bluuebunny/crossref_metadata_2025_split huggingface dataset:
Intraocular inflammation after intravitreal injection of aflibercept 8 mg for treatment-refractory neovascular age-related macular degeneration: a case report
Authors: Hashiya Nozomu , Maruko Ichiro , Miyaguchi Yuri , Maruko Ruka , Hasegawa Taiji , Iida Tomohiro 
Published: November 2024

Abstract

Background To report a case of intraocular inflammation (IOI) after intravitreal injection of aflibercept 8 mg for treatment-refractory neovascular age-related macular degeneration.Case presentation An 80-year-old man with diabetes mellitus had neovascular age-related macular degeneration refractory to treatment with aflibercept 2 mg. Despite ten injections of faricimab, the exudation remained, and we switched to brolucizumab, which resulted in a mild IOI. The IOI improved with only topical steroids, and we switched back to aflibercept 2 mg for the exudation. However, the exudation remained, and we decided to switch to aflibercept 8 mg after careful discussion with the patient. Two weeks later, he experienced minor ocular pain and photophobia. One month later, although a dry macula was achieved, severe visual impairment occurred due to anterior chamber inflammation, retinal vasculitis, and retinal vascular occlusion. We diagnosed the severe IOI following aflibercept 8 mg and immediately started steroid eye drops and a sub-Tenon injection of triamcinolone acetonide. Although the inflammation resolved, his visual acuity did not improve.Conclusions This case demonstrated a potential dose-dependent inflammatory response following aflibercept 8 mg, which did not occur with aflibercept 2 mg in patients with a history of intraocular inflammation.
Neuropeptide Y regulation of dental pulp neurogenic inflammation provoked by tooth bleaching agents
Authors: Caviedes-Bucheli Javier , Pérez-Villota Mario , Aucú-Miño Karolina , Escobar-Mafla Diana , Muñoz-Alvear Hernan Dario , Rios-Osorio Nestor , Gomez-Sosa Jose Francisco , Diaz-Barrera Luis Eduardo , Cristancho Edgar Güiza – , Munoz Hugo Roberto 
Published: November 2024

Abstract

Background: To assess the neuropeptide Y (NPY) expression in healthy human dental pulp following tooth bleaching with three in-office hydrogen peroxide-based systems: Opalescence Boost© (Ultradent Products, South Jordan, UT), Pola Office© (SDI, Victoria, Australia), and Zoom© (Zoom! Bleaching System; Discuss Dental, Culver City, CA).
Methods: This observational study was designed following the STROBE guidelines. Forty dental pulps were collected from healthy premolars scheduled for extraction for orthodontic reasons. Teeth were divided into four groups containing ten healthy premolars each: Control group (n= 10): the teeth were not exposed to dental bleaching agents (healthy pulps assessed for normal/basal NPY values). Pola Office system group (n= 10): application of Pola office (35% H2O2) for 8 minutes. Opalescent Boost system group (n= 10): application of Opalescent Boost (40% H2O2) for 20 minutes. Zoom system group (n= 10): application of Zoom! (25% H2O2 + cold blue light) for 15 minutes. We rigorously followed the manufacturer's instructions for all bleaching systems. Following the extractions, the pulpal tissue was collected, placed in a 4% formaldehyde solution in Eppendorf tubes, and processed. NPY levels were measured using enzyme-linked immunosorbent assay (ELISA).
Results: The expression levels of NPY in the dental pulp of human premolars showed the lowest value in the control group (0.0263 pmol/mg of pulp tissue), followed by the Zoom group (0.0314 pmol/mg of pulp tissue). An increase in expression was observed in the Pola Office group (0.0399 pmol/mg of tissue), with the highest NPY expression found in the Opalescence Boost group (0.0441 pmol/mg of pulp tissue), which demonstrated a significant difference compared to the control group (P = 0.03).
Conclusion: NPY - whose expression is closely correlated with the bleaching agent's concentration and the intensity of the inflammatory response - mediates the pulp's responses to tooth bleaching agent-induced neurogenic pulp inflammation. NPY is expressed in the dental pulp in both pathological and physiological conditions. The highest NPY expression values were found in OPALESCENCE BOOST® followed by POLA OFFICE®, and ZOOM®.
Trial registration: Clinical Trials.gov ID NCT06606236.
Edge Polarization Topology Integrated with Sliding Ferroelectricity in Moiré System
Authors: Zhong Ni , Fan Wen-cheng , Guan Zhao , Wei Lu-qi , Xu Hao-wen , Tong Wen-yi , Tian Ming , Wan Neng , Yao Cheng-shi , Zheng Jun-ding , Wang Ya-Qiong , Chen Bin-bin , Xiang Pinghua , Duan Chun-Gang 
Published: November 2024

Abstract

Van der Waals moiré heterostructure have been found to exhibits a robust interfacial ferroelectricity down to atomic thickness, and discovering and understanding the complex polarization state in moiré systems is of fundamental interest to condensed-matter research. In this study, we examined the moiré ferroelectricity in twisted h-BN heterostructure by piezoresponse force microscopy. Due to atomic reconstruction, triangular moiré patterns are detected, and we have directly observed sliding ferroelectricity in the center of triangular moiré patterns as well as robust in-plane polarization topology emerging at the boundary of adjacent triangles, which we call edge ferroelectricity. The edge ferroelectricity possesses non-trivial vortex polarization topology. Our calculations trace the origin of this phenomenon to joined piezoelectric effects with sliding ferroelectricity. The present work provides intuitive insights to explore the unique moiré ferroelectricity in a non-polar background matrix, paving the way for potential ultrathin nonvolatile memory applications.
Long-term Climate Change Trends: 43-Year Analysis of Climatic Data in Gangotri National Park, Western Himalaya
Authors: Bansal Deepali , Talukdar Gautam 
Published: November 2024

Abstract

Understanding long-term climatic patterns is essential for assessing climate change impacts and informing adaptation strategies. This study aims to examine the temporal variations in climate from 1981 to 2023 in Gangotri National Park, Western Himalaya. Using NASA’s Modern-Era Retrospective analysis for Research and Applications (MERRA-2), we analysed daily, monthly, and annual agroclimatology data for maximum (Tmax) and minimum (Tmin) temperatures (°C), corrected precipitation (mm/day), and relative humidity (RH, %) at 2 meters. The datasets were processed using R statistical software (version 4.4.1), and seasonal trends were evaluated with linear regression models to quantify the rate of change and statistical significance (p < 0.05). The analysis revealed that Tmax significantly decreased during the monsoon season, with an average decline of 0.01°C per year (p < 0.005), while Tmin increased during both the monsoon and post-monsoon seasons by 0.04°C per year and by 0.02°C per year in summer (p < 0.05). Precipitation trends indicated a substantial rise during the monsoon (0.057 mm per year) and winter (0.016 mm per year), indicating more intense rainfall. RH also increased across all seasons, with the higher rises in summer (0.31% per year) and post-monsoon (0.30% per year). These findings suggest that the observed shifts in these parameters may have a substantial influence on park's distinctive ecosystems. Validation using HOBO fine-scale microclimate loggers confirmed consistent seasonal trends between observational data and NASA POWER estimates, with no significant differences in trendlines (p > 0.05), demonstrating the reliability of NASA POWER for long-term climate studies in this region.
Generating Learning Guides for Medical Education with Large Language Models and Statistical Analysis of Test Results
Authors: Atanet Iván Roselló , Tomova Mihaela , Sieg Miriam , Sehy Victoria , Mäder Patrick , März Maren 
Published: November 2024

Abstract

Background: The Progress Test Medizin (PTM) is a formative test for medical students issued twice a year by the Charité-Universitätsmedizin Berlin. We used data from the PTM to automatically generate personalized feedback designed to identify the knowledge gaps of test participants as exactly as possible with the help of large language models and statistical analysis.
Methods: We have developed a seven-step approach to fulfil the purpose of this study. Firstly, a large language model (ChatGPT 4.0) identified keywords in the form of MeSH terms from all 200 questions of one PTM run. These keywords were checked against the list of medical terms included in the MeSH thesaurus published by the National Library of Medicine (NLM). Meanwhile, answer patterns of PTM questions were also analysed to find whether, given a pair of questions (A,B), knowing the answer to A has a large effect on improving the chance of knowing the answer to B. If that is the case, we say that question A is a precursor question to question B. With this information, we obtained series of questions related to specific MeSH terms and used them to develop a framework that allowed us to assess the performance of PTM participants and compose personalized feedback structured around a curated list of medical topics. A clustering procedure was also applied to construct benchmarking sets.
Results: We simulated the generation of personalized feedback for 1,401 PTM participants out of their test results, thereby producing specific information about their knowledge regarding a number of topics ranging from 34 to 243. Substantial knowledge gaps were found in 14.67% to 21.76% of rated learning topics, depending on the benchmarking set considered.
Conclusion: We designed and tested a method to generate student feedback covering up to 243 medical topics defined by MeSH terms. The feedback received by students in the later stages of their studies was more detailed, as they tend to face more questions matching their knowledge level.
Evaluation of Imipenem-Relebactam Susceptibility Testing in Carbapenem Resistant Pseudomonas aeruginosa: Comparison of Sensititre microplates, Disc diffusion, and MTS gradient strips with broth microdilution
Authors: Biguenet Adrien , Devoos Léa , Rousselot Julie , Bour Maxime , Fournier Damien , Jeannot Katy 
Published: November 2024

Abstract

Four commercial tests EUMDROXFÒ Sensititre microplates, MTS gradient strips, and disc diffusion were evaluated for imipenem-relebactam susceptibility in 148 ceftazidime and imipenem-resistant Pseudomonas aeruginosa strains, using broth microdilution (BMD) as the reference. EUMDROXFÒ Sensititre microplates showed the highest accuracy (CA=93.2%, EA=93.9%, and bias difference +21.8%). While the MTS gradient strips showed acceptable performance (CA= 84.5%; EA=89.9%, and difference of bias =21.0%), the disc method misclassified 25.8% (16/62) of the resistant strains. Given these results, considering an Area of Technical Uncertainty (ATU) for disc diffusion (21-24 mm) in imipenem-relebactam testing is recommended for P. aeruginosa strains.
An experimental study of the mechanically induced crystallization in filled natural rubber: relevant variables, kinetics and effect of loading history
Authors: Bihan Anthony Le , Marco Yann , Saux Vincent Le , Chazeau Laurent , Chenal Jean-Marc , Champy Clément , Warneboldt Iona , Charrier Pierre 
Published: November 2024

Abstract

Modelling crystallization under stretch is a key topic for fatigue design of rubber-like antivibration parts. Nevertheless, the industrial compounds used exhibit dissipative visco-elastic behaviour that complicates splitting between mechanical and time effects on the phase change and its kinetic, and thus the characterization and modelling of the phenomenon. In this study, we consider an industrial recipe for load/unload tension tests over a range of strain rates leading to non-equilibrium cases, and a multi-relaxation cyclic test combining static and monotonic steps (ANH). A systematic analysis of the triplet \{strain, stress, crystallinity ratio\} and their derivatives was applied, taking advantage of well resolved in-situ synchrotron measurements and providing insights into crystallization and melting kinetics and effects of loading history on these ones. The results obtained for this fully formulated material and various loads representative of service conditions provide a precious database to challenge the existing mechanical models aiming at describing the cristallinity index and crystallization/melting kinetics.
Impact of the first COVID-19 lockdown on HIV diagnosis in England: an observational time series study
Authors: Muhammed Feysal Kemal , Kirwan Peter D , Tabassum Tasnuva , Lester James , Samartsidis Pantelis , Brown Alison E , De Angelis Daniela , Chiavenna Chiara , Presanis Anne M 
Published: November 2024

Abstract

Background:
Sexually transmitted infections (STIs) and Human Immunodeficiency Virus (HIV) prevention and care services in England were unavoidably disrupted by the Coronavirus Disease of 2019 (COVID-19) lockdowns, and it is likely incidence was also affected. Estimates of HIV incidence rely on data on trends in new diagnoses, so understanding how much these diagnosis trends changed during lockdown is crucial for estimating incidence.
Methods:
We estimated the magnitude of the effect of the first United Kingdom (UK) national COVID-19 lockdown in March 2020 on subsequent HIV diagnoses, using the quarterly time series of observed diagnoses during the period 2004 to 2020. Using a Bayesian structural time series model we estimated the counterfactual number of new HIV diagnoses in the post-lockdown period that would have occurred in the absence of lockdown. This counterfactual was constructed using ''synthetic controls'', time series correlated with HIV diagnoses pre-lockdown that were themselves unaffected by the lockdown, based on Google Trend data. The effect was estimated as the difference between the counterfactual and the observed number of diagnoses, for strata defined by gender, sexual orientation, age, and whether or not the diagnosis was late.
Results:
All strata had greater than 50% posterior probability that the first COVID-19 lockdown had an effect on HIV diagnosis, with the highest posterior probability of an effect (99%) in heterosexual men aged 50+ with late diagnosis. The estimated overall number of missed diagnoses was 97 (95% prediction interval [-688, 850]). We found that the effect size varied by stratum and by time since the lockdown, with the largest effect in the first quarter after the lockdown. The effect then reduced, as HIV testing, and consequent diagnosis, caught up during the summer of 2020.
Conclusions:
As the number of new HIV diagnoses made in England and estimated incidence fell consistently in the five years pre-pandemic, we hypothesise that the effect of the lockdown may have been less pronounced than it could have been if the epidemic had not already been in decline. Public health campaigns which encouraged HIV home testing during and after the lockdown may also have helped to mitigate the effect.
Trace determination of uric acid based on double C-type five-frequency resonant metamaterial sensor
Authors: Sun Zhen , Liu Guocui , Yuan Tingting , Xu Xujun , Liu Jianjun , Du Yong 
Published: November 2024

Abstract

A rapid trace detection method for uric acid (UA) content based on terahertz metamaterial sensor was developed. UA is the final oxide of purine metabolism in human body and one of serum inflammatory markers. The continuous increase of serum UA content can cause a series of diseases in human body, so the detection and analysis of UA has been widely used in clinical diagnosis and treatment of diseases in recent years. The traditional methods for detecting UA have the disadvantages of high cost of reagents and complicated chemical analysis. However, the detection method of terahertz metamaterial sensor solves this problem well, with fast detection speed, small reagent dosage, non-destructive without labels, and no damage to the analyte. In this work, a double-C-type multi-resonant metamaterial sensor based on aluminum and polyimide materials is designed. The sensor has five resonant peaks in the frequency range of 0.1-1.0 THz, and the highest sensitivity is 208 GHz/RIU, which can be applied to high sensitivity trace detection. The results show that the minimum detection limit of the sensor is 0.001 mg/mL, which is lower than the minimum content of UA in healthy human body. It is expected to be applied to the detection of biomedicine and other fields.
A novel long short-term memory-adaptive feedback-correction gain extended Kalman filter for the high-precision state-of-charge estimation of lithium-ion batteries
Authors: Appiah Emmanuel , Wang Shunli , Takyi-Aninakwa Paul , Bobobee Etse Dablu , Koukoyi Ebenezer 
Published: November 2024

Abstract

Assessing the state of charge (SOC) is essential in guaranteeing the precise and effective use of lithium-ion batteries in electric vehicles and smart devices. For these batteries to continue to be dependable, safe to use, and have an appropriate service life in a variety of applications, such as electric vehicles and portable electronics, accurate SOC estimation by the battery management system (BMS) is essential. To examine the effects of training and testing variables on SOC estimate accuracy, this study makes use of transfer learning in a long short-term memory (LSTM) network. It also focuses on applying an adaptive feedback correction-gain extended Kalman filter (AFGEKF) and an EKF, using independently provided operational data and LSTM-estimated SOCs for performance optimization. Through iteration, this method improves denoising and SOC accuracy in a range of working conditions. The comprehensive results show that the ideal mean absolute error, mean squared error, and mean absolute percentage error are 0.4544%, 0.7326%, and 0.9371% for the LSTM model; 0.3069%, 0.4093%, and 0.3577% for the LSTM-EKF model; and 0.14687%, 0.3169%, and 0.2492% for the proposed LSTM-AFGEKF model at 0°C, 25°C, and 45°C using a ternary battery. The study shows that the training and testing hyperparameters of LSTM have a substantial impact on the accuracy of SOC estimations. Furthermore, the proposed LSTM-AFGEKF model’s capacity to provide precise SOC estimations serves as a good model with high computational efficiency.
Experimental and numerical investigation on seismic behavior of existing masonry corner wall strengthened with external thin reinforced concrete column
Authors: Li Guo-Lei , Yu Qian-Qian , Gu Xiang-Lin , Li Xiang , Zhang Hong 
Published: November 2024

Abstract

To improve anti-collapse behavior of an existing masonry structure under earthquake action, a new thin beam-column system was proposed. In order to verify the efficiency of the strengthening method, as one of the series projects, this paper focuses on the cooperative work behavior of an existing masonry wall and an externally added reinforced concrete thin column under horizontal cyclic loads. Six 1/2 scaled specimens of existing masonry corner wall were built. Among them, three specimens were strengthened with externally added thin reinforced concrete columns. Then quasi-static tests were conducted to study the seismic behavior of the walls under different axial loads and cyclic horizontal loads from different directions. Meanwhile, a discrete element model (DEM) for corner walls under cyclic loads was proposed and the simulated response results of the walls under cyclic loads were verified with the corresponding experimental data. Based on the verified simulation methods, parameter analyses were conducted. Test and simulation results showed that the externally added thin reinforced concrete column could work together well with an existing masonry corner wall by using the construction measures proposed in this paper. The strengthening effect of the externally added reinforced concrete column for a corner masonry wall was significant. As the wall thickness increased, the strengthening efficiency for a corner wall would decrease. As the strength of concrete increased, the increment of the lateral load bearing capacity of a strengthened wall would be negligible. The thin reinforced concrete column could be used in the thin beam-column system to enhance the integrity of an existing masonry building.
Boosting Commit Classification with Contrastive Learning
Authors: Tong Jiajun , Wang Zhixiao , Rui Xiaobin 
Published: November 2024

Abstract

Commit Classification (CC) is an important task in software maintenance, which helps software developers classify commit changes into different types according to their nature and purpose. However, existing models need lots of manually labeled data for model fine-tuning, when training samples are insufficient, ensuring the performance of commit classification becomes very critical. The scarcity of data also leads to the problem of poor model generalization ability, resulting in satisfactory performance only on specific tasks. Moreover, they often ignore the sentence-level semantic information in the commit message, which is essential for discovering the difference between diverse commits, especially for fewshot scenarios. In this work, we propose to boost commit classification with contrastive learning. This method can solve the CC problem in fewshot scenarios. To augment the training datasets and improve the generalization ability of our proposed method, we generate additional training samples by Semantic Prototype, which is defined as a representative embedding for a group of semantically similar instances. To produce meaningful and discriminating sentence-level vectors for each commit in a pair, we employ a pretrained Sentence-Transformer as the embedding layer. The network then learns to maximize the distance in the latent space for positive pairs and minimize it for negative pairs, leading to a fine-tuned Sentence-Transformer with fixed weights for the downstream commit classification task. Extensive experiments on two open available datasets demonstrate that our framework, though simple, can solve the CC problem effectively even in fewshot scenarios. It not only achieves state-of-the-art performance but also improves the adaptability of the model without requiring a large number of training samples for fine-tuning. The code, data, and trained models are available at https://github.com/CUMT-GMSC/CommitFit.
Stacked Nanowire FET for CMOS Technology Scaling
Authors: Kodali Dhanesh , Pant Divyanshu , Udutha Siddanth Chandra , Therese Brintha , kanthale Onkar 
Published: November 2024

Abstract

As CMOS technology nears its scaling limits, stacked nanosheet and nanowire field-effect transistors (FETs) have emerged as key candidates for extending Moore's Law. These architectures offer superior electrostatic control, reduced short-channel effects, and high drive current due to their gate-all-around (GAA) structure. Stacked configurations enhance effective channel width, providing higher performance in compact footprints. This paper analyzes the electrostatic behavior, drive current, and leakage characteristics of these devices for sub-5 nm nodes. Challenges in material integration and fabrication complexity are discussed, with simulation and experimental results supporting their potential for next-generation CMOS scaling.