the Local Group. These are the first unambiguous CNM detections in a low-metallicity dwarf galaxy outside the Magellanic Clouds. The Local Group L-band Survey (LGLBS) enabled these detections, due to its high spatial (15 pc for H i emission) and spectral (0.4 km s−1) resolution. We introduce LGLBS and describe a custom pipeline for searching for H i absorption at high angular resolution and extracting associated H i emission. A detailed Gaussian decomposition and radiative transfer analysis of the NGC 6822 detections reveals five CNM components, with key properties: a mean spin temperature of 32 ± 6 K, a mean CNM column density of 3.1 × 1020 cm−2, and CNM mass fractions of 0.33 and 0.12 for the two sightlines. Stacking nondetections does not reveal low-level signals below our median optical depth sensitivity of 0.05. One detection intercepts a star-forming region, with the H i absorption profile encompassing the CO (2−1) emission, indicating coincident molecular gas and a depression in high-resolution H i emission. We also analyze a nearby sightline with deep, narrow H i self-absorption dips, where the background warm neutral medium is attenuated by intervening CNM. The association of CNM, CO, and Hα emissions suggests a close link between the colder, denser H i phase and star formation in NGC 6822.

Frequency-of-seeing curves (psychometric functions) for perimetric stimuli in age-related macular degeneration.

Frequency-of-seeing (FoS) curves (psychometric functions) for perimetric stimuli have been widely used in computer simulations of new visual field test procedures. FoS curves for age-related macular degeneration (AMD) are not available in the literature and are needed for the development of improved microperimetry test procedures, which are of particular interest for use as clinical trial endpoints. Data were refitted from a previous study to generate FoS curves for 20 participants with AMD, each tested at nine locations within the central 10°. Stimulus parameters, background luminance and dB scale were matched to the MAIA-2 microperimeter, and stimuli were presented in a method of constant stimuli to build up FoS curves over multiple runs. FoS curves were fitted with a modified cumulative Gaussian function. The relationship between sensitivity and slope of fitted FoS curves was modelled by robust linear regression, producing models both with and without an eccentricity parameter. FoS curves were satisfactorily fitted to data from 174 visual field locations in 20 participants (age 65-83 years, 11 female). Each curve was made up of a median of 243 (range 177-297) stimulus presentations over a median of 12 (range 9-32) levels. Median sensitivity was 25.5 dB (range 3.8-31.4 dB). The median slope (SD of fitted function) was 1.6 dB (range 0.5-8.5 dB). As in previous studies of other conditions, the slope of fitted FoS curves increased as sensitivity decreased (p < 0.001). FoS are provided for participants with AMD, as well as models of the relationship between sensitivity and slope. These fitted models and data may be useful for computer simulation studies of microperimetry procedures. Full details of the fitted curves are provided as supporting information.

Potential to Identify the Neutrino Mass Ordering with Reactor Antineutrinos in JUNO

Abstract The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose neutrino experiment under construction in South of China. This paper presents an updated estimate of JUNO's sensitivity to the neutrino mass ordering using the reactor antineutrinos emitted from eight nuclear reactor cores in the Taishan and Yangjiang nuclear power plants. This measurement is planned by studying the fine interference pattern caused by quasi-vacuum oscillations in the oscillated antineutrino spectrum at a baseline of 52.5~km and is completely independent of the CP violating phase and the neutrino mixing angle θ23. The sensitivity is obtained through a joint analysis of JUNO and TAO detectors utilizing the best available knowledge to date about the location and overburden of the JUNO experimental site, the local and global nuclear reactors, the JUNO and TAO detectors responses, the expected event rates and spectra of signal and backgrounds, and the systematic uncertainties of the analysis inputs. It is found that a 3σ median sensitivity to reject the wrong mass ordering hypothesis can be reached with an exposure of about 6.5 years × 26.6~GW thermal power.Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Article funded by SCOAP3 and published under licence by Chinese Physical Society and the Institute of High Energy Physics of the Chinese Academy of Science and the Institute of Modern Physics of the Chinese Academy of Sciences and IOP Publishing Ltd.

Establishing Clinical Trial Endpoints in Selecting Patients for RPGR Retinal Gene Therapy.

Clinical trials for X-linked retinitis pigmentosa (RP) often assess retinal structure using optical coherence tomography (OCT) and function using microperimetry to evaluate initial eligibility and endpoints. Therefore, we seek to determine which parameters might be most sensitive in screening new patients for enrollment. Thirty-one patients (62 eyes) with confirmed retinitis pigmentosa GTPase regulator (RPGR) mutations attending Oxford Eye Hospital were included in this retrospective analysis. Outer retinal structure was investigated by measuring the remaining ellipsoid zone (EZ) and external limiting membrane (ELM) on OCT. Visual function was evaluated by using 10-2 microperimetry mean sensitivity. The median age of patients with RPGR was 31 years (interquartile range [IQR] = 22-39 years). For the right and left eyes, respectively, the median EZ length through the foveal section was 921 µm (IQR = 607-1570) and 865 µm (IQR = 508-1442) and median ELM length was 2056 µm (IQR = 1336-2764) and 1860 µm (IQR = 1152-2680). Similarly, the median microperimetry sensitivity (MS) was 2.0 decibel (dB; IQR = 0.4-5.4) and 1.1 dB (IQR = 0.1-5.4). Linear mixed model regression analysis showed that EZ was significantly positively correlated to ELM (P < 0.001, R² = 0.931). EZ and ELM were significantly correlated with the microperimetry sensitivity with exponential relationship (P < 0.001, R² = 0.71 and 0.72, respectively). Using the exponential equation of regression line, EZ below approximately 600 µm (RE = 637 µm, 95% confidence interval [CI] = 397-877, LE = 586 µm, 95% CI = 356-817) results in microperimetry sensitivity of approximately 0 dB. There was high degree of inter-eye symmetry for progression of EZ, ELM, and microperimetry sensitivity. Age was significantly correlated with the analyzed parameters (P < 0.001), although with low R² for each of them. EZ may comprise a surrogate biomarker for prediction of visual function in X-linked RP caused by mutations in RPGR and, in turn, identification of appropriate patients for enrollment in clinical trials. As expected, age plays a role in predicting potential biomarkers and visual function, however, it should not be used to preclude patients for gene therapy due to the poor correlation and heterogeneity of disease onset. Biomarkers of visual function in RPGR-associated RP may lead to identification of appropriate patients for enrollment in clinical trials.

Compressive Syndrome of the Ostio-fibrous Tunnel at Carp Level - A literary review

CTS tunnel syndrome is characterized by compression of the median nerve inside the carpal tunnel, at the level of the wrist. This disease still has unknown etiology, but it presents itself in a secondary way when related to other pathologies. Compression of the median nerve in the carpal tunnel occurs in most cases of nonspecific chronic flexor tenosynovitis, and it can also occur in many other nosological entities or lesions that occupy space in the tunnel. The prevalence of ostiofibrous tunnel compression syndrome at the carpal level is estimated to be between 4% and 5% of the population, ranging from 51 to 125:100,000, and it occurs more frequently in females, especially between 40 and 60 years of age. The main clinical manifestations include paresthesia in the territory of median nerve sensitivity (particularly in the thumb, index, middle and radial aspect of the ring finger), hyperesthesia (less frequent hypo or anesthesia), wrist pain that may radiate to the fingers, hands, elbows, and in some cases, even the shoulder (such symptoms may disappear with movement of the limb or massage). Diagnosis must be clinical, based on the patient's history and physical examination. For the physical examination, the Tinel and Phalen tests can be used. Complementary tests are used to make a diagnosis, the most recommended being: electroneuromyography (ENMG), magnetic resonance imaging (MRI), computed tomography (CT) and ultrasonography (US). Treatment can be conservative or surgical. The conservative way consists of adopting techniques to alleviate symptoms, such as pain control through wrist orthoses for immobilization, injections and medication, alternative therapies and physiotherapy. Surgical treatment aims to decompress the median nerve and reduce pressure on the carpal tunnel. This approach is adopted when conservative treatment does not lead to an improvement in the clinical condition within 6 to 8 weeks. This paper aims to review the medical aspects of ostiofibrous tunnel compression syndrome at the level of the carpus. The methodology used is analytical in nature through a literature review. The data extracted in the research were from the US National Library of Medicine (PubMed) with the descriptors: median nerve, wrist and carpal tunnel syndrome, according to the Health Sciences Descriptors (DeCS). To perform the crossing of these, the Boolean operator AND was used. 43 articles were found and after inclusion and exclusion criteria, 36 articles were used. Those used for inclusion: articles in English, Portuguese or Spanish, between June 2015 and July 2021, and that were available in full and for free online access. Studies that did not address the relevant concept for achieving the objective were excluded; repeated studies; according to criteria of methodological quality. Thus, the medical evaluation must be performed in a specific way, making it essential to collect the clinical history, physical exams and complementary exams in order to choose to improve conservative therapy and, later, surgical, if necessary. These facts will allow a greater therapeutic success, allowing the greatest well-being to the patient.

A GMRT 610 MHz radio survey of the North Ecliptic Pole (NEP, ADF-N) / Euclid Deep Field North

ABSTRACT This paper presents a 610 MHz radio survey covering 1.94 deg$^2$ around the North Ecliptic Pole (NEP), which includes parts of the ${\it AKARI}$ (ADF-N) and Euclid, Deep Fields North. The median 5$\sigma$ sensitivity is 28 $\mu$Jy beam$^{-1}$, reaching as low as 19 $\mu$Jy beam$^{-1}$, with a synthesized beam of 3.6 arcsec $\times$ 4.1 arcsec. The catalogue contains 1675 radio components, with 339 grouped into multicomponent sources and 284 ‘isolated’ components likely part of double radio sources. Imaging, cataloguing, and source identification are presented, along with preliminary scientific results. From a non-statistical sub-set of 169 objects with multiwavelength AKARI and other detections, luminous infrared galaxies (LIRGs) represent 66 ${{\ \rm per\ cent}}$ of the sample, ultra-luminous infrared galaxies (ULIRGs) 4 ${{\ \rm per\ cent}}$, and sources with L$_{IR}$ &amp;lt; 10$^{11}$ $\mathrm{ L}_{\odot }\,$ 30${{\ \rm per\ cent}}$. In total, 56 ${{\ \rm per\ cent}}$ of sources show some AGN presence, though only seven are AGN-dominated. ULIRGs require three times higher AGN contribution to produce high-quality SED fits compared to lower luminosity galaxies, and AGN presence increases with AGN fraction. The PAH mass fraction is not significant, although ULIRGs have about half the PAH strength of lower IR-luminosity galaxies. Higher luminosity galaxies show gas and stellar masses an order of magnitude larger, suggesting higher star formation rates. For LIRGs, AGN presence increases with redshift, indicating that part of the total luminosity could be contributed by AGN activity rather than star formation. Simple cross-matching revealed 13 ROSAT QSOs, 45 X-ray sources, and 61 sub-mm galaxies coincident with GMRT radio sources.