bluuebunny/crossref_metadata_2025 huggingface dataset:
Ultrabright quantum dots assisted in vivo NIR-II fluorescence microscopic imaging for brain metastases in triple-negative breast cancer
Authors: Gao Yuxiang , Zhang Chi , Zhu Lijun , Du Zhong , Ma Rong , Guo Le , Alifu Nuernisha , Zhang Xueliang
Published: March 2024

Triple-negative breast cancer (TNBC) is an aggressive and often fatal disease, especially since the brain metastasis of TNBC has been a particularly severe manifestation. However, brain metastasis in TNBC at early stages often lacks noticeable symptoms, making it challenging to detect. Near-infrared II (NIR-II) fluorescence microscopic imaging obtains long wavelength, which enables reduced scattering, high spatial resolution and minimal autofluorescence, it is also a favorable imaging method for tumor diagnosis. PbS@CdS quantum dots (QDs) are one of the popular NIR-II fluorescence nanoprobes for well brightness. In this study, NIR-II emissive PbS@CdS QDs were utilized and further encapsulated with thiol-terminated poly(ethylene oxide) (SH-PEG, MW = 5000) to form PbS@CdS@PEG QDs nanoparticles (NPs). The obtained PbS@CdS@PEG QDs NPs were then characterized and further studied in detail. The PbS@CdS@PEG QDs NPs had large absorption spectra, exhibited strong NIR-II fluorescence emission at approximately 1300[Formula: see text]nm, and possessed good NIR-II fluorescence properties. Then, the mice model of early-stage brain metastases of TNBC was established, and the PbS@CdS@PEG QDs NPs were injected into the tumor-bearing mice for NIR-II fluorescence microscopic bioimaging. The brain vessels and tumors of the living mice were detected with high spatial resolution under the NIR-II fluorescence microscopic imaging system with irradiation of 808[Formula: see text]nm laser. The tumor tissues were further restricted and prepared as thin slices. The NIR-II fluorescence signals were collected from the tumor slices with high spatial resolution and signal-to-background ratio (SBR). Thus, the PbS@CdS@PEG QDs NPs-assisted NIR-II fluorescence microscopic system can effectively achieve targeting brain metastases of TNBC imaging, offering a novel and promising approach for TNBC-specific diagnosis.
Photoacoustic/ultrasound dual-modal imaging of human nails: A pilot study
Authors: Wen Yanting , Huang Yijie , Huang Lin , Wu Dan , Zhong Renbin , Jiang Shixie , Zhang Yudi , Liu Ting , Liu Xiaotian , Jiang Huabei
Published: March 2024

Traditional diagnostic techniques including visual examination, ultrasound (US), and magnetic resonance imaging (MRI) have limitations of in-depth information for the detection of nail disorders, resolution, and practicality. This pilot study, for the first time, evaluates a dual-modality imaging system that combines photoacoustic tomography (PAT) with the US for the multiparametric quantitative assessment of human nail. The study involved a small cohort of five healthy volunteers who underwent PAT/US imaging for acquiring the nail unit data. The PAT/US dual-modality imaging successfully revealed the fine anatomical structures and microvascular distribution within the nail and nail bed. Moreover, this system utilized multispectral PAT to analyze functional tissue parameters, including oxygenated hemoglobin, deoxyhemoglobin, oxygen saturation, and collagen under tourniquet and cold stimulus tests to evaluate changes in the microcirculation of the nail bed. The quantitative analysis of multispectral PAT reconstructed images demonstrated heightened sensitivity in detecting alterations in blood oxygenation levels and collagen content within the nail bed, under simulated different physiological conditions. This pilot study highlights the potential of PAT/US dual-modality imaging as a real-time, noninvasive diagnostic modality for evaluating human nail health and for early detection of nail bed pathologies.
Research and application of push model based on digital twin for complex solutions
Authors: Wang Hui , Deng Zhiji , Liu Ming , Wang Yiyou , Zhang Yadong , Kong Weisheng , Wu Rongji
Published: March 2024

In order to solve the selection problems such as multiple products, complex scenarios, and difficulty in supporting complex solutions, the product selection push model for complex solutions was proposed, the selection cloud platform was set up, and the platform architecture, composition, and operating mechanism were given. Based on the digital twin five-dimensional framework, the modeling process of the product selection cloud platform was detailed, and in the demand stage, the function model of the model was determined by the collaborative filter algorithm, which gave a comprehensive description of its functions. Through the construction of multi-platform fusion and algorithm model, the active push, scheme automatic process, and verification function of solution product selection have been realized. The solution has been applied in the selection of the smart security community scheme, verifying the effectiveness and practicality of the platform.
An online model-free adaptive learning control solution for robotic arms
Authors: Moodi Mostafa , Hassanzadeh Kiavash , Merapala Tharun , Gascon Vincent , Abouheaf Mohammed
Published: March 2024

This paper focuses on the online control of a class of nonlinear dynamical systems, specifically robotic manipulators. Solutions utilizing Proportional–Integral–Derivative (PID) control schemes are employed to control the joints of robotic manipulators. However, the existing control strategies utilize fixed gains, which do not fully account for the inherent nonlinearity of the dynamical structure or the dynamics of reference-tracking error. Additionally, the individual joint’s dynamic performance is optimized independently from the performance of other joints. This work introduces an adaptive integral Reinforcement Learning algorithm to control a four-DoF robotic arm in real time. This is done using a model-free Value Iteration process implemented in a continuous-time mode. The solution does not assume any knowledge of the dynamics of the robot arm and does not require any initial admissible control strategy to proceed with the adaptive learning solution. The self-learning algorithm provides adaptable strategies to control the turntable, forearm, bicep, and wrist joints of the robotic arm. The performance of the adaptive learning solution is compared with those of Proportional–Integral–Derivative and high-order model-free adaptive control schemes to highlight its effectiveness.
SnackBot: A modular miniature robot design and simulation
Authors: Chen Zhenmin , Zhao Chun
Published: March 2024

Modular robots carry the unique advantage of changing their shape according to different tasks and environments. In order to study the lightweight and flexible combination characteristics of modular robots, this paper presents a two-degree-of-freedom pluggable modular robot and its simulation method, named SnackBot. SnackBot is composed of several independently operating modules. Each module contains a control unit, a micro battery, and two DC motors that drive the module joints. In order to predict robot actions and monitor the state of the robot, the simulation model of SnackBot is built and a virtual environment is established in Ignition Gazebo. The virtual robot and physical robot are synchronously controlled in the environment of the Robot Operating System 2 (ROS2). Finally, experimental results verify the feasibility of the SnackBot.
Frequency-dependent ferroelectric and electrocaloric properties in barium titanate-based ceramics based on Maxwell relations
Authors: Shu Wanting , Li Hong , Huang Yanli , Lin Cong , Wu Xiao , Gao Min , Lin Tengfei , Zhao Chunlin
Published: March 2024

In this work, the frequency dependence of ferroelectric and electrocaloric properties in barium titanate-based ceramics was studied based on Maxwell relations. It is found that the maximum and remnant polarization will decrease while the coercive field increases a lot with rising frequency from 0.1 to 10[Formula: see text]Hz, indicating that polarization rotation and domain switching become difficult at high frequencies. The electrocaloric properties show the different frequency dependence at different phase structures. Isothermal entropy change ([Formula: see text]) and adiabatic temperature change ([Formula: see text]) are similar around/above Curie temperature ([Formula: see text], showing tiny frequency dependence. However, [Formula: see text] and [Formula: see text] display the obvious frequency dependence below [Formula: see text], especially in the orthorhombic–tetragonal phase-transition region with a stable ferroelectric phase, and this frequency dependence becomes more obvious under a low-electric field. It is also found that increasing the frequency can weaken the electric field dependence of electrocaloric strength. This work gives a general profile of frequency dependence for electrocaloric properties in ferroelectric ceramics.
Efficient piezocatalytic properties of Na0.5Bi0.5TiO3 nanoparticles for dye degradation and hydrogen peroxide production
Authors: Liang Bocheng , Zhu Xiaoting , Yu Haimiao , Zhang Yongcheng , Ye Wanneng
Published: March 2024

Piezocatalysis has emerged as a promising environmental remediation technique, and the exploration of environmentally friendly and high-performance piezocatalysts is crucial for their practical applications. In this work, the bismuth sodium titanate (Na[Formula: see text]Bi[Formula: see text]TiO3 (NBT)) exhibited efficient piezocatalytic activity toward typical organic pollutants degradation, including acid orange 7, methylene blue, rhodamine B and methyl orange. Notably, rhodamine B was degraded by 98.1% within 30[Formula: see text]min with a reaction rate constant of 0.130[Formula: see text]min[Formula: see text]. Furthermore, the NBT achieved a hydrogen peroxide production efficiency of 538[Formula: see text][Formula: see text]mol/g⋅h without the sacrificial agent, indicating that the NBT is a superior piezocatalyst for dye degradation and hydrogen peroxide generation. This work demonstrated that by using mechanical energy, the NBT can be used for degrading organic pollutants in wastewater and hydrogen peroxide generation.
Water Footprint Flow and Vulnerability of China’s Provincial Energy Sector
Authors: Sun Han , Meng Zhenghao , Chen Lu , Yang Xiaohui , Ni Shan
Published: March 2024

Water and energy are mutually reinforcing elements in production and consumption activities, forming an inseparable relationship. This study, conducted at the provincial level, investigates the intricate connections between energy water footprint flow, water shortage, and water pollution. It aims to elucidate the vulnerability of provincial energy sectors to water use, fostering regional coordinated management for sustainable energy and water development. Utilizing a multi-regional input–output model, the research analyzes both the quantity and quality of water resources. The water footprint of China’s provincial energy sector is quantified and scrutinized. Furthermore, an evaluation index system is constructed based on the water footprint concept. The entropy weight TOPSIS method is then applied to assess the vulnerability of inter-provincial energy sector water footprints. The key findings are as follows: (1) The energy water footprint has shifted from water resource-deficient areas, such as the northwest, North China, and northeast, to economically developed regions with relatively abundant water resources, such as East China and South China. This reveals that the transfer of water footprints in economic activities does not entirely alleviate water shortages in China and may even exacerbate shortages in certain water-deficient areas. (2) The graywater footprint of the energy sector is significantly larger than the blue water footprint and water shortage footprint. (3) The production and supply departments of power and heat emerge as the largest contributors to the transfer of energy and water footprints from water shortage areas to water-rich areas, followed by coal mining and dressing departments. (4) Top 10 Vulnerable Provinces: Tibet, Zhejiang, Ningxia, Tianjin, Jiangsu, Henan, Hunan, Beijing, Chongqing, and Hebei are identified as the top 10 provinces vulnerable to water footprints in the energy sector. This vulnerability closely correlates with the energy sector’s dependence on water footprints and production leverage. Recommendations include optimizing the energy mix and establishing stable external water footprint supply channels for provinces with high water footprint dependence. Provinces with high production leverage are advised to accelerate innovation and application of water-saving technologies in key energy sectors, increase the utilization of clean energy, and enhance resource recycling. National government departments should focus on controlling and reducing water pollutants in the energy sector across regions, addressing water shortages by purifying water bodies and improving water quality.
Assessment of the Economic Consequences of Riverbank Erosion: The Case of the South Morava River, Serbia
Authors: Langović Marko , Popović Svetlana , Dragićević Slavoljub , Stojanović Žaklina , Manić Emilija
Published: March 2024

Landscape transformation, degradation and destruction are caused by fluvial processes as the predominant erosive processes in Serbia. The loss of arable land due to riverbank erosion is permanent, and the economic consequences are, therefore, especially pronounced. The primary aim of this study was to quantify the intensity of riverbank erosion in the lower part of the South Morava River (Serbia) during period 1924–2020, evaluate its economic consequences, and conduct a cost benefit analysis of revetment investments. The economic effects of riverbank erosion were analyzed by means of land loss and reduction in the quantity of agricultural production. An interdisciplinary research approach was applied using specific methodological procedures to calculate the riverbank erosion and soil (land) loss intensity (geographic information system-GIS), the economic consequences of riverbank erosion (ECRE), and the results of an investment decision-making model. The results showed that 202.6[Formula: see text]ha of arable land was lost during the observed period, the value of arable land loss was almost 622,000[Formula: see text]USD, and the loss in agricultural production was 7.5 million USD (discount rate 3.7%). The model is seen as the main research output and can be used for the assessment of long-term capital-intensive infrastructure projects in developing countries. The analysis identified the river segments that are economically viable for investments in riverbank revetments to preserve the largest area of fertile agricultural land. The results are especially valuable for river channel management, environmental planners and policy-makers, who deal with decisions regarding planning and the protection of bank erosion in areas of particular interest.
Classification of Fats and Oils Based on Raman Spectroscopy and Deep Learning
Authors: Liu Jia-Zhen , Yang Si-Wei , Xie Yu-Hao , Zhang Yue-Jiao , Tian Jing-Hua , Zheng Shisheng , Liang Pei , Li Jian-Feng
Published: March 2024

Raman spectroscopy is a popular technology for material identification, but it encounters difficulties when distinguishing components with closely related substances by intuition and experience. The distinction of fats and oils is a typical example, which is significant in the food industry. In this work, the Raman spectroscopy and deep learning algorithm are combined to analyze closely related animal fats (lard, butter, mutton fat and chicken fat) and vegetable oils (soybean oil and peanut oil) in a dataset. A deep neural network founded on the VGG architecture with attention mechanism is developed, reaching an accuracy of 100% for fats and oils classification. By combining Raman spectroscopy with deep learning, this research provides a potent technique for tackling the identification of similar substances.
mRNA Vaccines and Therapeutics: Current Science and Implications for Future Development
Authors: de Alwis Ruklanthi , Shin Eui-Cheol , Tam John S. , Leong Jane , Luu Co
Published: March 2024

The recent success of messenger RNA (mRNA) vaccines against SARS-CoV-2 has highlighted the value of mRNA technology to combat infectious diseases. Several decades of investigation on mRNA-based approaches hold promise for further application of mRNA in developing vaccines and therapeutics. The core components of mRNA-based vaccines are one or more mRNA sequences, which are synthesized by in vitro transcription and surrounded by a vehicle designed to stably deliver the mRNA across cell membranes. Currently, all approved SARS-CoV-2 mRNA vaccines use a lipid nanoparticle (LNP) as a delivery vehicle. Modifications in both the mRNA and LNP components can optimize mRNA stability, protein expression, and immunogenicity. The technology of mRNA-based vaccines enables a platform-based approach to development, as the same core components are used for each agent. This review discusses the key components of current mRNA-based vaccines and the advantages of the platform approach for vaccine development, mechanisms of mRNA vaccine-associated immune responses, and safety. Current challenges and future directions for mRNA technology are also described, including the expansion of the mRNA platform to develop novel therapeutics.
DeepMedFeature: An Accurate Feature Extraction and Drug-Drug Interaction Model for Clinical Text in Medical Informatics
Authors: Malik M. Shoaib , Jawad Sara , Moqurrab Syed Atif , Srivastava Gautam
Published: March 2024

Drug-drug interactions (DDIs) are an important biological phenomenon which can result in medical errors from medical practitioners. Drug interactions can change the molecular structure of interacting agents which may prove to be fatal in the worst case. Finding drug interactions early in diagnosis can be pivotal in side-effect prevention. The growth of big data provides a rich source of information for clinical studies to investigate DDIs. We propose a hierarchical classification model which is double-pass in nature. The first pass predicts the occurrence of an interaction and then the second pass further predicts the type of interaction such as effect, advice, mechanism, and int. We applied different deep learning algorithms with Convolutional Bi-LSTM (ConvBLSTM) proving to be the best. The results show that pre-trained vector embeddings prove to be the most appropriate features. The F1-score of the ConvBLSTM algorithm turned out to be 96.39% and 98.37% in Russian and English language respectively which is greater than the state-of-the-art systems. According to the results, it can be concluded that adding a convolution layer before the bi-directional pass improves model performance in the automatic classification and extraction of drug interactions, using pre-trained vector embeddings such as Fasttext and Bio-Bert.
Stackelberg-Pareto Synthesis
Authors: Bruyère Véronique , Fievet Baptiste , Raskin Jean-François , Tamines Clément
Published: March 2024

We study the framework of two-player Stackelberg games played on graphs in which Player 0 announces a strategy and Player 1 responds rationally with a strategy that is an optimal response. While it is usually assumed that Player 1 has a single objective, we consider here the new setting where he has several. In this context, after responding with his strategy, Player 1 gets a payoff in the form of a vector of Booleans corresponding to his satisfied objectives. Rationality of Player 1 is encoded by the fact that his response must produce a Pareto-optimal payoff given the strategy of Player 0. We study for several kinds of ω-regular objectives the Stackelberg-Pareto Synthesis problem which asks whether Player 0 can announce a strategy which satisfies his objective, whatever the rational response of Player 1. We show that this problem is fixed-parameter tractable for games in which objectives are all reachability, safety, Büchi, co-Büchi, Boolean Büchi, parity, Muller, Streett, or Rabin objectives. We also show that this problem is
NEXPTIME
-complete except for the cases of Büchi objectives for which it is
NP
-complete and co-Büchi objectives for which it is in
NEXPTIME
and
NP
-hard. The problem is already
NP
-complete in the simple case of reachability objectives and graphs that are trees.
Impact of Haptic Feedback in High Latency Teleoperation for Space Applications
Authors: Louca Joe , Eder Kerstin , Vrublevskis John , Tzemanaki Antonia
Published: March 2024

Remote manipulation is a key enabler for upcoming space activities such as in-orbit servicing and manufacture (IOSM). However, due to the large distances involved, these systems encounter unavoidable signal delays, which can lead to poor performance and users adopting a disjointed, “move-and-wait” style of operation. We use a robot arm teleoperated with a haptic controller to test the impact of haptic feedback on delayed (up to 2.6 s: Earth-Moon communications) teleoperation performance for two example IOSM-style tasks.

This user study showed that increased latency reduced performance in all metrics recorded. In real-time teleoperation, haptic feedback showed improvements in success rate, accuracy, contact force, velocity, and trust, but, of these, only the improvements to contact forces and moving velocity were also seen at higher latencies. Accuracy and trust improvements were lost, or even reversed, at higher latencies. Results varied between the two tasks, highlighting the need for further research into the range of task types to be encountered in teleoperated space activities. This study also provides a framework by which to explore how features other than haptic feedback can impact both performance and trust in delayed teleoperation.
Unsupervised Multimodal Machine Translation for Low-resource Distant Language Pairs
Authors: Tayir Turghun , Li Lin
Published: March 2024

Unsupervised machine translation (UMT) has recently attracted more attention from researchers, enabling models to translate when languages lack parallel corpora. However, the current works mainly consider close language pairs (e.g., English-German and English-French), and the effectiveness of visual content for distant language pairs has yet to be investigated. This article proposes an unsupervised multimodal machine translation model for low-resource distant language pairs. Specifically, we first employ adequate measures such as transliteration and re-ordering to bring distant language pairs closer together. We then use visual content to extend masked language modeling and generate visual masked language modeling for UMT. Finally, empirical experiments are conducted on our distant language pair dataset and the public Multi30k dataset. Experimental results demonstrate the superior performance of our model, with BLEU score improvements of 2.5 and 2.6 on translation for distant language pairs English-Uyghur and Chinese-Uyghur. Moreover, our model also brings remarkable results for close language pairs, improving 2.3 BLEU compared with the existing models in English-German.
Response of Garlic (Allium sativum L.) Yield to Combined Biochar, Lime, and Inorganic‐Fertilizer Rates in the Case of Gimbi District, Western Ethiopia
Authors: Kenea Solomon Abeba , Goshu Tolera Abera , Chimdessa Kinati
Published: March 2024

Soil degradation due to acidity is a serious problem in western Ethiopia, which would lower soil productivity and crop production. Implementing integrated soil fertility management is the most efficient approach for enhancing agronomic efficacy and boosting crop output while addressing the issue of soil acidity. This experimental study aimed to investigate the effect of combined coffee husk biochar (CHB), soil test‐based value lime (STV), and inorganic‐fertilizer (NPSB‐fertilizer) rates on the optimum yield of garlic in Gimbi district, western Ethiopia. The field experiment was conducted during the 2022 cropping season on two sites. The experiment comprised 14 treatments laid out in a randomized complete block design with three replicates. Application of integrated biochar, lime, and inorganic‐fertilizer rates showed a highly significant impact (p  < 0.01) on the yield and yield components of garlic. The treatment unit with T11 ha−1 had the significantly highest total fresh biomass yield (TFBY) per plant for both Farm‐1 (65.9 g) and Farm‐2 (75.3 g). Bulb yield fresh weight per plant (BWp) was highest in treatments of T4 and T8 ha−1 in Farm‐1 (27.7–28.1 g) and in treatments of T4 and T11 ha−1, in Farm‐2 (31.4–31.6 g). Marketable bulb yield (MBY) was significantly highest in treatments; T4, T7, T8, and T11 ha−1 in Farm‐1 (8.5–9.3 tons ha−1) and Farm‐2 (10.1–10.5 tons ha−1). Therefore, by combining, the application of 10 tons of biochar (CHB) + 75% of lime (STV) + 75% of inorganic‐fertilizer (NPSB‐fertilizer) ha−1 in strongly acid soil, and 7.5 tons of CHB + 50% of STV + 50% of NPSB‐fertilizer ha−1 rates in very strongly acid soil are recommended for garlic production in Gimbi district, western Ethiopia, and similar areas. In order to draw firm conclusions, future research on more sites is necessary because this study was logically limited to two sites.
Maxillary Protrusion with Severe Overjet Treated by Maxillary Anterior Alveolar Osteotomy
Authors: Tomita Daisuke , Haga Shugo , Tanzawa Fumi , Miyazawa Taira , Nakano Haruhisa , Maki Koutaro
Published: March 2024

In addition to affecting oral functions such as temporomandibular joint function, mastication, and speech, malocclusion caused by skeletal maxillary prognathism also entails sociopsychological implications. Surgical orthodontic treatment to improve occlusion and oral function and to correct esthetic disharmony is important to improve patients’ quality of life. We report the case of a 32‐year‐old woman who visited our hospital with a chief complaint of proclined upper front teeth. Clinical examination revealed maxillary overgrowth and severe labial inclination of the maxillary incisors with palatal gingival recession. The incisal protrusion was corrected with a maxillary anterior alveolar osteotomy—a surgical orthodontic method that could improve the overbite without causing excessive lingual inclination, while also minimizing orthodontic movement of the maxillary anterior teeth. This treatment is generally indicated in cases of maxillary prognathism with a relatively stable occlusal relationship in the molar region. As a result of the treatment, the patient’s chief complaint improved and a long‐term functional occlusion was achieved. This paper outlines the pre‐ and posttreatment skeletal and occlusal changes.
The Short Version of Approaches and Study Skills Inventory for Students: A Confirmatory Factor Analysis in Vietnamese Nursing Students
Authors: Bui Hien Thi , Huyen Nguyen Thi Hoa , Phan Hong Anh , Hoang Hue Thi , Tran Duong Thuy , Hoang Van Lan
Published: March 2024

Students’ academic performance is substantially influenced by their learning approaches, which reflect their intentions when confronted with a learning situation and the corresponding strategies they employ to fulfil these intentions. Since there was no validated questionnaire that aimed to assess students’ learning approaches in the context of Vietnam, the purpose of this study is to validate a Vietnamese short version of the approaches and study skills inventory for students (ASSIST). A cross-sectional study involved translation and validation with a group of Vietnamese undergraduate nursing students. This questionnaire was translated by two independent bilingual experts and reviewed by a team of two other experts. To test the internal reliability, Cronbach’s alpha was used with 102 nursing students in a nursing school. Regarding construct validity, the study checked whether the original three subscales fit the data by using confirmatory factor analysis in a group of 1340 nursing students from ten nursing schools across Vietnam. The result indicated that the internal consistency of the Vietnamese ASSIST short version was good; Cronbach’s alpha of the total scale was 0.89. Cronbach’s alphas for deep, strategic, and surface approaches were 0.82, 0.89, and 0.70, respectively. By using confirmatory factor analysis, the model of three subscales showed a moderate fit (X2/df = 7.097, p<0.01, CFI = 0.927, TLI = 0.886, and RMSEA = 0.067). As such, this finding supported the proposed three-factor structure of the short version of ASSIST in the context of Vietnam, which will be a useful tool for educators and educational institutions to assess students’ learning approaches initially.
Ecoepidemiology of Chagas Disease in a Biological Corridor in Southeastern Mexico: A Promising Approach to Understand the Risk of Chagas Disease
Authors: Cruz-Alegría Ingrid Yazmin , Santos-Hernández Nancy Gabriela , Ruiz-Castillejos Christian , Ruan-Soto Juan Felipe , Moreno-Rodríguez Adriana , Flores-Villegas Any Laura , Gutiérrez-Jiménez Javier , Hernández-Mijangos Luis Arturo , Espinoza-Medinilla Eduardo Estanislao , Vidal-López Dolores Guadalupe , De Fuentes-Vicente José Antonio
Published: March 2024

Ecoepidemiology is an emerging field that attempts to explain how biotic, environmental, and even social factors influence the dynamics of infectious diseases. Particularly in vector-borne diseases, the study under this approach offers us an overview of the pathogens, vectors, and hosts that coexist in a given region and their ecological determinants. As a result of this, risk predictions can be established in a changing environment and how it may impact human populations. This paper is aimed at evaluating some ecoepidemiological characteristics of Chagas disease in a natural reserve in southeastern Mexico that borders human settlements. We carry out a cross-sectional study in 2022 where we search insects manually and with light traps. We set traps for small mammals and bats and conducted interviews with the inhabitants living around the study site. We identified the presence of Triatoma dimidiata and T. huehuetenanguensis species with a percentage of TcI T. cruzi infection of 68.4% (95% CI: 66.9-69.9). Temperature and humidity were not determining factors for the probability of insect capture. Of the 108 wild mammals (Chiroptera, Rodentia, and Didelphimorphia), none was infected with T. cruzi. Knowledge about Chagas disease in nearby inhabitants is poor, and some characteristics were found on the periphery of dwellings that could offer a refuge for insect vectors. With this information, surveillance strategies can be generated in the study area that reduce the risk of transmission of T. cruzi parasite to humans, and it is expected to motivate the use of this field in future research.
DUSP22 Ameliorates Endothelial‐to‐Mesenchymal Transition in HUVECs through Smad2/3 and MAPK Signaling Pathways
Authors: Chen Lu , Su Hongyu , Tao Zekai , Liang Cui , Liu Zhongzhao , Dong Yiming , Zheng Peipei , Liu Yuan
Published: March 2024

Endothelial‐to‐mesenchymal transition (EndMT) is the process by which endothelial cells lose their endothelial properties and acquire mesenchymal characteristics. Dual‐specific protein phosphatase 22 (DUSP22) inactivates various protein kinases and transcription factors by dephosphorylating serine/threonine residues: hence, it plays a key role in many diseases. The aim of this study was to explore the functional role of DUSP22 in EndMT. In the transforming growth factor‐β‐induced EndMT model in human umbilical vein endothelial cells (HUVECs), we observed a downregulation of DUSP22 expression. This DUSP22 deficiency could aggravate EndMT. Conversely, the overexpression of DUSP22 could ameliorate EndMT. We used signaling pathway inhibitors to verify our results and found that DUSP22 could regulate EndMT through the smad2/3 and the mitogen‐activated protein kinase (MAPK) signaling pathways. In summary, DUSP22 ameliorates EndMT in HUVECs in vitro through the smad2/3 and MAPK signaling pathways.
Predicting Personal Exposure to PM2.5 Using Different Determinants and Machine Learning Algorithms in Two Megacities, China
Authors: Li Na , Li Yunpu , Xu Dongqun , Liu Zhe , Li Ning , Chartier Ryan , Chang Junrui , Wang Qin , Xu Chunyu
Published: March 2024

The primary aim of this study is to explore the utility of machine learning algorithms for predicting personal PM2.5 exposures of elderly participants and to evaluate the effect of individual variables on model performance. Personal PM2.5 was measured on five consecutive days across seasons in 66 retired adults in Beijing (BJ) and Nanjing (NJ), China. The potential predictors were extracted from routine monitoring data (ambient PM2.5 concentrations and meteorological factors), basic questionnaires (personal and household characteristics), and time‐activity diary (TAD). Prediction models were developed based on either traditional multiple linear regression (MLR) or five advanced machine learning methods. Our results revealed that personal PM2.5 exposures were well predicted by both MLR and machine learning models with predictors extracted from routine monitoring data, which was indicated by the high nested cross‐validation (CV) R2 ranging from 0.76 to 0.88. The addition of predictors from either the questionnaire or TAD did not improve predictive accuracy for all algorithms. The ambient PM2.5 concentrations were the most important predictor. Overall, the random forest, support vector machine, and extreme gradient boosting algorithms outperformed the reference MLR method. Compared with the traditional MLR approach, the CV R2 of the RF model increased up to 7% (from 0.82 ± 0.13 to 0.88 ± 0.10), while the RMSE reduced up to 18% (from 19.8 ± 5.4 to 16.3 ± 4.5) in BJ.
Case of Circulating Tumor Cells Discovered in Extensive Deep Venous Thrombosis in a Patient with Known Urothelial Carcinoma
Authors: Yetiskul Ekrem , Kimyaghalam Ali , Khan Shahkar , Grabie Yisroel , Rizvi Taqi A. , Khan Salman
Published: March 2024

Background. Currently, minimal data are available to explore the composition of venous thromboembolism in patients with cancer. This case report discusses a presentation of venous thromboembolism in a patient with high-grade urothelial carcinoma and highlights the pathology findings in thrombi. Case Presentation. A 55-year-old female who was diagnosed with high-grade urothelial carcinoma with multiple metastases developed an extensive deep vein thrombosis in her left lower extremity. Endovascular revascularization was indicated due to left lower extremity pain and swelling not responsive to anticoagulation. A mechanical thrombectomy was performed, and samples were sent for pathology. Pathologic examination discovered minute fragments of metastatic carcinoma, admixed with laminated blood clots (thrombus). The morphology of metastatic carcinoma and the immunostain profile were compatible with metastatic carcinoma of bladder origin. Conclusion. Cancer is a well-known risk factor for developing VTEs, and it is estimated that approximately 4–20% of cancer patients will experience VTE at some stage, the rate being the highest in the initial period following diagnosis. Annually, 0.5% of cancer patients will experience thrombosis compared with a 0.1% incidence rate in the general population (Elyamany et al., 2014). Despite knowing the increased incidence of VTEs in cancer patients, there are few studies to date that analyze the composition of thrombi in patients with cancer.
Genetically Engineered Microorganisms and Their Impact on Human Health
Authors: Mahdizade Ari Marzie , Dadgar Leila , Elahi Zahra , Ghanavati Roya , Taheri Behrouz
Published: March 2024

The emergence of antibiotic-resistant strains, the decreased effectiveness of conventional therapies, and the side effects have led researchers to seek a safer, more cost-effective, patient-friendly, and effective method that does not develop antibiotic resistance. With progress in synthetic biology and genetic engineering, genetically engineered microorganisms effective in treatment, prophylaxis, drug delivery, and diagnosis have been developed. The present study reviews the types of genetically engineered bacteria and phages, their impacts on diseases, cancer, and metabolic and inflammatory disorders, the biosynthesis of these modified strains, the route of administration, and their effects on the environment. We conclude that genetically engineered microorganisms can be considered promising candidates for adjunctive treatment of diseases and cancers.
The Relationship between Perceived Social Support and Bullying Behavior in Nursing Education among Nursing Students: The Mediating Role of Positive Psychological Capital
Authors: Ding Xing , Yang Li-Wei , Huang Ya-Lian , Yang Huan-Huan , Zhao Xuan , Jiang Qi , Gao Hao-Yue , Liu Rui-An
Published: March 2024

Purpose. To examine the mediating role of positive psychological capital in the relationship between nursing students’ perceived social support and bullying behavior in nursing education. Design and Methods. In May 2021, a sample of 1196 nursing students majoring in nursing at a medical college was conveniently selected as the study population. A set of questionnaires, including a General Information Questionnaire, Perceived Social Support Scale (PSSS), Positive Psychological Capital Questionnaire (PPQ), and Bullying Behaviors in Nursing Education Scale (BBNE), were utilized to examine and analyze the relationships among the variables. Findings. The scores of nursing students’ PSSS, PPQ, and BBNE were 68.19 ± 11.90, 123.97 ± 18.74, and 13.31 ± 9.24, respectively. There was a negative correlation (r = −0.421,P<0.01) between perceived social support and bullying behaviors in nursing education among nursing students. Positive psychological capital partially mediated the relationship between perceived social support and bullying behaviors in nursing education, accounting for 19.88% of the total effect. Further analysis revealed that self-efficacy, resilience, optimism, and hope in positive psychological capital each played a partial mediating role in the impact of nursing students’ perceived social support on bullying behaviors in nursing education, with the ratio of indirect effect to total effect being 5.38%, 7.74%, 6.09%, and 5.34%, respectively. Practice Implications. The impact of perceived social support on bullying behaviors in nursing education is substantial among nursing students, and it can indirectly influence bullying behaviors through positive psychological capital. Nursing educators should pay attention to nursing students with lower levels of social support, and they can decrease the occurrence of bullying behaviors by enhancing social support and fostering higher levels of psychological capital.
Variations in Gut Microbiome Profiles Based on Different DNA Extraction Methods: A Comparative Study
Authors: Liu Yameng , Zhong Xianchun , Wang Yibin , Wang Kanglong , Li Cuina , Liang Xinyu , Lin Suqin , Zhou Ru , Xie Cen
Published: March 2024

Objectives. To compare four DNA extraction methods for recovering bacterial DNA from mammalian faecal samples. Methods and Results. Three commercial kits from QIAamp, TIANamp, and MAGEN, together with the classic cetyltrimethylammonium bromide (CTAB) method, were evaluated for their performance in extracting bacterial DNA from the gut microbiota of five mammals, including humans (Homo sapiens), macaques (Macaca mulatta), dogs (Canis lupus familiaris), golden hamsters (Mesocricetus auratus), and mice (Mus musculus). First, we assessed the efficiency of the four methods based on DNA yield, purity, and integrity. Then, we investigated the impact of these methods on microbial composition and diversity and examined the relative abundance of dominant phyla bacteria based on 16S rRNA gene sequencing and real-time quantitative PCR. Our results showed that the CTAB method yielded relatively larger amounts of DNA, while the MAGEN kit yielded more Firmicutes DNA and reflected the true status of the microbiota more accurately. Conclusions. Of the four methods tested, the traditional CTAB method and commercial MAGEN kit accomplished the best performance in terms of DNA concentration and Firmicutes abundance across most of the tested species. As the CTAB method and MAGEN kit exhibited different advantages, we further tested and compared their DNA extraction performance on a defined microbiota comprising six strains from four dominant phyla to see which one better reflected the true status of the microbiota. In conclusion, the MAGEN kit was found to be superior to the CTAB method, as the testing results were closer to that of the defined microbiota.
Empowering Radiographers: A Call for Integrated AI Training in University Curricula
Authors: Rawashdeh Mohammad A. , Almazrouei Sara , Zaitoun Maha , Kumar Praveen , Saade Charbel
Published: March 2024

Background. Artificial intelligence (AI) applications are rapidly advancing in the field of medical imaging. This study is aimed at investigating the perception and knowledge of radiographers towards artificial intelligence. Methods. An online survey employing Google Forms consisting of 20 questions regarding the radiographers’ perception of AI. The questionnaire was divided into two parts. The first part consisted of demographic information as well as whether the participants think AI should be part of medical training, their previous knowledge of the technologies used in AI, and whether they prefer to receive training on AI. The second part of the questionnaire consisted of two fields. The first one consisted of 16 questions regarding radiographers’ perception of AI applications in radiology. Descriptive analysis and logistic regression analysis were used to evaluate the effect of gender on the items of the questionnaire. Results. Familiarity with AI was low, with only 52 out of 100 respondents (52%) reporting good familiarity with AI. Many participants considered AI useful in the medical field (74%). The findings of the study demonstrate that nearly most of the participants (98%) believed that AI should be integrated into university education, with 87% of the respondents preferring to receive training on AI, with some already having prior knowledge of AI used in technologies. The logistic regression analysis indicated a significant association between male gender and experience within the range of 23-27 years with the degree of familiarity with AI technology, exhibiting respective odds ratios of 1.89 (COR=1.89) and 1.87 (COR=1.87). Conclusions. This study suggests that medical practices have a favorable attitude towards AI in the radiology field. Most participants surveyed believed that AI should be part of radiography education. AI training programs for undergraduate and postgraduate radiographers may be necessary to prepare them for AI tools in radiology development.
Analytical Approximate Solutions of Caputo Fractional KdV‐Burgers Equations Using Laplace Residual Power Series Technique
Authors: Burqan Aliaa , Khandaqji Mona , Al-Zhour Zeyad , El-Ajou Ahmad , Alrahamneh Tasneem
Published: March 2024

The KdV‐Burgers equation is one of the most important partial differential equations, established by Korteweg and de Vries to describe the behavior of nonlinear waves and many physical phenomena. In this paper, we reformulate this problem in the sense of Caputo fractional derivative, whose physical meanings, in this case, are very evident by describing the whole time domain of physical processing. The main aim of this work is to present the analytical approximate series for the nonlinear Caputo fractional KdV‐Burgers equation by applying the Laplace residual power series method. The main tools of this method are the Laplace transform, Laurent series, and residual function. Moreover, four attractive and satisfying applications are given and solved to elucidate the mechanism of our proposed method. The analytical approximate series solution via this sweet technique shows excellent agreement with the solution obtained from other methods in simple and understandable steps. Finally, graphical and numerical comparison results at different values of α  are provided with residual and relative errors to illustrate the behaviors of the approximate results and the effectiveness of the proposed method.
Suppression of Inflammation in Adipocyte-Macrophage Coculture by Passion Fruit Seed Extract: Insights into the p38 and NF-ҡB Pathway
Authors: Chusongdam Sukanya , Woonnoi Wanwipha , Moolsup Furoida , Aenglong Chakkapat , Chonpathompikunlert Pennapa , Tanasawet Supita , Saetan Jirawat , Sukketsiri Wanida
Published: March 2024

Obesity, which is characterized by chronic low-grade inflammation, involves the infiltration of immune cells into adipose tissue, leading to the secretion of inflammatory cytokines and subsequent inflammation. Therefore, the aim of this study was to examine the potential of passion fruit seed extract (PSEE) in mitigating lipopolysaccharide (LPS)-induced inflammation in a coculture system comprising macrophages and adipocytes. PSEE demonstrated significant reductions in reactive oxygen species (ROS) and nitric oxide (NO) levels, primarily achieved through the downregulation of inducible nitric oxide synthase (iNOS) protein expression in LPS-induced adipocyte-macrophage cocultures. Furthermore, PSEE effectively suppressed the secretion of TNF-α and IL-1β by attenuating the gene expression of these cytokines, as well as other inflammation-related genes such as MMP-2, IL-6, and MCP-1. Notably, PSEE exhibited potent inhibitory effects on the p38 and NF-κB signaling pathways, thus alleviating inflammation in the LPS-induced adipocyte-macrophage cocultures. Additionally, PSEE led to a decrease in the expression of ACC, HSL, and FaSN, while aP2 and ATGL showed increased expression in LPS-induced cocultured macrophages and adipocytes. These findings suggest that passion fruit seed extract effectively combats inflammation by suppressing the p38 and NF-κB signaling pathways, resulting in reduced levels of proinflammatory cytokines, NO, and ROS production.
Performance Prediction and Optimization of Single‐Piston Free Piston Expander‐Linear Generator Based on Machine Learning and Genetic Algorithm
Authors: Li Jian , Zuo Zhengxing , Jia Boru , Feng Huihua , Zhang Hongguang , Mei Bingang
Published: March 2024

This paper proposed a single‐piston free piston expander‐linear generator (SFPE‐LG) prototype applied to organic Rankine cycle systems. Two valve timing control strategies, namely, time control strategy (TCS) and position control strategy (PCS), were developed. Based on the experimental data, a back propagation neural network (BPNN) prediction model was established. The effects of structural parameters such as neural network layers, transfer function, training function, hidden layer nodes, and learning rate on the prediction accuracy of this BPNN model were discussed. The training and prediction accuracy of the BPNN model was verified using 5‐fold cross‐validation and Wilcoxon signed‐rank test. Moreover, the BPNN model was integrated with a genetic algorithm to predict and optimize the maximum output power of the SFPE‐LG. The results showed that the BPNN model used to predict the motion characteristics and output performance of the SFPE‐LG exhibits strong learning ability and high prediction accuracy. Notably, the prediction accuracy of the BPNN model is significantly higher under the PCS compared to TCS. The effect of hidden layer nodes on mean square error (MSE) and correlation coefficient (R) is greater than that of the learning rate. When the number of hidden layer nodes exceeds 30, the BPNN model consistently achieves low MSE and high R. The optimization results showed that the SFPE‐LG can obtain a maximum output power of 141.69 W under the TCS, when the working parameters are inlet pressure of 0.7 MPa, intake duration of 35 ms, load resistance of 67 Ω, and expansion duration of 104 ms, respectively.