bluuebunny/crossref_metadata_2025_split huggingface dataset:
Preoperative fasting: knowledge, attitude, and practice of postgraduate trainees at a tertiary care hospital—an observational study
Authors: Gupta Nipun , Patnaik Subhasish , Lakkegowda Lahareesh B. , Jaiswal Alok , Dwivedi Deepak 
Published: March 2022

Abstract
Background
Limiting the preoperative fasting period positively impact surgical patient outcome. In tertiary care hospitals, preoperative fasting instructions are mostly implemented by postgraduates. This study was conducted to evaluate the knowledge, attitude, and practice of postgraduate trainees of surgical specialties and anesthesiology regarding preoperative fasting. This observational study was conducted at a tertiary care hospital in May 2020. Consenting 166 postgraduate trainees of anesthesiology and surgical specialties was asked to respond to a questionnaire consisting of nine questions. The primary outcome was the knowledge status of postgraduate trainees regarding preoperative fasting guidelines and secondary outcomes being their attitude and practice of the same. Categorical data were presented as numbers and percentages.

Results
Out of 166 respondents, 149 (89.8%) and 94 (56.6%) respondents could not correctly describe the practice guidelines published by the American Society of Anaesthesiology pertaining to preoperative fasting in adult and pediatric patients respectively. Of the total respondents, 146 (87.9%) and 108 (65.1%) gave incorrect preoperative fasting instructions to adult and pediatric patients, respectively. The source of information regarding fasting guidelines was seniors amongst 89 (53.6%) respondents. Fifty-one (30.7%) respondents were unaware of the benefits of limiting preoperative fasting.

Conclusions
There is an imminent need for change in the knowledge, attitude, and practices of residents with regards to preoperative fasting practices. A holistic approach with formal teaching and development of SOP can achieve the change of practice of “NPO after midnight.”
Assessment of wetland potential and bibliometric review: a critical analysis of the Ramsar sites of India
Authors: Biswas Roy Malabika , Nag Swetasree , Halder Sudipa , Kumar Roy Pankaj 
Published: March 2022

Abstract
Background
This study focussed on effective bibliometric analysis of research on Ramsar sites in India within the last 30 years using VOS viewer software. The study focused on the estimation of the current areal extent and meteorological impact on wetland together with bibliometric analysis. The online data source of Web of Science has been used to extract all the relevant documents on Indian Ramsar sites published within a period of 1989 to 2020.

Main body
The main objective is to analyze the trend of research on the Indian Ramsar site both qualitatively and quantitatively. The peak of research growth has been lifted after the twentieth century as most of the Indian water bodies have been designated or put into the land from this period. A geographical location-based mapping was prepared based on the number of publications during the study period to observe the growing research interest in wetland studies in India. The highest publications have been documented on East Calcutta Wetland located in West Bengal followed by Loktak Lake in Manipur, Deepor Beel in Assam and others. The most frequently used keywords were “Ramsar site”, “India”, “wetland”, “wetlands”, “remote sensing” etc. India published most of the documents on wetland studies in the Indian context. England, Australia, Canada also had various international collaborative works with Indian researchers.

Conclusions
The research output shows an upward research trend on Indian Ramsar sites both quantitative and qualitatively. Such kind of research can provide a panoramic view towards the worldwide research trend and help to generate further effective approaches for the betterment of our environment.
Water bodies are potential hub for spatio-allotment of cell-free nucleic acid and pandemic: a pentadecadal (1969–2021) critical review on particulate cell-free DNA reservoirs in water nexus
Authors: Igere Bright Esegbuyota , Onohuean Hope , Nwodo Uchechukwu U. 
Published: March 2022

Abstract
Background
In recent times, there had been report of diverse particulate nucleic acid-related infections and diseases which have been associated with endemic, sporadic, and pandemic reports spreading within water nexus. Some of such disease cases were seldom reported in earlier years of technological advancement and research based knowledge-scape. Although the usefulness of water, wastewater treatment systems, water regulatory organizations and water re-use policy in compliant regions remains sacrosanct, it has been implicated in diverse gene distribution.

Main body
A cosmopolitan bibliometric and critical assessment of cell-free DNA reservoir in water bodies was determined. This is done by analysing retrieved pentadecadal scientific publications in Scopus and Pubmed centre database, determining the twelve-monthly publication rates of related articles, and a content-review assessment of cell-free nucleic acids (cfNAs) in water environment. Our results revealed thirty-eight metric documents with sources as journals and books that conform to the inclusion criteria. The average reports/publication rate per year shows 16.7, while several single and collaborating authors are included with a collaboration index of 4.31. A zero average citation per document and citation per year indicate poor research interest and awareness.

Short conclusion
It is important to note that a redirected interest to studies on cfNAs in water environments would encourage advancement of water treatment strategies to include specific approaches on the removal of cfNAs, membrane vesicles or DNA reservoirs, plasmids or extra-chromosomal DNA and other exogenous nucleic acids from water bodies. It may also lead to a generational development/improvement of water treatment strategies for the removals of cfNAs and its members from water bodies.
Development and comparison of membrane separation schemes for byproduct recovery from Egyptian rice straw black liquor
Authors: Abulnour Abdelghani M. G. , El Sayed Marwa M. , Tewfik Shadia R. , Hani Heba A. , Sorour Mohamed H. , Shalaan Hayam 
Published: March 2022

Abstract
Background
Recovery of valuable ingredients from black liquor could lead to an environmentally and economically sound bioethanol production technology. In this work, two schemes comprising hybrid membrane systems incorporating ultrafiltration (UF) and nanofiltration (NF) are developed for the recovery of lignin, silica rich and cellulose/hemicellulose hydrolysates byproducts from alkaline pretreated rice straw.

Methods
The first scheme (I) comprises UF, NF and thermal vapor compression (TVC), while, the second scheme (II) includes UF, 2 stages of NF and 2 TVC units. Further treatments are suggested to produce solid byproducts with an economic value. Furthermore, material balance of the two schemes based on 1000 m3/d of black liquor and the main design features and comparative direct cost indicators of the main adopted units were deduced using WT Cost II© software.

Results
Results revealed that about (80–90%) yield of recovered byproducts from both schemes with equivalent amounts of 9.5, 5.5 and 18.5 ton/d of lignin, silica rich and cellulose/hemicellulose hydrolysates dry products, respectively. Moreover, reusable water recovery approaches 26% and 70% for schemes (I) and (II), respectively.

Conclusions
Further, the wastewater generated from scheme (II) is 2.9 times folds scheme (I) which improves the environmental impact of the former. Preliminary cost indicators revealed that both schemes have almost the same total direct capital cost.
Preoperative prediction of lymph node metastasis using deep learning-based features
Authors: Cattell Renee , Ying Jia , Lei Lan , Ding Jie , Chen Shenglan , Serrano Sosa Mario , Huang Chuan 
Published: March 2022

Abstract
Lymph node involvement increases the risk of breast cancer recurrence. An accurate non-invasive assessment of nodal involvement is valuable in cancer staging, surgical risk, and cost savings. Radiomics has been proposed to pre-operatively predict sentinel lymph node (SLN) status; however, radiomic models are known to be sensitive to acquisition parameters. The purpose of this study was to develop a prediction model for preoperative prediction of SLN metastasis using deep learning-based (DLB) features and compare its predictive performance to state-of-the-art radiomics. Specifically, this study aimed to compare the generalizability of radiomics vs DLB features in an independent test set with dissimilar resolution. Dynamic contrast-enhancement images from 198 patients (67 positive SLNs) were used in this study. Of these subjects, 163 had an in-plane resolution of 0.7 × 0.7 mm2, which were randomly divided into a training set (approximately 67%) and a validation set (approximately 33%). The remaining 35 subjects with a different in-plane resolution (0.78 × 0.78 mm2) were treated as independent testing set for generalizability. Two methods were employed: (1) conventional radiomics (CR), and (2) DLB features which replaced hand-curated features with pre-trained VGG-16 features. The threshold determined using the training set was applied to the independent validation and testing dataset. Same feature reduction, feature selection, model creation procedures were used for both approaches. In the validation set (same resolution as training), the DLB model outperformed the CR model (accuracy 83% vs 80%). Furthermore, in the independent testing set of the dissimilar resolution, the DLB model performed markedly better than the CR model (accuracy 77% vs 71%). The predictive performance of the DLB model outperformed the CR model for this task. More interestingly, these improvements were seen particularly in the independent testing set of dissimilar resolution. This could indicate that DLB features can ultimately result in a more generalizable model.
Simple headache revealed a rare lymphoma: Waldenstrom macroglobulinemia with unique markers: a case report and review of the literature
Authors: Awad Ahmed K. , Elbadawy Merihan A. , Boury Maty , Rivera Amanda , Motawea Karam , Shah Jaffer , Parnia Shanli , Varney Joseph 
Published: March 2022

Abstract
Background
Waldenstrom macroglobulinemia (WM) is a rare lymphoma with an incidence rate of 3 per million people per year, with approximately 1000 to 1500 new cases diagnosed each year in the USA. It is primarily seen in Caucasian males with a median age of 70 years old. Patients are most often asymptomatic, but WM can manifest itself with constitutional symptoms such as lethargy, bleeding, organomegaly, and neurological or fundoscopic abnormalities. WM is characterized by immunoglobulin M (IgM) monoclonal gammopathy, lymphocytic infiltration of bone marrow, and normocytic anemia due to bone marrow replacement.

Case presentation
Our patient is a Hispanic 67-year-old female that presents with one month of intermittent band-like bilateral headache accompanied by dizziness, light-headedness, nausea, and blurred vision. A thorough diagnostic workup was performed, including serum protein electrophoresis (SPEP) with serum immunofixation (SIFE) showing an M spike and IgM kappa. Bone marrow biopsy was significant for lymphoplasmacytic infiltration with nodular B cells (CD19+, CD20+, CD22+). Computerized Tomography (CT) imaging showed splenomegaly in the patient. Treatment was provided for hyperviscosity syndrome with plasmapheresis twice. The patient reported improvement of her symptoms and was then scheduled for chemotherapy. Throughout 7 months, our patient received multiple cycles of bortezomib, dexamethasone, and rituximab. While her symptoms improved her psychiatric status got progressively worse.

Conclusion
It is important not to neglect symptoms such as a headache, which may seem small, but could serve as a clue in the diagnosis of Waldenstrom's macroglobulinemia.
Using Implementation Mapping to develop and test an implementation strategy for active learning to promote physical activity in children: a feasibility study using a hybrid type 2 design
Authors: Walker Timothy J. , Kohl Harold W. , Bartholomew John B. , Green Charles , Fernández Maria E. 
Published: March 2022

Abstract
Background
Incorporating physical movement in the teaching of academic content (active learning) is a promising approach to improve children’s health and academic performance. Despite documented benefits, implementation of active learning remains challenging for schools. The aims of this study are to develop an implementation strategy to support the delivery of active learning in elementary schools and examine the impact of the developed implementation strategy on the implementation and effectiveness of active learning.

Methods
Aim 1 will use Implementation Mapping, which is a multi-step approach that guides the use of theory, stakeholder input, and existing literature to develop a scientifically based implementation strategy for active learning in elementary schools. Aim 2 will feature a feasibility study to examine the impact of the implementation strategy on both implementation and effectiveness outcomes, consistent with a Hybrid Type 2 design. Acceptability and implementation fidelity will be the primary implementation outcomes, and student physical activity levels will be the primary effectiveness outcome. We will recruit two elementary schools within our partner district, and one will be randomly assigned to receive usual support while the other will receive the newly developed implementation strategy. Participants from each school will complete baseline, 6-, and 12-month assessments. Bayesian statistical approaches will quantitatively examine preliminary effectiveness outcomes. We will also use an embedded mixed methods approach to triangulate findings.

Discussion
This study’s innovative overarching conceptual framework (centered on Implementation Mapping) will inform the development and testing of the implementation strategy. This study also uses methodological approaches optimal for feasibility studies, including mixed methods and Bayesian statistics. As a result, we will be able to gain a thorough understanding about the feasibility and preliminary effectiveness of the implementation strategy, which will inform subsequent research and practice for implementing active learning in schools.

Trial registration
ClinicalTrials.gov, NCT05048433, registered on September 8, 2021.
Intelligent system based comparative analysis study of SARS-CoV-2 spike protein and antigenic proteins in different types of vaccines
Authors: Touati Rabeb , Elngar Ahmed A. 
Published: March 2022

Abstract
Background
Coronaviruses, members of the Coronavirinae subfamily in the Coronaviridae family, are enveloped and positive-stranded RNA viruses that infect animals and humans, causing intestinal and respiratory infections. Coronavirus disease 2019 (COVID-19) is caused by a novel coronavirus, named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). This disease appeared, for the first time (December 2019), in China and has spread quickly worldwide causing a large number of deaths. Considering the global threat, the World Health Organization (WHO) has declared, in March 2020, COVID-19 as a pandemic. Many studies suggest the great effect of the existing vaccines to protect against symptomatic cases of death by the COVID-19 virus. This paper, proposes to compare the main antigenic proteins sequences of the existing vaccines with Spike (S) protein of the SARS-CoV-2 genome. Our choice of S protein is justified by the major role that plays it in the receptor recognition and membrane fusion process based on an intelligent system. Herein, we focus on finding a correlation between S protein and compulsory vaccines in the countries that have a less death number by COVID-19 virus. In this work, we have used a combination of coding methods, signal processing, and bioinformatic techniques with the goal to localize the similar patterns between the S gene of the SARS-Cov-2 genome and 14 investigated vaccines.

Results
A total of 8 similar sequences which have a size more than 6 amino acids were identified. Further, these comparisons propose that these segments can be implicated in the immune response against COVID-19, which may explain the wide variation by country in the severity of this viral threat.

Conclusions
Our in silico study suggests a possible protective effect of Poliovirus, HIB, Hepatitis B, PCV10, Measles, Mumps, and Rubella (MMR) vaccines against COVID-19.
Least-squares reverse time migration with a multiplicative Cauchy constraint
Authors: Yao Gang , Wu Bo , da Silva Nuno V. , Debens Henry A. , Wu Di , Cao Jingjie 
Published: March 2022

One of reverse time migration’s main limitations is that an unscaled adjoint operator is prone to produce images with low resolution, inaccurate amplitudes, and even artifacts. Least-squares reverse time migration (LSRTM) has been introduced to mitigate this inadequacy via the use of an approximation to an inverse operator. LSRTM suffers from its own limitations, most importantly from poor condition, which often manifests itself as image artifacts. One approach to ameliorate this issue is to constrain the optimization problem by introducing a penalty term to the cost function. Penalizing estimated parameters for sparsity is one such constraint that has been shown to be effective. A drawback of this technique is that it introduces a trade-off between data fitting and image sparsity. Furthermore, if using the Cauchy constraint, an additional trade-off is introduced due to the requirement to estimate a hyperparameter. We introduce an alternative approach that mitigates these trade-offs by combining a multiplicative cost function with an effective means for determining the Cauchy hyperparameter. We also introduce a new formulation of the multiplicative cost function that avoids over-penalization by the constraint via the introduction of a relaxation term. Finally, we seek to improve the computational efficiency by introducing a new approach for computing the step length. As such, our method introduces three novel aspects to constrained LSRTM: (1) a relaxed multiplicative cost function, (2) semiautomatic estimation of the Cauchy hyperparameter, and (3) efficient computation of the step length. We discuss the theory and implementation, followed by application to three synthetic data sets and a real ultrasonic data set. Given the presence of large salt bodies, elasticity, and noise, along with the directivity of piezoelectric ultrasonic transducers, these data sets provide a challenging test of the approach outlined. Results demonstrate that our method is robust in handling the challenges imposed by these scenarios.
Improved kinetic Monte Carlo models for computational and analytical investigations of the magnetic properties of finite-size atomic chains
Authors: Kolesnikov S. V. , Kolesnikova I. N. 
Published: March 2022

Abstract
Two improved kinetic Monte Carlo (kMC) models for investigations of the magnetic properties of finite-size atomic chains are presented. These models take into account the possible noncollinearity of magnetic moments. The spontaneous remagnetization of ferromagnetic Co chains on the Pt(997) surface and antiferromagnetic Fe chains on the

surface is investigated in the framework of our models. The results are compared with the results of the simple kMC model. It is also shown that a single domain-wall approximation can be successfully used to estimate the reversal time of the magnetization. Therefore, the improved kMC models can be used for analytical calculations as well as for computer simulations.
sIL-2R: A Novel Diagnostic Biomarker for Primary Adrenal Lymphoma
Authors: Yu Kai , Xie Wei , Zhao Lianling , Wang Hanxiao , Ren Yan , Zheng Chang , Chen Xixiao , Su Zhenli , Tang Yeyi , Zhou Fangli , Tian Haoming , Zhu Yuchun , Chen Tao 
Published: March 2022

Abstract

Context
Primary adrenal lymphoma (PAL) is difficult to distinguish from other adrenal masses. Soluble interleukin-2 receptor (sIL-2R) is a diagnostic biomarker for nodal non-Hodgkin lymphoma, whose association with PAL is unknown.

Objective
The aim of this study was to determine the diagnostic utility of serum sIL-2R for Patients with PAL.

Design
Prospective cohort study.

Settings and Participants
A total of 118 patients with adrenal masses who were willing to be tested for levels of serum sIL-2R from a tertiary hospital between 2019 and 2021 were included.

Main Outcomes and Measures
Serum sIL-2R and lactate dehydrogenase (LDH) levels.

Results
Patients with PAL had significantly higher sIL-2R levels than those of patients with other adrenal masses with indetermined and benign computed tomography (CT) features (both Ps &lt; 0.001). The LDH levels of patients with PAL were also significantly higher than those of patients with other adrenal masses with indeterminate and benign CT features (both Ps &lt; 0.001). Good discrimination of patients with PAL from other patients (PAL vs other adrenal masses with indeterminate CT features/non-PAL) was achieved with an area under the receiver operating characteristic curve (AUC) of 0.984 (95% CI, 0.95-1)/0.992 (95% CI, 0.975-1.000) using the serum levels of sIL-2R and further improved (AUC = 0.998, 95% CI, 0.994-1.000; AUC = 0.999, 95% CI, 0.996-1.000) after adjusting by LDH category.

Conclusions
For the first time, we have identified that serum sIL-2R and LDH category-adjusted sIL-2R levels have good diagnostic performances for PAL.
An adaptive calibration technique for thermistor with varying temperature coefficient and reference resistance
Authors: Venkata Santhosh Krishnan , Roy Binoy Krishna , Nair Nanditha 
Published: March 2022

Background: A thermistor is a nonlinear sensor requiring a precise calibration technique to achieve accurate temperature measurements. This paper attempts to design a calibration technique employing artificial neural network (ANN) algorithms. The present work fulfills the following objectives: (i) to cover 100% input range in the linearity range measurement; (ii) to make the measurement technique adaptive to variations in reference resistance and thermistor temperature coefficient using a calibration technique. Methods: An ANN-based calibration circuit is cascaded to the data conversion circuit. Optimized ANN is trained with linear data independent of reference resistance and temperature coefficient effects on thermistor output. ANN optimization is performed by comparing various schemes, algorithms, and numbers of hidden layers to achieve a minimum mean square error and a regression close to 1. Results: The proposed technique provides a linear relationship for the system over the entire input range and avoids the requirement of repeated calibrations each time the thermistor is replaced. Practical data are used to validate the proposed measurement technique. Conclusions: The objectives and proposed technique have been demonstrated by results with a root mean square percentage error of 1.8%.