bluuebunny/crossref_metadata_2025_split huggingface dataset:
Integrating bulk and single-cell RNA sequencing data reveals the relationship between intratumor microbiome signature and host metabolic heterogeneity in breast cancer
Authors: Chen Fangyue , Yang Jun , Guo Youxiang , Su Dongwei , Sheng Yuan , Wu Yanmei 
Published: March 2023

IntroductionNowadays, it has been recognized that gut microbiome can indirectly modulate cancer susceptibility or progression. However, whether intratumor microbes are parasitic, symbiotic, or merely bystanders in breast cancer is not fully understood. Microbial metabolite plays a pivotal role in the interaction of host and microbe via regulating mitochondrial and other metabolic pathways. And the relationship between tumor-resident microbiota and cancer metabolism remains an open question.Methods1085 breast cancer patients with normalized intratumor microbial abundance data and 32 single-cell RNA sequencing samples were retrieved from public datasets. We used the gene set variation analysis to evaluate the various metabolic activities of breast cancer samples. Furthermore, we applied Scissor method to identify microbe-associated cell subpopulations from single-cell data. Then, we conducted comprehensive bioinformatic analyses to explore the association between host and microbe in breast cancer.ResultsHere, we found that the metabolic status of breast cancer cells was highly plastic, and some microbial genera were significantly correlated with cancer metabolic activity. We identified two distinct clusters based on microbial abundance and tumor metabolism data. And dysregulation of the metabolic pathway was observed among different cell types. Metabolism-related microbial scores were calculated to predict overall survival in patients with breast cancer. Furthermore, the microbial abundance of the specific genus was associated with gene mutation due to possible microbe-mediated mutagenesis. The infiltrating immune cell compositions, including regulatory T cells and activated NK cells, were significantly associated with the metabolism-related intratumor microbes, as indicated in the Mantel test analysis. Moreover, the mammary metabolism-related microbes were related to T cell exclusion and response to immunotherapy.ConclusionsOverall, the exploratory study shed light on the potential role of the metabolism-related microbiome in breast cancer patients. And the novel treatment will be realized by further investigating the metabolic disturbance in host and intratumor microbial cells.
Identification and validation of IgG N-glycosylation biomarkers of esophageal carcinoma
Authors: Pan Huiying , Wu Zhiyuan , Zhang Haiping , Zhang Jie , Liu Yue , Li Zhiwei , Feng Wei , Wang Guiqi , Liu Yong , Zhao Deli , Zhang Zhiyi , Liu Yuqin , Zhang Zhe , Liu Xiangtong , Tao Lixin , Luo Yanxia , Wang Xiaonan , Yang Xinghua , Zhang Feng , Li Xia , Guo Xiuhua 
Published: March 2023

IntroductionAltered Immunoglobulin G (IgG) N-glycosylation is associated with aging, inflammation, and diseases status, while its effect on esophageal squamous cell carcinoma (ESCC) remains unknown. As far as we know, this is the first study to explore and validate the association of IgG N-glycosylation and the carcinogenesis progression of ESCC, providing innovative biomarkers for the predictive identification and targeted prevention of ESCC.MethodsIn total, 496 individuals of ESCC (n=114), precancerosis (n=187) and controls (n=195) from the discovery population (n=348) and validation population (n=148) were recruited in the study. IgG N-glycosylation profile was analyzed and an ESCC-related glycan score was composed by a stepwise ordinal logistic model in the discovery population. The receiver operating characteristic (ROC) curve with the bootstrapping procedure was used to assess the performance of the glycan score.ResultsIn the discovery population, the adjusted OR of GP20 (digalactosylated monosialylated biantennary with core and antennary fucose), IGP33 (the ratio of all fucosylated monosyalilated and disialylated structures), IGP44 (the proportion of high mannose glycan structures in total neutral IgG glycans), IGP58 (the percentage of all fucosylated structures in total neutral IgG glycans), IGP75 (the incidence of bisecting GlcNAc in all fucosylated digalactosylated structures in total neutral IgG glycans), and the glycan score are 4.03 (95% CI: 3.03-5.36, P&lt;0.001), 0.69 (95% CI: 0.55-0.87, P&lt;0.001), 0.56 (95% CI: 0.45-0.69, P&lt;0.001), 0.52 (95% CI: 0.41-0.65, P&lt;0.001), 7.17 (95% CI: 4.77-10.79, P&lt;0.001), and 2.86 (95% CI: 2.33-3.53, P&lt;0.001), respectively. Individuals in the highest tertile of the glycan score own an increased risk (OR: 11.41), compared with those in the lowest. The average multi-class AUC are 0.822 (95% CI: 0.786-0.849). Findings are verified in the validation population, with an average AUC of 0.807 (95% CI: 0.758-0.864).DiscussionOur study demonstrated that IgG N-glycans and the proposed glycan score appear to be promising predictive markers for ESCC, contributing to the early prevention of esophageal cancer. From the perspective of biological mechanism, IgG fucosylation and mannosylation might involve in the carcinogenesis progression of ESCC, and provide potential therapeutic targets for personalized interventions of cancer progression.
Antibacterial properties of antimicrobial peptide HHC36 modified polyetheretherketone
Authors: Gao Weijia , Han Xiao , Sun Duo , Li Yongli , Liu Xiaoli , Yang Shihui , Zhou Zhe , Qi Yuanzheng , Jiao Junjie , Zhao Jinghui 
Published: March 2023

IntroductionPolyetheretherketone (PEEK) is considered to be a new type of orthopedic implant material due to its mechanical properties and biocompatibility. It is becoming a replacement for titanium (Ti) due to its near-human-cortical transmission and modulus of elasticity. However, its clinical application is limited because of its biological inertia and susceptibility to bacterial infection during implantation. To solve this problem, there is an urgent need to improve the antibacterial properties of PEEK implants.MethodsIn this work, we fixed antimicrobial peptide HHC36 on the 3D porous structure of sulfonated PEEK (SPEEK) by a simple solvent evaporation method (HSPEEK), and carried out characterization tests. We evaluated the antibacterial properties and cytocompatibility of the samples in vitro. In addition, we evaluated the anti-infection property and biocompatibility of the samples in vivo by establishing a rat subcutaneous infection model.ResultsThe characterization test results showed that HHC36 was successfully fixed on the surface of SPEEK and released slowly for 10 days. The results of antibacterial experiments in vitro showed that HSPEEK could reduce the survival rate of free bacteria, inhibit the growth of bacteria around the sample, and inhibit the formation of biofilm on the sample surface. The cytocompatibility test in vitro showed that the sample had no significant effect on the proliferation and viability of L929 cells and had no hemolytic activity on rabbit erythrocytes. In vivo experiments, HSPEEK can significantly reduce the bacterial survival rate on the sample surface and the inflammatory reaction in the soft tissue around the sample.DiscussionWe successfully loaded HHC36 onto the surface of SPEEK through a simple solvent evaporation method. The sample has excellent antibacterial properties and good cell compatibility, which can significantly reduce the bacterial survival rate and inflammatory reaction in vivo. The above results indicated that we successfully improved the antibacterial property of PEEK by a simple modification strategy, making it a promising material for anti-infection orthopedic implants.
Different and unified responses of soil bacterial and fungal community composition and predicted functional potential to 3 years’ drought stress in a semiarid alpine grassland
Authors: Wan Qian , Li Lei , Liu Bo , Zhang Zhihao , Liu Yalan , Xie Mingyu 
Published: March 2023

IntroductionSoil microbial communities are key to functional processes in terrestrial ecosystems, and they serve as an important indicator of grasslands status. However, the responses of soil microbial communities and functional potential to drought stress in semiarid alpine grasslands remain unclear.MethodsHere, a field experiment was conducted under ambient precipitation as a control, −20% and −40% of precipitation to explore the responses of soil microbial diversity, community composition, and predicted functional potential to drought stress in a semiarid alpine grassland located in the northwest of China. Moreover, 16S rRNA gene and ITS sequencing were used to detect bacterial and fungal communities, and the PICRUST and FUNGuild databases were used to predict bacterial and fungal functional groups.ResultsResults showed drought stress substantially changes the community diversity of bacteria and fungi, among which the bacteria community is more sensitive to drought stress than fungi, indicating that the diversity or structure of soil bacteria community could serve as an indicator of alpine grasslands status. However, the fungal community still has difficulty maintaining resistance under excessive drought stress. Our paper also highlighted that soil moisture content, plant diversity (Shannon Wiener, Pieiou, and Simpson), and soil organic matter are the main drivers affecting soil bacterial and fungal community composition and predicted functional potential. Notably, the soil microbial functional potential could be predictable through taxonomic community profiles.ConclusionOur research provides insight for exploring the mechanisms of microbial community composition and functional response to climate change (longer drought) in a semiarid alpine grassland.
Bidirectional Mendelian randomization study of psychiatric disorders and Parkinson’s disease
Authors: Wu Qi , Liu Shulin , Huang Xiurong , Liu Jiabin , Wang Yige , Xiang Yaqing , Tang Xuxiong , Xu Qian , Yan Xinxiang , Tang Beisha , Guo Jifeng 
Published: March 2023

IntroductionAlthough the relationship between psychiatric disorders and Parkinson’s disease (PD) has attracted continuous research attention, the causal linkage between them has not reached a definite conclusion.MethodsTo identify the causal relationship between psychiatric disorders and PD, we used public summary-level data from the most recent and largest genome-wide association studies (GWASs) on psychiatric disorders and PD to conduct a bidirectional two-sample Mendelian randomization (MR). We applied stringent control steps in instrumental variable selection using the Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) method to rule out pleiotropy. The inverse-variance weighted (IVW) method was used to identify the causal relationship between psychiatric disorders and PD. Multiple MR analysis methods, including MR-Egger, weighted-median, and leave-one-out analyses, were used for sensitivity analysis, followed by heterogeneity tests. Further validation and reverse MR analyses were conducted to strengthen the results of the forward MR analysis.ResultsThe lack of sufficient estimation results could suggest a causal relationship between psychiatric disorders and PD in the forward MR analysis. However, the subsequent reverse MR analysis detected a causal relationship between PD and bipolar disorder (IVW: odds ratios [OR] =1.053, 95% confidence interval [CI] =1.02–1.09, p = 0.001). Further analysis demonstrated a causal relationship between genetically predicted PD and the risk of bipolar disorder subtype. No pleiotropy or heterogeneity was detected in the analyses.DiscussionOur study suggested that while psychiatric disorders and traits might play various roles in the risk of developing PD, PD might also be involved in the risk of developing psychiatric disorders.
How often is occult atrial fibrillation in cryptogenic stroke causal vs. incidental? A meta-analysis
Authors: Chaisinanunkul Napasri , Khurshid Shaan , Buck Brian H. , Rabinstein Alejandro A. , Anderson Christopher D. , Hill Michael D. , Fugate Jennifer E. , Saver Jeffrey L. 
Published: March 2023

IntroductionLong-term cardiac monitoring studies have unveiled low-burden, occult atrial fibrillation (AF) in some patients with otherwise cryptogenic stroke (CS), but occult AF is also found in some individuals without a stroke history and in patients with stroke of a known cause (KS). Clinical management would be aided by estimates of how often occult AF in a patient with CS is causal vs. incidental.MethodsThrough a systematic search, we identified all case–control and cohort studies applying identical long-term monitoring techniques to both patients with CS and KS. We performed a random-effects meta-analysis across these studies to determine the best estimate of the differential frequency of occult AF in CS and KS among all patients and across age subgroups. We then applied Bayes' theorem to determine the probability that occult AF is causal or incidental.ResultsThe systematic search identified three case–control and cohort studies enrolling 560 patients (315 CS, 245 KS). Methods of long-term monitoring were implantable loop recorder in 31.0%, extended external monitoring in 67.9%, and both in 1.2%. Crude cumulative rates of AF detection were CS 47/315 (14.9%) vs. KS 23/246 (9.3%). In the formal meta-analysis, the summary odds ratio for occult AF in CS vs. KS in all patients was 1.80 (95% CI, 1.05–3.07), p = 0.03. With the application of Bayes' theorem, the corresponding probabilities indicated that, when present, occult AF in patients with CS is causal in 41.2% (95% CI, 15.5–77.7%) of patients. Analyses stratified by age suggested that detected occult AF in patients with CS was causal in 62.3% (95 CI, 0–87.1%) of patients under the age of 65 years and 28.5% (95 CI, 0–63.7%) of patients aged 65 years and older but estimates had limited precision.ConclusionCurrent evidence is preliminary, but it indicates that in cryptogenic stroke when occult AF is found, it is causal in about 41.2% of patients. These findings suggest that anticoagulation therapy may be beneficial to prevent recurrent stroke in a substantial proportion of patients with CS found to have occult AF.
Neuro-oncological augmented reality planning for intracranial tumor resection
Authors: Van Gestel Frederick , Frantz Taylor , Buyck Felix , Geens Wietse , Neuville Quentin , Bruneau Michaël , Jansen Bart , Scheerlinck Thierry , Vandemeulebroucke Jef , Duerinck Johnny 
Published: March 2023

BackgroundBefore starting surgery for the resection of an intracranial tumor, its outlines are typically marked on the skin of the patient. This allows for the planning of the optimal skin incision, craniotomy, and angle of approach. Conventionally, the surgeon determines tumor borders using neuronavigation with a tracked pointer. However, interpretation errors can lead to important deviations, especially for deep-seated tumors, potentially resulting in a suboptimal approach with incomplete exposure. Augmented reality (AR) allows displaying of the tumor and critical structures directly on the patient, which can simplify and improve surgical preparation.MethodsWe developed an AR-based workflow for intracranial tumor resection planning deployed on the Microsoft HoloLens II, which exploits the built-in infrared-camera for tracking the patient. We initially performed a phantom study to assess the accuracy of the registration and tracking. Following this, we evaluated the AR-based planning step in a prospective clinical study for patients undergoing resection of a brain tumor. This planning step was performed by 12 surgeons and trainees with varying degrees of experience. After patient registration, tumor outlines were marked on the patient's skin by different investigators, consecutively using a conventional neuronavigation system and an AR-based system. Their performance in both registration and delineation was measured in terms of accuracy and duration and compared.ResultsDuring phantom testing, registration errors remained below 2.0 mm and 2.0° for both AR-based navigation and conventional neuronavigation, with no significant difference between both systems. In the prospective clinical trial, 20 patients underwent tumor resection planning. Registration accuracy was independent of user experience for both AR-based navigation and the commercial neuronavigation system. AR-guided tumor delineation was deemed superior in 65% of cases, equally good in 30% of cases, and inferior in 5% of cases when compared to the conventional navigation system. The overall planning time (AR = 119 ± 44 s, conventional = 187 ± 56 s) was significantly reduced through the adoption of the AR workflow (p &lt; 0.001), with an average time reduction of 39%.ConclusionBy providing a more intuitive visualization of relevant data to the surgeon, AR navigation provides an accurate method for tumor resection planning that is quicker and more intuitive than conventional neuronavigation. Further research should focus on intraoperative implementations.
Developmental changes in brain activity of heterozygous Scn1a knockout rats
Authors: Tahara Mayu , Higurashi Norimichi , Hata Junichi , Nishikawa Masako , Ito Ken , Hirose Shinichi , Kaneko Takehito , Mashimo Tomoji , Sakuma Tetsushi , Yamamoto Takashi , Okano Hirotaka James 
Published: March 2023

IntroductionDravet syndrome (DS) is an infantile-onset developmental and epileptic encephalopathy characterized by an age-dependent evolution of drug-resistant seizures and poor developmental outcomes. Functional impairment of gamma-aminobutyric acid (GABA)ergic interneurons due to loss-of-function mutation of SCN1A is currently considered the main pathogenesis. In this study, to better understand the age-dependent changes in the pathogenesis of DS, we characterized the activity of different brain regions in Scn1a knockout rats at each developmental stage.MethodsWe established an Scn1a knockout rat model and examined brain activity from postnatal day (P) 15 to 38 using a manganese-enhanced magnetic resonance imaging technique (MEMRI).ResultsScn1a heterozygous knockout (Scn1a+/−) rats showed a reduced expression of voltage-gated sodium channel alpha subunit 1 protein in the brain and heat-induced seizures. Neural activity was significantly higher in widespread brain regions of Scn1a+/− rats than in wild-type rats from P19 to P22, but this difference did not persist thereafter. Bumetanide, a Na+-K+-2Cl− cotransporter 1 inhibitor, mitigated hyperactivity to the wild-type level, although no change was observed in the fourth postnatal week. Bumetanide also increased heat-induced seizure thresholds of Scn1a+/− rats at P21.ConclusionsIn Scn1a+/− rats, neural activity in widespread brain regions increased during the third postnatal week, corresponding to approximately 6 months of age in humans, when seizures most commonly develop in DS. In addition to impairment of GABAergic interneurons, the effects of bumetanide suggest a possible contribution of immature type A gamma-aminobutyric acid receptor signaling to transient hyperactivity and seizure susceptibility during the early stage of DS. This hypothesis should be addressed in the future. MEMRI is a potential technique for visualizing changes in basal brain activity in developmental and epileptic encephalopathies.
A RUSBoosted tree method for k-complex detection using tunable Q-factor wavelet transform and multi-domain feature extraction
Authors: Li Yabing , Dong Xinglong 
Published: March 2023

BackgroundK-complex detection traditionally relied on expert clinicians, which is time-consuming and onerous. Various automatic k-complex detection-based machine learning methods are presented. However, these methods always suffered from imbalanced datasets, which impede the subsequent processing steps.New methodIn this study, an efficient method for k-complex detection using electroencephalogram (EEG)-based multi-domain features extraction and selection method coupled with a RUSBoosted tree model is presented. EEG signals are first decomposed using a tunable Q-factor wavelet transform (TQWT). Then, multi-domain features based on TQWT are pulled out from TQWT sub-bands, and a self-adaptive feature set is obtained from a feature selection based on the consistency-based filter for the detection of k-complexes. Finally, the RUSBoosted tree model is used to perform k-complex detection.ResultsExperimental outcomes manifest the efficacy of our proposed scheme in terms of the average performance of recall measure, AUC, and F10-score. The proposed method yields 92.41 ± 7.47%, 95.4 ± 4.32%, and 83.13 ± 8.59% for k-complex detection in Scenario 1 and also achieves similar results in Scenario 2.Comparison to state-of-the-art methodsThe RUSBoosted tree model was compared with three other machine learning classifiers [i.e., linear discriminant analysis (LDA), logistic regression, and linear support vector machine (SVM)]. The performance based on the kappa coefficient, recall measure, and F10-score provided evidence that the proposed model surpassed other algorithms in the detection of the k-complexes, especially for the recall measure.ConclusionIn summary, the RUSBoosted tree model presents a promising performance in dealing with highly imbalanced data. It can be an effective tool for doctors and neurologists to diagnose and treat sleep disorders.
Brain-machine interface based on transfer-learning for detecting the appearance of obstacles during exoskeleton-assisted walking
Authors: Quiles Vicente , Ferrero Laura , Iáñez Eduardo , Ortiz Mario , Gil-Agudo Ángel , Azorín José M. 
Published: March 2023

IntroductionBrain-machine interfaces (BMIs) attempt to establish communication between the user and the device to be controlled. BMIs have great challenges to face in order to design a robust control in the real field of application. The artifacts, high volume of training data, and non-stationarity of the signal of EEG-based interfaces are challenges that classical processing techniques do not solve, showing certain shortcomings in the real-time domain. Recent advances in deep-learning techniques open a window of opportunity to solve some of these problems. In this work, an interface able to detect the evoked potential that occurs when a person intends to stop due to the appearance of an unexpected obstacle has been developed.Material and methodsFirst, the interface was tested on a treadmill with five subjects, in which the user stopped when an obstacle appeared (simulated by a laser). The analysis is based on two consecutive convolutional networks: the first one to discern the intention to stop against normal walking and the second one to correct false detections of the previous one.Results and discussionThe results were superior when using the methodology of the two consecutive networks vs. only the first one in a cross-validation pseudo-online analysis. The false positives per min (FP/min) decreased from 31.8 to 3.9 FP/min and the number of repetitions in which there were no false positives and true positives (TP) improved from 34.9% to 60.3% NOFP/TP. This methodology was tested in a closed-loop experiment with an exoskeleton, in which the brain-machine interface (BMI) detected an obstacle and sent the command to the exoskeleton to stop. This methodology was tested with three healthy subjects, and the online results were 3.8 FP/min and 49.3% NOFP/TP. To make this model feasible for non-able bodied patients with a reduced and manageable time frame, transfer-learning techniques were applied and validated in the previous tests, and were then applied to patients. The results for two incomplete Spinal Cord Injury (iSCI) patients were 37.9% NOFP/TP and 7.7 FP/min.