bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of High-Pressure Homogenization on the Functional and Emulsifying Properties of Proteins Recovered from Auxenochlorella pyrenoidosa
Authors: Katsimichas Alexandros , Katsouli Maria , Spantidos Nikolaos , Giannakourou Maria C. , Taoukis Petros
Published: December 2024

Auxenochlorella pyrenoidosa is a microalga that stands out due to its high protein content. The objective of this work was to study the effect of high-pressure homogenization (HPH) on the recovery of proteins from A. pyrenoidosa and their application as functional emulsifiers. Untreated and HPH-treated (400–800 bar, 1 and 4 passes) aqueous cellular suspensions were incubated at 40 °C for 6 h. The aqueous extracts were collected, the proteins were precipitated at pH 3, and the Auxenochlorella pyrenoidosa protein concentrates (APPC) were lyophilized. Increasing HPH pressure and number of passes (400–800 bar, 1 and 4 passes) improved protein recovery yield up to 57%. Higher HPH pressures also reduced α-helix and β-sheet structures, exposing the hydrophobic protein core. This protein modification led to APPCs with increased oil-holding capacity (2.83 g oil/g APPC). The surface tension of APPC solutions reached a minimum value of 28.6 mN/m at an APPC concentration of 2% w/w. The APPCs from untreated and HPH-treated biomass were used to stabilize nanoemulsions (2–6% sunflower oil), comparing one-step homogenization (high-speed homogenization) with the two-step homogenization method (combining high-speed and high-pressure homogenization). The two-step method led to significantly smaller oil droplets with narrow size distribution, leading to stable nanoemulsions with improved resistance to centrifugation and heating–cooling cycles. Due to APPC’s great emulsifying properties, A. pyrenoidosa proteins have a promising potential for various applications such as delivery systems stabilization. Additionally, the low energy requirements, continuous processing capability, and scalability of HPH make it a suitable process for industrial applications.
Architectural Lighting Simulations as a Method to Evaluate Emotions on Cultural Heritage Building Facades
Authors: Balafoutis Thanos
Published: December 2024

This research concerns the exterior lighting of historic buildings and cultural heritage monuments. Its objective is to organize a methodology for the study of facades, to record the individual or grouped morphological and decorative elements of the facades, and to organize the steps to achieve a presentation of different ways of lighting these elements. This presentation is made by an experimental digital lighting simulation, leading the researcher to discover the relationship between light and the architectural element being illuminated. Finally, the results of the simulations are evaluated by experts in the field of lighting, who attest to the emotions generated by the observation of the different lighting scenarios, while an attempt is then made to synthesize these results on an entire building facade, to determine whether this synthesis of the individual lighting effects is practicable. The analysis of the results reveals the trends in each lighting scenario, leading to a variety of emotions, whether they arise from a specific morphological element or from the entire facade.
Computer Architecture for Industrial Training Evaluation
Authors: Gutiérrez Luz E. , Guerrero Carlos A. , Betts Mark M. , Jabba Daladier , Nieto Wilson , López-Ospina Héctor A.
Published: December 2024

Companies have tried to innovate in their training processes to increase their productivity indicators, reduce equipment maintenance costs, and improve the work environment. The use of Augmented Reality (AR) has been one of the implemented strategies to upgrade training processes, since it optimizes, through User Interface (UI) Design, experiences designed for users (UX) that are focused on education and training contexts. This research describes the definition and implementation of an IT architecture based on the ISO/IEC/IEEE 42010 standard using the Zachman and Kruchten frameworks. The methodological proposal presents an architecture seen from a business perspective, taking into account the strategic and technological components of the organization under a strategic alignment approach. The result is a six-layer architecture: The Government Strategy Layer (1) that accounts for the strategic component; the Business Layer (2) that presents the business management perspective; the Information Layer (4) that defines the metrics system: efficiency through task time, effectiveness through tasks completed, and satisfaction with overall satisfaction. In the Data Layer (4), the data collected with the metrics are structured in an industrial scenario with a cylinder turning process on a Winston Lathe. The experiment was carried out with two groups of 272 participants. In the Systems and Applications Layer (5), two applications were designed: a web client and a mobile application with augmented reality, and finally, the Networks and Infrastructure Layer (6), which delivers the two functional applications. The architecture validation was carried out using the mobile application. The analysis of the results showed a significance value of less than 0.001 in the three indicators: efficiency, effectiveness, and satisfaction in the Levene test and Student’s t-test. To corroborate the results, a test of equality of means with the Mann–Whitney U was carried out, showing that the three indicators presented significantly different values in the two experimental groups of this study. Thus, the group trained with the application obtained better results in the three indicators. The proposed architecture is adaptable to other training contexts. Information, data, and systems and application layers allowed for the exchange of training processes so that the augmented reality application is updated according to the new requirements.
Sustained Wind Forecasts from the High-Resolution Rapid Refresh Model: Skill Assessment and Bias Mitigation
Authors: Fovell Robert G. , Capps Scott B.
Published: December 2024

We examine the skill associated with sustained wind forecasts in the High-Resolution Rapid Refresh (HRRR) model, extending and enhancing previous work. Some utilities use numerical weather prediction models like the HRRR to anticipate electrical transmission line shutdowns for public safety reasons, increasing the importance of forecast accuracy and motivating the need to understand sources of bias and differences among observation networks. We demonstrate that the HRRR forecasts for airport stations are very good albeit with a tendency to underpredict the highest wind speeds and at the windiest locations. Forecasts for non-airport networks are much less accurate owing to a variety of factors, including differences in the way winds are measured and the environments they are measured in, and this results in predictions with excessive temporal variation relative to observations. We demonstrate a practical approach to modifying sustained wind forecasts so that they are more useful proxies for conditions being observed in the field.
Optimization of Wastewater Treatment Through Machine Learning-Enhanced Supervisory Control and Data Acquisition: A Case Study of Granular Sludge Process Stability and Predictive Control
Authors: Gulshin Igor , Kuzina Olga
Published: December 2024

This study presents an automated control system for wastewater treatment, developed using machine learning (ML) models integrated into a Supervisory Control and Data Acquisition (SCADA) framework. The experimental setup focused on a laboratory-scale Aerobic Granular Sludge (AGS) reactor, which utilized synthetic wastewater to model real-world conditions. The machine learning models, specifically N-BEATS and Temporal Fusion Transformers (TFTs), were trained to predict Biological Oxygen Demand (BOD5) values using historical data and real-time influent contaminant concentrations obtained from online sensors. This predictive approach proved essential due to the absence of direct online BOD5 measurements and an inconsistent relationship between BOD5 and Chemical Oxygen Demand (COD), with a correlation of approximately 0.4. Evaluation results showed that the N-BEATS model demonstrated the highest accuracy, achieving a Mean Absolute Error (MAE) of 0.988 and an R2 of 0.901. The integration of the N-BEATS model into the SCADA system enabled precise, real-time adjustments to reactor parameters, including sludge dose and aeration intensity, leading to significant improvements in granulation stability. The system effectively reduced the standard deviation of organic load fluctuations by 2.6 times, from 0.024 to 0.006, thereby stabilizing the granulation process within the AGS reactor. Residual analysis suggested a minor bias, likely due to the limited number of features in the model, indicating potential improvements through additional data inputs. This research demonstrates the value of machine learning-driven predictive control for wastewater treatment, offering a resilient solution for dynamic environments. By facilitating proactive management, this approach supports the scalability of wastewater treatment technologies while enhancing treatment efficiency and operational sustainability.
Hydrothermal Pre-Carbonization Triggers Structural Reforming Enabling Pore-Tunable Hierarchical Porous Carbon for High-Performance Supercapacitors
Authors: Kang Cuihua , Zuo Mingyuan , Qiu Chang , Zeng Fanda , Wang Yuehui , Chen Zhuo , Liang Tingting , Qiu Daping
Published: December 2024

The engineering of pore structures has great significance in the development of high-performance carbon-based supercapacitor electrode materials. Herein, we have successfully transformed jujube pits into hierarchical porous carbon (HJPC-4) with excellent capacitive properties via a unique hydrothermal–carbonization–activation strategy. Hydrothermal pretreatment is essential to regulate the supermesoporous and macroporous structure of samples and their superior electrochemical performances. Owing to the large ion-accessible, remarkable supermesoporous and macroporous pore volume, HJPC-4 exhibited ultra-high specific capacitance (6 M KOH: 316 F g−1 at 1 A g−1; EMIMBF4: 204 F g−1 at 1 A g−1), excellent rate performance (6 M KOH: 231 F g−1 at 100 A g−1; EMIMBF4: 154 F g−1 at 30 A g−1), outstanding cycling stability (6 M KOH: the retention rate is 92.11% after 60,000 cycles at 10 A g−1; EMIMBF4: the retention rate is 80% after 10,000 cycles at 5 A g−1), and ultimate energy/power density up to 91.09 Wh kg−1/24.25 kW kg−1 in EMIMBF4 two-electrode systems. This work presents unique insights into the effect of the pore structure of carbon-based materials on their capacitive energy storage.
Hypertensive Response to Exercise as an Early Marker of Disease Development
Authors: Kosowski Wojciech , Aleksandrowicz Krzysztof
Published: December 2024

Arterial hypertension is one of the world’s leading risk factors for death and disability. With the number of people living with this disease doubling between 1990 and 2019 from 650 million to 1.3 billion, it is a global burden that increases mortality from cardiovascular and kidney diseases. It is extremely important to use all possible diagnostic methods, indicating the possibility of early detection that subsequently leads to effective prevention of disease development. The phenomenon called hypertensive response to exercise (HRE) is one such method. The HRE is defined as a pathological, excessive increase in blood pressure as a result of exposure to the stressor, which is physical exercise. There is no consensus about precise cutoffs in the definition of this condition, which is most commonly diagnosed based on systolic blood pressure (SBP) ≥ 210 mm Hg in men and ≥190 mm Hg in women at peak exercise intensity. The fact that exercise hypotension is a pathologic sign is universally accepted. Accumulating data deliver the information that HRE is also connected to higher overall cardiovascular risk. It was demonstrated that HRE is associated with functional and structural impairment of the left ventricle and the future development of hypertension. HRE should act as a warning signal of increased cardiovascular risk, leading to the need for profound clinical care.
Reference Intervals (RIs) of the Bone Turnover Markers (BTMs) in Children and Adolescents: A Proposal for Effective Use
Authors: Brescia Vincenzo , Lovero Roberto , Fontana Antonietta , Zerlotin Roberta , Colucci Silvia Concetta , Grano Maria , Cazzolla Angela Pia , Di Serio Francesca , Crincoli Vito , Faienza Maria Felicia
Published: December 2024

Background/Objectives: Bone turnover markers (BTMs) can provide information on the bone growth of apparently healthy children and adolescents or useful results in the diagnosis and monitoring of the disease condition, comparing them with appropriate reference intervals (RIs). The aim of this study was to establish the RI for the BTM [specific bone alkaline phosphatase (BALP), carboxy-terminal cross-linked collagen type I telopeptide (CTX), N-terminal propeptide pro-collagen type I (PINP), osteocalcin (OC), resistant to acid tartrate phosphatase isoform 5b (TRAcP-5b)] on serum samples from children and adolescents. Method: 202 samples from children and adolescents (ages 1–18 years) (51.48% male), considered apparently healthy. The biomarker was analyzed on automatic immunometric equipment (TGSTA Technogenetics) and the IDS-iSYS automated system kits The RI of the studied parameters was calculated according to CLSI Guideline C28-A3 with stratification by age and sex. Evaluation of the distribution of values and the meaning of the biomarker concentrations were used to calculate general and specific RI for an age group. Results: BTM concentrations vary with pubertal growth. The pattern of change differs for each bone marker. General and age-specific RI were calculated: 1–14 years, 15–18 years for BALP and CTX; 1–13 years, 14–18 years for Oc and PINP and 1–12 years, 13–18 years for TRAcP. Discussion and Conclusions: Concentrations for biomarker studied vary with age and gender. The proof of concentrations with insignificant changes until puberty led to identification of two groups of RI relating to the covariables (age and sex) for each biomarker.
The Effects of Synthetic Polymers on the Release Patterns of Bupivacaine Hydrochloride from Sodium Hyaluronate Hydrogels
Authors: Wójcik-Pastuszka Dorota , Iwaszkiewicz Roksana , Musiał Witold
Published: December 2024

Background: Using hydrogels for the controlled release of drugs is beneficial for patients, who then receive the proper dose of the medicinal substance. In addition, the formulation can provide more consistent drug absorption while reducing the frequency of dosing. Objectives: The aim of this investigation is to propose a novel HA (sodium hyaluronate)-based hydrogel for intra-articular injection doped with synthetic polymers and incorporated with bupivacaine hydrochloride (Bu) as a local anesthetic. The other aim of this study is to reveal the effects of the formulation’s ingredients on its viscosity and the relationship between the hydrogel’s viscosity and drug release. Methods: First, HA-based hydrogels doped with synthetic polymers and incorporated with Bu were prepared. A study of the hydrogels’ viscosities was performed using a rotational viscometer. Release tests were carried out by employing a paddle-over-disk apparatus following the USP/Ph.Eur guidelines. The drug concentrations in the acceptor fluid were analyzed spectrophotometrically. Results: It was found that the viscosity of the hydrogels doped with synthetic polymers was higher than the viscosity of the hydrogels made with only HA. The viscosity of the hydrogels doped with AX (ammonium acryloyldimethyltaurate/VP copolymer) was the highest, measuring 6750 ± 160 cP and 12623 ± 379 cP with and without Bu, respectively. The results of the kinetic experiment indicate that the Higuchi and Korsmeyer–Peppas models best described the drug release. Bu was released the most slowly from the formulation doped with AX. The release rate constants obtained from the Higuchi and Korsmeyer–Peppas models were kH = 4.4 ± 0.2 mg × min−1/2 and kK-P = 3.4 ± 0.2 × 10−2 min–N, respectively. The half-release time, calculated using the Higuchi model, was the longest for the formulation doped with AX, at 199.5 ± 17.6 min. Conclusions: This indicates that the incorporation of AX into the hydrogel may prolong the drug dissolution. The hydrogel doped with AX was the optimal formulation for the controlled release of Bu.
Harnessing the Power of AI to Improve Detection, Monitoring, and Public Health Interventions for Japanese Encephalitis
Authors: Xiao Junhua , Kendal Evie , Kwa Faith A. A.
Published: December 2024

Japanese Encephalitis (JE) is the leading cause of viral encephalitis in regions with endemic Japanese Encephalitis Virus (JEV) infections. Background/Objectives: The aim of this review is to consider the potential role of artificial intelligence (AI) to improve detection, monitoring and public health interventions for JE. Discussion: As climate change continues to impact mosquito population growth patterns, more regions will be affected by mosquito-borne diseases, including JE. Improving diagnosis and surveillance, while continuing preventive measures, such as widespread vaccination campaigns in endemic regions, will be essential to reduce morbidity and mortality associated with JEV. Conclusions: With careful integration, AI mathematical and mechanistic models could be useful tools for combating the growing threat of JEV infections globally.
Graph Neural Networks in Brain Connectivity Studies: Methods, Challenges, and Future Directions
Authors: Mohammadi Hamed , Karwowski Waldemar
Published: December 2024

Brain connectivity analysis plays a crucial role in unraveling the complex network dynamics of the human brain, providing insights into cognitive functions, behaviors, and neurological disorders. Traditional graph-theoretical methods, while foundational, often fall short in capturing the high-dimensional and dynamic nature of brain connectivity. Graph Neural Networks (GNNs) have recently emerged as a powerful approach for this purpose, with the potential to improve diagnostics, prognostics, and personalized interventions. This review examines recent studies leveraging GNNs in brain connectivity analysis, focusing on key methodological advancements in multimodal data integration, dynamic connectivity, and interpretability across various imaging modalities, including fMRI, MRI, DTI, PET, and EEG. Findings reveal that GNNs excel in modeling complex, non-linear connectivity patterns and enable the integration of multiple neuroimaging modalities to provide richer insights into both healthy and pathological brain networks. However, challenges remain, particularly in interpretability, data scarcity, and multimodal integration, limiting the full clinical utility of GNNs. Addressing these limitations through enhanced interpretability, optimized multimodal techniques, and expanded labeled datasets is crucial to fully harness the potential of GNNs for neuroscience research and clinical applications.
Introducing and Validating the Multiphasic Evidential Decision-Making Matrix (MedMax) for Clinical Management in Patients with Intrahepatic Cholangiocarcinoma
Authors: Ramouz Ali , Adeliansedehi Ali , Khajeh Elias , März Keno , Michael Dominik , Wagner Martin , Müller-Stich Beat Peter , Mehrabi Arianeb , Majlesara Ali
Published: December 2024

Background: Despite the significant advancements of liver surgery in the last few decades, the survival rate of patients with liver and pancreatic cancers has improved by only 10% in 30 years. Precision medicine offers a patient-centered approach, which, when combined with machine learning, could enhance decision making and treatment outcomes in surgical management of ihCC. This study aims to develop a decision support model to optimize treatment strategies for patients with ihCC, a prevalent primary liver cancer. Methods: The decision support model, named MedMax, was developed using three data sources: studies retrieved through a systematic literature review, expert opinions from HPB surgeons, and data from ihCC patients treated at Heidelberg University Hospital. Expert opinions were collected via surveys, with factors rated on a Likert scale, while patient data were used to validate the model’s accuracy. Results: The model is structured into four decision-making phases, assessing diagnosis, treatment modality, surgical approach, and prognosis. Prospectively, 44 patients with ihCC were included for internal primary validation of the model. MedMax could predict the appropriate treatment considering the resectability of the lesions in 100% of patients. Also, MedMax could predict a decent surgical approach in 77% of the patients. The model proved effective in making decisions regarding surgery and patient management, demonstrating its potential as a clinical decision support tool. Conclusions: MedMax offers a transparent, personalized approach to decision making in HPB surgery, particularly for ihCC patients. Initial results show high accuracy in treatment selection, and the model’s flexibility allows for future expansion to other liver tumors and HPB surgeries. Further validation with larger patient cohorts is required to enhance its clinical utility.
Minichromosome Maintenance Proteins: From DNA Replication to the DNA Damage Response
Authors: Malysa Agnes , Zhang Xiaohong Mary , Bepler Gerold
Published: December 2024

The DNA replication machinery is highly conserved from bacteria to eukaryotic cells. Faithful DNA replication is vital for cells to transmit accurate genetic information to the next generation. However, both internal and external DNA damages threaten the intricate DNA replication process, leading to the activation of the DNA damage response (DDR) system. Dysfunctional DNA replication and DDR are a source of genomic instability, causing heritable mutations that drive cancer evolutions. The family of minichromosome maintenance (MCM) proteins plays an important role not only in DNA replication but also in DDR. Here, we will review the current strides of MCM proteins in these integrated processes as well as the acetylation/deacetylation of MCM proteins and the value of MCMs as biomarkers in cancer.
Managing Major Life Changes: An Exploratory Study Using the Bridges Transitions Framework to Help Foster Youth Prepare for Discharge
Authors: Nesmith Ande A.
Published: December 2024

Background: Adolescents in foster care endure frequent disruptive transitions, often culminating in discharge to independent living rather than reunification or adoption. Former foster youth fare poorly once on their own, with high rates of homelessness and social disconnection. This study explored the use of the Bridges Transitions Framework near the end of placement to help youth cope with the transition to adulthood. Methods: In this exploratory study, the framework was integrated into a foster agency’s programming; then, we assessed outcomes using administrative data and youth interviews. Thirty-five youth participated. Status of employment, education, and social support was collected 18 months after exposure to the framework. Results: The participants reported moderate to high levels of social support, which is often limited or absent among foster care leavers. Relative to rates reported in state-level foster care data, participants had substantially higher rates of school enrollment after discharge. With very few empirically assessed models available for this population that specifically address internal coping with such substantial life changes, the Transitions Framework offers a tool that may help foster youth navigate aging out of care. Securing lasting and meaningful social support and employment and completing education remain elusive for former foster youth. Conclusions: To confirm the utility of the Transitions Framework, it is recommended to assess it with a large sample and matched comparison group over time.
Artificial Intelligence in Pediatric Electrocardiography: A Comprehensive Review
Authors: Leone David M. , O’Sullivan Donnchadh , Bravo-Jaimes Katia
Published: December 2024

Artificial intelligence (AI) is revolutionizing healthcare by offering innovative solutions for diagnosis, treatment, and patient management. Only recently has the field of pediatric cardiology begun to explore the use of deep learning methods to analyze electrocardiogram (ECG) data, aiming to enhance diagnostic accuracy, expedite workflows, and improve patient outcomes. This review examines the current state of AI-enhanced ECG interpretation in pediatric cardiology applications, drawing insights from adult AI-ECG research given the progress in this field. It describes a broad range of AI methodologies, investigates the unique challenges inherent in pediatric ECG analysis, reviews the current state of the literature in pediatric AI-ECG, and discusses potential future directions for research and clinical practice. While AI-ECG applications have demonstrated considerable promise, widespread clinical adoption necessitates further research, rigorous validation, and careful consideration of equity, ethical, legal, and practical challenges.
Correction: Guo et al. Spoon-like Beams Generated with Exponential Phases. Coatings 2022, 12, 322
Authors: Guo Lei , Xia Tian , Xu Yiping , Xiong Yan , Leng Xian , Tao Shaohua , Tian Yonghong , Cheng Shubo
Published: December 2024

Following publication, concerns were raised regarding the relevance of a few references in this publication [...]
Bioinspired Blockchain Framework for Secure and Scalable Wireless Sensor Network Integration in Fog–Cloud Ecosystems
Authors: Rehman Abdul , Alharbi Omar
Published: December 2024

WSNs are significant components of modern IoT systems, which typically operate in resource-constrained environments integrated with fog and cloud computing to achieve scalability and real-time performance. Integrating these systems brings challenges such as security threats, scalability bottlenecks, and energy constraints. In this work, we propose a bioinspired blockchain framework aimed at addressing those challenges through the emulation of biological immune adaptation mechanisms, such as the self-recovery of swarm intelligence. It integrates lightweight blockchain technology with bioinspired algorithms, including an AIS for anomaly detection and a Proof of Adaptive Immunity Consensus mechanism for secure resource-efficient blockchain validation. Experimental evaluations give proof of the superior performance reached within this framework: up to 95.2% of anomaly detection accuracy, average energy efficiency of 91.2% when the traffic flow is normal, and latency as low as 15.2 ms during typical IoT scenarios. Moreover, the framework has very good scalability since it can handle up to 500 nodes with only a latency of about 6.0 ms.
WGAN-DL-IDS: An Efficient Framework for Intrusion Detection System Using WGAN, Random Forest, and Deep Learning Approaches
Authors: Gul Shehla , Arshad Sobia , Saeed Sanay Muhammad Umar , Akram Adeel , Azam Muhammad Awais
Published: December 2024

The rise in cyber security issues has caused significant harm to tech world and thus society in recent years. Intrusion detection systems (IDSs) are crucial for the detection and the mitigation of the increasing risk of cyber attacks. False and disregarded alarms are a common problem for traditional IDSs in high-bandwidth and large-scale network systems. While applying learning techniques to intrusion detection, researchers are facing challenges mainly due to the imbalanced training sets and the high dimensionality of datasets, resulting from the scarcity of attack data and longer training periods, respectively. Thus, this leads to reduced efficiency. In this research study, we propose a strategy for dealing with the problems of imbalanced datasets and high dimensionality in IDSs. In our efficient and novel framework, we integrate an oversampling strategy that uses Generative Adversarial Networks (GANs) to overcome the difficulties introduced by imbalanced datasets, and we use the Random Forest (RF) importance algorithm to select a subset of features that best represent the dataset to reduce the dimensionality of a training dataset. Then, we use three deep learning techniques, Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), to classify the attacks. We implement and evaluate this proposed framework on the CICIDS2017 dataset. Experimental results show that our proposed framework outperforms state-of-the-art approaches, vastly improving DL model detection accuracy by 98% using CNN.
COVID-19: Lessons from the Past to Inform the Future of Healthcare
Authors: Mattiuzzi Camilla , Lippi Giuseppe
Published: December 2024

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its global spread have left an indelible mark, disrupting multiple aspects of human life. It is therefore crucial to retrospectively analyze the factors that have contributed more to the initial inefficiency of the global response, thus enhancing preparedness and proactively addressing the risk of similar events occurring in the future. Critical areas were identified based on our expertise. Relevant bibliographic references were subsequently gathered through an open search of scientific databases to substantiate the concepts discussed in this article. The key issues that hindered an effective response to coronavirus disease 2019 (COVID-19) are numerous and multifaceted, and some of these will be critically examined in this article, including delayed identification of the pathogen, inadequate public health preparedness, inadequate therapeutic management, and deficiencies in laboratory diagnostics. From this analysis, key areas for improvement emerge to ensure more efficient responses to future health crises, including (i) enhancing and strengthening health information systems, (ii) improving pandemic preparedness and response planning, (iii) developing a resilient healthcare workforce, (iv) increasing investment in research and development, (v) expanding the use of telemedicine and digital health, (vi) ensuring universal access to healthcare, and (vii) improving public health communication and trust.
The Effect of the Film Thickness, Cooling Rate, and Solvent Evaporation on the Formation of L-Menthol Ring-Banded Spherulites
Authors: Kovács Tamás , Kovács Tamás , Detrich Márton , Gazdag Ferenc , Itatani Masaki , Lagzi István
Published: December 2024

Periodic pattern formation is a prominent phenomenon in chemical, physical, and geochemical systems. This phenomenon can arise from various processes, such as the reaction and mass transport of chemical species, solidification, or solvent evaporation. We investigated the formation of ring-banded spherulites of l-menthol using a thin liquid film in a Petri dish. We found that the film thickness and cooling rate strongly influence the generation of crystallization patterns. We performed two-dimensional numerical simulations using the Cahn–Hilliard model to support the experimentally observed trend on the dependence of the layer thickness on the periodicity of the generated macroscopic patterns. In a specific scenario, we observed the formation of rings consisting of needle-like crystals on the cover of the Petri dish. This phenomenon was due to the evaporation of the menthol and its subsequent crystallization. In addition to these findings, we created crystallization patterns by solvent evaporation (using tert-butyl alcohol, methyl alcohol, and acetone).
Evaluating [68Ga]-Ga PSMA PET/CT for Detecting Prostate Cancer Recurrence Post-High-Intensity Focused Ultrasound and Brachytherapy: A Single-Center Retrospective Study
Authors: Di Giorgio Andrea , Rapa Marco , Civollani Simona , Farolfi Andrea , Fanti Stefano
Published: December 2024

Focal therapy offers a promising approach for treating localized prostate cancer (PC) with minimal invasiveness and potential cost benefits. High-intensity focused ultrasound (HIFU) and brachytherapy (BT) are among these options but lack long-term efficacy data. Patient follow-ups typically use biopsies and multiparametric MRI (mpMRI), which often miss recurrences. PET/CT with PSMA has emerged as a promising tool for detecting residual disease or recurrence post-treatment, offering higher sensitivity and specificity than traditional imaging. We retrospectively reviewed patients who underwent [68Ga]Ga-PSMA-11 PET/CT for biochemical recurrence (BCR) after HIFU or brachytherapy from 2016 to 2024. Out of 22 patients, 32% had HIFU and 68% had brachytherapy. The median time from treatment to PET scan was 77 months, with a median PSA level of 3 ng/mL. [68Ga]Ga-PSMA-11 PET/CT identified PC recurrence in 63.6% of cases. Of these, 50% showed prostate recurrence, 14% had lymph node involvement, and 28% had metastatic disease. Focal therapies like HIFU and brachytherapy are effective and minimally invasive options for localized PC. [68Ga]Ga-PSMA-11 PET/CT is valuable for detecting recurrence or residual disease, enhancing post-treatment surveillance.
Real-World Efficacy and Safety of Avelumab Plus Axitinib in Metastatic Renal Cell Carcinoma: Results from the Ambispective RAVE-Renal Study
Authors: Tsimafeyeu Ilya , Chubenko Vyacheslav , Baklanova Olga , Kalpinskiy Alexey , Safina Sufia , Lebedinets Andrei , Petkau Vladislav , Parsadanova Elvira , Turganova Maria , Shkurat Aleksei , Tovbik Natalia , Tkacheva Elena , Anzhiganova Yulia , Novikova Olga , Bragina Varvara , Zukov Ruslan , Orlova Rashida
Published: December 2024

Background: The RAVE-Renal study was conducted to evaluate the real-world efficacy and safety of avelumab plus axitinib as a first-line therapy for patients with metastatic renal cell carcinoma (mRCC). Methods: RAVE-Renal was a multicenter, noninterventional, ambispective study with both retrospective and prospective components. The study included adult patients with histologically confirmed mRCC, measurable disease per RECIST version 1.1, and no prior systemic therapy. Patients received avelumab (800 mg intravenously every 2 weeks) plus axitinib (5 mg orally twice daily). The primary endpoints were median progression-free survival (PFS) and objective response rate (ORR). The secondary endpoints included median OS, 1-year overall survival (OS) rate, and safety. Results: A total of 125 patients from 13 sites were enrolled, with a median follow-up of 16.1 months. The median age was 61.0 years. The study population comprised 35.3% favorable, 49% intermediate, and 15.7% poor IMDC risk patients. The median PFS was 14.9 months (95% CI, 11.72–19.08). The ORR was 44.3% (95% CI, 32.5–56.1). The clinical benefit rate was 93.4%. The 1-year OS rate was 71.2%, with the median OS not reached. Any-grade treatment-related adverse events (TRAEs) occurred in 99 (79.2%) cases, including grade ≥3 TRAEs in 24 (19.2%). Conclusions: Avelumab in combination with axitinib showed clinical benefits in a real-world setting, consistent with findings from a pivotal trial. The regimen was effective and well tolerated across various patient subgroups.
Open Partial Horizontal Laryngectomy as a Conservative Salvage Treatment for Laser-Recurrent Laryngeal Cancer: A Multi-Institutional Series
Authors: Crosetti Erika , Borello Andrea , Bertolin Andy , Santos Izabela Costa , Fantini Marco , Arrigoni Giulia , Bertotto Ilaria , Sprio Andrea Elio , Dias Fernando Luiz , Rizzotto Giuseppe , Succo Giovanni
Published: December 2024

Early-stage laryngeal cancer (T1-T2) is commonly treated with organ-preserving techniques such as transoral laser microsurgery (TOLMS) or radiation therapy (RT), both providing comparable oncological outcomes but differing in functional results. Local recurrence occurs in approximately 10% of cases, making salvage surgery a crucial therapeutic option. This multi-institutional study investigates the efficacy of open partial horizontal laryngectomy (OPHL) as a salvage treatment, following recurrent laryngeal squamous-cell carcinoma (LSCC) after failed TOLMS. This analysis includes 66 patients who underwent OPHL between 1995 and 2017, reporting favorable oncological outcomes with overall survival (OS) of 87.4%, disease-specific survival (DSS) of 93.4%, and disease-free survival (DFS) of 85.5%. A recurrence rate of 10.6% was observed post-salvage OPHL, with vascular invasion and advanced pathological staging identified as significant predictors of recurrence. OPHL emerged as an effective organ-preserving alternative to total laryngectomy (TL) in select patients, especially those with limited tumor spread and preserved laryngeal function. The study highlights the importance of careful patient selection and thorough preoperative assessment to improve outcomes, positioning OPHL as a key option in treating recurrent laryngeal cancer and offering oncological control while preserving laryngeal functions.
Artificial Intelligence and Ontologies for the Management of Heritage Digital Twins Data
Authors: Felicetti Achille , Niccolucci Franco
Published: December 2024

This study builds upon the Reactive Heritage Digital Twin paradigm established in prior research, exploring the role of artificial intelligence in expanding and enhancing its capabilities. After providing an overview of the ontological model underlying the RHDT paradigm, this paper investigates the application of AI to improve data analysis and predictive capabilities of Heritage Digital Twins in synergy with the previously defined RHDTO semantic model. The structured nature of ontologies is highlighted as essential for enabling AIs to operate transparently, minimising hallucinations and other errors that are characteristic challenges of these technologies. New classes and properties within RHDTO are introduced to represent the AI-enhanced functions. Finally, some case studies are provided to illustrate how integrating AI within the RHDT framework can contribute to enriching the understanding of cultural information through interconnected data and facilitate real-time monitoring and preservation of cultural objects.
Harnessing Convolutional Neural Networks for Automated Wind Turbine Blade Defect Detection
Authors: Spajić Mislav , Talajić Mirko , Pejić Bach Mirjana
Published: December 2024

The shift towards renewable energy, particularly wind energy, is rapidly advancing globally, with Southeastern Europe and Croatia, in particular, experiencing a notable increase in wind turbine construction. The frequent exposure of wind turbine blades to environmental stressors and operational forces requires regular inspections to identify defects, such as erosion, cracks, and lightning damage, in order to minimize maintenance costs and operational downtime. This study aims to develop a machine learning model using convolutional neural networks to simplify the defect detection process for wind turbine blades, enhancing the efficiency and accuracy of inspections conducted by drones. The model leverages transfer learning on the YOLOv7 architecture and is trained on a dataset of 231 images with 246 annotated defects across eight categories, achieving a mean average precision of 0.76 at an intersection over the union threshold of 0.5. This research not only presents a robust framework for automated defect detection but also proposes a methodological approach for future studies in deep learning for structural inspections, highlighting significant economic benefits and improvements in inspection quality and speed.
Youth Suicide in Japan: Exploring the Role of Subcultures, Internet Addiction, and Societal Pressures
Authors: Imataka George , Shiraishi Hideaki
Published: December 2024

Background: Youth suicide remains a significant public health concern in Japan, driven by multifaceted factors such as academic pressures, social isolation, bullying, and family dysfunction. Recent societal changes, including the rise of internet addiction and subcultural influences from anime, manga, and gaming, have further shaped the psychological landscape of Japanese youth. The COVID-19 pandemic has exacerbated these challenges, intensifying feelings of loneliness and anxiety about the future. Methods: This study explores the impact of these factors on youth suicide risk through a systematic review of existing literature and statistical data, focusing on trends from 2000 to 2024. Results: In 2023, 513 school-aged youth in Japan died by suicide, marking persistently high rates. High school students accounted for the majority of cases, followed by middle and elementary school students. Key risk factors include intense academic expectations, cyberbullying, and internet addiction, which are often compounded by cultural stigmas surrounding mental health. Subcultures offer both solace and potential alienation, influencing youth emotions in complex ways. The COVID-19 pandemic has also worsened mental health issues and heightened suicide risks among this vulnerable group. Conclusions: The findings highlight the urgent need for comprehensive mental health support systems tailored to Japanese cultural contexts. Recommendations include enhancing access to school-based counseling, promoting family-based interventions, and implementing policies to regulate harmful online content. Additionally, efforts must address cultural attitudes that stigmatize mental health care. Collaborative societal and policy-level interventions are crucial for mitigating youth suicide and fostering a supportive environment for young people in Japan.
Leveraging Autofluorescence for Tumor Detection, Diagnosis, and Accurate Excision with Surgical Margin Assessment in Tumor Excision
Authors: Perdiou Antonis , Dumitrescu Ramona , Jumanca Daniela , Balean Octavia , Sava-Rosianu Ruxandra , Talpos Serban , Lalescu Dacian Virgil , Galuscan Atena
Published: December 2024

Background/Objectives: Oral cancer ranks among the top ten cancers globally, with a five-year survival rate below 50%. This study aimed to evaluate the effectiveness of autofluorescence-guided surgery compared to standard surgical methods in identifying tumor-free margins and ensuring complete excision. Methods: A prospective cohort of 80 patients was randomized into two groups: the control group underwent excision with a 10 mm margin based on clinical judgment, while the experimental group used autofluorescence guidance with a 5 mm margin beyond fluorescence visualization loss. Autofluorescence imaging was performed using the OralID device, which employs a 405 nm excitation laser to detect abnormal tissue. Ethical approval was obtained from the “Spitalul Clinic Municipal de Urgență Timișoara” Ethics Committee (approval number 08/26.02.2021), and the trial was registered at the University of Medicine and Pharmacy Timisoara (trial no. 59/25.11.2021). A double analysis was conducted: a primary analysis of the full cohort and a subgroup analysis focusing on squamous cell carcinoma (control: n = 19; experimental: n = 24). Histopathological analysis was the gold standard for margin evaluation, with margins coded as tumor-free margins (0), close (1), or infiltrated (2). Results: Statistically significant differences were observed in tumor-free margins between the control (73.17%) and experimental (97%) groups (p = 0.003). Subgroup analysis for SCC showed no significant difference (control: 84.21%; experimental: 95.83%; p = 0.306). Tumor location also differed significantly (p = 0.011), while other baseline variables, such as tumor type and patient characteristics, showed no significant differences. Conclusions: Autofluorescence-guided surgery improves the detection of tumor-free margins and may serve as an effective adjunct in oral cancer management. Larger studies are recommended to confirm these findings.
Long-Term Comparative Outcomes of Short Implants Versus Maxillary Sinus Elevation in Posterior Maxilla Rehabilitation
Authors: Anitua Eduardo , Piñas Laura , Alkhraisat Mohammad Hamdan
Published: December 2024

Background: Vertical atrophy of the maxilla has traditionally been treated with sinus lift procedures and implant placement, performed in one or two surgical stages. Subsequently, the transcrestal sinus lift technique was introduced, offering distinct advantages in terms of indications and reduced morbidity. Most recently, short implants have emerged as a valid alternative to these procedures, even in cases of severe horizontal resorption, allowing for direct placement in many cases. This study was designed to assess the clinical outcomes of short implant placement in alveolar ridges with severe bone atrophy, compared with conventional-length implants placed in areas undergoing conventional sinus elevation. Methods: A retrospective split-mouth study was conducted to compare conventional sinus elevation with standard-length implants versus short implants for addressing vertical bone atrophy in the posterior maxilla. The primary variable was the variation in the marginal bone level. The secondary variables were implant survival and complications. The evaluation of the statistical significance of the difference in categorical variables was accomplished by Chi-squared test or Fisher’s exact test. The comparison between the study groups in continuous variables was performed using Wilcoxon test. The statistical significance was set at p-value < 0.05. Results: The study sample consisted of 24 patients and a total of 73 dental implants. The lateral sinus elevation group (LSEG) included 39 implants, while the short implants group (SIG) included 32 implants. All prostheses were screw-retained. Changes in marginal bone levels indicated a marginal bone loss of less than 0.5 mm in both groups, with no statistically significant difference. In the LSEG, two cases of mucositis were identified, attributed to improper use of an interdental brush. Additionally, two cases of prosthetic screw fracture were reported in the LSEG as technical complications. Conclusions: Long-term outcome data have provided evidence that the use of short implants is comparable to a state-of-the-art procedure (sinus grafting and placement of implants) regarding implant survival, marginal bone remodeling, and complication rates.
Does Sex Matter in Obesity-Induced Periodontal Inflammation in the SSLepR Mutant Rats?
Authors: Alhashim Abdulmohsin , Capehart Kim , Tang Jocelyn , Saad Karim M. , Abdelsayed Rafik , Cooley Marion A. , Williams Jan M. , Elmarakby Ahmed A.
Published: December 2024

Introduction: The incidence of obesity has dramatically increased worldwide. Obesity has been shown to exacerbate the progression of periodontal disease. Studies suggest a sex difference in periodontitis, whereby males are more sensitive to periodontal inflammation compared to females. Aim: In the current study, it was hypothesized that obesity drives periodontal inflammation and bone loss in both sexes. Methodology: Utilizing leptin receptor mutant (SSLepR mutant) rats as a genetic model of obesity, 11–12-week-old male and female lean Dahl salt-sensitive (SS) rats and obese SSLepR mutant rats were used to investigate sex differences in obesity-induced periodontal inflammation. Results: Body weight, insulin, hemoglobin A1c and cholesterol levels were significantly elevated in the obese SSLepR mutant strain vs. the lean SS strain within the same sex. Sex differences in body weight and plasma hemoglobin A1c were only observed in obese SSLepR mutant rats, with males having significantly greater body weight and hemoglobin A1c vs. females. Plasma thiobarbituric acid reactive substances (TBARs) and monocyte chemoattractant protein-1 (MCP-1), markers of systemic oxidative stress and inflammation, respectively, were significantly elevated in obese SSLepR mutant rats vs. lean SS rats, with no sex differences in these parameters in either rat strains. Although micro-CT analyses of the maxillary first molar alveolar bone from obese SSLepR mutant rats revealed no evidence of bone loss and/or sex differences, immuno-histochemical analysis revealed significant elevations in periodontal IL-6 and decreases in IL-10 in obese SSLepR mutant rats vs. lean SS rats, with no apparent sex differences in these parameters. Conclusions: Obesity increases systemic and periodontal inflammation, without evidence of bone loss or apparent sex differences in SSLepR mutant rats.