bluuebunny/crossref_metadata_2025 huggingface dataset:
Diagnostic Accuracy of Urine Dipsticks for Urinary Tract Infection Diagnosis during Pregnancy: A Retrospective Cohort Study
Authors: Werter Dominique E. , Schneeberger Caroline , Geerlings Suzanne E. , de Groot Christianne J. M. , Pajkrt Eva , Kazemier Brenda M.
Published: June 2024

Objective: Urinary tract infections (UTIs) represent the most prevalent infections among pregnant women. Many pregnant women experience frequent voiding or lower abdominal pain during pregnancy due to physiologic changes. Due to the possible consequences of a UTI in pregnancy, pregnant women are more often tested for UTIs. This study aimed to assess the diagnostic accuracy of dipsticks in diagnosing UTIs in pregnant women while using the urine culture as the reference standard. Study design: This was a retrospective cohort study, conducted at two academic hospitals in the Netherlands among pregnant women. Pseudonymized data were collected from patient files. The results of the urine dipstick and the urine culture in pregnant women were linked. Additionally, nitrofurantoin prescriptions were linked to culture results. A positive urine culture was considered the reference test for a UTI. Results: Between 1 January 2017 and 28 February 2021, a total of 718 urine samples with leukocyte esterase dipstick results within 24 h of the urine culture were analyzed. Of these samples, a nitrite dipstick result was also available in 337 cases. Only 6.8% of the 718 urine samples yielded positive cultures. The sensitivity and specificity of leukocyte esterase were 75.5% and 40.4%, respectively; for nitrite, 72.0% sensitivity and 73.4% specificity were found. When at least one of the two tests was positive, the sensitivity and specificity were 92.0% and 27.9%, respectively. When both tests were positive, the sensitivity and specificity were 52.0% and 82.7%, respectively. In only 16.8% of the women to whom nitrofurantoin was prescribed, the urine cultures returned positive using a cut-off of 105 colony forming units/mL. Conclusion: The diagnostic performance of leukocyte esterase, nitrite, or their combination in clinical practice is lower than previously reported in study settings among pregnant women. A significant proportion of women treated with nitrofurantoin were found to have no UTI, suggesting potential over-prescription based on dipstick test results. Healthcare providers should be aware of this reduced performance in clinical practice and carefully weigh the risks of antibiotic treatment by suspicion of a UTI against the possibility of delayed treatment awaiting culture results in individual patients.
Solving a Multi-Objective Optimization Problem of a Two-Stage Helical Gearbox with Second-Stage Double Gear Sets Using the MAIRCA Method
Authors: Vu Duc-Binh , Tran Huu-Danh , Dinh Van-Thanh , Vu Duong , Vu Ngoc-Pi , Nguyen Van-Trang
Published: June 2024

This paper provides a novel application of the multi-criteria decision-making (MCDM) method to the multi-objective optimization problem (MOOP) of creating a two-stage helical gearbox (TSHG) with second-stage double gear sets (SDGSs). The aim of the study is to determine the optimum major design components for enhancing the gearbox efficiency while reducing the gearbox volume. In this work, three primary design parameters are chosen to accomplish this: the gear ratio of the first stage and the coefficients of the wheel face width (CWFW) of the first and second stages. Additionally, the study is conducted with two distinct objectives in mind: the lowest gearbox volume and the maximum gearbox efficiency. Moreover, phase 1 and phase 2, respectively, are the two stages of the MOOP. Phase 2 handles the MOOP to identify the ideal primary design factors as well as the single-objective optimization problem to minimize the difference between the variable levels. Additionally, the Multi-Attributive Ideal–Real Comparative Analysis (MAIRCA) approach is selected to deal with the MOOP. The results of the study are utilized to determine the ideal values for three crucial design parameters in order to create a TSHG with SDGSs.
Bioequivalence Studies of Highly Variable Drugs: An Old Problem Addressed by Artificial Neural Networks
Authors: Papadopoulos Dimitris , Karali Georgia , Karalis Vangelis D.
Published: June 2024

The bioequivalence (BE) of highly variable drugs is a complex issue in the pharmaceutical industry. The impact of this variability can significantly affect the required sample size and statistical power. In order to address this issue, the EMA and FDA propose the utilization of scaled limits. This study suggests the use of generative artificial intelligence (AI) algorithms, particularly variational autoencoders (VAEs), to virtually increase sample size and therefore reduce the need for actual human subjects in the BE studies of highly variable drugs. The primary aim of this study was to show the capability of using VAEs with constant acceptance limits (80–125%) and small sample sizes to achieve high statistical power. Monte Carlo simulations, incorporating two levels of stochasticity (between-subject and within-subject), were used to synthesize the virtual population. Various scenarios focusing on high variabilities were simulated. The performance of the VAE-generated datasets was compared to the official approaches imposed by the FDA and EMA, using either the constant 80–125% limits or scaled BE limits. To demonstrate the ability of AI generative algorithms to create virtual populations, no scaling was applied to the VAE-generated datasets, only to the actual data of the comparators. Across all scenarios, the VAE-generated datasets demonstrated superior performance compared to scaled or unscaled BE approaches, even with less than half of the typically required sample size. Overall, this study proposes the use of VAEs as a method to reduce the necessity of recruiting large numbers of subjects in BE studies.
Kinematic and Dynamic Modeling of 3DOF Variable Stiffness Links Manipulator with Experimental Validation
Authors: Ramzy Mina R. , Hammad Sherif , Maged Shady A.
Published: June 2024

Variable stiffness link (VSL) manipulators are robotic arms that can adjust their link stiffness in real time to improve their adaptability and precision. They are particularly useful in industrial environments where safe collaboration with human workers is required. However, modeling and controlling these non-linear systems is a major challenge due to their complexity. This research paper presents a mathematical model for a 3DOF VSL manipulator, which is the first step towards optimizing performance, improving safety, and reducing costs. The accuracy and reliability of the model are demonstrated through verification experiments that strengthen confidence in its validity for engineering and scientific research. This study contributes to the understanding of the dynamics of VSL manipulators and provides insights for future advances in the use of such robots. By using the proposed model, the efficiency and precision of VSL manipulators can be improved while ensuring safe human–robot interaction in various industrial applications.
Pressure-Reducing Design of 3D-Printed Diabetic Shoe Midsole Utilizing Auxetic Lattice Structure
Authors: Zhang Jifa , Lu Shizhu , Lin Yinyin , Wang Yang , Yi Xiaolie , Fang Wencheng
Published: June 2024

With the global rise in the prevalence of diabetes, diabetic patients need innovative footwear designs to reduce the risk of foot ulcers. This study examined the mechanical properties of diabetic shoe midsoles featuring auxetic lattice structures. Through the construction of finite element models and simulation, this research compared the biomechanical parameter differences in the plantar regions of the metatarsal head, midfoot, and hindfoot when wearing two types of auxetic midsoles with internal angles of 60° and 75° and a non-auxetic midsole with an internal angle of 90° under both walking and running conditions. Compared to the non-auxetic midsole, the auxetic midsoles significantly reduced the peak plantar pressure and optimized the pressure distribution across various plantar regions. Notably, the auxetic 60° midsole reduced the peak plantar pressure by 19.68–55.25% and 16.19–54.39% compared to the non-auxetic 90° midsole during walking and running, respectively. This study also verified that the auxetic midsoles exhibited greater adaptability and compliance to the plantar foot shape, contributing to reductions in plantar pressure in comparisons of deformation values and plantar contact areas across the different midsoles. Auxetic midsoles manufactured using 3D printing technology have significant potential to prevent diabetic foot ulcers and maintain human foot health. This research integrates insights and techniques from materials science and ergonomics, offering a new direction for footwear design.
AraFast: Developing and Evaluating a Comprehensive Modern Standard Arabic Corpus for Enhanced Natural Language Processing
Authors: Alrayzah Asmaa , Alsolami Fawaz , Saleh Mostafa
Published: June 2024

The research presented in the following paper focuses on the effectiveness of a modern standard Arabic corpus, AraFast, in training transformer models for natural language processing tasks, particularly in Arabic. In the study described herein, four experiments were conducted to evaluate the use of AraFast across different configurations: segmented, unsegmented, and mini versions. The main outcomes of the present study are as follows: Transformer models trained with larger and cleaner versions of AraFast, especially in question-answering, indicate the impact of corpus quality and size on model efficacy. Secondly, a dramatic reduction in training loss was observed with the mini version of AraFast, underscoring the importance of optimizing corpus size for effective training. Moreover, the segmented text format led to a decrease in training loss, highlighting segmentation as a beneficial strategy in Arabic NLP. In addition, using the study findings, challenges in managing noisy data derived from web sources are identified, which were found to significantly hinder model performance. These findings collectively demonstrate the critical role of well-prepared, segmented, and clean corpora in advancing Arabic NLP capabilities. The insights from AraFast’s application can guide the development of more efficient NLP models and suggest directions for future research in enhancing Arabic language processing tools.
Applicability of Digital Image Photogrammetry to Rapid Displacement Measurements of Structures in Restricted-Access and Controlled Areas: Case Study in Korea
Authors: Choi Chang-Hwan , Han Jung-Geun , Hong Gigwon
Published: June 2024

Critical facilities are generally located in areas with restricted or controlled access, making it difficult for experts to monitor the structural health of enclosed infrastructures. Hence, a case study was conducted in South Korea to evaluate the applicability of digital image photogrammetry using commercial imaging devices in order to quickly measure the structural deformations of infrastructures in such areas. The applicability evaluation involved measuring the displacement of mechanically stabilized earth (MSE) walls. In the experiment, the displacement of MSE walls was first measured using the traditional monitoring method and the results were compared with those obtained via digital image photogrammetry using commercial imaging devices such as digital cameras and cellphones. The measurement results obtained with the cellphone camera had a maximum error of approximately 20 mm when compared with the results of the traditional monitoring method. Because this is a significant error, even when considering the mechanical error in the traditional monitoring method’s result, it was determined that monitoring using a cellphone camera is infeasible. However, the experimental results of digital image photogrammetry using a digital camera showed a maximum error of approximately 9 mm. Although this is a sizable error, the method was assessed to be technically feasible.
Design and Implementation of a Two-Wheeled Self-Balancing Car Using a Fuzzy Kalman Filter
Authors: Ma Yimin , Meng Fanhao , Xiong Shuangshuang
Published: June 2024

To improve the upright balancing performance of the two-wheeled self-balancing car, this paper proposes an attitude estimation algorithm based on fuzzy Kalman filtering. Fuzzy logic is used to correct the inclination angle and angular velocity of the two-wheeled self-balancing car, thereby optimizing the state of the Kalman filter and ultimately improving the balancing performance of the car. This paper combines dual closed-loop PID control with the complementary filtering algorithm, Kalman filtering algorithm, and fuzzy Kalman filtering algorithm to conduct experiments on a physical two-wheeled self-balancing car. The experimental results validate the superiority of the fuzzy Kalman filtering algorithm proposed in this paper for improving the upright balancing performance of the two-wheeled self-balancing car.
AtomGID: An Atomic Gesture Identifier for Qualitative Spatial Reasoning
Authors: Bouchard Kevin , Bouchard Bruno
Published: June 2024

In this paper, we present a novel non-deep-learning-based approach for real-time object tracking and activity recognition within smart homes, aiming to minimize human intervention and dataset requirements. Our method utilizes discreet, easily concealable sensors and passive RFID technology to track objects in real-time, enabling precise activity recognition without the need for extensive datasets typically associated with deep learning techniques. Central to our approach is AtomGID, an algorithm tailored to extract highly generalizable spatial features from RFID data. Notably, AtomGID’s adaptability extends beyond RFID to other imprecise tracking technologies like Bluetooth beacons and radars. We validate AtomGID through simulation and real-world RFID data collection within a functioning smart home environment. To enhance recognition accuracy, we employ a clustering adaptation of the flocking algorithm, leveraging previously published Activities of Daily Living (ADLs) data. Our classifier achieves a robust classification rate ranging from 85% to 93%, underscoring the efficacy of our approach in accurately identifying activities. By prioritizing non-deep-learning techniques and harnessing the strengths of passive RFID technology, our method offers a pragmatic and scalable solution for activity recognition in smart homes, significantly reducing dataset dependencies and human intervention requirements.
Oxygen Self-Diffusion in Fluorite High Entropy Oxides
Authors: Chroneos Alexander
Published: June 2024

High-entropy oxides have recently attracted the interest of the community as a way of attuning the properties of oxides to energy applications. Here, we employ molecular dynamics simulations combined with empirical pair potential models to examine the predicted oxygen diffusivity of fluorite-structured high-entropy oxides. We show that lower levels of the dopants increase the overall diffusivity of the composition, but not to the levels of diffusion seen in yttria-doped zirconia. We attribute this to an increased resistance of the cation sublattice to the distortion that occurs through any multiple substitutions on the cation sublattice. To conclude, it is calculated that oxygen self-diffusion in high-entropy oxides is suppressed as compared to isostructural ternary oxides.
Dynamic Link Metric Selection for Traffic Aggregation and Multipath Transmission in Software-Defined Networks
Authors: Rzym Grzegorz , Duliński Zbigniew , Chołda Piotr
Published: June 2024

Software-defined networks (SDNs) are expanding their presence beyond laboratories, campus networks, ISPs, and data centre networks, moving into various domains. Although originally designed for campus networks, SDNs face scalability challenges, especially with the use of OpenFlow. Addressing these challenges requires innovative traffic management mechanisms to efficiently handle the growing number of connected devices and the increasing volume of traffic from various types of applications. This article proposes an innovative method for link weight selection that incorporates multipath transmission and flow aggregation in the SDNs. This novel approach improves resource utilization in two key ways. First, it involves the preservation of bandwidth during congestion. Second, it minimizes internal resource usage, as illustrated by a reduction in the number of table entries in switches. Resources undergo optimization through the introduction of a novel mechanism for flow aggregation. This novel mechanism, coupled with multipath transmission, enables adaptive responses to dynamic changes in network conditions. The aggregation process leads to a reduced number of flow entries in the core switches compared to the conventional operation of OpenFlow. The proposed scenarios for link weight allocation allow for a reduction in the number of entries in the core switches by up to 99%. The application of the proposed method also results in an increase of 58% in traffic transmission.
Study of Diagnostic Accuracy: Fundus Photography vs. Optical Coherence Tomography
Authors: Moriche Carretero Manuel , Sánchez Parejo Ana de los Reyes , Martínez Pérez Clara , Revilla Amores Remedios , Pérez Gómez Ángel , Biarnés Pérez Marc
Published: June 2024

(1) Background: This study aimed to determine the diagnostic accuracy that optical coherence tomography (OCT) can add to fundus photography (FP) in assessing the condition of the retinal posterior pole. (2) Methods: We conducted two blocks of analysis: First, the posterior pole of each eye was examined using an FP non-mydriatic imaging device. Second, OCT was used in addition to FP. After consolidating the specific diagnostic criteria, the assessments were evaluated using two blinded independent investigation groups (by optometrists, and by ophthalmologists who were considered the gold standard). (3) Results: We calculated the diagnostic accuracy of FP compared to OCT and found that they had similar sensitivity. FP had a slightly higher specificity (p-value: 0.01), and OCT had a higher kappa coefficient with 0.50 (95% CI: 0.46–0.55) vs. 0.39 (95% CI: 0.34–0.45) for FP. (4) Conclusions: On the basis that the role of the optometrist in Spain is not to diagnose but to detect lesions and refer patients to an ophthalmologist, the results of this study support the use of OCT, which provided gradable images in almost all examined eyes (97.5%), compared to FP (73.5%). However, optometrists need a detailed and standardized guide in order to conduct evaluations according to the ophthalmologist’s criteria.
Expanding the Psychological Domain of Technological Acceptance: The Use of Smart Wearable Devices in Leisure
Authors: Su Jie , Sun Xiaodong , Wang Junhui
Published: June 2024

This study introduces a new integrated framework by incorporating the Unified Theory of Acceptance and Use of Technology as the technological acceptance component, combined with satisfaction and perceived risk as the psychological acceptance components. The aim is to explore consumers’ behavioral intentions toward smart wearable devices and their expectations for the industry. The findings reveal that all research hypotheses were met as anticipated. Notably, the relatively low influence of Facilitating Conditions within the technological acceptance part and the prominent Perceived Functional Value within the psychological acceptance part highlight consumers’ concerns about the compatibility of smart wearable device ecosystems and their satisfaction with basic functionalities. This conclusion indicates that the market needs to address and improve the challenges posed by multi-brand product ecosystems and that addressing the lack of innovation reduces the emotional connection between products and consumers.
Hermite–Hadamard–Mercer-Type Inequalities for Three-Times Differentiable Functions
Authors: Ciurdariu Loredana , Grecu Eugenia
Published: June 2024

In this study, an integral identity is given in order to present some Hermite–Hadamard–Mercer-type inequalities for functions whose powers of the absolute values of the third derivatives are convex. Several consequences and three applications to special means are given, as well as four examples with graphics which illustrate the validity of the results. Moreover, several Hermite–Hadamard–Mercer-type inequalities for fractional integrals for functions whose powers of the absolute values of the third derivatives are convex are presented.
Predicting the Potential Distribution of Hypericum perforatum under Climate Change Scenarios Using a Maximum Entropy Model
Authors: Hao Yulan , Dong Pengbin , Wang Liyang , Ke Xiao , Hao Xiaofeng , He Gang , Chen Yuan , Guo Fengxia
Published: June 2024

H. perforatum, as one of the Traditional Chinese Medicinal materials, possesses a variety of pharmacological activities and high medicinal value. However, in recent years, the wild resources of H. perforatum have been severely depleted due to global climate change and human activities, and artificial cultivation faces problems such as unstable yield and active ingredient content. This poses a serious obstacle to the development and utilization of its resources. Therefore, this experiment took H. perforatum as the research object and used 894 distribution records of H. perforatum and 36 climatic environmental factors, using the MaxEnt model and GIS technology to explore the main climatic factors affecting the distribution of H. perforatum. Additionally, by utilizing the principles of ecological niche theory, the potential suitable distribution regions of H. perforatum across past, present, and future timelines were predicted, which can ascertain the dynamics of its spatial distribution patterns and the trend of centroid migration. The results indicate that the main environmental factors affecting the geographical distribution of H. perforatum are solar radiation in April (Srad4), solar radiation in September (Srad9), mean temperature of driest quarter (Bio9), solar radiation in November (Srad11), annual mean temperature (Bio1), and annual precipitation (Bio12). Under future climate scenarios, there is a remarkable trend of expansion in the suitable distribution areas of H. perforatum. The centroid migration indicates a trend of migration towards the northwest direction and high-altitude areas. These results can provide a scientific basis for formulating conservation and sustainable use management strategies for H. perforatum resources.
Deep Learning and Neural Architecture Search for Optimizing Binary Neural Network Image Super Resolution
Authors: Su Yuanxin , Ang Li-minn , Seng Kah Phooi , Smith Jeremy
Published: June 2024

The evolution of super-resolution (SR) technology has seen significant advancements through the adoption of deep learning methods. However, the deployment of such models by resource-constrained devices necessitates models that not only perform efficiently, but also conserve computational resources. Binary neural networks (BNNs) offer a promising solution by minimizing the data precision to binary levels, thus reducing the computational complexity and memory requirements. However, for BNNs, an effective architecture is essential due to their inherent limitations in representing information. Designing such architectures traditionally requires extensive computational resources and time. With the advancement in neural architecture search (NAS), differentiable NAS has emerged as an attractive solution for efficiently crafting network structures. In this paper, we introduce a novel and efficient binary network search method tailored for image super-resolution tasks. We adapt the search space specifically for super resolution to ensure it is optimally suited for the requirements of such tasks. Furthermore, we incorporate Libra Parameter Binarization (Libra-PB) to maximize information retention during forward propagation. Our experimental results demonstrate that the network structures generated by our method require only a third of the parameters, compared to conventional methods, and yet deliver comparable performance.
Energy Self-Sufficiency of Smaller Rural Centers: Experimental Approaches
Authors: Nicolini Elvira
Published: June 2024

Inland areas have been affected by demographic and economic decline over the past decades. New economic models, which are more focused on a humane quality of life, encourage a revival of these territories as newer, healthier places for living. This paper focuses on minor centers, rethought as energy communities and how these can sustain themselves and become new places of living. The first part of the research critically analyzes current strategies of SECAPs (Sustainable Energy and Climate Action Plans) in smaller historic urban centers. The second part of the paper starts with the typological, morphological, and technological interscalar analysis of two case studies, testing a repeatable expeditious knowledge collection and an intervention method on them. For urban environments, the hypothesized interventions include the management of energy production from renewable sources that are compatible with the presence and value of urban and built heritage; concerning rural territories, an agro-energy park is proposed. The document aims to provide a repeatable method for planning strategic actions within SECAPs in smaller urban centers with a high historical connotation. The case studies show that energy self-sufficiency can be an opportunity to valorize the urban center while favoring environmental sustainability and local development.
Molecular Profiling and the Interaction of Somatic Mutations with Transcriptomic Profiles in Non-Melanoma Skin Cancer (NMSC) in a Population Exposed to Arsenic
Authors: Jasmine Farzana , Argos Maria , Khamkevych Yuliia , Islam Tariqul , Rakibuz-Zaman Muhammad , Shahriar Mohammad , Shea Christopher R. , Ahsan Habibul , Kibriya Muhammad G.
Published: June 2024

Exposure to inorganic arsenic (As) is recognized as a risk factor for non-melanoma skin cancer (NMSC). We followed up with 7000 adults for 6 years who were exposed to As. During follow-up, 2.2% of the males and 1.3% of the females developed basal cell carcinoma (BCC), while 0.4% of the male and 0.2% of the female participants developed squamous cell carcinoma (SCC). Using a panel of more than 400 cancer-related genes, we detected somatic mutations (SMs) in the first 32 NMSC samples (BCC = 26 and SCC = 6) by comparing paired (tissue–blood) samples from the same individual and then comparing them to the SM in healthy skin tissue from 16 participants. We identified (a) a list of NMSC-associated SMs, (b) SMs present in both NMSC and healthy skin, and (c) SMs found only in healthy skin. We also demonstrate that the presence of non-synonymous SMs in the top mutated genes (like PTCH1, NOTCH1, SYNE1, PKHD1 in BCC and TP53 in SCC) significantly affects the magnitude of differential expressions of major genes and gene pathways (basal cell carcinoma pathways, NOTCH signaling, IL-17 signaling, p53 signaling, Wnt signaling pathway). These findings may help select groups of patients for targeted therapy, like hedgehog signaling inhibitors, IL17 inhibitors, etc., in the future.
Combination Therapy Approach to Overcome the Resistance to PI3K Pathway Inhibitors in Gynecological Cancers
Authors: Ibanez Kristen R. , Huang Tzu-Ting , Lee Jung-Min
Published: June 2024

The PI3K signaling pathway plays an essential role in cancer cell proliferation and survival. PI3K pathway inhibitors are now FDA-approved as a single agent treatment or in combination for solid tumors such as renal cell carcinoma or breast cancer. However, despite the high prevalence of PI3K pathway alterations in gynecological cancers and promising preclinical activity in endometrial and ovarian cancer models, PI3K pathway inhibitors showed limited clinical activity in gynecological cancers. In this review, we provide an overview on resistance mechanisms against PI3K pathway inhibitors that limit their use in gynecological malignancies, including genetic alterations that reactivate the PI3K pathway such as PIK3CA mutations and PTEN loss, compensatory signaling pathway activation, and feedback loops causing the reactivation of the PI3K signaling pathway. We also discuss the successes and limitations of recent clinical trials aiming to address such resistance mechanisms through combination therapies.
Off-the-Shelf Cord-Blood Mesenchymal Stromal Cells: Production, Quality Control, and Clinical Use
Authors: Montemurro Tiziana , Lavazza Cristiana , Montelatici Elisa , Budelli Silvia , La Rosa Salvatore , Barilani Mario , Mei Cecilia , Manzini Paolo , Ratti Ilaria , Cimoni Silvia , Brasca Manuela , Prati Daniele , Saporiti Giorgia , Astori Giuseppe , Elice Francesca , Giordano Rosaria , Lazzari Lorenza
Published: June 2024

Background Recently, mesenchymal stromal cells (MSCs) have gained recognition for their clinical utility in transplantation to induce tolerance and to improve/replace pharmacological immunosuppression. Cord blood (CB)-derived MSCs are particularly attractive for their immunological naivety and peculiar anti-inflammatory and anti-apoptotic properties. Objectives: The objective of this study was to obtain an inventory of CB MSCs able to support large-scale advanced therapy medicinal product (ATMP)-based clinical trials. Study design: We isolated MSCs by plastic adherence in a GMP-compliant culture system. We established a well-characterized master cell bank and expanded a working cell bank to generate batches of finished MSC(CB) products certified for clinical use. The MSC(CB) produced by our facility was used in approved clinical trials or for therapeutic use, following single-patient authorization as an immune-suppressant agent. Results: We show the feasibility of a well-defined MSC manufacturing process and describe the main indications for which the MSCs were employed. We delve into a regulatory framework governing advanced therapy medicinal products (ATMPs), emphasizing the need of stringent quality control and safety assessments. From March 2012 to June 2023, 263 of our Good Manufacturing Practice (GMP)-certified MSC(CB) preparations were administered as ATMPs in 40 subjects affected by Graft-vs.-Host Disease, nephrotic syndrome, or bronco-pulmonary dysplasia of the newborn. There was no infusion-related adverse event. No patient experienced any grade toxicity. Encouraging preliminary outcome results were reported. Clinical response was registered in the majority of patients treated under therapeutic use authorization. Conclusions: Our 10 years of experience with MSC(CB) described here provides valuable insights into the use of this innovative cell product in immune-mediated diseases.
Blockade of Sialylation with Decrease in Polysialic Acid Levels Counteracts Transforming Growth Factor β1-Induced Skin Fibroblast-to-Myofibroblast Transition
Authors: Fioretto Bianca Saveria , Rosa Irene , Tani Alessia , Andreucci Elena , Romano Eloisa , Sgambati Eleonora , Manetti Mirko
Published: June 2024

Aberrant sialylation with overexpression of the homopolymeric glycan polysialic acid (polySia) was recently reported in fibroblasts from fibrotic skin lesions. Yet, whether such a rise in polySia levels or sialylation in general may be functionally implicated in profibrotic activation of fibroblasts and their transition to myofibroblasts remains unknown. Therefore, we herein explored whether inhibition of sialylation could interfere with the process of skin fibroblast-to-myofibroblast transition induced by the master profibrotic mediator transforming growth factor β1 (TGFβ1). Adult human skin fibroblasts were pretreated with the competitive pan-sialyltransferase inhibitor 3-Fax-peracetyl-Neu5Ac (3-Fax) before stimulation with recombinant human TGFβ1, and then analyzed for polySia expression, cell viability, proliferation, migratory ability, and acquisition of myofibroblast-like morphofunctional features. Skin fibroblast stimulation with TGFβ1 resulted in overexpression of polySia, which was effectively blunted by 3-Fax pre-administration. Pretreatment with 3-Fax efficiently lessened TGFβ1-induced skin fibroblast proliferation, migration, changes in cell morphology, and phenotypic and functional differentiation into myofibroblasts, as testified by a significant reduction in FAP, ACTA2, COL1A1, COL1A2, and FN1 gene expression, and α-smooth muscle actin, N-cadherin, COL1A1, and FN-EDA protein levels, as well as a reduced contractile capability. Moreover, skin fibroblasts pre-administered with 3-Fax displayed a significant decrease in Smad3-dependent canonical TGFβ1 signaling. Collectively, our in vitro findings demonstrate for the first time that aberrant sialylation with increased polySia levels has a functional role in skin fibroblast-to-myofibroblast transition and suggest that competitive sialyltransferase inhibition might offer new therapeutic opportunities against skin fibrosis.
68Ga-PSMA PET/CT in Recurrent Prostate Cancer after Radical Prostatectomy Using PSMA-RADS Version 2.0
Authors: Masselli Gabriele , Sollaku Saadi , De Angelis Cristina , Polettini Elisabetta , Gualdi Gianfranco , Casciani Emanuele
Published: June 2024

Background: 68Ga-PSMA PET/CT is superior to standard-of-care imaging for detecting regional and distant metastatic recurrent prostate cancer. The objective of our study was to evaluate the performance of 68Ga-PSMAPET/CT in our patient population, using the new PSMA-RADS version 2.0. Methods: A total of 128 patients scanned with 68Ga-PSMA PET/CT for detection of recurrence after RP were analyzed with PSMA-RADS version 2.0. For the analysis of the detection rate, categories PSMA-RADS 3 to 5 were considered as “positive for malignancy” and 1–2 as “negative”. Results: According to PSMA-RADS v2.0, we classified patients as follows: 23 patients without PSMA-RADS because they were negative; PSMA-RADS 1: 10 patients; PSMA-RADS 2: 4 patients; PSMA-RADS 3A: 11 patients; PSMA-RADS 3B: 2 patients; PSMA-RADS 3C: 2 patients; PSMA-RADS 3D: 2 patients; PSMA-RADS 4: 13 patients; PSMA-RADS 5: 61 patients. Conclusions: The overall detection rate of 68Ga-PSMA PET/CT was 71%. By dividing the patients into fourgroups according to PSA level before examination, we obtained the following detection rates: PSA < 0.2 ng/mL 38%; 0.2 ≤ PSA < 0.5 ng/mL 57%; 0.5 ≤ PSA ≤ 1 ng/mL 77%; and PSA > 1 ng/mL 95%. Conclusion: Using PSMA-RADS version 2.0, we obtained detection rate values comparable with recent literature both in absolute terms and in relation to different PSA levels.
Involvement of Sclera in Lattice Retinal Degeneration: An Optical Coherence Tomography Study
Authors: Maltsev Dmitrii S. , Kulikov Alexey N. , Burnasheva Maria A. , Vasiliev Alexander S. , Kalinicheva Yana A. , Kazak Alina A.
Published: June 2024

The aim of the study was to evaluate the local status of the sclera in lattice retinal degeneration. Patients with lattice degeneration, snail-track degeneration, or horseshoe retinal breaks were included. One lesion of a single eye in each patient was captured with cross-sectional optical coherence tomography (OCT) along and across the greatest lesion dimension. The maximum height of scleral indentation was measured and compared between different lesion types and between lattice lesions with and without retinal breakage or local detachment. The correlation between the maximum height of the scleral indentation of lattice lesions and the age of the patients was calculated. Seventy-five eyes of 75 patients (44.4 ± 14.7 years; 35 males and 30 females) were included. OCT showed variable local scleral indentation in 52 out of 55 (94.5%) lattice lesions, in five out of nine (55.5%) snail-tack lesions, and in three out of eleven (27.3%) horseshoe breaks. The maximum scleral indentation within lattice lesions, snail-tack lesions, and horseshoe breaks was 227.2 ± 111.3, 22.0 ± 49.2, and 88.5 ± 48.4 µm, respectively (p < 0.001 for snail-tack lesions and horseshoe breaks compared to lattice lesions). Lattice lesions with retinal breaks and/or local retinal detachment had statistically significantly lower scleral indentation than those without (p = 0.01). The height of the scleral indentation of lattice lesions was positively correlated with patient age (r = 0.51, p = 0.03). In conclusion, scleral indentation is one of the hallmarks of lattice retinal degeneration and may be associated with a reduced risk of rhegmatogenous retinal detachment.
Long-Term Results of Surgical Treatment for Popliteal Artery Entrapment Syndrome
Authors: Mansour Wassim , Miceli Francesca , Di Girolamo Alessia , Dajci Ada , Marzano Antonio , Cangiano Rocco , Ascione Marta , di Marzo Luca
Published: June 2024

Introduction: Popliteal artery entrapment syndrome (PAES) is a rare disease of the lower limbs, mainly affecting young patients, due to extrinsic compression of the neurovascular bundle at the popliteal fossa. The aim of this study was to describe our experience during a median 15-year period. Methods: Patients treated for PAES in our institution from 1979 to 2024 were included. Preoperative, intraoperative, and postoperative data were analyzed. Results: A total of 47 patients with a total of 78 limbs were treated. Duplex ultrasound with active maneuvers was performed in all limbs (100%). Angiography was performed in almost all patients (97.4%), computed tomography angiography in 56 (71.8%), and magnetic resonance angiography in 22 (28.2%). Concerning surgical treatment, musculotendinous section was performed in 60 limbs (76.9%), and autologous venous bypass was achieved in 18 limbs (23.1%). The rates for freedom from target lesion revascularization—meaning that no significant stenosis or occlusion during follow-up required revascularization—and 15-year primary patency were 92.4% and 98%, respectively. Conclusion: Long-term results of surgical treatment for PAES seem to be very satisfying. Myotomy with or without arterial reconstruction using venous bypass can lead to good patency at 15 years of follow-up.
Impact of Al Alloying/Doping on the Performance Optimization of HfO2-Based RRAM
Authors: He Huikai , Yuan Xiaobo , Wu Wenhao , Lee Choonghyun , Zhao Yi , Liu Zongfang
Published: June 2024

Al alloying/doping in HfO2-based resistive random-access memory (RRAM) has been proven to be an effective method for improving the low-resistance state (LRS) retention. However, a detailed understanding of Al concentration on oxygen vacancy migration and resistive switching (RS) behaviors still needs to be included. Herein, the impact of Al concentration on the RS properties of the TiN/Ti/HfAlO/TiN RRAM devices is addressed. Firstly, it is found that the forming voltage, SET voltage, and RESET voltage can be regulated by varying the Al doping concentration. Moreover, we have demonstrated that the device with 15% Al shows the minimum cycle-to-cycle variability (CCV) and superior endurance (over 106). According to density-functional theory (DFT) calculations, it is found that the increased operation voltage, improved uniformity, and improved endurance are attributed to the elevated migration barrier of oxygen vacancy through Al doping. In addition, LRS retention characteristics of the TiN/Ti/HfAlO/TiN devices with different Al concentrations are compared. It is observed that the LRS retention is greatly enhanced due to the suppressed lateral diffusion process of oxygen vacancy through Al doping. This study demonstrates that Al alloying/doping greatly affects the RS behaviors of HfO2-based RRAM and provides a feasible way to improve the RS properties through changing the Al concentration.
Evaluation of Two Digital Wound Area Measurement Methods Using a Non-Randomized, Single-Center, Controlled Clinical Trial
Authors: Casanova-Lozano Lorena , Reifs-Jiménez David , Martí-Ejarque Maria , Reig-Bolaño Ramon , Grau-Carrión Sergi
Published: June 2024

A prospective, single-center, non-randomized, pre-marketing clinical investigation was conducted with a single group of subjects to collect skin lesion images. These images were subsequently utilized to compare the results obtained from a traditional method of wound size measurement with two novel methods developed using Machine Learning (ML) approaches. Both proposed methods automatically calculate the wound area from an image. One method employs a two-dimensional system with the assistance of an external calibrator, while the other utilizes an Augmented Reality (AR) system, eliminating the need for a physical calibration object. To validate the correlation between these methods, a gold standard measurement with digital planimetry was employed. A total of 67 wound images were obtained from 41 patients between 22 November 2022 and 10 February 2023. The conducted pre-marketing clinical investigation demonstrated that the ML algorithms are safe for both the intended user and the intended target population. They exhibit a high correlation with the gold standard method and are more accurate than traditional methods. Additionally, they meet the manufacturer’s expected use. The study validated the performance, safety, and usability of the implemented methods as a valuable tool in the measurement of skin lesions.
Game-Theory-Based Design and Analysis of a Peer-to-Peer Energy Exchange System between Multi-Solar-Hydrogen-Battery Storage Electric Vehicle Charging Stations
Authors: Duan Lijia , Yuan Yujie , Taylor Gareth , Lai Chun Sing
Published: June 2024

As subsidies for renewable energy are progressively reduced worldwide, electric vehicle charging stations (EVCSs) powered by renewable energy must adopt market-driven approaches to stay competitive. The unpredictable nature of renewable energy production poses major challenges for strategic planning. To tackle the uncertainties stemming from forecast inaccuracies of renewable energy, this study introduces a peer-to-peer (P2P) energy trading strategy based on game theory for solar-hydrogen-battery storage electric vehicle charging stations (SHS-EVCSs). Firstly, the incorporation of prediction errors in renewable energy forecasts within four SHS-EVCSs enhances the resilience and efficiency of energy management. Secondly, employing game theory’s optimization principles, this work presents a day-ahead P2P interactive energy trading model specifically designed for mitigating the variability issues associated with renewable energy sources. Thirdly, the model is converted into a mixed integer linear programming (MILP) problem through dual theory, allowing for resolution via CPLEX optimization techniques. Case study results demonstrate that the method not only increases SHS-EVCS revenue by up to 24.6% through P2P transactions but also helps manage operational and maintenance expenses, contributing to the growth of the renewable energy sector.
Geometry Optimization of Stratospheric Pseudolite Network for Navigation Applications
Authors: Qu Yi , Wang Sheng , Feng Hui , Liu Qiang
Published: June 2024

A stratospheric pseudolite (SP) is a pseudolite installed on a stratospheric airship. A stratospheric pseudolite network (SPN) is composed of multiple SPs, which shows promising potential in navigation applications because of its station-keeping capability, long service duration, and flexible deployment. Most traditional research about SPN geometry optimization has centered on geometric dilution of precision (GDOP). However, previous research rarely dealt with the topic of how SPN geometry configuration not only affects its GDOP, but also affects its energy balance. To obtain an optimal integrated performance, this paper employs the proportion of energy consumption in energy production as an indicator to assess SPN energy status and designs a composite indicator including GDOP and energy status to assess SPN geometry performance. Then, this paper proposes an SPN geometry optimization algorithm based on gray wolf optimization. Furthermore, this paper implements a series of simulations with an SPN composed of six SPs in a specific service area. Simulations show that the proposed algorithm can obtain SPN geometry solutions with good GDOP and energy balance performance. Also, simulations show that in the supposed scenarios and the specific area, a higher SP altitude can improve both GDOP and energy balance, while a lower SP latitude can improve SPN energy status.
An Approach to Deepfake Video Detection Based on ACO-PSO Features and Deep Learning
Authors: Alhaji Hanan Saleh , Celik Yuksel , Goel Sanjay
Published: June 2024

The rapid advancement of deepfake technology presents significant challenges in detecting highly convincing fake videos, posing risks such as misinformation, identity theft, and privacy violations. In response, this paper proposes an innovative approach to deepfake video detection by integrating features derived from ant colony optimization–particle swarm optimization (ACO-PSO) and deep learning techniques. The proposed methodology leverages ACO-PSO features and deep learning models to enhance detection accuracy and robustness. Features from ACO-PSO are extracted from the spatial and temporal characteristics of video frames, capturing subtle patterns indicative of deepfake manipulation. These features are then used to train a deep learning classifier to automatically distinguish between authentic and deepfake videos. Extensive experiments using comparative datasets demonstrate the superiority of the proposed method in terms of detection accuracy, robustness to manipulation techniques, and generalization to unseen data. The computational efficiency of the approach is also analyzed, highlighting its practical feasibility for real-time applications. The findings revealed that the proposed method achieved an accuracy of 98.91% and an F1 score of 99.12%, indicating remarkable success in deepfake detection. The integration of ACO-PSO features and deep learning enables comprehensive analysis, bolstering precision and resilience in detecting deepfake content. This approach addresses the challenges involved in facial forgery detection and contributes to safeguarding digital media integrity amid misinformation and manipulation.
Mitigation of Adversarial Attacks in 5G Networks with a Robust Intrusion Detection System Based on Extremely Randomized Trees and Infinite Feature Selection
Authors: Baldini Gianmarco
Published: June 2024

Intrusion Detection Systems (IDSs) are an important tool to mitigate cybersecurity threats in the ICT infrastructures. Preferable properties of the IDSs are the optimization of the attack detection accuracy and the minimization of the computing resources and time. A signification portion of IDSs presented in the research literature is based on Machine Learning (ML) and Deep Learning (DL) elements, but they may be prone to adversarial attacks, which may undermine the overall performance of the IDS algorithm. This paper proposes a novel IDS focused on the detection of cybersecurity attacks in 5G networks, which addresses in a simple but effective way two specific adversarial attacks: (1) tampering of the labeled set used to train the ML algorithm, (2) modification of the features in the training data set. The approach is based on the combination of two algorithms, which have been introduced recently in the research literature. The first algorithm is the Extremely Randomized Tree (ERT) algorithm, which enhances the capability of Decision Tree (DT) and Random Forest (RF) algorithms to perform classification in data sets, which are unbalanced and of large size as IDS data sets usually are (legitimate traffic messages are more numerous than attack related messages). The second algorithm is the recently introduced Infinite Feature Selection algorithm, which is used to optimize the choice of the hyper-parameter defined in the approach and improve the overall computing efficiency. The result of the application of the proposed approach on a recently published 5G IDS data set proves its robustness against adversarial attacks with different degrees of severity calculated as the percentage of the tampered data set samples.