bluuebunny/crossref_metadata_2025_split huggingface dataset:
Application and practice of “Hand as Foot” analogy teaching method in clinical teaching of Obstetrics and Gynecology
Authors: Sun JiaXin , Wang Jia , Su Mei , Chang WenZhong , Hu YuChong , Tang PeiJuan 
Published: September 2022

Abstract
Background
“Hand as Foot analogy teaching method” was proposed by the orthopedic team of the Affiliated Hospital of Inner Mongolia Medical University, and applied in orthopedic teaching, the teaching effect is remarkable. In view of the teaching characteristics of obstetrics and gynecology course, we applied analogy teaching method in clinical teaching. This teaching method can strengthen students’ understanding and memory of female reproductive anatomy, mechanism of labor and related diseases, and is favored by teachers and students.
Methods
Combined with PPT + props, the “Hand as Foot analogy teaching method” was used to teach key and difficult issues of obstetrics and gynecology, mainly aimed at undergraduates practicing in obstetrics and gynecology department of Inner Mongolia Peoples Hospital.
Results
“Hand as Foot analogy teaching method” contrasted human limbs with female reproductive anatomy, which is easier for students to understand and memorize, and is generally popular among medical students.
Conclusions
Based on the evolution of human beings, “Hand as Foot analogy teaching method” is an innovative and feasible clinical teaching method. The teaching method simplifies complex problems, visualizes abstract problems, and enables clinical medical students to get rid of boring and redundant medical knowledge learning process. On the one hand, “Hand as Foot analogy teaching method” makes up for the shortcomings of traditional multimedia teaching, on the other hand, it also shows its unique advantages. Besides, this teaching method has greatly improved clinical teaching effect, and it is worth promoting in the course of obstetrics and gynecology.
Cost consequences of unscheduled emergency admissions in cancer patients in the last year of life
Authors: McFerran Ethna , Cairnduff Victoria , Elder Ray , Gavin Anna , Lawler Mark 
Published: September 2022

Abstract
Objectives
Cancer is a leading cause of death. At end-of-life healthcare utilisation and expenditure peak. Understanding care patterns and quantifying the likely benefits from service reconfigurations may influence rates of hospital admission and deaths.
Methods
Using prevalence-based retrospective data from the Northern Ireland General Registrar’s Office linked by cancer diagnosis to Patient Administration episode data for unscheduled-emergency-care (1stJanuary 2014 to 31st December 2015), we estimate unscheduled-emergency-care costs in the last year of life and hypothetical resources released by reductions in length-of-stay for cancer patients who died in 2015. Linear regression examined patient characteristics affecting length-of-stay.
Results
3134 cancer patients used 60,746 days of unscheduled-emergency-care (average 19.5 days). Of these, 48.9% had ≥1 admission during their last 28 days of life. Total estimated cost was £28,684,261, averaging £9,200 per person.
Lung cancer patients had the highest proportion of admissions (23.2%, mean length-of-stay = 17.9 days, mean cost=£7,224). Highest service use and total cost was in those diagnosed at Stage IV (38.4%), required 22,099 days of care, costing £9,629,014. Palliative care support, identified in 25.5% of patients, contributed £1,322,328. A 3-day reduction in mean length-of-stay with a 10% reduction in admissions, could reduce costs by £7.37 million. Regression analyses explained 41% of length-of-stay variability.
Conclusions
The cost burden from unscheduled care use in the last year of life of cancer patients is significant. Opportunities to prioritise service reconfiguration for high-costing users emphasized lung and colorectal cancers offering the greatest potential to influence outcomes.
Fire resistance prediction for reinforced concrete columns: A comparative application of artificial neural network and random tree methods
Authors: Tijani Ibrahim , Lawal Abiodun Ismail , Kwon Sangki 
Published: September 2022

Abstract
The most common index for passive fire protection systems of built environments is fire resistance. Existing models for determining the fire resistance of reinforced concrete (RC) columns apply to certain conditions. Meanwhile, accurate determination of fire resistance of RC columns will aid in designing sustainable RC columns, saves lives, and protect property. This study leveraged on the power of soft computing methods – artificial neural network (ANN) and random tree (RT) – in predicting the fire resistance of RC columns. Ninety-six observations were assembled from existing fire tests on RC columns. Nine parameters are the predictors used to predict the fire resistance of RC columns. With respect to model predictive performance, the ANN model slightly outperformed the RT model. Genetic algorithm was also used to generate useful mathematical model for fire resistance prediction. The results of the current study highlight the merit of using soft computing (SC) methods in structural fire engineering applications given their extraordinary ability to comprehend multidimensional phenomena with high prediction accuracy.
Identification of a mycobacterial hydrazidase, an isoniazid-hydrolyzing enzyme
Authors: Sakiyama Arata , Saren Chaogetu , Kaneko Yukihiro , Oinuma Ken-Ichi 
Published: September 2022

Abstract
There is decades-old evidence that some mycobacteria, including Mycobacterium avium and Mycobacterium smegmatis, produce hydrazidase, an enzyme that can hydrolyze the first-line anti-tubercular agent isoniazid. Despite its importance as a potential resistance factor, no studies have attempted to reveal its identity. In this study, we aimed to isolate and identify M. smegmatis hydrazidase, characterize it, and evaluate its impact on isoniazid resistance. We determined the optimal condition under which M. smegmatis produced the highest amount of hydrazidase, purified the enzyme by column chromatography, and identified it by peptide mass fingerprinting. It was revealed to be PzaA, an enzyme known as pyrazinamidase/nicotinamidase whose physiological role remains unknown. The kinetic constants suggested that this amidase with broad substrate specificity prefers amides to hydrazides as a substrate. Interestingly, however, of the five tested compounds, including amides, only isoniazid served as an efficient inducer of pzaA transcription, as revealed by quantitative reverse transcription PCR. Moreover, high expression of PzaA was confirmed to be beneficial for the survival of M. smegmatis in the presence of isoniazid. Our findings suggest a possible role for PzaA and other hydrazidases yet to be identified as an intrinsic isoniazid resistance factor of mycobacteria.
Investigating and comparing the performance of meta-heuristic algorithms in feature selection and software fault prediction
Authors: Norouzi Mohsen , Arshaghi Ali 
Published: September 2022

Abstract
Meta‑Heuristic algorithms are optimization techniques that provide the optimal solution through processes of repeated exploration and exploitation of the entire search space. Feature selection is also an important and prominent process in the field of machine learning that reduces data dimensions. This paper examines and compares nature-inspired meta-heuristic algorithms for feature selection to increase the accuracy of software fault prediction. Researchers cannot easily select meta-heuristic algorithms as a suitable method for their research due to their great variety and multiplicity. In this paper, by describing the feature selection techniques and its methods, the application of meta-heuristic algorithms in different fields, such as swarm intelligence and binary methods of these algorithms has been investigated. Also, by introducing 18 meta-heuristic algorithms in 6 different categories and evaluating each of them, a suitable analysis has been provided to researchers so that they can easily and with the highest efficiency choose the appropriate algorithm and method of their work. In the papers presented so far, meta-heuristic algorithms have been studied from only one aspect, while in this article, while studying different types of research, they have tried to study and evaluate them from different aspects. The effectiveness of the combination of three meta-heuristic algorithms, developed butterfly flame, bee colony and developed wall, was tested on 20 data sets. the proposed method in 17 datasets was able to improve the result of 7 datasets.
The Scientific Performance of the Microchannel X-ray Telescope on board the SVOM Mission
Authors: Götz D. , Boutelier M. , Burwitz V. , Chipaux R. , Cordier B. , Feldman C. , Ferrando P. , Fort A. , Gonzalez F. , Gros A. , Hussein S. , Duigou J.-M. Le , Meidinger N. , Mercier K. , Meuris A. , Pearson J. , Renault-Tinacci N. , Robinet F. , Schneider B. , Willingale R. 
Published: September 2022

Abstract
The Microchannel X-ray Telescope (MXT) will be the first focusing X-ray telescope based on a "Lobster-Eye" optical design to be flown on Sino-French mission SVOM. SVOM will be dedicated to the study of Gamma-Ray Bursts and more generally time-domain astrophysics. The MXT telescope is a compact (focal length ∼ 1.15 m) and light (< 42 kg) instrument, sensitive in the 0.2–10 keV energy range. It is composed of an optical system, based on micro-pore optics (MPOs) of 40 µm pore size, coupled to a low-noise pnCDD X-ray detector. In this paper we describe the expected scientific performance of the MXT telescope, based on the End-to-End calibration campaign performed in fall 2021, before the integration of the SVOM payload on the satellite.
Energy-aware edge server placement using the improved butterfly optimization algorithm
Authors: Asghari Ali , Sayadi Marjan , Azgomi Hossein 
Published: September 2022

Abstract
Cloud service providers transfer some of their resources to the proximity of their users in order to increase the quality of services provided to them. Proper placement of servers, considering the number of service demands in different parts of the network, not only plays an important role in providing better services to users but also causes more effective use of resources and reduces their energy consumption. Some related research has been done in this context. However, designing a model that can meet the needs of both the users and the service providers has received less attention. On the other hand, most researchers use discrete models to select a number of candidate locations for resource deployment, while the proposed method explores the entire search area to find optimal locations for server placement. The proposed method (ESPB) using butterfly optimization algorithm(BOA), DVFS technic, and coral reefs optimization algorithm(CRO) seeks to find the best locations for edge servers. In the first step, BOA is used to find the best locations for resource deployment. Then the CRO algorithm is used to map between the optimal locations and the servers. The experiments show that the proposed method can effectively save energy and reduces network latency.
The Effect of a Mindfulness-Based Stress Reduction Program on Professional Quality of Life and Perceived Stress Level of the Pediatric Hematology-Oncology Nurses
Authors: SARIBUDAK Tugba PEHLİVAN , SEMERCİ Remziye , Çalışkan Behice Belkıs , Yıldız Güldem , Altınel Nazife , KAPANCI Mreyem 
Published: September 2022

Abstract
The authors have requested that this preprint be removed from Research Square.
Establishment and validation of a prediction model for the first recurrence of Budd-Chiari syndrome after endovascular treatment: A large sample size, single-center retrospective study
Authors: Wang Zhongkai , Wang Ziwei , Zhang Zhiyuan , Li Jiandong , Pan Zhiyang , Zu Maoheng , Xu Hao 
Published: September 2022

Abstract
Objective
To investigate the independent risk factors for the first recurrence after endovascular management in patients with Budd-Chiari syndrome (BCS), and to establish a prediction model for predicting recurrence in target patients.
Methods
BCS patients who underwent endovascular treatment in the Affiliated Hospital of Xuzhou Medical University from January 2010 to December 2015 were retrospectively examined, with their clinical, laboratory test, and imaging data collected and analyzed. Independent risk factors for recurrence were identified, and a prediction model was established and validated.
Results
A total of 450 patients met the filtering criteria, and 102 patients recurred during the follow-up. The median follow-up time was 87 months, ranging from 1 to 137 months. The 1-, 3-, 5- and 10-year cumulative recurrence rate was 9.11% (6.41%-11.73%), 17.35% (13.77%-20.78%), 20.10% (16.30%-23.72%), and 23.06% (18.86%-27.04%), respectively. Liver cirrhosis, ascites, thrombosis, and obstructed HV + AHV (all three main HVs and AHVs obstruct) are independent risk factors, while age is an independent protective factor. The risk score = (-0.385981 * Age/10)་(0.0404184 * PT)་(0.0943423 * CRE/10)་(0.0157053 * LDH/10)་(0.592179 * LC)་(0.896034 * Ascite)་(0.691346 * Thrombosis)་(0.886741 * (HV + AHV)). A nomogram was provided for better clinical application. Patients with a risk score < 1.57 were stratified as the low-risk group while those ≥ 1.57 as the high-risk group (P < 0.001).
Conclusion
Liver cirrhosis, ascites, thrombosis, and obstructed HV + AHV are independent risk factors for the first recurrence, age is an independent protective factor. The prediction model can effectively and conveniently predict the risk of recurrence and screen out patients at a high recurrence risk.
Degradation of paper products due to volatile organic compounds
Authors: Alam Izhar , Sharma Chhaya 
Published: September 2022

Abstract

Paper and packaging materials are lignocellulose-based natural polymers that release acetic acid, aldehydes, alcohol, and ester-based volatile organic compounds (VOCs) due to ageing. These VOCs start degrading the paper and its products and decline their mechanical strength properties. The reactivity of paper of unbleached wheat straw pulp towards acetic acid and hexanaldehyde, a kind of more harsh VOC than others was studied during this study. The papers were exposed to these volatile compounds for 90 days in an air-tight vessel under ambient environmental conditions. The results showed that hexanaldehyde was more destructive than acetic acid in terms of cellulose degradation. The paper properties like, tensile, tear and burst index, viscosity, pH and carbonyl group content were measured. The growth of the carbonyl group, evidence of the ageing effects in the paper, detected more in the paper exposed to acetic acid. However, the strength of paper properties declined more with hexanal. Thus, the results strongly recommend that VOCs could be the leading cause of the degradation of paper and its products, which may generate due to natural or artificial ageing.
Experimental mechanical polishing protocol performed continuously increases the durability of acrylic resin-based prosthetic materials.
Authors: Assis Ellen Lima , Menezes Antonia Thayná Santos , Lobato Mário Lucas Facundo , Peixoto Raniel Fernandes , Pinto-Fiamengui Lívia Maria Sales , Regis Romulo 
Published: September 2022

Abstract
Objectives To evaluate the effect of a mechanical polishing protocol (MPP) performed continuously on the surface roughness and color stability of a denture base acrylic resin and artificial teeth exposed to different pigmenting substances.
Materials and Methods Seventy specimens of each material (n=10) were exposed to: distilled water (DW, negative control); cigarette smoke (CS); immersion in coffee (CF); association CS+CF; and association of each pigmenting condition to the MPP (CS+MPP, CF+MPP and CS+CF+MPP). The MPP used aluminum oxide paste and felt wheel coupled to an electric motor. Color stability and surface roughness were evaluated by standard procedures at baseline and after 12 and 24 days. Two-way mixed ANOVA was used for statistical analysis.
Results In most experimental conditions, the polishing controlled the roughness changes, making roughness values significantly lower (p<0.05) than those observed in the negative control group, as well as significantly reduced (p<0.05)the color change even for the groups submitted to the association of cigarette smoke and coffee in both materials.
Conclusions The proposed MPP, when carried out at biweekly intervals, was able to control the increase in surface roughness and reduce the color change on the tested acrylic resin-based prosthetic materials.
Clinical Relevance Continuous polishing of removable dental prostheses can bring more durability, reducing the deleterious effect of agents such as cigarette and coffee. As it is a simple procedure to perform, the development of a portable device would allow the polishing to be done at home by the users of these prostheses.
Evaluation of a Two Step Testing Algorithm to Improve Diagnostic Accuracy and Stewardship of Clostridioides difficile Infections
Authors: Bettger Caitlin C. , Giancola Stephanie E. , Cybulski Robert J. , Okulicz Jason F. , Barsoumian Alice E. 
Published: September 2022

Abstract
In response to national guidelines, we implemented a two-step testing algorithm for Clostridioides difficile in an effort to improve diagnostic accuracy. Following implementation, we analyzed treatment frequency between discordant and concordant patients. We found that the majority of discordant cases were treated with no significant differences in patient characteristics or outcomes between the concordant and discordant groups. Additionally, there were no differences in outcomes when discordant patients were further stratified by treatment status. Given little added diagnostic accuracy with the addition of EIA toxin testing, our facility resumed diagnosis by PCR testing alone. Further studies are needed to investigate alternative processes for improvement in diagnostic accuracy aside from toxin EIA testing including stool submission criteria and educational programs.
Prevalence and factors associated with suicidal ideation, cannabis, and alcohol misuse during the COVID-19 pandemic in Saskatchewan: findings from a joint-effect modeling
Authors: Adeyinka Daniel , Novik Nuelle , Novotna Gabriela , Bartram Mary , Gabrys Robert , Muhajarine Nazeem 
Published: September 2022

Abstract
Background
Generally, pandemics such as COVID-19 take an enormous toll on people’s lives. As the pandemic extends into the second year, growing attention has been paid to the multiple adverse mental health and behavioral issues, such as suicidal ideation and substance misuse. However, the interplay of suicidality and substance misuse during the pandemic has been limited. We aimed to investigate the prevalence of co-occurrence of suicide ideation, alcohol and cannabis misuse, and the factors that are associated with these co-occurrences in the province of Saskatchewan during the COVID-19 pandemic.
Methods
We performed a multivariable trivariate probit regression on a sample of 666 Saskatchewan adolescents and adults (16 years or older), drawn from the cycle 10 data collection (March 2022) of the Mental Health Commission of Canada, and Canadian Centre on Substance Use and Addiction (MHCC-CCSA) dataset.
Results
The prevalence of suicidal ideation was higher among respondents who reported both problematic cannabis and alcohol use (25.8%) than single users of alcohol (23.2%) and cannabis (18.7%). Younger respondents (16–34 years) and those who reported other coping behaviors that were not particularly adaptive or helpful were independent factors that were associated with the common experience of suicide ideation, problematic cannabis, and alcohol use. Having a diagnosis of mental health disorders either before or during the pandemic, and the perceived inability to bounce back after the pandemic (low resilience) are strong correlates of suicidal ideation. Those who lived alone, between 35 and 55 years of age were more likely to report problematic alcohol use. Those who reported coping strategies that were deemed more adaptive, who reported pandemic stress, and declared a LGBTQIA2S + identity had higher probability of problematic cannabis use.
Conclusions
As the pandemic persists, improving access to suicide and substance use interventions for the vulnerable groups identified in this study may be impactful.
Classification of Pressure and Diabetic Chronic Wound Tissue Images with Deep Learning Methods
Authors: Eldem Hüseyin , Ülker Erkan , Işıklı Osman Yaşar 
Published: September 2022

Abstract
Pressure wounds are skin injuries that occur due to prolonged inactivity of the patient. They reduce blood flow. They cause muscle and skin ischemia, which leads to necrosis and cell death. The automatic classification of pressure sores according to international principles allows rapid recommendation of appropriate treatment methods. In this way, life quality of patients will be increased by ensuring that the treatments to be applied are efficient. Diabetes foot wounds are among the current serious and chronic diseases and are among the wound types that threaten life and reduce the quality of life. In the literature, there are mostly studies that make binary classification (ulcer-nonulcer) showing which type pressure and diabetic foot ulcers belong to. In this study, the impact of recently popular deep learning methods in image processing for tissue classification of pressure and diabetic wound images is discussed. Deep Convolutional Neural Network (CNN) architectures were used to determine which of the granule, necrotic and slough classes wound images belong to, according to their characteristics on the tissue. In this study, in which 19 CNN architectures were discussed, CNN architectures that give successful results on the dataset were observed. In addition, the effect of Maximum Epoch Number (ME), Initial learning rate (LR) and Minibatch size (MBS) parameter values used in the architectures were also researched. In the experiments performed on 1045 pressure and 1045 diabetic wound images collected from different patients from the Wound Care Unit of Karaman Education and Research Hospital, the best results were obtained with the AlexNet architecture with an Accuracy value of 95.83%. Optimum values of ME, LR and MBS parameter values were also found in parameter optimization tests. Comparisons were made with the results of studies on similar wound datasets in the literature, and it was shown that AlexNet and VGG16 architectures achieved competitive results.