bluuebunny/crossref_metadata_2025_split huggingface dataset:
Clustering of critically ill patients using an individualized learning approach enables dose optimization of mobilization in the ICU
Authors: Fuest Kristina E. , Ulm Bernhard , Daum Nils , Lindholz Maximilian , Lorenz Marco , Blobner Kilian , Langer Nadine , Hodgson Carol , Herridge Margaret , Blobner Manfred , Schaller Stefan J. 
Published: January 2023

AbstractBackgroundWhile early mobilization is commonly implemented in intensive care unit treatment guidelines to improve functional outcome, the characterization of the optimal individual dosage (frequency, level or duration) remains unclear. The aim of this study was to demonstrate that artificial intelligence-based clustering of a large ICU cohort can provide individualized mobilization recommendations that have a positive impact on the likelihood of being discharged home.MethodsThis study is an analysis of a prospective observational database of two interdisciplinary intensive care units in Munich, Germany. Dosage of mobilization is determined by sessions per day, mean duration, early mobilization as well as average and maximum level achieved. A k-means cluster analysis was conducted including collected parameters at ICU admission to generate clinically definable clusters.ResultsBetween April 2017 and May 2019, 948 patients were included. Four different clusters were identified, comprising “Young Trauma,” “Severely ill & Frail,” “Old non-frail” and “Middle-aged” patients. Early mobilization (< 72 h) was the most important factor to be discharged home in “Young Trauma” patients (ORadj10.0 [2.8 to 44.0],p < 0.001). In the cluster of “Middle-aged” patients, the likelihood to be discharged home increased with each mobilization level, to a maximum 24-fold increased likelihood for ambulating (ORadj24.0 [7.4 to 86.1],p < 0.001). The likelihood increased significantly when standing or ambulating was achieved in the older, non-frail cluster (ORadj4.7 [1.2 to 23.2],p = 0.035 and ORadj8.1 [1.8 to 45.8],p = 0.010).ConclusionsAn artificial intelligence-based learning approach was able to divide a heterogeneous critical care cohort into four clusters, which differed significantly in their clinical characteristics and in their mobilization parameters. Depending on the cluster, different mobilization strategies supported the likelihood of being discharged home enabling an individualized and resource-optimized mobilization approach.Trial Registration: Clinical Trials NCT03666286, retrospectively registered 04 September 2018.
Clinical and serological association of plasma 25-hydroxyvitamin D (25(OH)D) levels in lupus and the short-term effects of oral vitamin D supplementation
Authors: Kavadichanda Chengappa , Singh Pratibha , Maurya Supriya , Tota Sneha , Kiroubagarin Aberaame , Kounassegarane Deepika , Anand Swathi , Negi Vir Singh , Aggarwal Amita 
Published: January 2023

Abstract
Background and objectives
Data on the association of vitamin D levels and clinical phenotype and disease activity in systemic lupus erythematosus (SLE) is controversial. Further, the optimal dose of oral vitamin D supplementation in SLE is not clear. Thus, the present study was designed to determine the association of plasma vitamin D levels with clinical phenotype, disease variables and serology in a large, cohort of SLE from South Asia and to evaluate the short-term effect of two different dosage regimens of oral vitamin D supplementation on disease flares and plasma vitamin D levels.

Methods
This is a two-phase study. Phase I was a cross-sectional analytical study of patients from north (26.85° N) and south India (11.94° N). Plasma 25-hydroxyvitamin-D(25(OH)D) was measured, and its association with demography, serology, disease activity, Galectin-9 and CXCL-10 was analysed. In phase II, patients with SLEDAI-2KG < 10 and on stable immunosuppression were randomised to receive either high dose (weekly 60,000 U*5, followed by 60,000 U monthly) or routine dose (30,000 U monthly) oral vitamin D. Outcomes were assessed at 6 months

Results
Phase I included 702 patients with a mean age of 29.46 + 10.7 years. The median plasma vitamin D was 22.83 (13.8–31.8) ng/ml. Deficiency (< 20 ng/ml) was seen in 41.5% of patients. Patients from South India had higher vitamin D levels (27.06 ± 20.21 ng/dl) as compared to North India (17.15 ± 16.07 ng/ml) (p < 0.01). Univariate analyses demonstrated weak negative correlation of vitamin D with SLEDAI2K and positive correlation with age. Galactin-9 had modest correlation with SLEDAI2K but not with vitamin D levels. On multiple linear regression, centre of recruitment (β = 4.37) and age (β = 0.18) predicted (p < 0.05) plasma vitamin D levels.
In the phase II, 91 randomised to 2 groups completed 6 months. Median change in plasma vitamin D levels was more in high dose (9.5 versus 2.6 ng/ml; p = 0.04). There were 14 SLE flares and six minor adverse events which were equal across both groups.

Conclusion
Vitamin D deficiency is common in SLE. Geographical location of residence is the major determinant rather than the disease activity. The IFN regulated proteins reflect disease activity independent of vitamin D levels. High-dose oral vitamin D supplementation seems safe and more effective in improving vitamin D levels in SLE.

Trial registration
The second phase of this study was a registered randomised controlled trial CTRI/2019/06/019658 [registered on: 14/06/2019].
Deep reinforcement learning-based adaptive modulation for OFDM underwater acoustic communication system
Authors: Cui Xuerong , Yan Peihao , Li Juan , Li Shibao , Liu Jianhang 
Published: January 2023

AbstractDue to the time-varying and space-varying characteristics of the underwater acoustic channel, the communication process may be seriously disturbed. Thus, the underwater acoustic communication system is facing the challenges of alleviating interference and improving communication quality and communication efficiency through adaptive modulation. In order to select the optimal modulation mode adaptively and maximize the system throughput ensuring that the bit error rate (BER) meets the transmission requirements, this paper introduces deep reinforcement learning (DRL) into orthogonal frequency division multiplexing acoustic communication system. The adaptive modulation is mapped into a Markov decision process with unknown state transition probability. Thereby, the underwater communication channel environment is regarded as the state of DRL, and the modulation mode is regarded as action. The system returns channel state information (CSI) and signal–noise ratio in every time slot through the feedback link. Because the Deep Q-Network optimizes in the changing state space of each time slot, it is suitable for a variety of different CSI. Finally, simulations in different underwater environments (SWellEx-96) show that the proposed adaptive modulation scheme can obtain lower BER and improve the system throughput effectively.
Magnitude of early postoperative hypoxemia and its associated factors among adult patients who undergo emergency surgery under general anesthesia at Jimma Medical Center, Jimma, Southwest Ethiopia, 2021: a prospective observational study
Authors: Berhanu Mitiku , Dadi Negashu , Mengistu Berhanu , Muluken Zemenu , Tolesa Ashenafi , Tageza Tajera , Kalbesa Megersa , Tesfaye Gezahegn , Zawdie Belay 
Published: January 2023

Abstract
Purpose
Emergency surgical procedures involve considerable risks. Among these, early postoperative hypoxemia (EPH) is a frequent anesthetic complication in the post-anesthetic care unit (PACU). There is a great concern for EPH among health professionals, specifically, those providing emergency surgery during the nighttime. This raised anesthesia-ended time-related risk of EPH question. Thus, this study aimed to determine the magnitude of EPH and its associated factors among adult patients who undergo emergency surgery under general anesthesia.

Methods
A prospective observational study through a consecutive sampling technique was conducted. Binary logistic regression analysis was used to identify associated risk factors. All variables that were found statistically significant on bivariable analysis were entered into a multivariable logistic regression analysis.

Result
Of 352 patients who had undergone emergency surgery, 149 (42.3%) patients developed EPH. Factors significantly associated with EPH were anesthesia ended during nighttime (AOR = 1.76, 95%CI [1.01, 3.05], p = 0.045), ASA III (AOR = 12.35, 95%CI: [4.5, 34.02], p ≤ 0.001), age greater than 55 (AOR = 3.2, 95%CI: [1.7, 5.91], p ≤ 0.001), surgery duration greater than 2 h (AOR = 2.012, 95%CI: [1.2, 3.51], p = 0.014), hypotension (AOR = 10.3, 95%CI: [2.4, 44.16], p = 0.002), muscular strength score zero (AOR = 2.944, 95%CI: [1.8, 4.82], p ≤ 0.001), and preoperative oxygen saturation less than 95% (AOR = 2.371, 95%CI: [1.35,4.16], p = 0.003).

Conclusion
The magnitude of EPH among patients who have undergone emergency surgery was high and thus recommended that oxygen should be provided timely to decrease it. Identified risk factors were night-time ended anesthesia, ASA III, age greater than 55, surgery duration greater than 2 h, hypotension, muscular strength score zero, and preoperative oxygen saturation less than 95%. This study found anesthesia ended during early morning favors early morning early postoperative hypoxemia (EMEPH). To avert EMEPH, the anesthetist should avoid factors that favor the circadian rhythm of the lung-based early morning anesthesia augmented EPH.
A novel scoring system for early assessment of the risk of the COVID-19-associated mortality in hospitalized patients: COVID-19 BURDEN
Authors: Imanieh Mohammad Hossein , Amirzadehfard Fatemeh , Zoghi Sina , Sehatpour Faezeh , Jafari Peyman , Hassanipour Hamidreza , Feili Maryam , Mollaie Maryam , Bostanian Pardis , Mehrabi Samrad , Dashtianeh Reyhaneh , Feili Afrooz 
Published: January 2023

Abstract
Background
Corona Virus Disease 2019 (COVID-19) presentations range from those similar to the common flu to severe pneumonia resulting in hospitalization with significant morbidity and/or mortality. In this study, we made an attempt to develop a predictive scoring model to improve the early detection of high risk COVID-19 patients by analyzing the clinical features and laboratory data available on admission.

Methods
We retrospectively included 480 consecutive adult patients, aged 21–95, who were admitted to Faghihi Teaching Hospital. Clinical and laboratory features were collected from the medical records and analyzed using multiple logistic regression analysis. The final data analysis was utilized to develop a simple scoring model for the early prediction of mortality in COVID-19 patients. The score given to each associated factor was based on the coefficients of the regression analyses.

Results
A novel mortality risk score (COVID-19 BURDEN) was derived, incorporating risk factors identified in this cohort. CRP (> 73.1 mg/L), O2 saturation variation (greater than 90%, 84–90%, and less than 84%), increased PT (> 16.2 s), diastolic blood pressure (≤ 75 mmHg), BUN (> 23 mg/dL), and raised LDH (> 731 U/L) were the features constituting the scoring system. The patients are triaged to the groups of low- (score < 4) and high-risk (score ≥ 4) groups. The area under the curve, sensitivity, and specificity for predicting mortality in patients with a score of ≥ 4 were 0.831, 78.12%, and 70.95%, respectively.

Conclusions
Using this scoring system in COVID-19 patients, the patients with a higher risk of mortality can be identified which will help to reduce hospital care costs and improve its quality and outcome.
Differential vulnerability of the dentate gyrus to tauopathies in dementias
Authors: Kawles Allegra , Minogue Grace , Zouridakis Antonia , Keszycki Rachel , Gill Nathan , Nassif Caren , Coventry Christina , Zhang Hui , Rogalski Emily , Flanagan Margaret E. , Castellani Rudolph , Bigio Eileen H. , Mesulam M. Marsel , Geula Changiz , Gefen Tamar 
Published: January 2023

AbstractThe dentate gyrus (DG), a key hippocampal subregion in memory processing, generally resists phosphorylated tau accumulation in the amnestic dementia of the Alzheimer’s type due to Alzheimer’s disease (DAT-AD), but less is known about the susceptibility of the DG to other tauopathies. Here, we report stereologic densities of total DG neurons and tau inclusions in thirty-two brains of human participants with autopsy-confirmed tauopathies with distinct isoform profiles—3R Pick’s disease (PiD, N = 8), 4R corticobasal degeneration (CBD, N = 8), 4R progressive supranuclear palsy (PSP, N = 8), and 3/4R AD (N = 8). All participants were diagnosed during life with primary progressive aphasia (PPA), an aphasic clinical dementia syndrome characterized by progressive deterioration of language abilities with spared non-language cognitive abilities in early stages, except for five patients with DAT-AD as a comparison group. 51% of total participants were female. All specimens were stained immunohistochemically with AT8 to visualize tau pathology, and PPA cases were stained for Nissl substance to visualize neurons. Unbiased stereological analysis was performed in granule and hilar DG cells, and inclusion-to-neuron ratios were calculated. In the PPA group, PiD had highest mean total (granule + hilar) densities of DG tau pathology (p < 0.001), followed by CBD, AD, then PSP. PPA-AD cases showed more inclusions in hilar cells compared to granule cells, while the opposite was true in PiD and CBD. Inclusion-to-neuron ratios revealed, on average, 33% of all DG neurons in PiD cases contained a tau inclusion, compared to ~ 7% in CBD, 2% in AD, and 0.4% in PSP. There was no significant difference between DAT-AD and PPA-AD pathologic tau burden, suggesting that differences in DG burden are not specific to clinical phenotype. We conclude that the DG is differentially vulnerable to pathologic tau accumulation, raising intriguing questions about the structural integrity and functional significance of hippocampal circuits in neurodegenerative dementias.
Household food insecurity and coping strategies among rural households in Kedida Gamela District, Kembata-Tembaro zone, Southern Ethiopia: mixed-methods concurrent triangulation design
Authors: Yohannes Gizachew , Wolka Eskinder , Bati Temesgen , Yohannes Tadele 
Published: January 2023

Abstract
Background
Household food insecurity is a state in which household members experienced limited or uncertain physical and economic access to safe, plenty, and healthy food to meet the dietary needs for a fruitful, healthy, and active life. Food insecurity continues to be a major development and public health problem across the globe, having adverse consequences. This study was done to assess household food insecurity and to explore coping strategies in Kedida Gamela District, Southern Ethiopia.

Method
A cross-sectional study complemented with the qualitative inquiry was carried-out from August to November 2020. Multistage sampling was used to select study subjects. A total sample of 597 households was selected randomly using up to date family folder list in the district as a sampling frame. For the qualitative study, 16 food-insecure households were selected randomly from food in secured households. Quantitative data were entered using Ep-Data 3.1 and exported to SPSS 20 for analysis. Bivariate analysis was carried out to see the crude association between each independent variable and outcome variable. P-value < 0.05 and 95%CI for adjusted odds ratios (AOR) were used to declare the significance of the associations. The qualitative data were analyzed using thematic analysis.

Result
The findings of this study showed that 76% of the households were food insecure. Being female-headed households [AOR: 2.82:CI(1.10, 7.24)], absence of formal education [(AOR: 9.75:CI (3.7, 11.31)], lack of engagement in non-farm farm activities [(AOR; 3.30: CI (1.86, 5.96)], absence of credit service [AOR:4.04; C I (2.11,7.73)], presence of dependent household members [AOR: 3.47;(2.91,6.34)], poorest wealth status [AOR;9.86:CI (3.72, 15.85)] were factors significantly associated with food insecurity of the households. Food insecure households employed different coping strategies with the respective level of food insecurity.

Conclusion
The findings of this study indicated that household food insecurity was higher in the study area. Moreover, sex, educational status, wealth status of the households; engagement of households in off/non-farm farm activities, credit service, and active and inactive labor force were significantly associated with household food insecurity.
Food insecure households practice different coping strategies with respective food insecurity levels from the less severe strategy of eating inedible, fewer-quality foods to the most severe of migrating and begging for food.
Planning and exhaustively implementing sustainable food security programs should get due attention.
Examining a technology-focused language teacher community on Facebook during a crisis situation
Authors: Ito Yurika 
Published: January 2023

AbstractDue to the chaos and confusion caused by the sudden transition from face-to-face teaching to online and remote teaching in early 2020, numerous language teachers had no choice but to rely on online communities on social networking sites. The current study therefore examined how some language teachers were utilising online communities on Facebook during the COVID-19 pandemic. Employing a mixed-methods approach, data were mainly collected through: (1) an eight-month observation of a technology-focused language teacher community on Facebook to identify different types of posts generated by its members before and during the COVID-19 pandemic (n = 340); (2) a questionnaire to understand the community members’ backgrounds and experiences of being in the community (n = 51); (3) semi-structured interviews with some of the questionnaire participants (n = 13); and (4) a post-interview questionnaire (n = 12) to get a better understanding of their responses. A content analysis of online posts and community members’ responses suggest that language teacher communities on Facebook were supporting teachers during the stressful periods of the pandemic professionally and emotionally. The main findings are discussed in terms of the benefits and drawbacks of using online language teacher communities for professional purposes. The overall goal of the study is to offer much-needed answers on how pre-existing communities can be used to assist language teachers in times of a crisis.
Relating plasticity to dislocation properties by data analysis: scaling vs. machine learning approaches
Authors: Hiemer Stefan , Fan Haidong , Zaiser Michael 
Published: January 2023

AbstractPlasticity modelling has long relied on phenomenological models based on ad-hoc assumption of constitutive relations, which are then fitted to limited data. Other work is based on the consideration of physical mechanisms which seek to establish a physical foundation of the observed plastic deformation behavior through identification of isolated defect processes (’mechanisms’) which are observed either experimentally or in simulations and then serve to formulate so-called physically based models. Neither of these approaches is adequate to capture the complexity of plastic deformation which belongs into the realm of emergent collective phenomena, and to understand the complex interplay of multiple deformation pathways which is at the core of modern high performance structural materials. Data based approaches offer alternative pathways towards plasticity modelling whose strengths and limitations we explore here for a simple example, namely the interplay between rate and dislocation density dependent strengthening mechanisms in fcc metals.
A radiographic, deep transfer learning framework, adapted to estimate lung opacities from chest x-rays
Authors: Vardhan Avantika , Makhnevich Alex , Omprakash Pravan , Hirschorn David , Barish Matthew , Cohen Stuart L. , Zanos Theodoros P. 
Published: January 2023

AbstractChest radiographs (CXRs) are the most widely available radiographic imaging modality used to detect respiratory diseases that result in lung opacities. CXR reports often use non-standardized language that result in subjective, qualitative, and non-reproducible opacity estimates. Our goal was to develop a robust deep transfer learning framework and adapt it to estimate the degree of lung opacity from CXRs. Following CXR data selection based on exclusion criteria, segmentation schemes were used for ROI (Region Of Interest) extraction, and all combinations of segmentation, data balancing, and classification methods were tested to pick the top performing models. Multifold cross validation was used to determine the best model from the initial selected top models, based on appropriate performance metrics, as well as a novel Macro-Averaged Heatmap Concordance Score (MA HCS). Performance of the best model is compared against that of expert physician annotators, and heatmaps were produced. Finally, model performance sensitivity analysis across patient populations of interest was performed. The proposed framework was adapted to the specific use case of estimation of degree of CXR lung opacity using ordinal multiclass classification. Acquired between March 24, 2020, and May 22, 2020, 38,365 prospectively annotated CXRs from 17,418 patients were used. We tested three neural network architectures (ResNet-50, VGG-16, and ChexNet), three segmentation schemes (no segmentation, lung segmentation, and lateral segmentation based on spine detection), and three data balancing strategies (undersampling, double-stage sampling, and synthetic minority oversampling) using 38,079 CXR images for training, and validation with 286 images as the out-of-the-box dataset that underwent expert radiologist adjudication. Based on the results of these experiments, the ResNet-50 model with undersampling and no ROI segmentation is recommended for lung opacity classification, based on optimal values for the MAE metric and HCS (Heatmap Concordance Score). The degree of agreement between the opacity scores predicted by this model with respect to the two sets of radiologist scores (OR or Original Reader and OOBTR or Out Of Box Reader) in terms of performance metrics is superior to the inter-radiologist opacity score agreement.
Directly and instantly seeing through random diffusers by self-imaging in scattering speckles
Authors: Liu Jietao , Yang Wenhong , Song Guofeng , Gan Qiaoqiang 
Published: January 2023

AbstractImaging through scattering medium is challenging but important for different applications. Most advances rely on computational image reconstruction from scattering signals. In these conventional investigations, speckles were always treated as scrambled grainy patterns. Directly seeing through scattering diffusers has never been realized. Here, we report a new strategy to see through random diffusers directly using self-imaging of speckles. By analyzing the physics, a direct observation strategy through scattering media is reported with improved image quality. Using this method, we experimentally demonstrated reconstruction-free real-time imaging of static and moving objects with their actual orientation information under single-wavelength and white light illumination. We also proposed a modified speckle autocorrelation imaging (SAI) method inspired by the self-imaging results. Importantly, our strategy requires no pre-calibration or acquisition of point-spread-function, no active control of wavefronts or complicated equipment, nor iterations or carefully adjusted parameters, paving the way towards rapid and high-quality imaging through scattering diffusers.