bluuebunny/crossref_metadata_2025_split huggingface dataset:
Intermediate-Intensity Autologous Hematopoietic Stem Cell Transplantation Reduces Serum Neurofilament Light Chains and Brain Atrophy in Aggressive Multiple Sclerosis
Authors: Mariottini Alice , Marchi Leonardo , Innocenti Chiara , Di Cristinzi Maria , Pasca Matteo , Filippini Stefano , Barilaro Alessandro , Mechi Claudia , Fani Arianna , Mazzanti Benedetta , Biagioli Tiziana , Materozzi Francesca , Saccardi Riccardo , Massacesi Luca , Repice Anna Maria 
Published: February 2022

BackgroundAutologous haematopoietic stem cell transplantation (AHSCT) is highly effective in reducing new inflammatory activity in aggressive multiple sclerosis (MS). A remarkable decrease of serum neurofilament light chains (sNfL) concentration, a marker of axonal damage, was reported in MS following high-intensity regimen AHSCT, but hints for potential neurotoxicity had emerged. sNfL and brain atrophy were therefore analysed in a cohort of patients with aggressive MS treated with intermediate-intensity AHSCT, exploring whether sNfL might be a reliable marker of disability progression independent from new inflammation (i.e. relapses and/or new/gadolinium-enhancing MRI focal lesions).MethodssNfL concentrations were measured using SIMOA methodology in peripheral blood from relapsing-remitting (RR-) or secondary-progressive (SP-) MS patients undergoing AHSCT (MS AHSCT), collected before transplant and at months 6 and 24 following the procedure. sNfL measured at a single timepoint in SP-MS patients not treated with AHSCT without recent inflammatory activity (SP-MS CTRL) and healthy subjects (HD) were used as controls. The rate of brain volume loss (AR-BVL) was also evaluated by MRI in MS AHSCT cases.ResultsThirty-eight MS AHSCT (28 RR-MS; 10 SP-MS), 22 SP-MS CTRL and 19 HD were included. Baseline median sNfL concentrations were remarkably higher in the MS AHSCT than in the SP-MS CTRL and HD groups (p = 0.005 and &lt;0.0001, respectively), and levels correlated with recent inflammatory activity. After a marginal (not significant) median increase observed at month 6, at month 24 following AHSCT sNfL concentrations decreased compared to baseline by median 42.8 pg/mL (range 2.4–217.3; p = 0.039), reducing by at least 50% in 13 cases, and did not differ from SP-MS CTRL (p = 0.110) but were still higher than in HD (p &lt; 0.0001). Post-AHSCT AR-BVL normalised in 55% of RR-MS and in 30% of SP-MS. The effectiveness and safety of AHSCT were aligned with the literature.ConclusionsNfL concentrations correlated with recent inflammatory activity and were massively and persistently reduced by intermediate-intensity AHSCT. Association with response to treatment assessed by clinical or MRI outcomes was not observed, suggesting a good sensitivity of sNfL for recent inflammatory activity but low sensitivity in detecting ongoing axonal damage independent from new focal inflammation.
Core Sets of Kinematic Variables to Consider for Evaluation of Gait Post-stroke
Authors: Nedergård Heidi , Schelin Lina , Liebermann Dario G. , Johansson Gudrun M. , Häger Charlotte K. 
Published: February 2022

BackgroundInstrumented gait analysis post-stroke is becoming increasingly more common in research and clinics. Although overall standardized procedures are proposed, an almost infinite number of potential variables for kinematic analysis is generated and there remains a lack of consensus regarding which are the most important for sufficient evaluation. The current aim was to identify a discriminative core set of kinematic variables for gait post-stroke.MethodsWe applied a three-step process of statistical analysis on commonly used kinematic gait variables comprising the whole body, derived from 3D motion data on 31 persons post-stroke and 41 non-disabled controls. The process of identifying relevant core sets involved: (1) exclusion of variables for which there were no significant group differences; (2) systematic investigation of one, or combinations of either two, three, or four significant variables whereby each core set was evaluated using a leave-one-out cross-validation combined with logistic regression to estimate a misclassification rate (MR).ResultsThe best MR for one single variable was shown for theDuration of single-support(MR 0.10) orDuration of 2nd double-support(MR 0.11)phase, corresponding to an 89–90% probability of correctly classifying a person as post-stroke/control. AddingPelvissagittal ROMto either of the variablesSelf-selected gait speedorStride length, alternatively addingAnkle sagittal ROMto theDuration of single-stance phase, increased the probability of correctly classifying individuals to 93–94% (MR 0.06). Combining three variables decreased the MR further to 0.04, suggesting a probability of 96% for correct classification. These core sets contained: (1) a spatial (Stride/Step length) or a temporal variable (Self-selected gait speed/Stance time/Swing timeorDuration of 2nd double-support), (2)Pelvissagittal ROMorAnkle plantarflexion during push-off, and (3)Arm Posture ScoreorCadenceor a knee/shoulder joint angle variable. Adding a fourth variable did not further improve the MR.ConclusionA core set combining a few crucial kinematic variables may sufficiently evaluate post-stroke gait and should receive more attention in rehabilitation. Our results may contribute toward a consensus on gait evaluation post-stroke, which could substantially facilitate future diagnosis and monitoring of rehabilitation progress.
Neural Correlates of Attachment Representation in Patients With Borderline Personality Disorder Using a Personalized Functional Magnet Resonance Imaging Task
Authors: Bernheim Dorothee , Buchheim Anna , Domin Martin , Mentel Renate , Lotze Martin 
Published: February 2022

BackgroundFear of abandonment and aloneness play a key role in the clinical understanding interpersonal and attachment-specific problems in patients with borderline personality disorder (BPD) and has been investigated in previous functional Magnet Resonance Imaging (fMRI) studies. The aim of the present study was to examine how different aspects of attachment representations are processed in BPD, by using for the first time an fMRI attachment paradigm including personalized core sentences from the participants’ own attachment stories. We hypothesized that BPD patients would show increased functional involvement of limbic brain regions associated with fear and pain (e.g., the amygdala and the anterior cingulate cortex) when presented personalized attachment relevant stimuli representing loneliness compared to healthy controls (HC).MethodsWe examined the attachment classifications of 26 female BPD patients and 26 female HC using the Adult Attachment Projective Picture System (AAP). We used an fMRI-adapted attachment paradigm to investigate the neural correlates of attachment. All participants were presented three personalized (vs. neutral) sentences extracted from their AAP attachment narrative, combined with standardized AAP pictures representing being alone (monadic) or in interactive (dyadic) attachment situations.ResultsAs expected, the classification of unresolved attachment was significantly greater in BPD compared to HC. BPD patients showed increased fMRI-activation in brain areas associated with fear, pain, and hyperarousal than HC when presented with personalized attachment-relevant alone stimuli. In particular, pictures with monadic attachment situations induced greater anterior medial cingulate cortex, anterior insula, amygdala, thalamus and superior temporal gyrus activation in the patient group.ConclusionThe results point to increased fMRI-activation in areas processing emotional distress and painful experiences in BPD patients. In particular, the emotional cascade reflecting attachment distress was evoked by combining monadic pictures, representing abandonment and aloneness, with the patients’ personalized narrative material. Our results confirmed and replicated previous results that illustrate once again the high relevance of aloneness and feelings of abandonment for BPD in the context of attachment trauma. Moreover, our results support the hypothesis of hypermentalization in response to attachment distress as a core feature of social-cognitive impairment in BPD associated with common treatment implications across different therapeutic orientations.
Executive Performance Is Associated With Rest-Activity Rhythm in Nurses Working Rotating Shifts
Authors: Zhao Xiuli , Tang Quan , Feng Zhengzhi , Zhào Hóngyi 
Published: February 2022

PurposeRest-activity rhythms (RAR) is one of the most fundamental biological patterns that influence basic physical and mental health, as well as working performance. Nowadays, with the utilization of actigraphy device in detecting and analyzing RAR objectively, evidence has confirmed that RAR could be interrupted by shift work. Nurses need to participate shift work in their daily routine. The aim of the present study is to identify the RAR of nurses working rotating shifts and find out the relationship between RAR and executive function.MethodsA total of 11 female nurses on day-shift (DS), 11 female nurses on rotating-shift (RS) were recruited. Demographic features, non-parametric variables of RAR as well as sleep variables according to actigraphy data, and a series of cognitive function evaluations during work time were collected.ResultsThe Student’s t-test comparison revealed that RS group nurses showed significant shorter total sleep time (TST), lower level of interdaily stability (IS) and relative amplitude (RA), as well as poorer executive performance compared with DS nurses. The linear regression analysis demonstrated that several aspects of executive performance such as choice reaction test (CRT) (reflecting attention and concentration) and trail making test (TMT) -B (reflecting cognitive flexibility) were associated with IS in RS group nurses.ConclusionThese results elucidated that RAR was disrupted for nurses working rotating shifts, and executive performance was associated with IS from day to day in nurses working rotating shifts.
A Comprehensive Risk Assessment and Stratification Model of Papillary Thyroid Carcinoma Based on the Autophagy-Related LncRNAs
Authors: Mu Yongrun , Song Fuling , Yuan Kai , Zhang Zili , Lu Yan , Fu Rongzhan , Zhou Dongsheng 
Published: February 2022

BackgroundPapillary thyroid carcinoma (PTC) is one of the most common malignant carcinomas in the endocrine system, and it has a growing incidence worldwide. Despite the development of diagnosis and treatment modalities for thyroid carcinoma, the outcome remains uncertain. Autophagy participates in the process of cancer invasion, malignancy, metastasis, and drug resistance. Emerging research has shown that long noncoding RNAs (lncRNAs) play an important role in the process of different types of cancers. However, the interaction between the process of autophagy and lncRNA and the value of autophagy-related lncRNA for risk assessment, prediction of drug sensitivity, and prognosis prediction in PTC patients remains unknown.Materials and MethodsWe screened 1,283 autophagy-related lncRNAs and identified 144 lncRNAs with prognostic value in The Cancer Genome Atlas (TCGA) cohorts. Univariate and multivariate Cox regression analyses were used to establish the prognosis-related autophagy-related lncRNA risk classification consisting of 10 lncRNAs to indicate the level of risk, according to which the patients were grouped into high-risk group and low risk-group.ResultsThe high-risk group had dramatically worse overall survival compared with the low-risk group. Cox regression analysis was performed to confirm the independent prognostic value of the autophagy-related lncRNA risk stratification, and the time-dependent receiver operating characteristic curves of the risk stratification were 0.981 (1 year), 0.906 (3 years), and 0.963 (5 years). LncRNA CRNDE (LINC00180) is overexpressed in the tumor, and its high expression matched with poorer survival state. So, we chose it for further experiment. Finally, knockdown of the CRNDE in PTC increased the sensitivity to sorafenib.ConclusionCollectively, we successfully established a novel risk stratification for PTC based on the expression profiles of autophagy-related lncRNAs.
AKR1B10 as a Potential Novel Serum Biomarker for Breast Cancer: A Pilot Study
Authors: Cao Zhe , Delfino Kristin , Tiwari Vivek , Wang Xin , Hannan Abdul , Zaidi Fawwad , McClintock Andrew , Robinson Kathy , Zhu Yun , Gao John , Cao Deliang , Rao Krishna 
Published: February 2022

BackgroundAldo-keto reductase 1B10 (AKR1B10) is a secretory protein that is upregulated in breast cancer.ObjectiveThis case-controlled pilot study evaluated the serum level of AKR1B10 in healthy women and patients with a localized or metastatic breast cancer.MethodsAKR1B10 levels were measured by ELISA and IHC in several patient cohorts.ResultsOur data showed that serum AKR1B10 was significantly elevated in patients with localized (6.72 ± 0.92 ng/ml) or metastatic (7.79 ± 1.13 ng/ml) disease compared to cancer-free healthy women (1.69 ± 0.17 ng/ml) (p&lt;0.001); the serum AKR1B10 was correlated with its expression in tumor tissues, but not with the tumor burden, molecular subtypes or histological stages. After surgical removal of primary tumors, the serum AKR1B10 was rapidly decreased within 3 days and plateaued at a level similar to that of healthy controls in most patients. ROC curve analysis suggested the optimal diagnostic cut-off value of serum AKR1B10 at 3.456 ng/ml with AUC 0.9045 ± 0.0337 (95% CI 0.8384 – 0.9706), sensitivity 84.75% (95% CI 73.01% to 92.78%), and specificity 93.88% (95% CI 83.13% to 98.72%).ConclusionsThese data indicate the potential value of serum AKR1B10 as a biomarker of breast cancer.
Machine Learning-Based Radiological Features and Diagnostic Predictive Model of Xanthogranulomatous Cholecystitis
Authors: Zhou Qiao-Mei , Liu Chuan-Xian , Zhou Jia-Ping , Yu Jie-Ni , Wang You , Wang Xiao-Jie , Xu Jian-Xia , Yu Ri-Sheng 
Published: February 2022

BackgroundXanthogranulomatous cholecystitis (XGC) is a rare benign chronic inflammatory disease of the gallbladder that is sometimes indistinguishable from gallbladder cancer (GBC), thereby affecting the decision of the choice of treatment. Thus, this study aimed to analyse the radiological characteristics of XGC and GBC to establish a diagnostic prediction model for differential diagnosis and clinical decision-making.MethodsWe investigated radiological characteristics confirmed by the RandomForest and Logistic regression to establish computed tomography (CT), magnetic resonance imaging (MRI), CT/MRI models and diagnostic prediction model, and performed receiver operating characteristic curve (ROC) analysis to prove the effectiveness of the diagnostic prediction model.ResultsBased on the optimal features confirmed by the RandomForest method, the mean area under the curve (AUC) of the ROC of the CT and MRI models was 0.817 (mean accuracy = 0.837) and 0.839 (mean accuracy = 0.842), respectively, whereas the CT/MRI model had a considerable predictive performance with the mean AUC of 0.897 (mean accuracy = 0.906). The diagnostic prediction model established for the convenience of clinical application was similar to the CT/MRI model with the mean AUC and accuracy of 0.888 and 0.898, respectively, indicating a preferable diagnostic efficiency in distinguishing XGC from GBC.ConclusionsThe diagnostic prediction model showed good diagnostic accuracy for the preoperative discrimination of XGC and GBC, which might aid in clinical decision-making.
Second Primary Lung Adenocarcinoma After Intensity-Modulated Radiotherapy for Nasopharyngeal Carcinoma
Authors: Xue Fen , Niu Xiaoshuang , Hu Chaosu , He Xiayun 
Published: February 2022

ObjectiveThe improvement of the efficacy of intensity-modulated radiotherapy (IMRT) for nasopharyngeal cancer (NPC) has prolonged the survival of patients, and the incidence of the second tumor has gradually increased. Among them, second primary lung adenocarcinoma (SPLAC) attributes the highest incidence. This study aimed to determine the long-term risk of SPLAC in NPC patients after IMRT.MethodsFrom May 2005 to May 2018, a total of 1,102 non-metastatic NPC patients who received IMRT in our hospital were enrolled, and the incidence and efficacy of SPLAC were followed up in the long term.ResultsOver a median follow-up period of 66 months, a total of 22 cases of SPLAC were observed, with an incidence of 2.0%. The 1-, 2-, 3-, 4-, and 5-year cumulative risks of SPLAC were 0.4%, 0.7%, 0.8%, 1.1%, and 1.7%, respectively. During follow-up, 90.9% (20/22) of the SPLAC detected was in early stage, and the recurrence rate of surgery alone was 5.3% (1/19).ConclusionIn NPC patients, the proportion of SPLAC after IMRT was similar to that of the normal population, and most of them were found in early stage during follow-up, with good surgical efficacy.
A Deep Learning Model for Preoperative Differentiation of Glioblastoma, Brain Metastasis and Primary Central Nervous System Lymphoma: A Pilot Study
Authors: Tariciotti Leonardo , Caccavella Valerio M. , Fiore Giorgio , Schisano Luigi , Carrabba Giorgio , Borsa Stefano , Giordano Martina , Palmisciano Paolo , Remoli Giulia , Remore Luigi Gianmaria , Pluderi Mauro , Caroli Manuela , Conte Giorgio , Triulzi Fabio , Locatelli Marco , Bertani Giulio 
Published: February 2022

BackgroundNeuroimaging differentiation of glioblastoma, primary central nervous system lymphoma (PCNSL) and solitary brain metastasis (BM) remains challenging in specific cases showing similar appearances or atypical features. Overall, advanced MRI protocols have high diagnostic reliability, but their limited worldwide availability, coupled with the overlapping of specific neuroimaging features among tumor subgroups, represent significant drawbacks and entail disparities in the planning and management of these oncological patients.ObjectiveTo evaluate the classification performance metrics of a deep learning algorithm trained on T1-weighted gadolinium-enhanced (T1Gd) MRI scans of glioblastomas, atypical PCNSLs and BMs.Materials and MethodsWe enrolled 121 patients (glioblastoma: n=47; PCNSL: n=37; BM: n=37) who had undergone preoperative T1Gd-MRI and histopathological confirmation. Each lesion was segmented, and all ROIs were exported in a DICOM dataset. The patient cohort was then split in a training and hold-out test sets following a 70/30 ratio. A Resnet101 model, a deep neural network (DNN), was trained on the training set and validated on the hold-out test set to differentiate glioblastomas, PCNSLs and BMs on T1Gd-MRI scans.ResultsThe DNN achieved optimal classification performance in distinguishing PCNSLs (AUC: 0.98; 95%CI: 0.95 - 1.00) and glioblastomas (AUC: 0.90; 95%CI: 0.81 - 0.97) and moderate ability in differentiating BMs (AUC: 0.81; 95%CI: 0.70 - 0.95). This performance may allow clinicians to correctly identify patients eligible for lesion biopsy or surgical resection.ConclusionWe trained and internally validated a deep learning model able to reliably differentiate ambiguous cases of PCNSLs, glioblastoma and BMs by means of T1Gd-MRI. The proposed predictive model may provide a low-cost, easily-accessible and high-speed decision-making support for eligibility to diagnostic brain biopsy or maximal tumor resection in atypical cases.
Multimodal MRI Image Decision Fusion-Based Network for Glioma Classification
Authors: Guo Shunchao , Wang Lihui , Chen Qijian , Wang Li , Zhang Jian , Zhu Yuemin 
Published: February 2022

PurposeGlioma is the most common primary brain tumor, with varying degrees of aggressiveness and prognosis. Accurate glioma classification is very important for treatment planning and prognosis prediction. The main purpose of this study is to design a novel effective algorithm for further improving the performance of glioma subtype classification using multimodal MRI images.MethodMRI images of four modalities for 221 glioma patients were collected from Computational Precision Medicine: Radiology-Pathology 2020 challenge, including T1, T2, T1ce, and fluid-attenuated inversion recovery (FLAIR) MRI images, to classify astrocytoma, oligodendroglioma, and glioblastoma. We proposed a multimodal MRI image decision fusion-based network for improving the glioma classification accuracy. First, the MRI images of each modality were input into a pre-trained tumor segmentation model to delineate the regions of tumor lesions. Then, the whole tumor regions were centrally clipped from original MRI images followed by max–min normalization. Subsequently, a deep learning-based network was designed based on a unified DenseNet structure, which extracts features through a series of dense blocks. After that, two fully connected layers were used to map the features into three glioma subtypes. During the training stage, we used the images of each modality after tumor segmentation to train the network to obtain its best accuracy on our testing set. During the inferring stage, a linear weighted module based on a decision fusion strategy was applied to assemble the predicted probabilities of the pre-trained models obtained in the training stage. Finally, the performance of our method was evaluated in terms of accuracy, area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), etc.ResultsThe proposed method achieved an accuracy of 0.878, an AUC of 0.902, a sensitivity of 0.772, a specificity of 0.930, a PPV of 0.862, an NPV of 0.949, and a Cohen’s Kappa of 0.773, which showed a significantly higher performance than existing state-of-the-art methods.ConclusionCompared with current studies, this study demonstrated the effectiveness and superiority in the overall performance of our proposed multimodal MRI image decision fusion-based network method for glioma subtype classification, which would be of enormous potential value in clinical practice.