bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysis of the Correlation between Percutaneous Balloon Compression Treatment Frequency and Facial Numbness in Trigeminal Neuralgia
Authors: Wang Jianye , Zhang Geng , Jiang Shiwei , Shou Jixin , Cheng Sen , Guan Haibo , Gao Haidong , Yang Peng , Zhao Tailiang , Wang Bingbing 
Published: January 2024

Abstract

Percutaneous balloon compression (PBC) offers substantial immediate relief from the severe pain associated with trigeminal neuralgia (TN). To investigate the effect of compression Frequency during balloon compression on facial numbness in patients with primary trigeminal neuralgia, we conducted a nested case–control study. A retrospective analysis was performed on clinical data from 150 TN patients treated with PBC from October 2017 to May 2022. Of these, 60 patients underwent a single PBC compression, while the remaining 90 underwent two compressions. The Barrow Neurological Institute Pain Intensity (BNI-P) score was employed to measure the severity of pain. The Barrow Neurological Institute facial numbness score (BNI-N) was used to assess the degree of postoperative pain relief and facial numbness. At time point T1, the BNI-P scores for the “two compressions” group were significantly lower than those for the “single compression” group (P < 0.05). From T2 to T5, no significant differences in BNI-P scores were observed between the groups. For the BNI-N score, there was no notable difference between the groups at T0, while from T1 to T4, the “double compressions” group exhibited significantly lower BNI-N scores than the “single compression” group (P < 0.05). At T5, no significant difference in BNI-N scores was observed between the groups. The application of two compressions in PBC is a safe and effective surgical method that not only maintains the efficacy of the procedure but also significantly reduces the degree and duration of postoperative facial numbness.
A method for determining the time-frequency characteristics of localized impulse components in continuous signals
Authors: Filimonova Natalia , Specovius-Neugebauer Maria , Friedmann Elfriede 
Published: January 2024

Abstract
Background: We propose a method for analyzing the dynamics of signals of different nature. The method aims to find the time-frequency characteristics of the local impulse components for continuous signals. Thereby, we separate the information about the impulse shape from the time of its appearance. The method is based on a mathematical model of invariant pattern recognition by a visual system combined with a wavelet analysis method. Here we use Krawtchouk functions to model the response of the receptive fields of the lateral geniculate nucleus and, in addition, as the mother wavelet.
Results: The inclusion of saccades in the model of the visual system allows us to extract invariant features of the signals. In addition, a parameter is included in the model that controls the front-to-back ratio of the basis functions. This means that the signal decomposition is not solely based on one particular set of basis functions, but on a family of basis functions parametrized by the shift transformation and an asymmetry parameter, which allows a better approximation of asymmetric impulse components.
Conclusion: Our method makes it possible to study both the time of emergence of the impulse components of continuous signals and their spectral characteristics, which in turn can be used for their identification. Applying the method to biological signals of various nature, we could identify and localize a blinking, a muscle response due to emotions and find times of changes in a driver’s stress state, etc. Potentially, other applications of nonstationary signals with impulse components can also be considered.
Negative conspecific plant-soil feedback on alien plants co-growing with natives is partly mitigated by another alien
Authors: Chen Duo , Kleunen Mark van 
Published: January 2024

Abstract

Background and Aims
Naturalized alien and native plants can impact each other directly when they grow next to each other, but also indirectly through their soil legacies. These alien-native interactions can also be modified by the presence of a third alien or native species. However, it is unknown how the performance of co-growing alien and native species is affected by their soil legacies and by the presence of an additional species.
Methods
In our two-phase plant-soil-feedback experiment, soils were first conditioned by growing four naturalized alien and four native species separately. We then grew all 16 pairwise alien-native species combinations on soil conditioned by the respective alien species, on soil conditioned by the respective native species, on a mixture of soils conditioned by both species or on control soil. We also grew the two test plants with an additional alien or native species or without additional species.
Results
Soil conditioning, and particularly conspecific soil conditioning, reduced growth of the alien and native test plants. The addition of another species also reduced growth of the test plants. However, the negative conspecific soil-legacy effect on alien test plants was partly alleviated when the additional species was also alien.
Conclusion
The negative conspecific plant-soil feedback for both alien and native plants in our study could promote their coexistence. However, as partial alleviation of negative conspecific effects on alien plants occurred with an additional alien species, the chances of coexistence of alien and native species might decrease when there are multiple alien species present.
Associations between per- and poly-fluoroalkyl substance (PFAS) exposure and immune responses among women in the California Teachers Study: a cross-sectional evaluation
Authors: Cauble Emily L. , Reynolds Peggy , Epeldegui Marta , Andra Syam S. , Narasimhan Srinivasan , Pulivarthi Divya , Von Behren Julie , Goldberg Debbie , Spielfogel Emma S. , Lacey James V. , Wang Sophia S. 
Published: January 2024

Abstract
Background Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants that have been shown to cause or are related to various health outcomes and diseases.Methods In this cross-sectional study nested in the California Teachers Study cohort, we measured PFAS exposure (9 analytes) in serum, and further evaluated the 4 PFAS analytes (PFHxS, PFNA, PFOA, PFOS) with detection levels of > 80%, in relation to 16 systemic inflammatory/immune markers in two multiplex serum-based assays from blood collected at one point in time. Study participants (n = 722) were female, completed a questionnaire regarding various health measures and behaviors, and donated a blood sample between 2013–2016. The association between PFAS analytes and immune markers (dichotomized by median) were evaluated by calculating odds ratios (OR) and 95% confidence intervals (CI), in both linear and logistic regression models, adjusted for age.Results The median age of our study population was 61 years (range = 40–95 years), of whom 99.7% had detectable levels of at least one PFAS analyte. The prevalence of PFAS analytes was strongly correlated with age, with those aged 40–49 years (youngest group) having the lowest PFAS burden and those aged 75 + years (oldest group) with the highest detectable levels. Statistically significant positive associations were observed between each ng/mL increase in PFHxS (OR = 1.34), PFOA (OR = 1.15), and PFOS (OR = 1.53) levels with BAFF levels above the median (compared to below the median). There was a 1.47-fold increase of elevated PFHxS with TNF-RII (above median) and a 1.38-fold increase with IL2Rα. We observed statistically significant inverse associations between PFOA and IL6 (OR = 0.79), and between PFNA with sCD14 (OR = 0.86) and CD27 (OR = 0.80). Risk estimates for logistic regression models were consistent. No significant positive associations were observed between PFNA exposure and any immune marker.Conclusions PFAS exposure was associated with altered levels of circulating inflammatory/immune markers. If validated, our results may suggest potential immune mechanisms underlying associations between the different PFAS analytes and adverse health outcomes.
Development and validation of a machine learning-based risk prediction model for post-stroke cognitive impairment
Authors: Zhong Xia , Li Jing , Lv Shunxin , Zhang Mengdi , Qu Ying , Wang Rui , Jiao Huachen 
Published: January 2024

Abstract
Background
Machine learning (ML) risk prediction models for post-stroke cognitive impairment (PSCI) are still far from optimal. This study aims to generate a reliable predictive model for predicting PSCI in Chinese individuals using ML algorithms.
Methods
We collected data on 494 individuals who were diagnosed with acute ischemic stroke (AIS) and hospitalized for this condition from January 2022 to November 2023 at a Chinese medical institution. All of the observed samples were divided into a training set (70%) and a validation set (30%) at random. Logistic regression combined with the least absolute shrinkage and selection operator (LASSO) regression was utilized to efficiently screen the optimal predictive features of PSCI. We utilized seven different ML models (LR, XGBoost, LightGBM, AdaBoost, GNB, MLP, and SVM) and compared their performance for the resulting variables. We used five-fold cross-validation to measure the model's area under the curve (AUC), sensitivity, specificity, accuracy, F1 score and PR values. SHAP analysis provides a comprehensive and detailed explanation of our optimized model's performance.
Results
PSCI was identified in 58.50% of the 494 eligible AIS patients. The most predictive features of PSCI are HAMD-24, FBG, age, PSQI, and paraventricular lesion. The XGBoost model, among the 7 ML prediction models for PSCI developed based on the best predictive features, demonstrates superior performance, as indicated by its AUC (0.961), sensitivity (0.931), specificity (0.889), accuracy (0.911), F1 score (0.926), and AP value (0.967).
Conclusion
The XGBoost model developed on HAMD-24, FBG, age, PSQI, and paraventricular lesion performance is exceptional in predicting the risk of PSCI. It provide clinicians with a reliable tool for early screening of patients with cognitive impairment and effective treatment decisions in stroke patients.
Graves Ophthalmopathy a neglected comorbidity of Graves’ Disease; A Detailed investigation and management of Sixty Eight Patients in a tertiary healthcare center.
Authors: akkuş gamze , ulaş burak , binokay hülya , odabas fulya , soysal reyhan , öz altan , sert murat 
Published: January 2024

Abstract
Aim: To compare the measurements of macular thickness, intraocular pressure and retinal fiber nerve layer (RFNL) and hormone parameters before and after antithyroid therapy in patients with Graves’ Ophthalmopathy (GO).
Methods: A prospective observational study conducted at a tertiary care center. Patients with GO were included and scored (Clinical Activity Score, CAS) according to EUGOGO guideline. The participants underwent optical coherence tomography (OCT) and OCT angiography (OCT-A) of the peripapillary and macular areas. Baseline and follow-up hormone parameters including free T3, free T4, Thyroid stimulating hormone (TSH), Thyroid receptor autoantibodies and intravitreal measurements (RFNL, macular thickness, intraocular pressure) were performed and compared in the current study.
Results: During the 24 weeks, CAS was significantly decreased (0.5±0.8 vs 0.1±0.4, p=0.00). Although measurements of Intraocular pressure (14.9±2.8 vs 14.2±1.9), RFNL (100.2±9.05 vs 99.9±8.7) and macular thickness (274.7±42.9 vs 271.2±43.3) were between baseline and were similar after antithyroid therapy, baseline RFNL measurements were significantly correlated with serum TRAbs (p=0.008), fT3 (p=0.01), fT4 (p=0.003), negatively. Baseline intraocular pressure and macular thickness were also negatively correlated with serum TSH levels.
Conclusion: Serum fT3, fT4 and TRAbs levels were the main significant factor for the evaluation of intraocular structure, especially retinal nerve in patients with GO.
Quantization of Action for Elementary Particles and the Principle of Least Action
Authors: Wen Shuming 
Published: January 2024

Abstract

The uncertainty principle is a fundamental principle of quantum mechanics, but its exact mathematical expression cannot obtain correct results when used to solve theoretical problems such as the energy levels of hydrogen atoms, one-dimensional deep potential wells, one-dimensional harmonic oscillators, and double-slit experiments. Even after approximate treatment, the results obtained are not completely consistent with those obtained by solving Schrödinger’s equation. This indicates that further research on the uncertainty principle is necessary. Therefore, using the de Broglie matter wave hypothesis, we quantize the action of an elementary particle in natural coordinates and obtain the quantization condition and a new deterministic relation. Using this quantization condition, we obtain the energy level formulas of an elementary particle in different conditions in a classical way that is completely consistent with the results obtained by solving Schrödinger’s equation. A new physical interpretation is given for the particle eigenfunction independence of probability for an elementary particle: an elementary particle is in a particle state at the space-time point where the action is quantized, and in a wave state in the rest of the space-time region. The space-time points of particle nature and the wave regions of particle motion constitute the continuous trajectory of particle motion. When an elementary particle is in a particle state, it is localized, whereas in the wave state region, it is nonlocalized.
Weakly-supervised label distribution feature selection via label-specific features and label correlation
Authors: Shu Wenhao , Hu Jiayu , Qian Wenbin 
Published: January 2024

Abstract

Label Distribution Learning (LDL) stands out as an innovative method to address the challenges posed by label ambiguity. Current LDL algorithms are predominantly designed for datasets with comprehensive supervised information. However, in real-world scenarios, it’s common to encounter partial missing labels within the label space. This phenomenon disrupts the structure and correlation between labels, posing a challenge to the precise design of learning algorithms. Furthermore, in label distribution learning, it also faces the effect of high feature dimensionality. To tackle this, adopting pre-processing methods like feature selection becomes crucial, aiming to trim down the data dimensionality. Motivated by this, a weakly-supervised label distribution feature selection algorithm based on label correlation is proposed in this paper. First, to handle weakly-supervised label distribution data, a two-stage incomplete label distribution learning method (IncomLDLTS) to recover missing labels by exploiting label-independent prediction based on label-specific features is proposed. Second, a minimum correlation label feature selection algorithm (MCLFS) to enhance the performance for complete label distribution data is designed, which employs the interaction information metric to explore the label correlation to obtain a minimum correlation label, then features about each label based on label-specific features and feature redundancy are captured. Finally, based on six representative evaluation metrics, our experiments across 14 datasets affirm the effectiveness of our approach, not only in restoring missing labels but also in choosing essential features, leading to enhanced classification accuracy.
Combined assessment of Lysine and N-acetyl cadaverine levels assist as a potential biomarker of the smoker periodontitis
Authors: Rashid Md Haroon , Kumar Sandhya Pavan , kumar Y. Pavan , Lakshmi Brinda Goda , Mamillapalli Anitha 
Published: January 2024

Abstract

Periodontitis is an inflammatory condition of supporting structures of teeth leading to attachment and bone loss. Cigarette smoking is the single most important and modifiable risk factor with 5 to 20-fold susceptibility for periodontal diseases. Reverse smoking is a peculiar habit of smoking where the lit end is kept inside the mouth, which is predominant in the northern coastal districts of Andhra Pradesh. Polyamines are biologically active amines involved in tissue regeneration and modulation of inflammation. The study aimed to evaluate polyamines and check their utility as a marker in detection of periodontitis among different groups. Total Polyamine levels showed significant increase in reverse smokers with periodontitis when compared to the other groups. Qualitative analysis by thin layer chromatography showed three polyamine bands with varying intensity among the different groups. Mass spectrometric and NMR analyses of the three bands identified them as N1, N8- diacetyl spermidine, N-acetyl cadaverine and lysine. Most significantly elevated levels of lysine was observed in the smoker and reverese smoker periodontitis groups when compared to healthy and non-smoker periodontitis groups. The significanlty elevated levels of N-acetyl cadaverine could be responsible for the more destruction of periodontium in the reverse smoker group. Antioxidant potential decreased significantly in different smoker periodontitis groups. The present study suggests that the quantitative analysis of salivary polyamines, lysine and N-acetyl cadaverine can aid as an easy noninvasive diagnostic method for assessing the periodontal status, especially in smokers.
Construction method of multi-source Dougong point cloud dataset
Authors: Zhou Caochenyu , Dong Youqiang , Hou Miaole 
Published: January 2024

Abstract
Dougong, a distinctive component of ancient wooden architecture, holds significant importance for the preservation and restoration of such structures. In the realm of cultural heritage preservation, the application of deep learning has gradually expanded, demonstrating remarkable effectiveness. Point cloud serving as a crucial source for Dougong, encapsulates various information, enabling support for tasks like Dougong point cloud classification and completion. The quality of Dougong datasets directly impacts the outcomes of deep learning, as they serve as the foundational data support for these tasks. However, due to the inherent characteristics of Dougong, such as coplanarity and occlusion, acquiring point cloud data is challenging, resulting in poor data quality and organizational difficulties. To address this, our study employs three data acquisition methods—real scanning, photo-generated point clouds, and model-sampled point clouds—to substantially augment the Dougong point cloud dataset. Further, through data augmentation, we enhance the dataset's volume and generalize its characteristics. This effort culminates in the creation of the Dougong Point Cloud Dataset (DG Dataset), poised to support deep learning tasks related to Dougong scenarios.
Predictive value of coronary calcifications for future cardiac events in asymptomaticpatients with severe chronic kidney disease
Authors: Greif Martin , Lackermair Korbinian , Wessely Matthias , von Ziegler Franz , Becker Alexander 
Published: January 2024

Abstract
Background: Coronary calcification are a well established risk factor for cardiovascular events. The following retrospective study determined the predictive value of coronary calcification in a specific group of patients with chronic kidney disease.
Methods: We included 1094 asymptomatic patients (724 men, 370 women, age 62±9.3 years) referred for cardiological examination. Patents were divided into two groups depending on their renal function. Coronary calcification were determined with a multi-slice CT scanner. For quantification of coronary calcification the Agatston score was calculated. Over a mean follow up period of 6.2±1.3 years we observed the rate of cardiovascular events (185 events, 61 myocardial infarctions, 103 revascularisations, 21 cardiac deaths).
Results: The calcium score was significantly higher in patients with severe kidney disease (GFR ≤ 30 ml/min/1,72m²) compared with those with normal to moderate reduced renal function (GFR ≥ 30 ml/min/1,72m²) (207±190 vs.121±169 p≤0.001). The event rate in patients with severe impaired renal function was significantly higher compared to patients with normal to moderate reduced renal function. (20.6% vs 14.8% p=0.0001) The hazard ratio for cardiovascular events increased constantly with the calcium score in both groups .Still the hazard ratio in patients with severe kidney disease was significantly lower compared to patients in corresponding groups with regular to moderate reduced renal function.(7.3 vs 9.3 p=0.01). No cardiac events were observed in patients with a calcium score of 0.
Conclusions: We could demonstrate that risk prediction with the calcium score is possible in patients with severe chronic kidney disease even if the calcium score overestimates the risk for future cardiovascular events in compare to patients with normal to moderate reduced renal function.
The Effectiveness of Using Eye Movement Desensitization and Reprocessing Therapy on Reducing the Severity of Symptoms among Individuals Diagnosed with Post-Traumatic Stress Disorder: A Systematic Review
Authors: Haddad Rabia H. , Alhusamiah Bushra Kh. , Haddad Razan H. , Hamdan-Mansour Ayman M. , Abuhashish Younis H. 
Published: January 2024

Abstract
Background: Post Traumatic Stress Disorder (PTSD) is a global-concern mental illness that occurs due to exposure to a traumatic life experience such as a natural disaster, a severe accident, or a physical or sexual assault in adulthood or childhood. The consequence of PTSD warrants mental health professionals to find effective interventions to maintain and restore mental-well-being. Therefore, selection of intervention is a cornerstone element in deciding successful interventions.
Objectives: The aim of this systematic review was to evaluate and summarize the effectiveness of using eye movement desensitization and reprocessing (EMDR) intervention among individuals diagnosed with PTSD. As well as to highlight the standardized EMDR-based-interventional protocol.
Data source: The relevant literature was extracted and conducted using the following online bibliographic databases: Cumulative Index to Nursing and Allied Health Literature (CINAHL), ProQuest, MEDLINE, Google Scholar, SAGE Journals, and Science Direct and by hand checking reference lists of past similar reviews.
Methods: This Systematic review is guided according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards. Several online databases were used in searching for eligible studies in peer-reviewed journals relevant to the study keywords. The included studies were Systematic reviews and clinical trials which used EMDR intervention among patients experiencing PTSD symptoms, older than 18 years and were published in the English language from 2015 to 2022. Three researchers independently contributed to study selections, data extraction and study evaluations from different aspects: including quality assessment, risk of bias, and study synthesis.
Results: A total of eight published studies met the inclusion criteria and were included in this systematic review; four articles were randomized controlled trials and four were systematic review and meta-analysis studies. In all included studies, the EMDR was utilized as a primary psychotherapy intervention for PTSD symptoms. The results of this extensive and comprehensive review showed that EMDR is an effective psychotherapeutic intervention to reduce and control the severity of symptoms among individuals with PTSD.
Conclusions: This Systematic review gives an overview and explains strong supportive evidence for effectiveness of the EMDR interventions among individuals with PTSD. Therefore, EMDR therapy could be assumed as one standard treatment option for PTSD aiming at reducing treatment duration, and cost of treatment, and restoring the mental well-being and functionality of those suffering from PTSD.