bluuebunny/crossref_metadata_2025_split huggingface dataset:
Acupuncture in Diabetic Peripheral Neuropathy - Protocol for the Randomized, Multicenter ACUDPN Trial.
Authors: Dietzel Joanna , Hörder Sebastian , Habermann Isabel , Roll Stephanie , Ortiz Miriam , Willich Stefan , Hahn Katrin , Meyer-Hamme Gesa , Hammes Michael , Irnich Dominik , Wortmann Velia , Linde Klaus , Hummelsberg Josef , Wullinger Michael , Nögel Rainer , Schröder Sven , Brinkhaus Benno 
Published: February 2021

Abstract
Background: Acupuncture is used to treat patients with diabetic peripheral neuropathy; however, the evidence is unclear. We present the design and methodology of the ACUDPN (ACUpuncture in Diabetic Peripheral Neuropathy) trial, which investigates the effectiveness of acupuncture for the treatment of diabetic peripheral neuropathy (DPN) symptoms. The aim of this study is to investigate whether acupuncture is effective for the treatment of DPN symptoms.Methods: This study is a two-armed, randomized, controlled, parallel group, open-label, confirmatory, multicenter trial (8-week intervention period plus 16 weeks of follow-up). Physicians in outpatient units in Germany who specialize in acupuncture treatment will treat 110 diabetes type II patients with clinical symptoms of peripheral neuropathy in the feet and legs with signs of neuropathy according to nerve conduction testing. The patients will be randomized in a 1:1 ratio to one of the following two groups: (a) semi-standardized acupuncture plus routine care or (b) routine care alone. Acupuncture will consist of 12 treatments per patient over 8 weeks. The primary outcome will be the overall DPN-related complaints in the extremities after 8 weeks as measured by the Visual Analog Scale (VAS). Further outcome measures will include DPN-related pain, the Neuropathic Pain Symptom Inventory (NPSI), Diabetic Peripheral Neuropathic Pain Impact (DPNPI) scores and nerve conduction parameters of the sural nerve at weeks 8, 16 and 24.Discussion: The results of this trial will be available in 2021 and will help clarify whether acupuncture can be considered effective for the treatment of DPN with regard to the subdimensions of the neuropathic clinical picture.Trial registration: ClinicalTrials.gov: NCT03755960. Registered on November 19, 2018, https://clinicaltrials.gov/ct2/show/study/NCT03755960.
Comparative Analysis of Machine Learning Algorithms for Multi-Syndrome Classification of Neurodegenerative Syndromes
Authors: Lampe Leonie , Niehaus Sebastian , Huppertz Hans-Jürgen , Merola Alberto , Reinelt Janis , Mueller Karsten , Anderl-Straub Sarah , Fassbender Klaus , Fliessbach Klaus , Jahn Holger , Kornhuber Johannes , Lauer Martin , Prudlo Johannes , Schneider Anja , Synofzik Matthis , Danek Adrian , Diehl-Schmid Janine , Otto Markus , Villringer Arno , Egger Karl , Hattingen Elke , Hilker-Roggendorf Rüdiger , Schnitzler Alfons , Südmeyer Martin , Oertel Wolfgang , Kassubek Jan , Höglinger Günther , Schroeter Matthias L. 
Published: February 2021

Abstract
Importance The entry of artificial intelligence into medicine is pending. Several methods have been used for predictions of structured neuroimaging data, yet nobody compared them in this context.Objective Multi-class prediction is key for building computational aid systems for differential diagnosis. We compared support vector machine, random forest, gradient boosting, and deep feed-forward neural networks for the classification of different neurodegenerative syndromes based on structural magnetic resonance imaging.Design, Setting, and Participants Atlas-based volumetry was performed on multi-centric T1weighted MRI data from 940 subjects, i.e. 124 healthy controls and 816 patients with ten different neurodegenerative diseases, leading to a multi-diagnostic multi-class classification task with eleven different classes.Interventions n.a.Main Outcomes and Measures Cohen’s Kappa, Accuracy, and F1-score to assess model performance.Results Over all, the neural network produced both the best performance measures as well as the most robust results. The smaller classes however were better classified by either the ensemble learning methods or the support vector machine, while performance measures for small classes were comparatively low, as expected. Diseases with regionally specific and pronounced atrophy patterns were generally better classified than diseases with wide-spread and rather weak atrophy.Conclusions and Relevance Our study furthermore underlines the necessity of larger data sets but also calls for a careful consideration of different machine learning methods that can handle the type of data and the classification task best.Trial Registration n.a.
Timing and Causes of Death in Severe COVID-19 Patients
Authors: ROQUETAILLADE Charles de , Bredin Swann , Lascarrou Jean-Baptiste , Soumagne Thibaud , Cojocaru Mariana , Chousterman Benjamin Glenn , Leclerc Maxime , Gouhier Albin , Piton Gaël , Pène Frederic , Stoclin Annabelle , Llitjos Jean-François 
Published: February 2021

Abstract
Introduction: Although early identified as a deadly infectious disease, the precise mortality rate of the most severe forms of COVID-19 is a matter of debate. To the best of our knowledge, no study investigated so far, the causes that ultimately led to death as well as the relation between timing and causes of death.Material & Methods: We performed a retrospective study in eight ICU within eight French hospitals. All consecutive adult patients (aged≥ 18 years old) admitted in the ICU with a PCR-confirmed SARS-CoV-2 infection and acute respiratory failure were included in the analysis. Causes and timing of death were reported based on medical records. A binomial logistic regression statistical analysis was performed to identify the determinants associated with ICU-mortality.Results: From March 1st, 2020 to April 28th, 287 patients were admitted to ICU for SARS-CoV-2 related acute respiratory failure. COVID-19 related multiple organ dysfunction syndromes (MODS) was the leading cause of death (29%, n=27/93). End-of-life decisions occurred in 25% of patients (n=23/93). Secondary infections-related MODS accounted for 21% of ICU death, with a majority of ventilator-associated pneumonia. Fatal ischemic events (venous or arterial) occurred in 12% of patients. Refractory hypoxemia was a relatively uncommon cause of death and occurred only in 8 cases (9%). Regarding the timing of death, only one death occurred during the first three days of ICU admission. Determinants associated with ICU-mortality in logistic regression were age >65, requirement for vasopressors, renal replacement therapy and extracorporeal membrane oxygenation.Conclusion: Our data suggest the existence of a specific pattern of outcome in severe COVID-19 patients compared to severe bacterial and viral pneumonia, consisting in a high proportion of delayed COVID-19 related MODS.
A Phase Ii Dose Escalation Study of Intraarterial (Hepatic) Adult Human Bone Marrow Derived, Cultured, Pooled, Allogeneic Mesenchymal Stromal Cells&nbsp;(Stempeucel®) in Patients with Alcoholic Liver Cirrhosis
Authors: Gupta Pawan Kumar , Chullikana Anoop , Seetharam Raviraja N , Kolkundar Udaykumar , P Shivashankar , Viswanathan Pachaiyappan , Chandrashekar Mithun , Thej Charan , KV Prasanth , Abraham Jijy , Majumdar Anish Sen 
Published: February 2021

Abstract
Background: Alcoholic liver cirrhosis is an end stage alcoholic liver disease with poor prognosis. The definitive treatment of alcoholic liver cirrhosis is orthotopic liver transplantation, which is expensive, requires long term immunosuppression and is limited by supply of organs. Being an unmet medical need, cell therapy is under investigation for alcoholic liver cirrhosis.Aims: This study was designed primarily for assessing the safety and feasibility of administering stempeucel® through the hepatic artery in alcoholic liver cirrhosis and secondarily to assess possible efficacy and dose-response.Methods: Forty patients with alcoholic cirrhosis (18-65 years/Child Pugh class B or C/Model for End-Stage Liver Disease score of minimum 10) were included in 4 groups: 2.5 million cells/kg Body Weight (2.5M Cell) and respective control (2.5M Control); 5 million cells/kg Body Weight (5M Cell) and respective control (5M Control) with 10 patients in each group. Cell groups received stempeucel® administered via hepatic artery by catheterization through femoral artery (Seldinger technique) and Standard Protocol of Care. Control group received Standard Protocol of Care. Patients were followed up at 1 week, 1 month, 3 months and 6 months. Safety evaluations included clinical examination, Electrocardiogram and laboratory investigations. Efficacy evaluations included liver function test, Model for End-Stage Liver Disease score, Child Pugh score, Short Form-36 questionnaire, liver stiffness using Fibroscan (Transient Elastography), and liver volume using Computerized Tomography scan. Results: stempeucel® injection was well tolerated. Common treatment emergent adverse events were in Gastrointestinal disorders, General disorders and administration site conditions and Infections and infestations. Most of the treatment emergent adverse events were unrelated / remotely related to stempeucel®. Thirty serious adverse events occurred in 10 patients (3 in 2.5M Cell, 5 in 5M Cell and one each in control groups). Three patients died due to SAEs: Two in 2.5M and one in 5M Cell group, none were related to stempeucel®. There was no significant difference in efficacy evaluations at 6 months versus baseline compared to control at both the dose levels of stempeucel®. Statistically significant improvement was seen in 2.5M group compared to control group in Short Form-36 score: bodily pain, mental component summary, vitality and social functioning. Conclusions: stempeucel® was safe, well tolerated and subjective improvement in few component scores of Short Form-36 was seen 2.5M cell group.Trial registrationClinicaltrials.gov: NCT01591200Clinical Trial Registry – India: CTRI/2009/091/000432
Analysis and construction of a competitive endogenous RNA regulatory network of baicalin-induced apoptosis in human osteosarcoma cells
Authors: Lan Haifeng , Wang Haiyan , Gao Mi , Zhang Jiahuan , Yi Erkang , Liang Chunxiao , Xiong Xiaoxiao , Chen Xing , Wu Qinghua , Chen Ruikun , Hong Wei 
Published: February 2021

Abstract
Background: Baicalin is an extract from the traditional Chinese herb Scutellaria baicalensis. Recent literature reports have shown the potential of baicalin to treat osteosarcoma (OS). However, the transcriptome-level mechanism of baicalin-mediated antitumor effects in OS has not yet been investigated. The aim of this study was to analyze the competitive endogenous RNA (ceRNA) regulatory network involved in baicalin-induced apoptosis of OS cells.Methods: In this study, CCK-8 and flow cytometry assays were used to detect the antitumor effects of baicalin on human OS MG63 cells. Furthermore, whole transcriptome sequencing was employed to establish the long noncoding RNA (lncRNA), microRNA (miRNA), and mRNA profiles.Results: Baicalin could inhibit MG63 cell proliferation and could induce apoptosis. A total of 58 lncRNAs, 31 miRNAs, and 2136 mRNAs in the baicalin-treated MG63 cells were identified as differentially expressed RNAs compared to those in control cells. Of those, 2 lncRNAs, 3 miRNAs, and 18 mRNAs were included in the ceRNA regulatory network. The differentially expressed RNAs were confirmed by using quantitative real-time PCR (qRT-PCR). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to identify the target genes and signaling pathways of the aberrantly expressed RNAs.Conclusions: By identifying the ceRNA network, our results provide new information about the possible molecular basis of baicalin, which might be able to be used for OS treatment.
Evaluation of Animal Welfare in Extensive and Intensive Dairy Farms and its Correlation to Infectious Diseases
Authors: Licitra Francesca , Perillo Laura , Antoci Francesco , Piccione Giuseppe , Giannetto Claudia , Salonia Rosario , Monteverde Vincenzo , Cascone Giuseppe 
Published: February 2021

Abstract
Background: The welfare of farm animals has become a growing concern in recent years. This is the first study that assesses dairy cow welfare by the application of an impartial, reproducible, functional check-list based on risk analysis which provides a numerical animal welfare index to each farm. We tested the effect of two different management conditions, housing with free access to pasture and indoor housing, on dairy cows kept in 36 farms and concurrently carried out the evaluation of different infectious diseases. Animal welfare assessment was performed in each farm through the CReNBA’s check-list. Moreover, the prevalence of Mycobacterium avium subsp. paratuberculosis, Chlamydiophila abortus, Neospora caninum, Bovine Herpesvirus specific antibodies IgB and IgE and of Bovine Viral Diarrhea virus was tested in each farm through ELISA serological test. Unpaired t-Test was applied to assess differences among the two experimental groups; while, Kendall’s Tau coefficient (T) was determined to investigate the relationship between studied variables. Results: The result of each area provided an indication of the burden and importance of each of these on the final calculation of the animal welfare value. ELISA was positive with different mean percentages. The application of Unpaired t-Test showed that the intensive housing system group had higher percentages of Bovine Herpesvirus specific antibodies IgB compared to the extensive housing system group. The application of Kendall’s Tau coefficient showed different correlations between studied areas and infectious diseases. Conclusion: The hazard analysis areas assessed farming and management conditions of the farms, but these may have different effects as they are regulated by the animals’ ability to adapt and are therefore less important when establishing the final welfare value. Those farms with the highest prevalence of infections could be associated with a poor level of business management. A proper evaluation of the problems encountered and a more accurate application of the check-list could prevent and control the spread of infections in farms. Our results want to be a contribution in breeding which provides farmers and veterinarians the instruments to improve animal welfare and farm business performance.
Deep Learning Model for Screening Sepsis Using Electrocardiography
Authors: Kwon Joon-myoung , Lee Ye Rang , Jung Min-Seung , Lee Yoon-Ji , Jo Yong-Yeon , Kang Da-Young , Lee Soo Youn , Cho Yong-Hyeon , Shin Jae-Hyun , Ban Jang-Hyeon , Kim Kyung-Hee 
Published: February 2021

Abstract
Background: Sepsis is a life-threatening organ dysfunction and is a major healthcare burden worldwide. Although sepsis is a medical emergency that requires immediate management, it is difficult to screen the occurrence of sepsis. In this study, we propose an artificial intelligence based on deep learning-based model (DLM) for screening sepsis using electrocardiography (ECG).Methods: This retrospective cohort study included 46,017 patients who admitted to two hospitals. 1,548 and 639 patients underwent sepsis and septic shock. The DLM was developed using 73,727 ECGs of 18,142 patients and internal validation was conducted using 7,774 ECGs of 7,774 patients. Furthermore, we conducted an external validation with 20,101 ECGs of 20,101 patients from another hospital to verify the applicability of the DLM across centers.Results: During the internal and external validation, the area under the receiver operating characteristic curve (AUC) of an DLM using 12-lead ECG for screening sepsis were 0.901 (95% confidence interval 0.882–0.920) and 0.863 (0.846–0.879), respectively. During internal and external validation, AUC of an DLM for detecting septic shock were 0.906 (95% CI = 0.877–0.936) and 0.899 (95% CI = 0.872–0.925), respectively. The AUC of the DLM for detecting sepsis using 6-lead and single-lead ECGs were 0.845–0.882. A sensitivity map showed that the QRS complex and T wave was associated with sepsis. Subgroup analysis was conducted using ECGs from 4,609 patients who admitted with infectious disease, The AUC of the DLM for predicting in-hospital mortality was 0.817 (0.793–0.840). There was a significant difference in the prediction score of DLM using ECG according to the presence of infection in the validation dataset (0.277 vs 0.574, p<0.001), including severe acute respiratory syndrome coronavirus 2 (0.260 vs 0.725, p=0.018).Conclusions: The DLM demonstrated reasonable performance for screening sepsis using 12-, 6-, and single-lead ECG. The results suggest that sepsis can be screened using not only conventional ECG devices, but also diverse life-type ECG machine employing the DLM, thereby preventing irreversible disease progression and mortality.
Genetics components of rice root architecture and carbon isotopic fractionation parameters: a tracer for breeding in a water saving irrigation management
Authors: Brito Giovani , Concenço Germani , Costa Vladimir Eleodoro , Fagundes Paulo Ricardo Reis , Silva-Filho João Luis , Parfitt José Maria Barbat , Magalhães Ariano , Silva Giovana Tavares , Jardim Thaís Murias , Luccas Nathália Furtado , Scivittaro Walkyria Bueno 
Published: February 2021

Abstract
Phenotyping is the major bottleneck in the effort to develop varieties of rice (Oryza sativa L) suitable for grow under a water saving irrigation management, such as alternate wetting and drying irrigation technique (AWD). In order to analyze if the genotypic variability for carbon isotope discrimination (CID) in rice leaves could be used as a relatively high-throughput tracer to early select superior genotypes highlighting improved root architecture traits when submitted to AWD, a set of twenty varieties grown under semi-natural conditions were submitted to two water irrigation regimes, continuous flooding (CF) and AWD cycles. Coefficients of genetic variance (π2g) obtained for root architecture, micro-morphological and physiological traits were significant for all of them regardless of adopted irrigation system, except to mean root diameter. The three significant principal components (PCs) with eigenvalue > 1, explain the most of the total variation across cycles and water regimes. For most of analyzed traits, the values of heritability coefficients were higher regardless of adopted irrigation management and trait category; for CID, the magnitudes of broad heritability at individual level (greater than 0.80) were similar in the two irrigation techniques, evidencing that the success of selection is independent of irrigation management. The higher CID values after three AWD cycles are associated to varieties with higher total root length and volume. To our knowledge, this is the first study demonstrating the potential application of CID as tracer to select root architecture traits in rice when water-saving irrigation management are of concern.
Novel Urinary Glycan Biomarkers Predict Cardiovascular Events in Patients with Type 2 Diabetes: A Multicenter Prospective Study with 5-year Follow up (U-CARE Study 2)
Authors: Mise Koki , Imamura Mariko , Yamaguchi Satoshi , Watanabe Mayu , Higuchi Chigusa , Katayama Akihiro , Miyamoto Satoshi , Uchida Haruhito A. , Nakatsuka Atsuko , Eguchi Jun , Hida Kazuyuki , Nakato Tatsuaki , Tone Atsuhito , Teshigawara Sanae , Matsuoka Takashi , Kamei Shinji , Murakami Kazutoshi , Shimizu Ikki , Miyashita Katsuhiro , Ando Shinichiro , Nunoue Tomokazu , Yoshida Michihiro , Yamada Masao , Shikata Kenichi , Wada Jun 
Published: February 2021

Abstract
BackgroundAlthough various biomarkers predict cardiovascular event (CVE) in patients with diabetes, the relationship of urinary glycan profile with CVE in patients with diabetes remains unclear.MethodsAmong 680 patients with type 2 diabetes, we examined the baseline urinary glycan signals binding to 45 lectins with different specificities. Primary outcome was defined as cardiovascular event including cardiovascular disease, stroke, and peripheral arterial disease.ResultsDuring approximately 5-year follow-up period, 62 patients reached the endpoint. Cox proportional hazards analysis revealed that urinary glycan signals binding to two lectins were significantly associated with the outcome after adjustment for known indicators of CVE and for false discovery rate, as well as increased model fitness. Hazard ratios for these lectins (+ 1 SD for the glycan index) were: UDA (recognizing glycan: mixture of Man5 to Man9): 1.78 (95% CI: 1.24–2.55, P = 0.002) and Calsepa (High-Man [Man2-6]): 1.56 (1.19–2.04, P = 0.001). Common glycan binding to these lectins was high-mannose type of N-glycans. Moreover, adding glycan index for UDA to a model including known factors of CVE improved the outcome prediction (Difference of Harrel’s C-index: 0.028 [95% CI: 0.001–0.055, P = 0.044], net reclassification improvement at 5-year risk increased by 0.368 [0.045–0.692, P = 0.026], and the Akaike information criterion and Bayesian information criterion decreased from 725.7 to 716.5, and 761.8 to 757.2, respectively).ConclusionsThe urinary excretion of high-mannose glycan may be a valuable marker for predicting CVE in patients with type 2 diabetes, which provide the rationale to explore the mechanism underlying abnormal N-glycosylation in CVE of patients with diabetes.Trial registrationThis study was registered with the University Hospital Medical Information Network in June 26th 2012 (Clinical trial number: UMIN000011525, URL: https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000013482).
Role of Tumor Mutation Burden-related Signatures in the Prognosis and Immune Microenvironment of Pancreatic Ductal Adenocarcinoma
Authors: Tang Rong , Liu Xiaomeng , Wang Wei , Hua Jie , Xu Jin , Liang Chen , Meng Qingcai , Liu Jiang , Zhang Bo , Yu Xianjun , Shi Si 
Published: February 2021

Abstract
Background High tumor mutation burden (TMB) has gradually become a sensitive biomarker for predicting the response to immunotherapy in many cancers, including lung, bladder and head and neck cancers. Nonetheless, whether high TMB could predict the response to immunotherapy and prognosis in pancreatic ductal adenocarcinoma (PDAC), a classic “cold” tumor, remained obscure. Hence, it is significant to investigate the role of genes related to TMB (TRGs) in PDAC.Methods The transcriptome and mutation data of PDAC was downloaded from The Cancer Genome Atlas-Pancreatic Adenocarcinoma (TCGA). Five independent external datasets of PDAC were chosen to validate parts of our results. qRT-PCR and immunohistochemical staining were also performed to promote the reliability of this study. Results The median overall survival (OS) was significantly increased in TMB_low group compared with the counterpart with higher TMB score after tumor purity adjusted (P = 0.03). 718 differentially expressed TRGs were identified and functionally enriched in some oncogenic pathways. 67 TRGs were associated with OS in PDAC. A prognostic model for the OS was constructed and showed a high predictive accuracy (AUC = 0.849). We also found TMB score was associated with multiple immune components and signatures in tumor microenvironment. In addition, we identified a PDAC subgroup featured with TMBlowMSIhigh was associated with prolonged OS and a key molecule, ANKRD55, potentially mediating the survival benefits.Conclusion This study analyzed the biological function, prognosis value, implications for mutation landscape and potential influence on immune microenvironment of TRGs in PDAC, which contributed to get aware of the role of TMB in PDAC. Future studies are expected to investigate how these TRGs regulate the initiation, development or repression of PDAC.