bluuebunny/crossref_metadata_2025_split huggingface dataset:
Early diagnosis of sepsis using an E-health application for a clinical early warning system outside of the intensive care unit: a case report
Authors: Ghazali Daniel Aiham , Kenway Philippe , Choquet Christophe , Casalino Enrique 
Published: May 2022

Abstract
Background
Elderly and frail patients who are unable to call for help in case of vital distress can develop complications during their hospitalization. As a supplement to clinical monitoring by the nursing staff, these patients can also be monitored in real time, with the Sensium E-health technology. An application notifies clinical staff of any change in their vital signs (heart rate, respiratory rate, temperature) outside of normal ranges, suggestive of physiological decline. Nurses and physicians are notified of these abnormal changes by email and also via mobile application (iPhone or iPad), allowing early intervention to prevent further deterioration.

Case presentation
An 86-year-old Caucasian female, with chronic kidney disease, was hospitalized in our medical unit for pyelonephritis associated with a moderate deterioration of serum creatinine. Remote continuous monitoring allowed us to diagnose clinical deterioration early and adjust her treatment. The treatment improved her clinical condition and amended the secondary sepsis with circulation failure in 2 days.

Conclusions
The prognosis for patients with acute complicated pyelonephritis is much worse than for those with uncomplicated pyelonephritis. Remote continuous monitoring might be helpful to early diagnose urosepsis. This technology leads to improved prognosis of patients without initial vital distress, allowing early treatment and admission to intensive care unit.
Mesenchymal stromal cells (MSCs) and their exosome in acute liver failure (ALF): a comprehensive review
Authors: Shokravi Samin , Borisov Vitaliy , Zaman Burhan Abdullah , Niazvand Firoozeh , Hazrati Raheleh , Khah Meysam Mohammadi , Thangavelu Lakshmi , Marzban Sima , Sohrabi Armin , Zamani Amir 
Published: May 2022

AbstractRecently, mesenchymal stromal cells (MSCs) and their derivative exosome have become a promising approach in the context of liver diseases therapy, in particular, acute liver failure (ALF). In addition to their differentiation into hepatocytes in vivo, which is partially involved in liver regeneration, MSCs support liver regeneration as a result of their appreciated competencies, such as antiapoptotic, immunomodulatory, antifibrotic, and also antioxidant attributes. Further, MSCs-secreted molecules inspire hepatocyte proliferation in vivo, facilitating damaged tissue recovery in ALF. Given these properties, various MSCs-based approaches have evolved and resulted in encouraging outcomes in ALF animal models and also displayed safety and also modest efficacy in human studies, providing a new avenue for ALF therapy. Irrespective of MSCs-derived exosome, MSCs-based strategies in ALF include administration of native MSCs, genetically modified MSCs, pretreated MSCs, MSCs delivery using biomaterials, and also MSCs in combination with and other therapeutic molecules or modalities. Herein, we will deliver an overview regarding the therapeutic effects of the MSCs and their exosomes in ALF. As well, we will discuss recent progress in preclinical and clinical studies and current challenges in MSCs-based therapies in ALF, with a special focus on in vivo reports.
SemClinBr - a multi-institutional and multi-specialty semantically annotated corpus for Portuguese clinical NLP tasks
Authors: Oliveira Lucas Emanuel Silva e , Peters Ana Carolina , da Silva Adalniza Moura Pucca , Gebeluca Caroline Pilatti , Gumiel Yohan Bonescki , Cintho Lilian Mie Mukai , Carvalho Deborah Ribeiro , Al Hasan Sadid , Moro Claudia Maria Cabral 
Published: May 2022

Abstract
Background
The high volume of research focusing on extracting patient information from electronic health records (EHRs) has led to an increase in the demand for annotated corpora, which are a precious resource for both the development and evaluation of natural language processing (NLP) algorithms. The absence of a multipurpose clinical corpus outside the scope of the English language, especially in Brazilian Portuguese, is glaring and severely impacts scientific progress in the biomedical NLP field.

Methods
In this study, a semantically annotated corpus was developed using clinical text from multiple medical specialties, document types, and institutions. In addition, we present, (1) a survey listing common aspects, differences, and lessons learned from previous research, (2) a fine-grained annotation schema that can be replicated to guide other annotation initiatives, (3) a web-based annotation tool focusing on an annotation suggestion feature, and (4) both intrinsic and extrinsic evaluation of the annotations.

Results
This study resulted in SemClinBr, a corpus that has 1000 clinical notes, labeled with 65,117 entities and 11,263 relations. In addition, both negation cues and medical abbreviation dictionaries were generated from the annotations. The average annotator agreement score varied from 0.71 (applying strict match) to 0.92 (considering a relaxed match) while accepting partial overlaps and hierarchically related semantic types. The extrinsic evaluation, when applying the corpus to two downstream NLP tasks, demonstrated the reliability and usefulness of annotations, with the systems achieving results that were consistent with the agreement scores.

Conclusion
The SemClinBr corpus and other resources produced in this work can support clinical NLP studies, providing a common development and evaluation resource for the research community, boosting the utilization of EHRs in both clinical practice and biomedical research. To the best of our knowledge, SemClinBr is the first available Portuguese clinical corpus.
Monitoring of circulating monocyte HLA-DR expression in a large cohort of intensive care patients: relation with secondary infections
Authors: de Roquetaillade C. , Dupuis C. , Faivre V. , Lukaszewicz A. C. , Brumpt C. , Payen D. 
Published: May 2022

Abstract
Introduction
The reports of an early and profound acquired immunodepression syndrome (AIDs) in ICU patients had gained sufficient credence to modify the paradigm of acute inflammation. However, despite several articles published on AIDs and its assessment by monocytic HLA-DR monitoring, several missing informations remained: 1—Which patients’ are more prone to benefit from mHLA-DR measurement, 2—Is the nadir or the duration of the low mHLA-DR expression the main parameter to consider? 3—What are the compared performances of leukocytes’ count analyses (lymphocyte, monocyte).

Material and method
We conducted an observational study in a surgical ICU of a French tertiary hospital. A first mHLA-DR measurement (fixed flow cytometry protocol) was performed within the first 3 days following admission and a 2nd, between day 5 and 10. The other collected parameters were: SAPS II and SOFA scores, sex, age, comorbidities, mortality and ICU-acquired infections (IAI). The associations between mHLA-DR and outcomes were tested by adjusted Fine and Gray subdistribution competing risk models.

Results
1053 patients were included in the study, of whom 592 had a 2nd mHLA-DR measurement. In this cohort, 223 patients (37.7%) complicated by IAI. The initial decrement in mHLA-DR was not associated with the later occurrence of IAI, (p = 0.721), however, the persistence of a low mHLA-DR (< 8000 AB/C), measured between day 5 and day 7, was associated with the later occurrence of IAI (p = 0.01). Similarly, a negative slope between the first and the second value was significantly associated with subsequent IAI (p = 0.009). The best performance of selected markers was obtained with the combination of the second mHLA-DR measurement with SAPSII on admission. Persisting lymphopenia and monocytopenia were not associated with later occurrence of IAI.

Conclusion
Downregulation of mHLA-DR following admission is observed in a vast number of patients whatever the initial motif for admission. IAI mostly occurs among patients with a high severity score on admission suggesting that immune monitoring should be reserved to the most severe patients. The initial downregulation did not preclude the later development of IAI. A decreasing or a persisting low mHLA-DR expression below 8000AB/C within the first 7 days of ICU admission was independently and reliably associated with subsequent IAI among ICU patients with performances superior to leukocyte subsets count alone.
A Caputo discrete fractional-order thermostat model with one and two sensors fractional boundary conditions depending on positive parameters by using the Lipschitz-type inequality
Authors: Alzabut Jehad , Selvam A. George Maria , Dhineshbabu Raghupathi , Tyagi Swati , Ghaderi Mehran , Rezapour Shahram 
Published: May 2022

AbstractA thermostat model described by a second-order fractional difference equation is proposed in this paper with one sensor and two sensors fractional boundary conditions depending on positive parameters by using the Lipschitz-type inequality. By means of well-known contraction mapping and the Brouwer fixed-point theorem, we provide new results on the existence and uniqueness of solutions. In this work by use of the Caputo fractional difference operator and Hyer–Ulam stability definitions we check the sufficient conditions and solution of the equations to be stable, while most researchers have examined the necessary conditions in different ways. Further, we also establish some results regarding Hyers–Ulam, generalized Hyers–Ulam, Hyers–Ulam–Rassias, and generalized Hyers–Ulam–Rassias stability for our discrete fractional-order thermostat models. To support the theoretical results, we present suitable examples describing the thermostat models that are illustrated by graphical representation.
OpenIaC: open infrastructure as code - the network is my computer
Authors: Rong Chunming , Geng Jiahui , Hacker Thomas J. , Bryhni Haakon , Jaatun Martin G. 
Published: May 2022

AbstractModern information systems are built fron a complex composition of networks, infrastructure, devices, services, and applications, interconnected by data flows that are often private and financially sensitive. The 5G networks, which can create hyperlocalized services, have highlighted many of the deficiencies of current practices in use today to create and operate information systems. Emerging cloud computing techniques, such as Infrastructure-as-Code (IaC) and elastic computing, offer a path for a future re-imagining of how we create, deploy, secure, operate, and retire information systems. In this paper, we articulate the position that a comprehensive new approach is needed for all OSI layers from layer 2 up to applications that are built on underlying principles that include reproducibility, continuous integration/continuous delivery, auditability, and versioning. There are obvious needs to redesign and optimize the protocols from the network layer to the application layer. Our vision seeks to augment existing Cloud Computing and Networking solutions with support for multiple cloud infrastructures and seamless integration of cloud-based microservices. To address these issues, we propose an approach named Open Infrastructure as Code (OpenIaC), which is an attempt to provide a common open forum to integrate and build on advances in cloud computing and blockchain to address the needs of modern information architectures. The main mission of our OpenIaC approach is to provide services based on the principles of Zero Trust Architecture (ZTA) among the federation of connected resources based on Decentralized Identity (DID). Our objectives include the creation of an open-source hub with fine-grained access control for an open and connected infrastructure of shared resources (sensing, storage, computing, 3D printing, etc.) managed by blockchains and federations. Our proposed approach has the potential to provide a path for developing new platforms, business models, and a modernized information ecosystem necessary for 5G networks.
Impulsiveness as potential moderators of the relation between social media dependence and eating disorders risk
Authors: He Zhonghua , Yang Weili 
Published: May 2022

Abstract
Background
Social media dependence (SMD) and eating disorders (ED) risk are often thought to be inextricably linked. Because social media dependence often precedes an ED, predicts poor outcome, and persists even after recovery from an ED, it is important to examine whether certain factors have the ability to potentially attenuate or intensify SMD’s effect on eating disorders.

Methods
In the current study, we examined one possible moderating factor: impulsiveness. 767 undergraduates (mean age = 18.78 years, SD = 1.20) in Shaanxi province of China, completed anonymous questionnaires regarding social media dependence, eating disorders, impulsiveness.

Results
Revealed that non-planned impulsiveness (NPI) moderated the relation between SMD and ED risk. Individuals who were low in SMD and who reported low levels of NPI reported much lower levels of ED risk than those with low SMD and high NPI. However, Individuals who were high in SMD and who reported low levels of NPI reported much higher levels of ED risk than those with high SMD and high NPI. Contrary to our hypotheses, Motor impulsiveness and cognitive impulsiveness did not emerge as moderators of the relation between SMD and ED risk.

Conclusions
Results provide growing support that factors that interact with SMD can lessen or aggravate SMD’s effect on ED risk. These findings can be beneficial to our understanding of how and when social media dependence impacts undergraduates’ eating disorders risk.
Sestrin2 in cancer: a foe or a friend?
Authors: Ala Moein 
Published: May 2022

AbstractSestrin2 is a conserved antioxidant, metabolism regulator, and downstream of P53. Sestrin2 can suppress oxidative stress and inflammation, thereby preventing the development and progression of cancer. However, Sestrin2 attenuates severe oxidative stress by activating nuclear factor erythroid 2-related factor 2 (Nrf2), thereby enhancing cancer cells survival and chemoresistance. Sestrin2 inhibits endoplasmic reticulum stress and activates autophagy and apoptosis in cancer cells. Attenuation of endoplasmic reticulum stress and augmentation of autophagy hinders cancer development but can either expedite or impede cancer progression under specific conditions. Furthermore, Sestrin2 can vigorously inhibit oncogenic signaling pathways through downregulation of mammalian target of rapamycin complex 1 (mTORC1) and hypoxia-inducible factor 1-alpha (HIF-1α). Conversely, Sestrin2 decreases the cytotoxic activity of T cells and natural killer cells which helps tumor cells immune evasion. Sestrin2 can enhance tumor cells viability in stress conditions such as glucose or glutamine deficiency. Cancer cells can also upregulate Sestrin2 during chemotherapy or radiotherapy to attenuate severe oxidative stress and ER stress, augment autophagy and resist the treatment. Recent studies unveiled that Sestrin2 is involved in the development and progression of several types of human cancer. The effect of Sestrin2 may differ depending on the type of tumor, for instance, several studies revealed that Sestrin2 protects against colorectal cancer, whereas results are controversial regarding lung cancer. Furthermore, Sestrin2 expression correlates with metastasis and survival in several types of human cancer such as colorectal cancer, lung cancer, and hepatocellular carcinoma. Targeted therapy for Sestrin2 or regulation of its expression by new techniques such as non-coding RNAs delivery and vector systems may improve cancer chemotherapy and overcome chemoresistance, metastasis and immune evasion that should be investigated by future trials.
Accuracy of deep learning-based integrated tooth models by merging intraoral scans and CBCT scans for 3D evaluation of root position during orthodontic treatment
Authors: Lee Suk-Cheol , Hwang Hyeon-Shik , Lee Kyungmin Clara 
Published: May 2022

Abstract
Objective
This study aimed to evaluate the accuracy of deep learning-based integrated tooth models (ITMs) by merging intraoral scans and cone-beam computed tomography (CBCT) scans for three-dimensional (3D) evaluation of root position during orthodontic treatment and to compare the fabrication process of integrated tooth models (ITMs) with manual method.

Material and methods
Intraoral scans and corresponding CBCT scans before and after treatment were obtained from 15 patients who completed orthodontic treatment with premolar extraction. A total of 600 ITMs were generated using deep learning technology and manual methods by merging the intraoral scans and CBCT scans at pretreatment. Posttreatment intraoral scans were integrated into the tooth model, and the resulting estimated root positions were compared with the actual root position at posttreatment CBCT. Discrepancies between the estimated and actual root position including average surface differences, arch widths, inter-root distances, and root axis angles were obtained in both the deep learning and manual method, and these measurements were compared between the two methods.

Results
The average surface differences of estimated and actual ITMs in the manual method were 0.02 mm and 0.03 mm for the maxillary and mandibular arches, respectively. In the deep learning method, the discrepancies were 0.07 mm and 0.08 mm for the maxillary and mandibular arches, respectively. For the measurements of arch widths, inter-root distances, and root axis angles, there were no significant differences between estimated and actual models both in the manual and in the deep learning methods, except for some measurements. Comparing the two methods, only three measurements showed significant differences. The procedure times taken to obtain the measurements were longer in the manual method than in the deep learning method.

Conclusion
Both deep learning and manual methods showed similar accuracy in the integration of intraoral scans and CBCT images. Considering time and efficiency, the deep learning automatic method for ITMs is highly recommended for clinical practice.
Classification of mastoid air cells by CT scan images using deep learning method
Authors: Khosravi Mohammad , Jabbari Moghaddam Yalda , Esmaeili Mahdad , Keshtkar Ahmad , Jalili Javad , Tayefi Nasrabadi Hamid 
Published: May 2022

AbstractPurposeMastoid abnormalities show different types of ear illnesses, however inadequacy of experts and low accuracy of diagnostic demand a new approach to detect these abnormalities and reduce human mistakes. The manual analysis of mastoid CT scans is time-consuming and labor-intensive. In this paper the first and robust deep learning-based approaches is introduced to diagnose mastoid abnormalities using a large database of CT images obtained in the clinical center with remarkable accuracy.MethodsIn this paper, mastoid abnormalities are classified using the Xception based Convolutional Neural Network (CNN) model, with optimizer Adamax into five categories (Complete pneumatized, Opacification in pneumatization, Partial pneumatization, Opacification in partial pneumatization, None pneumatized). For this reason, a total of 24,800 slides of 152 patients were selected that include the mastoid from most upper to the lowest part of the middle ear cavity to complete the construction of the proposed deep neural network model.ResultsThe proposed model had the best accuracy of 87.80% (based on grader 1) and 88.44% (based on grader 2) on the 20th epoch and 87.70% (based on grader 1) and 87.56% (based on grader 2) on average and also significantly faster than other types of implemented architectures in terms of the computer running time (in seconds). The 99% confidence interval of the average accuracy was 0.012 which means that the true accuracy is 87.80% and 87.56% ± 1.2% that indicates the power of the model.ConclusionsThe manual analysis of ear cavity CT scans is often time-consuming and prone to errors due to various inter- or intra operator variability studies. The proposed method can be used to automatically analyze the middle ear cavity to classify mastoid abnormalities, which is markedly faster than most types of models with the highest accuracy.
Monte Carlo modelling of a compact CZT-based gamma camera with application to 177Lu imaging
Authors: Roth Daniel , Larsson Erik , Ljungberg Michael , Sjögreen Gleisner Katarina 
Published: May 2022

Abstract
Background
Semiconductor gamma-camera systems based on cadmium zinc telluride (CZT) detectors present new challenges due to an energy-response that includes effects of low-energy tailing. In particular, such energy tails produce effects that need to be considered when imaging radionuclides with multiple emissions such as $$^{177}{\mathrm {Lu}}$$

177

Lu

. Monte Carlo simulation can be used to investigate the behaviour of such systems and optimise their use, provided that the detector model closely reflects the real physical detector. The aim of this work is to develop a CZT model applicable for simulation of CZT-based gamma cameras.

Methods
The equations describing the charge transport and signal induction are considered in three dimensions and are solved numerically, and the CZT model is then realised by coupling the detector-response to the photon-transport handled by the SIMIND Monte Carlo program. The CZT model is tuned to reproduce experimentally measured energy spectra of a hand-held gamma camera system for multiple radionuclides ($$^{99\mathrm {m}}{\mathrm {Tc}}$$

99
m

Tc

, $$^{123}{\mathrm {I}}$$

123

I

and $$^{177}{\mathrm {Lu}}$$

177

Lu

) and parallel-hole collimators (MEGP, LEHR) as well as an uncollimated system.

Results
Overall, the model results agree well with measurements across the range of experimental conditions. The applicability of the model is demonstrated by separating energy spectra into components to investigate the interference of high-energy photons on lower energy-windows, where pronounced effects of low-energy tailing for $$^{177}{\mathrm {Lu}}$$

177

Lu

are observed.

Conclusions
The developed model provides understanding of the specifics of the camera response and is expected to be helpful for future optimisation of gamma camera applications.