bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessment of ocular neuropathic pain following vitreoretinal surgery using 23-gauge sclerotomy
Authors: Niktinat Hanieh , Yousefshahi Fardin , Latifi Golshan , Fadakar Kaveh , Kalantaritarari Farid , Fooladi Marjan Imani , Goudarzi Mehrdad , Ebrahimiadib Nazanin 
Published: July 2022

Abstract

Objective
To investigate the presence of persistent ocular pain after 23-gauge pars plana vitreoretinal surgery
Method
This is a prospective study including patients who underwent 23-gauge vitrectomy or silicone removal with local or general anesthesia. We evaluated the symptoms of ocular neuropathic pain before and two months after surgery using the Brief Pain Inventory (BPI) questionnaire, and the impact of reported ocular symptoms on quality of life. Correlation between ocular pain and factors such as patient demographics and underlying conditions were also assessed.
Result
75 patients with a mean age of 58.93 ± 12.05 years were evaluated. 44 (58.7%) were males. 67 (89.3%) patients underwent vitrectomy and eight had silicone removal surgery, all using 23-gauge instruments. there was an increasing trend in eye pain scores two months after surgery which did not reach a statistical significance. However, the analgesic consumption increased from 4% before surgery to 17.3% two months after surgery. Patients who reported increased analgesic usage two months after surgery, scored worse in items of the quality-of-life questionnaire. In addition, patients who reported ocular and facial pain and photophobia before surgery had higher chances of analgesic consumption after surgery (p-value: 0.03, 0.003, and 0.001, respectively). Those who reported migraine headaches had higher chances of reporting eye symptoms, postoperatively (p-value: 0.041).
Conclusion
Surgeries involving 23-gauge incisions may not induce ocular neuropathic pain. However, increased use of analgesics after surgery was observed.
Safe water reuse through a quasi-natural water cycle
Authors: Wang Xiaochang C. 
Published: July 2022

Abstract
Safe water reuse usually means the provision of reclaimed water with its quality meeting the criteria for safeguarding human health and aquatic ecology. Regarding this, people often overstress the technological magic in terms of hazardous substances removal by engineered processes while ignoring the utilization of nature's power to provide an additional barrier for safety control. Based on an understanding of the function of the hydrological cycle to secure water quality and the fact that water reuse is through a water loop, the author presented his viewpoint on creating a quasi-natural water cycle for water reuse. Existing data showed the remarkable effects of emerging pollutants removal and associated reduction of biotoxicities from the reclaimed water during open-storage under a quasi-natural condition. Adsorption by soil particles, sunlight-induced photolysis and purification by aquatic life were thought to be the major actions contributing to effective water quality improvement in a quasi-natural manner. Safe water reuse through a quasi-natural water cycle can thus be a recommendable strategy for system design in accordance with the purposes of water use.
Therapeutic Application of Nanoparticles in Hepatitis Diseases:
A Narrative Review (2011-2021)
Authors: Mehrizi Tahereh Zadeh , Ardestani Mehdi Shafiee 
Published: July 2022

Purpose:
Hepatitis, an inflammation of the liver parenchyma, is a viral disease. Addressing the
challenges of hepatitis is very important. Therefore, using nanoparticles (NPs) in solving the problems
of hepatitis diagnosis and treatment can be considered a promising approach.
To the best of our knowledge, there are few studies to review the most widely used and effective
NPs in the field of hepatitis. A literature review was performed on the publications available on the
subject matter from 2011 to 2021. The keywords in different combinations such as “hepatitis,”
“nanobiosensor,” “nanoparticles,” “drug delivery,” “vaccination,” “HBV,” and “HCV” were
searched in databases of PubMed and Scopus. The collected data were then analyzed.
Our review study for introducing the widely used metallic, polymeric and carbon-based NPs with
more promising effects in the field of hepatitis virus infection shows that the most effective metallic
NPs were gold nanoparticles for designing detection sensors. Also, among polymeric NPs, chitosan
NPs seem to be the best nanocarriers in drug delivery and vaccination for hepatitis and
among carbon-based NPs, carbon dots had more promising effects for biosensing of hepatitis.
According to the results, it is suggested that more studies could be conducted on these NPs for
further studies on hepatitis as well as other viral infectious diseases.
Computational Approaches for Investigating Disease-causing Mutations in Membrane Proteins: Database Development, Analysis and Prediction
Authors: Kulandaisamy Arulsang , Ridha Fathima , Frishman Dmitrij , Gromiha M. Michael 
Published: July 2022

Abstract:
Membrane proteins (MPs) play an essential role in a broad range of cellular functions, serving as transporters, enzymes, receptors, and communicators, and about ~60% of membrane proteins are primarily used as drug targets. These proteins adopt either α-helical or β-barrel structures in the lipid bilayer of a cell/organelle membrane. Mutations in membrane proteins alter their structure and function, and may lead to diseases. Data on disease-causing and neutral mutations in membrane proteins are available in MutHTP and TMSNP databases, which provide additional features based on sequence, structure, topology, and diseases. These databases have been effectively utilized for analysing sequence and structure-based features in disease-causing and neutral mutations in membrane proteins, exploring disease-causing mechanisms, elucidating the relationship between sequence/structural parameters and diseases, and developing computational tools. Further, machine learning-based tools have been developed for identifying disease-causing mutations using diverse features, such as evolutionary information, physicochemical properties, atomic contacts, contact potentials, and the contribution of different energetic terms. These membrane protein-specific tools are helpful in characterizing the effect of new variants in the whole human membrane proteome. In this review, we provide a discussion of the available databases for disease-causing mutations in membrane proteins, followed by a statistical analysis of membrane protein mutations using sequence and structural features. In addition, available prediction tools for identifying disease-causing and neutral mutations in membrane proteins will be described with their performances. This comprehensive review provides deep insights into designing mutation-specific strategies for different diseases.
Magnetic Nanoparticles Molecularly Imprinted Polymers: A Review
Authors: Ramin Nursyahera Azreen , Asman Saliza , Ramachandran Muggundha Raoov , Saleh Noorashikin Md , Mat Ali Zalilah Murni 
Published: July 2022

Abstract:
The molecularly imprinted polymers (MIPs) technology, which has been around since the 1970s, has grown in popularity in recent decades. MIPs have shown to be a useful approach for determining target molecules in complicated matrices containing other structurally similar and related chemicals. Despite MIPs have intrinsic polymer features such as stability, robustness, and low-cost production, traditional MIPs have a number of drawbacks. Surface molecular imprinting appears to be an alternative approach that can address some of the drawbacks of traditional MIP by anchoring shells to the surface of matrix carriers such as nanoparticles. The incorporation of nanoparticles into the polymeric structure of MIPs can improve their properties or provide novel capabilities. Magnetic nanoparticles have been widely explored for their separation and extraction capability. Magnetic components in MIP can help develop a regulated rebinding process, allowing magnetic separation to substitute centrifugation and filtration stages in a simple and cost-effective strategy. Polymers are created directly on the surface of a magnetic substrate to create a unique material termed magnetic molecularly imprinted polymer (MMIP). These materials have been widely used to extract molecules from complex matrices in a variety of applications, especially in environmental, food, and biological studies. This paper seeks to summarize and discuss the nanoparticle synthesis and magnetic nanoparticle combination in the MIP preparation. The novel applications of MMIP in environmental, food, and biological analysis are also discussed in this paper.
Effects of Fungal Fermentation on Cellulase Activity Along with the Solubility and Protein Yield on Different Economically Important Substrates
Authors:   South Dakota State University, Raihan Mohammad Karamchand , Alhomodi Ahmad ,   South Dakota State University, Berhow Mark ,   USDA, Gibbons William ,   South Dakota State University, Karki Bishnu ,   South Dakota State University
Published: July 2022

Plant-based agricultural residues are readily available. However, due to the presence of several undesirable plant components such as high starch content, low protein yield, phytic acid, saponins, phenolics, etc., these feedstocks need to be processed prior to their end use1. Fermentation technology has been successful in bringing some of these feedstocks to the animal feed and human food markets by improving the nutritional composition through microbial metabolic activity2. Submerged state fermentation (SMF) is an effective way of controlling fermentation parameters (pH, temperature, agitation, etc.) to achieve high product yield and improve the quality 1,2. In this study, our goal is to use fungal fermentation to enhance the proximate composition of three different feedstocks [dry pea protein (DPP), dehulled yellow pea (DHP), and distiller's dried grains with solubles (DDGS)]. Filamentous fungi have shown varied responses with regards to cellulase production depending upon the substrate composition, leading to a change in the structural and chemical composition of the substrate3. Hence, estimation of cellulases production during submerged fermentation of different feedstocks would generate the knowledge that can be implemented to optimize the fermentation process needed for upgrading the nutritional, and economic value of the agricultural commodities The specific objectives of this study were to: 1.	Ferment three substrates using three generally recognized as safe (GRAS) microbes (Aureobasidium pullulans, Neurospora crassa and Trichoderma reesei) for 120 h under submerged conditions. 2.	Determine and compare the proximate compositions of substrates with their unfermented counterparts. Estimate the microbial cellulase activities at 120 h of fermentation.
Consumer-Generated Discourse on Cannabis as a Medicine: Scoping Review of Techniques
Authors: Khademi Habibabadi Sedigheh , Hallinan Christine , Bonomo Yvonne , Conway Mike 
Published: July 2022

Background
Medicinal cannabis is increasingly being used for a variety of physical and mental health conditions. Social media and web-based health platforms provide valuable, real-time, and cost-effective surveillance resources for gleaning insights regarding individuals who use cannabis for medicinal purposes. This is particularly important considering that the evidence for the optimal use of medicinal cannabis is still emerging. Despite the web-based marketing of medicinal cannabis to consumers, currently, there is no robust regulatory framework to measure clinical health benefits or individual experiences of adverse events. In a previous study, we conducted a systematic scoping review of studies that contained themes of the medicinal use of cannabis and used data from social media and search engine results. This study analyzed the methodological approaches and limitations of these studies.

Objective
We aimed to examine research approaches and study methodologies that use web-based user-generated text to study the use of cannabis as a medicine.

Methods
We searched MEDLINE, Scopus, Web of Science, and Embase databases for primary studies in the English language from January 1974 to April 2022. Studies were included if they aimed to understand web-based user-generated text related to health conditions where cannabis is used as a medicine or where health was mentioned in general cannabis-related conversations.

Results
We included 42 articles in this review. In these articles, Twitter was used 3 times more than other computer-generated sources, including Reddit, web-based forums, GoFundMe, YouTube, and Google Trends. Analytical methods included sentiment assessment, thematic analysis (manual and automatic), social network analysis, and geographic analysis.

Conclusions
This study is the first to review techniques used by research on consumer-generated text for understanding cannabis as a medicine. It is increasingly evident that consumer-generated data offer opportunities for a greater understanding of individual behavior and population health outcomes. However, research using these data has some limitations that include difficulties in establishing sample representativeness and a lack of methodological best practices. To address these limitations, deidentified annotated data sources should be made publicly available, researchers should determine the origins of posts (organizations, bots, power users, or ordinary individuals), and powerful analytical techniques should be used.
Leveraging Representation Learning for the Construction and Application of a Knowledge Graph for Traditional Chinese Medicine: Framework Development Study
Authors: Weng Heng , Chen Jielong , Ou Aihua , Lao Yingrong 
Published: July 2022

Background
Knowledge discovery from treatment data records from Chinese physicians is a dramatic challenge in the application of artificial intelligence (AI) models to the research of traditional Chinese medicine (TCM).

Objective
This paper aims to construct a TCM knowledge graph (KG) from Chinese physicians and apply it to the decision-making related to diagnosis and treatment in TCM.

Methods
A new framework leveraging a representation learning method for TCM KG construction and application was designed. A transformer-based Contextualized Knowledge Graph Embedding (CoKE) model was applied to KG representation learning and knowledge distillation. Automatic identification and expansion of multihop relations were integrated with the CoKE model as a pipeline. Based on the framework, a TCM KG containing 59,882 entities (eg, diseases, symptoms, examinations, drugs), 17 relations, and 604,700 triples was constructed. The framework was validated through a link predication task.

Results
Experiments showed that the framework outperforms a set of baseline models in the link prediction task using the standard metrics mean reciprocal rank (MRR) and Hits@N. The knowledge graph embedding (KGE) multitagged TCM discriminative diagnosis metrics also indicated the improvement of our framework compared with the baseline models.

Conclusions
Experiments showed that the clinical KG representation learning and application framework is effective for knowledge discovery and decision-making assistance in diagnosis and treatment. Our framework shows superiority of application prospects in tasks such as KG-fused multimodal information diagnosis, KGE-based text classification, and knowledge inference–based medical question answering.
Predicting Psychological Symptoms When Facebook’s Digital Well-being Features Are Used: Cross-sectional Survey Study
Authors: Barsova Tamara , Cheong Zi Gi , Mak Ann R , Liu Jean CJ 
Published: July 2022

Background
Prior research has linked social media usage to poorer mental health. To address these concerns, social media platforms have introduced digital well-being tools to help users monitor their engagement. Nonetheless, little is known about the effectiveness of these tools.

Objective
In this study, we focused on Facebook to assess users’ awareness and usage of the following six Facebook well-being tools: the Unfollow, Snooze, Off-Facebook Activity, Your Time on Facebook, Set Daily Reminders, and Notification Settings features. Additionally, we examined whether the use of these tools was associated with better mental health outcomes.

Methods
We conducted a cross-sectional survey of 598 Facebook users. The survey comprised questions about (1) baseline Facebook use, (2) the adoption of Facebook’s digital well-being tools, and (3) participant demographics. These were used to predict the primary outcome measure—scores on the 21-item Depression, Anxiety, and Stress Scale.

Results
Most participants (580/598, 97%) knew about Facebook’s digital well-being tools, but each tool was used by only 17.4% (104/598) to 55.5% (332/598) of participants. In turn, the use of two tools was associated with better well-being; although participants who spent more time on Facebook reported higher levels of depression, anxiety, and stress, those who managed their feed content or notifications by using the Unfollow or Notification Settings features had lower scores on each of these measures. However, the use of the Snooze, Off-Facebook Activity, Your Time on Facebook, or Set Time Reminder features was not associated with lower depression, anxiety, or stress scores.

Conclusions
Of the 6 Facebook digital well-being tools, only 2 were associated with better mental health among users. This underscores the complexity of designing social media platforms to promote user welfare. Consequently, we urge further research into understanding the efficacy of various digital well-being tools.

Trial Registration
ClinicalTrials.gov NCT04967846; https://clinicaltrials.gov/ct2/show/study/NCT04967846
Multiple representation-based mobile apps with learning cycle 7e model on colligative properties of solutions
Authors: Widarti Hayuni Retno , Rokhim Deni Ainur , Rahmaniyah Nuril Maqfiroh Sarosah 
Published: July 2022

<p>Los fenómenos químicos se pueden explicar utilizando niveles de representación que consisten en representaciones macroscópicas, submicroscópicas y simbólicas. La explicación de los niveles de representación o conocidas como representaciones múltiples en el aprendizaje de la química es muy importante para proporcionar a los estudiantes una comprensión completa del concepto de material que se estudia. Uno de los temas de la química que requiere múltiples explicaciones de representación son las propiedades coligativas de las soluciones. La investigación tiene como objetivo desarrollar materiales didácticos de aplicaciones móviles basados en representación múltiple con modelo de ciclo de aprendizaje 7e sobre las propiedades materiales coligativas de soluciones que es factible como recurso material. Investigación que se lleva a cabo el desarrollo de la investigación utilizando el modelo 4D Thiagarajan, pero la investigación se limita solo a tres etapas, a saber, las etapas de definición, diseño y desarrollo, y luego continúa con la revisión del producto final de los materiales didácticos. La prueba de validación de materiales didácticos fue realizada por 2 profesores de química y 1 profesor de química. Los aspectos de la validación incluyen la evaluación de aspectos del contenido, la construcción, el idioma, los medios y la conformidad con la sintaxis del ciclo de aprendizaje 7e. La prueba de legibilidad fue realizada por 20 estudiantes de la clase XII IPA SMA Negeri 1 Tumpang. Los aspectos de la prueba de legibilidad incluyen la evaluación de la apariencia, el material de presentación y los beneficios. Los datos obtenidos fueron analizados por porcentaje e interpretados para determinar la factibilidad del material didáctico desarrollado. Los resultados de la prueba de validación obtuvieron un porcentaje del 84% (muy factible) y una prueba de legibilidad del 92% (muy factible). Esto muestra que los materiales didácticos de múltiples aplicaciones móviles basadas en representaciones con el modelo de ciclo de aprendizaje 7e sobre las propiedades coligativas de las soluciones que se han desarrollado son muy factibles para ser utilizados como materiales didácticos.</p><div> </div>
Prototyping Artificial Intelligent Classifiers using Deep Learning
Authors: Chege Herman Njoroge 
Published: July 2022

Point 1: Deep learning algorithms are revolutionizing how
hypothesis generation, pattern recognition, and prediction occurs in the
sciences. In the life sciences, particularly biology and its subfields,
the use of deep learning is slowly but steadily increasing. However,
prototyping or development of tools for practical applications remains
in the domain of experienced coders. Furthermore, many tools can be
quite costly and difficult to put together without expertise in
Artificial intelligence (AI) computing.
Point 2: We built a biological species classifier that
leverages existing open-source tools and libraries. We designed the
corresponding tutorial for users with basic skills in python and a
small, but well-curated image dataset. We included annotated code in
form of a Jupyter Notebook that can be adapted to any image dataset,
ranging from satellite images, animals to bacteria. The prototype
developer is publicly available and can be adapted for citizen science
as well as other applications not envisioned in this paper.
Point 3: We illustrate our approach with a case study of 219
images of 3 three seastar species. We show that with minimal parameter
tuning of the AI pipeline we can create a classifier with superior
accuracy. We include additional approaches to understand the
misclassified images and to curate the dataset to increase accuracy.
Point 4: The power of AI approaches is becoming increasingly
accessible. We can now readily build and prototype species classifiers
that can have a great impact on research that requires species
identification and other types of image analysis. Such tools have
implications for citizen science, biodiversity monitoring, and a wide
range of ecological applications.
Integration accuracy of craniofacial cone-beam computed tomography images with three-dimensional facial scans according to different registration areas
Authors: Aljawad Hussein , Kang Nara , Lee Kyungmin Clara 
Published: July 2022

ABSTRACT

Objectives
To evaluate the integration accuracy of cone-beam computed tomography (CBCT) images with three-dimensional (3D) facial scans according to different registration areas.

Materials and Methods
Twenty-five patients (14 males and 11 females), with a mean age of 19.0 ± 11.3 years, were included in this study. Each patient underwent CBCT and facial scans on the same day in an upright position. The facial scans were integrated with the corresponding soft-tissue images of CBCT scans. Three methods were used to integrate the two imaging modalities based on the facial regions scanned: R1, only the forehead and nasal bridge area were included; R2, the right and left malar area were included; and R3, the forehead, nasal bridge, and malar areas were included. The integration accuracy between the facial scans and CBCT images was evaluated by color-mapping methods and average surface distances, calculated by measuring the 3D distances between the surface points on the two superimposed images.

Results
The average surface differences between facial scans and CBCT images were less than 1.0 mm in all three methods. The R3 method showed fewer differences between the facial scans and CBCT images than the other methods did.

Conclusions
Facial scans obtained using a low-cost facial scanner showed clinically acceptable performance. The integration accuracy of facial and CBCT scans can be increased by including the forehead, nasal bridge, and malar areas as registration areas.