bluuebunny/crossref_metadata_2025_split huggingface dataset:
Elongational rheology of 2, 3 and 4 polymer stars connected by linear backbone chains
Authors: Hirschberg Valerian , Schußmann Max G. , Röpert Marie-Christin , Goecke Anika , Wilhelm Manfred , Wagner Manfred H. 
Published: May 2024

AbstractWe consider the elongational rheology of model polystyrene topologies with 2, 3 and 4 stars, which are connected by one (2-star or “Pom-Pom”), two (3-star) and three (4-star) linear backbone chains. The number of arms of each star varies from qa = 3 to 24, the molecular weight of the arms from Mw,a = 25 kg/mol to 300 kg/mol, and the backbone chains from Mw,b = 100 kg/mol to 382 kg/mol. If the length of the arm is shorter than the length of the backbone, i.e. Mw,a < Mw,b, and despite the vastly different topologies considered, the elongational stress growth coefficient can be modeled by the Hierarchical Multi-mode Molecular Stress Function (HMMSF) model, based exclusively on the linear-viscoelastic characterization and a single nonlinear parameter, the dilution modulus. If the length of the arms of the stars is similar or longer than the length of the backbone chain (Mw,a ≥ Mw,b) connecting two stars, the impact of the backbone chain on the rheology vanishes and the elongational stress growth coefficient is dominated by the star topology showing similar features of the elongational stress growth coefficient as those of linear polymers.
Graphical Abstract
Oxidative stress and NF-KB/iNOS inflammatory pathway as innovative biomarkers for diagnosis of drowning and differentiating it from postmortem submersion in both fresh and saltwater in rats
Authors: Adel Rana , Ibrahim Manar Fouli Gaber , Elsayed Samar Hisham , Yousri Nada A. 
Published: May 2024

Abstract
Background
Finding a dead body in water raises an issue concerning determining the cause of death as drowning because of the complex pathophysiology of drowning. In addition, the corpse may be submersed postmortem.

Objective
Evaluate the role of oxidative stress markers and NF-KB/iNOS inflammatory pathway as diagnostic biomarkers in drowning and whether they could differentiate freshwater from saltwater drowning.

Methods
This study included forty-five adult male albino rats classified into five groups: control group (C), Freshwater-drowned group (FD), Freshwater postmortem submersion group (FPS), saltwater-drowned group (SD), and saltwater postmortem submersion group (SPS). After the autopsy, the rats' lungs in each group were prepared for histological, immunohistochemical (caspase 3, TNF-α, NF-kB, COX-2 & iNOS), biochemical studies; MDA, NOx, SOD, GSH, VCAM-1, COX-2; and RT-PCR for the relative quantification of NF-kB and iNOS genes expression.

Results
Lung oxidative markers were significantly affected in drowned groups than in postmortem submersion groups. Inflammatory pathway markers were also significantly increased in the drowned groups, with concern that all markers were significantly affected more in saltwater than in freshwater drowned group.

Conclusions
It is concluded that the tested markers can be used accurately in diagnosing drowning and differentiating it from postmortem submersion with a better understanding of the mechanism of death in drowning as both mechanisms, inflammatory and oxidative stress, were revealed and involved.
Vulnerability profiles of workers and the relation with burnout symptoms: results from the Netherlands working conditions survey
Authors: Bouwens Luuk , van Zon Sander K.R. , Peijen Roy , Vooijs Marloes 
Published: May 2024

Abstract
Introduction
Unfavorable working conditions may place workers in a vulnerable position in the labour market, but studies on the clustering of these factors and their relation to burnout symptoms are lacking. This study aims to identify subgroups of workers in potentially vulnerable positions in the labour market and examine whether burnout symptoms differ across the established subgroups.

Methods
This study utilizes cross-sectional data from 2019 of the Netherlands Working Conditions Survey (n = 55,283). Working conditions included employment contracts, working hours, multiple jobs, tenure, physical strain, autonomy, and workload. Burnout symptoms were measured with five items on a 7-point Likert scale. Latent Class Analysis was used to identify vulnerability subgroups based on working conditions and educational level. Wilcoxon rank-sum tests were used to examine whether burnout symptoms differed between the identified subgroups.

Results
Three out of nine subgroups (i.e., classes 4, 6, and 7) presented combinations of multiple unfavourable working conditions. The vulnerability of class 4, characterized by low educational level, physically demanding work, low autonomy, and a high workload, was underscored by a significantly higher burnout symptom score (M = 2.91;SD = 0.97) compared to all other subgroups. Subgroups 3 (M = 2.69;SD = 1.43) and 8 (M = 2.41;SD = 1.41), without striking unfavourable conditions, had the second and third highest scores on burnout symptoms.

Conclusions
Determining vulnerability in the labour market is not straightforward as not all profiles that presented clusters of unfavourable working conditions scored high on burnout symptoms, and vice versa. Future research should investigate whether findings are similar to other mental health outcomes.
Training neuro-fuzzy using flower pollination algorithm to predict number of COVID-19 cases: situation analysis for twenty countries
Authors: Baştemur Kaya Ceren , Kaya Ebubekir 
Published: May 2024

AbstractPredicting the number of COVID-19 cases offers a reflection of the future, and it is important for the implementation of preventive measures. The numbers of COVID-19 cases are constantly changing on a daily. Adaptive methods are needed for an effective estimation instead of traditional methods. In this study, a novel method based on neuro-fuzzy and FPA is proposed to estimate the number of COVID-19 cases. The antecedent and conclusion parameters of the neuro-fuzzy model are determined by using FPA. In other words, neuro-fuzzy training is carried out with FPA. The number of COVID-19 cases belonging to twenty countries including USA, India, Brazil, Russian, France, UK, Italy, Spain, Argentina, Germany, Colombia, Mexico, Poland, Turkey, Iran, Peru, Ukraine, South Africa, the Netherlands and Indonesia is estimated. Time series is created using the number of COVID-19 cases. Daily, weekly and monthly estimates are realized by utilizing these time series. MSE is used as the error metric. Although it varies according to the example and problem type, the best training error values between 0.000398027 and 0.0286562 are obtained. These best test error values are between 0.0005607 and 0.409867. The best training and test error values are 0.000398027 and 0.0005607, respectively. In addition to FPA, the number of cases is also predicted with the algorithms such as particle swarm optimization, harmony search, bee algorithm, differential evolution and their performances are compared. Success score and ranking are created for all algorithms. The scores of FPA for the daily, weekly and monthly forecast are 71, 77 and 62, respectively. These scores have shown that neuro-fuzzy training based on FPA is successful than other meta-heuristic algorithms for all three prediction types in the short- and medium-term estimation of COVID-19 case numbers.
Revolutionizing Alzheimer’s detection: an advanced telemedicine system integrating Internet-of-Things and convolutional neural networks
Authors: Massoud Mohamed A. , El-Bouridy Mohamed E. , Ahmed Wael A. 
Published: May 2024

AbstractThe advent of Internet-of-Things (IoT)-based telemedicine systems has ushered in a new era of technology facilitating early diagnosis and prevention for distant patients. This is particularly crucial for severe illnesses such as Alzheimer's disease, encompassing memory loss and cognitive dysfunction that significantly impairs daily life, necessitating immediate medical attention. The surge in data from intelligent systems, sourced from diverse locations, has heightened complexity and diminished diagnostic accuracy. In response, this study proposes an innovative distributed learning-based classification model, leveraging a deep convolutional neural network (CNN) classifier. This model proficiently manages clinical data images from disparate sources, ensuring disease classification with high accuracy. The research introduces a novel system designed for automated Alzheimer's disease detection and healthcare delivery. Comprising two subsystems, one dedicated to Alzheimer's diagnosis with an impressive 94.91% accuracy using CNN, and another for healthcare treatment, delivering excellent results. Notably, the system is adaptable to various diseases post-training. The study emphasizes the model's robust performance, achieving an outstanding 94.91% accuracy after 200 training epochs, with a loss of 0.1158, and a validation accuracy of 96.60% with a loss of 0.0922 at training without noise and loss: 0.2938 - Accuracy: 0.8713 - val_loss: 0.2387 - val_accuracy: 0.9069 at CNN with noise. Precision, recall, and F1 scores are comprehensively presented in a classification report, underscoring the system's effectiveness in categorizing Mild Demented and Non-Demented cases. While acknowledging room for further enhancements, this study introduces a promising avenue for telemedicine systems. It significantly impacts the early diagnosis and treatment of Alzheimer's disease and related medical conditions, thereby advancing the healthcare sector and improving patients' quality of life. The inclusion of these quantitative results enhances the abstract's appeal to readers, providing a clearer understanding of the study's outcomes.
Cone-beam computed tomography evaluation of shaping ability of kedo-S square and fanta AF™ baby rotary files compared to manual K-files in root canal preparation of primary anterior teeth
Authors: El-Desouky Shaimaa S. , El Fahl Bassem N. , Kabbash Ibrahim A. , Hadwa Shimaa M. 
Published: May 2024

Abstract
Background
Pediatric rotary file systems were developed to solve manual file limitations. With many systems available, it may be tricky to select the most appropriate one.

Aim
to assess & compare Kedo-S Square, Fanta-AF™-Baby rotary files with manual K-file concerning removed dentin amount, canal transportation, centric ability & root canal taper using CBCT in primary anterior teeth.

Design
Extracted Seventy-five upper primary anterior teeth with intact 2/3 root length were collected and divided into three groups based on root canal instrumentation, group-I: prepared using K-file, group-II: prepared using Kedo-S Square, and group-III: prepared using Fanta AF™ Baby file. The teeth were imaged with CBCT before & following canal instrumentation. Then, the removed dentin amount was calculated at each root-canal level. The Kruskal-Wallis test was utilized to statistically analyze study data.

Result
The difference among the three groups was highly statistically significant at cervical & apical thirds concerning dentin thickness changes on both mesial & distal sides following canal preparation with the least removed dentin in the Kedo-S Square group(P < 0.0001). Regarding transportation & centering ability, a non-significant difference between the three groups was found. 80% of the Fanta AF™ Baby group had good-tapered preparation compared to the Kedo-S Square (72%) and K-file (40%) groups(P < 0.05).

Conclusion
Kedo-S Square was preferable to Fanta-AFTM-Baby & manual K-files in primary root canal preparation.
Cardiac comorbidities in McArdle disease: case report and systematic review
Authors: Hoxhaj Domeniko , Vadi Gabriele , Bianchi Lorenzo , Fontanelli Lorenzo , Torri Francesca , Siciliano Gabriele , Ricci Giulia 
Published: May 2024

Abstract
Introduction and methods
Myophosphorylase deficiency, also known as McArdle disease or Glycogen Storage Disease type V (GSD-V), is an autosomal recessive metabolic myopathy that results in impaired glycogen breakdown in skeletal muscle. Despite being labelled as a “pure myopathy,” cardiac involvement has been reported in some cases, including various cardiac abnormalities such as electrocardiographic changes, coronary artery disease, and cardiomyopathy. Here, we present a unique case of a 72-year-old man with GSD-V and both mitral valvulopathy and coronary artery disease, prompting a systematic review to explore the existing literature on cardiac comorbidities in McArdle disease.

Results
Our systematic literature revision identified 7 case reports and 1 retrospective cohort study. The case reports described 7 GSD-V patients, averaging 54.3 years in age, mostly male (85.7%). Coronary artery disease was noted in 57.1% of cases, hypertrophic cardiomyopathy in 28.5%, severe aortic stenosis in 14.3%, and genetic dilated cardiomyopathy in one. In the retrospective cohort study, five out of 14 subjects (36%) had coronary artery disease.

Discussion and conclusion
Despite McArdle disease primarily affecting skeletal muscle, cardiac involvement has been observed, especially coronary artery disease, the frequency of which was moreover found to be higher in McArdle patients than in the background population in a previous study from a European registry. Exaggerated cardiovascular responses during exercise and impaired glycolytic metabolism have been speculated as potential contributors. A comprehensive cardiological screening might be recommended for McArdle disease patients to detect and manage cardiac comorbidities. A multidisciplinary approach is crucial to effectively manage both neurological and cardiac aspects of the disease and improve patient outcomes. Further research is required to establish clearer pathophysiological links between McArdle disease and cardiac manifestations in order to clarify the existing findings.
Soil nitrogen dynamics affected by coffee (coffea arabica) canopy and fertilizer management in coffee-based agroforestry
Authors: Kurniawan Syahrul , Nugroho R Muhammad Yusuf Adi Pujo , Ustiatik Reni , Nita Istika , Nugroho Gabryna Auliya , Prayogo Cahyo , Anderson Christopher W. N. 
Published: May 2024

AbstractNutrient management in coffee-based agroforestry systems plays a critical role in soil nitrogen (N) cycling, but has not been well documented. The objective of this study was to evaluate the effect of coffee canopy management and fertilization on soil N dynamics. This study used a randomized complete block design (2 × 3 × 2) with four replications. There were three factors: 1) coffee canopy management (T1: Pruned, T2: Unpruned), 2) fertilizer type (O: Organic, I: Inorganic; M: 50% Organic + 50% Inorganic), and 3) fertilizer dose (D1: low, D2: medium, D3: high). Soil N dynamic indicators (i.e., total N, ammonium (NH4+), nitrate (NO3−), net N-NH4+, net N-NO3−, soil microbial biomass N) were measured at two soil sampling depths (0–20 cm and 20–40 cm). Results showed that pruning increased soil total N and microbial biomass N (MBN) by 10–56% relative to unpruned coffee trees. In contrast, the unpruned coffee canopy had 15–345% higher NH4+, NO3−, net N-NH4+, net N-NO3−, and microbial biomass N concentration than pruned coffee. Mixed fertilizer application increased NO3− and net N-NH4+ accumulation by 5–15% relative to inorganic and organic fertilizers. In addition, medium to high dose fertilization led to a 19–86% higher net N-NO3− concentration and microbial biomass N as compared to low dose fertilization. The treatment of no pruning and mixed fertilizer at low to medium doses was the optimal management strategy to maintain soil available N, while pruning combined with organic fertilizer has the potential to improve soil total N and MBN.
Suppressing the invasive common milkweed (Asclepias syriaca L.) saves soil moisture reserves
Authors: Tölgyesi Csaba , Tóth Viktória , Hábenczyus Alida Anna , Frei Kata , Tóth Benedek , Erdős László , Török Péter , Bátori Zoltán 
Published: May 2024

AbstractCommon milkweed (Asclepias syriaca L.) is a widespread invasive alien forb in dry sandy habitats of Central Europe. It adversely affects native plant and animal communities, but its ecosystem-level effects, particularly on hydrology, are little known. Since milkweed has an extensive, deep root system and large, broad leaves, we assumed a negative effect on the soil moisture content of the hosting ecosystem. Following the before-after control-impact protocol, we first compared the soil moisture content of the top 120 cm of the soil under seven milkweed stands to that of non-invaded reference sites. We then treated half of the stands by mechanically removing all aboveground milkweed biomass and repeated the comparative soil moisture measurements. We found that milkweed stands had significantly drier soils than reference grasslands during the growing season, but the soil under milkweed stands recharged to the level of the references in autumn and winter. However, the amount of moisture needed for this recharge was lost from deeper percolation to groundwater. Milkweed treatment prevented the depletion of moisture during the growing season, saving 21.6 l m−2 of water on average. Treatment did not affect non-milkweed plant biomass, thus, moisture patterns could fully be attributed to the milkweed stands. Our results reinforce the importance of milkweed suppression in invaded grasslands, as, besides enabling the recovery of the native grassland ecosystem, it promotes groundwater recharge, which is particularly important in the dry regions of Central Europe, currently facing severe aridification due to climate change and unfavourable land use trends.
A Pharmacogenomics-Based In Silico Investigation of Opioid Prescribing in Post-operative Spine Pain Management and Personalized Therapy
Authors: Lewandrowski Kai-Uwe , Sharafshah Alireza , Elfar John , Schmidt Sergio Luis , Blum Kenneth , Wetzel Franklin Todd 
Published: May 2024

Abstract
Considering the variability in individual responses to opioids and the growing concerns about opioid addiction, prescribing opioids for postoperative pain management after spine surgery presents significant challenges. Therefore, this study undertook a novel pharmacogenomics-based in silico investigation of FDA-approved opioid medications. The DrugBank database was employed to identify all FDA-approved opioids. Subsequently, the PharmGKB database was utilized to filter through all variant annotations associated with the relevant genes. In addition, the dpSNP (https://www.ncbi.nlm.nih.gov/snp/), a publicly accessible repository, was used. Additional analyses were conducted using STRING-MODEL (version 12), Cytoscape (version 3.10.1), miRTargetLink.2, and NetworkAnalyst (version 3). The study identified 125 target genes of FDA-approved opioids, encompassing 7019 variant annotations. Of these, 3088 annotations were significant and pertained to 78 genes. During variant annotation assessments (VAA), 672 variants remained after filtration. Further in-depth filtration based on variant functions yielded 302 final filtered variants across 56 genes. The Monoamine GPCRs pathway emerged as the most significant signaling pathway. Protein–protein interaction (PPI) analysis revealed a fully connected network comprising 55 genes. Gene–miRNA Interaction (GMI) analysis of these 55 candidate genes identified miR-16-5p as a pivotal miRNA in this network. Protein–Drug Interaction (PDI) assessment showed that multiple drugs, including Ibuprofen, Nicotine, Tramadol, Haloperidol, Ketamine, l-Glutamic Acid, Caffeine, Citalopram, and Naloxone, had more than one interaction. Furthermore, Protein–Chemical Interaction (PCI) analysis highlighted that ABCB1, BCL2, CYP1A2, KCNH2, PTGS2, and DRD2 were key targets of the proposed chemicals. Notably, 10 chemicals, including carbamylhydrazine, tetrahydropalmatine, Terazosin, beta-methylcholine, rubimaillin, and quinelorane, demonstrated dual interactions with the aforementioned target genes. This comprehensive review offers multiple strong, evidence-based in silico findings regarding opioid prescribing in spine pain management, introducing 55 potential genes. The insights from this report can be applied in exome analysis as a pharmacogenomics (PGx) panel for pain susceptibility, facilitating individualized opioid prescribing through genotyping of related variants. The article also points out that African Americans represent an important group that displays a high catabolism of opioids and suggest the need for a personalized therapeutic approach based on genetic information.

Graphical Abstract
Social Work Advocacy: Towards a Trauma-Informed Model
Authors: Tarshish Noam , David Paula , Krumer-Nevo Michal 
Published: May 2024

AbstractAdvocating for rights is widely regarded as an integral part of social work education and a key component of social work interventions with people in poverty and other marginalized populations. Current research also indicates that poverty and marginalization are strongly associated with trauma. However, models of social work advocacy fail to acknowledge and account for the effects of past and present trauma on marginalized populations. Similarly, principles stemming from a trauma-informed perspective (TIP) typically do not address advocacy of rights. This separation maintains the traditional social work divide between internal struggles and external obstacles, as well as the distinct approaches used to facilitate change in each realm. Based on a presentation of the main models of social work advocacy in light of their consideration of trauma, this article proposes an integrative model: the trauma-informed active take-up advocacy model (TI-ATA). We conclude by discussing case study illustrations and the applications and limitations of the integrative TI-ATA model to the social work field.
Too many cooks could spoil the broth: choice overload and the provision of ambulatory health care
Authors: Herwartz Helmut , Strumann Christoph 
Published: May 2024

AbstractPatient empowerment calls for an intensified participation of (informed) patients with more treatment opportunities to choose from. A growing body of literature argues that confronting consumers with too many opportunities can lead to a choice overload (CO) resulting in uncertainty that the selected alternative dominates all other options in the choice set. We examine whether there is a CO effect in the demand for ambulatory health care in Germany by analyzing the association of medical specialists supply on so-called patients’ health uncertainty. Further, we investigate if the CO effect is smaller in areas with a higher density of general practitioners (GPs). We find that patients who live in an area with a large supply of specialists are subject to a CO effect that is expressed by an increased health uncertainty. The coordinating role of GPs seems to be effective to reduce the CO effect, while preserving free consumer choice.