bluuebunny/crossref_metadata_2025_split huggingface dataset:
Combined forced oscillation and fractional-order modeling in patients with work-related asthma: a case–control study analyzing respiratory biomechanics and diagnostic accuracy
Authors: Tuza Fábio Augusto d´Alegria , de Sá Paula Morisco , Castro Hermano A. , Lopes Agnaldo José , de Melo Pedro Lopes 
Published: December 2020

AbstractBackgroundFractional-order (FrOr) models have a high potential to improve pulmonary science. These models could be useful for biomechanical studies and diagnostic purposes, offering accurate models with an improved ability to describe nature. This paper evaluates the performance of the Forced Oscillation (FO) associated with integer (InOr) and FrOr models in the analysis of respiratory alterations in work-related asthma (WRA).MethodsSixty-two individuals were evaluated: 31 healthy and 31 with WRA with mild obstruction. Patients were analyzed pre- and post-bronchodilation. The diagnostic accuracy was evaluated using the area under the receiver operating characteristic curve (AUC). To evaluate how well do the studied models correspond to observed data, we analyzed the mean square root of the sum (MSEt) and the relative distance (Rd) of the estimated model values to the measured resistance and reactance measured values.Results and discussionInitially, the use of InOr and FrOr models increased our understanding of the WRA physiopathology, showing increased peripheral resistance, damping, and hysteresivity. The FrOr model (AUC = 0.970) outperformed standard FO (AUC = 0.929), as well as InOr modeling (AUC = 0.838) in the diagnosis of respiratory changes, achieving high accuracy. FrOr improved the curve fitting (MSEt = 0.156 ± 0.340;Rd = 3.026 ± 1.072) in comparison with the InOr model (MSEt = 0.367 ± 0.991;Rd = 3.363 ± 1.098). Finally, we demonstrated that bronchodilator use increased dynamic compliance, as well as reduced damping and peripheral resistance.ConclusionsTaken together, these results show clear evidence of the utility of FO associated with fractional-order modeling in patients with WRA, improving our knowledge of the biomechanical abnormalities and the diagnostic accuracy in this disease.
Residential segregation, neighborhood violence and disorder, and inequalities in anxiety among Jewish and Palestinian-Arab perinatal women in Israel
Authors: Daoud Nihaya , Alfayumi-Zeadna Samira , Tur-Sinai Aviad , Geraisy Nabil , Talmud Ilan 
Published: December 2020

AbstractBackgroundResidential segregation can foster health inequality mechanisms by increasing stress related to neighborhood violence and disorder.AimsWe studied the association between neighborhood violence and disorder and inequalities in anxiety between two groups of perinatal Israeli women (Jewish, Palestinian-Arab) living in ethno-nationally segregated neighborhoods, and explored the influence of neighborhood characteristics; social support and chronic stress to this inequality.MethodsWe linked survey data on neighborhood violence and disorder, neighborhood social characteristics (collective efficacy, social capital and social support) and aggregate discrimination to neighborhood SES census data. The survey data was obtained from the “Family Relations, Violence and Health” study (2014–2015) and included a stratified national sample of women (Palestinian-Arab = 436, Jewish = 965) residing in 63 segregated neighborhoods. We conducted multi-variable logistic regression analysis for anxiety (measured based on State-trait Anxiety Inventory) using generalized estimating equation (GEE) to estimate odds ratios of the association with neighborhood violence and disorder (total score for 10 problems) while considering neighborhood characteristics (SES; social characteristics; aggregate discrimination), social support and chronic stress in different models for the total sample, and separately for Palestinian-Arab and Jewish women.ResultsPalestinian-Arab women had higher anxiety (60.5% vs. 42.1%, respectively) and higher severity of neighborhood violence and disorder (49.5% vs. 16.2%, respectively) compared to Jewish women. After considering individual and neighborhood variables, adjusted odds ratio (AOR) and 95% confidence intervals (CI) = 1.63, 1.04–2.56. The association between neighborhood violence and disorder and anxiety was significant for low vs. no problems in the final model for the total sample (AOR, 95%CI = 1.28, 1.00–1.64). Similarly, significant association was found only for low severity vs. no problems for Jewish women (1.40, 1.07–1.86). While among Palestinian-Arab women the association between neighborhood violence and disorder and anxiety rendered insignificant in the final model. Neighborhood social cohesion and social support were protective factors from anxiety in both groups, high neighborhood SES was protective factor only among Jewish women, and neighborhood aggregate discrimination was a risk factor only in Palestinian-Arab women.ConclusionsInequalities in anxiety related to neighborhood violence and disorder in ethno-national perinatal groups of women likely reflect residential segregation. Policies entrenching segregation might have affected neighborhood mechanisms (SES inequalities, aggregate discrimination and low social cohesion) that lead to higher stress and ethno-national inequalities in anxiety among perinatal women.
Location inference for hidden population with online text analysis
Authors: Liu Chuchu , Cao Ziqiang , Lu Xin 
Published: December 2020

AbstractBackgroundUnderstanding the geographic distribution of hidden population, such as men who have sex with men (MSM), sex workers, or injecting drug users, are of great importance for the adequate deployment of intervention strategies and public health decision making. However, due to the hard-to-access properties, e.g., lack of a sampling frame, sensitivity issue, reporting error, etc., traditional survey methods are largely limited when studying such populations. With data extracted from the very active online community of MSM in China, in this study we adopt and develop location inferring methods to achieve a high-resolution mapping of users in this community at national level.MethodsWe collect a comprehensive dataset from the largest sub-community related to MSM topics in Baidu Tieba, covering 628,360 MSM-related users. Based on users’ publicly available posts, we evaluate and compare the performances of mainstream location inference algorithms on the online locating problem of Chinese MSM population. To improve the inference accuracy, other approaches in natural language processing are introduced into the location extraction, such as context analysis and pattern recognition. In addition, we develop a hybrid voting algorithm (HVA-LI) by allowing different approaches to vote to determine the best inference results, which guarantees a more effective way on location inference for hidden population.ResultsBy comparing the performances of popular inference algorithms, we find that the classic gazetteer-based algorithm has achieved better results. And in the HVA-LI algorithms, the hybrid algorithm consisting of the simple gazetteer-based method and named entity recognition (NER) is proven to be the best to deal with inferring users’ locations disclosed in short texts on online communities, improving the inferring accuracy from 50.3 to 71.3% on the MSM-related dataset.ConclusionsIn this study, we have explored the possibility of location inferring by analyzing textual content posted by online users. A more effective hybrid algorithm, i.e., the Gazetteer & NER algorithm is proposed, which is conducive to overcoming the sparse location labeling problem in user profiles, and can be extended to the inference of geo-statistics for other hidden populations.
Emerging nanomedicines for effective breast cancer immunotherapy
Authors: Bahreyni Amirhossein , Mohamud Yasir , Luo Honglin 
Published: December 2020

AbstractBreast cancer continues to be the most frequently diagnosed malignancy among women, putting their life in jeopardy. Cancer immunotherapy is a novel approach with the ability to boost the host immune system to recognize and eradicate cancer cells with high selectivity. As a promising treatment, immunotherapy can not only eliminate the primary tumors, but also be proven to be effective in impeding metastasis and recurrence. However, the clinical application of cancer immunotherapy has faced some limitations including generating weak immune responses due to inadequate delivery of immunostimulants to the immune cells as well as uncontrolled modulation of immune system, which can give rise to autoimmunity and nonspecific inflammation. Growing evidence has suggested that nanotechnology may meet the needs of current cancer immunotherapy. Advanced biomaterials such as nanoparticles afford a unique opportunity to maximize the efficiency of immunotherapy and significantly diminish their toxic side-effects. Here we discuss recent advancements that have been made in nanoparticle-involving breast cancer immunotherapy, varying from direct activation of immune systems through the delivery of tumor antigens and adjuvants to immune cells to altering immunosuppression of tumor environment and combination with other conventional therapies.
Harm reduction via online platforms for people who use drugs in Russia: a qualitative analysis of web outreach work
Authors: Davitadze Arsen , Meylakhs Peter , Lakhov Aleksey , King Elizabeth J. 
Published: December 2020

AbstractBackgroundHarm reduction services to people who use drugs (PWUD) in Russia are insufficient in terms of quantity, government endorsement, and accessibility. The situation has recently deteriorated even further because of social distancing measures of the COVID-19 pandemic. Several organizations have started to provide some harm reduction services via online platforms by web outreach. However, little is known on how online outreach services are organized and implemented. Drawing on the example of St. Petersburg-based NGO “Humanitarian Action,” we explored web outreach work in Telegram instant messenger.MethodsOur data were comprised of 4 semi-structured interviews with the NGO staff and 301 cases of web outreach work with PWUD. We used thematic analysis to study the process of web outreach, harm reduction service provision, and needs of PWUD.ResultsThree stages of the process of web outreach work were identified: clients initiating communication, NGO workers addressing clients’ needs, and NGO workers receiving clients’ feedback. Communication proceeded in group chat or direct messages. Challenges in addressing clients’ needs happened when clients turned for help after hours, sent recorded voice messages, sent unclear messages, and/or were unwilling to transition to telephone communication. All web outreach workers reported receiving only positive feedback on their work. The needs of PWUD were categorized into two major themes, depending on whether they can be addressed fully or partially online. In cases of online only provision of services, web outreach workers helped PWUD treat minor injection drug use complications, obtain verified harm reduction information and receive general psychological support. In instances of partial online services provision, PWUD were assisted in getting treatment of severe injection drug use complications, overdoses, and in accessing offline medical, psychological, social, legal and harm reduction services.ConclusionsOur research demonstrated that web outreach work is a convenient tool for delivering some harm reduction services to PWUD either partially or completely online and for recruiting new clients (including hard-to-reach PWUD that avoid attending brick-and-mortar facilities). Harm reduction organizations should consider incorporating online harm reduction services into their activities. However, further research is needed to explore relative advantages and disadvantages of online harm reduction services.
Racial/ethnic differences in health-related quality of life among Hawaii adult population
Authors: Lim Eunjung , Davis James , Siriwardhana Chathura , Aggarwal Lovedhi , Hixon Allen , Chen John J. 
Published: December 2020

AbstractBackgroundThis study examined racial/ethnic differences in health-related quality of life (HRQOL) among adults and identified variables associated with HRQOL by race/ethnicity.MethodsThis study was conducted under a cross-sectional design. We used the 2011–2016 Hawaii Behavioral Risk Factor Surveillance System data. HRQOL were assessed by four measures: self-rated general health, physically unhealthy days, mentally unhealthy days, and days with activity limitation. Distress was defined as fair/poor for general health and 14 days or more for each of the other three HRQOL measures. We conducted multivariable logistic regressions with variables guided by Anderson’s behavioral model on each distress measure by race/ethnicity.ResultsAmong Hawaii adults, 30.4% were White, 20.9% Japanese, 16.8% Filipino, 14.6% Native Hawaiian and Pacific Islander (NHPI), 5.9% Chinese, 5.2% Hispanics, and 6.2% Other. We found significant racial/ethnic differences in the HRQOL measures. Compared to Whites, Filipinos, Japanese, NHPIs, and Hispanics showed higher distress rates in general health, while Filipinos and Japanese showed lower distress rates in the other HRQOL measures. Although no variables were consistently associated with all four HRQOL measures across all racial/ethnic groups, history of diabetes were significantly associated with general health across all racial/ethnic groups and history of depression was associated with at least three of the HRQOL measure across all racial/ethnic groups.ConclusionsThis study contributes to the literature on disparities in HRQOL and its association with other variables among diverse racial/ethnic subgroups. Knowing the common factors for HRQOL across different racial/ethnic groups and factors specific to different racial/ethnic groups will provide valuable information for identifying future public health priorities to improve quality of life and reduce health disparities.
Minimum clinical important difference for resilience scale specific to cancer: a prospective analysis
Authors: Ye Zeng Jie , Zhang Zhang , Tang Ying , Liang Jian , Zhang Xiao Ying , Hu Guang Yun , Sun Zhe , Liang Mu Zi , Yu Yuan Liang 
Published: December 2020

Abstract
Background
The minimum clinical important differences (MCIDs) of resilience instruments in patients with cancer have not been comprehensively described.
This study was designed to evaluate MCIDs of 10-item and 25-item resilience scales specific to cancer (RS-SC-10 and RS-SC-25).

Methods
From June 2015 to December 2018, RS-SCs were longitudinally measured in 765 patients with different cancer diagnoses at baseline (T0) and 3 months later (T1). The EORTC QLQ-C30, Connor-Davidson Resilience Scale, Hospital Anxiety and Depression Scale, and Allostatic Load Index were measured concurrently as anchors. Anchor-based methods (linear regression, within-group), distribution-based methods(within-group), and receiver operating characteristic curves (ROCs, within-subject) were performed to evaluate the MCIDs.

Results
623 of 765 (84.1%) patients had paired RS-SCs scores. Moderate correlations were identified between the change in RS-SCs and change in anchors (r = 0.38–0.44, all p < 0.001). Linear regression estimated + 8.9 and − 6.7 as the MCIDs of RS-SC-25, and + 3.4 and − 2.5 for RS-SC-10. Distribution-based methods estimated + 9.9 and − 9.9 as the MCIDs of RS-SC-25, and + 4.0 and − 4.0 for RS-SC-10. ROC estimated + 5.5 and − 4.5 as the MCIDs of RS-SC-25, and + 2.0 and − 1.5 for RS-SC-10.

Conclusions
The most reliable MCID is around 5 points for RS-SC-25 and 2 points for RS-SC-10. RS-SCs are more responsive to the worsening status of resilience in patients with cancer and these estimates could be useful in future resilience-based intervention trials.
Turnover among Australian general practitioners: a longitudinal gender analysis
Authors: Bardoel E. Anne , Russell Grant , Advocat Jenny , Mayson Susan , Kay Margaret 
Published: December 2020

AbstractBackgroundLittle is known about gender differences in general practitioner (GP) turnover. It is important to understand potential divergence given both the feminization of the Australian GP workforce and projected shortages of GPs.ObjectiveThere is increasing evidence that national health outcomes are related to the extent to which health care systems incorporate high quality primary care. Quality primary care is, in turn reliant on a stable general practice (GP) workforce. With the increasing feminization of medical schools, we sought to identify correlates of turnover in the GP workforce, separately for women and men, focusing particularly on part-time employment and child-rearing, and distinguishing effects related to either planned or unplanned turnover.MethodsAnnual responses from cohorts of at least 1900 women GPs and 2000 men GPs are used for up to eight waves of the Medicine in Australia—Balancing Employment and Life (MABEL) longitudinal survey of doctors. Descriptive and bivariate correlations are provided. Random effects ordered logit is applied to dependent variables for turnover intentions measuring intent to “leave direct care” or “leave medicine”. A behavioral measure of turnover is used in random effects logit regressions, with the exclusion or inclusion of the confounding intentions variables revealing correlates of unplanned or planned turnover.ResultsPart-time employment is associated with turnover intentions among both women (84% or 94% increase in the odds ratios or ORs) and particularly men (414% or 672%), and with actual turnover for women (150% or 49%) and for men (160% or 107%). Women GPs engage in more unplanned turnover than men: they are 85% more likely to engage in turnover after controlling for intentions. Unplanned turnover is concentrated among women below 40 years of age and with young children, even though both groups report below average turnover intentions.ConclusionAlthough further studies are needed to identify specific factors associated with GP turnover among women, the analysis highlights the need to focus on women GPs who are either young or have young children. Given the substantial personal and social investment required to produce GPs, it is wasteful to lose so many young women early in their careers.
A continuous data driven translational model to evaluate effectiveness of population-level health interventions: case study, smoking ban in public places on hospital admissions for acute coronary events
Authors: Bonakdari Hossein , Pelletier Jean-Pierre , Martel-Pelletier Johanne 
Published: December 2020

Abstract
Background
An important task in developing accurate public health intervention evaluation methods based on historical interrupted time series (ITS) records is to determine the exact lag time between pre- and post-intervention. We propose a novel continuous transitional data-driven hybrid methodology using a non-linear approach based on a combination of stochastic and artificial intelligence methods that facilitate the evaluation of ITS data without knowledge of lag time. Understanding the influence of implemented intervention on outcome(s) is imperative for decision makers in order to manage health systems accurately and in a timely manner.

Methods
To validate a developed hybrid model, we used, as an example, a published dataset based on a real health problem on the effects of the Italian smoking ban in public spaces on hospital admissions for acute coronary events. We employed a continuous methodology based on data preprocessing to identify linear and nonlinear components in which autoregressive moving average and generalized structure group method of data handling were combined to model stochastic and nonlinear components of ITS. We analyzed the rate of admission for acute coronary events from January 2002 to November 2006 using this new data-driven hybrid methodology that allowed for long-term outcome prediction.

Results
Our results showed the Pearson correlation coefficient of the proposed combined transitional data-driven model exhibited an average of 17.74% enhancement from the single stochastic model and 2.05% from the nonlinear model. In addition, data demonstrated that the developed model improved the mean absolute percentage error and correlation coefficient values for which 2.77% and 0.89 were found compared to 4.02% and 0.76, respectively. Importantly, this model does not use any predefined lag time between pre- and post-intervention.

Conclusions
Most of the previous studies employed the linear regression and considered a lag time to interpret the impact of intervention on public health outcome. The proposed hybrid methodology improved ITS prediction from conventional methods and could be used as a reliable alternative in public health intervention evaluation.
Can harmonisation of outcomes bridge the translation gap for pre-clinical research? A systematic review of outcomes measured in mouse models of type 2 diabetes
Authors: Harman Nicola L. , Sanz-Moreno Adrián , Papoutsopoulou Stamatia , Lloyd Katie A. , Ameen-Ali Kamar E. , Macleod Malcolm , Williamson Paula R. 
Published: December 2020

Abstract
Background
In pre-clinical research, systematic reviews have the potential to mitigate translational challenges by facilitating understanding of how pre-clinical studies can inform future clinical research. Yet their conduct is encumbered by heterogeneity in the outcomes measured and reported, and those outcomes may not always relate to the most clinically important outcomes. We aimed to systematically review outcomes measured and reported in pre-clinical in vivo studies of pharmacological interventions to treat high blood glucose in mouse models of type 2 diabetes.

Methods
A systematic review of pre-clinical in vivo studies of pharmacological interventions aimed at addressing elevated blood glucose in mouse models of type 2 diabetes was completed. Studies were screened for eligibility and outcomes extracted from the included studies. The outcomes were recorded verbatim and classified into outcome domains using an existing outcome taxonomy. Outcomes were also compared to those identified in a systematic review of registered phase 3/4 clinical trials for glucose lowering interventions in people with type 2 diabetes.

Results
Review of 280 included studies identified 532 unique outcomes across 19 domains. No single outcome, or domain, was measured in all studies and only 132 (21%) had also been measured in registered phase 3/4 clinical trials. A core outcome set, representing the minimum that should be measured and reported, developed for type 2 diabetes effectiveness clinical trials includes 18 core outcomes, of these 12 (71%) outcomes were measured and reported in one or more of the included pre-clinical studies.

Conclusions
There is heterogeneity of outcomes reported in pre-clinical research. Harmonisation of outcomes across the research pathway using a core outcome set may facilitate interpretation, evidence synthesis and translational success, and may contribute to the refinement of the use of animals in research.
Systematic review registration: The study was prospectively registered on the PROSPERO Database, registration number CRD42018106831