bluuebunny/crossref_metadata_2025_split huggingface dataset:
Artificial Intelligence Model for Detecting Duodenal Endoscopic Changes on Images of Functional Dyspepsia
Authors: Mihara Hiroshi , Nanjo Sohachi , Motoo Iori , Ando Takayuki , Fujinami Haruka , Yasuda Ichiro 
Published: April 2022

AbstractIntroduction and AimsRecently, it has been suggested that the duodenum may be the pathological locus of functional dyspepsia (FD), but there are few reports on the presence of duodenal changes on imaging in FD.MethodsDuodenal images acquired at our hospital with the presence or absence of the term “functional dyspepsia” on the electronic medical record, and H. pylori (HP) infection status on the Japan Endoscopy Database were obtained. The Google Cloud Platform AutoML Vision (single-label classification) was used to classify FD/HP current infection, versus FD/HP uninfected patients. We constructed an AI model to distinguish four groups, which included FD/HP current infection, FD/HP uninfected, non-FD/current infection, and non-FD/HP uninfected, and calculated the sensitivity, specificity, and AUC. Patient images with other organic diseases such as gastrointestinal cancer, peptic ulcer, postoperative abdominal organs, and gastroesophageal reflux disease were excluded. Narrow band imaging and dye-spread images were also excluded.ResultsThe overall AUC of the four groups was 0.47 (FD/HP current infection 0.20, FD/HP uninfected 0.35, non-FD/current infection 0.46, non-FD/HP uninfected 0.74). Next, using the same images, we constructed a model to determine the presence or absence of FD in HP infected patients only. The sensitivity, specificity, positive predictive value, negative predictive value, and AUC were 71.4%, 66.7%, 50.0%, 83.3%, and 0.84, respectively. However, when we constructed a model to determine the presence of FD only in uninfected HP patients, the sensitivity, specificity, positive predictive value, negative predictive value, and AUC were 58.3%, 100%, 100%, 77.3%, and 0.85, respectively.ConclusionThe results suggest that the duodenal imaging AL model may be able to determine the presence or absence of FD to a certain degree in HP-infected or uninfected patients.
Cancer-associated Lactobacillus iners are genetically distinct and associated with chemoradiation resistance in cervical cancer
Authors: Colbert Lauren E. , Karpinets Tatiana , El Alam Molly B. , Lynn Erica J. , Sammouri Julie , Lo David , Elnaggar Jacob H , Wang Rui , Harris Timothy A , Yoshida-Court Kyoko , Tomasic Katarina , Bronk Julianna K. , Yanamandra Ananta V. , Olvera Adilene V. , Carlin Lily G. , Sims Travis , Delgado Medrano Andrea Y. , Solley Travis , Eifel Patricia J. , Jhingran Anuja , Joyner Melissa , Lin Lilie , Ramondetta Lois M. , Futreal Andrew M. , Schmeler Kathleen M. , Mathew Geena , Dorta-Estremera Stephanie , Zhang Jianhua , Wu Xiaogang , Ajami Nadim J. , Taniguchi Cullen , Petrosino Joseph F. , Wargo Jennifer , Sastry K. Jagannadha , Okhuysen Pablo C. , Klopp Ann H. 
Published: April 2022

SUMMARYThis study identifies a novel pathotype of cervical cancer-associated Lactobacillus iners (L. iners) that results in chemoradiation resistance in vitro and is associated with poor patient survival. Cervical cancer affects over half a million women a year around the world. Treatment for women with locally advanced cancer is delivered with definitive chemoradiation (CRT) but is curative for only 60% of patients. There are few validated molecular markers to identify patients who will respond poorly to treatment. Tumor microbiome features are associated with treatment resistance in patients with colon and pancreatic cancers, and thus we investigated their role in the response of cervical cancer to therapy. We identified a strong association between poor clinical response to CRT and tumors dominated by L. iners. Cancer-associated L. iners promoted in vitro resistance of cervical cancer cells and modified the local tumor immunologic microenvironment, while non-cancer-associated L. iners did not. Assembly of genomes from cancer-derived L. iners also demonstrated pathogenic, metabolic, and immune functions not found in healthy patients.
Early Carotid Revascularization Rates, Procedural Distribution, and Hospital Density
Authors: Russeau Andrew , Bidare Deeksha , Walker Kyle E. , Kougias Panos , Mills Joseph L. , Barshes Neal R. 
Published: April 2022

AbstractEarly carotid revascularization (i.e. during the index hospitalization) may help reduce the risk of additional neurologic events without excess perioperative morbidity. We evaluated the relationship between rates of early carotid endarterectomy (CEA) or stenting (CAS) and hospital density/distribution within metropolitan areas of Texas. Patients with extracranial carotid artery stenosis and either stroke, transient ischemic attacks, or amaurosis were identified among all patients admitted from 2009 to 2013 to non-federal Texas hospitals within all 24 Texas metropolitan statistical areas (MSAs). Early CEA/CAS was defined as occurring during the index hospitalization. A Gini coefficient with bias correction factor was calculated to quantify the distribution of carotid procedures within an MSA. In total, 3,330 (15.4%) of the 21,665 metropolitan patients admitted to Texas hospitals with symptomatic carotid stenosis received early CEA/CAS. Only 263 (44%) of the 600 total hospitals where patients were admitted performed early CEA/CAS. An increasing proportion receiving early carotid CEA/CAS was inversely correlated with the procedural Gini coefficient (p=0.002) and directly correlated with the number of hospitals per 100K population (p=0.01). These two factors accounted for 51% of the variability among MSAs. Early CEA/CAS rates did not correlate with hospital volume or with level 1or 2 stroke centers within an MSA. Increasing the number of hospitals performing early carotid revascularization procedures (i.e. avoiding regionalization/concentration) may help increase the number of patients receiving early carotid revascularization for symptomatic carotid artery disease.
Augmenting gait in a population exhibiting foot drop with adaptive functional electrical stimulation
Authors: Robison Jeremiah , Gibbons Ren , Achelis Dean , Bent Brinnae , Wajda Doug , Webster Rebecca 
Published: April 2022

AbstractFunctional electrical stimulation (FES) can be used for improvement of gait in people with foot drop resulting from neurological disease or injury. However, existing FES solutions suffer from significant limitations. First, fixed placement electrode devices are difficult to tune stimulation for improved ankle dorsiflexion while maintaining balanced ankle eversion. Second, standard tilt-sensor-like triggers do not allow for adaptive, configurable stimulation sequencing algorithms.Here, we introduce the first FES system with adaptive current steering for an array of FES electrodes enabling precise control over dorsiflexor and evertor muscles, allowing for personalized treatment to correct key foot drop characteristics including dorsiflexion at heel strike and ankle inversion during swing phase.We share results of a pre-test, post-test study on thirty-two participants exhibiting symptoms of foot drop. The differences in pre-test versus post-test primary and secondary outcome measures were statistically significant (p<0.0125) within our cohort. With adaptive, current-steering FES, ankle dorsiflexion at heel strike increased an average 5.2°, and ankle inversion during swing phase was reduced by an average -3.6°, bringing the ankle to a more neutral position for stabilization.By significantly increasing ankle dorsiflexion at heel strike and decreasing ankle inversion during swing phase, adaptive FES enabled a more neutral ankle at heel strike, which is associated with greater ankle stability and decreased fall risk. Gait augmentation using adaptive, current-steering FES improved gait in a population exhibiting symptoms of foot drop.
Classifying sex with MRI
Authors: Ebel Matthis , Lotze Martin , Domin Martin , Neumann Nicola , Stanke Mario 
Published: April 2022

AbstractSex differences in the size of specific brain structures have been extensively studied but careful and reproducible statistical hypothesis testing to identify them produced overall small effect sizes and differences brains of males and females. On the other hand, multivariate statistical or machine learning methods that analyse MR images of the whole brain have reported respectable accuracies for the task of distinguishing males from females. However, most existing studies lacked a careful control for brain volume differences between sexes and, if done, their accuracy often declined to 70% or below. This raises questions on the relevance of accuracies achieved without careful control of overall volume. Also the potential applicability is uncertain insofar as the robustness of methods had rarely been tested or they suffered from poor accuracy when applied on a different cohort.We examined how accurately sex can be classified with multivariate methods from gray matter properties of the human brain when correcting for overall brain volume. We also tested, how robust machine learning classifiers are when predicting cross-cohort, i.e. when they are used on a different cohort than they were trained on. Further, we studied how their accuracy depends on the size of the training set. MRI data was used from two population based data sets of 3308 mostly older adults from the Study of Health in Pomerania (SHIP) and 1113 mostly younger adults from the Human Connectome Project (HCP), respectively. Our new open source program BraiNN is based on a 3D convolutional neural network and was compared with a simple logistic regression approach.When using the gold standard method of matching male and female participants for total intracranial volume, BraiNN achieved 86% accuracy when predicting sex on the same (SHIP) cohort and 73% accuracy when cross-predicting on the HCP cohort. Logistic regression achieved an accuracy >90% on the SHIP cohort, but required a large number of training examples to perform well and did not generalize well across cohorts. On the other hand, BraiNN lost less than 2% accuracy when the cohort size was reduced from 3308 to 1274.
A cross-sectional study of Q fever in Camels: risk factors for infection, the role of small ruminants and public health implications for desert-dwelling pastoral communities
Authors: Holloway Peter , Gibson Matthew , Nash Stephen , Holloway Tanja , Cardwell Jacqueline , Omari Bilal Al , Abu-Basha Ehab , Mangtani Punam , Guitian Javier 
Published: April 2022

AbstractQ fever represents an important ‘neglected zoonosis’, with high prevalences recorded across the Middle East region. Among rural desert-dwelling communities in the region, camel milk is largely consumed raw, due to perceptions of dromedaries as a uniquely clean livestock species mentioned in the Qur’an and Islamic hadith, while milk from other livestock species is usually boiled. As a result, camels present a unique public health threat among such communities from milk-borne pathogens, including C. burnetii. In view of this, an epidemiological survey was conducted among dromedary herds in southern Jordan between September 2017 and October 2018, including 404 camels from 121 randomly selected herds. In addition, 510 household members associated with these herds were interviewed regarding potential high-risk practices for zoonotic transmission. Weight adjusted camel population seroprevalence for C. burnetii was 49.6% (95% CI: 44.7 – 54.5), with evidence of maternally derived immunity in calves ≤6 months old. Adjusted herd-level prevalence was 76.0% (95%CI 72.7–80.2), with 30.4% (144/477) of individuals estimated to consume raw milk from infected herds monthly or more. Following multivariable logistic regression analysis, seropositive status in camels was found to be associated with increasing age, high herd tick burdens, keeping the herd together throughout the year including when calving, and owning larger (>50) sheep and goat flocks, with goats presenting a higher risk than sheep. Racing camel status was found to be protective. Socioculturally appropriate interventions aimed at raising awareness of potential risks associated with drinking raw camel milk, alongside appropriate livestock management interventions, should be considered.
Mapping the emergence of SARS-CoV-2 Omicron variants on a university campus
Authors: Weil Ana A. , Luiten Kyle G. , Casto Amanda M. , Bennett Julia C. , O’Hanlon Jessica , Han Peter D. , Gamboa Luis , McDermot Evan , Truong Melissa , Gottlieb Geoffrey S. , Acker Zack , Wolf Caitlin R. , Magedson Ariana , Chow Eric J. , Lo Natalie K. , Pothan Lincoln C. , McDonald Devon , Wright Tessa , McCaffrey Kathryn , Figgins Marlin D. , Englund Janet A. , Boeckh Michael , Lockwood Christina M. , Nickerson Deborah A. , Shendure Jay , Bedford Trevor , Hughes James P. , Starita Lea M. , Chu Helen Y. 
Published: April 2022

AbstractNovel variants continue to emerge in the SARS-CoV-2 pandemic. University testing programs may provide timely epidemiologic and genomic surveillance data to inform public health responses. We conducted testing from September 2021 to February 2022 in a university population under vaccination and indoor mask mandates. A total of 3,048 of 24,393 individuals tested positive for SARS-CoV-2 by RT-PCR; whole genome sequencing identified 209 Delta and 1,730 Omicron genomes of the 1,939 total sequenced. Compared to Delta, Omicron had a shorter median serial interval between genetically identical, symptomatic infections within households (2 versus 6 days, P=0.021). Omicron also demonstrated a greater peak reproductive number (2.4 versus 1.8) and a 1.07 (95% confidence interval: 0.58, 1.57; P<0.0001) higher mean cycle threshold value. Despite near universal vaccination and stringent mitigation measures, Omicron rapidly displaced the Delta variant to become the predominant viral strain and led to a surge in cases in a university population.
Which countries need COVID-19 vaccines the most? Development of a prioritisation tool
Authors: Jain Vageesh , Atun Rifat , Hansen Paul , Lorgelly Paula 
Published: April 2022

AbstractBackgroundAssessing relative needs for COVID-19 vaccines across countries has been challenging. The objective of this study was to identify the most important factors for assessing countries’ needs for vaccines, and to weight each, generating a scoring tool for prioritising countries.MethodsThe study was conducted between March and November 2021. The first stage involved a Delphi survey with a purposive and snowball sample of public health experts, to reach consensus on country-level factors for assessing relative needs for COVID-19 vaccines. The second stage involved a discrete choice experiment (DCE) to determine weights for the factors.ResultsThe study included 28 experts working across 13 different countries and globally. The Delphi survey found 37 factors related to needs. Nine of the most important factors were included in the DCE. Among these, the most important factor was the ‘proportion of overall population not fully vaccinated’ with a mean weight of 19.5, followed by ‘proportion of high-risk population not fully vaccinated’ (16.1), ‘health system capacity’ (14.2), ‘capacity to purchase vaccines’ (11.9) and the ‘proportion of the population clinically vulnerable’ (11.3).ConclusionsBy assessing relative needs, this scoring tool can build on existing methods to further the role of equity in global COVID-19 vaccine allocation.
Assessing clinical outcomes of vancomycin treatment in adult patients with penicillin-resistant Enterococcus faecium bacteremia
Authors: Sasano Hiroshi , Arakawa Ryutarou , Hanada Kazuhiko 
Published: April 2022

AbstractEnterococcal bacteremia is associated with high mortality and long-term hospitalization. Here, we aimed to investigate the clinical outcomes and evaluate risk factors for mortality in adult patients treated with vancomycin (VCM) for penicillin-resistant Enterococcus faecium (E. faecium) bacteremia. Data were collected from inpatients at a single university hospital between January 2009 and December 2020. The area under the curve (AUC) of VCM was calculated using the Bayesian approach. The primary outcome was 30-day in-hospital mortality. Univariate analysis showed significant differences in the combined use of vasopressors, history of the use of inactive antimicrobial agents against E. faecium, VCM plasma trough concentration, and renal dysfunction during VCM administration between 30-day mortality and survival groups. However, the AUC/minimum inhibitory concentration (MIC) was not significantly different. Multivariate analysis revealed that concomitant vasopressors were an independent risk factor for 30-day all-cause mortality (odds risk, 7.81; 95% confidence interval, 1.16–52.9; P = 0.035). VCM plasma trough concentrations and AUC/MIC in the mortality group were higher than those in the surviving group. Depending on the drug-susceptibility results of E. faecium, it is considered that the AUC/MIC is calculated at a double AUC value. No association between AUC/MIC and treatment effect in E. faecium bacteremia was assumed because the known target AUC/MIC was sufficiently achieved in the mortality group. When an immunocompromised host develops E. faecium bacteremia with septic shock, especially in situations where the hemodynamics of using a pressor agent is unstable, treating it exclusively by sufficient exposure to antibacterial agents may be difficult.
Can accurate demographic information about people who use prescription medications non-medically be derived from Twitter?
Authors: Yang Yuan-Chi , Al-Garadi Mohammed Ali , Love Jennifer S. , Cooper Hannah L. F. , Perrone Jeanmarie , Sarker Abeed 
Published: April 2022

AbstractTraditional surveillance mechanisms for nonmedical prescription medication use (NPMU) involve substantial lags. Social media-based approaches have been proposed for conducting close-to-real-time surveillance, but such methods typically cannot provide fine-grained statistics about subpopulations. We address this gap by developing methods for automatically characterizing a large Twitter NPMU cohort (n=288,562) in terms of age-group, race, and gender. Our methods achieved 0.88 precision (95%-CI: 0.84-0.92) for age-group, 0.90 (95%-CI: 0.85-0.95) for race, and 0.94 accuracy (95%-CI: 0.92-0.97) for gender. We compared the automatically-derived statistics for the NPMU of tranquilizers, stimulants, and opioids from Twitter to statistics reported in traditional sources (eg., the National Survey on Drug Use and Health). Our estimates were mostly consistent with the traditional sources, except for age-group-related statistics, likely caused by differences in reporting tendencies and representations in the population. Our study demonstrates that subpopulation-specific estimates about NPMU may be automatically derived from Twitter to obtain early insights.
Measuring Autonomic Symptoms with the Body Perception Questionnaire Short Form (BPQ-SF): Factor Analysis, Derivation of U.S. Adult Normative Values, and Association with Sensor-Based Physiological Measures
Authors: Kolacz Jacek , Chen Xiwei , Nix Evan J. , Roath Olivia K. , Holmes Logan G. , Tokash Clarissa , Porges Stephen W. , Lewis Gregory F. 
Published: April 2022

AbstractObjectiveAutonomic regulation of organ and tissues may give rise to disruptions of typical functions. The Body Perception Questionnaire Short Form (BPQ-SF) includes items that were developed to assess autonomic symptoms in daily life. This pair of studies aimed to establish previously unexplored psychometric properties of the BPQ-SF, develop normative values for clinical and research use, and validate the self-reports with sensor-based measures.MethodsStudy 1 reports exploratory and confirmatory factor analysis (CFA) on BPQ-SF autonomic reactivity items from a large U.S. population-based online study (n = 2048). In study 2, BPQ-SF scores were examined for associations with heart period, respiratory sinus arrhythmia (RSA), and skin conductance (SC) during seated leg lifts in a community sample (n = 62).ResultsStudy 1 results supported a 2-factor supra- and sub-diaphragmatic autonomic symptom solution (CFA:RMSEA= .040,CFI= .99,TLI= .99), though a 1-factor solution also fit the data well (RMSEA= .080,CFI= .99,TLI= .99). In study 2, flexible HP responses to lifts and rests were demonstrated at all autonomic symptom levels. However, low self-reported autonomic symptoms were associated with flexible dynamic RSA and SC, moderate symptoms with prolonged SC responses during rest periods, and high symptoms with little systematic changes in RSA and SC during leg lifts.ConclusionResults support the validity of self-reports of autonomic symptoms in research and clinical applications, with higher symptoms likely indicating impairment in autonomic flexibility.
Using Primary Care Text Data and Natural Language Processing to Monitor COVID-19 in Toronto, Canada
Authors: Meaney Christopher , Moineddin Rahim , Kalia Sumeet , Aliarzadeh Babak , Greiver Michelle 
Published: April 2022

AbstractObjectiveTo investigate whether a rule-based natural language processing (NLP) system, applied to primary care clinical text data, can be used to monitor COVID-19 viral activity in Toronto, Canada.DesignWe employ a retrospective cohort design. We include primary care patients with a clinical encounter between January 1, 2020 and December 31, 2020 at one of 44 participating clinical sites.Setting and ContextThe study setting is Toronto, Canada. During the study timeframe the city experienced a first wave of COVID-19 in spring 2020; followed by a second viral resurgence beginning in the fall of 2020.Methods and DataStudy objectives are descriptive. We use an expert derived dictionary, pattern matching tools and a contextual analyzer to classify documents as 1) COVID-19 positive, 2) COVID-19 negative, or 3) unknown COVID-19 status. We apply the COVID-19 biosurveillance system across three primary care electronic medical record text streams: 1) lab text, 2) health condition diagnosis text and 3) clinical notes. We enumerate COVID-19 entities in the clinical text and estimate the proportion of patients with a positive COVID-19 record. We construct a primary care COVID-19 NLP-derived time series and investigate its correlation with other external public health series: 1) lab confirmed COVID-19 cases, 2) COVID-19 hospitalizations, 3) COVID-19 ICU admissions, and 4) COVID-19 intubations.ResultsOver the study timeframe 1,976 COVID-19 positive documents, and 277 unique COVID-19 entities were identified in the lab text. 539 COVID-19 positive documents and 121 unique COVID-19 entities were identified in the health condition diagnosis text. And 4,018 COVID-19 positive documents, and 644 unique COVID-19 entities were identified in the clinical notes. A total of 196,440 unique patients were observed over the study timeframe, of which 4,580 (2.3%) had at least one positive COVID-19 document in their primary care electronic medical record. We constructed an NLP-derived COVID-19 time series describing the temporal dynamics of COVID-19 positivity status over the study timeframe. The NLP derived series correlates strongly with external public health series under investigation.ConclusionsUsing a rule-based NLP system we identified hundreds of unique COVID-19 entities, and thousands of COVID-19 positive documents, across millions of clinical text documents. Future work should continue to investigate how high quality, low-cost, passively collected primary care electronic medical record clinical text data can be used for COVID-19 monitoring and surveillance.