bluuebunny/crossref_metadata_2025 huggingface dataset:
Bioinformatic Analysis and Machine Learning Methods in Neonatal Sepsis: Identification of Biomarkers and Immune Infiltration
Authors: Jiang Zhou , Luo Yujia , Wei Li , Gu Rui , Zhang Xuandong , Zhou Yuanyuan , Zhang Songying
Published: June 2023

The disease neonatal sepsis (NS) poses a serious threat to life, and its pathogenesis remains unclear. Using the Gene Expression Omnibus (GEO) database, differentially expressed genes (DEGs) were identified and functional enrichment analyses were conducted. Three machine learning algorithms containing the least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) were applied to identify the optimal feature genes (OFGs). This study conducted CIBERSORT to present the abundance of immune infiltrates between septic and control neonates and assessed the relationship between OFGs and immune cells. In total, 44 DEGs were discovered between the septic and control newborns. Throughout the enrichment analysis, DEGs were primarily related to inflammatory signaling pathways and immune responses. The OFGs derived from machine learning algorithms were intersected to yield four biomarkers, namely Hexokinase 3 (HK3), Cystatin 7 (CST7), Resistin (RETN), and Glycogenin 1 (GYG1). The potential biomarkers were validated in other datasets and LPS-stimulated HEUVCs. Septic infants showed a higher proportion of neutrophils (p < 0.001), M0 macrophages (p < 0.001), and regulatory T cells (p = 0.004). HK3, CST7, RETN, and GYG1 showed significant correlations with immune cells. Overall, the biomarkers offered promising insights into the molecular mechanisms of immune regulation for the prediction and treatment of NS.
Insights into Healthcare Professionals’ Perceptions and Attitudes toward Nanotechnological Device Application: What Is the Current Situation in Glioblastoma Research?
Authors: Ragucci Federica , Sireci Francesca , Cavallieri Francesco , Rossi Jessica , Biagini Giuseppe , Tosi Giovanni , Lucchi Chiara , Molina-Pena Rodolfo , Ferreira Natalia Helen , Zarur Mariana , Ferreiros Alba , Bourgeois William , Berger François , Abal Miguel , Rousseau Audrey , Boury Frank , Alvarez-Lorenzo Carmen , Garcion Emmanuel , Pisanello Anna , Pavesi Giacomo , Iaccarino Corrado , Ghirotto Luca , Bassi Maria Chiara , Valzania Franco
Published: June 2023

Nanotechnology application in cancer treatment is promising and is likely to quickly spread worldwide in the near future. To date, most scientific studies on nanomaterial development have focused on deepening the attitudes of end users and experts, leaving clinical practice implications unexplored. Neuro-oncology might be a promising field for the application of nanotechnologies, especially for malignant brain tumors with a low-survival rate such as glioblastoma (GBM). As to improving patients’ quality of life and life expectancy, innovative treatments are worth being explored. Indeed, it is important to explore clinicians’ intention to use experimental technologies in clinical practice. In the present study, we conducted an exploratory review of the literature about healthcare workers’ knowledge and personal opinions toward nanomedicine. Our search (i) gives evidence for disagreement between self-reported and factual knowledge about nanomedicine and (ii) suggests the internet and television as main sources of information about current trends in nanomedicine applications, over scientific journals and formal education. Current models of risk assessment suggest time-saving cognitive and affective shortcuts, i.e., heuristics support both laypeople and experts in the decision-making process under uncertainty, whereas they might be a source of error. Whether the knowledge is poor, heuristics are more likely to occur and thus clinicians’ opinions and perspectives toward new technologies might be biased.
Design and Evaluation of Composite Magnetic Iron–Platinum Nanowires for Targeted Cancer Nanomedicine
Authors: Nana Abu Bakr , Marimuthu Thashree , Wamwangi Daniel , Kondiah Pierre P. D. , Choonara Yahya E.
Published: June 2023

The purpose of the study was to synthesize and investigate the influence of geometrical structure, magnetism, and cytotoxic activity on core–shell platinum and iron–platinum (Fe/Pt) composite nanowires (NWs) for potential application in targeted chemotherapeutic approaches. The Pt-NWs and Fe/Pt composite NWs were synthesized via template electrodeposition, using anodic aluminum oxide (AAO) membranes. The Fe/Pt composite NWs (Method 1) was synthesized using two electrodeposition steps, allowing for greater control of the diameter of the NW core. The Fe/Pt composite NWs (Method 2) was synthesized by pulsed electrodeposition, using a single electrolytic bath. The properties of the synthesized NWs were assessed by high-resolution transmission electron microscopy (HRTEM), Raman spectroscopy, powder X-ray diffraction (XRD), inductively coupled plasma–optical emission spectrometry (ICP-OES), vibrating-sample magnetometry (VSM), and surface charge (zeta potential). A microscopy image analysis of the NWs revealed the presence of high-aspect-ratio NWs with nominal diameters of 40–50 nm and lengths of approximately <4 µm. The obtained powder XRD patterns confirmed the presence of a polycrystalline structure for both Pt NWs and Fe/Pt composite NWs. The potential utility of the synthesized NW nanoplatforms for anticancer activity was investigated using Tera 1 cells and Mouse 3T3 cells. Pt-NWs displayed modest cytotoxic activity against Tera 1 cells, while the Fe/Pt composite NWs (both Methods 1 and 2) demonstrated enhanced cytotoxic activity compared to the Pt-NWs on Tera 1 cells. The Fe/Pt composite NWs (Method 1) displayed ferromagnetic behavior and enhanced cytotoxic activity compared to Pt-NWs on Tera 1 cells, thus providing a sound basis for future magnetically targeted chemotherapeutic applications.
Disrupted Topological Organization of Brain Network in Rats with Spatial Memory Impairments Induced by Acute Microwave Radiation
Authors: Wang Haoyu , Zhao Haixia , Li Chunfang , Dong Ji , Zhao Jianghao , Yue Hanlin , Lai Yunfei , Zhao Li , Wang Hui , Zhang Jing , Xu Xinping , Yao Binwei , Zhou Hongmei , Nie Binbin , Du Xiumin , Peng Ruiyun
Published: June 2023

Previous studies have suggested that microwave (MW) radiation with certain parameters can induce spatial memory deficits. However, the effect of MW on the topological organization of the brain network is still unknown. This work aimed to investigate the topological organization of the brain network in rats with spatial memory impairments induced by acute microwave (MW) radiation. The Morris water maze (MWM) test and resting-state functional magnetic resonance imaging were performed to estimate the spatial memory ability and brain network topological organization of the rats after MW exposure. Compared with the sham group, the rats exposed to 30 mW/cm2 1.5 GHz MW radiation exhibited a significantly decreased normalized clustering coefficient (γ) (p = 0.002) 1 d after the exposure and a prolonged average escape latency (AEL) (p = 0.014) 3 d after the exposure. Moreover, after 10 mW/cm2 1.5 GHz MW radiation, a significantly decreased γ (p = 0.003) was also observed in the rats, without any changes in AEL. In contrast, no adverse effects on AEL or topological parameters were observed after 9.375 GHz MW radiation. In conclusion, the rats with spatial memory deficits induced by MW radiation exhibited disruptions in the topological organization of the brain network. Moreover, these topological organization disruptions emerged earlier than behavioral symptom onset and could even be found in the rats without a decline in the performance of the spatial memory task. Therefore, it is possible to use the topological parameters of the brain network as early and sensitive indicators of the spatial memory impairments induced by acute MW radiation.
How Does Trait Mindfulness Weaken the Effects of Risk Factors for Adolescent Smartphone Addiction? A Moderated Mediation Model
Authors: Li Dengfeng , Xu Yang , Cao Shangqing
Published: June 2023

As a psychological resource of individuals, trait mindfulness is valuable in facilitating individuals to maintain attention intensity, increase efficiency, and alleviate stress and depression. It can also buffer against the risk factors of addictive behaviors. However, applied research combining trait mindfulness and smartphone addiction with the use of psychological resources is relatively scarce and needs further examination. We constructed a moderated mediation model based on compensatory Internet use and conservation of resources theory (OCR) to examine the effects of social anxiety on adolescent smartphone addiction and to describe how trait mindfulness “works” and “in what contexts it works better”. We analyzed 1570 adolescent subjects through a multistage stratified sampling method. Our findings revealed that social anxiety positively predicted smartphone addiction, while trait mindfulness was negatively associated with it. Furthermore, trait mindfulness mitigated smartphone addiction by reducing social anxiety, suggesting a mediating effect of social anxiety on this relationship. Meanwhile, the mediating effect was more pronounced among adolescents with left-behind experience; we found that left-behind experience partially moderated the relationship between social anxiety and smartphone addiction. Adolescents with left-behind experience had more significant compensatory media use with a higher risk of smartphone addiction. This study highlights the potential protective role of trait mindfulness in the development and maintenance of adolescent smartphone addiction. It provides empirical support for applying resource conservation theory and stress buffering theory in this context.
Impact of Social Interaction on Customer Engagement in China’s Social Commerce—A Moderated Chain Mediation Model
Authors: Li Xuexin , Tian Ligang , Wang Shulin
Published: June 2023

With the emergence of social commerce, customer engagement is increasingly considered as an important influencing factor for enterprises to maintain a competitive advantage. Despite the extensive literature examining the determinants of customer engagement in social commerce from the perspectives of platform functions and technical dimensions, discussions on social interaction remain rare. Based on a sample dataset of 460 valid questionnaires collected via an online survey within China, using the structural equation model, this study attempts to investigate the effect of social interaction on customer engagement. Specifically, it divides social interaction into two dimensions, namely information-oriented and relation-oriented interactions. It is found that both informational and relational interactions are essential for driving customer engagement. Social presence and customer trust sequentially mediate the effect of social interaction to enhance customer engagement. In other words, social interaction enhances the sense of social presence, which in turn heightens customer trust, ultimately spurring a greater customer engagement. Self-construal moderates the relationship between social interaction and customer engagement. For interdependent customers, the effect of social interaction on customer trust is particularly significant. This study provides novel insights into how and when social interaction shapes customer engagement, highlighting the mechanisms and boundary conditions involved in this relationship within a social commerce context, which can also offer practical guidance for platforms and merchants seeking to facilitate greater engagement among customers.
Ventilation Strategies for Highly Occupied Public Environments: A Review
Authors: Zhang Yongzhi , Guo Zengrui , Zhuo Lanting , An Nirui , Han Yifei
Published: June 2023

In urban public transportation and highly diversified air environments, air pollutant exposure is becoming an increasing concern in terms of public health and personal safety. Herein, the scientific literature on air quality and virus transmission in densely crowded environments is reviewed to determine effective control methods. The research results are classified on the basis of different crowded environments. Much research has been conducted on pollutants in subways and buses. High particulate matter concentrations in public transportation are still a serious problem, but few studies on the spread of viruses exist. With existing types of ventilation systems, increasing local exhaust may be an efficient way to remove pollutants. Air quality sensors should be distributed in densely crowded spaces to achieve real-time display of pollutant concentration data. When pollution levels exceed the safe values, scientifically designed ventilation and filtration schemes should be implemented to reduce the pollution levels. Occupant activities are among the important factors that make pollutant transmission more complex. The analysis results herein contribute to the assessment of indoor pollutant concentrations and the protection of occupants from cross-infection.
Evaluating Savings Potentials Using Energy Retrofitting Measures for a Residential Building in Jeddah, KSA
Authors: Felimban Ahmed , Knaack Ulrich , Konstantinou Thaleia
Published: June 2023

Residential buildings in the Kingdom of Saudi Arabia (KSA) contribute to nearly half of the overall electricity consumption in the building stock, highlighting their significant role in energy consumption. While an upgraded energy code has been established and enforced for new buildings, existing buildings continue to operate at the same level of energy consumption. Therefore, there is a need for further energy upgrades in existing buildings. This study evaluates the energy savings potential of various energy retrofitting measures for a case study in Jeddah, KSA. Data from previous studies and current practices were collected and analyzed. Different energy upgrade measures, such as windows replacement, wall insulation upgrade, roof insulation upgrade, and air conditioning unit replacement, were selected and evaluated using a digital simulation tool called Design-Builder. The simulation results were compared to understand the potential percentage of energy savings. The average annual energy consumption (AAEC) was used as the primary performance indicator to compare the energy savings among the scenarios. The results demonstrate significant reductions in energy consumption for the proposed scenarios. Furthermore, the study examined the significant impact of uncertainties, specifically, the infiltration rate and AC setback temperature, on AAEC. In conclusion, the proposed scenarios have the potential to achieve substantial energy savings, ranging from 25% to 66%, depending on the number of energy retrofitting interventions employed. The findings of this study can serve as a useful reference for similar energy retrofitting projects.
Improvement of the Mechanical Properties of Mortars Manufactured with Partial Substitution of Portland Cement by Kaolinitic Clays
Authors: Astudillo Beatriz , Martín Domingo A. , Costafreda Jorge L. , Presa Leticia , Sanjuán Miguel A. , Parra José Luis
Published: June 2023

Presently, the search for urgent solutions to mitigate climate change has become a global priority. One of the most important challenges is the characterization, standardization, and technology of sustainable natural raw materials, which will significantly improve the quality of common types of cement, the production process of which emits large amounts of greenhouse gases into the atmosphere. This work is focused on the study of natural kaolinitic clays (NKC) from the eastern part of the Iberian Peninsula and its main objective is to define and normalize their properties as natural pozzolanic materials. This research consists of an initial study to determine the morphological and chemical properties using SEM and XRF. Furthermore, the physical properties of the samples were studied, such as thermic treatment (TT), Blane particle finesse (BPF), real density (RD) and apparent density (AD), porosity (P), volume stability (VS) and start and final setting time (SFST). On the other hand, technological analyses were carried out as follows: chemical analysis (CATQ), pozzolanicity (CAP), mechanical compression strength tests at 7, 28, and 90 days (MCST) as well as the ultrasonic pulse velocity (UPV). XRF results indicated that the SiO2 content (49.9–51.0%) of kaolinitic clay in its natural state (NKC) increases to 57.41 and 58.10%, respectively, when calcined (CKC). The chemical analysis of pozzolanicity established that the NKC does not show pozzolanic activity during the first 8 and 15 days; however, once calcinated, its pozzolanic reactivity increases substantially. On the other hand, the results of the mechanical stress tests (MCST) indicate an exponential increase in mechanical resistance from 7 to 90 days, which is higher in mortars made with CKC; similarly, and according to the results of the calculation of the Resistant Activity Index (RAI), it shows that the substitutions of Portland cement (PC) by NKC are effective between the ranges of 10 and 25%, while in the case of the substitution of PC by CKC, all formulations (10, 25 and 40%) are effective. This research establishes that the kaolinitic clays of the east of the Iberian Peninsula can be considered quality pozzolanic materials, capable of partially replacing Portland cement. The results presented here could be used as guidelines for the understanding and application of natural pozzolanic materials contributing to the improvement of types of cement, mortars, and concretes, which would positively affect the quality and preservation of the environment as well as the sustainability of eco-efficient construction materials.
Building Detection in High-Resolution Remote Sensing Images by Enhancing Superpixel Segmentation and Classification Using Deep Learning Approaches
Authors: Benchabana Ayoub , Kholladi Mohamed-Khireddine , Bensaci Ramla , Khaldi Belal
Published: June 2023

Accurate building detection is a critical task in urban development and digital city mapping. However, current building detection models for high-resolution remote sensing images are still facing challenges due to complex object characteristics and similarities in appearance. To address this issue, this paper proposes a novel algorithm for building detection based on in-depth feature extraction and classification of adaptive superpixel shredding. The proposed approach consists of four main steps: image segmentation into homogeneous superpixels using a modified Simple Linear Iterative Clustering (SLIC), in-depth feature extraction using an variational auto-encoder (VAE) scale on the superpixels for training and testing data collection, identification of four classes (buildings, roads, trees, and shadows) using extracted feature data as input to an Convolutional Neural Network (CNN), and extraction of building shapes through regional growth and morphological operations. The proposed approach offers more stability in identifying buildings with unclear boundaries, eliminating the requirement for extensive prior segmentation. It has been tested on two datasets of high-resolution aerial images from the New Zealand region, demonstrating superior accuracy compared to previous works with an average F1 score of 98.83%. The proposed approach shows potential for fast and accurate urban monitoring and city planning, particularly in urban areas.
Electrocatalytic Hydrodechlorination Using Supported Atomically Precise Gold Nanoclusters under Flow-Through Configuration
Authors: Zhao Zhiyuan , Yan Haochen , Liu Fuqiang , Yao Jie , You Shijie , Liu Yanbiao
Published: June 2023

We developed and optimized an electrocatalytic filtration system to catalytically hydrodechlorinate chlorophenolic compounds. A key part of the system was the cathode, which consisted of a filter constructed with electroactive carbon nanotubes (CNTs) functionalized with atomically precise gold nanoclusters (AuNCs). In the functional membrane electrode, the AuNCs attached to the CNTs functioned as a highly effective hydrodechlorination catalyst. Additionally, the ligands of the AuNCs facilitated the binding of the AuNCs with the CNT and protected the Au core from agglomeration. Atomic H* was the primary reactive species in the system, but direct reduction by cathode electrons also contributed to the elimination of 2,4-dichlorophenol (2,4-DCP) by hydrodechlorination. The generated atomic H* was able to break the C–Cl bond to achieve the rapid hydrodechlorination of 2,4-DCP into phenol, with 91.5% 2,4-DCP removal within 120 min. The AuNC catalysts attached to the CNT exceeded the best catalytic activity of larger nanoparticles (e.g., AuNPs), while the flow-through construction performed better than a standard batch reactor due to the convection-enhanced mass transport. The study provides an environmentally friendly strategy for the elimination of pervasive halogenated organic contaminants using a highly efficient, stable and recyclable system for hydrodechlorination that integrates nanofiltration and electrochemistry.
Recent Advances in Transition Metal Phosphide Nanocatalysts for H2 Evolution and CO2 Reduction
Authors: Shaheen Saman , Ali Syed Asim , Mir Umar Farooq , Sadiq Iqra , Ahmad Tokeer
Published: June 2023

Green hydrogen energy has captivated researchers and is regarded as a feasible option for future energy-related aspirations. The emerging awareness of renewable energy-driven hydrogen generation and carbon dioxide reduction calls for the use of unconventional schematic tools in the fabrication of nanocatalyst systems. Transition metal phosphides are state-of-art, cost-effective, noble-metal-free materials that have been comprehensively examined for sustainable energy-driven applications. Recent reports on these advanced functional materials have cemented their candidature as high-performance catalytic systems for hydrogen production and for carbon dioxide conversion into value-added chemical feedstock. Bimetallic NiCoP (238.2 mmol g−1 h−1) exhibits top-notch catalytic competence toward photocatalytic HER that reveals the energy-driven application of a pristine class of TMPs, whereas heterostructured Ni2P/CdS was found to be fit for photochemical CO2 reduction, as well as for HER. On the other hand, pristine Ni2P was recently ascertained as an efficient electrocatalytic system for HER and CO2RR applications. A wide array of physicochemical modulations, such as compositional and structural engineering, defect generation, and facet control, have been used for improving the catalytic efficiency of transition metal phosphide nanostructures. In this review, we succinctly discuss the proficiency of transition metal phosphides in green hydrogen production and carbon dioxide conversion via photochemical and electrochemical pathways. We detail the significance of their structural properties and brief the readers about the synthetic advancements without deviating from our goal of summarizing the recent achievements in energy-driven applications.
Coal Calorific Value Detection Technology Based on NIRS-XRF Fusion Spectroscopy
Authors: Li Jiaxuan , Gao Rui , Zhang Yan , Wang Shuqing , Zhang Lei , Yin Wangbao , Jia Suotang
Published: June 2023

Calorific value is an important index for evaluating coal quality, and it is important to achieve the rapid detection of calorific value to improve production efficiency. In this paper, a calorific value detection method based on NIRS-XRF fusion spectroscopy is proposed, which utilizes NIRS to detect organic functional groups and XRF to detect inorganic ash-forming elements in coal. NIRS, XRF and NIRS-XRF fusion spectrum were separately used to establish partial least squares (PLS) regression models for coal calorific value, and better prediction performance was obtained by using fusion spectrum (the determination coefficient of calibration set (R2) was 0.98, the root mean square error of prediction set (RMSEP) was 0.19 MJ/kg, the average relative deviation for prediction (MARDP) was 0.95%). The variable selection is very important for model performance. The effective variables were extracted using Pearson correlation coefficients to further optimize the prediction model, and the evaluation indexes of the optimized model are R2 = 0.99, RMSEP = 0.16 MJ/kg and MARDP = 0.70%. In addition, the repeatability of the proposed method was briefly evaluated. The results show that the proposed method is an effective analysis method to detect the calorific value of coal, which provides a new idea and technique for coal quality detection.
Functionalized Carbon-Nanotubes-Based Thin-Film Transistor Sensor for Highly Selective Detection of Methane at Room Temperature
Authors: Ji Feifan , Hu Jinyong , Zhang Yong
Published: June 2023

Gas sensors based on carbon nanotubes (CNTs) as channel materials have been widely considered as promising candidates for the detection of toxic gas. However, effectively detecting methane (CH4) with CNTs-based sensors remains challenging because nonpolar CH4 molecules find it difficult to directly interact with CNTs. Herein, a functionalized CNTs-based thin-film transistor (TFT) sensor is proposed for the highly effective detection of CH4 at room temperature, where CNTs with high semiconductor purity are used as the main TFT channel. The VO2 and Pd nanoparticles serve as surface-active agents to modify the CNTs, and the surface-modified CNTs-based gas sensor exhibits excellent gas-sensing properties for the detection of CH4. In particular, the Pd@VO2 composite-modified CNTs-based TFT sensor has excellent sensitivity to CH4 in the detection range of 50 to 500 ppm. The detection limit is as low as 50 ppm, and the sensor exhibits excellent selectivity and superior repeatability. The improved gas-sensing properties of the CNTs-based gas sensor is primarily attributed to the modification of the sensitive channel that can promote the electronic interaction between CH4 and gas-sensing materials. This study provides guidance for the development of high-performance CH4 sensors operating at room temperature.
Longer Brace Duration Is Associated with Lower Stress Levels and Better Quality of Life in Adolescents with Idiopathic Scoliosis
Authors: Di Maria Fabrizio , Testa Gianluca , Carnazza Michela , Testaì Martina , Pavone Vito
Published: June 2023

Physical and psychological health concerns have been raised due to either spine deformity or orthotic treatment in adolescents with idiopathic scoliosis. To determine whether orthotic bracing duration affects psychological stress and health-related quality of life, a sample of 46 patients (8M, 38F, aged 11–17 years, regularly treated with bracing) with moderate or moderate to severe idiopathic scoliosis were separated into two groups based on whether their treatment duration was up to six months or longer. The brace-related levels of stress and quality of life were investigated in both groups by using the Bad Sobernheim Stress Questionnaire and the Italian Spine Youth Quality of Life, respectively. The questionnaire scores were categorized as low, mean, and high. Our statistical analysis considered the proportion of patients falling into the three categories and the difference in crude score rates between the two groups. Values were considered significant at p < 0.05. The proportion of patients with brace-related stress and impaired quality of life was significantly lower in the group treated longer compared to the group that had only received treatment for up to six months (chi-square test, p < 0.0001). Overall, mean ± SD BSSQ scored 8.72 ± 4.91 and 12.89 ± 4.65 in group 1 and group 2, respectively (Mann–Whitney U test, p = 0.008), while ISYQoL scored 19.94 ± 8.21 and 16.07 ± 6.12, respectively. We argue that the differences could depend on both the physical and psychological adaptation patients make to their brace and that more support should be provided to patients when they start to wear their brace.
Low Doses of Melatonin to Improve Sleep in Children with ADHD: An Open-Label Trial
Authors: Checa-Ros Ana , Muñoz-Hoyos Antonio , Molina-Carballo Antonio , Viejo-Boyano Iris , Chacín Maricarmen , Bermúdez Valmore , D’Marco Luis
Published: June 2023

Objective. Only a few studies assessing the sleep effects of low doses of melatonin (aMT) have been performed in the past, most of them in adults, and only one in subjects with attention-deficit/hyperactivity disorder (ADHD). The aim of this study was to provide evidence of the changes induced by aMT doses as low as 1 mg in the sleep pattern of pediatric patients with ADHD under treatment with methylphenidate (MPH). Methods. Children and adolescents (7–15 years) with ADHD who were receiving extended-release MPH were recruited. A seven-week sleep diary was collected prior to starting a four-week treatment with 1 mg of aMT (30 min before bedtime). Seven-day actigraphic assessments of sleep were performed before and after treatment. Results. Twenty-seven patients (17 males, 62.96%) participated in the study, who had been receiving MPH for 1.57 (1.11) months. A significant increase in sleep duration (TST) was observed after one month of treatment (463 (49) min to 485 (41) min; p < 0.040), with nonsignificant improvements in sleep-onset latency (SOL), nocturnal awakenings, or sleep efficiency. Only minor adverse effects were reported. Conclusion. Low doses of melatonin (1 mg) are able to increase TST in children and adolescents with ADHD receiving treatment with psychostimulants, with an adequate tolerability profile. Further placebo-controlled trials adjusting the time of aMT administration to the individual circadian profile should explore the effects of low doses of this hormone to shorten SOL in this population of patients.
High CCL2 Levels Detected in CSF of Patients with Pediatric Pseudotumor Cerebri Syndrome
Authors: Genizi Jacob , Berger Lotan , Mahajnah Muhammad , Shlonsky Yulia , Golan-Shany Orit , Romem Azriel , Halevy Ayelet , Nathan Keren , Sharkia Rajech , Zalan Abdelnaser , Kessel Aharon , Cohen Rony
Published: June 2023

Pseudotumor cerebri (PTC) is a disorder characterized by increased intracranial pressure in the absence of a structural lesion or other identifiable cause. Cytokines, which are involved in the regulation of immune responses and inflammation, have been implicated in the pathogenesis of PTC. In a prospective, cross-sectional study at three centers in Israel, we analyzed cerebrospinal fluid (CSF) samples from 60 children aged 0.5–18 years, including 43 children with a definitive diagnosis of PTC and a control group of 17 children. Levels of IL-4, IL-10, IL-17, CCL2, CCL7, CCL8, CCL13, BDNF, and IFN-γ were measured using ELISA kits. Levels of CCL2 were significantly higher in the PTC group compared to the control group (p < 0.05), with no other significant differences in the measured cytokines between the two groups. The groups did not differ significantly in clinical presentation, imaging, treatment, or ophthalmic findings. Our findings provide preliminary evidence that CCL2 may be involved in the pathogenesis of PTC and may serve a potential target for therapy in PTC.
Multivariable Projections of Caries-Free Prevalence and the Associated Factors from 2019 to 2030 among Schoolchildren Aged 6, 12 and 16-Year-Old in Malaysia
Authors: Najihah Lokman , Wan Husin Wan Zakiyatussariroh , Marhazlinda Jamaludin
Published: June 2023

This study identified caries-free associated factors and conducted multivariable projections of the caries-free prevalence until 2030 among six-, 12-, and 16-year-old schoolchildren in Malaysia. It was a secondary data analysis of caries-free prevalence and potential associated factors obtained from the Health Information Management System (HIMS), Department of Statistics Malaysia (DOSM), and Food Balance Sheets (FBS). Multiple linear regression and regression with ARMA errors were employed to determine the associated factors and predict the caries-free prevalence from 2019 or 2020 until 2030 for the six-, 12-, and 16-year-old groups, respectively. Gross Domestic Product (GDP) and household income, sugar consumption, and water fluoridation were significantly associated with caries-free status, with the most impactful in all age groups being water fluoridation. With the projected values of the associated factors, the caries-free prevalence in schoolchildren of all age groups in Malaysia is predicted to increase in the next decade. Similar to the past decade, the prevalence trend will remain the highest among the 12-year-olds and the lowest among six-year-olds. Caries-free prevalence was predicted to increase by 9.10%, 15.52%, and 15.10% in the six-, 12-, and 16-year-old groups, respectively. The prevalence multiplied the highest at four times greater than in the past ten years among 16-year-olds, compared with less than 2% in the six- and 12-year-old groups. In conclusion, by factoring in economic factors, sugar consumption, water fluoridation, and age groups, the caries-free prevalence of schoolchildren in Malaysia is projected to increase at different rates in the next decade until 2030. Thus, strategic oral health plans to recognise effective promotion programmes and strengthen others for each age group are crucial.
Pharmacological and Pathological Effects of Mulberry Leaf Extract on the Treatment of Type 1 Diabetes Mellitus Mice
Authors: Luo Liru , Fan Wei , Qin Jingping , Guo Shiyin , Xiao Hang , Tang Zhonghai
Published: June 2023

This study investigated the pharmacological and pathological effects of aqueous mulberry leaf extract on type 1 diabetes mellitus mice induced with an intraperitoneal injection of streptozotocin (STZ). Diabetic mice were randomized into six groups: control (normal group), model, metformin-treated mice, and high-dose, medium-dose, and low-dose mulberry. The mulberry-treated mice were divided into high-, medium-, and low-dose groups based on the various doses of aqueous mulberry leaf extract during gavage. The efficacy of the six-week intervention was evaluated by measuring levels of fasting plasma glucose, alkaline phosphatase, alanine aminotransferase, aspartate transaminase, blood urea nitrogen, gamma-glutamyl transferase, glucose, high-density lipoprotein cholesterol, lactate dehydrogenase, and low-density lipoprotein cholesterol and recording body weight. Results revealed that mulberry leaf extract exhibited an ideal hypoglycemic effect, and the high-dose group was the most affected. Histology analysis, glycogen staining and apoptosis detection were used to study the extract’s effects on the liver, kidney, and pancreatic cells of diabetic mice, enabling the assessment of its effectiveness and complications on a clinical and theoretical basis. It was shown that a certain concentration of aqueous mulberry leaf extract repaired the islet cells of type 1 diabetes mellitus mice, promoting normal insulin secretion. Herein, it was confirmed that mulberry leaf could be used to develop new hypoglycemic drugs or functional health food with broad applicability.
Bio-Based Impregnated Resin Preparation for Aldehyde-Free Decorative Paper Production
Authors: Wang Jin , Liu Jun , Long Ling
Published: June 2023

With the growing concern for environmental protection and personal health, utilizing bio-based impregnated resin has become a sustainable approach for producing aldehyde-free decorative paper and in-house decorations. Our current work focuses on the preparation of an aldehyde-free resin (AFR) by formulating quaternized cellulose nanofibrils (QCNFs, Ave. width 10 ± 3 nm, Ave. length of and >500 nm) with aqueous acrylate emulsion. We analyzed the synthesized QCNFs, acrylate emulsion, and AFR by using various methods, including FTIR, XPS, XRD, TGA/DTG, and rheometer, to evaluate their applicability for impregnated paper processing. At a low coating weight of 4.0 g/m2, a 30.8% increase and 4.9-times increase in tensile strength and contact angle were detected, respectively. Meanwhile, the free aldehyde emission from the AFR-coated paper was found to be 0.1 mg/L even at a high coating weight of 18.8 g/m2, which is far below the E0 level requirement in the JAS 234:2003 criteria. Therefore, the surface coating of the decorative base paper was found to be competitive in covering the porous structure of the paper, reinforcing its mechanical strength, and providing high water resistance. Moreover, the lower free aldehyde emission from the AFR-coated paper ensures its safety and potential application in house decoration products.
Temperature Field Analytical Solution for OGFC Asphalt Pavement Structure
Authors: Qi Lin , Yu Baoyang , Zhao Zhonghua , Zhang Chunshuai
Published: June 2023

The change law of the temperature field of an open-graded friction course (OGFC) asphalt pavement was studied. The thermal conductivity of OGFC asphalt mixtures with different oil–stone ratios was measured using a thermal-conductivity tester. The relationship between the oil–stone ratio and thermal conductivity was established, which was then used as the boundary condition of the temperature field. Using mathematical and physical methods based on thermodynamics and heat-transfer principles, an analytical solution of the temperature field of the OGFC asphalt pavement structure was developed. Data from an outdoor test of large Marshall specimens were compared with the analytical solution of the temperature field to verify the correctness of the model. The results show that the analytical model of the OGFC asphalt pavement structure temperature field can predict the temperature changes at different oil–stone ratios, times, and depths (from the road surface). The differences between the predicted results and test data at 0.01, 0.02, and 0.03 m from the road surface were 0.5, 0.7, and 0.9 °C, respectively, confirming that this study can be used to provide reference information for the design of OGFC asphalt pavement structures.
Assessing COVID-19 Effects on Inflation, Unemployment, and GDP in Africa: What Do the Data Show via GIS and Spatial Statistics?
Authors: Gotu Butte , Tadesse Habte
Published: June 2023

What are the effects of Corona Virus Disease 19 (COVID-19) on inflation, unemployment, and GDP in Africa? Using geo-coded cross-sectional data taken from the World Health Organization and International Monetary Fund, we investigate the spatial distribution of COVID-19 and its effects on inflation, unemployment, and Gross Domestic Product (GDP) in Africa by employing the Geographic Information System (GIS), multivariate analysis of covariance (MANCOVA), and spatial statistics. The entire dataset was analyzed using Stata, ArcGIS, and R software. The result shows (1) that there is evidence of a spatial pattern of COVID-19 cases and death rate clustering behavior in Africa, verifying the existence of spatial autocorrelation. The result also reveals (2) that COVID-19 has a negative effect on unemployment, inflation, and GDP in Africa. We confirmed that (3) temperature, rainfall, and humidity were statistically significantly associated with the spread of the COVID-19 pandemic in Africa. The comparison of the GDP of African countries before and after the pandemic shows (4) a large decrease in GDP, the highest in Seychelles (23 percent). The result of the study shows (5) that there has been a significant increase in inflation and unemployment rates in all countries since the outbreak of the pandemic as compared to the time before the outbreak. There is also evidence that (6) there is a significant relationship between death rate due to COVID-19 and population density; temperature with COVID-19 cases and death rate; and precipitation with death rate due to COVID-19. Therefore, respective governments and the international community need to pay attention to controlling/reducing the impact of COVID-19 on inflation, unemployment, and GDP, focusing on the indicated demographic and environmental variables.
Gallium-Modified Zinc Oxide Thin Films Prepared by Chemical Solution Deposition
Authors: Stojanoska Izabela , Kmet Brigita , Uršič Hana , Kuscer Danjela
Published: June 2023

Gallium-doped ZnO (GZO) thin films on glass, which can be used as transparent electrodes, were prepared using a spin coating technique. Thermal analysis and Fourier-transform infrared spectroscopy of the dried precursor solution of Zn acetate and Ga nitrate dissolved in ethanol with diethanolamine confirmed the decomposition of the organic components upon heating and the formation of ZnO at 450 °C. The thin films fired at 600 °C in oxygen and air, and the films annealed at 400 °C in Ar/H2, were polycrystalline, 140 nm thick, and exhibited a homogeneous microstructure with 50 nm grains and a smooth surface, as shown by X-ray powder diffraction and scanning electron and atomic force microscopy. The sheet resistance Rs measured using the 4-probe technique showed a change in Rs within 80 days for all samples. The Rs of the GZO thin films annealed in oxygen and air with values of MΩ/sq decreased over time. Rs values of 150 kΩ/sq were obtained for GZO thin films annealed in Ar/H2, but the Rs increased over time. We suggest that the degradation of Rs is related to the adsorption of water on GZO and that the responses depend on the nature of the defects in the GZO lattice.
Preparation, Properties, and Applications of Near Stoichiometric Lithium Tantalate Crystals
Authors: Xiao Xuefeng , Si Jiashun , Liang Shuaijie , Xu Qingyan , Zhang Huan , Ma Lingling , Yang Cui , Zhang Xuefeng
Published: June 2023

Lithium tantalate crystal is widely used in optical devices, infrared detectors and surface acoustic wave devices because of its excellent piezoelectric, acousto-optic and nonlinear optical properties. The Li content of near stoichiometric lithium tantalate (NSLT) crystal is higher than that of congruent lithium tantalate (CLT) crystal. Therefore, the performance of NSLT crystal is better than that of CLT crystal in some aspects. This article reviews the physical properties, preparation methods and current research status in acoustics and optics of NSLT crystals. It also looks forward to the improvement of NSLT crystal preparation methods and their applications in surface acoustic wave (SAW) filters and optics. With the increase of Li content, the acoustic performance of NSLT crystals is expected to be comprehensively improved, achieving the application of SAW filters in 5G communication.
Dataset of Linkability Networks of Ethereum Accounts Involved in NFT Trading of Top 15 NFT Collections
Authors: Tošić Aleksandar , Hrovatin Niki , Vičič Jernej
Published: June 2023

In this paper, we present subgraphs of Ethereum wallets involved in NFT trades of the top 15 ERC721 NFT collections. To obtain the subgraphs, we have extracted the Ethereum transaction graph from a live Ethereum node and filtered out exchanges, mining pools, and smart contracts. For each of the selected collections, we identified the set of accounts involved in NFT trading, which we used to perform a breadth-first search in the Ethereum transaction graph to obtain a subgraph. These subgraphs can offer insight into the linkability of accounts participating in NFT trading on the Ethereum blockchain.
A Cardiac Deep Learning Model (CDLM) to Predict and Identify the Risk Factor of Congenital Heart Disease
Authors: Pachiyannan Prabu , Alsulami Musleh , Alsadie Deafallah , Saudagar Abdul Khader Jilani , AlKhathami Mohammed , Poonia Ramesh Chandra
Published: June 2023

Congenital heart disease (CHD) is a critical global public health concern, particularly when it comes to newborn mortality. Low- and middle-income countries face the highest mortality rates due to limited resources and inadequate healthcare access. To address this pressing issue, machine learning presents an opportunity to develop accurate predictive models that can assess the risk of death from CHD. These models can empower healthcare professionals by identifying high-risk infants and enabling appropriate care. Additionally, machine learning can uncover patterns in the risk factors associated with CHD mortality, leading to targeted interventions that prevent or reduce mortality among vulnerable newborns. This paper proposes an innovative machine learning approach to minimize newborn mortality related to CHD. By analyzing data from infants diagnosed with CHD, the model identifies key risk factors contributing to mortality. Armed with this knowledge, healthcare providers can devise customized interventions, including intensified care for high-risk infants and early detection and treatment strategies. The proposed diagnostic model utilizes maternal clinical history and fetal health information to accurately predict the condition of newborns affected by CHD. The results are highly promising, with the proposed Cardiac Deep Learning Model (CDLM) achieving remarkable performance metrics, including a sensitivity of 91.74%, specificity of 92.65%, positive predictive value of 90.85%, negative predictive value of 55.62%, and a miss rate of 91.03%. This research aims to make a significant impact by equipping healthcare professionals with powerful tools to combat CHD-related newborn mortality, ultimately saving lives and improving healthcare outcomes worldwide.
Investigating Beta-Variational Convolutional Autoencoders for the Unsupervised Classification of Chest Pneumonia
Authors: Akila Serag Mohamed , Imanov Elbrus , Almezhghwi Khaled
Published: June 2023

The world’s population is increasing and so is the challenge on existing healthcare infrastructure to cope with the growing demand in medical diagnosis and evaluation. Although human experts are primarily tasked with the diagnosis of different medical conditions, artificial intelligence (AI)-assisted diagnoses have become considerably useful in recent times. One of the critical lung infections, which requires early diagnosis and subsequent treatment to reduce the mortality rate, is pneumonia. There are different methods for obtaining a pneumonia diagnosis; however, the adoption of chest X-rays is popular since it is non-invasive. The AI systems for a pneumonia diagnosis using chest X-rays are often built on supervised machine-learning (ML) models, which require labeled datasets for development. However, collecting labeled datasets is sometimes infeasible due to constraints such as human resources, cost, and time. As such, the problem that we address in this paper is the unsupervised classification of pneumonia using unsupervised ML models including the beta-variational convolutional autoencoder (β-VCAE) and other variants, such as convolutional autoencoders (CAE), denoising convolutional autoencoders (DCAE), and sparse convolutional autoencoders (SCAE). Namely, the pneumonia classification problem is cast into an anomaly detection to develop the aforementioned ML models. The experimental results show that pneumonia can be diagnosed with high recall, precision, f1-score, and f2-score using the proposed unsupervised models. In addition, we observe that the proposed models are competitive with the state-of-the-art models, which are trained on a labeled dataset.