bluuebunny/crossref_metadata_2025_split huggingface dataset:
Implementing nudges to promote utilization of low tidal volume ventilation (INPUT): a stepped-wedge, hybrid type III trial of strategies to improve evidence-based mechanical ventilation management
Authors: Kerlin Meeta Prasad , Small Dylan , Fuchs Barry D. , Mikkelsen Mark E. , Wang Wei , Tran Teresa , Scott Stefania , Belk Aerielle , Silvestri Jasmine A. , Klaiman Tamar , Halpern Scott D. , Beidas Rinad S. 
Published: August 2021

Abstract
Background
Behavioral economic insights have yielded strategies to overcome implementation barriers. For example, default strategies and accountable justification strategies have improved adherence to best practices in clinical settings. Embedding such strategies in the electronic health record (EHR) holds promise for simple and scalable approaches to facilitating implementation. A proven-effective but under-utilized treatment for patients who undergo mechanical ventilation involves prescribing low tidal volumes, which protects the lungs from injury. We will evaluate EHR-based implementation strategies grounded in behavioral economic theory to improve evidence-based management of mechanical ventilation.

Methods
The Implementing Nudges to Promote Utilization of low Tidal volume ventilation (INPUT) study is a pragmatic, stepped-wedge, hybrid type III effectiveness implementation trial of three strategies to improve adherence to low tidal volume ventilation. The strategies target clinicians who enter electronic orders and respiratory therapists who manage the mechanical ventilator, two key stakeholder groups. INPUT has five study arms: usual care, a default strategy within the mechanical ventilation order, an accountable justification strategy within the mechanical ventilation order, and each of the order strategies combined with an accountable justification strategy within flowsheet documentation. We will create six matched pairs of twelve intensive care units (ICUs) in five hospitals in one large health system to balance patient volume and baseline adherence to low tidal volume ventilation. We will randomly assign ICUs within each matched pair to one of the order panels, and each pair to one of six wedges, which will determine date of adoption of the order panel strategy. All ICUs will adopt the flowsheet documentation strategy 6 months afterwards. The primary outcome will be fidelity to low tidal volume ventilation. The secondary effectiveness outcomes will include in-hospital mortality, duration of mechanical ventilation, ICU and hospital length of stay, and occurrence of potential adverse events.

Discussion
This stepped-wedge, hybrid type III trial will provide evidence regarding the role of EHR-based behavioral economic strategies to improve adherence to evidence-based practices among patients who undergo mechanical ventilation in ICUs, thereby advancing the field of implementation science, as well as testing the effectiveness of low tidal volume ventilation among broad patient populations.

Trial registration
ClinicalTrials.gov, NCT04663802. Registered 11 December 2020.
Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care
Authors: Dong Junzi , Feng Ting , Thapa-Chhetry Binod , Cho Byung Gu , Shum Tunu , Inwald David P. , Newth Christopher J. L. , Vaidya Vinay U. 
Published: August 2021

Abstract
Background
Acute kidney injury (AKI) in pediatric critical care patients is diagnosed using elevated serum creatinine, which occurs only after kidney impairment. There are no treatments other than supportive care for AKI once it has developed, so it is important to identify patients at risk to prevent injury. This study develops a machine learning model to learn pre-disease patterns of physiological measurements and predict pediatric AKI up to 48 h earlier than the currently established diagnostic guidelines.

Methods
EHR data from 16,863 pediatric critical care patients between 1 month to 21 years of age from three independent institutions were used to develop a single machine learning model for early prediction of creatinine-based AKI using intelligently engineered predictors, such as creatinine rate of change, to automatically assess real-time AKI risk. The primary outcome is prediction of moderate to severe AKI (Stage 2/3), and secondary outcomes are prediction of any AKI (Stage 1/2/3) and requirement of renal replacement therapy (RRT). Predictions generate alerts allowing fast assessment and reduction of AKI risk, such as: “patient has 90% risk of developing AKI in the next 48 h” along with contextual information and suggested response such as “patient on aminoglycosides, suggest check level and review dose and indication”.

Results
The model was successful in predicting Stage 2/3 AKI prior to detection by conventional criteria with a median lead-time of 30 h at AUROC of 0.89. The model predicted 70% of subsequent RRT episodes, 58% of Stage 2/3 episodes, and 41% of any AKI episodes. The ratio of false to true alerts of any AKI episodes was approximately one-to-one (PPV 47%). Among patients predicted, 79% received potentially nephrotoxic medication after being identified by the model but before development of AKI.

Conclusions
As the first multi-center validated AKI prediction model for all pediatric critical care patients, the machine learning model described in this study accurately predicts moderate to severe AKI up to 48 h in advance of AKI onset. The model may improve outcome of pediatric AKI by providing early alerting and actionable feedback, potentially preventing or reducing AKI by implementing early measures such as medication adjustment.
First hemispheric report of invasive tick species Haemaphysalis punctata, first state report of Haemaphysalis longicornis, and range expansion of native tick species in Rhode Island, USA
Authors: Tufts Danielle M. , Diuk-Wasser Maria A. 
Published: August 2021

Abstract
Background
Invasive arthropod vectors and the range expansions of native vectors can lead to public and veterinary health concerns, as these vectors may introduce novel pathogens or spread endemic pathogens to new locations. Recent tick invasions and range expansion in the USA has been attributed to climate and land use change, an increase in global travel, and importations of exotic animals.

Methods
A 10-year surveillance study was conducted on Block Island, Rhode Island, from 2010 to 2020 including sampling ticks from small mammal and avian hosts.

Results
We report the discovery and establishment of the red sheep tick (Haemaphysalis punctata) for the first time in the western hemisphere and in the US. This invasive species was first collected in 2010 on Block Island, was collected continuously throughout the study, and was collected from an avian host. We document the first report of the invasive Asian longhorned tick (Haemaphysalis longicornis) in the state of Rhode Island, first observed at our sites in 2018. Finally, we present data on the range expansion and establishment of two native tick species, the lone star tick and the rabbit tick, on Block Island.

Conclusion
This study emphasized the importance of long-term surveillance to detect changes in tick host communities, including invasive and expanding native vectors of potential significance to humans and wildlife.

Graphical abstract
Microbiome diversity declines while distinct expansions of Th17, iNKT, and dendritic cell subpopulations emerge after anastomosis surgery
Authors: Vomhof-DeKrey Emilie E. , Stover Allie , Basson Marc D. 
Published: August 2021

Abstract
Background
Anastomotic failure causes morbidity and mortality even in technically correct anastomoses. Initial leaks must be prevented by mucosal reapproximation across the anastomosis. Healing is a concerted effort between intestinal epithelial cells (IECs), immune cells, and commensal bacteria. IEC TLR4 activation and signaling is required for mucosal healing, leading to inflammatory factor release that recruits immune cells to limit bacteria invasion. TLR4 absence leads to mucosal damage from loss in epithelial proliferation, attenuated inflammatory response, and bacteria translocation. We hypothesize after anastomosis, an imbalance in microbiota will occur due to a decrease in TLR4 expression and will lead to changes in the immune milieu.

Results
We isolated fecal content and small intestinal leukocytes from murine, Roux-en-Y and end-to-end anastomoses, to identify microbiome changes and subsequent alterations in the regulatory and pro-inflammatory immune cells 3 days post-operative. TLR4+ IECs were impaired after anastomosis. Microbiome diversity was reduced, with Firmicutes, Bacteroidetes, and Saccharibacteria decreased and Proteobacteria increased. A distinct TCRβhi CD4+ T cells subset after anastomosis was 10–20-fold greater than in control mice. 84% were Th17 IL-17A/F+ IL-22+ and/or TNFα+. iNKT cells were increased and TCRβhi. 75% were iNKT IL-10+ and 13% iNKTh17 IL-22+. Additionally, Treg IL-10+ and IL-22+ cells were increased. A novel dendritic cell subset was identified in anastomotic regions that was CD11bhi CD103mid and was 93% IL-10+.

Conclusions
This anastomotic study demonstrated a decrease in IEC TLR4 expression and microbiome diversity which then coincided with increased expansion of regulatory and pro-inflammatory immune cells and cytokines. Defining the anastomotic mucosal environment could help inform innovative therapeutics to target excessive pro-inflammatory invasion and microbiome imbalance.
Data of bacterial community dynamics resulting from total rumen content exchange in beef cattle
Authors: Clemmons Brooke A. , Henniger Madison T. , Myer Phillip R. 
Published: August 2021

Abstract
Objectives
Extensive efforts have been made to characterize the rumen microbiome under various conditions. However, few studies have addressed the long-term impacts of ruminal microbiome dysbiosis and the extent of host control over microbiome stability. These data can also inform host-microbial symbioses. The objective was to develop preliminary data to measure the changes that occur in the rumen bacterial communities following a rumen content exchange to understand the effects major perturbations may impart upon the rumen microbiome, which may be host-driven.

Data description
We report here an initial rumen content exchange between two SimAngus (Simmental/Angus) non-pregnant, non-lactating cows of ~ 6 years of age weighing 603.4 ± 37.5 kg. To measure bacterial community succession and acclimation following the exchange, rumen content was collected via rumen cannula at the beginning of the study immediately prior to and following the rumen content exchange, and weekly for 12 weeks. The V4 hypervariable region of the 16S rRNA gene was targeted for DNA sequencing and bacterial analysis. Over 12 weeks, numerous genera and diversity varied, before partial return to pre-exchange metrics. These preliminary data help support potential host control for the rumen microbiome, aiding in efforts to define bovine host-microbe relationships.
Research on application of GPS-based wireless communication system in highway landslide
Authors: Xiong Zhiwen 
Published: August 2021

AbstractMachine learning is a branch of the field of artificial intelligence. Deep learning is a complex machine learning algorithm that has unique advantages in image recognition, speech recognition, natural language processing, and industrial process control. Deep learning has It is widely used in the field of wireless communication. Prediction of geological disasters (such as landslides) is currently a difficult problem. Because landslides are difficult to detect in the early stage, this paper proposes a GPS-based wireless communication continuous detection system and applies it to landslide deformation monitoring to achieve early treatment and prevention. This article introduces the GPS multi-antenna detection system based on deep learning wireless communication, and introduces the time series analysis method and its application. The test results show that the GPS multi-antenna detection system of the wireless communication network has great advantages in response time, with high accuracy and small error. The horizontal accuracy is controlled at 0–2 mm and the vertical accuracy is about 1 mm. The analysis method is simple and efficient, and can obtain good results for short-term deformation prediction.
Estimation of divergence measures on time scales via Taylor’s polynomial and Green’s function with applications in q-calculus
Authors: Ansari Iqrar , Khan Khuram Ali , Nosheen Ammara , Pečarić Ðilda , Pečarić Josip 
Published: August 2021

AbstractTaylor’s polynomial and Green’s function are used to obtain new generalizations of an inequality for higher order convex functions containing Csiszár divergence on time scales. Various new inequalities for some divergence measures in quantum calculus andh-discrete calculus are also established.
Spatial distribution and determinants of iron supplementation among pregnant women in Ethiopia: a spatial and multilevel analysis
Authors: Agegnehu Chilot Desta , Tesema Getayeneh Antehunegn , Teshale Achamyeleh Birhanu , Alem Adugnaw Zeleke , Yeshaw Yigizie , Kebede Sewnet Adem , Liyew Alemneh Mekuriaw 
Published: August 2021

Abstract
Background
Maternal anemia continues as a global public health concern particularly in developing countries including Ethiopia. It is associated with an increased risk of maternal death, obstetric complications, preterm birth, and low birth weight. Even though maternal anemia is the commonest problem in Ethiopia, there is limited evidence on the spatial distribution and determinants of iron supplementation. Therefore, this study aimed to investigate the spatial distribution and determinants of iron supplementation among pregnant women in Ethiopia.

Method
A secondary data analysis was conducted based on the 2016 Ethiopian Demographic and Health Survey (EDHS) data. A total weighted sample of 7589 women was included for analysis. For the spatial analysis; ArcGIS version 10.6, and SaT Scan version 9.6 statistical software were employed to explore the spatial distribution, and to identify significant hotspot areas of iron supplementation in Ethiopia. For the determinant factors, multilevel logistic regression analysis was fitted to identify significant individual and community level determinants of iron supplementation. Deviance, Median Odds Ratio (MOR), and Intra-class Correlation Coefficient (ICC) were used for model comparison and for assessing model fitness. Variables with a p-value of less than 0.2 in the bivariable analysis were considered in the multivariable multilevel analysis. In the multivariable multilevel analysis, the Adjusted Odds Ratio (AOR) with 95% Confidence Interval (CI) was used to declare significant determinants of iron supplementation.

Results
The spatial distribution of iron supplementation was significantly varied across the country with Global Moran’s index value of 0.3 (p < 0.001). The SaTScan analysis identified a total of 271 significant clusters, of these 89 clusters were primary clusters located in the Southwest Somali and Central Oromia regions (LLR = 66.69, P < 0.001, RR = 1.35). ANC visit (AOR = 3.66, 95%CI: 3.21, 417), community education [AOR = 1.31, 95%CI, 1.07, 1.59), media exposure (AOR = 1.33, 95%CI: 1.15, 1.53), distance to health facility (AOR = 1.32, 95%CI: 1.16, 1.50), region and household wealth index were statistically significant determinant factors of iron supplementation.

Conclusion
Iron supplementation among pregnant women were significantly varied across the country. Therefore, the finding of this study could help to design effective public health interventions targeting areas with low iron supplementation and maternal health services should be delivered in all areas of our country. Besides, public health programs should enhance iron supplementation through promoting ANC visits, media exposure, and giving special emphasis to marginalized and remote areas.
Characteristics of seismicity in the southern Okinawa Trough and their relation to back-arc rifting processes
Authors: Arai Ryuta 
Published: August 2021

AbstractThe southern part of the Okinawa Trough forms a narrow back-arc rift basin where evidence for submarine volcanoes and active hydrothermal venting is observed. The region is also known to cause large crustal earthquakes frequently which often accompany a rapid increase in seismicity rate. Although such swarm-like activities are common in active volcanic regions and are considered to be primarily induced by crustal fluid flows, potential interactions between tectonic, magmatic and hydrologic processes have been poorly examined in the southern Okinawa Trough despite these processes happening in the proximity. Here, I report the spatial and statistical characteristics of seismic activity in the southern Okinawa Trough and discuss their relation to other rifting-related phenomena. Most of the earthquakes with magnitude greater than 5 are localized around the rift axes (the Yaeyama Rift and the Yonaguni Rift) where seismic reflection data indicated the presence of solidified magmatic intrusions into the shallow sedimentary layers. I found the areas around the rift axes show low b values of < 0.8 and suggest that stress changes directly induced by dike intrusions beneath the rift axes control the occurrence of swarm activities. On the contrary, regions with high b values (> 1.2) are found around the Ishigaki Knoll and the Hatoma Knoll. These two areas are located between the rift axes and the Ryukyu Islands and correspond to potential submarine volcanoes proposed by seafloor bathymetry and seismic reflection images. This result may constrain the location of the volcanic front in the region.
Impact of cancer and cardiovascular disease on in-hospital outcomes of COVID-19 patients: results from the american heart association COVID-19 cardiovascular disease registry
Authors: Tehrani David M. , Wang Xiaoyan , Rafique Asim M. , Hayek Salim S. , Herrmann Joerg , Neilan Tomas G. , Desai Pooja , Morgans Alicia , Lopez-Mattei Juan , Parikh Rushi V. , Yang Eric H. 
Published: August 2021

Abstract
Background
While pre-existing cardiovascular disease (CVD) appears to be associated with poor outcomes in patients with Coronavirus Disease 2019 (COVID-19), data on patients with CVD and concomitant cancer is limited. The purpose of this study is to evaluate the effect of underlying CVD and CVD risk factors with cancer history on in-hospital mortality in those with COVID-19.

Methods
Data from symptomatic adults hospitalized with COVID-19 at 86 hospitals in the US enrolled in the American Heart Association’s COVID-19 CVD Registry was analyzed. The primary exposure was cancer history. The primary outcome was in-hospital death. Multivariable logistic regression models were adjusted for demographics, CVD risk factors, and CVD. Interaction between history of cancer with concomitant CVD and CVD risk factors were tested.

Results
Among 8222 patients, 892 (10.8%) had a history of cancer and 1501 (18.3%) died. Cancer history had significant interaction with CVD risk factors of age, body mass index (BMI), and smoking history, but not underlying CVD itself. History of cancer was significantly associated with increased in-hospital death (among average age and BMI patients, adjusted odds ratio [aOR] = 3.60, 95% confidence interval [CI]: 2.07–6.24; p < 0.0001 in those with a smoking history and aOR = 1.33, 95%CI: 1.01—1.76; p = 0.04 in non-smokers). Among the cancer subgroup, prior use of chemotherapy within 2 weeks of admission was associated with in-hospital death (aOR = 1.72, 95%CI: 1.05–2.80; p = 0.03). Underlying CVD demonstrated a numerical but statistically nonsignificant trend toward increased mortality (aOR = 1.18, 95% CI: 0.99—1.41; p = 0.07).

Conclusion
Among hospitalized COVID-19 patients, cancer history was a predictor of in-hospital mortality. Notably, among cancer patients, recent use of chemotherapy, but not underlying CVD itself, was associated with worse survival. These findings have important implications in cancer therapy considerations and vaccine distribution in cancer patients with and without underlying CVD and CVD risk factors.
Influence of ABO blood groups and demographic characteristics on the prevalence of type 2 diabetes in Lagos, southwest Nigeria
Authors: Yahaya Tajudeen O. , Oladele Esther O. , Mshelia Mari B. , Sifau Mutiu O. , Fashola Oluwatosin D. , Bunza M. D. A. , Nathaniel Josephine 
Published: August 2021

Abstract
Background
The literature shows that ABO blood groups and demographic characteristics influence susceptibility to type 2 diabetes mellitus (T2DM) and may be used to stem the rising incidence of the disease. However, these associations vary geographically, which necessitates the need to determine the association in every locality. This study determined the ABO blood groups and demographic characteristics of 486 type 2 diabetic patients with no family history in selected hospitals in Lagos, Nigeria.

Results
The results showed that age class 50–59 years (35.8%) was the most susceptible to T2DM, while age class 30–39 years (8.6%) was the least. Female diabetics accounted for 56.8%, while males constituted 43.2%. Diabetes was less prevalent among singles (9.5%), while married couples had the highest prevalence (55.6%). Primary school leavers were the least diabetic (9.9%), while secondary school leavers were the most diabetic (41.6%). Approximately 59.05% of the patients were overweight, while 5.35% were underweight. Blood group O positive was the most prevalent (38.5%; p < 0.05), followed by A positive (20.2%; p < 0.05), and the least was AB negative (4.5%). Severe conditions were less common among groups O positive and A positive (p < 0.05) compared to other blood groups. Thus, the relatively high prevalence of T2DM among blood groups O and A could be due to natural selection owing to their fitness.

Conclusions
ABO blood groups and demographic characteristics influenced the prevalence of T2DM in the city. Residents with blood groups O and A positive should pay special attention to T2DM.
Magnetic resonance imaging and spectroscopy in evaluation of hypoxic ischemic encephalopathy in pediatric age group
Authors: Elshal Fatma Ibrahim Soliman , Elshehaby Walid Ahmed , Dawoud Mahmoud Abd elaziz , Shaban Ekhlas Abdelmonem 
Published: August 2021

Abstract
Background
Hypoxic ischemic encephalopathy is a major cause of pediatric mortality and morbidity, with possible long-term neurologic sequel, such as cerebral palsy. With improvements in care of at-risk neonates, more children survive. This makes it increasingly important to assess, soon after birth, the prognosis of children with hypoxic-ischemic encephalopathy. The aim of the study was to assess the additive role of magnetic resonance spectroscopy over conventional MRI in diagnosis and early prediction of pathological motor development in neonates with hypoxic ischemic encephalopathy.

Results
MRS ratios showed significant difference between unfavorable and normal outcome infants. MRS ratios as Lac/Cr, NAA/Cr and NAA/Cho within basal ganglia, thalamus and white matter can significantly differentiate between patients with normal and pathological outcome at 1 year.
Lac/Cr positively correlates with the severity of HIE. Both NAA/Cr and NAA/Cho negatively correlate with the severity of the disease. Ratios cutoff values as Lac/Cr above 0.38 and 0.42 in basal ganglia and white matter, respectively, NAA/Cr below 0.9 and 0.8 in basal ganglia and occipital white matter, respectively, and NAA/Cho below 0.29 and 0.31 in basal ganglia and frontal white matter, respectively, were significantly predictive of pathological outcome.

Conclusion
High Lac/Cr, low NAA/Cr and low NAA/Cho ratios within examined regions of the brain including deep grey matter nuclei as well as white matter are associated with an adverse outcome in infants with perinatal asphyxia. MRS is an accurate quantitative MR biomarker within the neonatal period for prediction of neurodevelopmental outcome after perinatal HIE. MRS may be useful in early clinical management decisions, and counseling parents thereby ensuring appropriate early intervention and rehabilitation.