bluuebunny/crossref_metadata_2025 huggingface dataset:
The prolonged health sequelae “of the COVID-19 pandemic” in sub-Saharan Africa: a systematic review and meta-analysis
Authors: Alie Melsew Setegn , Tesema Getayeneh Antehunegn , Abebe Gossa Fetene , Girma Desalegn
Published: January 2025

Background

Survivors of coronavirus disease 2019 (COVID-19) often face persistent and significant challenges that affect their physical, mental, and financial wellbeing, which can significantly diminish their overall quality of life. The emergence of new symptoms or the persistence of existing ones after a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) diagnosis has given rise to a complex clinical issue known as “long COVID-19” (LC). This situation has placed additional strain on global healthcare systems, underscoring the urgent need for ongoing clinical management of these patients. While numerous studies have been conducted on the long-term effects of COVID-19, our systematic review, and meta-analysis, is the first of its kind in sub-Saharan Africa, providing a comprehensive understanding of the situation in the region and highlighting the necessity for continuous clinical management.

Objective

This study aimed to systematically synthesize evidence on the persistent sequelae of COVID-19 and their predictors in sub-Saharan Africa.

Methods

A thorough search was conducted across multiple databases, including PubMed/MEDLINE, Web of Science, Google/Google Scholar, African online journals, and selected reference lists, from the inception of these databases until January 12, 2024. A meta-analysis of proportions was conducted using the random-effects restricted maximum-likelihood model. The association between various factors was also analyzed to determine the pooled factors that influence long COVID-19 outcomes.

Results

Our comprehensive analysis of 16 research articles, involving a total of 18,104 participants revealed a pooled prevalence of COVID-19 sequelae at 42.1% (95% CI: 29.9–55.4). The long-term health sequelae identified in this review included persistent pulmonary sequelae (27.5%), sleep disturbance (22.5%), brain fog (27.4%), fatigue (17.4%), anxiety (22.3%), and chest pain (13.2%). We also found a significant association was observed between comorbidities and long COVID-19 sequelae [POR = 4.34 (95% CI: 1.28–14.72)], providing a comprehensive understanding of the factors influencing long COVID-19 outcomes.

Conclusion

COVID-19 can have long-lasting effects on various organ systems, even after a person has recovered from the infection. These effects can include brain fog, pulmonary symptoms, sleep disturbances, anxiety, fatigue, and other neurological, psychiatric, respiratory, cardiovascular, and general symptoms. It is crucial for individuals who have recovered from COVID-19 to receive careful follow-up care to assess and reduce any potential organ damage and maintain their quality of life.

Systematic review registration

Clinicaltrial.gov, identifier CRD42024501158.
Gender stereotypes and professional experiences of female nurses in Türkiye
Authors: Aca Zeynep , Kırcal-Şahin Arzu , Özdemir Akın , Kaymakcı Yavuz Selim
Published: January 2025

Introduction

Gender roles and stereotypes play a significant role in shaping the nursing profession, perpetuating systemic inequities that negatively impact professional experiences and healthcare system efficiency. In Türkiye, patriarchal norms and systemic disparities exacerbate these workplace challenges, particularly for female nurses.

Methods

This qualitative study utilized semi-structured interviews with 13 female nurses working in intensive care units to examine the influence of societal expectations, workplace discrimination, and institutional policies on gender inequities in nursing.

Results

The findings reveal that cultural norms, family influence, and constrained career planning often channel women into nursing, reinforcing perceptions of the profession as an extension of caregiving roles. While participants rejected the notion of nursing as a “women’s profession,” their narratives highlighted the pervasive impact of gendered expectations. Additionally, political favoritism and nepotism were identified as factors exacerbating workplace challenges, reflecting broader systemic issues in Türkiye’s labor market. The normalization of gender norms and their internalization by female nurses further complicate efforts to combat discrimination.

Discussion

The study underscores the necessity for policy interventions to address systemic gender inequities in nursing. Recommendations include implementing mandatory gender equality education within healthcare institutions, stricter enforcement of anti-violence laws, and the establishment of psychological and legal support systems for workplace violence victims. Additional measures, such as childcare support and regulations against marital status-based discrimination, are essential to mitigate inequities. By addressing societal, cultural, and institutional factors, this research provides actionable strategies for healthcare organizations and policymakers to promote equity and improve sector efficiency.
Impact of COVID-19 pandemic restrictions and relaxation on pediatric respiratory virus hospitalizations in Southwest China: a retrospective study
Authors: Gong Ting , He Fengqi , Zhao Yongle , Lu Shuya , Tian Daiyin
Published: January 2025

Background

Research from multiple regions indicates that Coronavirus disease of 2019 (COVID-19) pandemic has altered the epidemiological characteristics of respiratory viruses.

Methods

This single-center retrospective study included children hospitalized for lower respiratory tract infections (LRTIs) from 2017 to mid-2024. Data on respiratory syncytial virus (RSV), adenovirus (ADV), influenza A virus (IAV), influenza B virus (IBV), and parainfluenza virus (PIV)—were collected and analyzed across pre-pandemic, pandemic, and post-pandemic periods.

Results

A total of 59173 children were included. Detection rates were 34.97% (8553/24459) pre-pandemic, 21.93% (4621/21067) during the pandemic, and 21.64% (2953/13647) post-pandemic. Mixed infections rates significantly decreased during the pandemic (0.78%) and post-pandemic (0.78%) compared to pre-pandemic levels (1.63%) (P < 0.001). RSV was the most prevalent virus across all periods (P < 0.001). Following non-pharmaceutical interventions (NPIs) in 2020, detection rates declined significantly. After restrictions eased in 2021, RSV, PIV, and IBV surged, while IAV continued to decline until its resurgence in spring 2023 (P < 0.001). Certain viruses showed altered seasonal patterns: during the spring and summer of 2021, RSV detection rates exceeded those of previous years, while the expected winter 2022 peak was delayed by approximately six months. IAV peaks disappeared during winters of 2020 and 2021, peaking in spring 2023. IBV peaked in autumn 2021 and winter 2023. PIV had an unusual rise in summer 2020, peaking in autumn, delayed by about five months. The peak in the first half of 2024 returned to its normal timing. ADV appeared to be unaffected, with increased detection in 2022, and decreased in 2023 and 2024.

Conclusion

Epidemiological patterns of respiratory viruses have been altered by multiple influencing factors. The significance of vaccination, hygiene practices, public health interventions and etc. was emphasized in addressing pandemics. Additionally, ongoing dynamic monitoring is essential due to the evolving epidemiology.
Digital Revolution in Agriculture: Using Predictive Models to Enhance Agricultural Performance Through Digital Technology
Authors: Vărzaru Anca Antoaneta
Published: January 2025

Digital innovation in agriculture has become a powerful force in the modern world as it revolutionizes the agricultural sector and improves the sustainability and efficacy of farming practices. In this context, the study examines the effects of digital technology, as reflected by the digital economy and society index (DESI), on key agricultural performance metrics, including agricultural output and real labor productivity per person. The paper develops a strong analytical method for quantifying these associations using predictive models, such as exponential smoothing, ARIMA, and artificial neural networks. The method fully illustrates how economic and technological components interact, including labor productivity, agricultural output, and GDP per capita. The results demonstrate that digital technologies significantly impact agricultural output and labor productivity. These findings illustrate the importance of digital transformation in modernizing and improving agriculture’s overall efficacy. The study’s conclusion highlights the necessity of integrating digital technology into agricultural policy to address productivity problems and nurture sustainable growth in the sector.
The Combinatorial Fusion Cascade as a Neural Network
Authors: Nesterov-Mueller Alexander
Published: January 2025

The combinatorial fusion cascade provides a surprisingly simple and complete explanation for the origin of the genetic code based on competing protocodes. Although its molecular basis is only beginning to be uncovered, it represents a natural pattern of information generation from initial signals and has potential applications in designing more-efficient neural networks. By utilizing the properties of the combinatorial fusion cascade, we demonstrate its embedding into deep neural networks with sequential fully connected layers using the dynamic matrix method and compare the resulting modifications. We observe that the Fiedler Laplacian eigenvector of a combinatorial cascade neural network does not reflect the cascade architecture. Instead, eigenvectors associated with the cascade structure exhibit higher Laplacian eigenvalues and are distributed widely across the network. We analyze a text classification model consisting of two sequential transformer layers with an embedded cascade architecture. The cascade shows a significant influence on the classifier’s performance, particularly when trained on a reduced dataset (approximately 3% of the original). The properties of the combinatorial fusion cascade are further examined for their application in training neural networks without relying on traditional error backpropagation.
Milk Performance and Grazing Behaviour of Cinisara Cows Supplemented with Low- and High-Polyphenols Faba Bean Varieties
Authors: Lanza Massimiliano , Ponte Marialetizia , Pipi Marianna , Bonanno Adriana , Di Grigoli Antonino , Avondo Marcella , Tumino Serena
Published: January 2025

The high degradability of crude protein (CP) from fresh grass can impair its utilization in ruminants. The presence of a moderate level of polyphenolic substances in the diet of grazing animals could help to overcome this problem. The study aimed to evaluate the effects of supplementation with two varieties of faba bean, with different polyphenol contents, on milk yield and quality, as well as on grazing behaviour, in Cinisara grazing cows. A total of 30 cows, homogeneous per days in milk (61 ± 29) and milk yield (12.9 kg ± 3.2), fed on a mixed pasture (CP 17.9% DM) and supplied with grains (an equal mixture of barley, oats, and wheat) and mixed hay (CP 10.2% DM), were assigned to three dietary treatments and supplemented as follows: control group (C, 10 cows), 1 kg of hay, 6 kg of grains mixture, and 0.4 kg soybean meal; low polyphenols group (LP, 10 cows), 1 kg of hay, 4 kg of grains mixture, and 2 kg/d of faba bean var. Torrelama (total polyphenols 4.4 mg GAE/g DM; CP 28.4% DM); high polyphenols group (HP, 10 cows), 2 kg of hay, 4 kg of grains mixture, and 2 kg/d of faba bean var. Fanfare (total polyphenols 16.4 mg GAE/g DM; CP 28.9% DM). All groups were allowed to graze for 20 h/d on natural pasture forage. The HP supplement tended to increase the milk yield compared to that of the LP and C groups (17.1 vs. 15.3 and 14.6 kg/d, respectively; p = 0.057) but reduced the protein (3.20 vs. 3.39 and 3.47%; p = 0.009) and casein proportions (2.45 vs. 2.67 and 2.74%, respectively; p = 0.007) compared to those from the LP and C treatments. HP milk also showed a higher milk urea nitrogen (MUN) value compared to that of C milk, while an intermediate level was measured in LP milk (25.5 vs. 22.9 and 20.9 mg/dl, respectively; p = 0.036). No dietary effect was evident in the milk fatty acid profile. Eating time at pasture and biting rate were not affected by supplementation. The results do not seem to suggest a difference in the efficiency of use of supplements with different polyphenol contents. In any case, they seem to demonstrate that the use of faba beans, regardless of their polyphenol content, represents a valid alternative to soyabeans, also taking into account the tendency for an increased milk yield found by integrating pasture grazing with faba beans possessing a high level of polyphenols, without significant worsening of the qualitative characteristics of the milk or negatively affecting grazing behaviour.
In Vitro Functional Validation of an Anti-FREM2 Nanobody for Glioblastoma Cell Targeting
Authors: Krapež Gloria , Šamec Neja , Zottel Alja , Katrašnik Mojca , Kump Ana , Šribar Jernej , Križaj Igor , Stojan Jurij , Romih Rok , Bajc Gregor , Butala Matej , Muyldermans Serge , Jovčevska Ivana
Published: January 2025

Background/Objectives: Glioblastomas are the most common brain malignancies. Despite the implementation of multimodal therapy, patient life expectancy after diagnosis is barely 12 to 18 months. Glioblastomas are highly heterogeneous at the genetic and epigenetic level and comprise multiple different cell subpopulations. Therefore, small molecules such as nanobodies, able to target membrane proteins specific to glioblastoma cells or specific cell types within the tumor are being investigated as novel tools to treat glioblastomas. Methods: Here, we describe the identification of such a nanobody and its in silico and in vitro validation. NB3F18, as we named it, is directed against the membrane-associated protein FREM2, overexpressed in glioblastoma stem cells. Results: Three dimensional in silico modeling indicated that NB3F18 and FREM2 form a stable complex. Surface plasmon resonance confirmed their interaction with moderate affinity. As we demonstrated by flow cytometry, NB3F18 binds to glioblastoma stem cells to a greater extent than to differentiated glioblastoma cells and astrocytes. Immunocytochemistry revealed surface localization of NB3F18 on glioblastoma stem cells, whereas cytoplasmic localization of NB3F18 was observed in other cell lines. NB3F18 was detected by transmission electron microscopy on the plasma membrane and in various compartments of the endocytic pathway, from endocytic vesicles to multivesicular bodies (endosomes) and lysosomes. Interestingly, NB3F18 was cytotoxic to glioblastoma stem cells. Conclusions: Collectively, NB3F18 has been qualified as an interesting tool to target glioblastoma cells and as a potential vehicle to deliver biological or pharmaceutical agents to these cells.
A Novel Analytical Model for Structural Analysis of Long-Span Hybrid Cable-Stayed Suspension Bridges
Authors: He Dongsheng , Qin Shunquan , Xiao Haizhu , Wu Suiwen
Published: January 2025

The hybrid cable-stayed suspension bridge is used to combine the advantages of cable-stayed and suspension bridges and hence has a broad prospect for application. The conventional simplified analytical models of the hybrid bridge are usually developed based on a schematic with the cable-stayed and suspension systems working separately without any overlapping zone, which cannot represent the modern hybrid bridge system. In this study, a novel analytical model is proposed based on the modified suspension–elastic foundation beam theory to estimate the mechanical performance and deflection of the hybrid bridge system with the consideration of the overlapped section between the suspension and stayed cables. The governing equations of the hybrid bridge system are developed based on the elastic foundation beam theory and the deflection theory, which are derived separately in the hybrid section, the pure suspension section and the cable-stayed section. The general solution of each section is presented. The Transfer Matrix Method is then employed to solve the unknowns from one end to the other, which are in turn used to solve the internal forces of the hybrid bridge system caused by the concentrated load. In addition, in view of no variation in the unstressed length of the main cable, the compatibility equation of the main cable is established with consideration of the longitudinal displacement of the main tower, which is used to derive the formulas for the internal force and deflection of the hybrid system. The model can be easily complied in any programming platform, such as Matlab, with simple input parameters, which can eliminate the complex finite element modeling process. Hence, it can be easily used in the preliminary design stage to determine the optimal size and layout of the bridge. Then, a case study is presented for the verification of the proposed model under a vertical load, which is simplified from the Xihoumen Bridge, a combined highway and railway bridge with a main span of 1488 m. Good agreement is obtained between the proposed model and the finite element method. Meanwhile, it is found that there exists a negative deflection zone for the main beam at a distance from the concentrated vertical load, which is mainly caused by the deflection of the main cables, leading to the cambering of the beam.
Self-Training Can Reduce Detection False Alarm Rate of High-Resolution Imaging Sonar
Authors: Han Jingqi , Fan Yue , He Zheng , You Zhenhang , Zhang Peng , Hu Zhengliang
Published: January 2025

Imaging sonar is a primary means of underwater detection, but it faces challenges of high false alarm rates in sonar image target detection due to factors such as reverberation, noise, and resolution. This paper proposes a method to improve the false alarm rate by self-training a deep learning detector on sonar images. Self-training automatically generates proxy classification tasks based on the sonar image target detection dataset, and pre-trains the deep learning detector through these proxy classification tasks to enhance its learning effectiveness of target and background features. This, in turn, improves the detector’s ability to distinguish between targets and backgrounds, thereby reducing the false alarm rate. For the first time, this paper conducts target detection experiments based on deep learning using high-resolution synthetic aperture sonar images at two frequencies. The results show that, under the conditions of equal or higher recall rates, this method can reduce the false alarm rate by 3.91% and 18.50% on 240 kHz and 450 kHz sonar images, respectively, compared to traditional transfer learning methods.
Urban Sound Recognition in Smart Cities Using an IoT–Fog Computing Framework and Deep Learning Models: A Performance Comparison
Authors: İşler Buket
Published: January 2025

Rapid urbanization presents significant challenges in energy consumption, noise control, and environmental sustainability. Smart cities aim to address these issues by leveraging information technologies to enhance operational efficiency and urban liveability. In this context, urban sound recognition supports environmental monitoring and public safety. This study provides a comparative evaluation of three machine learning models—convolutional neural networks (CNNs), long short-term memory (LSTM), and dense neural networks (Dense)—for classifying urban sounds. The analysis used the UrbanSound8K dataset, a static dataset designed for environmental sound classification, with mel-frequency cepstral coefficients (MFCCs) applied to extract core sound features. The models were tested in a fog computing architecture on AWS to simulate a smart city environment, chosen for its potential to reduce latency and optimize bandwidth for future real-time sound-recognition applications. Although real-time data were not used, the simulated setup effectively assessed model performance under conditions relevant to smart city applications. According to macro and weighted F1-score metrics, the CNN model achieved the highest accuracy at 90%, followed by the Dense model at 84% and the LSTM model at 81%, with the LSTM model showing limitations in distinguishing overlapping sound categories. These simulations demonstrated the framework’s capacity to enable efficient urban sound recognition within a fog-enabled architecture, underscoring its potential for real-time environmental monitoring and public safety applications.
Structural Health Assessment of a Reinforced Concrete Building in Valparaíso Under Seismic and Environmental Shaking: A Foundation for IoT-Driven Digital Twin Systems
Authors: Lozano-Allimant Sebastián , Lopez Alvaro , Gomez Miguel , Atencio Edison , Lozano-Galant José Antonio , Fingerhuth Sebastian
Published: January 2025

Structural health monitoring is vital for the safety and longevity of infrastructure, particularly in seismic zones. This study focuses on identifying the dynamic properties of a reinforced concrete building in Chile’s Valparaíso region. Using an experimental approach, the study compares ambient vibration records, seismic events (moment magnitude > 4), and data collected during adjacent construction activities. Force-balanced accelerometers were used for vibration measurements. The analysis employs the Stochastic Subspace Identification with Covariances (SSI-COV) method within an operational modal analysis framework to extract the building’s modal parameters without requiring artificial excitations. This technique effectively identifies modal characteristics under different vibration sources, making it suitable for evaluating the structural condition under diverse loading conditions. The findings reveal the building’s modes and frequencies, offering critical insights for maintenance and management of infrastructure. Little to no variations were observed in the identified frequencies of the building when working with different types of input data. These data support the integration of real-time IoT systems for continuous monitoring, providing a foundation for future digital twin applications. These advancements facilitate early deterioration detection, enhancing resilience in seismic environments.
Using Eye-Tracking Data to Examine Response Processes in Digital Competence Assessment for Validation Purposes
Authors: Bartolomé Juan , Garaizar Pablo , Loizaga Erlantz , Bastida Leire
Published: January 2025

Background: When measuring complex cognitive constructs, it is crucial to correctly design the evaluation items in order to trigger the intended knowledge and skills. Furthermore, assessing the validity of an assessment requires considering not only the content of the evaluation tasks, but also how examinees perform by engaging construct-relevant response processes. Objectives: We used eye-tracking techniques to examine item response processes in the assessment of digital competence. The eye-tracking observations helped to fill an ‘explanatory gap’ by providing data on the variation in response processes that cannot be captured by other common sources. Method: Specifically, we used eye movement data to validate the inferences made between claimed and observed behavior. This allowed us to interpret how participants processed the information in the items in terms of Area Of Interest (their size, placement, and order). Results and Conclusions: The gaze data provide detailed information about response strategies at the item level, profiling the examinees according to their engagement, response processes and performance/success rate. The presented evidence confirms that the response patterns of the participants who responded well do not represent an alternative to the interpretation of the results that would undermine the assessment criteria. Takeaways: Gaze-based evidence has great potential to provide complementary data about the response processes performed by examinees, thereby contributing to the validity argument.
Enhancing Place Attachment Through Developing Public Open Places: A Cross-Cultural Study in Gold Coast, Australia
Authors: Ghasemieshkaftaki Marzieh , Dupre Karine , Campbell Jennifer , Fernando Ruwan
Published: January 2025

Urban studies research has increasingly focused on placemaking and place attachment in public open places. While several studies have explored how immigrants interact with these places, this study investigates how cultural differences affect immigrants’ place attachment, providing a deeper understanding of inclusive urban design. Semi-structured interviews were conducted with 21 students from India, Iran, China, and Australia, in Southport, a preferred neighborhood for immigrants on the Gold Coast, Australia. NVivo software was used to analyze the data and extract themes. The findings highlighted that, despite universal factors such as natural environments and social opportunities, cultural factors are crucial in shaping individuals’ experiences.
A Novel Mechanics-Based Design for Overcorrection in Clear Aligner Orthodontics via Finite Element Analysis
Authors: Yang Sensen , Cheng Yumin
Published: January 2025

A simplified mechanics model of aligner–tooth interaction was developed to establish a precise computational method for overcorrection design in clear aligner orthodontics. Validated through finite element analysis and experiments, the results demonstrated that designing the movement of only the target teeth on the aligner leads to uneven force distribution on adjacent teeth, while an overcorrection design can evenly distribute the reaction force generated by pushing the target teeth to the anchorage teeth, reducing the maximum force on the anchorage teeth, minimizing unplanned tooth movement, and improving the efficacy of the designed tooth movement for all teeth.
Exploring Modeling Techniques for Soft Arms: A Survey on Numerical, Analytical, and Data-Driven Approaches
Authors: Liu Shengkai , Yu Hongfei , Ding Ning , He Xuchun , Liu Hengli , Zhang Jun
Published: January 2025

Soft arms, characterized by their compliance and adaptability, have gained significant attention in applications ranging from industrial automation to biomedical fields. Modeling these systems presents unique challenges due to their high degrees of freedom, nonlinear behavior, and complex material properties. This review provides a comprehensive overview of three primary modeling approaches: numerical methods, analytical techniques, and data-driven models. Numerical methods, including finite element analysis and multi-body dynamics, offer precise but computationally expensive solutions for simulating soft arm behaviors. Analytical models, rooted in continuum mechanics and simplified assumptions, provide insights into the fundamental principles while balancing computational efficiency. Data-driven approaches, leveraging machine learning and artificial intelligence, open new avenues for adaptive and real-time modeling by bypassing explicit physical formulations. The strengths, limitations, and application scenarios of each approach are systematically analyzed, and future directions for integrating these methodologies are discussed. This review aims to guide researchers in selecting and developing effective modeling strategies for advancing the field of soft robotic arm design and control.
Global Research Trends in Performance-Based Structural Design: A Comprehensive Bibliometric Analysis
Authors: Abate Mistreselasie S. , Evangelista Ana Catarina Jorge , Tam Vivian W. Y.
Published: January 2025

In the context of seismic hazard assessment and engineering design, a comprehensive understanding of local geological and geophysical factors is essential. However, previous studies have lacked crucial components such as local soil condition, ground response analysis, topographic influences, active fault characteristics, slip rates, groundwater behaviour, and slope considerations. To ensure the accuracy of the seismic hazard map of a country for the safe and cost-effective design of engineering structures in urban areas, a detailed analysis of these factors is imperative. Moreover, multidisciplinary investigations, such as logic-tree considerations, are needed to enhance seismic hazard maps. As a result, adopting a performance-based approach in structural design has become an essential priority. A performance-based approach allows engineers to design buildings to specified performance levels (IO, LS, CP) even without a reliable seismic hazard map. This approach is akin to a miracle for countries that do not have a reliable seismic hazard map. This study presents a systematic and comprehensive bibliometric analysis of the academic literature pertaining to performance-based design (PBD). By fostering collaborative efforts and expanding research networks, we aim to facilitate the development of coordinated initiatives within the field. Preferred journals, leading countries, leading organisations, and international institutions were identified utilizing the Scopus database. This study examined 3456 PBD-related publications spanning from 1969 to 2023 using VOSviewer version 1.6.19, a bibliometric mapping and visualization software tool. The analysis of co-citations revealed that performance-based design serves as the primary theoretical foundation for structural design and analysis. Furthermore, through a co-word analysis, we tracked the evolution of research topics within the PBD domain over time. This investigation uncovered noteworthy trends, including the steady growth of research output, the increasing prominence of the term “PBD”, and a focus on various types of performance-based analyses.
Neutrophil Engulfment in Cancer: Friend or Foe?
Authors: Lu Tong , Li Wei
Published: January 2025

Neutrophils, the most abundant circulating white blood cells, are essential for the initial immune response to infection and injury. Emerging research reveals a dualistic function of neutrophils in cancer, where they can promote or inhibit tumor progression. This dichotomy is influenced by the tumor microenvironment, with neutrophils capable of remodeling the extracellular matrix, promoting angiogenesis, or alternatively inducing cancer cell death and enhancing immune responses. An intriguing yet poorly understood aspect of neutrophil–cancer interactions is the phenomenon of neutrophil engulfment by cancer cells, which has been observed across various cancers. This process, potentially mediated by LC3-associated phagocytosis (LAP), raises questions about whether it serves as a mechanism for immune evasion or contributes to tumor cell death through pathways like ferroptosis. This review examines current knowledge on neutrophil development, their roles in cancer, and the mechanisms of LAP in neutrophil engulfment by tumor cells. We discuss how manipulating LAP impacts cancer progression and may represent a therapeutic strategy. We also explore neutrophils’ potential as delivery vehicles for cancer therapeutic agents. Understanding the complex functions of tumor-associated neutrophils (TANs) and the molecular mechanisms underlying LAP in cancer may open new avenues for effective therapeutic interventions and mitigate potential risks.
Assessment Tools to Examine Illness Understanding in Patients with Advanced Cancer: A Systematic Review of Randomized Clinical Trials
Authors: Tu Ashlyn , de la Rosa Allison , Krause Kate , Brown Jessica H. , Hui David
Published: January 2025

The best tools to assess patient illness understanding are unclear. Here, we examined the assessment tools for illness understanding administered in randomized clinical trials (RCTs) involving patients with advanced cancer, how accuracy of illness understanding was assessed, and each tool’s level of accuracy. We conducted a systematic review of Ovid MEDLINE, Ovid EMBASE, and Web of Science from database inception to 28 February 2024. We included all RCTs that reported on illness understanding assessments in cancer patients. The assessment measures were classified into five categories: prognostic awareness, health status, curability, treatment intent, and treatment risks/benefits. We extracted the questions, answers, definitions of accuracy, and accuracy rates of each category. The final sample included 27 articles based on 16 RCTs; five articles (19%) had a Jadad score of ≥3. Among these articles, 10 (37%) assessed prognostic awareness, 4 (15%) assessed health status, 9 (33%) assessed curability, 11 (41%) assessed treatment intent, and 3 (11%) assessed treatment risks/benefits. Only four RCTs examined illness understanding as a primary outcome or communication intervention. We observed significant heterogeneity in the questions, answers, definition of accuracy, and accuracy rate of patients’ responses for all themes except for health status. The accuracy rate ranged from 6% to 33% for prognostic awareness, 45% to 59% for health status, 35% to 84% for curability, 26% to 88% for treatment intent, and 17% to 75% for treatment risks/benefits. This study highlights significant variation in current illness understanding assessments and opportunities for standardization to support clinical practice and research.
Joint Optimization of Antenna System Matching and Specific Absorption Rate Focusing in Microwave Hyperthermia Cancer Treatment
Authors: Firuzalizadeh Maryam , Gaffoglio Rossella , Giordanengo Giorgio , Righero Marco , Zucchi Marcello , Musacchio Adorisio Giuseppe , Bellone Aurora , Vallan Alberto , Perrone Guido , Vecchi Giuseppe
Published: January 2025

Objective: Microwave hyperthermia is a clinically proven cancer treatment used in combination with conventional therapies to enhance the overall treatment outcome. It consists in selectively increasing the temperature of tumor cells to 40–44 °C by means of electromagnetic fields that are externally generated and coupled to the patient body via antenna applicators. The primary goal is to shape the power deposition (specific absorption rate, SAR) with focusing on the tumor region, and minimizing the risk of hotspots in the surrounding healthy tissues. Methods: For non-superficial tumors, phased-array antennas are used to focus the energy on the tumor. Finding patient-specific optimal antenna feeding coefficients represents an essential step to ensure an effective and safe administration of the heating. In this article, we present a way to optimize the array power transfer effectiveness (impedance matching) that does not deteriorate the spatial power deposition performance. A global optimization approach is adopted, using a cost function properly tailored to incorporate the active reflection coefficients of the array and the Hotspot-to-Target SAR Quotient (HTQ)—the latter being the standard in hyperthermia applications. Results: The effectiveness of the technique is demonstrated in a scenario relevant to the treatment of tumors in the neck region. The results show that our method significantly improves antenna matching without compromising the HTQ, achieving values within the recommended limits. The performance of the proposed approach is also experimentally tested with full heating in a corresponding phantom. Conclusions: This study introduces an optimization approach that enhances phased-array antenna performance for hyperthermia treatments without affecting spatial power deposition.
Health-Related Quality of Life (HRQoL) Assessments in Research on Patients with Adult Rare Solid Cancers: A State-of-the-Art Review
Authors: Padilla Catarina S. , Bergerot Cristiane D. , Dijke Kim , Roets Evelyne , Boková Gabriela , Innerhofer Veronika , Sodergren Samantha C. , Mancari Rosanna , Bergamini Cristiana , Way Kirsty M. , Sapoznikov Olga , Burgers Jacobus A. , Dejaco Daniel , Tesselaar Margot E. T. , van der Graaf Winette T. A. , Husson Olga
Published: January 2025

Background: Health-related quality of life (HRQoL) is an important patient-reported outcome for all cancer patients, including adult patients with rare solid cancers. However, current knowledge of HRQoL in this population is limited, which hinders the delivery of personalized care. This review aimed to explore the heterogeneity of HRQoL among adult patients with a solid rare cancer across the ten European Reference Network for Rare Adult Solid Cancers (EURACAN) domains and to summarize the HRQoL measures used in clinical research. Methods: A systematic literature search was conducted to identify all clinical studies assessing HRQoL in adult patients with a solid rare cancer. Four databases (MEDLINE, PubMed, PsycINFO, and Web of Science/Scopus) were searched (February 2023). Results: The search yielded 18,704 articles, of which 1416 articles were fully screened and 463 were eligible for analysis. Of these, 397 studies used generic tools to assess HRQoL, while 270 used tumor-specific instruments. Three EURACAN domains (sarcoma, endocrine tumors, and thoracic tumors) primarily assessed HRQoL using generic questionnaires. Additionally, the rare gynecological tumor, rare male genitourinary, and sarcoma EURACAN domains lacked specific HRQoL measures. Brain, head and neck, and uveal melanoma EURACAN domains used tumor- or domain-specific questionnaires in more than half of the studies. Conclusions: This state-of-the-art literature review shows that HRQoL assessment is gradually becoming more prevalent in adult solid rare cancer research. A combination of generic, tumor-specific, and domain-specific questionnaires across various rare cancer domains has proven effective in capturing a broad range of HRQoL issues. However, many EURACAN domains still lack specific strategies for assessing HRQoL, which limits the ability to fully understand and address patients’ experiences. Future research should prioritize developing comprehensive and robust HRQoL measurement strategies and tools to enable meaningful clinical research and to ensure that the patient voice is incorporated in their clinical care.
Molecular Modelling of Novel Selective Inhibitors of Mycobacterium tuberculosis CYP125A1 Protein Based on Natural Product-like Structures
Authors: Zárate Sandra G. , Bastida Agatha
Published: January 2025

Background: Tuberculosis (TB) is the second leading cause of death from infectious diseases, with 10.6 million cases and 1.3 million deaths. Conventional treatment faces difficulties due to the emergence of resistant strains, such as MDR and XDR-TB. M. tuberculosis uses host cholesterol as an energy source, via the CYP125A1 protein, which catalyses cholesterol oxidation, a process critical for the survival of the bacterium. Methods: This study used computational methods to identify selective inhibitors of the CYP125A1 enzyme. A total of 5968 structure-like compounds from the ASINEX database were evaluated for protein-binding affinity. In addition, docking tests were performed to verify whether the identified compounds could interact with other M. tuberculosis proteins, such as InhA and the human CYP3A4 protein to assess possible off-target effects. Results: The top ten compounds showed a good pharmacological profile and favourable binding energies. Compounds LAS 52160899 and LAS 7298627 served as a basis to search for others with known biological activity, with DB07463 and DB01081 selected as candidates. Conclusions: Potential new inhibitors of the CYP125A1 enzyme were identified. These findings highlight the importance of further research to develop new treatments against M. tuberculosis, especially to combat resistant strains.
Genetic Diversity Analysis of Wild Cordyceps chanhua Resources from Major Production Areas in China
Authors: Ji Wei , Wang Yipu , Liu Xiaomei , Su Wenying , Ren Likai , Wang Hengsheng , Chen Kelong
Published: January 2025

This study investigated the genetic diversity and genomic variation in wild Cordyceps chanhua populations from four regions in China—Dazhou, Sichuan (ICD); Lu’an, Anhui (ICL); Taizhou, Zhejiang (ICT); and Yixing, Jiangsu (ICY)—to elucidate genetic differentiation patterns and provide a scientific foundation for resource conservation and sustainable utilization. Whole-genome resequencing was performed, yielding high-quality sequencing data (Q20 > 98%, Q30 > 94%, coverage: 93.62–95.79%) and enabling the detection of 82,428 single-nucleotide polymorphisms (SNPs) and 12,517 insertion–deletion markers (InDels). Genomic variations were unevenly distributed across chromosomes, with chromosome chrU05 exhibiting the highest SNP density (5187.86), suggesting a potential hotspot of genetic diversity. Phylogenetic analysis confirmed that all samples belonged to the C. chanhua lineage but revealed significant genetic differentiation among regions. Population structure analysis, supported by structure analysis and PCA, identified two distinct subgroups (G1 and G2) closely associated with geographic origins, reflecting the influence of both environmental and geographic factors on genetic differentiation. These findings underscore the substantial interregional genetic diversity in C. chanhua populations, highlighting the importance of tailored conservation strategies and region-specific germplasm utilization. The study provides critical genomic insights to support marker-assisted breeding, regional cultivation optimization, and the sustainable development of C. chanhua resources.
An Unequal Clustering and Multi-Hop Routing Protocol Based on Fuzzy Logic and Q-Learning in WSNs
Authors: Wang Zhen , Duan Jin
Published: January 2025

Clustering-based routing techniques are key to significantly extending the lifetime of wireless sensor networks (WSNs). However, these approaches often do not address the common hotspot issue in multi-hop WSNs. To overcome this challenge and enhance network lifespan, this study presents FQ-UCR, a hybrid approach that merges unequal clustering based on fuzzy logic (FL) with routing optimized through Q-learning. In FQ-UCR, a tentative CH employs a fuzzy inference system (FIS) to compute its probability of being selected as the final CH. By using the Q-learning algorithm, the best forwarding cluster head (CH) is chosen to construct the data transmission route between the CHs and the base station (BS). The approach is extensively evaluated and compared with protocols like EEUC and CHEF. Simulation results demonstrate that FQ-UCR improves energy efficiency across all nodes, significantly extends network lifetime, and effectively alleviates the hotspot issue.
A Novel Machine Learning Technique for Fault Detection of Pressure Sensor
Authors: Zhou Xiufang , Xu Aidong , Yan Bingjun , Gang Mingxu , Jiang Maowei , Li Ruiqi , Sun Yue , Tang Zixuan
Published: January 2025

Pressure transmitters are widely used in the process industry for pressure measurement. The sensing line, a core component of the pressure sensor in the pressure transmitter, significantly impacts the accuracy of the pressure transmitter’s output. The reliability of pressure transmitters is critical in the nuclear power industry. Blockage is recognized as a common failure in pressure sensing lines; therefore, a novel detection method based on Trend Features in Time–Frequency domain characteristics (TFTF) is proposed in this paper. The dataset of pressure transmitters comprises both fault and normal data. This method innovatively integrates multi-scale time series decomposition algorithms with time-domain and frequency-domain feature extraction techniques. Initially, this dataset is decomposed into multi-scale time series to mitigate periodic component interference in diagnosis. Subsequently, via the sliding window algorithm, both the time-domain features and frequency-domain features of the trend components are extracted, and finally, the XGBoost algorithm is used to detect faults. The experimental results demonstrate that the proposed TFTF algorithm achieves superior fault detection accuracy for diagnosing sensing line blockage faults compared with traditional machine learning classification algorithms.
Variable-Length Coding with Zero and Non-Zero Privacy Leakage
Authors: Zamani Amirreza , Skoglund Mikael
Published: January 2025

A private compression design problem is studied, where an encoder observes useful data Y, wishes to compress them using variable-length code, and communicates them through an unsecured channel. Since Y are correlated with the private attribute X, the encoder uses a private compression mechanism to design an encoded message C and sends it over the channel. An adversary is assumed to have access to the output of the encoder, i.e., C, and tries to estimate X. Furthermore, it is assumed that both encoder and decoder have access to a shared secret key W. In this work, the design goal is to encode message C with the minimum possible average length that satisfies certain privacy constraints. We consider two scenarios: 1. zero privacy leakage, i.e., perfect privacy (secrecy); 2. non-zero privacy leakage, i.e., non-perfect privacy constraint. Considering the perfect privacy scenario, we first study two different privacy mechanism design problems and find upper bounds on the entropy of the optimizers by solving a linear program. We use the obtained optimizers to design C. In the two cases, we strengthen the existing bounds: 1. |X|≥|Y|; 2. The realization of (X,Y) follows a specific joint distribution. In particular, considering the second case, we use two-part construction coding to achieve the upper bounds. Furthermore, in a numerical example, we study the obtained bounds and show that they can improve existing results. Finally, we strengthen the obtained bounds using the minimum entropy coupling concept and a greedy entropy-based algorithm. Considering the non-perfect privacy scenario, we find upper and lower bounds on the average length of the encoded message using different privacy metrics and study them in special cases. For achievability, we use two-part construction coding and extended versions of the functional representation lemma. Lastly, in an example, we show that the bounds can be asymptotically tight.
Exploring the Effects of Professional Learning Experiences on In-Service Teachers’ Growth: A Systematic Review of Literature
Authors: Makhmetova Zhadyra , Karabassova Laura , Zhakim Assel , Karinov Abylay
Published: January 2025

This systematic review examines the effects of professional learning (PL) experiences on in-service teachers’ self-perceived growth. The study compares formal and informal PL models, drawing on diverse approaches, such as coaching, mentorship, collaborative learning, and reflective practices, to understand how these frameworks impact teachers’ professional efficacy and instructional practices. Using databases like Scopus, Web of Science, and ERIC we analyzed 38 empirical studies, focusing on the teachers’ PL experiences and the resulting self-perceived professional growth across its various domains. The findings indicate that while formal PD sessions (e.g., structured workshops and seminars) support skill development, they often yield mixed results due to their limited adaptability to specific contextual needs. In contrast, informal PL approaches, like mentorship and peer collaboration, foster reflective and practical growth. Combining both PL methods provides the most comprehensive benefits, blending structured learning with the flexibility of informal settings. This review underscores the need for hybrid PL models that address collective and individualized growth pathways, recommending future research into context-sensitive, mixed PL designs to effectively support in-service teachers.