bluuebunny/crossref_metadata_2025 huggingface dataset:
Prediction of Lymph Node Metastasis in T1 Colorectal Cancer Using Artificial Intelligence with Hematoxylin and Eosin-Stained Whole-Slide-Images of Endoscopic and Surgical Resection Specimens
Authors: Song Joo Hye , Kim Eun Ran , Hong Yiyu , Sohn Insuk , Ahn Soomin , Kim Seok-Hyung , Jang Kee-Taek
Published: May 2024

According to the current guidelines, additional surgery is performed for endoscopically resected specimens of early colorectal cancer (CRC) with a high risk of lymph node metastasis (LNM). However, the rate of LNM is 2.1–25.0% in cases treated endoscopically followed by surgery, indicating a high rate of unnecessary surgeries. Therefore, this study aimed to develop an artificial intelligence (AI) model using H&E-stained whole slide images (WSIs) without handcrafted features employing surgically and endoscopically resected specimens to predict LNM in T1 CRC. To validate with an independent cohort, we developed a model with four versions comprising various combinations of training and test sets using H&E-stained WSIs from endoscopically (400 patients) and surgically resected specimens (881 patients): Version 1, Train and Test: surgical specimens; Version 2, Train and Test: endoscopic and surgically resected specimens; Version 3, Train: endoscopic and surgical specimens and Test: surgical specimens; Version 4, Train: endoscopic and surgical specimens and Test: endoscopic specimens. The area under the curve (AUC) of the receiver operating characteristic curve was used to determine the accuracy of the AI model for predicting LNM with a 5-fold cross-validation in the training set. Our AI model with H&E-stained WSIs and without annotations showed good performance power with the validation of an independent cohort in a single center. The AUC of our model was 0.758–0.830 in the training set and 0.781–0.824 in the test set, higher than that of previous AI studies with only WSI. Moreover, the AI model with Version 4, which showed the highest sensitivity (92.9%), reduced unnecessary additional surgery by 14.2% more than using the current guidelines (68.3% vs. 82.5%). This revealed the feasibility of using an AI model with only H&E-stained WSIs to predict LNM in T1 CRC.
Navigating the Maze: Exploring Non-Oncological Complexities in Non-Small-Cell Lung Cancer
Authors: Marghescu Angela-Ștefania , Vlăsceanu Silviu , Preda Mădălina , Țigău Mirela , Dumitrache-Rujinski Ștefan , Leonte Diana Gabriela , Măgheran Elena Doina , Tudor Adrian , Bădărău Ioana Anca , Georgescu Livia , Costache Mariana
Published: May 2024

Pulmonary oncological pathologies are an important public health problem and the association with other pulmonary lesions may pose difficulties in diagnosis and staging or require different treatment options. To address this complexity, we conducted a retrospective observational study at the Marius Nasta Institute of Pneumophthisiology, Bucharest, Romania. Our study focused on patients admitted in 2019 with non-small-cell lung carcinoma and associated pulmonary lesions identified through surgical resection specimens. Among the 314 included patients, multiple pulmonary nodules were observed on macroscopic examination, with 12% (N = 37) exhibiting nonmalignant etiologies upon microscopic examination. These findings underscore the challenge of preoperative staging. Patients with coexisting nonmalignant lesions were similar in age, smoking habits, and professional or environmental exposure by comparison with those who presented only malignant lesions. The presentation of coexisting malignant and nonmalignant lesions may pose difficulties in diagnosing and staging pulmonary cancer.
Role of 18F-FDG PET/CT in Head and Neck Squamous Cell Carcinoma: Current Evidence and Innovative Applications
Authors: Caldarella Carmelo , De Risi Marina , Massaccesi Mariangela , Miccichè Francesco , Bussu Francesco , Galli Jacopo , Rufini Vittoria , Leccisotti Lucia
Published: May 2024

This article provides an overview of the use of 18F-FDG PET/CT in various clinical scenarios of head–neck squamous cell carcinoma, ranging from initial staging to treatment-response assessment, and post-therapy follow-up, with a focus on the current evidence, debated issues, and innovative applications. Methodological aspects and the most frequent pitfalls in head–neck imaging interpretation are described. In the initial work-up, 18F-FDG PET/CT is recommended in patients with metastatic cervical lymphadenectomy and occult primary tumor; moreover, it is a well-established imaging tool for detecting cervical nodal involvement, distant metastases, and synchronous primary tumors. Various 18F-FDG pre-treatment parameters show prognostic value in terms of disease progression and overall survival. In this scenario, an emerging role is played by radiomics and machine learning. For radiation-treatment planning, 18F-FDG PET/CT provides an accurate delineation of target volumes and treatment adaptation. Due to its high negative predictive value, 18F-FDG PET/CT, performed at least 12 weeks after the completion of chemoradiotherapy, can prevent unnecessary neck dissections. In addition to radiomics and machine learning, emerging applications include PET/MRI, which combines the high soft-tissue contrast of MRI with the metabolic information of PET, and the use of PET radiopharmaceuticals other than 18F-FDG, which can answer specific clinical needs.
One-Step Synthesis of High-Efficiency Oxygen Evolution Reaction Catalyst FeSx(Y/MB) with High Temperature Resistance and Strong Alkali
Authors: Wang Jing , Feng Lingling , Zhao Zikang , Wang Yan , Zhang Ying , Song Shan , Sun Shengwei , Zhou Junshuang , Gao Faming
Published: May 2024

Given the energy crisis and escalating environmental pollution, the imperative for developing clean new energy is evident. Hydrogen has garnered significant attention owing to its clean properties, high energy density, and ease of storage and transportation. This study synthesized four types of catalysts—FeS(DI/MB), FeS(ET/MB), Fe(DI/MB), and Fe(ET/MB)—using two distinct solution systems: DI/MB and ET/MB. The FeS(DI/MB) catalyst, synthesized using the layered solution system (DI/MB), demonstrates a uniformly distributed and dense nanosheet structure, exhibiting excellent resistance to strong bases and superior catalytic properties. The FeS(DI/MB) electrode showed OER overpotentials of 460 mV and 318 mV in 1 M and 6 M, respectively, at current densities of up to 500 mA cm−2. Under industrial electrolysis test conditions, the FeS(DI/MB) electrode required only 262 mV to achieve a current density of 500 mA cm−2, operating in a high-temperature, strong alkaline environment of 6 M at 60 °C. Furthermore, the FeS(DI/MB) electrode exhibited excellent OER catalytic activity and stability, as evidenced by a 60 h stability test These findings provide valuable insights into the preparation of iron nickel sulfide-based catalysts, and further in-depth and comprehensive exploration is anticipated to yield the excellent catalytic performance of these catalysts in the realm of electrolytic water hydrogen production.
Optimization of Desulfurization Process via Choline Phosphotungstate Coupled with Persulfate Using Response Surface Methodology
Authors: Zhang Yinke , Xu Hang
Published: May 2024

Using a simple acid-base neutralization method, a Ch-PW solid catalyst was synthesized by mixing choline hydroxide (ChOH) and phosphotungstic acid (HPW) at a 2:1 molar ratio in an aqueous solution. This catalyst was combined with a 20 wt.% potassium peroxymonosulfate (PMS) solution, using acetonitrile (ACN) as the extraction solvent to create an extraction catalytic oxidative desulfurization system. The optimal desulfurization conditions were determined through response surface methodology, targeting the highest desulfurization rate: 0.99 g of Ch-PW, 1.07 g of PMS, 2.5 g of extraction solvent, at a temperature of 50.48 °C. The predicted desulfurization rate was 90.79%, compared to an experimental rate of 93.64%, with a deviation of 3.04%. A quadratic model correlating the desulfurization rate with the four conditions was developed and validated using ANOVA, which also quantified the impact of each factor on the desulfurization rate: PMS > ACN > Ch-PW > temperature. GC-MS analysis identified the main oxidation product as DBTO2, and the mechanism of desulfurization in this system was further explored.
Study of Oxygen Reduction Reaction on Polycrystalline Rhodium in Acidic and Alkaline Media
Authors: Golubović Jelena , Varničić Miroslava , Štrbac Svetlana
Published: May 2024

This study examines the kinetics and mechanism of the oxygen reduction reaction (ORR) on a polycrystalline rhodium electrode (Rh(poly)) in acidic and alkaline media, using rotating disc electrode measurements. This study found that the ORR activity of the Rh(poly) electrode decreases in the order of 0.1 M NaOH > 0.1 M HClO4 > 0.05 M H2SO4 concerning the half-wave potentials. The Tafel slopes for ORR on Rh(poly) in the cathodic direction are 60 and 120 mV dec−1 at low and high overpotentials, respectively, in perchloric acid and alkaline solutions. However, strongly adsorbed sulfate anions hinder the ORR on Rh(poly) in sulfuric acid, leading to higher Tafel slopes. The highest ORR activity of Rh(poly) in an alkaline media suggests the promoting role of the specifically adsorbed OH− anions and RhOH. In all cases, ORR on Rh(poly) proceeds through the 4e-series reaction pathway.
Preconditioning of MSCs for Acute Neurological Conditions: From Cellular to Functional Impact—A Systematic Review
Authors: Serrenho Inês , Ferreira Susana Alves , Baltazar Graça
Published: May 2024

This systematic review aims to gather evidence on the mechanisms triggered by diverse preconditioning strategies for mesenchymal stem cells (MSCs) and their impact on their potential to treat ischemic and traumatic injuries affecting the nervous system. The 52 studies included in this review report nine different types of preconditioning, namely, manipulation of oxygen pressure, exposure to chemical substances, lesion mediators or inflammatory factors, usage of ultrasound, magnetic fields or biomechanical forces, and culture in scaffolds or 3D cultures. All these preconditioning strategies were reported to interfere with cellular pathways that influence MSCs’ survival and migration, alter MSCs’ phenotype, and modulate the secretome and proteome of these cells, among others. The effects on MSCs’ phenotype and characteristics influenced MSCs’ performance in models of injury, namely by increasing the homing and integration of the cells in the lesioned area and inducing the secretion of growth factors and cytokines. The administration of preconditioned MSCs promoted tissue regeneration, reduced neuroinflammation, and increased angiogenesis and myelinization in rodent models of stroke, traumatic brain injury, and spinal cord injury. These effects were also translated into improved cognitive and motor functions, suggesting an increased therapeutic potential of MSCs after preconditioning. Importantly, none of the studies reported adverse effects or less therapeutic potential with these strategies. Overall, we can conclude that all the preconditioning strategies included in this review can stimulate pathways that relate to the therapeutic effects of MSCs. Thus, it would be interesting to explore whether combining different preconditioning strategies can further boost the reparative effects of MSCs, solving some limitations of MSCs’ therapy, namely donor-associated variability.
Pharmacological Blockade of the Adenosine A2B Receptor Is Protective of Proteinuria in Diabetic Rats, through Affecting Focal Adhesion Kinase Activation and the Adhesion Dynamics of Podocytes
Authors: Mendoza-Soto Pablo , Jara Claudia , Torres-Arévalo Ángelo , Oyarzún Carlos , Mardones Gonzalo A. , Quezada-Monrás Claudia , San Martín Rody
Published: May 2024

Induction of the adenosine receptor A2B (A2BAR) expression in diabetic glomeruli correlates with an increased abundance of its endogenous ligand adenosine and the progression of kidney dysfunction. Remarkably, A2BAR antagonism protects from proteinuria in experimental diabetic nephropathy. We found that A2BAR antagonism preserves the arrangement of podocytes on the glomerular filtration barrier, reduces diabetes-induced focal adhesion kinase (FAK) activation, and attenuates podocyte foot processes effacement. In spreading assays using human podocytes in vitro, adenosine enhanced the rate of cell body expansion on laminin-coated glass and promoted peripheral pY397-FAK subcellular distribution, while selective A2BAR antagonism impeded these effects and attenuated the migratory capability of podocytes. Increased phosphorylation of the Myosin2A light chain accompanied the effects of adenosine. Furthermore, when the A2BAR was stimulated, the cells expanded more broadly and more staining of pS19 myosin was detected which co-localized with actin cables, suggesting increased contractility potential in cells planted onto a matrix with a stiffness similar to of the glomerular basement membrane. We conclude that A2BAR is involved in adhesion dynamics and contractile actin bundle formation, leading to podocyte foot processes effacement. The antagonism of this receptor may be an alternative to the intervention of glomerular barrier deterioration and proteinuria in the diabetic kidney disease.
CPEB3 Maintains Developmental Competence of the Oocyte
Authors: Lamacova Lucie , Jansova Denisa , Jiang Zongliang , Dvoran Michal , Aleshkina Daria , Iyyappan Rajan , Jindrova Anna , Fan Heng-Yu , Jiao Yuxuan , Susor Andrej
Published: May 2024

Mammalian oocyte development depends on the temporally controlled translation of maternal transcripts, particularly in the coordination of meiotic and early embryonic development when transcription has ceased. The translation of mRNA is regulated by various RNA-binding proteins. We show that the absence of cytoplasmic polyadenylation element-binding protein 3 (CPEB3) negatively affects female reproductive fitness. CPEB3-depleted oocytes undergo meiosis normally but experience early embryonic arrest due to a disrupted transcriptome, leading to aberrant protein expression and the subsequent failure of embryonic transcription initiation. We found that CPEB3 stabilizes a subset of mRNAs with a significantly longer 3’UTR that is enriched in its distal region with cytoplasmic polyadenylation elements. Overall, our results suggest that CPEB3 is an important maternal factor that regulates the stability and translation of a subclass of mRNAs that are essential for the initiation of embryonic transcription and thus for embryonic development.
Ceramic Matrix Composites: Classifications, Manufacturing, Properties, and Applications
Authors: Shrivastava Shriya , Rajak Dipen Kumar , Joshi Tilak , Singh Dwesh K. , Mondal D. P.
Published: May 2024

Ceramic matrix composites (CMCs) are a significant advancement in materials science and engineering because they combine the remarkable characteristics of ceramics with the strength and toughness of fibers. With their unique properties, which offer better performance and endurance in severe settings, these advanced composites have attracted significant attention in various industries. At the same time, lightweight ceramic matrix composites (LCMCs) provide an appealing alternative for a wide range of industries that require materials with excellent qualities such as high-temperature stability, low density, corrosion resistance, and excellent mechanical performance. CMC uses will expand as production techniques and material research improve, revolutionizing aerospace, automotive, and other industries. The effectiveness of CMCs primarily relies on the composition of their constituent elements and the methods employed in their manufacturing. Therefore, it is crucial to explore the functional properties of various global ceramic matrix reinforcements, their classifications, and the manufacturing techniques used in CMC fabrication. This study aims to overview a diverse range of CMCs reinforced with primary fibers, including their classifications, manufacturing techniques, functional properties, significant applications, and global market size.
Survey of Thai Physicians’ Practice in Pediatric Septic Shock
Authors: Niamsanit Sirapoom , Saengthongpitag Teerapat , Uppala Rattapon , Sitthikarnkha Phanthila , Techasatian Leelawadee , Saengnipanthkul Suchaorn
Published: May 2024

(1) Background: Sepsis management in children is crucial, especially in emergency services. This study aims to evaluate Thai physicians’ knowledge gaps in the emergency management of sepsis in children and to evaluate their adherence to the current sepsis clinical practice guidelines. (2) Methods: This is a cross-sectional survey of Thai physicians’ management of septic shock in children. The survey was conducted through online questionnaires from March 2019–April 2019. (3) Results: Of the 366 responders, 362 (98.9%) were completed. Most of the responders were general practitioners (89.2%) and pediatricians (10.8%). The time from positive sepsis screening to being evaluated by physicians within 15 min was reported by 83.9%. The most common choice of fluid resuscitation was normal saline solution (77.3%). The practice of a fluid loading dose (20 mL/kg) consistent with the guidelines was 56.3%. The selection of the first vasoactive agent in warm shock (norepinephrine) and cold shock (epinephrine) according to recommendations in the guidelines was 74.3% and 36.2%, respectively. There was a significant difference between general practitioners and pediatricians in terms of knowledge about initial fluid resuscitation and the optimal vasoactive agent in cold shock (p-value < 0.001). In the multivariate model, factors associated with the guideline-based decision-making of vasoactive agent choice for cold shock were specialist training (pediatrician) and the completion of sepsis management training certification, with adjusted odds ratios (AORs) of 7.81 and 2.96, but working experience greater than ten years was inconsistent with the guideline-based decision-making (AOR 0.14). (4) Conclusions: Thai clinicians were unfamiliar with pediatric sepsis therapy standards, specifically the quantity of early fluid resuscitation and the appropriate vasoactive medications for cold shock. To encourage adherence to the guidelines, we propose a regularly required training course on pediatric sepsis management.
Proteogenomics in Nephrology: A New Frontier in Nephrological Research
Authors: Chavali Kavya , Coker Holley , Youngblood Emily , Karaduta Oleg
Published: May 2024

Proteogenomics represents a transformative intersection in nephrology, uniting genomics, transcriptomics, and proteomics to unravel the molecular intricacies of kidney diseases. This review encapsulates the methodological essence of proteogenomics and its profound implications in chronic kidney disease (CKD) research. We explore the proteogenomic pipeline, highlighting the integrated analysis of genomic, transcriptomic, and proteomic data and its pivotal role in enhancing our understanding of kidney pathologies. Through case studies, we showcase the application of proteogenomics in clear cell renal cell carcinoma (ccRCC) and Autosomal Recessive Polycystic Kidney Disease (ARPKD), emphasizing its potential in personalized treatment strategies and biomarker discovery. The review also addresses the challenges in proteogenomic analysis, including data integration complexities and bioinformatics limitations, and proposes solutions for advancing the field. Ultimately, this review underscores the prospective future of proteogenomics in nephrology, particularly in advancing personalized medicine and providing novel therapeutic insights.
Advancing Sustainable Decision Making in Additive Manufacturing: A Comprehensive Review of Multi-Criteria Decision Making Approaches
Authors: Alves Adriana S. F. , Oliveira J. P. , Godina Radu
Published: May 2024

Additive manufacturing (AM) is one of the technologies of Industry 4.0 that has been contributing to the development of different manufacturing industries. The integration of sustainability concepts into additive manufacturing has been gaining attention among researchers. This integration is essential in the development of AM technologies and can be a significant asset in terms of decision making for organizations. This work aims to present a concise literature review on the integration of decision making, especially multi-criteria decision making, and sustainability into the AM environment. The literature on this topic currently possesses a total of fifteen documents, which were analyzed in this work. Some developments on this topic have been achieved in domains such as material selection, process selection and challenges, and drivers’ analysis of sustainable AM. This review shows that even though there has been an effort in recent years to integrate sustainability into additive manufacturing, there is still a long road to the development of this topic for the future, and so some recommendations for future research paths are presented.
Teaching Medical Procedural Skills for Performance
Authors: Taraporewalla Kersi , Barach Paul , van Zundert André
Published: May 2024

Procedures are a core element of medical professional practice. Today’s training approach was formulated in the mid-twentieth century based on a computer analogue of the brain. Despite minor modifications, the system has remained relatively unchanged for the past 70 years. It delivers competence. However, competence is not reliable performance. The inability to adapt to the variety of patients and variations in the performance environments, such as the operating room, results in patient morbidity and mortality. There is a need for changes in the development and training of medical procedural skills based on current theories of skill acquisition, movement theory, and motor control. Achieving optimal performance necessitates the ability to adapt through training in diverse patient and performance environments rather than merely imitating prescribed movements. We propose a novel model of training, the Constraints-Led Approach, which allows for robust training by altering the factors affecting skill acquisition and lifelong learning.
Application of Machine Learning in Predicting Perioperative Outcomes in Patients with Cancer: A Narrative Review for Clinicians
Authors: Brydges Garry , Uppal Abhineet , Gottumukkala Vijaya
Published: May 2024

This narrative review explores the utilization of machine learning (ML) and artificial intelligence (AI) models to enhance perioperative cancer care. ML and AI models offer significant potential to improve perioperative cancer care by predicting outcomes and supporting clinical decision-making. Tailored for perioperative professionals including anesthesiologists, surgeons, critical care physicians, nurse anesthetists, and perioperative nurses, this review provides a comprehensive framework for the integration of ML and AI models to enhance patient care delivery throughout the perioperative continuum.
Discrepancy in the Location of Prostate Cancer Indicated on Biparametric Magnetic Resonance Imaging and Pathologically Diagnosed Using Surgical Specimens
Authors: Tomioka Masayuki , Nakane Keita , Kawase Makoto , Iinuma Koji , Kato Daiki , Kawase Kota , Taniguchi Tomoki , Tobisawa Yuki , Sugino Fumiya , Kaga Tetsuro , Kato Hiroki , Matsuo Masayuki , Kito Yusuke , Saigo Chiemi , Suzui Natsuko , Ito Takayasu , Miyazaki Tatsuhiko , Takeuchi Tamotsu , Koie Takuya
Published: May 2024

Accurate diagnosis of the localization of prostate cancer (PCa) on magnetic resonance imaging (MRI) remains a challenge. We aimed to assess discrepancy between the location of PCa pathologically diagnosed using surgical specimens and lesions indicated as possible PCa by the Prostate Imaging Reporting and Data System on MRI. The primary endpoint was the concordance rate between the site of probable clinically significant PCa (csPCa) identified using biparametric MRI (bpMRI) and location of PCa in the surgical specimen obtained using robot-assisted total prostatectomy. Among 85 lesions identified in 30 patients; 42 (49.4%) were identified as possible PCa on MRI. The 85 PCa lesions were divided into positive and negative groups based on the bpMRI results. None of the patients had missed csPCa. Although the diagnostic accuracy of bpMRI was relatively high for PCas located in the middle of the prostate (p = 0.029), it was relatively low for PCa located at the base of the prostate, all of which were csPCas. Although current modalities can accurately diagnose PCa, the possibility that PCa is present with multiple lesions in the prostate should be considered, even if MRI does not detect PCa.
TAFRO Syndrome on 18F-FDG-PET/CT: An Appealing Diagnostic Tool
Authors: Jaafari Ayoub , Taheri Nadim , Mansour Sohaïb , El Bouhali Saïf-Eddine , Attou Rachid
Published: May 2024

TAFRO syndrome (TS) is a recently recognized and heterogenous systemic disease characterized by a confluence of symptoms: thrombocytopenia (T), anasarca (A), fever (F), reticulin myelofibrosis (R), and organomegaly (O). First described in Japan in 2010, the pathogenesis remains unclear and includes various clinical conditions such as malignancies, rheumatologic disorders, infections, and “Polyneuropathy, Organomegaly, Endocrinopathy, Monoclonal plasma cell disorder, and Skin changes” (POEMS) syndrome. Due to its heterogeneous presentation and potential life-threatening delays in diagnosis, accurate diagnosis is crucial. According to the literature, no specific imaging modality has been recommended for the work-up of patients with suspected TS. Here, we report a case of TS and its management using 18F-FDG-PET/CT imaging as an attractive complementary diagnostic tool.
Incisor and Soft Tissue Characteristics of Adult Bimaxillary Protrusion Patients among Different Skeletal Anteroposterior Classifications
Authors: Siangloy Thitirat , Charoemratrote Chairat
Published: May 2024

The objective of this study was to investigate the upper incisors (U1), lower incisors (L1), and soft tissue profiles of bimaxillary protrusion (BM) adult patients among skeletal Class I (BM1), II (BM2) and III (BM3). Understanding these characteristics would be useful for incisor and lip diagnostics in different skeletal classifications. Fourteen linear and twelve angular variables of the incisors and lips were evaluated in 214 lateral cephalograms (BM1 = 91, BM2 = 84, BM3 = 39). ANOVA and Bonferroni tests compared the measurements. BM1 and BM3 exhibited a greater U1 position and U1 inclination than the norms, while BM2 presented only a greater U1 position than the norms but normal U1 inclination. BM1 and BM3 had a significantly greater U1 position than BM2. BM1 and BM2 demonstrated a greater L1 position and L1 inclination than the norms, whereas BM3 displayed only a greater L1 position than the norms but normal L1 inclination. BM2 had the most anterior L1 position, whereas BM3 had the least anterior position. Only BM2 had a longer anterior dental height (ADH) than the norms, while BM1 and BM3 had a normal ADH and the significantly shortest ADH, respectively. Only BM1 had a normal upper incisor display at rest (U1R), while BM2 and BM3 displayed an increased and decreased U1R, respectively, with significant differences among the three groups. The most significantly protruded upper and lower lips were presented in BM2, but these were exhibited the least in BM3. The most significant acute nasolabial angle (NLA) was found in BM3, whereas BM2 presented the least acute NLA. A normal lip–chin–throat angle (LCTA) was observed in BM1 and BM3, while only BM2 had a greater LCTA than the norms. The most significant obtuse LCTA was found in BM2, while BM3 had the least obtuse LCTA. Therefore, both U1 and L1 in all groups presented protrusion and proclination, except for U1 in BM2, while L1 in BM3 exhibited normal inclination. The ADH and U1R were increased in BM2 but decreased in BM3. The most acute NLA was found in BM3, whereas the least acute was found in BM2. The most obtuse LCTA was in BM2, while the least was in BM3.
Scoping Review of Experimental and Clinical Evidence and Its Influence on Development of the Suction Ureteral Access Sheath
Authors: Yuen Steffi Kar Kei , Traxer Olivier , Wroclawski Marcelo Langer , Gadzhiev Nariman , Chai Chu Ann , Lim Ee Jean , Giulioni Carlo , De Stefano Virgilio , Nedbal Carlotta , Maggi Martina , Sarica Kemal , Castellani Daniele , Somani Bhaskar , Gauhar Vineet
Published: May 2024

The ureteral access sheath (UAS) has been a boon and a bane in flexible ureteroscopy (FURS), with its merits and demerits well established. Its design and dimensions were instrumental in reshaping the way flexible scopes were used and were key adjuncts to establishing retrograde intrarenal surgery (RIRS) as a standard of care in the endourological management of renal stones. With the ever-changing landscape of RIRS over the decades shaped by technological advancements in lasers and flexible scopes, the UAS has also continuously evolved. The utility of suction in endourology has recently changed the way RIRS is performed and is a game changer for FURS outcomes. With strong clinical and experimental evidence to support its use, the UAS has undergone a transformative change in the recent past, with its ability to monitor intrarenal pressure and provide a superior vacuum-cleaner effect that improves the trifecta of RIRS, namely an improved single-stage stone-free rate (SFR), minimise complications, and reduce reinterventions. Our comprehensive review outlines the key clinical and experimental evidence and traces the developments that were key to modifying the traditional UAS into a flexible and navigable suction ureteric access sheath (FANS) and highlights how the design and modifications, in turn, influence the ability to push the boundaries of RIRS.
Housing the Teacher Workforce: A Scoping Review
Authors: Medlin Lauren , Eacott Scott , Gilbert Catherine , MacDonald Katrina , Pettit Christopher J.
Published: May 2024

Globally, education systems are faced with dual workforce crises: a shortage of teachers and a lack of affordable housing. Attracting and retaining teachers through improved renumeration, working conditions, and quality preparation have been central. However, initiatives to attract and retain teachers mean little if the workforce cannot find appropriate (quality and affordable) housing within commuting distance to their workplaces. The present study undertakes a scoping review of research on the intersection of housing and the school education workforce. Specifically, we examine the volume, variety, and characteristics of evidence through the question of ‘What empirical studies have been published on the relationship between housing and the school education workforce?’ Online databases were used to identify 23 studies published in 2000–2024 from Australia, China, England, Kenya, Malaysia, New Zealand, Tanzania, Uganda, the UK, and the USA. Publications drew on a range of methods and housing was rarely the focal unit of analysis. This study finds that beyond establishing unaffordability through salary and housing costs ratios, and the peripheral inclusion of housing issues in studies, there is insufficient published peer reviewed evidence available to purposefully inform and measure interventions. Greater interdisciplinarity is required in research to highlight the complexity of issues at the intersection of housing (availability, affordability, and distance from workplaces) and workforce distribution. More rigorous data should be collected to support robust reporting on the state of housing for the school education workforce to deliver the type of evidence necessary to develop targeted and tailored interventions to improve outcomes for the workforce and ultimately students.
Teaching Biology Lessons Using Digital Technology: A Contextualized Mixed-Methods Study on Pre-Service Biology Teachers’ Enacted TPACK
Authors: Aumann Alexander , Schnebel Stefanie , Weitzel Holger
Published: May 2024

Pre-service biology teachers must apply Technological Pedagogical and Content Knowledge (TPACK) acquired at university in real classroom situations to utilize the instructional potential of digital technologies for teaching biology. So far, there is little evidence on how pre-service biology teachers translate TPACK into teaching practice. The present study addresses this gap by accompanying 42 pre-service biology teachers in planning, implementing, and reflecting on a biology lesson as part of their internship semester at school. Data were collected via written lesson plans, videotaped lesson observations, and stimulated-recall reflection interviews and evaluated by applying a sequential explanatory mixed-method design. The results indicate that pre-service biology teachers enact their TPACK by focusing on technology with the content of the subject receding into the background. In addition, pre-service biology teachers focus particularly on aspects that serve to structure the lesson, rather than on aspects of student activation. The use of emerging technologies in the classroom seems to lead to insecurity among pre-service biology teachers for various reasons, whereby surface characteristics and structuring lesson aspects are focused. Within the sample, we can distinguish between two types of TPACK enactment: the split-focus type separates between content and technology, whereas the novelty-focus type systematically links content and technology, utilizing the technology as a tool for subject teaching.
Promoting Sustainability Together with Parents in Early Childhood Education
Authors: Sihvonen Pilvi , Lappalainen Riikka , Herranen Jaana , Aksela Maija
Published: May 2024

This multimethod study investigated an environmental recycling project in a Finnish kindergarten group, tailored for children aged 4 to 6, as part of early childhood environmental education for sustainable development. We aimed to identify the main drivers of sustainable lifestyles in the families of kindergarten children and evaluate the project’s effective practices. We utilized a qualitative approach using interviews with parents and feedback from teachers and employed the Following a Thread approach alongside inductive thematic analysis. The results highlight the crucial role of Finland’s socio-cultural context, including the emphasis on free play in natural settings and the encouragement of exploration under gentle guidance, in fostering sustainable behaviors among children. Additionally, the parents’ strong environmental sensitivity was manifested in their will to engage with their surrounding community to promote sustainability. The results underscore the importance of collaboration between parents and educators in promoting environmental awareness from an early age. The study advocates for policy changes to ensure that children and teachers have access to natural environments during day care, suggesting that integrating free play in nature with hands-on recycling activities can significantly contribute to sustainable education. Moreover, this topic should be further investigated in different living environments.
A Method to Explore the Best Mixed-Effects Model in a Data-Driven Manner with Multiprocessing: Applications in Public Health Research
Authors: Han Hyemin
Published: May 2024

In the present study, I developed and tested an R module to explore the best models within the context of multilevel modeling in research in public health. The module that I developed, explore.models, compares all possible candidate models generated from a set of candidate predictors with information criteria, Akaike information criterion (AIC), and Bayesian information criterion (BIC), with multiprocessing. For testing, I ran explore.models with datasets analyzed in three previous studies in public health, which assumed candidate models with different degrees of model complexity. These three studies examined the predictors of psychological well-being, compliance with preventive measures, and vaccine intent during the COVID-19 pandemic. After conducting model exploration with explore.models, I cross-validated the nomination results with calculated model Bayes Factors to examine whether the model exploration was performed accurately. The results suggest that explore.models using AIC and BIC can nominate best candidate models and such nomination outcomes are supported by the calculated model Bayes Factors. In particular, all the identified models are superior to the full models in terms of model Bayes Factors. Also, by employing AIC and BIC with multiprocessing, explore.models requires a shorter processing time than model Bayes Factor calculations. These results indicate that explore.models is a reliable, valid, and feasible tool to conduct data-driven model exploration with datasets collected from multiple groups in research on health psychology and education.
Smart IoT Irrigation System Based on Fuzzy Logic, LoRa, and Cloud Integration
Authors: Artetxe Eneko , Barambones Oscar , Martín Toral Imanol , Uralde Jokin , Calvo Isidro , del Rio Asier
Published: May 2024

Natural resources must be administered efficiently to reduce the human footprint and ensure the sustainability of the planet. Water is one of the most essential resources in agriculture. Modern information technologies are being introduced in agriculture to improve the performance of agricultural processes while optimizing water usage. In this scenario, artificial intelligence techniques may become a very powerful tool to improve efficiency. The introduction of the edge/fog/cloud paradigms, already adopted in other domains, may help to organize the services involved in complex agricultural applications. This article proposes the combination of several modern technologies to improve the management of hydrological resources and reduce water waste. The selected technologies are (1) fuzzy logic, used for control tasks since it adapts very well to the nonlinear nature of the agricultural processes, and (2) long range (LoRa) technology, suitable for establishing large distance links among the field devices (sensors and actuators) and the process controllers, executed in a centralized way. The presented approach has been validated in the laboratory by means of a control scheme aimed at achieving an adequate moisture level in the soil. The control algorithm, based on fuzzy logic, can use the weather forecast, obtained as a cloud service, to reduce water consumption. For testing purposes, the dynamics of the water balance model of the soil were implemented as hardware in the loop, executed in a dSPACE DS1104. Experiments proved the viability of the presented approach since the continuous space state output controller achieved a water loss reduction of 23.1% over a 4-day experiment length compared to a traditional on/off controller. The introduction of cloud services for weather forecasting improved the water reduction by achieving an additional reduction of 4.07% in water usage.
Transfer Learning with Multi-Sequence MRI for Segmentation of Autosomal Dominant Polycystic Kidney Disease Using U-Net
Authors: Kwon Min-Seok , Jung Yeon-Soon , Park Jung-Gu , Ahn Yeh-Chan
Published: May 2024

In recent studies, the measurement of total kidney volume, a primary indicator for the diagnosis and treatment of renal diseases, has been advanced through artificial-intelligence-driven automated segmentation. However, the limited quantity of medical data remains a persistent challenge, with its scarcity negatively impacting the outcomes of machine learning algorithms. In this study, we have enhanced the accuracy of machine learning for disease diagnosis by employing various MRI sequences commonly used during renal imaging. We created a model for kidney segmentation using U-Net and performed single training, joint training, and transfer learning using MRI images from two sequences based on SSFP and SSFSE. Ultimately, during transfer learning, we achieved the highest accuracy with a Dice coefficient of 0.951 and a mean difference of 2.05% (−3.47%, 7.57%) in Bland–Altman analysis for SSFP. Similarly, for SSFSE, we obtained a Dice coefficient of 0.952 and a mean difference of 4.33% (−7.05%, 15.71%) through Bland–Altman analysis. This demonstrates our ability to enhance prediction accuracy for each sequence by leveraging different sequences.
Deep-Reinforcement-Learning-Based Collision Avoidance of Autonomous Driving System for Vulnerable Road User Safety
Authors: Chen Haochong , Cao Xincheng , Guvenc Levent , Aksun-Guvenc Bilin
Published: May 2024

The application of autonomous driving system (ADS) technology can significantly reduce potential accidents involving vulnerable road users (VRUs) due to driver error. This paper proposes a novel hierarchical deep reinforcement learning (DRL) framework for high-performance collision avoidance, which enables the automated driving agent to perform collision avoidance maneuvers while maintaining appropriate speeds and acceptable social distancing. The novelty of the DRL method proposed here is its ability to accommodate dynamic obstacle avoidance, which is necessary as pedestrians are moving dynamically in their interactions with nearby ADSs. This is an improvement over existing DRL frameworks that have only been developed and demonstrated for stationary obstacle avoidance problems. The hybrid A* path searching algorithm is first applied to calculate a pre-defined path marked by waypoints, and a low-level path-following controller is used under cases where no VRUs are detected. Upon detection of any VRUs, however, a high-level DRL collision avoidance controller is activated to prompt the vehicle to either decelerate or change its trajectory to prevent potential collisions. The CARLA simulator is used to train the proposed DRL collision avoidance controller, and virtual raw sensor data are utilized to enhance the realism of the simulations. The model-in-the-loop (MIL) methodology is utilized to assess the efficacy of the proposed DRL ADS routine. In comparison to the traditional DRL end-to-end approach, which combines high-level decision making with low-level control, the proposed hierarchical DRL agents demonstrate superior performance.
Boundary Gaussian Distance Loss Function for Enhancing Character Extraction from High-Resolution Scans of Ancient Metal-Type Printed Books
Authors: Lee Woo-Seok , Choi Kang-Sun
Published: May 2024

This paper introduces a novel loss function, the boundary Gaussian distance loss, designed to enhance character segmentation in high-resolution scans of old metal-type printed documents. Despite various printing defects caused by low-quality printing technology in the 14th and 15th centuries, the proposed loss function allows the segmentation network to accurately extract character strokes that can be attributed to the typeface of the movable metal type used for printing. Our method calculates deviation between the boundary of predicted character strokes and the counterpart of the ground-truth strokes. Diverging from traditional Euclidean distance metrics, our approach determines the deviation indirectly utilizing boundary pixel-value difference over a Gaussian-smoothed version of the stroke boundary. This approach helps extract characters with smooth boundaries efficiently. Through experiments, it is confirmed that the proposed method not only smoothens stroke boundaries in character extraction, but also effectively eliminates noise and outliers, significantly improving the clarity and accuracy of the segmentation process.
Personalized Feedback in Massive Open Online Courses: Harnessing the Power of LangChain and OpenAI API
Authors: Morales-Chan Miguel , Amado-Salvatierra Hector R. , Medina José Amelio , Barchino Roberto , Hernández-Rizzardini Rocael , Teixeira António Moreira
Published: May 2024

Studies show that feedback greatly improves student learning outcomes, but achieving this level of personalization at scale is a complex task, especially in the diverse and open environment of Massive Open Online Courses (MOOCs). This research provides a novel method for using cutting-edge artificial intelligence technology to enhance the feedback mechanism in MOOCs. The main goal of this research is to leverage AI’s capabilities to automate and refine the MOOC feedback process, with special emphasis on courses that allow students to learn at their own pace. The combination of LangChain—a cutting-edge framework specifically designed for applications that use language models—with the OpenAI API forms the basis of this work. This integration creates dynamic, scalable, and intelligent environments that can provide students with individualized, insightful feedback. A well-organized assessment rubric directs the feedback system, ensuring that the responses are both tailored to each learner’s unique path and aligned with academic standards and objectives. This initiative uses Generative AI to enhance MOOCs, making them more engaging, responsive, and successful for a diverse, international student body. Beyond mere automation, this technology has the potential to transform fundamentally how learning is supported in digital environments and how feedback is delivered. The initial results demonstrate increased learner satisfaction and progress, thereby validating the effectiveness of personalized feedback powered by AI.
Machine Learning-Based Anomaly Detection for Securing In-Vehicle Networks
Authors: Alfardus Asma , Rawat Danda B.
Published: May 2024

In-vehicle networks (IVNs) are networks that allow communication between different electronic components in a vehicle, such as infotainment systems, sensors, and control units. As these networks become more complex and interconnected, they become more vulnerable to cyber-attacks that can compromise safety and privacy. Anomaly detection is an important tool for detecting potential threats and preventing cyber-attacks in IVNs. The proposed machine learning-based anomaly detection technique uses deep learning and feature engineering to identify anomalous behavior in real-time. Feature engineering involves selecting and extracting relevant features from the data that are useful for detecting anomalies. Deep learning involves using neural networks to learn complex patterns and relationships in the data. Our experiments show that the proposed technique have achieved high accuracy in detecting anomalies and outperforms existing state-of-the-art methods. This technique can be used to enhance the security of IVNs and prevent cyber-attacks that can have serious consequences for drivers and passengers.
A Case Study of a Tiny Machine Learning Application for Battery State-of-Charge Estimation
Authors: Giazitzis Spyridon , Sakwa Maciej , Leva Sonia , Ogliari Emanuele , Badha Susheel , Rosetti Filippo
Published: May 2024

Growing battery use in energy storage and automotive industries demands advanced Battery Management Systems (BMSs) to estimate key parameters like the State of Charge (SoC) which are not directly measurable using standard sensors. Consequently, various model-based and data-driven approaches have been developed for their estimation. Among these, the latter are often favored due to their high accuracy, low energy consumption, and ease of implementation on the cloud or Internet of Things (IoT) devices. This research focuses on creating small, efficient data-driven SoC estimation models for integration into IoT devices, specifically the Infineon Cypress CY8CPROTO-062S3-4343W. The development process involved training a compact Convolutional Neural Network (CNN) and an Artificial Neural Network (ANN) offline using a comprehensive dataset obtained from five different batteries. Before deployment on the target device, model quantization was performed using Infineon’s ModusToolBox Machine Learning (MTB-ML) configurator 2.0 software. The tests show satisfactory results for both chosen models with a good accuracy achieved, especially in the early stages of the battery lifecycle. In terms of the computational burden, the ANN has a clear advantage over the more complex CNN model.