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Spin-lock (SL) pulses have been proposed to directly detect neuronal activity otherwise inaccessible through standard functional magnetic resonance imaging. However, the practical limits of this technique remain unexplored. Key challenges in SL-based detection include ultra-weak signal variations, sensitivity to magnetic field inhomogeneities, and potential contamination from blood oxygen level-dependent effects, all of which hinder the reliable isolation of neuronal signals. This pilot study evaluates the performance of the stimulus-induced rotary saturation (SIRS) technique to map visual stimulation response in the human cortex. A rotary echo spin-lock (RESL) preparation followed by a 2D echo planar imaging readout was used to investigate 12 healthy subjects at rest and during continuous exposure to 8 Hz flickering light. The SL amplitude was fixed to the target neuroelectric oscillations at that frequency. The signal variance was used as contrast metric, and two alternative post-processing pipelines (regression-filtering-rectification and normalized subtraction) were statistically evaluated. Higher variance in the SL signal was detected in four of the 12 subjects. Although group-level analysis indicated activation in the occipital pole, analysis of variance revealed that this difference was not statistically significant, highlighting the need for comparable control measures and more robust preparations. Further optimization in sensitivity and robustness is required to noninvasively detect physiological neuroelectric activity in the human brain., (© 2024 The Author(s). NMR in Biomedicine published by John Wiley & Sons Ltd.)

Background/Objectives : While human papillomavirus (HPV) is well known for its role in cervical cancer, it also affects vaginal cancers. Although colposcopy offers a comprehensive examination of the female genital tract, its diagnostic accuracy remains suboptimal. Integrating artificial intelligence (AI) could enhance the cost-effectiveness of colposcopy, but no AI models specifically differentiate low-grade (LSILs) and high-grade (HSILs) squamous intraepithelial lesions in the vagina. This study aims to develop and validate an AI model for the differentiation of HPV-associated dysplastic lesions in this region. Methods : A convolutional neural network (CNN) model was developed to differentiate HSILs from LSILs in vaginoscopy (during colposcopy) still images. The AI model was developed on a dataset of 57,250 frames (90% training/validation [including a 5-fold cross-validation] and 10% testing) obtained from 71 procedures. The model was evaluated based on its sensitivity, specificity, accuracy and area under the receiver operating curve (AUROC). Results : For HSIL/LSIL differentiation in the vagina, during the training/validation phase, the CNN demonstrated a mean sensitivity, specificity and accuracy of 98.7% (IC95% 96.7-100.0%), 99.1% (IC95% 98.1-100.0%), and 98.9% (IC95% 97.9-99.8%), respectively. The mean AUROC was 0.990 ± 0.004. During testing phase, the sensitivity was 99.6% and 99.7% for both specificity and accuracy. Conclusions : This is the first globally developed AI model capable of HSIL/LSIL differentiation in the vaginal region, demonstrating high and robust performance metrics. Its effective application paves the way for AI-powered colposcopic assessment across the entire female genital tract, offering a significant advancement in women's healthcare worldwide.

Humans have been driving a global erosion of species richness for millennia, but the consequences of past extinctions for other dimensions of biodiversity-functional and phylogenetic diversity-are poorly understood. In this work, we show that, since the Late Pleistocene, the extinction of 610 bird species has caused a disproportionate loss of the global avian functional space along with ~3 billion years of unique evolutionary history. For island endemics, proportional losses have been even greater. Projected future extinctions of more than 1000 species over the next two centuries will incur further substantial reductions in functional and phylogenetic diversity. These results highlight the severe consequences of the ongoing biodiversity crisis and the urgent need to identify the ecological functions being lost through extinction.

Background: Acute myeloid leukaemia (AML) is a bone marrow malignancy with poor prognosis. One of several treatments for AML is midostaurin combined with intensive chemotherapy (MIC), currently approved for FLT3 mutation-positive (FLT3-MP) AML. However, many patients carrying FLT3 mutations are refractory or experience an early relapse following MIC treatment, and might benefit more from receiving a different treatment. Development of a stratification method that outperforms FLT3 mutational status in predicting MIC response would thus benefit a large number of patients., Methods: We employed mass spectrometry phosphoproteomics to analyse 71 diagnosis samples of 47 patients with FLT3-MP AML who subsequently received MIC. We then used machine learning to identify biomarkers of response to MIC, and validated the resulting predictive model in two independent validation cohorts (n = 20)., Findings: We identified three distinct phosphoproteomic AML subtypes amongst long-term survivors. The subtypes showed similar duration of MIC response, but different modulation of AML-implicated pathways, and exhibited distinct, highly-predictive biomarkers of MIC response. Using these biomarkers, we built a phosphoproteomics-based predictive model of MIC response, which we called MPhos. When applied to two retrospective real-world patient test cohorts (n = 20), MPhos predicted MIC response with 83% sensitivity and 100% specificity (log-rank p < 7∗10 -5 , HR = 0.005 [95% CI: 0-0.31])., Interpretation: In validation, MPhos outperformed the currently-used FLT3-based stratification method. Our findings have the potential to transform clinical decision-making, and highlight the important role that phosphoproteomics is destined to play in precision oncology., Funding: This work was funded by Innovate UK grants (application numbers: 22217 and 10054602) and by Kinomica Ltd., Competing Interests: Declaration of interests WEB—is an employee at Kinomica, owns Kinomica share options, Kinomica funded attendance and travel to conferences, named on a Kinomica patent; LN—is an employee at Kinomica, owns Kinomica share options, Kinomica funded attendance and travel to conferences; SFP—is an employee at Kinomica, owns Kinomica share options; AEC—is an employee at Kinomica, owns Kinomica share options, Kinomica funded attendance and travel to conferences; NN—is an employee at Kinomica, owns Kinomica share options, Kinomica funded attendance and travel to conferences; JAC—is an employee at Kinomica, owns Kinomica share options, Kinomica funded attendance and travel to conferences; DNP—is an employee at Kinomica, owns Kinomica share options, Kinomica funded attendance and travel to conferences; PMC—is an employee at Kinomica, owns Kinomica share options, Kinomica funded attendance and travel to conferences; JK—is an employee at Kinomica, owns Kinomica share options; HRF—no conflict of interest; BP—no conflict of interest; PG—no conflict of interest; AA—no conflict of interest; AJA—received a honorarium for speaking engagements from Astellas; AT—received consultant fees from Kinomica for the role of Programme Director; AW—owns Kinomica share options, funded attendance and travel to conferences; GG—no conflict of interest; MDM—no conflict of interest; JGG—Kinomica co-founder, owns Kinomica share options; DJB—co-founder of Kinomica, owns Kinomica share options, named on Kinomica patents, Kinomica funded attendance and travel to conferences, received honoraria from Kinomica in a consulting role; PRC—co-founder and director of Kinomica, owns Kinomica share options, named on Kinomica patents, Kinomica funded attendance and travel to conferences, received honoraria from Kinomica in a consulting role; ADD—is an employee at Kinomica (CTO), named on Kinomica patents, Kinomica funded attendance and travel to conferences owns Kinomica share options., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Artificial intelligence (AI) has emerged as a transformative tool across several specialties, namely gastroenterology, where it has the potential to optimize both diagnosis and treatment as well as enhance patient care. Coloproctology, due to its highly prevalent pathologies and tremendous potential to cause significant mortality and morbidity, has drawn a lot of attention regarding AI applications. In fact, its application has yielded impressive outcomes in various domains, colonoscopy being one prominent example, where it aids in the detection of polyps and early signs of colorectal cancer with high accuracy and efficiency. With a less explored path but equivalent promise, AI-powered capsule endoscopy ensures accurate and time-efficient video readings, already detecting a wide spectrum of anomalies. High-resolution anoscopy is an area that has been growing in interest in recent years, with efforts being made to integrate AI. There are other areas, such as functional studies, that are currently in the early stages, but evidence is expected to emerge soon. According to the current state of research, AI is anticipated to empower gastroenterologists in the decision-making process, paving the way for a more precise approach to diagnosing and treating patients. This review aims to provide the state-of-the-art use of AI in coloproctology while also reflecting on future directions and perspectives.

Background: Magalhães et al. demonstrated that the incidence of acute kidney injury was high in hospitalized patients with COVID-19 and that the second wave was associated with greater severity; however, the mortality rates were similar between the two periods. This may reflect both the effectiveness of vaccines and the constant learning that frontline professionals gained throughout the pandemic to provide greater support to their patients., Background: ◼ Renal involvement was frequent in patients with COVID-19 and related to worse outcomes., Background: ◼ Diuretic use, mechanical ventilation, proteinuria, hematuria, age, and creatine phosphokinase and D-dimer levels were risk factors for acute kidney injury., Background: ◼ Acute kidney injury, mechanical ventilation, elevated SOFA Score, and elevated ATN-ISS were associated with mortality., Background: ◼ The second wave was associated with greater severity; however, the mortality rates were similar between the two periods., Background: ◼ This may reflect the effectiveness of vaccines and the constant learning that frontline professionals gained throughout the pandemic., Objective: This study aimed to evaluate the incidence of acute kidney injury in hospitalized Brazilian patients with COVID-19 and identify the risk factors associated with its development and prognosis during the two waves of the disease., Methods: We performed a prospective cohort study of hospitalized patients with COVID-19 at a public university hospital in São Paulo from March 2020 to May 2021., Results: Of 887 patients hospitalized with COVID-19, 54.6% were admitted to the intensive care unit. The incidence of acute kidney injury was 48.1%, and the overall mortality rate was 38.9%. Acute kidney replacement therapy was indicated for 58.8% of the patients. The factors associated with acute kidney injury were diuretic use (odds ratio [OR] 2.2, 95%CI= 1.2-4.1, p=0.01), mechanical ventilation (OR= 12.9, 95%CI= 4.3-38.2, p<0.0001), hematuria(OR= 2.02, 95%CI= 1.1-3.5, p<0.0001), chronic kidney disease (OR= 2.6, 95%CI= 1.2-5.5, p=0.009), age (OR= 1.03, 95%CI= 1.01-1.07, p=0.02), and elevated creatine phosphokinase (OR= 1.02, 95%CI= 1.01-1.07, p=0.02) and D-dimer levels (OR= 1.01, 95%CI= 1.01-1.09, p<0.0001). Mortality was higher among those with acute kidney injury (OR= 1.12, 95%CI= 1.02-2.05, p=0.01), elevated Sequential Organ Failure Assessment Scores (OR= 1.35, 95%CI= 1.1-1.6, p=0.007), elevated Acute Tubular Necrosis-Injury Severity Score (ATN-ISS; (OR= 96.4, 95%CI= 4.8-203.1, p<0.0001), and who received mechanical ventilation (OR= 12.9, 95%CI= 4.3-38.2, p<0.0001). During the second wave, the number of cases requiring mechanical ventilation (OR= 1.57, 95%CI= 1.01-2.3, p=0.026), with proteinuria (OR= 1.44, 95%CI= 1.01-2.1, p=0.04), and with higher ATN-ISS Scores (OR= 40.9, 95%CI= 1.7-48.1, p=0.04) was higher than that during the first wave., Conclusion: Acute kidney injury was frequent in hospitalized patients with COVID-19, and the second wave was associated with greater severity. However, mortality rates were similar between the two periods, which may reflect both the effectiveness of vaccines and the constant learning that frontline professionals gained throughout the pandemic to provide greater support to their patients., Registry of Clinical Trials: RBR-62y3h7.

The growing integration of software in healthcare, particularly the rise of standalone software as a medical device (SaMD), is transforming digestive medicine, a field heavily reliant on medical imaging for both diagnosis and therapeutic interventions. This narrative review aims to explore the impact of SaMD on digestive healthcare, focusing on the evolution of these tools and their regulatory and ethical challenges. Our analysis highlights the exponential growth of SaMD in digestive healthcare, driven by the need for precise diagnostic tools and personalized treatment strategies. This rapid advancement, however, necessitates the parallel development of a robust regulatory framework to ensure SaMDs are transparent and deliver universal clinical benefits without the introduction of bias or harm. In addition, the discussion highlights the importance of adherence to the FAIR principles for data management-findability, accessibility, interoperability, and reusability. However, enhanced accessibility and interoperability require rigorous protocols to ensure compliance with data protection guidelines and adequate data security, both of which are crucial for effective integration of SaMDs into clinical workflows. In conclusion, while SaMDs hold significant promise for improving patients' outcomes in digestive medicine, their successful integration into clinical workflow depends on rigorous data protection protocols and clinical validation. Future directions include the need for adequate clinical and real-world studies to demonstrate that these devices are safe and well-suited to healthcare settings.

Colon capsule endoscopy (CCE) enables a comprehensive, non-invasive, and painless evaluation of the colon, although it still has limited indications. The lengthy reading times hinder its wider implementation, a drawback that could potentially be overcome through the integration of artificial intelligence (AI) models. Studies employing AI, particularly convolutional neural networks (CNNs), demonstrate great promise in using CCE as a viable option for detecting certain diseases and alterations in the colon, compared to other methods like colonoscopy. Additionally, employing AI models in CCE could pave the way for a minimally invasive panenteric or even panendoscopic solution. This review aims to provide a comprehensive summary of the current state-of-the-art of AI in CCE while also addressing the challenges, both technical and ethical, associated with broadening indications for AI-powered CCE. Additionally, it also gives a brief reflection of the potential environmental advantages of using this method compared to alternative ones.

In the aquaculture industry, biocides are routinely used to treat parasitosis in fish, and researchers are continually developing sustainable alternatives that can replace these harsh chemicals. In this context, the objective of this study was to evaluate the effectiveness of a new natural compound, BiokosTM, for the treatment against Epistylis sp. in Carassius auratus fish. The infestation was identified by the presence of whitish plaques on the integument of five animals, and the diagnosis was confirmed through skin scrapings. BiokosTM is a lipopeptide derived from the bacteria Pseudomonas that can destroy the functionality of the cell membrane of ciliated protozoa. The action of BiokosTM does not harm animals and the environment because the compound degrades into amino acids and fatty acids within days. A 0.15 m3 (150 L) aquarium was treated with an Ich-AwayTM water conditioner manufactured by the Danish company Sundew ApS, which has BiokosTM as the active ingredient. Six tablets were added to the water daily for two days, and new skin scrapings were performed. The fish were clinically well and no longer possessed lesions or parasites. The results obtained indicate that BiokosTM can be an innovative and more sustainable alternative for controlling epistyliasis in ornamental fish.

Antarctica terrestrial ecosystems are facing the most threats from global climate change, which is altering plant composition greatly. These transformations may cause major reshuffling of soil community composition, including functional traits and diversity, and therefore affect ecosystem processes in Antarctica. We used high-throughput sequencing analysis to investigate soil nematodes under 3 dominant plant functional groups (lichens, mosses, and vascular plants) and bare ground in the Antarctic region. We calculated functional diversity of nematodes based on their diet, life histories, and body mass with kernel density n-dimensional hypervolumes. We also calculated taxonomic and functional beta diversity of the nematode communities based on Jaccard dissimilarity. The presence of plants had no significant effect on the taxonomic richness of nematodes but significantly increased nematode functional richness. The presence of plants also significantly decreased taxonomic beta diversity (homogenization). Only mosses and vascular plants decreased nematode functional beta diversity, which was mostly due to a decreased effect of the richness difference component. The presence of plants also increased the effect of deterministic processes potentially because environmental filtering created conditions favorable to nematodes at low trophic levels with short life histories and small body size. Increasing plant cover in the Antarctic due to climate change may lead to increased diversity of nematode species that can use the scarce resources and nematode taxonomic and functional homogenization. In a future under climate change, community restructuring in the region is possible., (© 2024 Society for Conservation Biology.)

Comparable data is essential to understand biodiversity patterns. While assemblage or community inventorying requires comprehensive sampling, monitoring focuses on as few components as possible to detect changes. Quantifying species, their evolutionary history, and the way they interact requires studying changes in taxonomic (TD), phylogenetic (PD) and functional diversity (FD). Here we propose a method for the optimization of sampling protocols for inventorying and monitoring assemblages or communities across these three diversity dimensions taking sampling costs into account. We used Iberian spiders and Amazonian bats as two case-studies. The optimal combination of methods for inventorying and monitoring required optimizing the accumulation curve of α-diversity and minimizing the difference between sampled and estimated β-diversity (bias), respectively. For Iberian spiders, the optimal combination for TD, PD and FD allowed sampling at least 50% of estimated diversity with 24 person-hours of fieldwork. The optimal combination of six person-hours allowed reaching a bias below 8% for all dimensions. For Amazonian bats, surveying all the 12 sites with mist-nets and 0 or 1 acoustic recorders was the optimal combination for almost all diversity types, resulting in >89% of the diversity and <10% bias with roughly a third of the cost. Only for phylogenetic α-diversity, the best solution was less clear and involved surveying both with mist nets and acoustic recorders. The widespread use of optimized and standardized sampling protocols and regular repetition in time will radically improve global inventory and monitoring of biodiversity. We strongly advocate for the global adoption of sampling protocols for both inventory and monitoring of taxonomic, phylogenetic and functional diversity., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Cardoso et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background and Aims: Current guidelines recommend bowel preparation before small-bowel capsule endoscopy (SBCE). However, the optimal protocol is yet to be defined. To determine the best timing for preparation in SBCE, we compared small-bowel visualization quality (SBVQ), diagnostic yield (DY), and patient-reported outcomes across 4 purgative regimens., Methods: In this prospective, randomized (1:1:1:1), multicenter study, patients with suspected small-bowel bleeding were randomized into 4 arms: G1 (1 L of polyethylene glycol + ascorbic acid [Moviprep, Norgine, Amsterdam, Netherlands] the night before SBCE), G2 (1 L in the morning up to 2 hours before SBCE), G3 (0.5 L up to 2 hours before SBCE + 0.5 L after the capsule reached the duodenum), and G4 (1 L after the capsule reached the duodenum). To assess DY, lesions were categorized as having high (P2) or low (P0 or P1) bleeding potential. SBVQ was assessed using the Brotz score. Transit times were measured, and patient tolerability was scored from 0 to 5, with higher scores indicating better tolerability., Results: A total of 387 patients were included, 59% female and with a median age of 73 years (interquartile range, 23). The examination completion rate was lower in G1 (90%, P < .001). Small-bowel transit time was shorter for patients receiving purgative during SBCE (G3 and G4, P = .001). SBVQ was better in patients receiving purgative after reaching the small bowel (P < .001): a median of 7 for G1, 8 for G2, and 9 for G3 and G4. The overall DY of patients receiving intraprocedure purgatives (G3 + G4) was superior (42.7 vs 31.3%, P = .02); significant differences were found in the second and third terciles. Likewise, G3 and G4 had higher angioectasia detection (P = .04). Patients' satisfaction was significantly superior for G4 (median, 4 points; interquartile range, 1)., Conclusions: The group that received the bowel preparation the night before SBCE had poorer outcomes. Intraprocedure purgative regimens reduced SBTT, enhanced visualization, improved DY, and increased angioectasia detection. G4 was the best-tolerated regimen., Competing Interests: Disclosure All authors disclosed no financial relationships., (Copyright © 2024 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.)

Background: Clinical prediction models can help identify high-risk patients and facilitate timely interventions. However, developing such models for rare diseases presents challenges due to the scarcity of affected patients for developing and calibrating models. Methods that pool information from multiple sources can help with these challenges., Methods: We compared three approaches for developing clinical prediction models for population screening based on an example of discriminating a rare form of diabetes (Maturity-Onset Diabetes of the Young - MODY) in insulin-treated patients from the more common Type 1 diabetes (T1D). Two datasets were used: a case-control dataset (278 T1D, 177 MODY) and a population-representative dataset (1418 patients, 96 MODY tested with biomarker testing, 7 MODY positive). To build a population-level prediction model, we compared three methods for recalibrating models developed in case-control data. These were prevalence adjustment ("offset"), shrinkage recalibration in the population-level dataset ("recalibration"), and a refitting of the model to the population-level dataset ("re-estimation"). We then developed a Bayesian hierarchical mixture model combining shrinkage recalibration with additional informative biomarker information only available in the population-representative dataset. We developed a method for dealing with missing biomarker and outcome information using prior information from the literature and other data sources to ensure the clinical validity of predictions for certain biomarker combinations., Results: The offset, re-estimation, and recalibration methods showed good calibration in the population-representative dataset. The offset and recalibration methods displayed the lowest predictive uncertainty due to borrowing information from the fitted case-control model. We demonstrate the potential of a mixture model for incorporating informative biomarkers, which significantly enhanced the model's predictive accuracy, reduced uncertainty, and showed higher stability in all ranges of predictive outcome probabilities., Conclusion: We have compared several approaches that could be used to develop prediction models for rare diseases. Our findings highlight the recalibration mixture model as the optimal strategy if a population-level dataset is available. This approach offers the flexibility to incorporate additional predictors and informed prior probabilities, contributing to enhanced prediction accuracy for rare diseases. It also allows predictions without these additional tests, providing additional information on whether a patient should undergo further biomarker testing before genetic testing., (© 2024. The Author(s).)

Background: Capsule endoscopy (CE) is a valuable tool for assessing inflammation in patients with Crohn's disease (CD). The current standard for evaluating inflammation are validated scores (and clinical laboratory values) like Lewis score (LS), Capsule Endoscopy Crohn's Disease Activity Index (CECDAI), and ELIAKIM. Recent advances in artificial intelligence (AI) have made it possible to automatically select the most relevant frames in CE., Objectives: In this proof-of-concept study, our objective was to develop an automated scoring system using CE images to objectively grade inflammation., Design: Pan-enteric CE videos (PillCam Crohn's) performed in CD patients between 09/2020 and 01/2023 were retrospectively reviewed and LS, CECDAI, and ELIAKIM scores were calculated., Methods: We developed a convolutional neural network-based automated score consisting of the percentage of positive frames selected by the algorithm (for small bowel and colon separately). We correlated clinical data and the validated scores with the artificial intelligence-generated score (AIS)., Results: A total of 61 patients were included. The median LS was 225 (0-6006), CECDAI was 6 (0-33), ELIAKIM was 4 (0-38), and SB&#95;AIS was 0.5659 (0-29.45). We found a strong correlation between SB&#95;AIS and LS, CECDAI, and ELIAKIM scores (Spearman's r  = 0.751, r  = 0.707, r  = 0.655, p  = 0.001). We found a strong correlation between LS and ELIAKIM ( r  = 0.768, p  = 0.001) and a very strong correlation between CECDAI and LS ( r  = 0.854, p  = 0.001) and CECDAI and ELIAKIM scores ( r  = 0.827, p  = 0.001)., Conclusion: Our study showed that the AI-generated score had a strong correlation with validated scores indicating that it could serve as an objective and efficient method for evaluating inflammation in CD patients. As a preliminary study, our findings provide a promising basis for future refining of a CE score that may accurately correlate with prognostic factors and aid in the management and treatment of CD patients., Competing Interests: J.P.S.F. is currently an employee of DigestAID – Digestive Artificial Intelligence Development., (© The Author(s), 2024.)

Background/Objectives : Proficient colposcopy is crucial for the adequate management of cervical cancer precursor lesions; nonetheless its limitations may impact its cost-effectiveness. The development of artificial intelligence models is experiencing an exponential growth, particularly in image-based specialties. The aim of this study is to develop and validate a Convolutional Neural Network (CNN) for the automatic differentiation of high-grade (HSIL) from low-grade dysplasia (LSIL) in colposcopy. Methods : A unicentric retrospective study was conducted based on 70 colposcopy exams, comprising a total of 22,693 frames. Among these, 8729 were categorized as HSIL based on histopathology. The total dataset was divided into a training (90%, n = 20,423) and a testing set (10%, n = 2270), the latter being used to evaluate the model's performance. The main outcome measures included sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and the area under the receiving operating curve (AUC-ROC). Results : The sensitivity was 99.7% and the specificity was 98.6%. The PPV and NPV were 97.8% and 99.8%, respectively. The overall accuracy was 99.0%. The AUC-ROC was 0.98. The CNN processed 112 frames per second. Conclusions : We developed a CNN capable of differentiating cervical cancer precursors in colposcopy frames. The high levels of accuracy for the differentiation of HSIL from LSIL may improve the diagnostic yield of this exam.

High-resolution anoscopy (HRA) plays a central role in the detection and treatment of precursors of anal squamous cell carcinoma (ASCC). Artificial intelligence (AI) algorithms have shown high levels of efficiency in detecting and differentiating HSIL from low-grade squamous intraepithelial lesions (LSIL) in HRA images. Our aim was to develop a deep learning system for the automatic detection and differentiation of HSIL versus LSIL using HRA images from both conventional and digital proctoscopes. A convolutional neural network (CNN) was developed based on 151 HRA exams performed at two volume centers using conventional and digital HRA systems. A total of 57,822 images were included, 28,874 images containing HSIL and 28,948 LSIL. Partial subanalyses were performed to evaluate the performance of the CNN in the subset of images acetic acid and lugol iodine staining and after treatment of the anal canal. The overall accuracy of the CNN in distinguishing HSIL from LSIL during the testing stage was 94.6%. The algorithm had an overall sensitivity and specificity of 93.6% and 95.7%, respectively (AUC 0.97). For staining with acetic acid, HSIL was differentiated from LSIL with an overall accuracy of 96.4%, while for lugol and after therapeutic manipulation, these values were 96.6% and 99.3%, respectively. The introduction of AI algorithms to HRA may enhance the early diagnosis of ASCC precursors, and this system was shown to perform adequately across conventional and digital HRA interfaces.

Aims/hypothesis: A precision medicine approach in type 2 diabetes could enhance targeting specific glucose-lowering therapies to individual patients most likely to benefit. We aimed to use the recently developed Bayesian causal forest (BCF) method to develop and validate an individualised treatment selection algorithm for two major type 2 diabetes drug classes, sodium-glucose cotransporter 2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP1-RA)., Methods: We designed a predictive algorithm using BCF to estimate individual-level conditional average treatment effects for 12-month glycaemic outcome (HbA 1c ) between SGLT2i and GLP1-RA, based on routine clinical features of 46,394 people with type 2 diabetes in primary care in England (Clinical Practice Research Datalink; 27,319 for model development, 19,075 for hold-out validation), with additional external validation in 2252 people with type 2 diabetes from Scotland (SCI-Diabetes [Tayside & Fife]). Differences in glycaemic outcome with GLP1-RA by sex seen in clinical data were replicated in clinical trial data (HARMONY programme: liraglutide [n=389] and albiglutide [n=1682]). As secondary outcomes, we evaluated the impacts of targeting therapy based on glycaemic response on weight change, tolerability and longer-term risk of new-onset microvascular complications, macrovascular complications and adverse kidney events., Results: Model development identified marked heterogeneity in glycaemic response, with 4787 (17.5%) of the development cohort having a predicted HbA 1c benefit >3 mmol/mol (>0.3%) with SGLT2i over GLP1-RA and 5551 (20.3%) having a predicted HbA 1c benefit >3 mmol/mol with GLP1-RA over SGLT2i. Calibration was good in hold-back validation, and external validation in an independent Scottish dataset identified clear differences in glycaemic outcomes between those predicted to benefit from each therapy. Sex, with women markedly more responsive to GLP1-RA, was identified as a major treatment effect modifier in both the UK observational datasets and in clinical trial data: HARMONY-7 liraglutide (GLP1-RA): 4.4 mmol/mol (95% credible interval [95% CrI] 2.2, 6.3) (0.4% [95% CrI 0.2, 0.6]) greater response in women than men. Targeting the two therapies based on predicted glycaemic response was also associated with improvements in short-term tolerability and long-term risk of new-onset microvascular complications., Conclusions/interpretation: Precision medicine approaches can facilitate effective individualised treatment choice between SGLT2i and GLP1-RA therapies, and the use of routinely collected clinical features for treatment selection could support low-cost deployment in many countries., (© 2024. The Author(s).)

Background and study aims Capsule endoscopy (CE) is commonly used as the initial exam for suspected mid-gastrointestinal bleeding after normal upper and lower endoscopy. Although the assessment of the small bowel is the primary focus of CE, detecting upstream or downstream vascular lesions may also be clinically significant. This study aimed to develop and test a convolutional neural network (CNN)-based model for panendoscopic automatic detection of vascular lesions during CE. Patients and methods A multicentric AI model development study was based on 1022 CE exams. Our group used 34655 frames from seven types of CE devices, of which 11091 were considered to have vascular lesions (angiectasia or varices) after triple validation. We divided data into a training and a validation set, and the latter was used to evaluate the model's performance. At the time of division, all frames from a given patient were assigned to the same dataset. Our primary outcome measures were sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and an area under the precision-recall curve (AUC-PR). Results Sensitivity and specificity were 86.4% and 98.3%, respectively. PPV was 95.2%, while the NPV was 95.0%. Overall accuracy was 95.0%. The AUC-PR value was 0.96. The CNN processed 115 frames per second. Conclusions This is the first proof-of-concept artificial intelligence deep learning model developed for pan-endoscopic automatic detection of vascular lesions during CE. The diagnostic performance of this CNN in multi-brand devices addresses an essential issue of technological interoperability, allowing it to be replicated in multiple technological settings., Competing Interests: Conflict of Interest João Ferreira is a paid employee of DigestAID. The remaining authors have no conflict of interest to declare., (The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

Background : Coronary artery calcification is a predictor of adverse outcomes after percutaneous coronary intervention (PCI). Intravascular lithotripsy (IVL) is a promising tool for the treatment of calcified lesions. The aim of this study was to assess the effectiveness and safety of IVL. Methods : A single-center observational study of PCI procedure, with assessment of the outcomes of patients undergoing PCI using IVL, was performed. Angiographic procedural success was used as the primary effectiveness endpoint. The primary safety endpoint was defined as a composite of cardiac death, myocardial infarction and target vessel revascularization within 30 days. Results : A total of 111 patients were included. Indications for PCI spanned the spectrum of chronic (53.2%) and acute coronary syndromes (43%). Lesion preparation before IVL was performed with non-compliant (42%), cutting or OPN (14.4%) balloons and with atherectomy techniques in 11% of procedures. Intravascular imaging was used in 21.6% of procedures. The primary effectiveness endpoint was achieved in 100% and the primary safety endpoint in 3.6% of procedures. Peri-procedural complications were minimal and successfully resolved. Conclusions : IVL was an effective and safe technique for the treatment of calcified coronary lesions. These findings contribute to the growing body of evidence supporting the use of IVL in the management of these challenging scenarios.

Globally, there is a concerning decline in many insect populations, and this trend likely extends to all arthropods, potentially impacting unique island biota. Native non-endemic and endemic species on islands are under threat due to habitat destruction, with the introduction of exotic, and potentially invasive, species, further contributing to this decline. While long-term studies of plants and vertebrate fauna are available, long-term arthropod datasets are limited, hindering comparisons with better-studied taxa. The Biodiversity of Arthropods of the Laurisilva of the Azores (BALA) project has allowed gathering comprehensive data since 1997 in the Azorean Islands (Portugal), using standardised sampling methods across islands. The dataset includes arthropod counts from epigean (pitfall traps) and canopy-dwelling (beating samples) communities, enriched with species information, biogeographic origins, and IUCN categories. Metadata associated with the sample protocol and events, like sample identifier, archive number, sampled tree species, and trap type are also recorded. The database is available in multiple formats, including Darwin Core, which facilitates the ecological analysis of pressing environmental concerns, such as arthropod population declines and biological invasions., (© 2024. The Author(s).)

Beneficial microorganisms for corals (BMCs), or probiotics, can enhance coral resilience against stressors in laboratory trials. However, the ability of probiotics to restructure the coral microbiome in situ is yet to be determined. As a first step to elucidate this, we inoculated putative probiotic bacteria (pBMCs) on healthy colonies of Pocillopora verrucosa in situ in the Red Sea, three times per week, during 3 months. pBMCs significantly influenced the coral microbiome, while bacteria of the surrounding seawater and sediment remained unchanged. The inoculated genera Halomonas, Pseudoalteromonas, and Bacillus were significantly enriched in probiotic-treated corals. Furthermore, the probiotic treatment also correlated with an increase in other beneficial groups (e.g., Ruegeria and Limosilactobacillus), and a decrease in potential coral pathogens, such as Vibrio. As all corals (treated and non-treated) remained healthy throughout the experiment, we could not track health improvements or protection against stress. Our data indicate that healthy, and therefore stable, coral microbiomes can be restructured in situ, although repeated and continuous inoculations may be required in these cases. Further, our study provides supporting evidence that, at the studied scale, pBMCs have no detectable off-target effects on the surrounding microbiomes of seawater and sediment near inoculated corals., (© 2024. The Author(s).)

Malignant peripheral nerve sheath tumour (MPNST) is an aggressive soft tissue sarcoma with a poor prognosis, affecting most commonly the extremities. The lungs constitute the most frequent location for distant metastases. Half of all MPNSTs arise in patients with neurofibromatosis type 1, while approximately 10% are radiation induced and the rest are sporadic.The authors present a pregnant woman in her 40s with a sporadic MPNST of the lower limb and with lung metastases at diagnosis. Treatment consisted of interilioabdominal amputation, followed by adjuvant chemotherapy. Partial response and disease stabilisation were achieved with chemotherapy.Surgical resection with negative margins is the only potentially curative therapy, while radiation therapy and chemotherapy might be useful in the neoadjuvant or adjuvant setting, but their advantage in survival is not demonstrated. In the reported case, chemotherapy permitted the achievement of partial response and stabilisation of the disease., Competing Interests: Competing interests: None declared., (© BMJ Publishing Group Limited 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Introduction: High-resolution anoscopy (HRA) is the gold standard for detecting anal squamous cell carcinoma (ASCC) precursors. Preliminary studies on the application of artificial intelligence (AI) models to this modality have revealed promising results. However, the impact of staining techniques and anal manipulation on the effectiveness of these algorithms has not been evaluated. We aimed to develop a deep learning system for automatic differentiation of high-grade squamous intraepithelial lesion vs low-grade squamous intraepithelial lesion in HRA images in different subsets of patients (nonstained, acetic acid, lugol, and after manipulation)., Methods: A convolutional neural network was developed to detect and differentiate high-grade and low-grade anal squamous intraepithelial lesions based on 27,770 images from 103 HRA examinations performed in 88 patients. Subanalyses were performed to evaluate the algorithm's performance in subsets of images without staining, acetic acid, lugol, and after manipulation of the anal canal. The sensitivity, specificity, accuracy, positive and negative predictive values, and area under the curve were calculated., Results: The convolutional neural network achieved an overall accuracy of 98.3%. The algorithm had a sensitivity and specificity of 97.4% and 99.2%, respectively. The accuracy of the algorithm for differentiating high-grade squamous intraepithelial lesion vs low-grade squamous intraepithelial lesion varied between 91.5% (postmanipulation) and 100% (lugol) for the categories at subanalysis. The area under the curve ranged between 0.95 and 1.00., Discussion: The introduction of AI to HRA may provide an accurate detection and differentiation of ASCC precursors. Our algorithm showed excellent performance at different staining settings. This is extremely important because real-time AI models during HRA examinations can help guide local treatment or detect relapsing disease., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of The American College of Gastroenterology.)

Purpose: To report clinical findings and prognostic factors for visual and morphological outcomes in patients with Acanthamoeba keratitis (AK)., Methods: Single-center, retrospective, longitudinal study of 51 cases of AK diagnosed by real-time polymerase chain reaction (RT-PCR) between March 2010 and October 2022. The primary outcome was the final best corrected visual acuity (BCVA). Poor visual outcome was defined as a final BCVA ≥ 1 logMAR unit, while good visual outcome was defined as a final BCVA < 1 logMAR unit. Eyes from these two groups were compared, regarding demographic and initial clinical variables, anti-Acanthamoeba treatment used, and complications of the disease. Early diagnosis was defined as ≤ 14 days from symptom onset to diagnostic confirmation and initiation of Acanthamoeba medical treatment. Multivariable logistic regression was used to determine predictors of poor visual outcome., Results: A total of 51 eyes from 46 patients diagnosed with AK, all contact lens (CL) wearers, were included in this study. Average follow-up was 39.0 ± 30.2 [total range 14-120] months. Thirty-one eyes (60.8 %) presented good visual outcome, with a lower baseline age (30.5 ± 9.0 vs. 42.3 ± 15.8; p = 0.020), better initial BCVA (0.8 ± 0.7 logMAR units vs. 1.3 ± 0.9 logMAR units; p = 0.047), higher rate of early diagnosis (45.2 % vs. 5.6 %; p = 0.004), and higher rate of therapeutic epithelial debridement (64.5 % vs. 10 %; p < 0.001). 20 eyes (39.2 %) presented poor visual outcome, with 12 eyes undergoing evisceration/enucleation (23.5 %). These 20 eyes presented a higher rate of complications (90 % vs. 61.3 %; p = 0.031). In multivariable analysis, early diagnosis of AK (OR 19.78; 95 % CI 2.07-189.11; p = 0.010) and therapeutic epithelial debridement (OR 19.02; 95 % CI 3.27-110.57; p = 0.001) were associated with a good visual outcome., Conclusions: In the present study, poor visual outcome was present in 39 % of affected eyes. Early AK diagnosis (≤14 days from symptom onset) and therapeutic epithelial debridement were associated with good final visual outcome., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 British Contact Lens Association. Published by Elsevier Ltd. All rights reserved.)

Background: With this publication, we contribute to the knowledge of the arachnofauna of Cabo Verde, focusing specifically on the Islands of Santo Antão and São Vicente. Data were obtained from samples collected as part of the project "Macaronesian Islands as a testing ground to assess biodiversity drivers at multiple scales" (FCT - MACDIV, 2015-2018). This project aimed to identify the factors influencing community assembly in Macaronesian islands. For the Cabo Verde Islands, we focused on dry habitats with the additional aim to revise the aracnofauna of this poorly-known fauna. We applied the COBRA (Conservation Oriented Biodiversity Rapid Assessment) sampling protocol in ten 50 m x 50 m dry shrub plots, with five on each of the two islands, using pitfall traps, sweep-netting and active search. Additional ad-hoc samples were also collected and reported., New Information: Our sampling of spiders from Cabo Verde (Santo Antão and São Vicente) yielded a total of 3,368 specimens, of which 1300 (39%) were adults. The samples include 21 families, 87 species, 18 of which are morphospecies awaiting formal identification or description at species level. Species in the families Oxyopidae (2 spp.) and Araneidae (8 spp.) were the most abundant, making up 49% of the specimens. From the 68 named species, 14 are endemic to Cabo Verde, 40 are native non-endemic and 14 are introduced. The colonisation status of Cithaeronreimoseri Platnick, 1991 is unknown. Endemic species accounted for 24% (n = 818) of the specimens and native non-endemic for 63% (n = 2122). A total of 29 species were new records for Cabo Verde, with 15 for Santo Antão, seven for São Vicente and seven for both Islands., (Luís Carlos Fonseca Crespo, Pedro Cardoso, Jagoba Malumbres-Olarte, Fernando Pereira, Maria Romeiras, Alejandra Ros-Prieto, François Rigal, Paulo A. V. Borges.)

Background and Aims: The presence of portal hypertension in cirrhotic patients is a major prognostic factor associated with the development of severe complications and increased mortality. The gold standard for diagnosing portal hypertension is the hepatic venous pressure gradient. More recently, spleen stiffness has emerged as a new and non-invasive diagnostic tool, and has already been included in the last Baveno VII guidelines. The exact prevalence of Helicobacter pylori infection, pre-malignant lesions and their relation to portal hypertension have never been described. The aim of our study was to evaluate the relationship between the presence of portal hypertension assessed via liver and spleen elastography and Helicobacter pylori infection and pre-malignant gastric lesions. Methods: An observational study was conducted, including consecutive patients admitted from December 2020 to December 2022. All patients underwent upper endoscopy and were also subjected to liver and spleen elastography (using the new probe of 100 Hz) by the same blinded operator in a tertiary center. Results: We included 155 cirrhotic patients, with a mean age of 64.1 years (±8.8), and 81.3% were male. The most common etiology was alcoholic liver disease (72.9%). The median value of liver stiffness measurement was 24.4 kPa [3.1-75.0], and the spleen stiffness measurement was 49.1 kPa [12.8-100.0]. Akin to endoscopic findings, 50.3% presented esophageal varices, 5.2% gastric atrophy, 11.6% gastric metaplasia, and 32.9% portal hypertension gastropathy. Regarding histologic findings, we found that 34.8% presented H. pylori infection, 35.5% gastric atrophy (OLGA 1-58.2%) and 38.7% gastric metaplasia (OLGIM 1-63.3%). Liver stiffness and spleen stiffness measurements were associated with the presence of portal hypertensive gastropathy ( p < 0.01), but not with H. pylori infection or pre-malignant gastric lesions. Conclusions : Although present in almost one third of cirrhotic patients, H. pylori infection and pre-malignant gastric lesions are not associated with liver stiffness and spleen stiffness measurements. On the other hand, we found an association between liver stiffness and spleen stiffness measurements and portal hypertensive gastropathy.

Background: Transfusion-transmitted malaria (TTM) is a public health problem in endemic and nonendemic areas. The Brazilian Ministry of Health (MH) requested the development of a nucleic acid amplification test (NAT) for the detection of Plasmodium spp. in public blood centers to increase blood safety., Study Design and Methods: The new Brazilian NAT kit named NAT PLUS HIV/HBV/HCV/Malaria Bio-Manguinhos was first implemented in HEMORIO, a public blood center in the city of Rio de Janeiro. Since October 1, 2022, this blood center has been testing all its blood donations for malaria in a pool of six plasma samples to detect Plasmodium spp. by real-time polymerase chain reaction (PCR)., Results: Since the implementation of the NAT PLUS platform until February 2023, HEMORIO has successfully received and tested 200,277 donations. The platform detected two asymptomatic donors in the city of Rio de Janeiro, which is a nonendemic region for malaria. Our analyses suggested a malaria from the Amazon region caused by Plasmodium vivax, in the first case, while an autochthonous transmission case by Plasmodium malariae was identified in the rural area of Rio de Janeiro state., Discussion: The NAT PLUS platform detects Plasmodium spp. in plasma samples with sensitivity capable of detecting subpatent infections. This is the first time worldwide that a group developed and implemented molecular diagnosis for Plasmodium spp. to be used by public blood centers to avoid TTM., (© 2024 AABB.)

This paper investigated the correlation between magnetic resonance spectroscopic imaging (MRSI) and magnetic resonance fingerprinting (MRF) in glioma patients by comparing neuro-oncological markers obtained from MRSI to T1/T2 maps from MRF. Data from 12 consenting patients with gliomas were analyzed by defining hotspots for T1, T2, and various metabolic ratios, and comparing them using Sørensen-Dice similarity coefficients (DSCs) and the distances between their centers of intensity (COIDs). The median DSCs between MRF and the tumor segmentation were 0.73 (T1) and 0.79 (T2). The DSCs between MRSI and MRF were the highest for Gln/tNAA (T1: 0.75, T2: 0.80, tumor: 0.78), followed by Gly/tNAA (T1: 0.57, T2: 0.62, tumor: 0.54) and tCho/tNAA (T1: 0.61, T2: 0.58, tumor: 0.45). The median values in the tumor hotspot were T1 = 1724 ms, T2 = 86 ms, Gln/tNAA = 0.61, Gly/tNAA = 0.28, Ins/tNAA = 1.15, and tCho/tNAA = 0.48, and, in the peritumoral region, were T1 = 1756 ms, T2 = 102 ms, Gln/tNAA = 0.38, Gly/tNAA = 0.20, Ins/tNAA = 1.06, and tCho/tNAA = 0.38, and, in the NAWM, were T1 = 950 ms, T2 = 43 ms, Gln/tNAA = 0.16, Gly/tNAA = 0.07, Ins/tNAA = 0.54, and tCho/tNAA = 0.20. The results of this study constitute the first comparison of 7T MRSI and 3T MRF, showing a good correspondence between these methods.

The aim of this study is to assess the functional, emotional, and social adaptation of pediatric patients submitted to a hand ray resection to treat a traumatic hand injury. Retrospective study of pediatric patients undergoing hand ray resection, in the period 2013-2022, performed in two hospital institutions. Inclusion criteria: age less than 18 years, primary/secondary resection due to traumatic etiology and follow-up time of more than 1 year. Data concerning demographic information, lesion location, traumatic mechanism and surgical description were collected. Functional outcomes (QuickDASH Score), social and emotional integration (PEDSQL) and satisfaction were evaluated. Seven patients were included, with male predominance (n = 4) and the right side was the most affected (n = 5). Median age at the time of surgery was 10 years (2-15). Primary ray resection, without previous revascularization, was performed in three patients. Secondary ray resection was performed in four patients. Three patients with an unsuccessful revascularization procedure attempt another patient with a sequela with severe deformity. All patients presented a good adaptation to daily living activities, with good pinch and grip function. The median follow-up was 4 years (min:1; max:9). The scores were collected on six out of seven patients. Median QuickDASH score of 14 (min:11; max:22), and PEDSQL[physical] of 95.31 (min:78.13; max:100) and PEDSQL[social] of 93.47 (min:66.3; max:100). Hand ray resection is a rare procedure in pediatric age and usually difficult for patients and parents to accept. However, it is a useful and safe technique that allows for an improvement in overall hand function and psychosocial readaptation in specific and selected cases. Level of evidence: Level of evidence IV - case series., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Artificial intelligence has yielded remarkably promising results in several medical fields, namely those with a strong imaging component. Gynecology relies heavily on imaging since it offers useful visual data on the female reproductive system, leading to a deeper understanding of pathophysiological concepts. The applicability of artificial intelligence technologies has not been as noticeable in gynecologic imaging as in other medical fields so far. However, due to growing interest in this area, some studies have been performed with exciting results. From urogynecology to oncology, artificial intelligence algorithms, particularly machine learning and deep learning, have shown huge potential to revolutionize the overall healthcare experience for women's reproductive health. In this review, we aim to establish the current status of AI in gynecology, the upcoming developments in this area, and discuss the challenges facing its clinical implementation, namely the technological and ethical concerns for technology development, implementation, and accountability.

Purpose: To report the 4-year outcomes of transepithelial accelerated corneal collagen crosslinking (TE-ACXL) in the treatment of eyes with progressive keratoconus (KC)., Methods: Eyes of patients who underwent TE-ACXL (6mW/cm 2 for 15 minutes) for progressive KC and presented 48 months of follow-up were included. Corrected distance visual acuity (CDVA), keratometry measurements (Kmax, maximum keratometry, Kmean, mean keratometry and Astg, corneal astigmatism), thinnest corneal thickness (PachyMin), and topographic, and tomographic indices (specifically the posterior radius of curvature from the 3.0 mm centered on the thinnest point of the cornea (PRC), and the D-index) were analysed preoperatively and every 12 months after TE-ACXL, up to 48 months. Progression after TE-ACXL was considered when eyes presented ≥1 criteria: (1) increase of ≥1D in Kmax or increase of ≥0.75D in Kmean or increase of ≥1D in Astg; (2) reduction of ≥0.085 mm in PRC; (3) decrease ≥5% in PachyMin., Results: 41 eyes from 30 patients were included, with a mean age at crosslinking of 20.90±4.69 years. There was a significant increase in Kmean (+0.64±1.04 D, p <0.001; +0.98 ± 1.49 D, p <0.001; +1.27±2.01 D, p <0.001; +1.13±2.00 D, p =0.006) and a significant decrease in PRC throughout follow-up (-0.12±0.22, p=0.002; -0.15±0.24, p <0.001; -0.17±0.43, p =0.021; -0.16±0.43, p =0.027). PachyMin decreased significantly at 36 and 48 months (-8.50±15.93 μm, p =0.004; -7.82±18.37, p =0.033). According to our progression criteria, there was a major progression rate throughout follow-up (57.1%, 61.1%, 58.8%, and 67.9%, respectively). Surgery and follow-up were uneventful in all subjects. Eleven eyes (26.8%) required further procedures, ≥36 months after the initial TE-ACXL, due to persistent progressive disease., Conclusion: TE-ACXL proved to be a safe therapeutic option for progressive KC. However, its efficacy is deemed unsatisfactory, as a notable proportion of affected eyes may continue to advance within a 4-year timeframe, necessitating additional procedures to halt the disease's course., Competing Interests: The authors have no conflicts of interest to declare for this work., (© 2024 Vilares-Morgado et al.)

Bats frequently inhabit caves and other subterranean habitats and play a critical role in subterranean food webs. With escalating threats to subterranean ecosystems, identifying the most effective measures to protect subterranean-roosting bats is critical. We conducted a meta-analysis to evaluate the effectiveness of conservation and management interventions for subterranean-roosting bats. We used network analyses to determine to what extent interventions for bats overlap those used for other subterranean taxa. We conducted our analyses with data extracted from 345 papers recommending a total of 910 conservation interventions. Gating of roost entrances was applied to preserve bat populations in 21 studies, but its effectiveness was unclear. Habitat restoration and disturbance reduction positively affected bat populations and bat behavior, respectively, in ≤4 studies. Decontamination was assessed in 2 studies and positively affected bat populations, particularly in studies focused on reducing fungal spores associated with white-nose syndrome in North America. Monitoring of bat populations as an effective conservation strategy was unclear and infrequently tested. Only 4% of bat studies simultaneously considered other subterranean organisms. However, effective interventions for bat conservation had similarities with all other organisms. If other subterranean organisms are considered when applying interventions to conserve bats, they might also benefit., (© 2023 The Authors. Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology.)

The role of capsule endoscopy and enteroscopy in managing various small-bowel pathologies is well-established. However, their broader application has been hampered mainly by their lengthy reading times. As a result, there is a growing interest in employing artificial intelligence (AI) in these diagnostic and therapeutic procedures, driven by the prospect of overcoming some major limitations and enhancing healthcare efficiency, while maintaining high accuracy levels. In the past two decades, the applicability of AI to gastroenterology has been increasing, mainly because of the strong imaging component. Nowadays, there are a multitude of studies using AI, specifically using convolutional neural networks, that prove the potential applications of AI to these endoscopic techniques, achieving remarkable results. These findings suggest that there is ample opportunity for AI to expand its presence in the management of gastroenterology diseases and, in the future, catalyze a game-changing transformation in clinical activities. This review provides an overview of the current state-of-the-art of AI in the scope of small-bowel study, with a particular focus on capsule endoscopy and enteroscopy.

Subterranean ecosystems (comprising terrestrial, semi-aquatic, and aquatic components) are increasingly threatened by human activities; however, the current network of surface-protected areas is inadequate to safeguard subterranean biodiversity. Establishing protected areas for subterranean ecosystems is challenging. First, there are technical obstacles in mapping three-dimensional ecosystems with uncertain boundaries. Second, the rarity and endemism of subterranean organisms, combined with a scarcity of taxonomists, delays the accumulation of essential biodiversity knowledge. Third, establishing agreements to preserve subterranean ecosystems requires collaboration among multiple actors with often competing interests. This perspective addresses the challenges of preserving subterranean biodiversity through protected areas. Even in the face of uncertainties, we suggest it is both timely and critical to assess general criteria for subterranean biodiversity protection and implement them based on precautionary principles. To this end, we examine the current status of European protected areas and discuss solutions to improve their coverage of subterranean ecosystems., (© 2024. The Author(s).)

This study aimed to characterize 300 Aeromonas spp. strains isolated from 123 ornamental fish of 32 different species presenting with septicemia, skin lesions, and/or eye lesions. Within the 300 strains, 53.0% were identified as A. veronii , 41.3% as A. hydrophila , and 5.7% as A. caviae . Among the six virulence genes investigated, the most frequent were act (90.3%) and aer (79.3%). More than 50% of A. hydrophila strains were positive for all the studied genes. A total of 30 virulence profiles were identified, with the five main profiles identified comprising 75% of strains. Only five strains were negative for all genes and were identified as A. caviae and A. veronii . The antimicrobial susceptibility profile was performed for 234 strains, with sulfonamides presenting more than 50% of the resistance rates. Susceptibility was observed mainly for cephalosporins, aminoglycosides, chloramphenicol and piperacillin-tazobactam. Multidrug resistance was detected in 82.5% of the studied strains, including A. caviae with 100% multidrug resistance, and A. hydrophila with 90.9% multidrug resistance. The SE-AFLP analysis resulted in 66 genotypes of A. hydrophila , 118 genotypes of A. veronii , and 14 genotypes of A. caviae , demonstrating the greater heterogeneity of A. veronii and A. caviae . However, no direct correlation was observed between the genotypes and the strains' origins or virulence and resistance profiles.

Epilepsy represents a condition in which abnormal neuronal discharges or the hyperexcitability of neurons occur with synchronicity, presenting a significant public health challenge. Prognostic factors, such as etiology, electroencephalogram (EEG) abnormalities, the type and number of seizures before treatment, as well as the initial unsatisfactory effects of medications, are important considerations. Although there are several third-generation antiepileptic drugs currently available, their multiple side effects can negatively affect patient quality of life. The inheritance and etiology of epilepsy are complex, involving multiple underlying genetic and epigenetic mechanisms. Different neurotransmitters play crucial roles in maintaining the normal physiology of different neurons. Dysregulations in neurotransmission, due to abnormal transmitter levels or changes in their receptors, can result in seizures. In this review, we address the roles played by various neurotransmitters and their receptors in the pathophysiology of epilepsy. Furthermore, we extensively explore the neurological mechanisms involved in the development and progression of epilepsy, along with its risk factors. Furthermore, we highlight the new therapeutic targets, along with pharmacological and non-pharmacological strategies currently employed in the treatment of epileptic syndromes, including drug interventions employed in clinical trials related to epilepsy.

Background: The handling of missing data is a challenge for inference and regression modelling. A particular challenge is dealing with missing predictor information, particularly when trying to build and make predictions from models for use in clinical practice., Methods: We utilise a flexible Bayesian approach for handling missing predictor information in regression models. This provides practitioners with full posterior predictive distributions for both the missing predictor information (conditional on the observed predictors) and the outcome-of-interest. We apply this approach to a previously proposed counterfactual treatment selection model for type 2 diabetes second-line therapies. Our approach combines a regression model and a Dirichlet process mixture model (DPMM), where the former defines the treatment selection model, and the latter provides a flexible way to model the joint distribution of the predictors., Results: We show that DPMMs can model complex relationships between predictor variables and can provide powerful means of fitting models to incomplete data (under missing-completely-at-random and missing-at-random assumptions). This framework ensures that the posterior distribution for the parameters and the conditional average treatment effect estimates automatically reflect the additional uncertainties associated with missing data due to the hierarchical model structure. We also demonstrate that in the presence of multiple missing predictors, the DPMM model can be used to explore which variable(s), if collected, could provide the most additional information about the likely outcome., Conclusions: When developing clinical prediction models, DPMMs offer a flexible way to model complex covariate structures and handle missing predictor information. DPMM-based counterfactual prediction models can also provide additional information to support clinical decision-making, including allowing predictions with appropriate uncertainty to be made for individuals with incomplete predictor data., (© 2023. The Author(s).)

Comparative extinction risk analysis-which predicts species extinction risk from correlation with traits or geographical characteristics-has gained research attention as a promising tool to support extinction risk assessment in the IUCN Red List of Threatened Species. However, its uptake has been very limited so far, possibly because existing models only predict a species' Red List category, without indicating which Red List criteria may be triggered. This prevents such approaches to be integrated into Red List assessments. We overcome this implementation gap by developing models that predict the probability of species meeting individual Red List criteria. Using data on the world's birds, we evaluated the predictive performance of our criterion-specific models and compared it with the typical criterion-blind modelling approach. We compiled data on biological traits (e.g. range size, clutch size) and external drivers (e.g. change in canopy cover) often associated with extinction risk. For each specific criterion, we modelled the relationship between extinction risk predictors and species' Red List category under that criterion using ordinal regression models. We found criterion-specific models were better at identifying threatened species compared to a criterion-blind model (higher sensitivity), but less good at identifying not threatened species (lower specificity). As expected, different covariates were important for predicting extinction risk under different criteria. Change in annual temperature was important for criteria related to population trends, while high forest dependency was important for criteria related to restricted area of occupancy or small population size. Our criteria-specific method can support Red List assessors by producing outputs that identify species likely to meet specific criteria, and which are the most important predictors. These species can then be prioritised for re-evaluation. We expect this new approach to increase the uptake of extinction risk models in Red List assessments, bridging a long-standing research-implementation gap., (© 2024 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)

Device-assisted enteroscopy (DAE) is capable of evaluating the entire gastrointestinal tract, identifying multiple lesions. Nevertheless, DAE's diagnostic yield is suboptimal. Convolutional neural networks (CNN) are multi-layer architecture artificial intelligence models suitable for image analysis, but there is a lack of studies about their application in DAE. Our group aimed to develop a multidevice CNN for panendoscopic detection of clinically relevant lesions during DAE. In total, 338 exams performed in two specialized centers were retrospectively evaluated, with 152 single-balloon enteroscopies (Fujifilm®, Porto, Portugal), 172 double-balloon enteroscopies (Olympus ® , Porto, Portugal) and 14 motorized spiral enteroscopies (Olympus ® , Porto, Portugal); then, 40,655 images were divided in a training dataset (90% of the images, n = 36,599) and testing dataset (10% of the images, n = 4066) used to evaluate the model. The CNN's output was compared to an expert consensus classification. The model was evaluated by its sensitivity, specificity, positive (PPV) and negative predictive values (NPV), accuracy and area under the precision recall curve (AUC-PR). The CNN had an 88.9% sensitivity, 98.9% specificity, 95.8% PPV, 97.1% NPV, 96.8% accuracy and an AUC-PR of 0.97. Our group developed the first multidevice CNN for panendoscopic detection of clinically relevant lesions during DAE. The development of accurate deep learning models is of utmost importance for increasing the diagnostic yield of DAE-based panendoscopy.

Groundwater is a vital ecosystem of the global water cycle, hosting unique biodiversity and providing essential services to societies. Despite being the largest unfrozen freshwater resource, in a period of depletion by extraction and pollution, groundwater environments have been repeatedly overlooked in global biodiversity conservation agendas. Disregarding the importance of groundwater as an ecosystem ignores its critical role in preserving surface biomes. To foster timely global conservation of groundwater, we propose elevating the concept of keystone species into the realm of ecosystems, claiming groundwater as a keystone ecosystem that influences the integrity of many dependent ecosystems. Our global analysis shows that over half of land surface areas (52.6%) has a medium-to-high interaction with groundwater, reaching up to 74.9% when deserts and high mountains are excluded. We postulate that the intrinsic transboundary features of groundwater are critical for shifting perspectives towards more holistic approaches in aquatic ecology and beyond. Furthermore, we propose eight key themes to develop a science-policy integrated groundwater conservation agenda. Given ecosystems above and below the ground intersect at many levels, considering groundwater as an essential component of planetary health is pivotal to reduce biodiversity loss and buffer against climate change., (© 2023 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)

In the early 2000s, the introduction of single-camera wireless capsule endoscopy (CE) redefined small bowel study. Progress continued with the development of double-camera devices, first for the colon and rectum, and then, for panenteric assessment. Advancements continued with magnetic capsule endoscopy (MCE), particularly when assisted by a robotic arm, designed to enhance gastric evaluation. Indeed, as CE provides full visualization of the entire gastrointestinal (GI) tract, a minimally invasive capsule panendoscopy (CPE) could be a feasible alternative, despite its time-consuming nature and learning curve, assuming appropriate bowel cleansing has been carried out. Recent progress in artificial intelligence (AI), particularly in the development of convolutional neural networks (CNN) for CE auxiliary reading (detecting and diagnosing), may provide the missing link in fulfilling the goal of establishing the use of panendoscopy, although prospective studies are still needed to validate these models in actual clinical scenarios. Recent CE advancements will be discussed, focusing on the current evidence on CNN developments, and their real-life implementation potential and associated ethical challenges.

The surge in the implementation of artificial intelligence (AI) in recent years has permeated many aspects of our life, and health care is no exception. Whereas this technology can offer clear benefits, some of the problems associated with its use have also been recognised and brought into question, for example, its environmental impact. In a similar fashion, health care also has a significant environmental impact, and it requires a considerable source of greenhouse gases. Whereas efforts are being made to reduce the footprint of AI tools, here, we were specifically interested in how employing AI tools in gastroenterology departments, and in particular in conjunction with capsule endoscopy, can reduce the carbon footprint associated with digestive health care while offering improvements, particularly in terms of diagnostic accuracy. We address the different ways that leveraging AI applications can reduce the carbon footprint associated with all types of capsule endoscopy examinations. Moreover, we contemplate how the incorporation of other technologies, such as blockchain technology, into digestive health care can help ensure the sustainability of this clinical speciality and by extension, health care in general.

Despite being central to the implementation of conservation policies, the usefulness of the International Union for Conservation of Nature (IUCN) Red List of Threatened Species is hampered by the 14% of species classified as data-deficient (DD) because information to evaluate these species' extinction risk was lacking when they were last assessed or because assessors did not appropriately account for uncertainty. Robust methods are needed to identify which DD species are more likely to be reclassified in one of the data-sufficient IUCN Red List categories. We devised a reproducible method to help red-list assessors prioritize reassessment of DD species and tested it with 6887 DD species of mammals, reptiles, amphibians, fishes, and Odonata (dragonflies and damselflies). For each DD species in these groups, we calculated its probability of being classified in a data-sufficient category if reassessed today from covariates measuring available knowledge (e.g., number of occurrence records or published articles available), knowledge proxies (e.g., remoteness of the range), and species characteristics (e.g., nocturnality); calculated change in such probability since last assessment from the increase in available knowledge (e.g., new occurrence records); and determined whether the species might qualify as threatened based on recent rate of habitat loss determined from global land-cover maps. We identified 1907 species with a probability of being reassessed in a data-sufficient category of >0.5; 624 species for which this probability increased by >0.25 since last assessment; and 77 species that could be reassessed as near threatened or threatened based on habitat loss. Combining these 3 elements, our results provided a list of species likely to be data-sufficient such that the comprehensiveness and representativeness of the IUCN Red List can be improved., (© 2023 The Authors. Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology.)

Current understanding of ecological and evolutionary processes underlying island biodiversity is heavily shaped by empirical data from plants and birds, although arthropods comprise the overwhelming majority of known animal species, and as such can provide key insights into processes governing biodiversity. Novel high throughput sequencing (HTS) approaches are now emerging as powerful tools to overcome limitations in the availability of arthropod biodiversity data, and hence provide insights into these processes. Here, we explored how these tools might be most effectively exploited for comprehensive and comparable inventory and monitoring of insular arthropod biodiversity. We first reviewed the strengths, limitations and potential synergies among existing approaches of high throughput barcode sequencing. We considered how this could be complemented with deep learning approaches applied to image analysis to study arthropod biodiversity. We then explored how these approaches could be implemented within the framework of an island Genomic Observatories Network (iGON) for the advancement of fundamental and applied understanding of island biodiversity. To this end, we identified seven island biology themes at the interface of ecology, evolution and conservation biology, within which collective and harmonized efforts in HTS arthropod inventory could yield significant advances in island biodiversity research., (© 2022 The Authors. Molecular Ecology published by John Wiley & Sons Ltd.)

After the Coronavirus pandemic, the importance of virus surveillance was highlighted, reinforcing the constant necessity of discussing and updating the methods for collection and diagnoses, including for other respiratory viruses. Although the nasopharyngeal swab is the gold-standard sample for detecting and genotyping SARS-CoV-2 and Influenza viruses, its collection is uncomfortable and requires specialized teams, which can be costly. During the pandemic, non-invasive saliva samples proved to be a suitable alternative for SARS-CoV-2 diagnosis, but for Influenza virus the use of this sample source is not recognized yet. In addition, most SARS-CoV-2 comparisons were conducted before the Omicron variant emerged. Here, we aimed to compare Influenza A and Omicron RT-qPCR analysis of nasopharyngeal swabs and saliva self-collection in paired samples from 663 individuals. We found that both nasopharyngeal swab and saliva collection are efficient for the diagnosis of Omicron (including sub-lineages) and for Influenza A, with high sensitivity and accuracy (>90%). The kappa index is 0.938 for Influenza A and 0.905 for SARS-CoV-2. These results showed excellent agreement between the two samples reinforcing saliva samples as a reliable source for detecting Omicron and highlighting saliva as a valid sample source for Influenza detection, considering this cheaper and more comfortable alternative.

Gastroenterology is increasingly moving towards minimally invasive diagnostic modalities. The diagnostic exploration of the colon via capsule endoscopy, both in specific protocols for colon capsule endoscopy and during panendoscopic evaluations, is increasingly regarded as an appropriate first-line diagnostic approach. Adequate colonic preparation is essential for conclusive examinations as, contrary to a conventional colonoscopy, the capsule moves passively in the colon and does not have the capacity to clean debris. Several scales have been developed for the classification of bowel preparation for colon capsule endoscopy. Nevertheless, their applications are limited by suboptimal interobserver agreement. Our group developed a deep learning algorithm for the automatic classification of colonic bowel preparation, according to an easily applicable classification. Our neural network achieved high performance levels, with a sensitivity of 91%, a specificity of 97% and an overall accuracy of 95%. The algorithm achieved a good discriminating capacity, with areas under the curve ranging between 0.92 and 0.97. The development of these algorithms is essential for the widespread adoption of capsule endoscopy for the exploration of the colon, as well as for the adoption of minimally invasive panendoscopy.