bluuebunny/crossref_metadata_2025_split huggingface dataset:
Developing a RadLex-Based Named Entity Recognition Tool for Mining Textual Radiology Reports: Development and Performance Evaluation Study
Authors: Tsuji Shintaro , Wen Andrew , Takahashi Naoki , Zhang Hongjian , Ogasawara Katsuhiko , Jiang Gouqian 
Published: July 2021

Background
Named entity recognition (NER) plays an important role in extracting the features of descriptions such as the name and location of a disease for mining free-text radiology reports. However, the performance of existing NER tools is limited because the number of entities that can be extracted depends on the dictionary lookup. In particular, the recognition of compound terms is very complicated because of the variety of patterns.

Objective
The aim of this study is to develop and evaluate an NER tool concerned with compound terms using RadLex for mining free-text radiology reports.

Methods
We leveraged the clinical Text Analysis and Knowledge Extraction System (cTAKES) to develop customized pipelines using both RadLex and SentiWordNet (a general purpose dictionary). We manually annotated 400 radiology reports for compound terms in noun phrases and used them as the gold standard for performance evaluation (precision, recall, and F-measure). In addition, we created a compound terms–enhanced dictionary (CtED) by analyzing false negatives and false positives and applied it to another 100 radiology reports for validation. We also evaluated the stem terms of compound terms by defining two measures: occurrence ratio (OR) and matching ratio (MR).

Results
The F-measure of cTAKES+RadLex+general purpose dictionary was 30.9% (precision 73.3% and recall 19.6%) and that of the combined CtED was 63.1% (precision 82.8% and recall 51%). The OR indicated that the stem terms of effusion, node, tube, and disease were used frequently, but it still lacks capturing compound terms. The MR showed that 71.85% (9411/13,098) of the stem terms matched with that of the ontologies, and RadLex improved approximately 22% of the MR from the cTAKES default dictionary. The OR and MR revealed that the characteristics of stem terms would have the potential to help generate synonymous phrases using the ontologies.

Conclusions
We developed a RadLex-based customized pipeline for parsing radiology reports and demonstrated that CtED and stem term analysis has the potential to improve dictionary-based NER performance with regard to expanding vocabularies.
The Classification of Six Common Skin Diseases Based on Xiangya-Derm: Development of a Chinese Database for Artificial Intelligence
Authors: Huang Kai , Jiang Zixi , Li Yixin , Wu Zhe , Wu Xian , Zhu Wu , Chen Mingliang , Zhang Yu , Zuo Ke , Li Yi , Yu Nianzhou , Liu Siliang , Huang Xing , Su Juan , Yin Mingzhu , Qian Buyue , Wang Xianggui , Chen Xiang , Zhao Shuang 
Published: July 2021

Background
Skin and subcutaneous disease is the fourth-leading cause of the nonfatal disease burden worldwide and constitutes one of the most common burdens in primary care. However, there is a severe lack of dermatologists, particularly in rural Chinese areas. Furthermore, although artificial intelligence (AI) tools can assist in diagnosing skin disorders from images, the database for the Chinese population is limited.

Objective
This study aims to establish a database for AI based on the Chinese population and presents an initial study on six common skin diseases.

Methods
Each image was captured with either a digital camera or a smartphone, verified by at least three experienced dermatologists and corresponding pathology information, and finally added to the Xiangya-Derm database. Based on this database, we conducted AI-assisted classification research on six common skin diseases and then proposed a network called Xy-SkinNet. Xy-SkinNet applies a two-step strategy to identify skin diseases. First, given an input image, we segmented the regions of the skin lesion. Second, we introduced an information fusion block to combine the output of all segmented regions. We compared the performance with 31 dermatologists of varied experiences.

Results
Xiangya-Derm, as a new database that consists of over 150,000 clinical images of 571 different skin diseases in the Chinese population, is the largest and most diverse dermatological data set of the Chinese population. The AI-based six-category classification achieved a top 3 accuracy of 84.77%, which exceeded the average accuracy of dermatologists (78.15%).

Conclusions
Xiangya-Derm, the largest database for the Chinese population, was created. The classification of six common skin conditions was conducted based on Xiangya-Derm to lay a foundation for product research.
Prediction of Readmission in Geriatric Patients From Clinical Notes: Retrospective Text Mining Study
Authors: Goh Kim Huat , Wang Le , Yeow Adrian Yong Kwang , Ding Yew Yoong , Au Lydia Shu Yi , Poh Hermione Mei Niang , Li Ke , Yeow Joannas Jie Lin , Tan Gamaliel Yu Heng 
Published: July 2021

Background
Prior literature suggests that psychosocial factors adversely impact health and health care utilization outcomes. However, psychosocial factors are typically not captured by the structured data in electronic medical records (EMRs) but are rather recorded as free text in different types of clinical notes.

Objective
We here propose a text-mining approach to analyze EMRs to identify older adults with key psychosocial factors that predict adverse health care utilization outcomes, measured by 30-day readmission. The psychological factors were appended to the LACE (Length of stay, Acuity of the admission, Comorbidity of the patient, and Emergency department use) Index for Readmission to improve the prediction of readmission risk.

Methods
We performed a retrospective analysis using EMR notes of 43,216 hospitalization encounters in a hospital from January 1, 2017 to February 28, 2019. The mean age of the cohort was 67.51 years (SD 15.87), the mean length of stay was 5.57 days (SD 10.41), and the mean intensive care unit stay was 5% (SD 22%). We employed text-mining techniques to extract psychosocial topics that are representative of these patients and tested the utility of these topics in predicting 30-day hospital readmission beyond the predictive value of the LACE Index for Readmission.

Results
The added text-mined factors improved the area under the receiver operating characteristic curve of the readmission prediction by 8.46% for geriatric patients, 6.99% for the general hospital population, and 6.64% for frequent admitters. Medical social workers and case managers captured more of the psychosocial text topics than physicians.

Conclusions
The results of this study demonstrate the feasibility of extracting psychosocial factors from EMR clinical notes and the value of these notes in improving readmission risk prediction. Psychosocial profiles of patients can be curated and quantified from text mining clinical notes and these profiles can be successfully applied to artificial intelligence models to improve readmission risk prediction.
Brain Mechanisms of Virtual Reality Breathing Versus Traditional Mindful Breathing in Pain Modulation: Observational Functional Near-infrared Spectroscopy Study
Authors: Hu Xiao-Su , Beard Katherine , Sherbel Mary Catherine , Nascimento Thiago D , Petty Sean , Pantzlaff Eddie , Schwitzer David , Kaciroti Niko , Maslowski Eric , Ashman Lawrence M , Feinberg Stephen E , DaSilva Alexandre F 
Published: July 2021

Background
Pain is a complex experience that involves sensory-discriminative and cognitive-emotional neuronal processes. It has long been known across cultures that pain can be relieved by mindful breathing (MB). There is a common assumption that MB exerts its analgesic effect through interoception. Interoception refers to consciously refocusing the mind’s attention to the physical sensation of internal organ function.

Objective
In this study, we dissect the cortical analgesic processes by imaging the brains of healthy subjects exposed to traditional MB (TMB) and compare them with another group for which we augmented MB to an outside sensory experience via virtual reality breathing (VRB).

Methods
The VRB protocol involved in-house–developed virtual reality 3D lungs that synchronized with the participants’ breathing cycles in real time, providing them with an immersive visual-auditory exteroception of their breathing.

Results
We found that both breathing interventions led to a significant increase in pain thresholds after week-long practices, as measured by a thermal quantitative sensory test. However, the underlying analgesic brain mechanisms were opposite, as revealed by functional near-infrared spectroscopy data. In the TMB practice, the anterior prefrontal cortex uniquely modulated the premotor cortex. This increased its functional connection with the primary somatosensory cortex (S1), thereby facilitating the S1-based sensory-interoceptive processing of breathing but inhibiting its other role in sensory-discriminative pain processing. In contrast, virtual reality induced an immersive 3D exteroception with augmented visual-auditory cortical activations, which diminished the functional connection with the S1 and consequently weakened the pain processing function of the S1.

Conclusions
In summary, our study suggested two analgesic neuromechanisms of VRB and TMB practices—exteroception and interoception—that distinctively modulated the S1 processing of the ascending noxious inputs. This is in line with the concept of dualism (Yin and Yang).
Effects of Message Framing on Cancer Prevention and Detection Behaviors, Intentions, and Attitudes: Systematic Review and Meta-analysis
Authors: Ainiwaer Abidan , Zhang Shuai , Ainiwaer Xiayiabasi , Ma Feicheng 
Published: July 2021

Background
With the increasing health care burden of cancer, public health organizations are increasingly emphasizing the importance of calling people to engage in long-term prevention and periodical detection. How to best deliver behavioral recommendations and health outcomes in messaging is an important issue.

Objective
This study aims to disaggregate the effects of gain-framed and loss-framed messages on cancer prevention and detection behaviors and intentions and attitudes, which has the potential to inform cancer control programs.

Methods
A search of three electronic databases (Web of Science, Scopus, and PubMed) was conducted for studies published between January 2000 and December 2020. After a good agreement achieved on a sample by two authors, the article selection (κ=0.8356), quality assessment (κ=0.8137), and data extraction (κ=0.9804) were mainly performed by one author. The standardized mean difference (attitude and intention) and the odds ratio (behaviors) were calculated to evaluate the effectiveness of message framing (gain-framed message and loss-framed message). Calculations were conducted, and figures were produced by Review Manager 5.3.

Results
The title and abstract of 168 unique citations were scanned, of which 53 were included for a full-text review. A total of 24 randomized controlled trials were included, predominantly examining message framing on cancer prevention and detection behavior change interventions. There were 9 studies that used attitude to predict message framing effect and 16 studies that used intention, whereas 6 studies used behavior to examine the message framing effect directly. The use of loss-framed messages improved cancer detection behavior (OR 0.76, 95% CI 0.64-0.90; P=.001), and the results from subgroup analysis indicated that the effect would be weak with time. No effect of framing was found when effectiveness was assessed by attitudes (prevention: SMD=0.02, 95% CI –0.13 to 0.17; P=.79; detection: SMD=–0.05, 95% CI –0.15 to 0.05; P=.32) or intentions (prevention: SMD=–0.05, 95% CI –0.19 to 0.09; P=.48; detection: SMD=0.02, 95% CI –0.26 to 0.29; P=.92) among studies encouraging cancer prevention and cancer detection.

Conclusions
Research has shown that it is almost impossible to change people's attitudes or intentions about cancer prevention and detection with a gain-framed or loss-framed message. However, loss-framed messages have achieved preliminary success in persuading people to adopt cancer detection behaviors. Future studies could improve the intervention design to achieve better intervention effectiveness.
Phytochemical Study of Capsaicinoids Extracted From Indian Varieties of Capsicum Annuum and Quantification of Capsaicin By Chromatographic Methods
Authors: Ms Aditi khandekar  , Ms Apeksha petare  , Ms Yashashri kolalle  , Ms Mayuri desai  , Ms Sayali gaikwad  , Ms Kajol chavan  , Ms Nimisha vijayan  , Dr Kanchan chitnis  
Published: July 2021

The flavour and aroma of chillies is because of the presence of capsaicinoids. Among the capsaicinoids, 90% of the pungency is due to the presence of capsaicin (8-methyl-N-vanillyl-trans-6-nonenamide) and dihydrocapsaicin. From the perspective of the food and pharmaceutical industry, capsaicin being used as an active component, there is a need to find a quicker method of estimating capsaicin. Also it is imperative to determine and standardize a fingerprint of chilli extract of different varieties, to define a pure sample containing capsaicin by determining a marker peak for capsaicin, using HPTLC. Hence in this study capsaicin was estimated by HPLC, GC-MS and HPTLC methods. This study has successfully demonstrated a quick chromatographic separation of capsaicin by HPLC with Rt of just 2.9 min. GC-MS showed the capsaicin peak at Rt of 25.9 min. The fingerprint results of HPTLC indicated capsaicin at Rf 0.03 using Toluene: Ethyl Acetate (70:3) as mobile phase and Iodine vapours as detecting reagent. Besides, the Methanolic extracts of Capsicum annuum showed good antimicrobial activity with very negligible antioxidant properties. Byadgi chilli extract showed the most intense red brown colour with λmax at 500 nm and it thus justified its use as the most preferred one for contributing to colour in food. Keeping in mind the importance in the food industry, and monitoring of raw materials in the pharmaceutical industry, the methods of HPTLC and HPLC can be used to authenticate chilli powders available in the market and in pharmaceutical products for quick estimation of capsaicin.
PARTICIPATORY LAND USE CONFLICT RESOLUTION: EFFORTS TOWARDS COMMUNITY COLLABORATIVE MANAGEMENT
Authors: Golar Golar , Massiri Sudirman Dg , Rauf Rustam Abd , Muis Hasriani , Paingi Syahril 
Published: July 2021

This research aims to determine the causes of land-use conflicts and formulate a participatory conflict resolution model of the Forest Management Unit (KPH) of Kulawi. Through interviews with respondents using the purposive sampling method. The study included the community around the forest area, local government, KPH, Department of Agriculture, BPDAS-HL, and local government. Data analysis was stakeholder's and land use conflict by using problem trees analysis. The study results show that the battle occurred not only because of the community's factor of accessing the area illegally but also because the KPH has not performed its function correctly. Some factors cause enforcement's failure, such as lack of understanding of the area and community empowerment. Some alternative solutions desired by the community: (1) The government through related agencies should supervise forest areas, (2) Clear boundaries between community-owned land, villages, and forest areas, (3) Community empowerment by utilizing potential forest product resources and (4) Granting management rights to community's forest claimed by the community. There are two models of conflict resolution to be offered. First, the KPH needs the collaboration of parties to resolve land-use conflicts. Second, community involvement as the main subject is significant in every stage of conflict resolution. The KPH supports improving community capacity related to competence in conflict resolution is indispensable.
Measuring Accounting Asset Informativeness
Authors: Chen Qi , Schipper Katherine , Zhang Ning 
Published: July 2021

ABSTRACT
We develop and validate an empirical measure of the informativeness of accounting assets in measuring firm-specific economic capital, an important determinant of both cash flows and intrinsic values. Our validation tests show that the asset informativeness measure is sensitive to differences in both accounting methods and implementation decisions at the firm level and corresponds to the way equity investors use the information in accounting assets. We find that accounting assets contain substantial information about firms' productive capacity (economic capital) and the information is not summarized in several earnings attributes often associated with earnings quality.
JEL Classifications: M41; G14; G32.
Effect of orthodontic force on dental pulp histomorphology and tissue factor expression:
Authors: Vitali Filipe Colombo , Cardoso Ihan Vitor , Mello Fernanda Weber , Flores-Mir Carlos , Andrada Ana Cristina , Dutra-Horstmann Kamile Leonardi , Duque Thais Mageste 
Published: July 2021

ABSTRACTObjectivesTo evaluate the effects of orthodontic force on histomorphology and tissue factor expression in the dental pulp.Materials and MethodsTwo reviewers comprehensively and systematically searched the literature in the following databases: Latin American and Caribbean Health Sciences, Embase, Cochrane, PubMed, Scopus, Web of Science, and Grey literature (Google Scholar, OpenGrey, and ProQuest) up to September 2020. According to the Population, Intervention, Comparison, Outcomes, Studies criteria, randomized clinical trials (RCTs) and observational studies that evaluated the effects of orthodontic force on dental pulp were included. Case series/reports, laboratory-based or animal studies, reviews, and studies that did not investigate the association between orthodontic force and pulpal changes were excluded. Newcastle-Ottawa Scale and Cochrane risk-of-bias tool were used to assess the risk of bias. The overall certainty level was evaluated with the Grading of Recommendations Assessment, Development and Evaluation tool.Results26 observational studies and five RCTs were included. A detailed qualitative analysis of articles showed a wide range of samples and applied methodologies concerning impact of orthodontic force on the dental pulp. The application of orthodontic force seems to promote several pulpal histomorphological changes, including tissue architecture, cell pattern, angiogenesis, hard tissue deposition, inflammation, and alteration of the expression levels of 14 tissue factors.ConclusionsAlthough the included articles suggest that orthodontic forces may promote histomorphological changes in the dental pulp, due to the very low-level of evidence obtained, there could be no well-supported conclusion that these effects are actually due to orthodontic movement. Further studies with larger samples and improved methods are needed to support more robust conclusions.
A Multitask Deep-Learning System to Classify Diabetic Macular Edema for Different Optical Coherence Tomography Devices: A Multicenter Analysis
Authors: Tang Fangyao , Wang Xi , Ran An-ran , Chan Carmen K.M. , Ho Mary , Yip Wilson , Young Alvin L. , Lok Jerry , Szeto Simon , Chan Jason , Yip Fanny , Wong Raymond , Tang Ziqi , Yang Dawei , Ng Danny S. , Chen Li Jia , Brelén Marten , Chu Victor , Li Kenneth , Lai Tracy H.T. , Tan Gavin S. , Ting Daniel S.W. , Huang Haifan , Chen Haoyu , Ma Jacey Hongjie , Tang Shibo , Leng Theodore , Kakavand Schahrouz , Mannil Suria S. , Chang Robert T. , Liew Gerald , Gopinath Bamini , Lai Timothy Y.Y. , Pang Chi Pui , Scanlon Peter H. , Wong Tien Yin , Tham Clement C. , Chen Hao , Heng Pheng-Ann , Cheung Carol Y. 
Published: July 2021

OBJECTIVE
Diabetic macular edema (DME) is the primary cause of vision loss among individuals with diabetes mellitus (DM). We developed, validated, and tested a deep learning (DL) system for classifying DME using images from three common commercially available optical coherence tomography (OCT) devices.

RESEARCH DESIGN AND METHODS
We trained and validated two versions of a multitask convolution neural network (CNN) to classify DME (center-involved DME [CI-DME], non-CI-DME, or absence of DME) using three-dimensional (3D) volume scans and 2D B-scans, respectively. For both 3D and 2D CNNs, we used the residual network (ResNet) as the backbone. For the 3D CNN, we used a 3D version of ResNet-34 with the last fully connected layer removed as the feature extraction module. A total of 73,746 OCT images were used for training and primary validation. External testing was performed using 26,981 images across seven independent data sets from Singapore, Hong Kong, the U.S., China, and Australia.

RESULTS
In classifying the presence or absence of DME, the DL system achieved area under the receiver operating characteristic curves (AUROCs) of 0.937 (95% CI 0.920–0.954), 0.958 (0.930–0.977), and 0.965 (0.948–0.977) for the primary data set obtained from CIRRUS, SPECTRALIS, and Triton OCTs, respectively, in addition to AUROCs &gt;0.906 for the external data sets. For further classification of the CI-DME and non-CI-DME subgroups, the AUROCs were 0.968 (0.940–0.995), 0.951 (0.898–0.982), and 0.975 (0.947–0.991) for the primary data set and &gt;0.894 for the external data sets.

CONCLUSIONS
We demonstrated excellent performance with a DL system for the automated classification of DME, highlighting its potential as a promising second-line screening tool for patients with DM, which may potentially create a more effective triaging mechanism to eye clinics.
Literature Review of Type 2 Diabetes Management and Health Literacy
Authors: Alsaedi Rulla , McKeirnan Kimberly 
Published: July 2021

Objective
The purpose of this literature review was to identify educational approaches addressing low health literacy for people with type 2 diabetes. Low health literacy can lead to poor management of diabetes, low engagement with health care providers, increased hospitalization rates, and higher health care costs. These challenges can be even more profound among minority populations and non-English speakers in the United States.

Methods
A literature search and standard data extraction were performed using PubMed, Medline, and EMBASE databases. A total of 1,914 articles were identified, of which 1,858 were excluded based on the inclusion criteria, and 46 were excluded because of a lack of relevance to both diabetes management and health literacy. The remaining 10 articles were reviewed in detail.

Results
Patients, including ethnic minorities and non-English speakers, who are engaged in diabetes education and health literacy improvement initiatives and ongoing follow-up showed significant improvement in A1C, medication adherence, medication knowledge, and treatment satisfaction. Clinicians considering implementing new interventions to address diabetes care for patients with low health literacy can use culturally tailored approaches, consider ways to create materials for different learning styles and in different languages, engage community health workers and pharmacists to help with patient education, use patient-centered medication labels, and engage instructors who share cultural and linguistic similarities with patients to provide educational sessions.

Conclusion
This literature review identified a variety of interventions that had a positive impact on provider-patient communication, medication adherence, and glycemic control by promoting diabetes self-management through educational efforts to address low health literacy.