bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deep learning based prediction of necessity for orthognathic surgery of skeletal malocclusion using cephalogram in Korean individuals
Authors: Shin WooSang , Yeom Han-Gyeol , Lee Ga Hyung , Yun Jong Pil , Jeong Seung Hyun , Lee Jong Hyun , Kim Hwi Kang , Kim Bong Chul 
Published: March 2021

AbstractBackgroundPosteroanterior and lateral cephalogram have been widely used for evaluating the necessity of orthognathic surgery. The purpose of this study was to develop a deep learning network to automatically predict the need for orthodontic surgery using cephalogram.MethodsThe cephalograms of 840 patients (Class ll: 244, Class lll: 447, Facial asymmetry: 149) complaining about dentofacial dysmorphosis and/or a malocclusion were included. Patients who did not require orthognathic surgery were classified as Group I (622 patients—Class ll: 221, Class lll: 312, Facial asymmetry: 89). Group II (218 patients—Class ll: 23, Class lll: 135, Facial asymmetry: 60) was set for cases requiring surgery. A dataset was extracted using random sampling and was composed of training, validation, and test sets. The ratio of the sets was 4:1:5. PyTorch was used as the framework for the experiment.ResultsSubsequently, 394 out of a total of 413 test data were properly classified. The accuracy, sensitivity, and specificity were 0.954, 0.844, and 0.993, respectively.ConclusionIt was found that a convolutional neural network can determine the need for orthognathic surgery with relative accuracy when using cephalogram.
Clinical characteristics, treatment status and complications in women with tube ovarian abscess and endometriosis: a retrospective study
Authors: Li Hui , Zhao Yan , Chang Xiao-hong , Wang Yue , Zhu Hong-lan 
Published: March 2021

Abstract
Background
The aim of our present study was to investigate the clinical characteristics, treatment status and complications in women with endometriosis (EM) and tube ovarian abscess (TOA) to determine the possible association between TOA and EM.

Methods
Medical records were used to analyze the clinical characteristics, treatment and complications. Twenty women who were diagnosed with TOA with EM were compared with 93 women diagnosed as having TOA without EM between January, 2008 and December, 2018.

Results
In this study, TOA patients with EM were significantly more likely to have a lower age range (20–39 years) than the non-EM group [11/20 (55.0%) vs 27/93 (29.0%)]. In addition, TOA patients with EM were associated with a significantly lower rate of parity [11/20 (55.0%) vs 75/93 (80.6%)], higher rates of infertility [8/20(40%) vs 0/93(0%)] and a significantly lower incidence of elevated blood platelet counts [5/20 (25%) vs 43/93 (46.2%)]. Furthermore, women with EM had greater blood loss (347 ± 445.77 vs 204.67 ± 289.46) and an increased complication rate [3/20(15%) vs 0/93(0%)]. Among the 3 patients who had complications in the EM group, 2 patients had septic shock and 1 patient had intestinal obstruction. And 1 case who had septic shock followed by IVF treatment. There was no significance difference on other factors.

Conclusions
The present study indicated that EM did not increase the difficulty and time of treatment in patients with TOA, but increased bleeding during surgery and serious complications. It is suggested that doctors should pay more attention to postoperative treatment and nursing in women with TOA and EM, especially those who have a history of recent infertility treatment and related procedures.
Evaluation of e-health (Seha) application: a cross-sectional study in Saudi Arabia
Authors: Alharbi Abeer , Alzuwaed Joharah , Qasem Hind 
Published: March 2021

Abstract
Background
The Ministry of Health in Saudi Arabia is expanding the country’s telemedicine services by using advanced technology in health services. In doing so, an e-health application (app), Seha, was introduced in 2018 that allows individuals to have face-to-face visual medical consultations with their doctors on their smartphones.

Objective
This study evaluated the effectiveness of the app in improving healthcare delivery by ensuring patient satisfaction with the care given, increasing access to care, and improving efficiency in the healthcare system.

Methods
A cross-sectional study design was used to assess the perceptions of users of the Seha app and non-users who continued with traditional health services. The data were collected using an online survey via Google Forms between June 2020 and September 2020. Independent t tests and chi-square (χ2) tests were conducted to answer the research questions.

Results
There was a significant difference between users and non-users in terms of ease of access to health services (t =  − 9.38, p < 0.05), with app users having a higher mean score (4.19 ± 0.91) than non-users (3.41 ± 1.00); satisfaction with health services (t =  − 6.33, p < 0.05), with users having a higher mean score (3.96 ± 0.91) than non-users (3.45 ± 0.94); and efficiency (only one visit needed for treatment) (t =  − 3.20, p < 0.05), with users having a higher mean score (3.71 ± 0.93) than non-users (3.45 ± 0.93). There were significant associations between the use of the Seha app and age (χ2 = 8.79, p < 0.05), gender (χ2 = 22.19, p < 0.05), region (χ2 = 19.74, p < 0.05), and occupation (χ2 = 22.05, p < 0.05). There were significant relationships between the three items (on access, satisfaction, and efficiency) and experiencing technical issues (t = 4.47, t = 8.11, and t = 3.24, respectively, p < 0.05), with users who faced technical problems having significantly lower mean scores for all three items.

Conclusion
This study provided evidence that the Seha app improved the delivery of healthcare in Saudi Arabia. Users of the app had a better health experience in terms of their perceived ease of access to healthcare services; their satisfaction with healthcare services; and the efficiency of the system, measured by the number of required doctor visits. Other factors that appeared to influence the use of the app included age, gender, usual source of care, and technical difficulties.
Primary care practices’ ability to predict future risk of expenditures and hospitalization using risk stratification and segmentation
Authors: Dorr David A. , Ross Rachel L. , Cohen Deborah , Kansagara Devan , Ramsey Katrina , Sachdeva Bhavaya , Weiner Jonathan P. 
Published: March 2021

Abstract
Background
Patients with complex health care needs may suffer adverse outcomes from fragmented and delayed care, reducing well-being and increasing health care costs. Health reform efforts, especially those in primary care, attempt to mitigate risk of adverse outcomes by better targeting resources to those most in need. However, predicting who is susceptible to adverse outcomes, such as unplanned hospitalizations, ED visits, or other potentially avoidable expenditures, can be difficult, and providing intensive levels of resources to all patients is neither wanted nor efficient. Our objective was to understand if primary care teams can predict patient risk better than standard risk scores.

Methods
Six primary care practices risk stratified their entire patient population over a 2-year period, and worked to mitigate risk for those at high risk through care management and coordination. Individual patient risk scores created by the practices were collected and compared to a common risk score (Hierarchical Condition Categories) in their ability to predict future expenditures, ED visits, and hospitalizations. Accuracy of predictions, sensitivity, positive predictive values (PPV), and c-statistics were calculated for each risk scoring type. Analyses were stratified by whether the practice used intuition alone, an algorithm alone, or adjudicated an algorithmic risk score.

Results
In all, 40,342 patients were risk stratified. Practice scores had 38.6% agreement with HCC scores on identification of high-risk patients. For the 3,381 patients with reliable outcomes data, accuracy was high (0.71–0.88) but sensitivity and PPV were low (0.16–0.40). Practice-created scores had 0.02–0.14 lower sensitivity, specificity and PPV compared to HCC in prediction of outcomes. Practices using adjudication had, on average, .16 higher sensitivity.

Conclusions
Practices using simple risk stratification techniques had slightly worse accuracy in predicting common outcomes than HCC, but adjudication improved prediction.
Prebiotic properties of Bacillus coagulans MA-13: production of galactoside hydrolyzing enzymes and characterization of the transglycosylation properties of a GH42 β-galactosidase
Authors: Aulitto Martina , Strazzulli Andrea , Sansone Ferdinando , Cozzolino Flora , Monti Maria , Moracci Marco , Fiorentino Gabriella , Limauro Danila , Bartolucci Simonetta , Contursi Patrizia 
Published: March 2021

Abstract
Background
The spore-forming lactic acid bacterium Bacillus coagulans MA-13 has been isolated from canned beans manufacturing and successfully employed for the sustainable production of lactic acid from lignocellulosic biomass. Among lactic acid bacteria, B. coagulans strains are generally recognized as safe (GRAS) for human consumption. Low-cost microbial production of industrially valuable products such as lactic acid and various enzymes devoted to the hydrolysis of oligosaccharides and lactose, is of great importance to the food industry. Specifically, α- and β-galactosidases are attractive for their ability to hydrolyze not-digestible galactosides present in the food matrix as well as in the human gastrointestinal tract.

Results
In this work we have explored the potential of B. coagulans MA-13 as a source of metabolites and enzymes to improve the digestibility and the nutritional value of food. A combination of mass spectrometry analysis with conventional biochemical approaches has been employed to unveil the intra- and extra- cellular glycosyl hydrolase (GH) repertoire of B. coagulans MA-13 under diverse growth conditions. The highest enzymatic activity was detected on β-1,4 and α-1,6-glycosidic linkages and the enzymes responsible for these activities were unambiguously identified as β-galactosidase (GH42) and α-galactosidase (GH36), respectively. Whilst the former has been found only in the cytosol, the latter is localized also extracellularly. The export of this enzyme may occur through a not yet identified secretion mechanism, since a typical signal peptide is missing in the α-galactosidase sequence. A full biochemical characterization of the recombinant β-galactosidase has been carried out and the ability of this enzyme to perform homo- and hetero-condensation reactions to produce galacto-oligosaccharides, has been demonstrated.

Conclusions
Probiotics which are safe for human use and are capable of producing high levels of both α-galactosidase and β-galactosidase are of great importance to the food industry. In this work we have proven the ability of B. coagulans MA-13 to over-produce these two enzymes thus paving the way for its potential use in treatment of gastrointestinal diseases.
sparrpowR: a flexible R package to estimate statistical power to identify spatial clustering of two groups and its application
Authors: Buller Ian D. , Brown Derek W. , Myers Timothy A. , Jones Rena R. , Machiela Mitchell J. 
Published: March 2021

AbstractBackgroundCancer epidemiology studies require sufficient power to assess spatial relationships between exposures and cancer incidence accurately. However, methods for power calculations of spatial statistics are complicated and underdeveloped, and therefore underutilized by investigators. The spatial relative risk function, a cluster detection technique that detects spatial clusters of point-level data for two groups (e.g., cancer cases and controls, two exposure groups), is a commonly used spatial statistic but does not have a readily available power calculation for study design.ResultsWe developedsparrpowRas an open-source R package to estimate the statistical power of the spatial relative risk function.sparrpowRgenerates simulated data applying user-defined parameters (e.g., sample size, locations) to detect spatial clusters with high statistical power. We present applications ofsparrpowRthat perform a power calculation for a study designed to detect a spatial cluster of incident cancer in relation to a point source of numerous environmental emissions. The conducted power calculations demonstrate the functionality and utility ofsparrpowRto calculate the local power for spatial cluster detection.ConclusionssparrpowRimproves the current capacity of investigators to calculate the statistical power of spatial clusters, which assists in designing more efficient studies. This newly developed R package addresses a critically underdeveloped gap in cancer epidemiology by estimating statistical power for a common spatial cluster detection technique.
Risk factors for breast cancer development by tumor characteristics among women with benign breast disease
Authors: Figueroa Jonine D. , Gierach Gretchen L. , Duggan Máire A. , Fan Shaoqi , Pfeiffer Ruth M. , Wang Yihong , Falk Roni T. , Loudig Olivier , Abubakar Mustapha , Ginsberg Mindy , Kimes Teresa M. , Richert-Boe Kathryn , Glass Andrew G. , Rohan Thomas E. 
Published: March 2021

AbstractBackgroundAmong women diagnosed with invasive breast cancer, 30% have a prior diagnosis of benign breast disease (BBD). Thus, it is important to identify factors among BBD patients that elevate invasive cancer risk. In the general population, risk factors differ in their associations by clinical pathologic features; however, whether women with BBD show etiologic heterogeneity in the types of breast cancers they develop remains unknown.MethodsUsing a nested case-control study of BBD and breast cancer risk conducted in a community healthcare plan (Kaiser Permanente Northwest), we assessed relationships of histologic features in BBD biopsies and patient characteristics with subsequent breast cancer risk and tested for heterogeneity of associations by estrogen receptor (ER) status, tumor grade, and size. The study included 514 invasive breast cancer cases (median follow-up of 9 years post-BBD diagnosis) and 514 matched controls, diagnosed with proliferative or non-proliferative BBD between 1971 and 2006, with follow-up through mid-2015. Odds ratios (ORs) and 95% confidence intervals (CIs) were obtained using multivariable polytomous logistic regression models.ResultsBreast cancers were predominantly ER-positive (86%), well or moderately differentiated (73%), small (74% < 20 mm), and stage I/II (91%). Compared to patients with non-proliferative BBD, proliferative BBD with atypia conferred increased risk for ER-positive cancer (OR = 5.48, 95% CI = 2.14–14.01) with only one ER-negative case,P-heterogeneity = 0.45. The presence of columnar cell lesions (CCLs) at BBD diagnosis was associated with a 1.5-fold increase in the risk of both ER-positive and ER-negative tumors, with a 2-fold increase (95% CI = 1.21–3.58) observed among postmenopausal women (56%), independent of proliferative BBD status with and without atypia. We did not identify statistically significant differences in risk factor associations by tumor grade or size.ConclusionMost tumors that developed after a BBD diagnosis in this cohort were highly treatable low-stage ER-positive tumors. CCL in BBD biopsies may be associated with moderately increased risk, independent of BBD histology, and irrespective of ER status.
Tamoxifen suppresses brain metastasis of estrogen receptor-deficient breast cancer by skewing microglia polarization and enhancing their immune functions
Authors: Wu Shih-Ying , Sharma Sambad , Wu Kerui , Tyagi Abhishek , Zhao Dan , Deshpande Ravindra Pramod , Watabe Kounosuke 
Published: March 2021

Abstract
Background
Brain metastasis of breast cancer exhibits exceedingly poor prognosis, and both triple negative (TN) and Her2+ subtypes have the highest incidence of brain metastasis. Although estrogen blockers are considered to be ineffective for their treatment, recent evidence indicates that estrogen blockade using tamoxifen showed certain efficacy. However, how estrogen affects brain metastasis of triple negative breast cancer (TNBC) remains elusive.

Methods
To examine the effect of estrogen on brain metastasis progression, nude mice were implanted with brain metastatic cells and treated with either estrogen supplement, tamoxifen, or ovariectomy for estrogen depletion. For clinical validation study, brain metastasis specimens from pre- and post-menopause breast cancer patients were examined for microglia polarization by immunohistochemistry. To examine the estrogen-induced M2 microglia polarization, microglia cells were treated with estrogen, and the M1/M2 microglia polarization was detected by qRT-PCR and FACS. The estrogen receptor-deficient brain metastatic cells, SkBrM and 231BrM, were treated with conditioned medium (CM) derived from microglia that were treated with estrogen in the presence or absence of tamoxifen. The effect of microglia-derived CM on tumor cells was examined by colony formation assay and sphere forming ability.

Results
We found that M2 microglia were abundantly infiltrated in brain metastasis of pre-menopausal breast cancer patients. A similar observation was made in vivo, when we treated mice systemically with estrogen. Blocking of estrogen signaling either by tamoxifen treatment or surgical resection of mice ovaries suppressed M2 microglial polarization and decreased the secretion of C-C motif chemokine ligand 5, resulting in suppression of brain metastasis. The estrogen modulation also suppressed stemness in TNBC cells in vitro. Importantly, estrogen enhanced the expression of signal regulatory protein α on microglia and restricted their phagocytic ability.

Conclusions
Our results indicate that estrogen promotes brain metastasis by skewing polarity of M2 microglia and inhibiting their phagocytic ability, while tamoxifen suppresses brain metastasis by blocking the M2 polarization of microglia and increasing their anti-tumor phagocytic ability. Our results also highlight a potential therapeutic utility of tamoxifen for treating brain metastasis of hormone receptor-deficient breast cancer.
Does smallholder farmers’ awareness of Sustainable Development Goal 2 improve household food security in the Northern Region of Ghana?
Authors: Mabe Franklin Nantui , Mumuni Eliasu , Sulemana Nashiru 
Published: March 2021

Abstract
Background
Sustainable Development Goal 2 aims at ending hunger, achieving food security, improving nutrition and promoting sustainable agriculture. Whilst some smallholder farmers are aware of this goal, others are not. The question that arises is whether or not awareness translates into food security. Therefore, this study assessed whether or not smallholder farmers’ awareness of Sustainable Development Goal 2 improves household food security in the Northern Region of Ghana.

Methods
The study used cross-sectional primary data collected from two districts and two municipalities in the region. An endogenous switching regression treatment effects model with ordered outcome was used to estimate the effects of smallholder farmers’ awareness of Sustainable Development Goal 2 on household food insecurity level.

Results
The age of household head, distance of households to the regional capital, membership of farmer-based organizations, access to e-extension, education, and ownership of radio are the key drivers of farmers’ awareness of Sustainable Development Goal 2. The results from the endogenous switching regression treatment effects model with ordered outcome showed that households who are aware of the second goal are more food secure than their counterparts.

Conclusions
It is therefore prudent for stakeholders promoting and championing Sustainable Development Goals to educate farmers on goal 2 as their awareness of the goal is critical to achieving food security.
Dynamic change of heart rate in the acute phase and clinical outcomes after intracerebral hemorrhage: a cohort study
Authors: You Shoujiang , Wang Yupin , Lu Zian , Chu Dandan , Han Qiao , Xu Jiaping , Liu Chun-Feng , Cao Yongjun , Zhong Chongke 
Published: March 2021

Abstract
Background
Dynamic change of heart rate in the acute phase and clinical outcomes after intracerebral hemorrhage (ICH) remains unknown. We aimed to investigate the associations of heart rate trajectories and variability with functional outcome and mortality in patients with acute ICH.

Methods
This prospective study was conducted among 332 patients with acute ICH. Latent mixture modeling was used to identify heart rate trajectories during the first 72 h of hospitalization after ICH onset. Mean and coefficient of variation of heart rate measurements were calculated. The study outcomes included unfavorable functional outcome, ordinal shift of modified Rankin Scale score, and all-cause mortality.

Results
We identified 3 distinct heart rate trajectory patterns (persistent-high, moderate-stable, and low-stable). During 3-month follow-up, 103 (31.0%) patients had unfavorable functional outcome and 46 (13.9%) patients died. In multivariable-adjusted model, compared with patients in low-stable trajectory, patients in persistent-high trajectory had the highest odds of poor functional outcome (odds ratio 15.06, 95% CI 3.67–61.78). Higher mean and coefficient of variation of heart rate were also associated with increased risk of unfavorable functional outcome (P trend < 0.05), and the corresponding odds ratios (95% CI) comparing two extreme tertiles were 4.69 (2.04–10.75) and 2.43 (1.09–5.39), respectively. Likewise, similar prognostic effects of heart rate dynamic changes on high modified Rankin Scale score and all-cause mortality were observed.

Conclusions
Persistently high heart rate and higher variability in the acute phase were associated with increased risk of unfavorable functional outcome in patients with acute ICH.
Deep learning approaches for natural product discovery from plant endophytic microbiomes
Authors: Aghdam Shiva Abdollahi , Brown Amanda May Vivian 
Published: March 2021

AbstractPlant microbiomes are not only diverse, but also appear to host a vast pool of secondary metabolites holding great promise for bioactive natural products and drug discovery. Yet, most microbes within plants appear to be uncultivable, and for those that can be cultivated, their metabolic potential lies largely hidden through regulatory silencing of biosynthetic genes. The recent explosion of powerful interdisciplinary approaches, including multi-omics methods to address multi-trophic interactions and artificial intelligence-based computational approaches to infer distribution of function, together present a paradigm shift in high-throughput approaches to natural product discovery from plant-associated microbes. Arguably, the key to characterizing and harnessing this biochemical capacity depends on a novel, systematic approach to characterize the triggers that turn on secondary metabolite biosynthesis through molecular or genetic signals from the host plant, members of the rich ‘in planta’ community, or from the environment. This review explores breakthrough approaches for natural product discovery from plant microbiomes, emphasizing the promise of deep learning as a tool for endophyte bioprospecting, endophyte biochemical novelty prediction, and endophyte regulatory control. It concludes with a proposed pipeline to harness global databases (genomic, metabolomic, regulomic, and chemical) to uncover and unsilence desirable natural products.