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Sentiment analysis (SA) is an effective method for determining public opinion. Social media posts have been the subject of much research, due to the platforms' enormous and diversified user bases that regularly share thoughts on nearly any subject. However, on posts composed by a text–image pair, the written description may or may not convey the same sentiment as the image. The present study uses machine learning models for the automatic sentiment evaluation of pairs of text and image(s). The sentiments derived from the image and text are evaluated independently and merged (or not) to form the overall sentiment, returning the sentiment of the post and the discrepancy between the sentiments represented by the text–image pair. The image sentiment classification is divided into four categories—"indoor" (IND), "man-made outdoors" (OMM), "non-man-made outdoors" (ONMM), and "indoor/outdoor with persons in the background" (IOwPB)—and then ensembled into an image sentiment classification model (ISC), that can be compared with a holistic image sentiment classifier (HISC), showing that the ISC achieves better results than the HISC. For the Flickr sub-data set, the sentiment classification of images achieved an accuracy of 68.50% for IND, 83.20% for OMM, 84.50% for ONMM, 84.80% for IOwPB, and 76.45% for ISC, compared to 65.97% for the HISC. For the text sentiment classification, in a sub-data set of B-T4SA, an accuracy of 92.10% was achieved. Finally, the text–image combination, in the authors' private data set, achieved an accuracy of 78.84%. [ABSTRACT FROM AUTHOR]

Introduction: COVID-19, caused by the SARS-CoV-2 virus, has been associated with oligosymptomatic cases or severe acute respiratory syndrome, with multiple organ failure and death. One of the most significant events for clinical outcomes is Acute Kidney Injury (AKI). It is known that AKI in COVID-19 is multifactorial, and the main mechanisms are cytokine storm, metabolic stress, use of nephrotoxic drugs, rhabdomyolysis, viral tropism to kidney tissues, and multiple organ failure. However, little is known about the impact of AKI clinical presentation and pathophysiological mechanisms on the outcome of patients affected by COVID-19. Objectives: To identify AKI clinical presentation and etiology, also known as phenotypes, and pathophysiological mechanisms, also known as subphenotypes, in patients affected by COVID-19 and associate them with death. This cohort and retrospective study evaluate the medical records of patients with SARS-CoV-2 infection admitted to a tertiary public hospital from 1 June 2020, to 31 July 2021, from admission to clinical outcome (hospital discharge or death). Clinical and laboratory data were analyzed during the hospitalization. Renal function was estimated by urine output and serum creatinine; therefore, the diagnosis and AKI classification were based on the 2012 KDIGO criteria. The occurrence of AKI was the inclusion criterion. According to clinical and laboratory presentations, we recognized two phenotypes of AKI (the direct and indirect impact of SARS-CoV-2 on the kidney) and several pathophysiological mechanisms. Subphenotypes of the direct impact of SARS-CoV-2 on kidneys were associated with Kidney Viral Tropism, Cytokine Storm, COVID-19-Related Multiple Organ Failure, and Mixed (more than one mechanism associated with COVID-19). Subphenotypes of indirect impact of SARS-CoV-2 on kidney phenotypes were Ischemic, Nephrotoxic due to rhabdomyolysis, and Septic. Univariate and multivariate analyses were performed to identify risk factors associated with death. Result: In total, 372 patients were included; 55.6% were male, 82.3% were Caucasians, and the mean age was 61.4 years. The majority of patients were admitted to the ICU (88.2%) and required mechanical ventilation (86.3%). AKI was predominantly KDIGO 3 (65.6%). When classifying our patients' AKI in two kidney phenotypes based on their clinical presentation, the direct impact of the SARS-CoV-2 phenotype was predominant (71,5%) and associated with higher mortality (83.8 vs. 46.3%, p = 0.001). Among the AKI pathophysiological mechanisms, Mixed—synergism of viral mechanisms—was the most prevalent (23.4%), followed by Viral Tropism (19.9%), Multiple Organ Failure—MOF (18%), Septic (15.6%), Ischemic (12.9%), and Cytokine Storm (10.2%). Mortality was high (73.1%). Logistic regression identified APACHE II, ATN-ISS, and the direct impact of SARS-CoV-2 on the kidney as factors associated with death, while ischemic AKI was associated with lower mortality. Conclusions: We can conclude that APACHE II and ATN-ISS scoring are clinical predictions of hospital mortality in COVID patients with AKI, as well as AKI etiology involving the direct impact of SARS-CoV-2 on the kidney, while ischemic pathophysiological mechanisms of AKI are associated with lower mortality. [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/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]

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]

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]

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]

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]

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]

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]

A cross-sectional study was conducted to biomechanically characterize Isabel's workout (30 snatch repetitions with 61 kg fixed weight), focusing on eventual changes in knee, hip and shoulder angles. A three-dimensional markerless motion capture system was used to collect data from 11 highly trained male crossfitters along the Isabel workout performed at maximal effort. The routine was analyzed globally and in initial, middle and final phases (10, 20 and 30 repetitions, respectively). Lift total time increased (1.51 ± 0.18 vs. 1.97 ± 0.20 s) and maximal lift velocity (2.64 ± 0.12 vs. 2.32 ± 0.13 m/s) and maximal lift power (15.58 ± 2.34 vs. 13.80 ± 2.49 W/kg) decreased from the initial to final phases, while the time from lift until the bar crossed the hip and shoulder (34.20 ± 4.00 vs. 27.50 ± 5.10 and 39.70 ± 16.80 vs. 30.90 ± 13.90%) decreased along the Isabel workout. In addition, a decrease in hip flexion was observed during the last two phases when the bar crosses the knee (62.62 ± 24.80 vs. 53.60 ± 19.99°). Data evidence a decrease in the power profile and a change in hip flexion throughout the Isabel workout, without compromising the other joints. [ABSTRACT FROM AUTHOR]

The conceptualization of insularity in the context of islands has been widely studied and debated, encompassing both geographical and identity aspects. The academic discussion on the definition of insularity and isolation in the context of islands remains a current topic, along with the economic, social, legal and environmental challenges faced by insular regions. This study aims to deepen the conceptual framework of insularity and structure its issues from various scientific perspectives through quantitative bibliometric analysis. Key focus areas and research gaps have been identified, including the need for enhanced economic policies, better marine resource management, more comprehensive urban development research and improved data on social and political issues. The conclusions emphasize the importance of prioritizing intervention areas and specific policies to address the unique challenges faced by insular regions in terms of sustainable development. [ABSTRACT FROM AUTHOR]

In this study, we quantified relevant biophysical characteristics of two elite rowers across a wide range of intensities. Two <40-year-old male and female Olympic and World Championship finalists performed a 7 × 3 min protocol plus 1 min maximal effort on a rowing ergometer. The intensity increase resulted in maximum values of 79.4 ± 2.4 and 69.7 ± 1.5 mL/min/kg for oxygen uptake, 179.3 ± 5.7 and 152.5 ± 2.9 L/min for ventilation, 170 ± 1 and 173 ± 0 bpm for heart rate, 10.6 and 15.8 mmol/L for blood lactate concentration, and 38.1 ± 0.03 and 38.8 ± 0.03 °C for core temperature for the male and female rowers. The percentage of power corresponding to a previously conducted maximum 2000 m rowing ergometer test and the work at each step increased from 49 to 127 and 42 to 103% and from 226.8 to 398.9 J and 174.0 to 250.0 J, from low to extreme intensities, for the male and female. Concurrently, there was a decrease in cycle length and propulsive time, followed by an increase in maximal handle drive velocity, with the rise in rowing intensity. These world-class rowers seem capable of maintaining physiological and technical profiles (and a remarkable capacity to generate substantial power) at this phase of their careers possibly due to long-term engagement in elite-level training. Biophysical data provide valuable referential information for guiding rowers to improve their performance. [ABSTRACT FROM AUTHOR]