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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. [ABSTRACT FROM AUTHOR]

The study of functional diversity (FD) provides ways to understand phenomena as complex as community assembly or the dynamics of biodiversity change under multiple pressures. Different frameworks are used to quantify FD, either based on dissimilarity matrices (e.g. Rao entropy, functional dendrograms) or multidimensional spaces (e.g. convex hulls, kernel‐density hypervolumes), each with their own strengths and limits. Frameworks based on dissimilarity matrices either do not enable the measurement of all components of FD (i.e. richness, divergence, and regularity), or result in the distortion of the functional space. Frameworks based on multidimensional spaces do not allow for comparisons with phylogenetic diversity (PD) measures and can be sensitive to outliers. We propose the use of neighbor‐joining trees (NJ) to represent and quantify FD in a way that combines the strengths of current frameworks without many of their weaknesses. Importantly, our approach is uniquely suited for studies that compare FD with PD, as both share the use of trees (NJ or others) and the same mathematical principles. We test the ability of this novel framework to represent the initial functional distances between species with minimal functional space distortion and sensitivity to outliers. The results using NJ are compared with conventional functional dendrograms, convex hulls, and kernel‐density hypervolumes using both simulated and empirical datasets. Using NJ, we demonstrate that it is possible to combine much of the flexibility provided by multidimensional spaces with the simplicity of tree‐based representations. Moreover, the method is directly comparable with taxonomic diversity (TD) and PD measures, and enables quantification of the richness, divergence and regularity of the functional space. [ABSTRACT FROM AUTHOR]

The traditional approach of single-word brand names faces constraints due to trademarks, prompting a shift towards fusing two or more words to craft unique and memorable brands, exemplified by brands such as SalesForce© or SnapChat©. Furthermore, brands such as Kodak©, Xerox©, Google©, Häagen-Dazs©, and Twitter© have become everyday names although they are not real words, underscoring the importance of brandability in the naming process. However, manual evaluation of the vast number of possible combinations poses challenges. Artificial intelligence (AI), particularly natural language processing (NLP), is emerging as a promising solution to address this complexity. Existing online brand name generators often lack the sophistication to comprehensively analyze meaning, sentiment, and semantics, creating an opportunity for AI-driven models to fill this void. In this context, the present document reviews AI, NLP, and text-to-speech tools that might be useful in innovating the brand name generation and evaluation process. A systematic search on Google Scholar, IEEE Xplore, and ScienceDirect was conducted to identify works that could assist in generating and evaluating brand names. This review explores techniques and datasets used to train AI models as well as strategies for leveraging objective data to validate the brandability of generated names. Emotional and semantic aspects of brand names, which are often overlooked in traditional approaches, are discussed as well. A list with more than 75 pivotal datasets is presented. As a result, this review provides an understanding of the potential applications of AI, NLP, and affective computing in brand name generation and evaluation, offering valuable insights for entrepreneurs and researchers alike. [ABSTRACT FROM AUTHOR]

There is currently exponential growth in the electric vehicle market, which will require an increase in the electrical grid capacity to meet the associated charging demand. If, on the one hand, the introduction of energy generation from renewable energy sources can be used to meet that requirement, the intermittent nature of some of these sources will challenge the mandatory real-time equilibrium between generation and consumption. In order to use most of the energy generated via these sources, mechanisms are required to manage the charging of batteries in electric vehicles, according to the levels of generation. An effective smart charging process requires communication and/or control mechanisms between the supply equipment and the electric vehicle, enabling the adjustment of the energy transfer according to the generation levels. At this level, the ISO 15118 standard supports high-level communication mechanisms, far beyond the basic control solutions offered through the IEC 61851-1 specification. It is, thus, relevant to evaluate it in smart charging scenarios. In this context, this paper presents the development of a charge emulation system using the ISO 15118 communication protocol, and it discusses its application for demand response purposes. The system comprises several modules developed at both ends, supply equipment and electric vehicles, and allows the exchange of data during an emulated charging process. The system also includes human interfaces to facilitate interactions with users at both ends. Tests performed using the implemented system have shown that it supports a demand response when integrated with a photovoltaic renewable energy source. The dynamic adjustment to charging parameters, based on real-time energy availability, ensures efficient and sustainable charging processes, reducing the reliance on the grid and promoting the use of renewable energy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Aim: Quantifying the relative contribution of environmental filtering versus limiting similarity in shaping communities is challenging because these processes often act simultaneously and their effect is scale‐dependent. Focusing on caves, island‐like natural laboratories with limited environmental variability and species diversity, we tested: (i) the relative contribution of environmental filtering and limiting similarity in determining community assembly in caves; (ii) how the relative contribution of these driving forces changes along environmental gradients. Location: Europe. Time period: Present. Major taxa studied: Subterranean spiders. Methods: We used data on distribution and traits for European cave spiders (n = 475 communities). We estimated the trait space of each community using probabilistic hypervolumes, and obtained estimations of functional richness independent of the species richness of each community via null modelling. We model functional diversity change along environmental gradients using generalized dissimilarity modelling. Results: Sixty‐three percent of subterranean spider communities exhibited a prevalence of trait underdispersion. However, most communities displayed trait dispersion that did not depart significantly from random, suggesting that environmental filtering and limiting similarity were both exerting equally weak or strong, yet opposing influences. Overdispersed communities were primarily concentrated in southern latitudes, particularly in the Dinaric karst, where there is greater subterranean habitat availability. Pairwise comparisons of functional richness across caves revealed these effects to be strongly scale‐dependent, largely varying across gradients of cave development, elevation, precipitation, entrance size and annual temperature range. Conversely, geographical distance weakly affected trait composition, suggesting convergence in traits among communities that are far apart. Main conclusions: Even systems with stringent environmental conditions maintain the potential for trait differentiation, especially in areas of greater habitat availability. Yet, the relative influence of environmental filtering and limiting similarity change with scale, along clear environmental gradients. The interplay of these processes may explain the assembly of species‐poor subterranean communities displaying high functional specialization. [ABSTRACT FROM AUTHOR]

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_AIS was 0.5659 (0–29.45). We found a strong correlation between SB_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. Plain language summary: Artificial intelligence in Crohn's disease: the development of an automated score for disease activity evaluation This study introduces an innovative AI-based approach to evaluate Crohn's Disease. The AI system automatically analyzes images from capsule endoscopy, focusing on finding ulcers and erosions to measure disease activity. The research reveals a robust correlation between the AI-generated score assessing inflammation in the small bowel and traditional clinical scores. This suggests that the AI solution could be a quicker and more consistent way to evaluate Crohn's Disease, speeding up the evaluation process and reducing manual scoring variability. While promising, the study acknowledges limitations and emphasizes the need for further validation with larger groups of patients. Overall, it represents a crucial step toward integrating AI into gastroenterology, offering a glimpse into a future of more objective and personalized Crohn's Disease evaluation. [ABSTRACT FROM AUTHOR]

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 [ABSTRACT FROM AUTHOR]

Simple Summary: High-resolution anoscopy (HRA) is crucial for spotting and treating early signs of anal cancer. The researchers created an artificial intelligence (AI) system to analyze HRA images and identify high-grade and low-grade lesions accurately. They trained a computer program with thousands of images, achieving a remarkable accuracy of 94.6%. The AI system proved effective across different examination methods, such as using acetic acid or lugol iodine, and even after treatment. This advancement could improve the early detection of anal cancer precursors, potentially saving lives. 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. [ABSTRACT FROM AUTHOR]

Verbenaceae is an important element of Neotropical flora, and Stachytarpheta is the second largest genus in the family. Whereas pollen morphology has helped in the differentiation of several taxonomic groups, palynological data on Stachytarpheta are still scarce, especially for Brazil, where its greatest species richness is concentrated. The present work aimed to analyze the pollen morphology of selected species of Stachytarpheta sect. Stachytarpheta, to aid in the taxonomy of the group. Pollen grains from 11 species were analyzed before and after chemical treatment, measured and treated statistically; multivariate analyses were also performed. The pollen grains of S. sect. Stachytarpheta are large or very large monads, that are radially symmetric and isopolar, with amb varying among triangular, subtriangular, subcircular, and quadrangular, and shape among subspheroidal, oblate-spheroidal, prolate-spheroidal, and subprolate. The three or four apertures are of the colpus type and narrow. The ornamentation is verrucate, with perforations at the base of the verrucae and isolated granules. The pollen grains also possess a margin and an anapertural membrane, which are the main characteristics useful for differentiating the species of the section. The size, amb, shape, apertural type, and ornamentation are also important for morphological differentiation, but multivariate analyses did not support the groups formed based on pollen characterization. An identification key is provided to distinguish the 11 studied species based on pollen morphology. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. Pioneering coral probiotics study shows in situ application reshapes the coral microbiome, boosting beneficial bacteria and reducing pathogens without affecting surrounding seawater and sediment microbiomes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Simple Summary: This study compared two brain imaging methods, 7T magnetic resonance spectroscopic imaging (MRSI), which can image metabolic processes, and 3T magnetic resonance fingerprinting (MRF), which can image magnetic relaxation times, in 12 people with brain tumors called gliomas. Our goal was to understand how well these two approaches corresponded to each other, and which metabolite or relaxation time map was closest to the clinical standard, a neuroradiologist's tumor segmentation. In order to do this, we defined hotspots for each method and compared their overlaps. Additionally, we investigated the region around the tumor to look for evidence of possible tumor infiltration. The results of this study could improve how we use magnetic resonance imaging to monitor gliomas in patients. 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 Cithaeron reimoseri 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. [ABSTRACT FROM AUTHOR]

Three-dimensional printing (3DP) technologies are characterized as a set of innovative manufacturing techniques that allow for the creation of complex and/or personalized three-dimensional physical objects on the work surface of a 3D printing machine (based on the computer-aided design (CAD) project designs of these parts). Three-dimensional printing techniques are widely used in various areas of knowledge, such as education, engineering, and biomedicine. Polymeric materials are widely used for these applications, mainly due to their desirable workability during part manufacturing, compatibility with other chemical materials, the wide range of polymers with different physical and chemical characteristics, and the possibility for recycling. The development of polymeric drug delivery systems (DDSs) by 3D printing is currently an active field of research, both in academia and industry, given the potential of this technique for medical purposes. In this context, this work reviews potential polymers for the production of drug delivery systems via 3D printing techniques. The demonstrations of the main 3DP techniques used for drug delivery applications include their working principles and advantages and how the technologies develop the final product. In addition, potential synthetic and natural polymers that are currently used in 3DP drug delivery devices are presented and discussed based on recent scientific studies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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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. [ABSTRACT FROM AUTHOR]

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/cm2 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The mostly Brazilian population present common disorders among young and adults peoples denominated aesthetic diseases, for example acne, melasma, psoriasis and nettle rash. Given this scenario, the development of a mobile application has become feasible, with technology growing exponentially, and, at the same time, there is also an increased interest for developing new ways of disseminating content in a practical way. In this study, the aim was to help nonprofessional, academics and health and aesthetic professionals. A large percentage were young people (74.7%), aged between 18 and 29 years old, but the target of the application is not exclusive to this profile. Our research resulted in good metrics, among them, the public studied is interested in paying to have the application on their smartphone. Thus, the virtual atlas of aesthetic dysfunctions demonstrated to be a tool for nonprofessional and professionals helping in the acquisition of information. [ABSTRACT FROM AUTHOR]

Stachytarpheta is mainly distributed in Brazil, where the knowledge of species richness is still in progress. Here, we describe S. meninii, a new micro‐endemic species from the Diamantina Plateau, Espinhaço Range. It is a shrub that can reach up to 1.8 meters in height, with abundant pedicellate glandular trichomes, viscous, aromatic leaves, inflorescences with oblong‐triangular bracts, and blue corollas, with a tube that is barely exserted from the calyx. The new species has been recorded from two gatherings from the same locality in the municipality of Serro, Minas Gerais state, and can be considered Critically Endangered (CR). Illustrations and a distribution map are provided for the recognition of S. meninii, along with an identification key for species of the genus found in Diamantina Plateau. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Simple Summary: Device-assisted enteroscopy is the only diagnostic and therapeutic exam capable of exploring the entire gastrointestinal tract. However, the diagnostic yield of this procedure is not sufficient enough to assure a cost-effective panendoscopy, and there is significant interobserver variability during the exam. Artificial intelligence tools have been proved to be beneficial in several areas of medicine, namely in Gastroenterology, with a strong image component. However, the development of deep learning models for application in device-assisted enteroscopy is still in an embryonic phase. The authors herein aimed to develop a multidevice convolutional neural network based on 338 exams performed in two renowned centers. The present model was able to accurately identify multiple clinically relevant lesions across the entire gastrointestinal tract, with an image processing time that favors its clinical applicability. The first worldwide panendoscopic model showed the potential of artificial intelligence in augmenting the accuracy and cost-effectiveness of device-assisted enteroscopy. 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. [ABSTRACT FROM AUTHOR]