bluuebunny/crossref_metadata_2025 huggingface dataset:
Parental Primary Language, Access to Care, And Parental Report of Developmental Delays Among Premature Infants
Authors: Sternad Lindsay , McPheeters Melissa , Heinrich Carolyn , Gonzales Gilbert , Patrick Stephen
Published: February 2024

Objectives: We sought to determine if premature infants of Spanish-speaking caregivers had poorer access to healthcare and higher prevalence of reported developmental delays compared to an English-speaking cohort.
Methods:This study used 2016-2018 National Survey of Children’s Health (NSCH) data. Our outcomes were (1) various measure of access to healthcare and (2) caregiver reported developmental delay.
Results: Premature children in Spanish-speaking families were less likely to have a medical home (29.1% vs. 49.3%, p<0.001) or primary care provider (58.0% vs. 73.7%, p<0.001); their caregivers were more likely to report their child having difficulty using their hands (28.1% vs. 11.3%, p=0.03) but less likely to report having a formal diagnosis of developmental delay (8.7% vs. 14.5%, p = 0.05).
Conclusions: In a national sample, Spanish-speaking families of premature infants reported difficulty accessing a medical home, preventative services, and a formal diagnosis of developmental delay, despite reporting more concerns about specific delays.
Measuring Unit for Synchronously Collecting Air Dose Rate and Measurement Position
Authors: Kawabata Kuniaki , Imabuchi Takashi , Shirasaki Norihito , Suzuki Soichiro , Ito Rintaro , Aoki Yuto , Omori Takazumi
Published: February 2024

This paper describes a measuring unit to realize the synchronous collection of air dose rate and measurement position for efficient dosimetry survey and data logging in a working space. The developed prototype comprises a three-dimensional light detection and ranging-based mapping part and a dosimetry part, which are integrated into a single measurement unit through an embedded computer that installs a ROS (Robot Operating System) framework. The unit can function as a standalone system with embedded batteries. Since it is portable, on-line data gathering in the workspace can be realized, thereby maintaining consistency between the air dose rate and the measurement position. In this paper, we describe the prototype system configuration and the experimental results obtained in the mockup test space and nuclear facility to discuss its performance.
Construction of bioactive electrospun microfibers with VEGF to facilitate the repair of infected wounds
Authors: Lin Xunxun , jiashaochuan Shaochuan , Zhou Boya , Liu Haibo , Li Xixi , Ling Yu , Wei Yanlin , Jiang Xiaoyang , Rong Yongxian , Li Hongmian
Published: February 2024

Biomimetic dressings are considered as one of the ideal medical dressings to combat bacterial infections, shorten the tissue regeneration process, and facilitate wound repair. Researchers are committed to developing a new generation of wound dressings with unique functions to promote wound repair and healing. Here, a strategy for composite nanofiber membrane construction based on natural biopolymers HA, Gel and PLGA with carrying vascular endothelial growth factor (VEGF) was provided for pro-angiogenic wound repair. The composite nanofiber membrane prepared by electrospinning technology can maintain its biomimetic properties and biochemical properties of the functional factors. The enhanced mechanical properties and flexibility enable it to fit the wound and have great biocompatibility. In addition, the highly efficiently loaded VEGF can be controllable released and targeted to the wound area, thereby promoting the exchange of nutrients and oxygen, proliferation and migration of endothelial cells, and the formation of vascular lumen, effectively accelerating tissue repair and wound healing while avoiding unnecessary drug diffusion. Therefore, composite nanofiber dressing of VEGF@PAG can break through the limitations of traditional patches and become ideal candidates for wound healing and related biomedical applications.
CT radiomics based model for differentiating malignant and benign small (≤20mm) solid pulmonary nodules
Authors: Sun Jing-Xi , Zhou Xuan-Xuan , Yu Yan-Jin , Wei Ya-Ming , Xu Qing-Song , Chen Shuang-Shuang
Published: February 2024

Background
At present, computed tomography (CT) radiomics-based models capable of evaluating small (≤ 20 mm) solid pulmonary nodules (SPNs) are lacking. Accordingly, the present study sought to develop a CT radiomics-based model capable of differentiating between benign and malignant small SPNs.
Methods
Between January 2019 and November 2021, this study enrolled consecutive patients presenting with small SPNs, randomly assigning these individuals to training and testing cohorts at an 8:2 ratio. CT images were processed to extract radiomics features, with a radiomics scoring model being developed based on the features selected in the training group through univariate and multivariate logistic regression analyses. The testing cohort was then used to validate the developed predictive model.
Results
In total, this study included 210 patients in the training (n = 168) and testing (n = 42) cohorts. Radiomics scores were ultimately calculated based on 9 selected CT radiomics features. Traditional CT and clinical risk factors associated with malignancy in SPNs included lobulation (P < 0.001), spiculation (P < 0.001), and a larger diameter (P < 0.001). The developed CT radiomics scoring model consisted of the following formula: X = -6.773 + 12.0705×radiomics score + 2.5313×lobulation + 3.1761×spiculation + 0.3253×diameter. The CT radiomics-based model, CT radiomics score, and clinicoradiological score were associated with area under the curve (AUC) values of 0.957, 0.945, and 0.853, respectively, in the training cohort, while the testing cohort exhibited corresponding AUC values of 0.943, 0.916, and 0.816.
Conclusions
The CT radiomics-based model designed in the present study offers valuable diagnostic accuracy when employed to distinguish between benign and malignant SPNs.
Resiliency-Constrained Placement and Sizing of Virtual Power Plants in the Distribution Network Considering Extreme Weather Events
Authors: Zadehbagheri Mahmoud , Pirouzi Sasan , Dehghan Mohammad , Kiani Mohammadjavad
Published: February 2024

The placement and scale of virtual power plants (VPPs) in distribution networks are the only topics covered in this article that pertain to the resilience of the grid to severe weather. This problem is framed as a two-objective optimization, where the predicted energy that the network would not deliver in the case of an earthquake or flood, and the annual planning cost of the VPP, are the two objective functions to be reduced. The constraints include the formula for VPP planning, limitations on network operation and resilience, and equations for AC power flow. Uncertainties about demand, renewable power, energy prices, and the supply of network hardware and VPP components are all taken into account in stochastic programming. The proposed technique achieves a single-objective formulation in the subsequent stage by the use of a Pareto optimization strategy based on the ε-constraint method. This article uses a solver based on a hybrid of Crow search algorithm (CSA) and sine cosine algorithm (SCA) to achieve the trustworthy optimal solution with lowest dispersion in the final response. In order to tackle the problem, the proposed system looks at how the VPP affects network resilience, scales it, and combines it with the hybrid evolutionary algorithm. In the end, the numerical findings verify that the optimal placement and dimensions of VPPs help to improve the operational, financial, and resilience status of the distribution network by applying the proposed problem to a 69-bus distribution network.
Lycopene Alleviates Deoxynivalenol-induced Toxicity in Porcine Intestinal Epithelial Cells by Mediating Mitochondrial Function
Authors: Wang Jie , Lu Fan , Gu Shanshen · , Dai Chaohui · , Xiao Yeyi · , Bao Wenbin · , Wang Haifei ·
Published: February 2024

Deoxynivalenol (DON) is widely found in food and feed, posing a threat to human and animal health. Lycopene (Lyc) is a natural plant extracts with significant antioxidant properties. This study was conducted to investigate the protective effects of Lyc on IPEC-J2 cells upon DON exposure. The detection of cell viability and trypan blue staining showed that Lyc alleviated cell damage and decreased cell apoptotic rate induced by DON. The analysis of reactive oxygen species (ROS) level and antioxidant parameter measurements showed that Lyc significantly down-regulated the content of ROS and restored antioxidant enzyme activity. Furthermore, mitochondrial membrane potential (ΔΨm) detection, mitochondrial DNA copy number (mtDNAcn) assay and adenosine triphosphate (ATP) concentration detection showed Lyc improved mitochondrial function after DON exposure. The results of transcriptome analysis, ROS detection and CCK8 assay suggested that Lyc may activated the oxidative phosphorylation (OXPHOS) to improve mitochondrial function. Conclusively, our results suggested that Lyc alleviated DON-induced oxidative stress by improving mitochondrial function through OXPHOS signaling pathway.
Recycling of Broken Glasswares Into an Adsorbate for Cationic and Anionic Dyes for Waste Water Treatment
Authors: Angayarkanny S. , Prabha J. , Sri M.
Published: February 2024

Chemically modified broken laboratory glass wares were proved to act as an efficient adsorbent for both anionic and cationic dyes from waste water. The glassware wastes were chemically modified with sodium hydroxide as silica gel, which are used for cationic dyes adsorption from wastewater. The said modified silica gel is again modified with cetyl trimethyl ammonium bromide (CTAB), which is a cationic amphiphile for anionic dye adsorption from wastewater. The both silica gel and modified silica gel were characterized with Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Analysis (EDAX) and Fourier Transform Infrared Spectroscopy (FTIR). The adsorptions of the dyes were confirmed visually as well as using UV- visible spectroscopy. The adsorption capacities of the modified silica gel were quantified using model dyes like methylene blue (MB) (cationic dye) and methyl orange (MO) (anionic dye). The adsorption was found to be 97.84 mg/g for methylene blue and 196.14 mg/g for methyl orange by the modified silica gel. The efficiency of the adsorption was quantified at various dosages, pH, initial concentrations and contact time. The mechanism of the adsorption is predicted by applying mathematical models. This study demonstrated an efficient way of recycling the non-biodegradable broken glassware waste from laboratories for the waste water treatment by modifying as an efficient adsorbate for both anionic and cationic dyes. This study doubly contributes towards the environmental protection by recycling hazardous broken glass wares by modifying that as an adsorbate towards clean water.
Taming numerical imprecision by adapting the KL divergence to negative probabilities
Authors: Pfahler Simon , Georg Peter , Schill Rudolf , Klever Maren , Grasedyck Lars , Spang Rainer , Wettig Tilo
Published: February 2024

The Kullback-Leibler (KL) divergence is frequently used in data science. For discrete distributions on large state spaces, approximations of probability vectors may result in a few small negative entries, rendering the KL divergence undefined. We address this problem by introducing a parameterized family of substitute divergence measures, the shifted KL (sKL) divergence measures. Our approach is generic and does not increase the computational overhead. We show that the sKL divergence shares important theoretical properties with the KL divergence and discuss how its shift parameters should be chosen. If Gaussian noise is added to a probability vector, we prove that the average sKL divergence converges to the KL divergence for small enough noise. We also show that our method solves the problem of negative entries in an application from computational oncology, the optimization of Mutual Hazard Networks for cancer progression using tensor-train approximations.
Community Pharmacists’ Perceptions and Experiences of Medicine Shortages in Disruptive Situations: A Qualitative Study
Authors: Bachoolall Rivana , Suleman Fatima
Published: February 2024

Background: Medicine shortages are a challenge in upper, lower and middle-income countries, including South Africa. In recent years, community pharmacists in Durban, South Africa, have experienced disruptions such as the COVID-19 pandemic, flooding, civil unrest and electricity disruptions. Little is known about the impact of these disruptive situations on medicine shortages in community pharmacies.
Aim: Exploring the perceptions of community pharmacists and their experiences with medicine shortages during the COVID-19 pandemic and other disruptive situations.
Method: Convenience and snowball sampling were used to recruit community pharmacists in Durban, South Africa. Semi-structured interviews were conducted in person or via an online video conferencing platform. All interviews were audio-recorded and transcribed verbatim. The transcripts were analysed thematically on NVivo 14 software, using the Framework Method.
Results:A total of 15 community pharmacists were interviewed. Five major themes emerged from thematic analysis, viz. the perceptions of medicine shortages, the impact of disruptive situations, the consequences of medicine shortages, mitigation strategies; and further suggestions and resources. Perceptions were that shortages were exacerbated by the disruptive situations. Participants perceived a negative financial impact on patients and pharmacies, with out-of-pocket costs affecting the former and loss of income affecting the latter. The mitigation strategies used were contacting stakeholders, medicine substitution and stock management.
Conclusion: Community pharmacists felt that medicine shortages required improved communication, collaboration, policies, notification systems and guidelines to mitigate the problem further.
Does the history of option quality affect nest site choice in the acorn ant?
Authors: Tyler Kelsi , Vassie Dominique , Sasaki Takao
Published: February 2024

During decision−making, animals consider not only the current but also the past quality of options. For example, when humans evaluate performance (e.g. sales) of employees, they do not only consider the average performance but also the trend of performance— ascending performance is often viewed as more favorable than descending performance. In our study, we test if non-human animals have a similar bias when they are evaluating options using house-hunting by the acorn ant, Temnothorax curvispinosus, as our model system. Our data show that when nest-site quality is static over time, ant colonies tend to prefer the nest site which was better (i.e. darker) between two nest options. However, when the nest quality changes—one improves and the other worsens—over time, more colonies choose the low-quality, but improving, nest than the high-quality, but worsening, nest. These results suggest that a continuous change of option quality may influence evaluation. We discuss alternative explanations for our results, possible mechanisms and potential ecological benefits for keeping track of the nest-site quality.
A Study on Stock Forecasting Using Deep Learning and Statistical Models
Authors: Jaiswal Aditya
Published: February 2024

Predicting a fast and accurate model for stock price forecasting is been a challenging task and this is an active area of research where it is yet to be found which is the best way to forecast the stock price. Machine learning, deep learning and statistical analysis techniques are used here to get the accurate result so the investors can see the future trend and maximize the return of investment in stock trading. This paper will review many deep learning algorithms for stock price forecasting. We use a record of s&p 500 index data for training and testing. The survey motive is to check various deep learning and statistical model techniques for stock price forecasting that are Moving Averages, ARIMA which are statistical techniques and LSTM, RNN, CNN, and FULL CNN which are deep learning models. It will discuss various models, including the Auto regression integration moving average model, the Recurrent neural network model, the long short-term model which is the type of RNN used for long dependency for data, the convolutional neural network model, and the full convolutional neural network model, in terms of error calculation or percentage of accuracy that how much it is accurate which measures by the function like Root mean square error, mean absolute error, mean squared error. The model can be used to predict the stock price by checking the low MAE value as lower the MAE value the difference between the predicting and the actual value will be less and this model will predict the price more accurately than other models.
Semantic segmentation of point clouds of ancient buildings based on weak supervision
Authors: Zhao Jianghong , Yu Haiquan , Hua Xinnan , Wang Xin , Yang Jia , Zhao Jifu , Xu Ailin
Published: February 2024

Semantic segmentation of point cloud of ancient buildings plays an important role in heritage building information modeling (HBIM). Since the point cloud annotation task of ancient architecture point cloud is characterized by strong specialization and large workload, which greatly restricts the application of point cloud semantic segmentation technology in the field of ancient architecture, this paper researches on the semantic segmentation method based on weak supervision for ancient architecture point cloud. Aiming at the problem of small differences between classes of ancient architectural components, this paper introduces a self-attention mechanism, which can effectively distinguish similar components. We also explore the insufficiency of position encoding in baseline to construct a high-precision point cloud semantic segmentation network model for ancient buildings. We call it SQN-DLA. using only 0.1% of the annotations in our homemade dataset and the public dataset ArCH, the mIoU reaches 66.02% and 58.03%, respectively, which is improved by 3.51% and 3.91% compared with baseline, respectively.
Cytogenetic determination of two species of lacewings (Neuroptera: Chrysopidae)
Authors: Acevedo Victoria Fernández , Gil Sergio Rodríguez , Schneider Marcela
Published: February 2024

The Chrysopidae family is the second most important family in the order Neuroptera. Chrysoperla externa and Chrysoperla argentina are two of the four chrysopid species of economic importance in Argentina. Meiotic studies of the species have indicated that have chromosome numbers of 2n = 12. The objective of this study was to analyze and describe the biology of C. externa and C. argentina chromosomes in terms of the cell cycle and karyotype because of the lack of exhaustive details of the karyotype or mitotic cycle of both species of lacewings. Chromosomes of C. externa were measured at the metaphase stage, C. argentina chromosomes were measured at the end of the mitotic prophase. Chromosomes that exhibited natural banding were paired together. Chromosomes that did not show marked differentiation were grouped according to the criterion of a similar chromosome contribution. The chromosomes are rod shaped without primary constriction with a gradual variation in size. In both species, sex chromosomes exhibited marked dimorphism. The X chromosome is among the largest chromosome pairs and the Y chromosome among the smallest. Chrysoperla externa shows natural chromosome banding that allows three of the six pairs that make up its genome to be distinguished. Chrysoperla argentina only two chromosome pairs exhibit natural banding. In conclusion, this study presents, for the first time, a proposal for nomenclature, identification of the chromosomes and kariotype of C. externa and C. argentina, accompanied by detailed information on their cell cycle.
The prognostic value of venous return system in patients with constrictive pericarditis after pericardial stripping: a retrospective cohort study
Authors: Chen Rongping , DU Wei , Liu Dawei
Published: February 2024

This study aimed to explore changes in the venous return system in patients with Constrictive pericarditis (CP) after pericardial stripping and examine their value in predicting mortality. An 8-year single-center retrospective cohort study including patients with CP after pericardial stripping surgery. Hemodynamic parameters were analyzed in 90 patients at 11 time points including before and after surgery and every 4 to 9 hours in the first 48 hours in the ICU (0h, t1-t3, 24h, t4-t6, 48h). Mean systemic filling pressure (Pmsf) and resistance to venous return (RVr) were significantly higher in patients who died (non-survival group) than survivors (P = 0.016 and 0.022, respectively). Pmsf at 0h, Pmsf at 24h, Pmsf − CVP at 0h, RVr before surgery, RVr at 24h, APACHE II score, and SOFA score were evaluated as predictors of 28-day mortality. The area under the receiver operating characteristic curve was largest for RVr before surgery (0.771; 95% confidence interval, 0.583–0.958; P = 0.034). SOFA combined with RVr before surgery had the best performance (area under the receiver operating characteristic curve, 0.883; 95% confidence interval, 0.758–1.000; P = 0.003). Pmsf, Pmsf − CVP, and RVr can be used as valid indicators for prognostic assessment in patients with constrictive pericarditis after pericardial stripping.
A Block Cipher Recognition Scheme Based on Deep Learning Transformer Algorithm
Authors: Yuan Ke , Zhang Bowen , Zhou Yuwei , Sun Hanlin , Yang Wei , Jia Chunfu
Published: February 2024

Cryptographic algorithms are the core of data encryption, and their identification is a prerequisite for in-depth analysis of cryptography. Cryptographic algorithm identification is the process of distinguishing or identifying encryption methods by analyzing potentially distinctive information in the ciphertext when the ciphertext is known. It serves as the foundation for cryptanalysis work. The machine learning-based approach for identifying cryptographic algorithms extracts ciphertext features and trains the machine learning algorithm to build a cryptographic system identification classifier. It has the characteristics of high accuracy, concise operation process, and strong practicability. The ciphertext's complexity and the inter-data interference both rise with the number of cryptographic algorithms. This will lead to a reduction in the recognition rate of traditional machine learning cryptographic algorithm recognition solutions, poor recognition stability, and huge challenges to recognition capabilities. Deep learning algorithms have the characteristics of strong learning ability, a large amount of data processing, wide-coverage, good adaptability, and portability, etc., and have become a hot spot in the realm of cryptographic algorithm identification. In this work, a cryptographic algorithm identification methodology based on the Transformer algorithm is proposed, and the ciphertext feature extraction approach in the NIST randomness test is improved. In the research on cryptographic algorithm identification, we selected five block cipher algorithms: AES, 3DES, Blowfish, CAST, and RC2 as the research objects, and conducted two-class and five-class recognition experiments. According to experimental data, the approach suggested in this article has superior accuracy and stability than the classic machine learning model when the ciphertext size and other experimental variables are the same. Among them, on ciphertext files ranging from 1kb to 512kb, compared to the accuracy of the conventional classic machine learning algorithms SVM, GNB, KNN, RF, and LR, the average recognition accuracy of the two categories is 0.855, which is 29% higher. 33.9%, 28.5%, 34.2%, and 29.9%. Compared with the MLP accuracy of the same deep learning algorithm, the accuracy is also improved by 9%. When conducting the five-category experiment, the recognition accuracy of the scheme put forward in this piece is not less than 0.4. When the ciphertext file is 512KB, the recognition accuracy is as high as 0.42, and the average recognition accuracy of the five categories is 0.412. It is far greater than the other five classic machine learning algorithms and significantly better than the 20% random classification accuracy. Other algorithms' recognition rates fluctuate as ciphertext file sizes change. Among them, the Transformer algorithm has the smallest fluctuation and the best overall effect.
Evolutionary adaptation perspectives on childcare with references to life history plasticity in the modern world: Brazil, Russia, and the USA
Authors: Semenova Olga , Figueredo Aurelio , Tokumaru Rosana , de Felipe Renata , Lucci Tania , Salmon Catherine , Patch Emily , Zambrano Rachel , Bytovskaya Marina
Published: February 2024

Introduction. The cooperative breeding framework suggests that help from extended family members with childrearing is important adaptation for our species survival, and it is universal. However, the degree of alloparental help may vary between societies, families, and over time. We hypothesized that maternal and paternal effort, as well as alloparental carе, would depend both upon resource availability (SЕS) and different mating opportunities for males and females in three countries: Brazil, Russia, and the USA. Methods. We analyzed the intergenerational interactions between family members during childcare via Structural Equation Modeling (SEM) in R-software. Online samples were collected from Brazil (N = 603), Russia (N = 620), and the USA (N = 314). Results and discussion. The results of our study are consistent with previous research on life history (LHT) plasticity, which has shown a negative correlation between low SES and parental effort. However, our models indicated a possible cultural difference in the estimates of poverty paths: in Brazilian and American samples, SES had a greater impact on paternal care than on maternal, while in Russia, poverty had a greater effect on mothers’ effort. This reversed effect size on maternal versus paternal effort in Russia may suggest that Russian mothers experience a trade-off between working outside the home and direct childcare, while Russian fathers may adopt a “faster” LHT strategy as they are the limited sex in the mating pool. Our findings also demonstrate that the parental effort of both parents was positively associated, indicating their mutualistic relationship. We also found that maternal grandparents usually compensate the lack of paternal effort, but their help, as well as the help of paternal grandparents, was indifferent to the poverty cues.
Solar Physics paper
Authors: ABDULAMEER NOUR , allawi habeeb , ujvari Balazs
Published: February 2024

This study examines the complex relationships among high-energy solar particle events (SEPs), solar flares, and coronal mass ejections (CMEs) at the onset of Solar Cycle 25. We investigate the sources and characteristics of high-energy solar particles and explore the dynamics of solar flares and CMEs within this evolving solar cycle.
While CMEs are often associated with SEPs, their occurrence isn't guaranteed due to the intricate interplay of magnetic fields and propagation conditions. Understanding this variability is crucial for improving predictive capabilities related to space weather events.
We leverage advanced observational techniques to analyze CMEs and assess their correlations with solar cycle activity. Our research also considers the potential impacts of these solar events on Earth's space weather and technology infrastructure.
Comparisons with previous solar cycles provide valuable insights into the evolving nature of solar activity. We conclude by discussing the current state of predictive modeling and forecasting, emphasizing the need for continued advancements in space weather prediction models. Overall, this study contributes to our understanding of solar physics and space weather, particularly regarding the dynamics of high-energy solar particle events and their relationships with flares and CMEs during Solar Cycle 25's early stages.
Approximately orthogonal non-negative Tucker decomposition with graph regularized for multiway dimensionality reduction
Authors: Gao Xiang , Lu Linzhang , Liu Qilong
Published: February 2024

Non-negative Tucker decomposition (NTD) is one of the renowned technique in feature extraction and representation for non-negative high-dimensional tensor data. The main focus behind NTD is how to factorize the data to get hold of a high quality data representation from multidimensional directions. However, NTD does not conserve the geometrical structure of the data space and does not consider relationship and property among columns of the factor matrices. In this paper, by using approximately orthogonal constraint and graph regularized constraint , we manage to capture nonlinear local features of data space and further enhance expressiveness of the NTD clustering method. First, based on the uni-side and bi-side approximate orthogonality, we propose two novel approximately orthogonal non-negative Tucker decomposition with graph regularized models, which not only in part make factor matrix tend to be orthogonality, but also preserve the geometrical information from high-dimensional tensor data. Then we develop the iterative updating algorithm depended on multiplicative update rule to solve the proposed models, and provide its convergence and computational complexity. Finally, we use numerical experimental results to demonstrate the effectiveness, robustness and efficiency of the proposed new methods on the real-world image datasets.
Effects of phenylephrine and ephedrine on the Analgesia Nociception Index (ANI) and Nociception Level (NOL)
Authors: Kuroki Masahiro , Onodera Yu , Okada Masayuki , Nakane Masaki
Published: February 2024

Background: There is growing evidence that perinatal HIV infection and exposure affect salivary pH and flow rate in children in most parts of the world, but not against the background of caries and the African demographic. This study aimed to evaluate the impact of HIV infection as well as exposure on salivary properties and their influence upon the dental caries experience among school-aged children in Nigeria.
Method: This cross-sectional study assessed the salivary flow rates and salivary pH of HIV infected and exposed school-aged (4-11) children receiving care at a Nigerian tertiary hospital. A total of 266 consenting participants which comprised of three groups as follows: 1) HIV Infected (HI) (n = 87), 2) HIV Exposed and Uninfected (HEU) (n=82) and 3) HIV Unexposed and Uninfected (HUU) (n=97) were recruited for the study. Questionnaires completed by parents/guardians were used for data collection. Three calibrated dentists performed oral examinations for dental caries. International Caries Detection and Assessment Scores (ICDAS) was used and presented as dmft/DMFT. Salivary pH was measured using MColourpHastTM pH indicator strips, while salivary flow rate was determined by collecting unstimulated whole saliva using the suction method. Data analysis relied on comparative statistics to determine the correlation between HIV exposure and infection on salivary pH and flow rates.
Result: Across the groups, (HI, HEU, and HUU) mean pH of the HI was significantly less than that of HEU and HUU. Similarly, there was a statistically significant difference in the SFR across the three groups (p = 0.004). Other variables such as gender, age and oral hygiene status expressed by the gingival inflammatory scores had no significant influence on the pH and SFR of study participants. There was a rather unexpected positive correlation of DMFT of HI and HEU groups with increasing salivary flow rate; though, the relationship was weak and not significant.
Conclusion: Perinatal HIV exposure and infection significantly impact salivary pH and flow rate among school-aged children in Nigeria. The findings of this study imply that HIV infection influenced the salivary pH, while HIV maternal exposure (without infection) impacted salivary flow rates when compared to the controls.
The Role and Mechanism of Dental Pulp Stem Cells in Highly Porous Microcarriers Forming New Micro-tissues to Accelerate Mandibular Bone Defect Repair
Authors: Shen RenZe , Wang YiCheng , Huang JiaCheng , Tan YongMei , Ke SongXia , Zhou ZiQing , Zheng WeiWen , Chen AiZheng , Ye ZhanChao
Published: February 2024

Objective
Dental pulp stem cells (DPSCs) exhibit strong differentiation and proliferation capabilities but are often discarded. This experiment aimed to prepare PLGA (poly lactic-co-glycolic acid) highly open porous microspheres (HOPMS) and form microscale cell carriers with DPSCs, exploring their efficacy and mechanism in promoting alveolar bone defect repair.
Methods
Rat dental pulp stem cells were cultured and identified. PLGA HOPMS were prepared and their porosity, pore size, degradation rate, and PH change were evaluated. Cytological experiments examined the effects of PLGA HOPMS on cell proliferation, mineralized nodule formation, mineralization gene, and inflammatory factor expression. Cells were transduced with lentivirus for fluorescence marking and co-cultured with PLGA HOPMS to form microscale cell carriers. Subcutaneous and intraosseous implantations were performed to investigate their biocompatibility, angiogenesis, and inflammatory response.
Results
The size and pore diameter of PLGA HOPM were adjustable. PLGA HOPMS promoted the formation of mineralized nodules, mineralization gene expression, and exhibited no cytotoxicity. Subcutaneous experiments demonstrated that microscale cell carriers had excellent biocompatibility with no significant immune rejection. Intraosseous experiments confirmed that this material promoted osteogenesis and angiogenesis. Conclusion: PLGA HOPMS loaded with DPSCs effectively promoted bone and vascular formation in alveolar bone defect repair, proving to be an excellent stem cell transplantation carrier.
Protective role of selenium-binding protein 1 (SELENBP1) in patients with ulcerative colitis: Cross-sectional study
Authors: Fonseca-Camarillo Gabriela , Furuzawa-Carballeda Janette , Priego-Ranero Ángel Alexis , Barreto-Zúñiga Rafael , Martínez-Benítez Braulio , Yamamoto-Furusho Jesus K
Published: February 2024

Background
The expression of selenium-binding protein 1 (SELENBP1), a molecule responsible for the absorption of selenium in the colon, is crucial for its immunoregulatory effect, but this phenomenon has not been studied in patients with UC. The present study aimed to determine the clinical outcome of SELENBP1 expression in colonic tissue from patients with UC.
Methods and Results
The relative mRNA expression of SELENBP1 was analyzed in 34 patients with UC and 20 patients as controls. All the statistical analyses were performed with SPSS 19. SELENBP1 gene expression was significantly lower in patients with active UC than in patients with UC in remission (P = 0.003) and within the controls (P = 0.04). Overexpression of the SELENBP1 gene was associated with a more benign clinical course characterized by initial activity and more than two years of prolonged remission (P = 0.003, OR = 23.7) and an intermittent clinical course (P = 0.001, OR = 47.5), mild histological activity (P = 0.05 OR = 0.11, IC = 1.00-1.41) and severe histological activity (P = 0.02, OR = 0.08, 95% CI = 0.008–0.866). SELENBP1-positive cells were found mainly in the inflammatory infiltrate of the submucosa and internal layers muscular and adventitia from patients with active UC compared to those in the control group (p ≤ < 0.001).
Conclusions
The upregulation of SELENBP1 was associated with a benign clinical course of UC. This is the first report suggesting the immunoregulatory role of SELENBP1 in patients with UC.
Ethnic Disparities in Breast Cancer Patterns in Brazil: Examining Findings from Population-Based Registries
Authors: Silva Jessé Lopes da , de Albuquerque Lucas Zanetti , Rodrigues Mariana Espírito Santo , Thuler Luiz Claudio Santos , de Melo Andréia Cristina
Published: February 2024

Purpose: To investigate breast cancer (BC) incidence and mortality rates among specific racial groups in Brazil.
Methods: BC incidence was evaluated from 2010 to 2015, using Brazilian Population-Based Cancer Registries, incorporating age-standardized ratios and annual average percentage change (AAPC). Clinical and sociodemographic data from 2000 to 2019 were obtained from Hospital-Based Cancer Registries. Mortality data from 2000 to 2020 were sourced from the National Mortality Information System, comparing White women and Black women.
Results: Across 13 Brazilian registries, 70,896 new BC cases were reported from 2010 to 2015. The median BC incidence rate was notably higher for White women (101.3 per 100,000) compared to Black women (59.7 per 100,000). In the general population, non-significant decrease in annual BC incidence was observed (AAPC = -1.2; p = 0.474). Black women were more likely to live in underdeveloped areas, have lower education levels, live without a partner, and have higher alcohol consumption as compared to White women. A higher proportion of Black women received advanced-stage diagnoses (60.1% versus 50.6%, p < 0.001). BC-related mortality analysis showed 271,002 recorded deaths, with significant increase in BC-specific mortality rates in both racial groups. Black women displayed an AAPC of +2.3% (p < 0.001), while White women demonstrated a moderately elevated AAPC of +0.6% (p < 0.001).
Conclusion: This study underscores the need for targeted policies to address disparities in access to early detection and proper treatment, particularly for Black women in underprivileged regions, aiming to improve the survival rates of Brazilian women grappling with BC.
A Prediction of PM10 Air Contamination Using Multiple Linear Regression
Authors: Kumar Lokesh , Kumar Gaurav
Published: February 2024

Sufficient assessment of air contaminants, mainly Particulate Matter, is necessary for effective and persuasive management of air quality. Numerous elements influence PM prediction, therefore it is essential to incorporate the most appropriate input variables to provide the most dependable forecasts. This work used the multiple linear regression technique to select the most appropriate variables for the model. The concentration of PM10 in this work is determined by measuring the amounts of nitrogen dioxide (NO2), air quality index (AQI), and sulfur dioxide (SO2).
Robotic Sorting of Zebrafish Embryos
Authors: Diouf Alioune , Sadak Ferhat , Gerena Edison , Mannioui Abdelkrim , Zizioli Daniela , Fassi Irene , Boudaoud Mokrane , Legnani Giovanni , Haliyo Sinan
Published: February 2024

Transcriptomics and metabolomics, two biological research fields that need large numbers of zebrafish embryos, require the removal of unfertilized or nonviable zebrafish embryos. Biologists routinely conduct the tedious, error-prone, and time-consuming manual sorting of embryos. We suggest a novel approach that combines deep learning and microfluidics for automated sorting to overcome this difficulty. To determine the developmental stage and viability of zebrafish eggs, we trained an optimized YOLOv5 model with 95.8% accuracy and a processing speed of 10.6 ms per frame, classifying them as dead, unfertilized, or alive. The eggs are contained in traps on a microfluidic chip using micro-pumps. After that, the deep learning system can identify and automatically sort the eggs according to their viability by positioning this chip on an XYZ motorized stage. The sorting experiment was conducted in two modes: without feedback and with feedback while using the dead egg position. The first one had a sorting success rate of 90% as opposed to 97.9% for the feedback mode with 3 seconds required for each dead egg. This automated approach provides a precise and efficient way to handle a large number of zebrafish embryos while also greatly reducing the work-load associated with manual sorting. The success rates attained demonstrate the usefulness and effectiveness of our suggested methodology, opening new avenues for biological research involving accurate embryo selection.
Hemospray® (Hemostatic powder TC-325) as monotherapy for acute gastrointestinal bleeding: a multi-center prospective study
Authors: Papaefthymiou Apostolis , Aslam Nasar , Hussein Mohamed , Alzoubaidi Durayd , Gross Seth A , Serna Alvaro De La , Varbobitis Ioannis , Hengehold Tricia A. , López Miguel Fraile , Fernández-Sordo Jacobo Ortiz , Rey Johannes W. , Hayee Bu , Despott Edward J. , Murino Alberto , Moreea Sulleman , Boger Phil , Dunn Jason M. , Mainie Inder , Mullady Daniel , Early Dayna , Latorre Melissa , Ragunath Krish , Anderson John T. , Bhandari Pradeep , Goetz Martin , Kiesslich Ralf , Coron Emmanuel , De Santiago Enrique Rodríguez , Gonda Tamas A. , O'Donnell Michael , Norton Benjamin , Telese Andrea , Simons-Linares Roberto , Haidry Rehan J
Published: February 2024

Background
Topical hemostatic powders are a reliable second-line approach in acute gastrointestinal (GI) bleeding (AGIB) treatment, according to the existing guidelines. Increasing evidence supports the use of hemostatic powder TC-325 (Hemospray®) as monotherapy in specific GI bleeding scenarios. This prospective, multi-center study evaluated the performance of TC-325 as monotherapy for GI hemorrhage.
Methods
Eighteen centres across Europe, and USA contributed between 2016 and 2022 to an international multicentre prospective registry. Adults with AGIB were eligible (melena, hematemesis, hematochezia, Glasgow-Blatchford score ≥ 1 or abnormal Oakland score), unless TC-325 was part of combined hemostasis (adjunctive to clips or thermocautery). The primary endpoint was immediate haemostasis. Secondary outcomes were rebleeding, 7- and 30-day mortality rates. Potential associations with risk factors were investigated with statistical significance set for p ≤ 0.05.
Results
One hundred and ninety patients were included (age range = 51–81, male:female = 2:1). Peptic ulcer (n = 48), upper GI malignancy (n = 79), post endoscopic treatment-related hemorrhage (n = 37), and lower GI lesions (n = 26) were diagnosed. The primary outcome was recorded in 96.3% (95%CI:92.6–98.5) with rebleeding in 17.4% (95%CI:11.9–24.1) when TC-325 was used as primary monotherapy. Post-hemostasis, 9.9% (95%CI:5.8–15.6) died within 7 days and 21.7% (95%CI:15.6–28.9) within 30 days. Regarding peptic ulcer, the immediate hemostasis was achieved in 88% (95%CI:75–95) and 26% (95%CI:13–43) rebled. Increased American Society of Anaesthesiologists (ASA) score was associated with mortality [OR:23.5 (95%CI:1.60–345); p = 0.02]. The primary outcome was achieved in 100% of cases with malignancy and post GI intervention bleeding, with rebleeding in 17% and 3.1%, respectively. Twenty-six patients received Hemospray® for lower GI bleeding, and in all but one the primary outcome was achieved.
Conclusions
TC-325 powder as monotherapy represents a safe and effective modality especially in malignancy- or post-endoscopic intervention-related bleeding. In peptic ulcer bleeding it could be helpful when the standard of care treatment is not feasible or unavailable, to stabilise patients.
Association between High-Density Lipoproteins (HDLs) and Prostate Specific Antigen (PSA): a cross-sectional study from NHANES database
Authors: Adan Mohamed Mohamoud , Hu Bowen , Yan Minbo Yan , Li Gonghui , Hidig Sakarie Mustafe
Published: February 2024

Background:
According to growing evidence, high-density lipoproteins (HDLs ) may raise PSA levels in the prostate. The link between HDL-C and PSA, on the other hand, is debatable and challenging. Hence, this research examined the relationship between HDL-C and PSA in men using the National Health and Nutrition Examination Survey (NHANES) database.
Methods:
We extracted NHANES data for five cycles from 2001 to 2010. The data used for analysis included PSA concentrations, sociodemographic data, and laboratory data. After the screening, 6,669 of 52,195 participants were included in the study. Participants were divided into 4 groups based on HDL-C quartiles. We analyzed categorical and continuous variables using weighted chi-square tests and linear regression models to compare differences between groups. We constructed 3 weighted multivariate linear regression models and assessed the association between HDL-C and PSA using a smoothed curve fit.
Results:
In our study, unadjusted and adjusted multivariate linear regression models revealed a significant positive association between prostate-specific antigen (PSA) concentrations and serum high-density lipoprotein cholesterol (HDL-C) levels. Specifically, each unit increase in HDL-C ratio was associated with an increase in PSA concentration by 0.470 ng/mL (P < 0.001) in the unadjusted model. In minimally adjusted models, accounting for socioeconomic and demographic factors, this correlation remained significant, with an increase of 0.408 ng/mL per unit increase in serum HDL-C (P < 0.001). Furthermore, the stratified analysis revealed various impacts based on socioeconomic status and HDL-C levels, with a significant interaction between household income and HDL-C levels (P = 0.037). Exclusion of subjects with low HDL-C levels strengthened the association, showing a significant increase in PSA concentration with higher HDL-C levels (0.50 ng/mL per 1 mmol/L increase, P = 0.009). Our findings suggest a nuanced relationship between HDL-C levels, socioeconomic factors, and PSA concentrations, highlighting the potential importance of considering these factors in prostate cancer screening and risk assessment.
Conclusion:
This study found a positive association between serum HDL-C and PSA concentrations in adult men in the United States without a prostate cancer diagnosis. Moreover, People with low HDL-C are more likely to be diagnosed with prostate cancer in the late stage of the disease. Hence, people with high levels of HDL-C should be tested for PSA to help early detection of prostate cancer.
UDC Image Restoration via Lightweight Dual-Domain Network
Authors: Zheng Zhuoran , Wang Di , Jia Xiuyi
Published: February 2024

With the development of full-screen devices, smartphone manufacturers have started to explore placing the camera behind the screen, offering larger display-to-body ratios. However, this under-display camera (UDC) imaging system causes varying degrees of degradation for captured RGB images. To address this issue, we propose a lightweight dual-domain network (LD2Net) for restoring UDC images. Specifically, first, the UDC image is decomposed into reflection and illumination images. Then, an amplitude-phase mutual guided block is designed to reconstruct the reflection image in the frequency domain and restore the attenuated highfrequency components. Meanwhile, we employ a multi-scale hybrid dilated convolution block to reconstruct the degeneracy of UDC images in the spatial domain. Finally, the feature maps of the spatial and frequency domain outputs are fused to recover a clear image. In addition, we propose a global perceptual loss function based on the Transformer framework to help LD2Net reconstruct clearer textures and colors. Extensive experiments show that our model can achieve superior results with relatively few parameters. The valid URL for our code is at https://github.com/zzr-idam/LD2Net.