bluuebunny/crossref_metadata_2025_split huggingface dataset:
Perfectionism in the anesthesiological environment
Authors: Kravets Olha V. , Yekhalov Vasyl V. , Stanin Dmytro M. , Krishtafor Daria A. , Martynenko Danylo A. 
Published: November 2023

Abstract
Background
Perfectionism today is understood as an individual's psychological conviction that the ideal can and should be achieved, and the imperfect result of work (physical, intellectual, etc.), in their opinion, has no right to exist.

The purpose of the study
Our goal was to investigate levels and types of perfectionism among anesthesiology interns in comparison with the indicators of practicing anesthesiologists.

Materials and methods
An anonymous survey of 92 anesthesiology interns and 124 practicing anesthesiologists was conducted according to the Big-Three Perfectionism Scale (BTPS).

Results
The mean general level of perfectionism was average, with the total BTPS score of 124,38 ± 14,47 out of 225 in interns and 105,97 ± 10,31 in practicing anesthesiologists (p < 0,05). Both interns and practicing doctors leaned toward rigid perfectionism (mean score 32,32 ± 3,32 out of 50 in interns and 33,33 ± 3,23—in practicing doctors, p < 0,05) and self-critical perfectionism, with the average score of 52,08 ± 4,37 out of 90 in interns and 42,87 ± 4,76 in postgraduates (p < 0,05). Narcissistic perfectionism is the factor with the least relative score in both groups (39,99 ± 7,61 out of 85 in interns and 29,77 ± 4,20 in practicing doctors, p < 0,05).

Conclusions
Neither anesthesiology interns nor practicing anesthesiologists in general exhibited high levels of perfectionism. In both groups there was a moderate leaning towards rigid and self-critical perfectionism, which indicates a tendency for the individuals to set high standards for themselves and base their own self-worth on meeting these standards. In interns, the general perfectionism levels were significantly higher than in practicing doctors. Also the self-critical type was more prominent among interns. This might indicate a sense of pressure to meet unrealistic outside expectations and an impostor syndrome which is common for the people at the start of their careers, but it’s also a significant risk factor for future burnout.
Aloe Barbadensis Miller (Aloe Vera)
Authors: Kaur Sukhdeep , Bains Kiran 
Published: November 2023

Abstract: Aloe Barbadensis Miller (Aloe Vera, AV) is a widely recognized for its diverse health-promoting, skin care, and medicinal properties. This narrative review provides a comprehensive overview of AV’s bioactive compounds, pharmacological activities, potential applications, its toxic and adverse effects, as well as the clinical evidence supporting AV’s efficacy in disease prevention. AV contains over 200 bioactive compounds, with the inner clear gel of the leaves containing the majority of these compounds. These include phenolic acids (274.5–307.5 mg/100 g), flavonoids. (3.63–4.70 g/kg), polysaccharides (3.82–6.55 g/kg), saponins, alkaloids, terpenoids, and anthraquinone derivatives. Findings from clinical studies involving both humans and animals highlight the therapeutic potential of AV across diverse health domains. The studies demonstrate AV’s efficacy in reducing blood glucose levels, exhibiting antioxidant and immunomodulatory effects, inducing apoptosis in cancer cells, protecting the liver from damage, and displaying antimicrobial properties. In the fields of dermatology and dentistry, AV has also been observed to promote skin and oral health. However, it is imperative to acknowledge potential risks, adhere to recommended dosages, and seek guidance from healthcare experts before employing AV as a natural therapeutic option. Moreover, considering safety concerns, further well-designed randomized controlled trials are necessary to substantiate the potential benefits of AV and comprehensively assess any associated risks.
Skillful Multi‐Month Predictions of Ecosystem Stressors in the Surface and Subsurface Ocean
Authors: Mogen Samuel C. , Lovenduski Nicole S. , Yeager Stephen , Keppler Lydia , Sharp Jonathan , Bograd Steven J. , Quiros Nathali Cordero , Di Lorenzo Emanuele , Hazen Elliott L. , Jacox Michael G. , Buil Mercedes Pozo 
Published: November 2023

AbstractAnthropogenic carbon emissions and associated climate change are driving rapid warming, acidification, and deoxygenation in the ocean, which increasingly stress marine ecosystems. On top of long‐term trends, short term variability of marine stressors can have major implications for marine ecosystems and their management. As such, there is a growing need for predictions of marine ecosystem stressors on monthly, seasonal, and multi‐month timescales. Previous studies have demonstrated the ability to make reliable predictions of the surface ocean physical and biogeochemical state months to years in advance, but few studies have investigated forecast skill of multiple stressors simultaneously or assessed the forecast skill below the surface. Here, we use the Community Earth System Model (CESM) Seasonal to Multiyear Large Ensemble (SMYLE) along with novel observation‐based biogeochemical and physical products to quantify the predictive skill of dissolved inorganic carbon (DIC), dissolved oxygen, and temperature in the surface and subsurface ocean. CESM SMYLE demonstrates high physical and biogeochemical predictive skill multiple months in advance in key oceanic regions and frequently outperforms persistence forecasts. We find up to 10 months of skillful forecasts, with particularly high skill in the Northeast Pacific (Gulf of Alaska and California Current Large Marine Ecosystems) for temperature, surface DIC, and subsurface oxygen. Our findings suggest that dynamical marine ecosystem prediction could support actionable advice for decision making.
Earthquake Early Warning Starting From 3 s of Records on a Single Station With Machine Learning
Authors: Lara Pablo , Bletery Quentin , Ampuero Jean‐Paul , Inza Adolfo , Tavera Hernando 
Published: November 2023

AbstractWe introduce the Ensemble Earthquake Early Warning System (E3WS), a set of Machine Learning (ML) algorithms designed to detect, locate, and estimate the magnitude of an earthquake starting from 3 s of P‐waves recorded by a single station. The system is made of six Ensemble ML algorithms trained on attributes computed from ground acceleration time series in the temporal, spectral, and cepstral domains. The training set comprises data sets from Peru, Chile, Japan, and the STEAD global data set. E3WS consists of three sequential stages: detection, P‐phase picking, and source characterization. The latter involves magnitude, epicentral distance, depth, and back azimuth estimation. E3WS achieves an overall success rate in the discrimination between earthquakes and noise of 99.9%, with no false positive (noise mis‐classified as earthquakes) and very few false negatives (earthquakes mis‐classified as noise). All false negatives correspond to M ≤ 4.3 earthquakes, which are unlikely to cause any damage. For P‐phase picking, the Mean Absolute Error is 0.14 s, small enough for earthquake early warning purposes. For source characterization, the E3WS estimates are virtually unbiased, have better accuracy for magnitude estimation than existing single‐station algorithms, and slightly better accuracy for earthquake location. By updating estimates every second, the approach gives time‐dependent magnitude estimates that follow the earthquake source time function. E3WS gives faster estimates than present alert systems relying on multiple stations, providing additional valuable seconds for potential protective actions.
Cloud Botany: Shallow Cumulus Clouds in an Ensemble of Idealized Large‐Domain Large‐Eddy Simulations of the Trades
Authors: Jansson Fredrik , Janssens Martin , Grönqvist Johanna H. , Siebesma A. Pier , Glassmeier Franziska , Attema Jisk , Azizi Victor , Satoh Masaki , Sato Yousuke , Schulz Hauke , Kölling Tobias 
Published: November 2023

AbstractSmall shallow cumulus clouds (<1 km) over the tropical oceans appear to possess the ability to self‐organize into mesoscale (10–100 km) patterns. To better understand the processes leading to such self‐organized convection, we present Cloud Botany, an ensemble of 103 large‐eddy simulations on domains of 150 km, produced by the Dutch Atmospheric Large Eddy Simulation model on supercomputer Fugaku. Each simulation is run in an idealized, fixed, larger‐scale environment, controlled by six free parameters. We vary these over characteristic ranges for the winter trades, including parameter combinations observed during the EUREC4A (Elucidating the role of clouds–circulation coupling in climate) field campaign. In contrast to simulation setups striving for maximum realism, Cloud Botany provides a platform for studying idealized, and therefore more clearly interpretable causal relationships between conditions in the larger‐scale environment and patterns in mesoscale, self‐organized shallow convection. We find that any simulation that supports cumulus clouds eventually develops mesoscale patterns in their cloud fields. We also find a rich variety in these patterns as our control parameters change, including cold pools lined by cloudy arcs, bands of cross‐wind clouds and aggregated patches, sometimes topped by thin anvils. Many of these features are similar to cloud patterns found in nature. The published data set consists of raw simulation output on full 3D grids and 2D cross‐sections, as well as post‐processed quantities aggregated over the vertical (2D), horizontal (1D) and all spatial dimensions (time‐series). The data set is directly accessible from Python through the use of the EUREC4A intake catalog.
Markers of intestinal mucositis to predict blood stream infections at the onset of fever during treatment for childhood acute leukemia
Authors: Weischendorff Sarah , Rathe Mathias , Petersen Malene Johanne , Weimann Allan , Enevold Christian , Nielsen Claus H. , Als-Nielsen Bodil , Nygaard Ulrikka , Moser Claus , Müller Klaus 
Published: November 2023

AbstractDespite chemotherapy-induced intestinal mucositis being a main risk factor for blood stream infections (BSIs), no studies have investigated mucositis severity to predict BSI at fever onset during acute leukemia treatment. This study prospectively evaluated intestinal mucositis severity in 85 children with acute leukemia, representing 242 febrile episodes (122 with concurrent neutropenia) by measuring plasma levels of citrulline (reflecting enterocyte loss), regenerating islet-derived-protein 3α (REG3α, an intestinal antimicrobial peptide) and CCL20 (a mucosal immune regulatory chemokine) along with the general neutrophil chemo-attractants CXCL1 and CXCL8 at fever onset. BSI was documented in 14% of all febrile episodes and in 20% of the neutropenic febrile episodes. In age-, sex-, diagnosis- and neutrophil count-adjusted analyses, decreasing citrulline levels and increasing REG3α and CCL20 levels were independently associated with increased odds of BSI (OR = 1.6, 1.5 and 1.7 per halving/doubling, all p < 0.05). Additionally, higher CXCL1 and CXCL8 levels increased the odds of BSI (OR = 1.8 and 1.7 per doubling, all p < 0.0001). All three chemokines showed improved diagnostic accuracy compared to C-reactive protein and procalcitonin. These findings underline the importance of disrupted intestinal integrity as a main risk factor for BSI and suggest that objective markers for monitoring mucositis severity may help predicting BSI at fever onset.
Targeting the up-regulated CNOT3 reverses therapeutic resistance and metastatic progression of EGFR-mutant non-small cell lung cancer
Authors: Jing Lin , Zhai Meng-En , Qian Mei-Rui , Li Yi-Ming , Han Ming-Wei , Wang Kun , Huang Wan , Nan Gang , Jiang Jian-Li 
Published: November 2023

AbstractLung cancer is the leading cause of cancer-related mortality worldwide. CNOT3, a subunit of the CCR4-NOT complex, has recently been suggested to be overexpressed in lung cancer and involved in tumor malignancy. However, its precise role and the underlying mechanisms still need to be fully revealed. In the present study, we found in lung cancer cells the expression of CNOT3 could be regulated by EGFR signaling pathway and c-Jun, a transcription factor downstream of EGFR, transcriptionally regulated its expression. Interestingly, CNOT3 could inversely regulate the expression of c-Jun via modulating its translation. Thus, a feedback loop existed between c-Jun and CNOT3. CNOT3 reduction post EGFR blockade facilitated the drug-induced cell death, and simultaneously inhibited cell proliferation via impacting TSC1/mTOR axis. Whereas, further up-regulation of the CNOT3 expression was observed in gefitinib-resistant cells, which dampened gefitinib sensitivity. Mechanically, the elevation of CNOT3 was induced by the bypass activation of HER2/c-Jun signaling. Depleting CNOT3 in vitro and in vivo sensitized the drug-resistant cells to gefitinib treatment and inhibited metastatic progression. These results give novel insights into the role of CNOT3 in lung cancer malignancy and provide a theoretical basis for the development of therapeutic strategies to solve acquired resistance to EGFR-TKIs.
Regenerating human skeletal muscle forms an emerging niche in vivo to support PAX7 cells
Authors: Hicks Michael R. , Saleh Kholoud K. , Clock Ben , Gibbs Devin E. , Yang Mandee , Younesi Shahab , Gane Lily , Gutierrez-Garcia Victor , Xi Haibin , Pyle April D. 
Published: November 2023

AbstractSkeletal muscle stem and progenitor cells including those derived from human pluripotent stem cells (hPSCs) offer an avenue towards personalized therapies and readily fuse to form human–mouse myofibres in vivo. However, skeletal muscle progenitor cells (SMPCs) inefficiently colonize chimeric stem cell niches and instead associate with human myofibres resembling foetal niches. We hypothesized competition with mouse satellite cells (SCs) prevented SMPC engraftment into the SC niche and thus generated an SC ablation mouse compatible with human engraftment. Single-nucleus RNA sequencing of SC-ablated mice identified the absence of a transient myofibre subtype during regeneration expressing Actc1. Similarly, ACTC1+ human myofibres supporting PAX7+ SMPCs increased in SC-ablated mice, and after re-injury we found SMPCs could now repopulate into chimeric niches. To demonstrate ACTC1+ myofibres are essential to supporting PAX7 SMPCs, we generated caspase-inducible ACTC1 depletion human pluripotent stem cells, and upon SMPC engraftment we found a 90% reduction in ACTC1+ myofibres and a 100-fold decrease in PAX7 cell numbers compared with non-induced controls. We used spatial RNA sequencing to identify key factors driving emerging human niche formation between ACTC1+ myofibres and PAX7+ SMPCs in vivo. This revealed that transient regenerating human myofibres are essential for emerging niche formation in vivo to support PAX7 SMPCs.
Efficacy and safety of eltrombopag in Chinese patients with refractory or relapsed severe aplastic anemia
Authors: Chang Hong , He Guangsheng , Fu Rong , Li Fei , Han Bing , Li Tao , Liu Lei , Mittal Hemant , Jin Hantao , Zhang Fengkui 
Published: November 2023

AbstractFor patients with severe aplastic anemia (SAA) in China who have had an insufficient response to the first-line treatment with hematopoietic stem cell transplantation or immunosuppressive therapy, there is no established standard of care other than transfusion support and treatment of infections. This non-randomized, open-label, Phase II multicenter trial investigated the efficacy and safety of eltrombopag in 20 adult Chinese patients with refractory or relapsed (r/r) SAA. The primary endpoint of hematologic response rate at Week 26, defined as the proportion of patients who met any of the International Working Group criteria, was observed in 70% (14/20) of patients, with more than 50% of these having at least bi-lineage response. Reduced red blood cell and platelet transfusion at Week 26 were observed in 57% (8/14) and 80% (8/10) of patients, respectively. Safety findings were consistent with the established safety profile of eltrombopag and no new safety signals were reported. None of the patients discontinued eltrombopag because of safety concerns. Although the sample size was small, this is the first prospective study to show that eltrombopag is efficacious and has a favorable safety profile in a Chinese patient population with r/r SAA.Trial registration: This trial is registered on ClinicalTrials.gov (NCT03988608); registered 17 June 2019.
Unveiling the combined effects of neutral dynamics and electrodynamic forcing on dayside ionosphere during the 3–4 February 2022 “SpaceX” geomagnetic storms
Authors: Kakoti Geetashree , Bagiya Mala S. , Laskar Fazlul I. , Lin Dong 
Published: November 2023

AbstractGeomagnetic storms of G1-class were observed on 3 and 4 February 2022, which caused the loss of 38 out of 49 SpaceX satellites during their launch due to enhanced neutral density. The effects of storm-time neutral dynamics and electrodynamics over the American sector during this minor storm have been investigated using Global Positioning System—total electron content (TEC) and Global‐scale Observations of the Limb and Disk (GOLD) mission measured thermospheric composition and temperature. Results revealed an unexpected feature in terms of increase in O/N2 and depletion in TEC over the American low-latitudes. This feature is in addition to the classic storm time ionospheric variations of enhancement in ionospheric electron density in presence of enhanced O/N2 and an intense equatorial electrojet (EEJ). Further, significant morning-noon electron density reductions were observed over the southern mid-high latitudes along the American longitudes. Results from Multiscale Atmosphere-Geospace Environment (MAGE) model simulations elucidated storm-induced equatorward thermospheric wind which caused the strong morning counter electrojet by generating the disturbance dynamo electric field. This further explains the morning TEC depletion at low-latitudes despite an increase in O/N2. Sub-storm related magnetospheric convection resulted in significant noon-time peak in EEJ on 4 February. Observation and modelling approaches together suggested that combined effects of storm-time neutral dynamic and electrodynamic forcing resulted in significant ionospheric variations over the American sector during minor geomagnetic storms.
Software defect prediction using learning to rank approach
Authors: Nassif Ali Bou , Talib Manar Abu , Azzeh Mohammad , Alzaabi Shaikha , Khanfar Rawan , Kharsa Ruba , Angelis Lefteris 
Published: November 2023

AbstractSoftware defect prediction (SDP) plays a significant role in detecting the most likely defective software modules and optimizing the allocation of testing resources. In practice, though, project managers must not only identify defective modules, but also rank them in a specific order to optimize the resource allocation and minimize testing costs, especially for projects with limited budgets. This vital task can be accomplished using Learning to Rank (LTR) algorithm. This algorithm is a type of machine learning methodology that pursues two important tasks: prediction and learning. Although this algorithm is commonly used in information retrieval, it also presents high efficiency for other problems, like SDP. The LTR approach is mainly used in defect prediction to predict and rank the most likely buggy modules based on their bug count or bug density. This research paper conducts a comprehensive comparison study on the behavior of eight selected LTR models using two target variables: bug count and bug density. It also studies the effect of using imbalance learning and feature selection on the employed LTR models. The models are empirically evaluated using Fault Percentile Average. Our results show that using bug count as ranking criteria produces higher scores and more stable results across multiple experiment settings. Moreover, using imbalance learning has a positive impact for bug density, but on the other hand it leads to a negative impact for bug count. Lastly, using the feature selection does not show significant improvement for bug density, while there is no impact when bug count is used. Therefore, we conclude that using feature selection and imbalance learning with LTR does not come up with superior or significant results.
The role of an amphiphilic helix and transmembrane region in the efficient acylation of the M2 protein from influenza virus
Authors: Meng Xiaorong , Templeton Clark , Clementi Cecilia , Veit Michael 
Published: November 2023

AbstractProtein palmitoylation, a cellular process occurring at the membrane-cytosol interface, is orchestrated by members of the DHHC enzyme family and plays a pivotal role in regulating various cellular functions. The M2 protein of the influenza virus, which is acylated at a membrane-near amphiphilic helix serves as a model for studying the intricate signals governing acylation and its interaction with the cognate enzyme, DHHC20. We investigate it here using both experimental and computational assays. We report that altering the biophysical properties of the amphiphilic helix, particularly by shortening or disrupting it, results in a substantial reduction in M2 palmitoylation, but does not entirely abolish the process. Intriguingly, DHHC20 exhibits an augmented affinity for some M2 mutants compared to the wildtype M2. Molecular dynamics simulations unveil interactions between amino acids of the helix and the catalytically significant DHHC and TTXE motifs of DHHC20. Our findings suggest that the binding of M2 to DHHC20, while not highly specific, is mediated by requisite contacts, possibly instigating the transfer of fatty acids. A comprehensive comprehension of protein palmitoylation mechanisms is imperative for the development of DHHC-specific inhibitors, holding promise for the treatment of diverse human diseases.
Multifractal analysis of cellular ATR-FTIR spectrum as a method for identifying and quantifying cancer cell metastatic levels
Authors: Barbora Ayan , Karri Sirish , Firer Michael A. , Minnes Refael 
Published: November 2023

AbstractCancer is a leading cause of mortality today. Sooner a cancer is detected, the more effective is the treatment. Histopathological diagnosis continues to be the gold standard worldwide for cancer diagnosis, but the methods used are invasive, time-consuming, insensitive, and still rely to some degree on the subjective judgment of pathologists. Recent research demonstrated that Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) spectroscopy can be used to determine the metastatic potential of cancer cells by evaluating their membrane hydration. In the current study, we demonstrate that the conversion of ATR-FTIR spectra using multifractal transformation generates a unique number for each cell line’s metastatic potential. Applying this technique to murine and human cancer cells revealed a correlation between the metastatic capacity of cancer cells within the same lineage and higher multifractal value. The multifractal spectrum value was found to be independent of the cell concentration used in the assay and unique to the tested lineage. Healthy cells exhibited a smaller multifractal spectrum value than cancer cells. Further, the technique demonstrated the ability to detect cancer progression by being sensitive to the proportional change between healthy and cancerous cells in the sample. This enables precise determination of cancer metastasis and disease progression independent of cell concentration by comparing the measured spectroscopy derived multifractal spectrum value. This quick and simple technique devoid of observer bias can transform cancer diagnosis to a great extent improving public health prognosis worldwide.