bluuebunny/crossref_metadata_2025_split huggingface dataset:
A comprehensive nomogram for assessing the prognosis of non-small cell lung cancer patients receiving immunotherapy: a prospective cohort study in China
Authors: Li Hongmei , Yuan Yuliang , Xu Qianjie , Liang Guangzhong , Hu Zuhai , Li Xiaosheng , Zhang Wei , Lei Haike 
Published: November 2024

ObjectiveIn China, lung cancer ranks first in both incidence and mortality among all malignant tumors. Non-small cell lung cancer (NSCLC) constitutes the vast majority of cases, accounting for 80% to 85% of cases. Immune checkpoint inhibitors (ICIs), either as monotherapies or combined with other treatments, have become the standard first-line therapy for NSCLC patients. This study aimed to establish a nomogram model for NSCLC patients receiving immunotherapy incorporating demographic information, clinical characteristics, and laboratory indicators.MethodsFrom January 1, 2019, to December 31, 2022, a prospective longitudinal cohort study involving 1321 patients with NSCLC undergoing immunotherapy was conducted at Chongqing University Cancer Hospital. Clinical and pathological characteristics, as well as follow-up data, were collected and analyzed. To explore prognostic factors affecting overall survival (OS), a Cox regression model was used to test the significance of various variables. Independent prognostic indicators were identified through multivariate analysis and then used to construct a nomogram prediction model. To validate the accuracy and practicality of this model, the concordance index (C-index), area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) were used to assess the predictive performance of the nomogram.ResultIn the final model, 11 variables from the training cohort were identified as independent risk factors for patients with NSCLC: age, KPS score, BMI, diabetes, targeted therapy, Hb, WBC, LDH, CRP, PLR, and LMR. The C-index for OS in the training cohort was 0.717 (95% CI, 0.689–0.745) and 0.704 (95% CI, 0.660–0.750) in the validation cohort. Calibration curves for survival probability showed good concordance between the nomogram predictions and actual observations. The AUCs for 1-year, 2-year, and 3-year OS in the training cohort were 0.724, 0.764, and 0.79, respectively, and 0.725, 0.736, and 0.818 in the validation cohort. DCA demonstrated that the nomogram model had a greater overall net benefit.ConclusionA prognostic model for OS in NSCLC patients receiving immunotherapy was established, providing a simple and reliable tool for predicting patient survival (https://icisnsclc.shinyapps.io/DynNomapp/). This model offers valuable guidance for clinicians in making treatment decisions and recommendations.
HLA class, calcineurin inhibitor levels, and the risk of graft failure in kidney recipients with de novo donor-specific antibodies
Authors: Béland Marc-Antoine , Lapointe Isabelle , Côté Isabelle , Lesage Julie , Houde Isabelle , Wagner Eric , Riopel Julie , Latulippe Eva , Désy Olivier , De Serres Sacha A. 
Published: November 2024

IntroductionDe novo donor-specific HLA antibody (dnDSA) are associated with poor outcomes. Whether this observation applies to both HLA class I and II dnDSA remains unclear.MethodsWe studied 1236 consecutive kidney recipients who had routine anti-HLA antibody surveillance post-transplant.ResultsDuring the screening period, 55/1236 (4.4%) patients developed dnDSA: 18 (33%) HLA-I only, 33 (60%) HLA-II only, and 4 (7%) both classes. Thirty patients experienced graft loss at a median of 39 months after dnDSA detection: 9/18 (50%) HLA-I only, 17/33 (52%) HLA-II only, and 4/4 (100%) both classes. A control group was created by matching patients with dnDSA to patients who did not develop DSA and had a functioning graft at the time of dnDSA detection in their respective cases. Compared with these controls, the risk estimates of graft loss were similar between patients with HLA-I only and HLA-II only dnDSA (aHR [95% CI] 2.7 [1.1-6.6], p=0.04 and 3.1 [1.5-6.6], p&lt;0.01 respectively). Additionally, the risk of graft loss decreased with increasing CNI trough levels following dnDSA detection (aHR 0.7 [0.6-0.9] for each increase in 1 ng/mL, p=0.02).ConclusionsThe prognosis of patients with dnDSA is similar regardless of the HLA class specificity. Lower calcineurin inhibitor levels predict graft loss in such patients.
Development of the interpretable typing prediction model for osteosarcoma and chondrosarcoma based on machine learning and radiomics: a multicenter retrospective study
Authors: Long Qing-Yuan , Wang Feng-Yan , Hu Yue , Gao Bo , Zhang Chuan , Ban Bo-Heng , Tian Xiao-Bin 
Published: November 2024

BackgroundOsteosarcoma and chondrosarcoma are common malignant bone tumors, and accurate differentiation between these two tumors is crucial for treatment strategies and prognosis assessment. However, traditional radiological methods face diagnostic challenges due to the similarity in imaging between the two.MethodsClinical CT images and pathological data of 76 patients confirmed by pathology from January 2018 to January 2024 were retrospectively collected from Guizhou Medical University Affiliated Hospital and Guizhou Medical University Second Affiliated Hospital. A total of 788 radiomic features, including shape, texture, and first-order statistics, were extracted in this study. Six machine learning models, including Random Forest (RF), Extra Trees (ET), AdaBoost, Gradient Boosting Tree (GB), Linear Discriminant Analysis (LDA), and XGBoost (XGB), were trained and validated. Additionally, the importance of features and the interpretability of the models were evaluated through SHAP value analysis.ResultsThe RF model performed best in distinguishing between these two tumor types, with an AUC value close to perfect at 1.00. The ET and AdaBoost models also demonstrated high performance, with AUC values of 0.98 and 0.93, respectively. SHAP value analysis revealed significant influences of wavelet-transformed GLCM and First Order features on model predictions, further enhancing diagnostic interpretability.ConclusionThis study confirms the effectiveness of combining machine learning with radiomic features in improving the accuracy and interpretability of osteosarcoma and chondrosarcoma diagnosis. The excellent performance of the RF model is particularly suitable for complex imaging data processing, providing valuable insights for the future.
Serum beta2-microglobulin and peripheral blood eosinophils for the assessment of severity and prognosis with omicron variant COVID-19 infection
Authors: Tan Jie , Fang Hanxi , Hu Xiao , Yue Ming , Yang Junling 
Published: November 2024

BackgroundThe Omicron variant’s high transmissibility has made it the most widespread novel coronavirus variant. Elevated serum β2-MG levels from viral infections and EOS’ role in viral clearance have garnered attention. However, their predictive value for Omicron’s severity and prognosis needs further exploration.MethodsThis retrospective study included 424 patients with confirmed COVID-19 Omicron variant admitted to the Second Hospital of Jilin University in Changchun, China, of whom 128 experienced in-hospital mortality. Patients were divided into high and low groups according to β2-MG and EOS levels; the relationship between disease severity and patient prognosis was analyzed.ResultsOur findings showed that severe-to-critical Omicron patients had higher β2-MG levels than mild-normal patients. Conversely, EOS levels were higher in mild-moderate cases. Both β2-MG and EOS levels normalized when Omicron patients tested negative for nucleic acid. Deceased Omicron patients had significantly lower pre-mortem EOS levels. Elevated β2-MG and lower EOS levels correlated with reduced overall survival. Multivariate COX regression analysis indicated that elevated β2-MG was an independent adverse prognostic factor for Omicron patients.ConclusionHigh serum β2-MG levels and low eosinophil levels upon admission correlate with omicron variant severity and prognosis. β2-MG is an independent risk factor for poor outcomes in omicron patients.
Identification of key genes to predict response to chemoradiotherapy and prognosis in esophageal squamous cell carcinoma
Authors: Cui Yingying , Wen Jing , Fu Jianhua , Leng Changsen 
Published: November 2024

BackgroundChemoradiotherapy is a crucial treatment modality for esophageal squamous cell carcinoma (ESCC). This study aimed to identify chemoradiotherapy sensitivity-related genes and analyze their prognostic value and potential associations with the tumor microenvironment in ESCC.MethodsUtilizing the Gene Expression Omnibus database, we identified differentially expressed genes between ESCC patients who achieved complete and incomplete pathological responses following chemoradiotherapy. Prognostic genes were then screened, and key genes associated with chemoradiotherapy sensitivity were determined using random survival forest analysis. We examined the relationships between key genes, infiltrating immune cells, and immunoregulatory genes. Additionally, drug sensitivity and enrichment analyses were conducted to assess the impact of key genes on chemotherapy responses and signaling pathways. A prognostic nomogram for ESCC was developed incorporating key genes, and its effectiveness was evaluated. Genome-wide association study data were employed to investigate chromosomal pathogenic regions associated with key genes.ResultsThree key genes including ATF2, SLC27A5, and ALOXE3 were identified. These genes can predict the sensitivity of ESCC patients to neoadjuvant chemoradiotherapy and hold significant clinical relevance in prognostication. These genes were also found to be significantly correlated with certain immune cells and immunoregulatory genes within the tumor microenvironment and were involved in critical tumor-related signaling pathways, including the epithelial-mesenchymal transition and P53 pathways. A nomogram was established to predict the prognosis of ESCC by integrating key genes with clinical stages, demonstrating favorable predictability and reliability.ConclusionThis study identified three key genes that predict chemoradiotherapy sensitivity and prognosis and are involved in multiple tumor-related biological processes in ESCC. These findings provide predictive biomarkers for chemoradiotherapy response and support the development of individualized treatment strategies for ESCC patients.
ISFM-SLAM: dynamic visual SLAM with instance segmentation and feature matching
Authors: Li Chao , Hu Yang , Liu Jianqiang , Jin Jianhai , Sun Jun 
Published: November 2024

IntroductionSimultaneous Localization and Mapping (SLAM) is a technology used in intelligent systems such as robots and autonomous vehicles. Visual SLAM has become a more popular type of SLAM due to its acceptable cost and good scalability when applied in robot positioning, navigation and other functions. However, most of the visual SLAM algorithms assume a static environment, so when they are implemented in highly dynamic scenes, problems such as tracking failure and overlapped mapping are prone to occur.MethodsTo deal with this issue, we propose ISFM-SLAM, a dynamic visual SLAM built upon the classic ORB-SLAM2, incorporating an improved instance segmentation network and enhanced feature matching. Based on YOLACT, the improved instance segmentation network applies the multi-scale residual network Res2Net as its backbone, and utilizes CIoU_Loss in the bounding box loss function, to enhance the detection accuracy of the segmentation network. To improve the matching rate and calculation efficiency of the internal feature points, we fuse ORB key points with an efficient image descriptor to replace traditional ORB feature matching of ORB-SLAM2. Moreover, the motion consistency detection algorithm based on external variance values is proposed and integrated into ISFM-SLAM, to assist the proposed SLAM systems in culling dynamic feature points more effectively.Results and discussionSimulation results on the TUM dataset show that the overall pose estimation accuracy of the ISFM-SLAM is 97% better than the ORB-SLAM2, and is superior to other mainstream and state-of-the-art dynamic SLAM systems. Further real-world experiments validate the feasibility of the proposed SLAM system in practical applications.
Multimodal robot-assisted English writing guidance and error correction with reinforcement learning
Authors: Wang Ni 
Published: November 2024

IntroductionWith the development of globalization and the increasing importance of English in international communication, effectively improving English writing skills has become a key focus in language learning. Traditional methods for English writing guidance and error correction have predominantly relied on rule-based approaches or statistical models, such as conventional language models and basic machine learning algorithms. While these methods can aid learners in improving their writing quality to some extent, they often suffer from limitations such as inflexibility, insufficient contextual understanding, and an inability to handle multimodal information. These shortcomings restrict their effectiveness in more complex linguistic environments.MethodsTo address these challenges, this study introduces ETG-ALtrans, a multimodal robot-assisted English writing guidance and error correction technology based on an improved ALBEF model and VGG19 architecture, enhanced by reinforcement learning. The approach leverages VGG19 to extract visual features and integrates them with the ALBEF model, achieving precise alignment and fusion of images and text. This enhances the model's ability to comprehend context. Furthermore, by incorporating reinforcement learning, the model can adaptively refine its correction strategies, thereby optimizing the effectiveness of writing guidance.Results and discussionExperimental results demonstrate that the proposed ETG-ALtrans method significantly improves the accuracy of English writing error correction and the intelligence level of writing guidance in multimodal data scenarios. Compared to traditional methods, this approach not only enhances the precision of writing suggestions but also better caters to the personalized needs of learners, thereby effectively improving their writing skills. This research is of significant importance in the field of language learning technology and offers new perspectives and methodologies for the development of future English writing assistance tools.
Elevated systemic inflammation response index is associated with poor outcomes in minor ischemic stroke
Authors: Li Jie , Zhang Ping , Chen Hong , Wang Yanfen , Han Yangyun , Wang Chun , Yi Xingyang 
Published: November 2024

ObjectivesPatients with minor ischemic stroke (MIS) have substantial disability rates at 90 days. Our study aimed to explore the association between the systemic inflammation response index (SIRI) and 3-month functional outcomes in patients with MIS.MethodsWe conducted a prospective observational study in patients with MIS [defined as a National Institutes of Health Stroke Scale (NIHSS) score of 0–3] admitted within 24 h from symptoms onset. Blood samples for the SIRI measurement were collected on admission. The primary outcome measure was poor outcomes at 90 days (defined as a modified Rankin Scale score of 2–6). Univariate and multivariate logistic analyses were performed to assess the association between the SIRI and the risk of 3-month poor outcomes.ResultsA total of 152 patients with MIS were enrolled, of which 24 cases (15.8%) had poor outcomes at 90 days. The median SIRI level was 1.27 [interquartile range (IQR), 0.77–1.92, ×10^9 /L] on admission. MIS patients with poor outcomes had higher levels of the SIRI than patients with good outcomes (poor outcomes: median, 1.93, IQR: 1.17–3.28, ×10^9 /L; good outcomes: median, 1.21, IQR: 0.71–1.80, ×10^9 /L; p = 0.003). The high SIRI level group (SIRI &gt;1.27 × 10^9 /L) had significantly higher rates of poor outcomes at 90 days (22.4% vs. 9.2%, p = 0.026). After adjusting for age, baseline NIHSS score, prehospital delay, Trial of Org 10,172 in Acute Stroke Treatment (TOAST) classification, and other confounders in multivariate analyses, an elevated SIRI level remained independently associated with an increased risk of poor outcomes in patients with MIS [odds ratio (OR): 1.57, 95% confidence interval (CI): 1.12–2.20; p = 0.010]. Meanwhile, a high level of the SIRI (&gt;1.27 × 10^9/L) was still an independent risk factor for 3-month poor outcomes (OR: 4.80, 95%CI: 1.51–15.29; p = 0.008) in MIS patients.ConclusionDisability at 90 days was common in patients with MIS. An elevated SIRI was associated with poor outcomes in MIS patients. The SIRI might be a promising biomarker candidate that can help identify high-risk MIS patients with poor outcomes for reaching individual therapeutic decisions in clinical trials.
Enhancing early Alzheimer's disease classification accuracy through the fusion of sMRI and rsMEG data: a deep learning approach
Authors: Liu Yuchen , Wang Ling , Ning Xiaolin , Gao Yang , Wang Defeng 
Published: November 2024

ObjectiveEarly detection and prediction of Alzheimer's Disease are paramount for elucidating neurodegenerative processes and enhancing cognitive resilience. Structural Magnetic Resonance Imaging (sMRI) provides insights into brain morphology, while resting-state Magnetoencephalography (rsMEG) elucidates functional aspects. However, inherent disparities between these multimodal neuroimaging modalities pose challenges to the effective integration of multimodal features.ApproachTo address these challenges, we propose a deep learning-based multimodal classification framework for Alzheimer's disease, which harnesses the fusion of pivotal features from sMRI and rsMEG to augment classification precision. Utilizing the BioFIND dataset, classification trials were conducted on 163 Mild Cognitive Impairment cases and 144 cognitively Healthy Controls.ResultsThe study findings demonstrate that the InterFusion method, combining sMRI and rsMEG data, achieved a classification accuracy of 0.827. This accuracy significantly surpassed the accuracies obtained by rsMEG only at 0.710 and sMRI only at 0.749. Moreover, the evaluation of different fusion techniques revealed that InterFusion outperformed both EarlyFusion with an accuracy of 0.756 and LateFusion with an accuracy of 0.801. Additionally, the study delved deeper into the role of different frequency band features of rsMEG in fusion by analyzing six frequency bands, thus expanding the diagnostic scope.DiscussionThese results highlight the value of integrating resting-state rsMEG and sMRI data in the early diagnosis of Alzheimer's disease, demonstrating significant potential in the field of neuroscience diagnostics.
Improvement effect of insulin resistance in one-day outpatient service by reducing stress adaptation disorders in patients with gestational diabetes mellitus
Authors: Feng Yan , Yu Quan , Gu Fuqian , Feng Qi , Zhang Yinghong 
Published: November 2024

AimConducted a one-day outpatient service for GDM patients, analyzed the relationship between stress adaptation disorder and insulin resistance in GDM patients after intervention, and tried to provide some new clues for the prevention and treatment of GDM, provide some theoretical basis for the multidisciplinary diagnosis and treatment model of GDM patients.Methods240 GDM women were included in this study, 120 women were included in one-day diabetes clinic management for GDM women as GDM Intervention Group, and 120 GDM women receiving regular dietary education as GDM Control Group. One-day diabetes clinic management including disease knowledge and dietary education, sports education and blood sugar monitoring and personalized issues and follow-up visits, and intervention time lasting for 1 month.ResultsAfter intervention, the concentration of 2-h postprandial blood glucose, and HOMA-IR were decreased in GDM Intervention Group, while weekly weight gain rate and insulin application rate were significantly lower than GDM Control Group (all p &lt; 0.05). Cortisol and MDA in GDM Intervention Group were significantly lower than GDM Control Group (both p &lt; 0.01). HOMA-IR was positively correlated with weight gain, E, NE and cortisol (r = 0.249, 0.242, 0.663, 0.313, all p &lt; 0.01), E and HOMA-IR were negatively correlated with SOD in GDM Intervention Group (r = −0.306, −0.213, both p &lt; 0.01).ConclusionThe intervention model in our study was based on the one-day outpatient comprehensive management model of diabetes, which improved the insulin resistance of GDM patients. The possible mechanism was related to the implementation of one-day outpatient intervention measures, which reduced the stress adaptation disorder and oxidative stress injury of GDM patients. At the same time, the implementation of intervention measures reduced the rate of weight gain, which can also alleviate insulin resistance to a certain extent. One-day outpatient treatment has a positive effect on improving insulin resistance in GDM women, which can reduce the risk of maternal and fetal complications.
Astragaloside IV alleviates inflammation and improves myocardial metabolism in heart failure mice with preserved ejection fraction
Authors: Wang Xiao , Chen Xinting , Wang Yuting , He Xinyu , Li Lan , Wang Xiaodan , Huang Yuting , Fan Guanwei , Ni Jingyu 
Published: November 2024

BackgroundHeart failure with preserved ejection fraction (HFpEF) has grown to become the dominant form of heart failure worldwide. However, no unequivocally effective treatment for HFpEF has been identified in clinical trials. In this study, we report that Astragaloside IV (AS-IV) can be used to treat HFpEF.MethodsMice were fed on a high-fat diet and given 0.5 g/L L-NAME (in drinking water) for 10 weeks to establish the HFpEF model. After 10th weeks, the HFpEF mice were given 10 mg/kg empagliflozin, 10 mg/kg AS-IV, or 20 mg/kg AS-IV for 4 weeks. The echocardiography, blood pressure, hemodynamics, heart failure biomarkers, collagen deposition and fibrosis, histopathology, and inflammation in HFpEF mice were evaluated. Metabolic profiling based on NMR measurements was also performed. Myocardial glucose and fatty acid metabolism were evaluated.ResultsAS-IV improves cardiac function and myocardial remodeling in HFpEF mice. AS-IV attenuates systemic inflammatory infiltration and myocardial inflammation levels in HFpEF mice by decreasing the expression of plasma inflammatory markers GDF15, CRP, IL1RL1, and MCP-1, NLRP3, IL-1β, Caspase-1, and IL-6 in the myocardium of HFpEF mice. Metabolomic analysis suggested that AS-IV improved cardiac glucose and fatty acid metabolism in HFpEF mice. Further studies showed that AS-IV significantly improved Complex I activity, increased ATP production, and elevated plasma NAD + levels; AS-IV also significantly improved pyruvate dehydrogenase activity and decreased pyruvate and lactate accumulation, thereby improving glucose metabolism in the hearts of HFpEF mice.ConclusionThese results provide novel evidence that Astragaloside IV alleviates inflammation and improves myocardial metabolism in HFpEF mice.