bluuebunny/crossref_metadata_2025_split huggingface dataset:
Seasonal Variances in General Urine Examination Findings among Al-Yarmouk Teaching Hospital Patients
Authors: Jameel Sarab K. , Abdullah M. Taha  , Abdulrazzaq Russell A. , Ali H. Hayyawi  , Mariam R. Khashea  , Oruba K. AL-Taiem  , Zakaria Y. Younus  , Marwan M. Hamid  
Published: September 2024

Background: Urinalysis is a common test done for several reasons; for instance, in UTI looking for, hematuria or pseudo-hematuria, pyuria, crystalluria and also looking for different microorganisms (etc...), many laboratories perform either a regular test light microscopy, or examine varying dilutions of urine sediment and fields of view with or without the use of stains or polarization, while other laboratories used analysis of urine using automated system. As being a human; the biological sex(female or male) and broadly influences diverse immune phenotypes, including immune responses to diseases at mucosal surfaces, all contribute to how an organism will respond to diseases of the urinary tract, on the other hand, a close relationship between four seasons and the incidence of UTI has been detected in various areas of the world, where the seasons alternate in a year, and the temperature fluctuates widely among these seasons.
Methods: present study was conducted on midstream sample was collected for acquiring accurate results from (68899) individuals (40800 females and 28099 males), these subjects were clinically suspected to have UTI, who were referred to the Department of Teaching Laboratories in AL-Yarmouk Teaching Hospital at Baghdad / Iraq, between January 2020 and December 2023, which examined by; Urilyzer 500 PRO (Analyticon) with Urine strips (dipstick) and Optical Microscope BA 210 (Motic).
Results: A total of 68899 urine specimens were tested, of which 28099 male (40.8%) and 40800 female (59.2%) with the highest percentage of urinalysis in 2022 (33.2%), furthermore, the higher percentage of urinalysis parameters was scored in 2021 as follows; pus cells 9012 (49.3%), red blood corpuscles 5284 (48.9%) and crystalluria 3956 (44.2%) with the most common type of crystal was amorphous urate 5032 (56.2%), while highest percentages of microorganisms 1050 (40.3%) in year 2022 , and Bacteria was exhibited the highest percentages 2036 (78.1%). Current study recorded significantly differences between males and females in all urine parameters which was higher percentage in female than male and in the Seasons of (Summer and Autumn) , during the four years of this study.
Conclusion: This study registered more accurate diagnostic results of UTI according to the urinary parameters in the microscopic routine urinalysis(centrifugation at 300xg at 5 min) when compared to machines (Urilyzer 500 PRO test/Urine strips) which frequently miss that , in addition, hot season in Iraq could affect to increase the seasonally variable incidence of UTI , as well, increased percentages and frequency of urinary parameters which observed in 2021-2022 out of four study years may indicates, high likelihood side effects of Covid-19 pandemic disease in study persons involved.
Environment Aspects and Daily Life-Threatening Risk Prediction for Improving Public Health Using Ensemble Learning Techniques
Authors: Dewangan Omprakash , Prakash Vasani Vaibhav 
Published: September 2024

Increasing work nature in the fastest world need relaxing to maintain public health is important. But the continuous imbalance of work nature in human daily life doesn’t have time to consecrate the health with leads life threatening facts such as work tension, management stress, job professional, family needs, improper periodic work cycle, and soon. By analyzing the daily public life threatening risk prediction based on Feature selection and classification to category the public health to recommend for phycological treatment. By suggesting treatment schedule are important to balance the public health Lifecyle to make stress free life to spent with nature. Most of prevailing techniques analyse the life-threatening issues, but the features are improperly to taken without the mutual relation cause poor accuracy in precision and classifications rate with more false prediction rate. To tackle this issue, to propose an optimized ensemble learning Techniques based on multi scalar support vector machine (MSSVM) with deep scaled XGboost classifier (DSXG-boost) to improve the prediction accuracy. Initially the public life Cycle dataset is collected and to make normalization using Min-max normalization. Then the Public Life Threat Impact Rate (PLTIR) is analysed to marginalize the heath affecting features. Then then stress margin dependencies feature limits are selected with support of MSSVM. The selected features are grouped in cluster margins and classified with deep scaled XG boost algorithm to predict the active and inactive heath margin be categorized by risk threaten class. The proposed system prove the prediction accuracy in higher precision are by selecting mutual dependencies of health affection feature limits and best recall rate to improve the performance. Based on the predicted class, the categorized life threaten risk peoples are recommends to make phycological treatment to protect the public life cycle.
A Unification of Fog-Cloud Computing for Data Agitation and Guard Intensification in Industrial Health Care Security
Authors: Biswas Debarghya , Tiwari Ankita 
Published: September 2024

The development of Fog computing is a decentralized environment in which the data processing, storage and applications are processed between the located server in a cloud environment. By increasing the Internet of things (IoT) and remote storage, the communication in cloud become more sophisticated by processing the data safely and securely. Healthcare data is important which contains medical information and processed centrality in the public environment through IoT, due to increasing security breaches they need to protect depends on the security applicants. All over the centralized data computing are accessed by virtual environment through remote protocol doesn’t provide safety in healthcare industry. To resolve this problem, to propose as Unification of fog-cloud computing for data agitation and guard intensification (DA-GI) in Industrial Health Care Security. The Medical Data Health-Care (MDHC) records are stored in Cloud datacenters and the Fog layer based on the guard intensity and the key is provoked for ingress the file. The activity logs are controlled and monitoring from cloud serves sustains the Activity Log, Risk Table and Health Records. To introduce a cryptographic approach based on advanced encryption standards (AES) to protect data and authenticity verification server. The key verification process based on security gateway, Fog cloud server depends on user access rights provided to the user. During the key validation, role of permission to the user s verified and agitate to allow access rights to the verified service access. The proposed system produce high security compared to the other system as well in all security concerns of role, authentication and verification to process data safely.
Secure Healthcare Data Storage and Transmission: A Review of Current Technologies and Future Directions
Authors: Haval Abhijeet Madhukar , Afzal Md 
Published: September 2024

The development of websites, applications, and the first social networks profoundly altered everyone's life and became the catalyst for advancement on a global scale. The days of immovable points, phones, and printing presses are long gone. But is everything really as perfect as it looks? Perhaps the most contentious thing in history is progress. We now have the freedom to express our ideas without fear, connect with individuals around the globe, and access a seemingly limitless amount of knowledge thanks to the Internet. But as time goes on, concerns about the cloudlessness of virtual existence become more and more prevalent; we have been captured by things that do not exist in reality. Apart from the well-known hazards like terrorism and global warming, the growth of the Internet has given rise to entirely unknown and novel perils that have infiltrated our life. We refer to this phenomena as "cybercrime". Any form of criminal activity carried out virtually is referred to as cybercrime. Ten to twenty years ago, this phenomena was known only to specialised specialists. IT industry, and it is currently a worldwide issue. Although everyone and the IT sector receive adequate security measures and equipment, cybercrime is nevertheless increasing at a very rapid pace in parallel. There are several security problems and cyberthreats in the modern world. With new technology emerging daily, we can predict major issues in the road. In this work a Secure Healthcare Data Storage and Transmission in WAN area is discussed.
Finding the Link Between Cyberbullying and Suicidal Behavior Among School Going Adolescents
Authors: K Gokul , D Dr Gokul , D Dr Keerthana , B Dr Nisha 
Published: September 2024

Background: Studies have shown that cyberbullying or online bullying has increased the risk of suicidal thoughts and suicidal behaviours. Although suicide or suicidal behaviours are not directly related to cyberbullying it can be said that cyberbullying increases depression rates and feelings of isolation which end up in suicidal calls. This research aims to understand the nexus between cyberbullying and suicidal behaviour. Methods and materials: A self-administered and anonymous online questionnaire was used to survey 410 higher secondary school adolescents aged 16 to 18 and the data analysis has been carried out by statistical analysis with the help of IBM-SPSS software of version 22. Results: The findings of the results have revealed that cyberbullying was more prevalent among the respondents than cyber victimization. There was a small fraction of the population that said that they had undergone suicidal behaviour due to cyberbullying. Between text messaging and multimedia messaging, the best platform for cyberbullying was multimedia messaging. The reason behind this was its usage around the world, its popularity, and its ease. Teenagers over the age of 17 years have reported the highest percentage share for suicide rates and most of them were female. Conclusion: It can be concluded that cyber bullying is a threat to those girls who is currently studying in 11th standard and or age 17 years whose parents are separated or not living together.
Exploration of Workaholic Characteristics of Married Women in Healthcare sector during Pandemic situations
Authors: Praveenadevi D. , Manimegalai V. , P Sreelatha , Bamini J , Kaushal Ashish Kumar , Fufa Gamachu 
Published: September 2024

Women in professional and family life are facing lot of challenges where they are struggling to manage their work pressure and family responsibilities. In healthcare sectors, the employees especially the nurses have to carry out shift work and have to perform morning, evening and night shifts. It is analysed and proved by many analysts that mostly the women from middleclass family life are subject to such work-life challenges in particular the women nurses especially during pandemic situations. This study focuses on Workaholism which is an extreme work force that leads to the disregard of others’ welfare and harmful effects faced by the women nurses especially who are married. In this aspect, the research was conducted to examine the workaholic characteristics of married women especially the nursing professionals during the pandemic period. The study was conducted by collecting data from women nurses from South part of India and the sample size derived for the study is 200. The findings reveal that Continuous thoughts of Work Involvement, Accepts Extra Work Time, Task Oriented, Compensation based on Work Performance and No Botheration about Work life Balance are the major workaholic characteristics of women nurses especially who are married.
Big Data Analytics Challenges and Opportunities in Heart Disease Recognition: Novel Dimensionality Reduction with Classification Approach
Authors: Sinha Dr. Anupa , Kumar Yalakala Dinesh 
Published: September 2024

Due to the current technological growth, a number of strategies have been developed, and more are being developed to eliminate problems that arise in many fields. Big Data techniques are employed to effectively stored health data due to the continual and massive volume of data created by the human body. Furthermore, the most important procedure is the classification of health data since it must be carried out precisely in order to diagnose cardiac disease early. The database images are various in size to reduce the dimension the Modified Principal Component Analysis (MPCA) Algorithm is used. the proposed MPCA algorithm is act as a feature selection model to pick features. One of the best and most effective techniques for classifying medical data is the Modified Deep Convolutional Neural Network (MDCNN). It has been shown to work for a variety of hospitalized patients. Consequently, the simulation results show that this proposal enhances classification accuracy in experimental research for the detection of heart ailment. Hence, the proposed method leads to an efficient usage of the resources and cost reduction. This approach assists the physician in taking suitable decision for giving a better treatment at right moment.
Development Of Electronic Data Analytics For Public Health Surveillance And Epidemiology
Authors: Deshmukh Dr. Shailesh Madhavrao , Salwadkar Mrunal 
Published: September 2024

The process of assessing patient data that is taken from various medical devices is known as health care analytics. The patient's condition is projected by the analytics, which helps the physician support the diagnosis. The data created are referred to as Electronic Health Records (EHR) / Electronic Medical Records (EMR). Large volumes of electronic health and medical records being produced, giving rise to the idea of medical big data. Medical data may be in its early stages of development or fully developed and organised. The data research community now has a new way to experiment with medical data and access the results of intelligent algorithms through testing or historical experiences. The vast amount of medical big data used in this study project served as the impetus for an attempt to handle the data using supervised classifiers and predictors from clever machine learning techniques. In healthcare, this analytical model is used to forecast illnesses. The goal of this project is to develop an intelligent framework for mining large amounts of medical data in order to assess public health issues and forecast illness occurrences.
Evaluation of Health Science Examination of Pharmacy Students Using Critical Thinking Ability
Authors: Gupta Khushboo , Sahu Kishor Kumar 
Published: September 2024

This research aims to find a link between how well pharmacy students did on the Health Science Examination (HSE) and how well they did on a task that tests their critical thinking skills (package insert). Pharmacy students in their first year were given the HSE as part of a spring term task to help them think critically. The box insert project was finished as part of a pharmacokinetics course in the same semester. The correlation analysis was used to check for a link between HSE scores and project grades. A weak but significant link existed between the learners' grades on the task and their overall HSE rating. Team-Based Learning (TBL) is a way of teaching that gets students to learn by doing and by getting them to think critically about how to solve problems. Given a weak but meaningful link with HSE scores, this study showed that a package insert task could be used to test pharmacy students' critical thinking skills.
Soil organic carbon stabilization by iron in permafrost regions of the Qinghai‐Tibet Plateau
Authors: Mu C. C. , Zhang T. J. , Zhao Q. , Guo H. , Zhong W. , Su H. , Wu Q. B. 
Published: September 2016

AbstractA close relationship exists between soil organic carbon (SOC) and reactive iron; however, little is known about the role of iron in SOC preservation in permafrost regions. We determined the amount of SOC associated with reactive iron phases (OC‐Fe) in the permafrost regions of the Qinghai‐Tibetan Plateau (QTP). The results showed that the percentage of OC‐Fe ranged between 0.9% and 59.5% in the upper 30 cm of soil and that the OC‐Fe represented 19.5 ± 12.3% of the total SOC pool. No clear vertical distribution pattern in OC‐Fe was present in the upper 1 m of soil. Throughout the year, the OC‐Fe accounted for relatively stable proportions of the total SOC pool. This study suggests that approximately 20% of SOC is a potential rusty OC pool in the permafrost regions of the QTP. Biogeochemical processes related to the reaction of iron may play important roles in soil carbon cycles in permafrost regions.
Automated identification of discrete, lightning‐generated, multiple‐dispersed whistler waves in C/NOFS‐VEFI very low frequency observations
Authors: Jacobson Abram R. , Holzworth Robert H. , Pfaff Robert , Heelis Roderick 
Published: September 2016

AbstractAutomated wave feature detection is required to efficiently analyze large archives of very low frequency broadband recordings for discrete whistler identification and feature extraction. We describe a new method to do this, even in the presence of simultaneous, multiple whistler phase dispersions. Previous techniques of whistler identification were unable to deal with simultaneous, multiple phase dispersions. We demonstrate the new method with data from the Vector Electric Field Investigation (VEFI) payload on the Communication/Navigation Outage Forecast System (C/NOFS) satellite, from the mission years 2008–2014.
Time‐lapse gravity data for monitoring and modeling artificial recharge through a thick unsaturated zone
Authors: Kennedy Jeffrey , Ferré Ty P. A. , Creutzfeldt Benjamin 
Published: September 2016

AbstractGroundwater‐level measurements in monitoring wells or piezometers are the most common, and often the only, hydrologic measurements made at artificial recharge facilities. Measurements of gravity change over time provide an additional source of information about changes in groundwater storage, infiltration, and for model calibration. We demonstrate that for an artificial recharge facility with a deep groundwater table, gravity data are more sensitive to movement of water through the unsaturated zone than are groundwater levels. Groundwater levels have a delayed response to infiltration, change in a similar manner at many potential monitoring locations, and are heavily influenced by high‐frequency noise induced by pumping; in contrast, gravity changes start immediately at the onset of infiltration and are sensitive to water in the unsaturated zone. Continuous gravity data can determine infiltration rate, and the estimate is only minimally affected by uncertainty in water‐content change. Gravity data are also useful for constraining parameters in a coupled groundwater‐unsaturated zone model (Modflow‐NWT model with the Unsaturated Zone Flow (UZF) package).
αCAR IGF‐1 vector targeting of motor neurons ameliorates disease progression in ALS mice
Authors: Eleftheriadou Ioanna , Manolaras Ioannis , Irvine Elaine E. , Dieringer Michael , Trabalza Antonio , Mazarakis Nicholas D. 
Published: September 2016

AbstractObjectiveWe have previously described the generation of coxsackievirus and adenovirus receptor (αCAR)‐targeted vector, and shown that intramuscular delivery in mouse leg muscles resulted in specific retrograde transduction of lumbar‐motor neurons (MNs). Here, we utilized the αCAR‐targeted vector to investigate the in vivo neuroprotective effects of lentivirally expressed IGF‐1 for inducing neuronal survival and ameliorating the neuropathology and behavioral phenotypes of the SOD1G93A mouse model of ALS.MethodsWe produced cell factories of IGF‐1 expressing lentiviral vectors (LVs) bearing αCAR or Vesicular Stomatitis Virus glycoprotein (VSV‐G) on their surface so as to compare neuroprotection from MN transduced versus muscle transduced cells. We performed intramuscular delivery of either αCAR IGF‐1 or VSVG IGF‐1 LVs into key muscles of SOD1G93A mice prior to disease onset at day 28. Motor performance, coordination and gait analysis were assessed weekly.ResultsWe observed substantial therapeutic efficacy only with the αCAR IGF‐1 LV pretreatment with up to 50% extension of survival compared to controls. αCAR IGF‐1 LV‐treated animals retained muscle tone and had better motor performance during their prolonged survival. Histological analysis of spinal cord samples at end‐stage further confirmed that αCAR IGF‐1 LV treatment delays disease onset by increasing MN survival compared with age‐matched controls. Intrastriatal injection of αCAR eGFP LV in rats leads to transduction of neurons and glia locally and neurons in olfactory bulb distally.InterpretationOur data are indicative of the efficacy of the αCAR IGF‐1 LV in this model and support its candidacy for early noninvasive neuroprotective therapy in ALS.
Bridging the Time Gap: A Copper/Zinc Oxide/Aluminum Oxide Catalyst for Methanol Synthesis Studied under Industrially Relevant Conditions and Time Scales
Authors: Lunkenbein Thomas , Girgsdies Frank , Kandemir Timur , Thomas Nygil , Behrens Malte , Schlögl Robert , Frei Elias 
Published: September 2016

AbstractLong‐term stability of catalysts is an important factor in the chemical industry. This factor is often underestimated in academic testing methods, which may lead to a time gap in the field of catalytic research. The deactivation behavior of an industrially relevant Cu/ZnO/Al2O3 catalyst for the synthesis of methanol is reported over a period of 148 days time‐on‐stream (TOS). The process was investigated by a combination of quasi in situ and ex situ analysis techniques. The results show that ZnO is the most dynamic species in the catalyst, whereas only slight changes can be observed in the Cu nanoparticles. Thus, the deactivation of this catalyst is driven by the changes in the ZnO moieties. Our findings indicate that methanol synthesis is an interfacially mediated process between Cu and ZnO.
Polymerization under Hypersaline Conditions: A Robust Route to Phenolic Polymer‐Derived Carbon Aerogels
Authors: Yu Zhi‐Long , Li Guan‐Cheng , Fechler Nina , Yang Ning , Ma Zhi‐Yuan , Wang Xin , Antonietti Markus , Yu Shu‐Hong 
Published: September 2016

AbstractPolymer‐derived carbon aerogels can be obtained by direct polymerization of monomers under hypersaline conditions using inorganic salts. This allows for significantly increased mechanical robustness and avoiding special drying processes. This concept was realized by conducting the polymerization of phenol–formaldehyde (PF) in the presence of ZnCl2 salt. Afterwards, the simultaneous carbonization and foaming process conveniently converts the PF monolith into a foam‐like carbon aerogel. ZnCl2 plays a key role, serving as dehydration agent, foaming agent, and porogen. The carbon aerogels thus obtained are of very low density (25 mg cm−3), high specific surface area (1340 m2 g−1), and have a large micro‐ and mesopore volume (0.75 cm3 g−1). The carbon aerogels show very promising potential in the separation/extraction of organic pollutants and for energy storage.
Deorphaning the Macromolecular Targets of the Natural Anticancer Compound Doliculide
Authors: Schneider Gisbert , Reker Daniel , Chen Tao , Hauenstein Kurt , Schneider Petra , Altmann Karl‐Heinz 
Published: September 2016

AbstractThe cyclodepsipeptide doliculide is a marine natural product with strong actin‐polymerizing and anticancer activities. Evidence for doliculide acting as a potent and subtype‐selective antagonist of prostanoid E receptor 3 (EP3) is presented. Computational target prediction suggested that this membrane receptor is a likely macromolecular target and enabled immediate in vitro validation. This proof‐of‐concept study demonstrates the in silico deorphanization of phenotypic screening hits as a viable concept for future natural‐product‐inspired chemical biology and drug discovery efforts.