bluuebunny/crossref_metadata_2025_split huggingface dataset:
Smartphone tests quantify lower extremities dysfunction in multiple sclerosis
Authors: Jin Kimberly , Kosa Peter , Bielekova Bibiana 
Published: November 2024

IntroductionIncreasing shortage of neurologists compounded by the global aging of the population have translated into suboptimal care of patients with chronic neurological diseases. While some patients might benefit from expanding telemedicine, monitoring neurological disability via telemedicine is challenging. Smartphone technologies represent an attractive tool for remote, self-administered neurological assessment. To address this need, we have developed a suite of smartphone tests, called neurological functional test suite (NeuFun-TS), designed to replicate traditional neurological examination. The aim of this study was to assess the ability of two NeuFun-TS tests—short walk and foot tapping—to quantify motor functions of lower extremities as assessed by a neurologist.MethodsA cohort of 108 multiple sclerosis (MS) patients received a full neurological examination, imaging of the brain, and completed the NeuFun-TS smartphone tests. The neurological exam was digitalized using the NeurEx™ platform, providing calculation of traditional disability scales, as well as quantification of lower extremities-specific disability. We assessed unilateral correlations of 28 digital biomarkers generated from the NeuFun-TS tests with disability and MRI outcomes and developed machine learning models that predict physical disability. Model performance was tested in an independent validation cohort.ResultsNeuFun-TS-derived digital biomarkers correlated strongly with traditional outcomes related to gait and lower extremities functions (e.g., Spearman ρ &gt; 0.8). As expected, the correlation with global disability outcomes was weaker, but still highly significant (e.g., ρ 0.46–0.65; p &lt; 0.001 for EDSS). Digital biomarkers also correlated with semi-quantitative imaging outcomes capturing locations that can affect lower extremity functions (e.g., ρ ~ 0.4 for atrophy of medulla). Reliable digital outcomes with high test-retest values showed stronger correlation with disability outcomes. Combining strong, reliable digital features using machine learning resulted in models that outperformed predictive power of best individual digital biomarkers in an independent validation cohort.DiscussionNeuFun-TS tests provide reliable digital biomarkers of lower extremity motor functions.
Attention model of EEG signals based on reinforcement learning
Authors: Zhang Wei , Tang Xianlun , Wang Mengzhou 
Published: November 2024

BackgroundApplying convolutional neural networks to a large number of EEG signal samples is computationally expensive because the computational complexity is linearly proportional to the number of dimensions of the EEG signal. We propose a new Gated Recurrent Unit (GRU) network model based on reinforcement learning, which considers the implementation of attention mechanisms in Electroencephalogram (EEG) signal processing scenarios as a reinforcement learning problem.MethodsThe model can adaptively select target regions or position sequences from inputs and effectively extract information from EEG signals of different resolutions at multiple scales. Just as convolutional neural networks benefit from translation invariance, our proposed network also has a certain degree of translation invariance, making its computational complexity independent of the EEG signal dimension, thus maintaining a lower learning cost. Although the introduction of reinforcement learning makes the model non differentiable, we use policy gradient methods to achieve end-to-end learning of the model.ResultsWe evaluated our proposed model on publicly available EEG dataset (BCI Competition IV-2a). The proposed model outperforms the current state-of-the-art techniques in the BCI Competition IV- 2a dataset with an accuracy of 86.78 and 71.54% for the subject-dependent and subject-independent modes, respectively.ConclusionIn the field of EEG signal processing, attention models that combine reinforcement learning principles can focus on key features, automatically filter out noise and redundant data, and improve the accuracy of signal decoding.
Using Long Short-Term Memory (LSTM) recurrent neural networks to classify unprocessed EEG for seizure prediction
Authors: Chambers Jordan D. , Cook Mark J. , Burkitt Anthony N. , Grayden David B. 
Published: November 2024

ObjectiveSeizure prediction could improve quality of life for patients through removing uncertainty and providing an opportunity for acute treatments. Most seizure prediction models use feature engineering to process the EEG recordings. Long-Short Term Memory (LSTM) neural networks are a recurrent neural network architecture that can display temporal dynamics and, therefore, potentially analyze EEG signals without performing feature engineering. In this study, we tested if LSTMs could classify unprocessed EEG recordings to make seizure predictions.MethodsLong-term intracranial EEG data was used from 10 patients. 10-s segments of EEG were input to LSTM models that were trained to classify the EEG signal. The final seizure prediction was generated from 5 outputs of the LSTM model over 50 s and combined with time information to account for seizure cycles.ResultsThe LSTM models could make predictions significantly better than a random predictor. When compared to other publications using the same dataset, our model performed better than several others and was comparable to the best models published to date. Furthermore, this framework could still produce predictions significantly better than chance when the experimental paradigm design was altered, without the need to reperform feature engineering.SignificanceRemoving the need to perform feature engineering is an advancement on previously published models. This framework can be applied to many different patients’ needs and a variety of acute interventions. Also, it opens the possibility of personalized seizure predictions that can be altered to meet daily needs.
Effect of endometriosis on prognosis of ovarian clear cell carcinoma: a 10-year retrospective study
Authors: He Yang , Cao Bo , Huang Yi 
Published: November 2024

IntroductionEndometriosis is considered as a precancerous lesion for OCCC; however its prognostic significance remains controversial. This study aims to evaluate the prognostic significance of endometriosis in patients with ovarian clear cell carcinoma (OCCC) and analyze the impact of other clinical pathological features on prognosis. Additionally, we also assess the role of laparoscopic surgery and chemotherapy in OCCC, hoping to provide evidence for improving the clinical diagnosis and treatment of OCCC.MethodsA retrospective analysis was conducted on medical records of 105 OCCC patients diagnosed and treated at the Gynecologic Cancer Center of Hubei Cancer Hospital in China from 2013 to 2022. Based on the presence or absence of endometriosis, OCCC patients were divided into two groups: a group with ovarian endometriosis consisting of 44 cases (41.9%) (EC-positive group) and a group without ovarian endometriosis consisting of 61 cases (58.1%)(EC-negative group). Clinical pathological characteristics, progression-free survival (PFS), and overall survival (OS) were compared between the two groups.ResultsThere were no statistically significant differences between the two groups in terms of age, CA125, tumor size, FIGO stage, adjuvant chemotherapy regimen, or chemotherapy efficacy (P&gt;0.05). Residual tumor after surgery, staging, site invasion involvement, presence of ascites, positive cytology in ascitic fluid, lymph node metastasis, and chemotherapy efficacy were predictive factors for recurrence among patients with statistical significance (P&lt;0.10); chemotherapy efficacy remained as independent predictors for recurrence (P&lt;0.05); staging and chemotherapy efficacy remained as independent predictors for survival (P&lt;0.05). There was no statistically significant difference observed between both groups regarding OS or PFS.ConclusionIn this study, co-existing endometriosis was not a prognostic factor for survival in patients with OCCC. The most important predictors of OS and PFS were FIGO stage and chemotherapy sensitivity. The intrinsic link between endometriosis and OCCC requires larger, better-designed prospective studies to draw more definitive conclusions.
Insulin-like growth factor-binding protein-3 is induced by tamoxifen and fulvestrant and modulates fulvestrant response in breast cancer cells
Authors: Flynn Keenan L. , Zheng Yan , Sowers Janel Y. , Masangya Nefretiri J. T. , Houston Kevin D. 
Published: November 2024

IntroductionInsulin-like growth factor binding protein-3 (IGFBP-3) exerts varying effects on estrogen receptor alpha (ERα)-positive and triple-negative breast cancer (TNBC) cells. In ERα-positive cells, IGFBP-3 is antiproliferative and proapoptotic. In contrast, IGFBP-3 stimulates proliferation in triple-negative breast cancer (TNBC) cells via EGFR activation.MethodsTo identify potential mechanisms that underlie the opposing effects of IGFBP-3 on these two breast cancer subtypes, IGFBP-3 expression was determined in cell line models of both ERα-positive breast cancer and TNBC, and cells were treated with antiestrogens tamoxifen and fulvestrant. Results and discussionMCF-7 and T-47D cells expressed low levels of IGFBP-3 when compared to MDA-MB-231 and MDA-MB-468 cells. MCF-7 cells with acquired resistance to the selective estrogen receptor degrader fulvestrant expressed high IGFBP-3 and MCF-7 cells with constitutive IGFBP-3 expression were fulvestrant resistant. IGFBP-3 expression was increased in all cell lines upon treatment with fulvestrant or the selective estrogen receptor modulator tamoxifen and both fulvestrant and tamoxifen increased TNBC cell proliferation. Further, IGFBP-3 expression was increased by treatment with the GPER1 agonist G-1 and attenuated upon treatment with P17, a YAP/TAZ inhibitor. These data suggest that IGFBP-3 modulates breast cancer cells and is a mediator of breast cancer cell response to fulvestrant and tamoxifen.
Diagnostic accuracy of linked color imaging and white light imaging for early gastric cancer and gastrointestinal metaplasia: a systematic review and meta-analysis
Authors: Duan Hui , Zhou Xinxu , Li Qian , Liu Liu , Wang Qiong , Wu Kaiwen , Jiang Lin , Sun Xiaobin 
Published: November 2024

BackgroundConventional white light imaging (WLI) frequently misses gastric cancer, resulting in a high rate of undiagnosed cases. This study compares the effectiveness of linked color imaging (LCI) and WLI in detecting early gastric cancer and gastrointestinal metaplasia, aiming to improve clinical diagnostic practices through evidence-based medical insights.MethodsThe QUADAS-2 tool evaluated the quality of the studies. Additionally, methods like Split Component Synthesis (SCS) were utilized to evaluate the diagnostic performance of LCI and WLI.ResultsEleven studies involving a total of 7836 patients were included in the meta-analysis. Comparative analysis revealed that LCI demonstrated a statistically significant superiority over WLI in terms of the detection rates of EGC and GIM (detection rate of EGC: LCI vs WLI, 85% vs. 56.7%, p=0.004, OR 4.78, 95% CI 2.33-9.82, I2 = 71%; detection rate of GIM: LCI vs WLI, 88.9% vs. 40.1%, p=0.0003, OR 9.94, 95% CI 5.59-17.68, I2 = 71%). Additionally, LCI exhibited better sensitivity and specificity for the diagnosis of EGC and GIM compared to WLI. For the entire cohort, the sensitivity of LCI for EGC detection was 80% (95% CI 71%-86%) with a specificity of 82% (95% CI 63%-92%), while for GIM detection, the sensitivity was 87% (95% CI 81%-92%) with a specificity of 85% (95% CI 77%-91%).ConclusionsThe detection efficiency of LCI for EGC and GIM is better than that of WLI, and LCI is recommended as the main screening method for EGC and GIM.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42023452140.
Segmentation of glioblastomas via 3D FusionNet
Authors: Guo Xiangyu , Zhang Botao , Peng Yue , Chen Feng , Li Wenbin 
Published: November 2024

IntroductionThis study presented an end-to-end 3D deep learning model for the automatic segmentation of brain tumors.MethodsThe MRI data used in this study were obtained from a cohort of 630 GBM patients from the University of Pennsylvania Health System (UPENN-GBM). Data augmentation techniques such as flip and rotations were employed to further increase the sample size of the training set. The segmentation performance of models was evaluated by recall, precision, dice score, Lesion False Positive Rate (LFPR), Average Volume Difference (AVD) and Average Symmetric Surface Distance (ASSD).ResultsWhen applying FLAIR, T1, ceT1, and T2 MRI modalities, FusionNet-A and FusionNet-C the best-performing model overall, with FusionNet-A particularly excelling in the enhancing tumor areas, while FusionNet-C demonstrates strong performance in the necrotic core and peritumoral edema regions. FusionNet-A excels in the enhancing tumor areas across all metrics (0.75 for recall, 0.83 for precision and 0.74 for dice scores) and also performs well in the peritumoral edema regions (0.77 for recall, 0.77 for precision and 0.75 for dice scores). Combinations including FLAIR and ceT1 tend to have better segmentation performance, especially for necrotic core regions. Using only FLAIR achieves a recall of 0.73 for peritumoral edema regions. Visualization results also indicate that our model generally achieves segmentation results similar to the ground truth.DiscussionFusionNet combines the benefits of U-Net and SegNet, outperforming the tumor segmentation performance of both. Although our model effectively segments brain tumors with competitive accuracy, we plan to extend the framework to achieve even better segmentation performance.
Impact of entrepreneurs' prior experience on their new ventures' early-stage performance: the mediation effect of entrepreneurial mindset level
Authors: Chen Sihui , Zhong Lichen , Wang Xiao , Chen Bolong 
Published: November 2024

IntroductionDoes an entrepreneur's prior experience affect the early-stage performance of new ventures? This study further explores the mediating effect of the entrepreneurial mindset, indicated by the quality of business plans, between entrepreneurs' prior experience and the early-stage performance of their new ventures.MethodsThe sample consists of 157 valid formal business plan documents from the “2021 Gusu Science and Technology Angel Program.” The study employs an evaluation system developed to measure the quality of these business plans and assess the entrepreneurial mindset levels.ResultsThe results reveal the mechanism through which prior experience influences early-stage performance. It indicates that entrepreneurs aiming for high long-term new venture performance should focus on learning and acquiring diverse experiences.DiscussionThe opportunity to write and evaluate their business plans can serve as a self-assessment tool for their entrepreneurial mindset using the evaluation system developed in this study. This approach can significantly enhance their performance and contribute to entrepreneurial success. Moreover, by employing this evaluation system, educators and educational institutions can better understand the entrepreneurial mindset levels of trainees (entrepreneurs) and tailor their courses more effectively.
The association between anthocyanin intake and myopia in adolescents: a cross-sectional study of NHANES
Authors: Chen Ying , Xu Qinglin , Lv Lu , Liu Yun , Zhang Zhidan , Yang Zhikuan 
Published: November 2024

AimThe study aimed to explore the relationship of anthocyanin and its subtypes with myopia in adolescents aged 12–17 years.MethodsAdolescents data for this cross-sectional study were extracted from the National Health and Nutrition Examination Survey (NHANES) 2007–2008. Anthocyanin and subtypes were obtained using the Nutrient Database for Dietary Studies codes. Myopia was defined as a spherical equivalent of −1.0 diopters or less. The relationships between anthocyanin and subtypes intake and myopia were determined utilizing weighted univariate and multivariate logistic regression models. The relationships were also explored in gender, leisure time, physical activity, sedentary activity, BMI, and serum cotinine subgroups.ResultsA total of 839 adolescents were included for further analysis, among them 245 have myopia. Malvidin (34.98%) was the subtype with the largest anthocyanin intake, followed by cyanidin (22.94%). Compared to adolescents without anthocyanin intake, total anthocyanin intake was related to a lower incidence of myopia (OR = 0.69, 95%CI: 0.51–0.92). Higher intake of cyanidin (OR = 0.69, 95%CI: 0.52–0.92), petunidin (OR = 0.64, 95%CI: 0.42–0.97), and delphinidin (OR = 0.71, 95%CI: 0.51–0.99) were associated with lower odds of myopia in adolescents. Higher total anthocyanin intake was related to lower odds of myopia in those females, leisure time physical activity ≥60 min/day, sedentary time &lt;8 h/day, overweight or obese, and serum cotinine ≥0.05 ng/ml.ConclusionHigher total anthocyanin intake, particularly cyanidin, petunidin, and delphinidin, was related to a lower incidence of myopia in adolescents. Increasing dietary anthocyanin intake may be an effective prevention strategy for ocular health.
Tetramethylpyrazine inhibits ferroptosis in spinal cord injury by regulating iron metabolism through the NRF2/ARE pathway
Authors: Tao Jingwei , Zhou Jingya , Zhu Hanjie , Xu Lin , Yang Jizhou , Mu Xiaohong , Fan Xiao 
Published: November 2024

BackgroundTetramethylpyrazine (TMP) is a natural alkaloid compound with antioxidant and neuroprotective effects. We hypothesized that TMP could exert neuroprotective effects by inhibiting ferroptosis through modulating iron metabolism, but its mechanism is unclear. Through in vivo and in vitro experiments, we have explored how TMP can regulate neurons’ iron metabolism through the NRF2/ARE pathway to Inhibit ferroptosis.MethodsIn the in vivo experiment, the effects of TMP on nerve function and secondary spinal cord injury were observed through behavioral tests and morphology staining. Transmission electron microscopy, molecular biology tests and immunofluorescence staining were used to investigate the role of TMP in the regulation of iron metabolism and ferroptosis through the Nrf2/ARE pathway. Using in vitro experiments to investigate the mechanism of TMP in inhibiting ferroptosis through the Nrf2/ARE pathway.ResultsFirstly, through in vivo experiments, we found that TMP improves motor function of rats with spinal cord injury, reduces spinal cord tissue damage and nerve cell death caused by secondary injury. Moreover, neuronal death and the formation of spinal cord cavities are inhibited by TMP. By regulating lipid peroxidation, TMP can inhibit mitochondrial damage and reduce ROS accumulation. Our study also demonstrated that TMP regulates iron metabolism through the NRF2/ARE pathway to inhibit ferroptosis and repair spinal cord injury. To further explore the regulatory mechanisms of TMP we down-regulating Nrf2 expression in subsequent in vitro experiments. We find that a key ferroptosis pathway, lipid peroxidation, can be regulated by TMP. Additionally, TMP inhibits iron overload-mediated ferroptosis by increasing Nrf2 transcriptional activity.ConclusionA regulatory effect of TMP on the NRF2/ARE pathway was found in both in vitro and in vivo experiments. It promotes the transcription and translation of iron metabolizing and antioxidant molecules. Our study explored the inhibitory effect of TMP on ferroptosis from the iron metabolism pathway and provided new ideas for the treatment of SCI.
Broiler breeder putative lipid biomarkers associated with sperm mobility
Authors: Bond A. , Mills K. M. , Ferreira C. R. , Harford I. , Flack B. , Long J. A. , Diehl K. 
Published: November 2024

IntroductionBiomarkers indicative of sperm mobility in broiler breeders would provide the ability to screen for fertility potential, with a positive correlation established between sperm mobility and fertilization potential. This study characterized the lipidome of seminal plasma (SP), sperm cell (SC), and whole semen (WS) isolated from broiler breeder roosters with different sperm mobility phenotypes across key timepoints of the semen production cycle.MethodsWS samples were collected from five high mobility roosters and five low mobility roosters during early, mid, and late semen production, with SP separated from SC by centrifugation. Using multiple reaction monitoring (MRM) profiling, a total of 3241 lipid species were identified in rooster semen across ten lipid classes. Metaboanalyst 6.0 was used to analyze the relative ion intensity for each lipid species due to sperm mobility phenotype through a t-test and due to timepoint through a one-way ANOVA, with lipid ontology enrichment analysis performed using LION. Metaboanalyst 6.0 was also used to perform biomarker analysis for the sperm mobility phenotype in WS samples.ResultsLipid class total abundance differed with sample type, sperm mobility phenotype, and timepoint. A total of 31, 99, and 112 lipid species were found to be different between low and high mobility males across timepoints in the SP, SC, and WS samples, respectively. Lipid ontology enrichment analysis revealed stark contrasts in lipid-based functions key to sperm survival, storage, and productivity between low and high sperm mobility phenotypes. Through biomarker analysis, 8 lipid species were identified as excellent sperm mobility biomarkers that could be detected in early and mid-semen production.DiscussionTimepoint based changes in lipid species were unique to each sperm mobility phenotype, with low sperm mobility roosters exhibiting a larger number of lipid species changes over the semen production cycle in the SP and SC when compared to high sperm mobility roosters. This is the first study to characterize poultry semen lipidome using MRM profiling. The lipid species identified between low and high sperm mobility roosters could be utilized in the poultry industry as potential biomarkers of fertility potential, with the ability to screen for the economical trait of fertility potential early in semen production.