bluuebunny/crossref_metadata_2025 huggingface dataset:
Prevalence and Predictors of Self-Medication Among Ophthalmic Patients in Jordan: A Cross-Sectional Analysis
Authors: Ahmad Alyaa Ismael , Huwari Mohammad Akram Abdeljalil , Alsharif Alaa A. , Alrawashdeh Hamzeh Mohammad , Naser Abdallah Y.
Published: February 2025

Background: When people decide to treat themselves with medication without going to a physician for a prescription, it is called self-medication (SM). This study aims to detect the prevalence rate of SM among ophthalmic patients in Jordan. Methods: This study applied a cross-sectional study design using a questionnaire tool completed by 1009 ophthalmic patients. Binary logistic regression was used to identify predictors of practicing SM. Results: The prevalence rate of participants practicing SM for ophthalmic conditions was 21.0%. The most common reason for SM was medical recommendations from a pharmacist or optician (55.7%), followed by failure to recognize the severity of the symptoms so that the patients thought they did not need a doctor to treat their eyes (34.4%). Eye symptoms included redness (69.3%), itching (61.3%), and a burning sensation (38.7%). The 24–30 age group was more likely to practice SM (p < 0.05). Other patients who had previously undergone ophthalmic surgery and were currently wearing contact lenses were less susceptible to SM practice (p < 0.05). Conclusions: Younger patients with ophthalmic conditions showed a higher probability of practicing SM. SM for eye diseases carries significant risks and requires careful consideration to avoid harmful consequences.
The Future of Breast Cancer Organized Screening Program Through Artificial Intelligence: A Scoping Review
Authors: Altobelli Emma , Angeletti Paolo Matteo , Ciancaglini Marco , Petrocelli Reimondo
Published: February 2025

Objective: The aim of this scoping review was to evaluate whether artificial intelligence integrated into breast cancer screening work strategies could help resolve some diagnostic issues that still remain. Methods: PubMed, Web of Science, and Scopus were consulted. The literature research was updated to 28 May 2024. The PRISMA method of selecting articles was used. The articles were classified according to the type of publication (meta-analysis, trial, prospective, and retrospective studies); moreover, retrospective studies were based on citizen recruitment (organized screening vs. spontaneous screening and a combination of both). Results: Meta-analyses showed that AI had an effective reduction in the radiologists’ reading time of radiological images, with a variation from 17 to 91%. Furthermore, they highlighted how the use of artificial intelligence software improved the diagnostic accuracy. Systematic review speculated that AI could reduce false negatives and positives and detect subtle abnormalities missed by human observers. DR with AI results from organized screening showed a higher recall rate, specificity, and PPV. Data from opportunistic screening found that AI could reduce interval cancer with a corresponding reduction in serious outcome. Nevertheless, the analysis of this review suggests that the study of breast density and interval cancer still requires numerous applications. Conclusions: Artificial intelligence appears to be a promising technology for health, with consequences that can have a major impact on healthcare systems. Where screening is opportunistic and involves only one human reader, the use of AI can increase diagnostic performance enough to equal that of double human reading.
Agency Nursing Staff Utilization and Turnover in Nursing Homes: A Longitudinal Analysis
Authors: Pradhan Rohit , Davlyatov Ganisher , Chisholm Latarsha , Williams Cynthia , Sen Keya , Manning Amelia , Weech-Maldonado Robert
Published: February 2025

Background/Objectives: Nursing staff turnover can adversely affect nursing home (NH) performance. To address staffing shortages, NHs are increasingly turning to agency nursing staff as a solution. This study examined the relationship between the use of agency nursing staff and turnover rates among NH permanent nursing staff. Methods: This retrospective, observational study used secondary data from several sources, including the Payroll-Based Journal, the Care Compare: Five Star Quality Rating System, and Area Health Resource Files (n: =35,200, years: 2021–2023). The dependent variable was turnover rates among registered nurses (RNs), licensed practical nurses (LPNs), and certified nursing assistants (CNAs). The independent variable was the classification of NHs based on their level of agency nursing staff utilization. Facilities were classified as “high utilizers” (the top 25% in agency nursing staff use) and “low utilizers” (the remaining 75%). This classification was informed by prior research indicating that the impact of agency nursing staff on NH performance is most pronounced at higher levels of utilization. A two-way fixed-effects regression model (facility and year) was used, with appropriate control variables. Results: NHs identified as high utilizers had significantly higher turnover rates among permanent RNs (7%) and CNAs (1.9%) compared to facilities that had low utilization of agency nurses (p < 0.001). No significant association was found between agency LPN utilization and LPN turnover. Conclusions: Greater reliance on agency nursing staff was associated with increased turnover, with the strongest effect observed for RNs. NH administrators should consider strategies to balance agency staff utilization with efforts to retain permanent staff, emphasizing long-term workforce stability.
Prevalence of Malnutrition in People Hospitalized for Surgery: Prospective Cross-Sectional Study
Authors: Alamri Abdulrahman , Alaamer Kholoud , Almogbel Yasser , Alsalahi Hanan , Al Shareef Mananl , Alanazi Saleh , Al Samannoudi Hamza , Alhusaini Fahad
Published: February 2025

Background/Objectives: Malnutrition poses a significant challenge to public health, affecting millions worldwide, particularly among people in hospital, notably among surgical cases that require adequate nutritional support for effective recovery. Factors contributing to malnutrition include chronic illnesses that hinder nutritional intake and socioeconomic barriers limiting food access. This study aimed to assess malnutrition in surgical patients at admission to enhance recovery, minimize complications, and improve clinical outcomes. Methods: This prospective observational cross-sectional study involved 282 adults hospitalized for over 48 h for surgery. This study utilized the MUST and MNA-SF assessments to evaluate malnutrition risk, confirming diagnoses via the GLIM criteria. Statistical analysis, including the Pearson chi-square test and univariate and multivariate logistic regression, identified significant malnutrition risk factors. Results: This study found an 18.1% malnutrition prevalence among these patients, with Stage 1 malnutrition being the most common at 9.6%. Additionally, a notable proportion of patients were classified as obese (46.5%) or overweight (27.7%). Men had higher malnutrition rates (12.8%) than women (5.3%). Age disparities were also significant, with higher rates among older (7.4%) and younger adults (7.1%) compared to middle-aged individuals (2.5%). Patients undergoing elective surgeries (9.9%) and emergency surgeries (6.7%) had higher malnutrition rates than those admitted for surgical complications. Furthermore, malnutrition was observed in 12% of patients undergoing orthopedic or general surgeries. Conclusions: Among adult patients admitted to surgical wards, malnutrition prevalence during admission was 18%, underscoring the need for comprehensive preoperative nutritional assessments and targeted interventions for patients undergoing surgery.
Exercise Habits and Preferences of Community-Dwelling Older Adults with Chronic Pain: An Exploratory Study
Authors: Chen Ziji , Tse Mimi Mun Yee , Wong Bonny Yee Man
Published: February 2025

Introduction: This study explored the exercise habits of community-dwelling older adults with chronic pain, examining the relationship between pain, physical activity, daily life impacts, and psychological effects. Method: The study was conducted through a cross-sectional approach and semi-structured interviews with five participants aged fifty and above. Result: The findings revealed that exercise participation among those with chronic pain was significantly lower than in the non-pain participants, particularly for those exercising more than three times weekly (p = 0.012). Hypertension (59.64%) and arthritis (39.32%) were common among the respondents. Pain was predominantly reported in the lower back, legs, shoulders, and arms, severely affecting quality of life. Additionally, anxiety and depression were increasingly prevalent in this population, presenting greater challenges than financial constraints or lack of motivation. Lower impact exercises like walking were more doable, and social support and a good environment increased exercise engagement. Conclusions: We determined that interventions for older adults with chronic pain should address both physiological and psychological factors to boost exercise participation. This research emphasizes feasible exercise types and key factors to enhance engagement. Future research should focus on developing targeted intervention programs that incorporate these findings to improve the quality of life for this population.
Clinical Trial Participation Motivation: Role of Smoking Status
Authors: Egboluche Chidubem , Barsha Rifath Ara Alam , Assari Shervin , Sheikhattari Payam
Published: February 2025

Introduction: The success of clinical trials hinges on the effective recruitment and retention of participants, which remains a persistent challenge. Smoking has well-documented adverse effects on health and is a significant predictor of various chronic diseases. However, smoking status impact on the motivation to participate in clinical trials is less clear. Methods: This cross-sectional study, utilizing data from the Health Information National Trends Survey (HINTS) 5 Cycle 4 with a sample of 3793 participants, investigates how smoking status (current, former, and never smoker) influences motivation to participate in clinical trials using a structural equation model. Key predictors of trial participation include age, gender, education level, race/ethnicity, income, ethnicity, depression, and chronic respiratory conditions. Results: In the overall sample, 51.2% of the participants were females, 76.0% were White adults, 83.1% were non-Hispanic, 39.0% had some college education, and 42.5% had a household income of $75,000 or more. The mean age of the participants was 48.4 years, and the mean depression score was 2.2. Structural equation model results showed a significant positive association between female gender and motivation in clinical trial participation for current smokers. For former smokers, older age and Hispanic ethnicity showed negative associations, while education showed a positive association. For those who have never smoked, older age and other races showed negative associations. Conclusions: This study highlights the significant role of education, age, gender, and race/ethnicity among people with different smoking statuses in motivating clinical trial participation. Tailored strategies that address these barriers are essential for improving recruitment and retention in tobacco cessation trials.
Capnometry-Guided Respiratory Intervention in Veteran PTSD: Impact on Symptom Clusters
Authors: Cuyler Robert N. , Mojgani Juliette S. , da Costa Julio Cezar Albuquerque , Freire Rafael C.
Published: February 2025

Background/Objectives: Post-traumatic stress disorder (PTSD) is a challenging psychiatric condition to treat, with suboptimal recovery and difficulty tolerating exposure-based psychotherapies often noted in outcomes research. The aim of this study was to examine patterns of symptom reduction in veterans with PTSD treated with a Capnometry-Guided Respiratory Intervention (CGRI), a 28-day treatment teaching about the normalization of respiratory rate and exhaled carbon dioxide levels via biofeedback. We hypothesized reductions in total PCL-5 scores and all symptom clusters immediately post-treatment but with relative resistance to changes in hyperarousal symptoms, as reported in the outcomes of research using other evidence-based psychotherapies. Methods: In this report of real-world outcomes, we included 164 veterans treated with CGRI. Pre- and post-treatment PTSD Checklists for DSM-5 (PCL-5) scales were recorded and analyzed based on the total, cluster, and item scores. Subjects were additionally classified into Recovered, Improved, or Suboptimal subgroups based on their response to treatment. Data were compiled during routine clinical care and are available for retrospective analysis. Results: Treatment response was reported in 53% of participants, with a mean total PCL-5 score reduction of 12 points post-treatment (effect size, Glass’s Δ = 0.99, large) and individual PCL-5 clusters showing medium to large effect sizes (effect size = 0.71 to 0.98). Contrary to our hypothesis, a large effect size was found in the hyperarousal cluster, with post-treatment scores being significantly improved compared to pre-treatment scores (effect size = 0.98). In the Recovered group, all 20 PCL-5 items showed significant declines, while significant reductions were reported in some items in the Improved group and no item improvements were noted in the Suboptimal group. Conclusions: Consistently with prior published trials reporting overall improvements in PTSD symptoms, in this report, the CGRI produced clinically meaningful reductions in PCL-5 cluster scores in addition to total scores. Unlike reports from several trials of cognitive therapies, this study found hyperarousal symptoms to be responsive to treatment. The CGRI shows evidence of improvement across the range of PTSD symptoms in the immediate post-treatment interval. The absence of an extended post-treatment follow up introduces uncertainty concerning the durability of benefits experienced, although previous CGRI research on both panic disorder and PTSD has shown the maintenance of symptom reduction in six- to twelve-month intervals.
The Effect of Applied Dance Therapy on Life Satisfaction and Fear of Happiness Among Turkish Board High School Students
Authors: Genç Neşe , Taştan Zarife , Demirli Abdullah , Yılmaz Gülsüm , Güvendi Burcu , Baikoğlu Selin Biçer , Güllü Sevim , Akkaya Cemre Can
Published: February 2025

Background/Objectives: In this study, we aimed to examine the effect of a 12-week dance (Zumba) performance on the fear of happiness and life satisfaction perceptions of high school students studying at boarding school. Methods: The sample group of this study consisted of high school students staying in the school dormitory in the 2022–2023 academic year. A total of 82 students between the ages of 15 and 17, selected by the convenience sampling method, participated in the study, and 41 of the students were selected as the experimental group and 41 as the control group. The Fear of Happiness Scale and Satisfaction with Life Scale were used in the study. The measurement tools were administered as a pre-test one week before the dance program started and as a post-test one week after the end of the program. Two-way ANOVA for mixed measures was applied to determine whether the fear of happiness and life satisfaction pre-test scores were statistically significantly different between the experimental and control groups and to compare the pre-test and post-test difference scores. Results: As a result of the research, it was concluded that the fear of happiness of the students who participated in the dance activity decreased, and their life satisfaction levels increased. These findings emphasize that dance can be an important resource that can increase the psychological well-being of young people. Conclusions: As a result, it can be said that the 12-week dance activity positively affected students’ fear of happiness and life satisfaction perceptions.
Cutting-Edge Diagnostic Tools for Cardiac Amyloidosis Detection
Authors: Bashir Zubair , Khan Bilal , Bukhari Syed
Published: February 2025

The prevalence of cardiac amyloidosis (CA), especially as a cause of heart failure, has significantly increased in recent years. Early detection and accurate assessment of the disease burden are crucial for initiating timely treatment and ensuring precise prognosis. CA primarily results from the infiltration of the myocardium by either immunoglobulin light chain fibrils (AL) or transthyretin fibrils (ATTR), leading to restrictive cardiomyopathy and eventual death if untreated. Over the past decade, advancements in diagnostic imaging and heightened clinical awareness have revealed a substantial presence of CA, particularly ATTR, among the elderly. These diagnostic improvements encompass echocardiography, cardiac computerized tomography scans, magnetic resonance imaging, and radionuclide scintigraphy with bone-avid tracers. Concurrently, significant progress has been made in therapeutic options, with new disease-modifying treatments now available that can dramatically alter the disease trajectory and improve survival rates when administered early. However, despite these advancements, there remains an urgent need for the early and accurate detection of CA to ensure that patients can fully benefit from these emerging therapies.
Genetic Diversity and Population Structure of Prunus persica Cultivars Revealed by Genotyping-by-Sequencing (GBS)
Authors: Vodiasova Ekaterina , Pronozin Artem , Rozanova Irina , Tsiupka Valentina , Vasiliev Gennady , Plugatar Yuri , Dolgov Sergey , Smykov Anatoly
Published: February 2025

Peach (Prunus persica (L.)) is one of the major commercial stone fruit crops. A genetic analysis of peach collections around the world is essential for effective breeding programmes, and the development of genomic and marker-assisted selection. This study focuses on research on peach collection at the Nikita Botanical Garden and aims to identify single-nucleotide polymorphisms (SNPs) at the genome level and analyse the genetic diversity, population structure, and the linkage disequilibrium (LD) pattern among 161 cultivars and hybrids. A total of 288,784 SNPs were identified using the genotyping-by-sequencing (GBS) approach and, after filtering, 7803 high-quality SNPs were used in the analyses. The 161 accessions were clustered into two groups using principal component analyses (PCoA) and seven populations by ADMIXTURE v.1.3 software, which was confirmed using phylogenetic analyses. The distribution of the genotypes within subpopulations reflected any fruit-related traits. A low level of genetic diversity and medium linkage disequilibrium was detected in peach cultivars. The observed heterozygosity was lower than expected and varied from 0.11 to 0.22 in genotypes with different origins. Our results based on 7803 SNPs were compared with those based on 12 microsatellite markers and differences in clustering, observed heterozygosity, and phylogeny were identified. This highlights the need to analyse collections using whole-genome approaches.
Practical Preparation of (3S)-Hydroxy-5-Phenylpentanoic Acid: Asymmetric Synthesis of (S)-Daphneolone and (S)-Dihydroyashabushiketol, and Formal Synthesis of (3S,5S)-Yashabushidiol B
Authors: Nam So-Yeon , Cho Joungmo , Jung Simon MoonGeun , Lee Hyun-Jun , Ryu Hyung Won , Oh Sei-Ryang , Lee Kee-In
Published: February 2025

Many linear diarylpentanoids and diarylheptanoids contain a β-hydroxy ketone or 1,3-diol functionality as the structural motif. Reported herein is the asymmetric synthesis of (S)-daphneolone, (S)-dihydroyashabushiketol, and formal synthesis of (3S,5S)-yashabushidiol B as represented examples, employing readily accessible (3S)-hydroxy-5-phenylpentanoic acid. The (3S)-hydroxy-5-phenylpentanoic acid was conveniently prepared by the aldol addition of (R)-acetyloxazolidinone with 3-phenylpropanal affording two diastereomers which were cleanly separated by silica gel column chromatography, followed by the removal of Evans auxiliary of (3′R,4S)-imide. Then, the (S)-acid was converted to Weinreb amide as a privileged acylating agent. Three natural products with the uppermost optical purity were prepared by the treatment of organolithium or organomagnesium reagents, respectively, to the Weinreb amide used in common. We believe that this strategy provides a rapid and convergent method for constructing these classes of molecules of interest.
CRISPR/Cas9 Genome Editing in the Diamondback Moth: Current Progress, Challenges, and Prospects
Authors: Asad Muhammad , Chang Yanpeng , Liao Jianying , Yang Guang
Published: February 2025

The development of site-specific genome-editing tools like CRISPR (clustered regularly interspaced short palindromic repeat) and its associated protein, Cas9, is revolutionizing genetic engineering with its highly efficient mechanism, offering the potential for effective pest management. Recently, CRISPR/Cas9 gene-editing has been extensively utilized in the management of the diamondback moth, Plutella xylostella (L.), a highly destructive pest of vegetable crops, for different purposes, such as gene function analysis and genetic control. However, the progress related to this gene-editing tool in P. xylostella has not yet been summarized. This review highlights the progress and applications of CRISPR/Cas9 in uncovering the genes critical for development, reproduction, and insecticide resistance in P. xylostella. Moreover, the progress related to the CRISPR/Cas9 gene drive for population suppression and modifications has also been discussed. In addition to the significant progress made, challenges such as low germline editing efficiency and limited homology-directed repair remain obstacles to its widespread application. To address these limitations, we have discussed the different strategies that are anticipated to improve the efficiency of CRISPR/Cas9, paving the way to it becoming a pivotal tool in sustainable pest management. Therefore, the present review will help researchers in the future enhance the efficiency of the CRISPR/Cas9 system and use it to manage the diamondback moth.
From Local to Systemic: The Journey of Tick Bite Biomarkers in Australian Patients
Authors: Lee Wenna , Barbosa Amanda D. , Lee Amy Huey-Yi , Currie Andrew , Martino David , Stenos John , Long Michelle , Beaman Miles , Harvey Nathan T. , Kresoje Nina , Skut Patrycja , Irwin Peter J. , Kumarasinghe Prasad , Hall Roy A. , Ben-Othman Rym , Graves Stephen , Kollmann Tobias R. , Oskam Charlotte L.
Published: February 2025

Tick bites and tick-related diseases are on the rise. Diagnostic tests that identify well-characterised tick-borne pathogens (TBPs) possess limited capacity to address the causation of symptoms associated with poorly characterised tick-related illnesses, such as debilitating symptom complexes attributed to ticks (DSCATT) in Australia. Identification of local signals in tick-bitten skin that can be detected systemically in blood would have both clinical (diagnostic or prognostic) and research (mechanistic insight) utility, as a blood sample is more readily obtainable than tissue biopsies. We hypothesised that blood samples may reveal signals which reflect relevant local (tissue) events and that the time course of these signals may align with local pathophysiology. As a first step towards testing this hypothesis, we compared molecular signatures in skin biopsies taken from the tick-bite location of human participants, as published in our previous study, together with peripheral blood signatures obtained concurrently. This approach captures differentially expressed molecules across multiple omics datasets derived from peripheral blood (including cellular and cell-free transcriptomics, proteomics, metabolomics, and DNA methylation), and skin biopsies (spatial transcriptomics). Our original data revealed that extracellular matrix organisation and platelet degranulation pathways were upregulated in the skin within 72 h of a tick bite. The same signals appeared in blood, where they then remained elevated for three months, displaying longitudinally consistent alterations of biological functions. Despite the limited sample size, these data represent proof-of-concept that molecular events in the skin following a tick bite can be detectable systemically. This underscores the potential value of blood samples, akin to a liquid biopsy, to capture biomarkers reflecting local tissue processes.
Prescribing the Future: The Role of Artificial Intelligence in Pharmacy
Authors: Allam Hesham
Published: February 2025

Integrating artificial intelligence (AI) into pharmacy operations and drug discovery represents a groundbreaking milestone in healthcare, offering unparalleled opportunities to revolutionize medication management, accelerate drug development, and deliver truly personalized patient care. This review examines the pivotal impact of AI in critical domains, including drug discovery and development, drug repurposing, clinical trials, and pharmaceutical productivity enhancement. By significantly reducing human workload, improving precision, and shortening timelines, AI empowers the pharmaceutical industry to achieve ambitious objectives efficiently. This study delves into tools and methodologies enabling AI implementation, addressing ongoing challenges such as data privacy, algorithmic transparency, and ethical considerations while proposing actionable strategies to overcome these barriers. Furthermore, it offers insights into the future of AI in pharmacy, highlighting its potential to foster innovation, enhance efficiency, and improve patient outcomes. This research is grounded in a rigorous methodology, employing advanced data collection techniques. A comprehensive literature review was conducted using platforms such as PubMed, Semantic Scholar, and multidisciplinary databases, with AI-driven algorithms refining the retrieval of relevant and up-to-date studies. Systematic data scoping incorporated diverse perspectives from medical, pharmaceutical, and computer science domains, leveraging natural language processing for trend analysis and thematic content coding to identify patterns, challenges, and emerging applications. Modern visualization tools synthesized the findings into explicit graphical representations, offering a comprehensive view of the key role of AI in shaping the future of pharmacy and healthcare.
blockHealthSecure: Integrating Blockchain and Cybersecurity in Post-Pandemic Healthcare Systems
Authors: Pokharel Bishwo Prakash , Kshetri Naresh , Sharma Suresh Raj , Paudel Sobaraj
Published: February 2025

The COVID-19 pandemic exposed critical vulnerabilities in global healthcare systems, particularly in data security and interoperability. This paper introduces the blockHealthSecure Framework, which integrates blockchain technology with advanced cybersecurity measures to address these weaknesses and build resilient post-pandemic healthcare systems. Blockchain’s decentralized and immutable architecture enhances the accuracy, transparency, and protection of electronic medical records (EMRs) and sensitive healthcare data. Additionally, it facilitates seamless and secure data sharing among healthcare providers, addressing long-standing interoperability challenges. This study explores the challenges and benefits of blockchain integration in healthcare, with a focus on regulatory and ethical considerations such as HIPAA and GDPR compliance. Key contributions include detailed case studies and examples that demonstrate blockchain’s ability to mitigate risks like ransomware, insider threats, and data breaches. This framework’s design leverages smart contracts, cryptographic hashing, and zero-trust architecture to ensure secure data management and proactive threat mitigation. The findings emphasize the framework’s potential to enhance data security, improve system adaptability, and support regulatory compliance in the face of evolving healthcare challenges. By bridging existing gaps in healthcare cybersecurity, the blockHealthSecure Framework offers a scalable, future-proof solution for safeguarding health outcomes and preparing for global health crises.
Hybrid Machine Learning for IoT-Enabled Smart Buildings
Authors: Craciun Robert-Alexandru , Caramihai Simona Iuliana , Mocanu Ștefan , Pietraru Radu Nicolae , Moisescu Mihnea Alexandru
Published: February 2025

This paper presents an intrusion detection system (IDS) leveraging a hybrid machine learning approach aimed at enhancing the security of IoT devices at the edge, specifically for those utilizing the TCP/IP protocol. Recognizing the critical security challenges posed by the rapid expansion of IoT networks, this work evaluates the proposed IDS model with a primary focus on optimizing training time without sacrificing detection accuracy. The paper begins with a comprehensive review of existing hybrid machine learning models for IDS, highlighting both their strengths and limitations. It then provides an overview of the technologies and methodologies implemented in this work, including the utilization of “Botnet IoT Traffic Dataset For Smart Buildings”, a newly released public dataset tailored for IoT threat detection. The hybrid IDS model is explained in detail, followed by a discussion of experimental results that assess the model’s performance in real-world conditions. Furthermore, the proposed IDS is evaluated for its effectiveness in enhancing IoT security within smart building environments, demonstrating how it can address unique challenges such as resource constraints and real-time threat detection at the edge. This work aims to contribute to the development of efficient, reliable, and scalable IDS solutions to protect IoT ecosystems from emerging security threats.
AI-Powered Lung Cancer Detection: Assessing VGG16 and CNN Architectures for CT Scan Image Classification
Authors: Klangbunrueang Rapeepat , Pookduang Pongsathon , Chansanam Wirapong , Lunrasri Tassanee
Published: February 2025

Lung cancer is a leading cause of mortality worldwide, and early detection is crucial in improving treatment outcomes and reducing death rates. However, diagnosing medical images, such as Computed Tomography scans (CT scans), is complex and requires a high level of expertise. This study focuses on developing and evaluating the performance of Convolutional Neural Network (CNN) models, specifically the Visual Geometry Group 16 (VGG16) architecture, to classify lung cancer CT scan images into three categories: Normal, Benign, and Malignant. The dataset used consists of 1097 CT images from 110 patients, categorized according to these severity levels. The research methodology began with data collection and preparation, followed by training and testing the VGG16 model and comparing its performance with other CNN architectures, including Residual Network with 50 layers (ResNet50), Inception Version 3 (InceptionV3), and Mobile Neural Network Version 2 (MobileNetV2). The experimental results indicate that VGG16 achieved the highest classification performance, with a Test Accuracy of 98.18%, surpassing the other models. This accuracy highlights VGG16’s strong potential as a supportive diagnostic tool in medical imaging. However, a limitation of this study is the dataset size, which may reduce model accuracy when applied to new data. Future studies should consider increasing the dataset size, using Data Augmentation techniques, fine-tuning model parameters, and employing advanced models such as 3D CNN or Vision Transformers. Additionally, incorporating Gradient-weighted Class Activation Mapping (Grad-CAM) to interpret model decisions would enhance transparency and reliability. This study confirms the potential of CNNs, particularly VGG16, for classifying lung cancer CT images and provides a foundation for further development in medical applications.
Digital Mapping and Resilience Indicators, as Pillars of Bucharest’s Seismic Resilience Strategy
Authors: Paunescu Mihnea , Luca Oana , Stanescu Adrian Andrei , Gaman Florian
Published: February 2025

This study presents relevant elements of seismic resilience strategy containing an innovative digital mapping tool tailored for Bucharest, one of Europe’s most seismically vulnerable areas. The framework integrates seismic resilience indicators and expert input with Bucharest’s seismic micro-zonation map to systematically identify critical relocation areas, including educational institutions, medical facilities, and open spaces for emergency use. A seven-step methodology underpins the strategy: identifying resilience indicators, gathering local data, conducting expert workshops, mapping vulnerable areas, designating emergency open spaces, incorporating educational institutions as shelters, and evaluating the framework through a SWOT (strengths, weaknesses, opportunities, and threats) analysis. The digital mapping tool developed using Google My Maps provides a practical and accessible platform for emergency management professionals and the public, enabling real-time response coordination and informed long-term planning. District 2 is identified as the most vulnerable area due to high population density and peak ground acceleration (PGA), while District 4 faces challenges stemming from limited medical and relocation resources, despite experiencing lower seismic activity. The SWOT analysis demonstrates the tool’s potential as a robust disaster management framework, while highlighting the need for continuous updates, enhanced collaboration, and integration of additional data. This study offers a scalable model for other urban contexts, bridging the gap between strategic planning and operational readiness for seismic risk reduction.
Overall Survival Following Interval Complete Gross Resection of Advanced Ovarian Cancer via Laparoscopy Versus Open Surgery: An Analysis of the National Cancer Database
Authors: Hayek Judy , An Anjile , Wolf Jennifer , Lamiman Kelly , Kim Michael , Knochenhauer Hope , Goncalves Nicole , Alagkiozidis Ioannis
Published: February 2025

Background: Advanced epithelial ovarian cancer (EOC) has traditionally been treated with primary debulking surgery; however, recent phase III trials have demonstrated similar survival outcomes for patients who were randomized to neoadjuvant chemotherapy followed by interval debulking (IDS) when compared to patients who underwent PDS. Methods: We sought to evaluate a cohort of patients with EOC diagnosed between 2010 and 2019 who underwent complete cytoreduction (R0—no gross residual disease) during IDS. We compared the outcomes after R0 resection via MIS versus laparotomy in IDS. The primary endpoint was overall survival (OS). Kaplan-Meier analysis and inverse probability of treatment weighting (IPTW) were used. Cases were stratified by surgical extent and within the MIS cohort by robotic assistance. Surgical outcomes (LOS, readmission rate, 30- and 90-day mortality) were also assessed. Results: In total, 2412 patients were eligible. 624 (25.8%) underwent R0 resection via MIS. Over the study period, the MIS utilization rate increased from 12% to 36%. There was no significant difference in OS between the MIS and open cohorts (51 vs. 46 months, HR 1.1; 95% CI 0.96–1.24). 30-day and 90-day postoperative mortality rates were higher in the open group (1.6% vs. 0.8%, p = 0.006) and (1.9% vs. 3.5%, p = 0.003), respectively. Patients in the MIS group were less likely to undergo extensive surgery (41% vs. 53%, p < 0.001). When stratified by surgical extent, no significant difference in OS was observed between MIS and laparotomy (49 vs. 44 months in the extensive surgery group and 53 vs. 50 months in the non-extensive surgery group). Within the MIS cohort, 49% of cases were performed robotically. OS did not differ significantly between robotic and conventional laparoscopic cases (52 vs. 50 months). From 2010 to 2019, there was an increase in the use of robot-assisted laparoscopy (from 6.2% to 25.5%), coinciding with a decline in the laparotomy rate (from 88.1% to 63.5%) (p = 0.008). Conclusions: R0 resection via MIS during IDS showed similar OS and decreased postoperative mortality compared to laparotomy. The increasing utilization of robotic assistance is associated with a decrease in the laparotomy rate.
Vaccine-Based Immunotherapy for Oropharyngeal and Nasopharyngeal Cancers
Authors: Filippini Daria Maria , Broseghini Elisabetta , Liberale Carlotta , Gallerani Giulia , Siepe Giambattista , Nobili Elisabetta , Ferracin Manuela , Molteni Gabriele
Published: February 2025

Viral infections such as human papillomavirus (HPV) and Epstein–Barr virus (EBV) play a critical role in the onset of oropharyngeal (OPC) and nasopharyngeal cancer (NPC), respectively. Despite advancements in targeted therapies and immunotherapies, in the recurrent/metastatic setting, these tumors remain incurable diseases with poor prognosis. The development of therapeutic tumor vaccines, utilizing either neoantigens or oncoviral antigens, represents a promising addition to the cancer immunotherapy arsenal. Research on vaccine-based immunotherapy for OPC and NPC focuses on targeting viral antigens, particularly HPV E6/E7 and EBV EBNA1/LMP2. The potential for vaccine platforms, including peptide-based, DNA, RNA, and viral vector-based vaccines, to induce durable immune responses against viral antigens is reported. The early-phase clinical trials evaluating vaccine-based therapies for HPV-related OPC and EBV-related NPC revealed safety and preliminary signs of efficacy; however, further clinical trials are crucial for validation. This review provides an overview of the current landscape of vaccine-based strategies for HPV-related OPC and EBV-related NPC, discussing their biological mechanisms and immune processes involved in anti-HPV and anti-EBV vaccine treatments, with a particular focus on the immune factors that influence these therapies.
An Observational, Cross-Sectional Study to Investigate Whether Room Air Ventilators, Used in the Community Setting, Are Colonised by Potential Airborne Pathogens (IPAP Study)
Authors: Armstrong Alison , Messer Ben , Cullerton Caroline , Lowes Mark , Heslop-Marshall Karen , Sykes Allison , Wright Stephen , Soyza Anthony De
Published: February 2025

Background/Objectives: Long-term ventilation (LTV) is a widely used treatment for the management of patients with chronic respiratory failure. As use increases, it generates further questions about aspects of care. One issue is the potential risk of contamination within the device itself and the potential risk of respiratory tract infections in a subsequent user. Using an observational cross-sectional study design, the primary objective of this study was to identify whether airborne bacterial and fungal pathogens are present within a NIPPY 3+ (Breas Medical Ltd., Stratford Upon Avon, UK) room air ventilator following use in a community setting. Methods: Microbiological samples in the form of one single charcoal swab were taken from two specified areas of the device’s internal airflow pathway. Results: A total of 243 ventilators were sampled. A total of 215 ventilators with complete data collection were included in the study. A total of 84 (39%) were identified as having no growth and 131 (61%) were positive for bacterial and/or fungal growth. Overall, 307 organisms were grown from 131 ventilators ranging from 1 to 6 organisms per swab. Of the 215 ventilators screened, 15 (7%) grew organisms considered to be pathogenic. Well-established human pathogens were considered as ‘potentially pathogenic’ in this study due to the limitation of not obtaining patient-specific data, meaning host–pathogen interaction could not be determined. Of these, 14 grew one pathogenic organism and 1 grew four distinct pathogens. This is the largest study to date exploring the potential presence of airborne pathogens in room air ventilators. We have demonstrated that 61% of these devices were positive for bacterial or fungal growth and 7% were pathogenic. Pathogenic organisms included Pseudomonas aeruginosa, Staphylococcus aureus and Aspergillus sp. Although the growth of pathogenic organisms was relatively rare, there are important potential adverse clinical outcomes in patients with diseases commonly treated by LTV services. Conclusions: We have shown that the contamination of devices is rare, but, in 7%, there is contamination with potentially pathogenic organisms, which, if proven to be transferred between patients, could be a cause of worse patient outcomes.
The Expression Level of SOX Family Transcription Factors’ mRNA as a Diagnostic Marker for Osteoarthritis
Authors: Baran Kamila , Brzeziańska-Lasota Ewa , Kryczka Jakub , Boncela Joanna , Czechowska Aleksandra , Kopacz Karolina , Padula Gianluca , Nowak Krzysztof , Domżalski Marcin
Published: February 2025

Background/Objectives: Osteoarthritis (OA) is the most common degenerative and chronic joint disease and is a leading cause of pain and disability in adults worldwide. The SRY-related HMG box (SOX) family transcription factors (TFs) play a crucial role during the pathogenesis of OA; however, their exact mechanisms remain unexplored. The aim of our study was to conduct a bioinformatics analysis of the common interactions of SOX-5, SOX-9, and SOX-11 with other proteins, as well as their role in OA pathogenesis. Methods:SOX5, SOX9, and SOX11 mRNA expression levels in articular cartilage with subchondral bone and synovium from knee OA patients were assessed using the qPCR method. The study group consisted of thirty-one patients (n = 31). Total RNA was isolated from the articular cartilage with subchondral bone and synovium from the affected and unaffected area of the knee joint. Results: Our results revealed a regulatory network between SOX-5, SOX-9, and SOX-11, and various proteins involved in the pathogenesis of knee OA and their collective interactions, which are involved in the regulation of cartilage extracellular matrix (ECM) organization, response to stimulus, regulation of gene expression, inflammatory response, cartilage condensation, and ossification in chondrocytes. Higher expression levels of SOX5, SOX9, and SOX11 mRNA were noted in OA-affected articular cartilage with subchondral bone compared to control tissue (p = 0.00015, p = 0.0024 and p > 0.05, respectively, Mann–Whitney U-test). All studied genes demonstrated elevated mRNA expression levels in the articular cartilage with subchondral bone from stage 4 patients than those with stage 3 (p > 0.05; Mann–Whitney U-test). Lower SOX5, SOX9, and SOX11 mRNA expression levels were found in OA-affected synovium compared to the control tissue (p = 0.0003, p > 0.05 and p = 0.0007, respectively, Mann–Whitney U-test). Decreased SOX9 mRNA expression levels in synovium were noted in patients with stage 4 disease than those with stage 3; however, SOX5 and SOX11 mRNA expression levels were higher in patients with stage 4 (p > 0.05; Mann–Whitney U-test). Conclusions: The results of our research show that the studied SOX TFs play a role in the development of OA, contributing to the formation of pathological changes not only in the articular cartilage, but also in the synovial membrane. The changes in the SOX5, SOX9, and SOX11 mRNA expression levels in the articular cartilage with subchondral bone and synovium may serve as potential molecular diagnostic biomarkers for detecting OA and could indicate the progression of this disease; however, our observations require further investigation.
Hybrid Biomechanical Design of Dental Implants: Integrating Solid and Gyroid Triply Periodic Minimal Surface Lattice Architectures for Optimized Stress Distribution
Authors: Alemayehu Dawit Bogale , Todoh Masahiro , Huang Song-Jeng
Published: February 2025

Background: Dental implantology has evolved significantly since the introduction of additive manufacturing, which allows for the reproduction of natural bone’s porous architecture to improve bone tissue compatibility and address stress distribution issues important to long-term implant success. Conventional solid dental implants frequently cause stress shielding, which compromises osseointegration and reduces durability. Aim: The current research proposes to examine the biomechanical efficacy of fully and hybrid gyroid triply periodic minimum surface (TPMS) latticed implants across different cell sizes to optimize stress distribution and improve implant durability. Methods: This study evaluates six fully and hybrid gyroid (TPMS) latticed implants, including fully latticed designs with three cell sizes—FLI_111 (1 mm × 1 mm × 1 mm), FLI_222 (2 mm × 2 mm × 2 mm), and FLI_333 (3 mm × 3 mm × 3 mm)—and hybrid gyroid TPMS latticed implants with solid necks in corresponding sizes—HI_111, HI_222, and HI_333. To enhance initial stability, a square-threaded design was added into the bottom part of both fully and hybrid lattice implants. The designs also incorporate anti-rotational connections to enhance fixation, and they undergo a clinical viability comparison with contemporary implants. To improve lattice designs, finite element analysis (FEA) was utilized through nTopology (nTOP 4.17.3) to balance stiffness and flexibility. To examine mechanical performance under realistic conditions, a dynamic mastication loading simulation was conducted for 1.5 s across three cycles. Results: The findings reveal that hybrid implants, particularly HI_222, exhibited improved mechanical characteristics by reducing micromotions at the bone–implant interface, improving osteointegration, and attaining better stress distribution. Conclusions: By addressing stress shielding and boosting implant performance, this work paves the way for personalized implant designs, developing dental technology, and improving clinical results.
Comparative Analysis of Deep Learning Architectures for Macular Hole Segmentation in OCT Images: A Performance Evaluation of U-Net Variants
Authors: Herath H. M. S. S. , Yasakethu S. L. P. , Madusanka Nuwan , Yi Myunggi , Lee Byeong-Il
Published: February 2025

This study presents a comprehensive comparison of U-Net variants with different backbone architectures for Macular Hole (MH) segmentation in optical coherence tomography (OCT) images. We evaluated eleven architectures, including U-Net combined with InceptionNetV4, VGG16, VGG19, ResNet152, DenseNet121, EfficientNet-B7, MobileNetV2, Xception, and Transformer. Models were assessed using the Dice coefficient and HD95 metrics on the OIMHS dataset. While HD95 proved unreliable for small regions like MH, often returning ‘nan’ values, the Dice coefficient provided consistent performance evaluation. InceptionNetV4 + U-Net achieved the highest Dice coefficient (0.9672), demonstrating superior segmentation accuracy. Although considered state-of-the-art, Transformer + U-Net showed poor performance in MH and intraretinal cyst (IRC) segmentation. Analysis of computational resources revealed that MobileNetV2 + U-Net offered the most efficient performance with minimal parameters, while InceptionNetV4 + U-Net balanced accuracy with moderate computational demands. Our findings suggest that CNN-based backbones, particularly InceptionNetV4, are more effective than Transformer architectures for OCT image segmentation, with InceptionNetV4 + U-Net emerging as the most promising model for clinical applications.
New Challenges in Marine Aquaculture Research
Authors: Matias Ana Catarina , Andrade Carlos
Published: February 2025

Marine aquaculture has witnessed remarkable growth in recent decades, contributing significantly to global food security and economic development [...]