LC-MS/MS platform-based serum untargeted screening reveals the diagnostic biomarker panel and molecular mechanism of breast cancer.

BACKGROUND: Breast cancer (BC), the most common form of malignant cancer affecting women worldwide, was characterized by heterogeneous metabolic disorder and lack of effective biomarkers for diagnosis. The purpose of this study is to search for reliable metabolite biomarkers of BC as well as triple-negative breast cancer (TNBC) using serum metabolomics approach. METHODS: In this study, an untargeted metabolomics technique based on ultra-high performance liquid chromatography combined with mass spectrometry (UHPLC-MS) was utilized to investigate the differences in serum metabolic profile between the BC group (n = 53) and non-BC group (n = 57), as well as between TNBC patients (n = 23) and non-TNBC subjects (n = 30). The multivariate data analysis, determination of the fold change and the Mann-Whitney U test were used to screen out the differential metabolites. Additionally, machine learning methods including receiver operating curve analysis and logistic regression analysis were conducted to establish diagnostic biomarker panels. RESULTS: There were 36 metabolites found to be significantly different between BC and non-BC groups, and 12 metabolites discovered to be significantly different between TNBC and non-TNBC patients. Results also showed that four metabolites, including N-acetyl-D-tryptophan, 2-arachidonoylglycerol, pipecolic acid and oxoglutaric acid, were considered as vital biomarkers for the diagnosis of BC and non-BC with an area under the curve (AUC) of 0.995. Another two-metabolite panel of N-acetyl-D-tryptophan and 2-arachidonoylglycerol was discovered to discriminate TNBC from non-TNBC and produced an AUC of 0.965. CONCLUSION: This study demonstrated that serum metabolomics can be used to identify BC specifically and identified promising serum metabolic markers for TNBC diagnosis.

Improvement of Rimocidin Biosynthesis by Increasing Supply of Precursor Malonyl-CoA via Over-expression of Acetyl-CoA Carboxylase in Streptomyces rimosus M527.

Precursor engineering is an effective strategy for the overproduction of secondary metabolites. The polyene macrolide rimocidin, which is produced by Streptomyces rimosus M527, exhibits a potent activity against a broad range of phytopathogenic fungi. It has been predicted that malonyl-CoA is used as extender units for rimocidin biosynthesis. Based on a systematic analysis of three sets of time-series transcriptome microarray data of S. rimosus M527 fermented in different conditions, the differentially expressed accsr gene that encodes acetyl-CoA carboxylase (ACC) was found. To understand how the formation of rimocidin is being influenced by the expression of the accsr gene and by the concentration of malonyl-CoA, the accsr gene was cloned and over-expressed in the wild-type strain S. rimosus M527 in this study. The recombinant strain S. rimosus M527-ACC harboring the over-expressed accsr gene exhibited better performances based on the enzymatic activity of ACC, intracellular malonyl-CoA concentrations, and rimocidin production compared to S. rimosus M527 throughout the fermentation process. The enzymatic activity of ACC and intracellular concentration of malonyl-CoA of S. rimosus M527-ACC were 1.0- and 1.5-fold higher than those of S. rimosus M527, respectively. Finally, the yield of rimocidin produced by S. rimosus M527-ACC reached 320.7 mg/L, which was 34.0% higher than that of S. rimosus M527. These results confirmed that malonyl-CoA is an important precursor for rimocidin biosynthesis and suggested that an adequate supply of malonyl-CoA caused by accsr gene over-expression led to the improvement in rimocidin production.

Predicting subcellular location of protein with evolution information and sequence-based deep learning.

BACKGROUND: Protein subcellular localization prediction plays an important role in biology research. Since traditional methods are laborious and time-consuming, many machine learning-based prediction methods have been proposed. However, most of the proposed methods ignore the evolution information of proteins. In order to improve the prediction accuracy, we present a deep learning-based method to predict protein subcellular locations. RESULTS: Our method utilizes not only amino acid compositions sequence but also evolution matrices of proteins. Our method uses a bidirectional long short-term memory network that processes the entire protein sequence and a convolutional neural network that extracts features from protein sequences. The position specific scoring matrix is used as a supplement to protein sequences. Our method was trained and tested on two benchmark datasets. The experiment results show that our method yields accurate results on the two datasets with an average precision of 0.7901, ranking loss of 0.0758 and coverage of 1.2848. CONCLUSION: The experiment results show that our method outperforms five methods currently available. According to those experiments, we can see that our method is an acceptable alternative to predict protein subcellular location.

Demographic Characteristics and Lifestyle Habits of Chinese Plasma Donors: A Multicenter Study.

BACKGROUND The demand for plasma and plasma products has increased in China, which has a short supply. Compared with whole blood donors, plasma donors and their donation behavior have received less attention. This study aimed to investigate the demographic characteristics and lifestyle habits of Chinese plasma donors. MATERIAL AND METHODS During 2018-2019, information on plasma donors was collected from blood product companies using a 25-item questionnaire, including sex, age, height, weight, blood group, donation frequency, occupation, smoking and drinking, and sleeping and dietary habits. RESULTS Among 15 497 plasma donors, 70.5% were women and 78.5% were aged 46-55 years. Among 4847 plasma donors, the average height of men was 169.5±6.2 cm and the average height of women was 157.0±4.6 cm. In addition, the average weight of men was 67.0±10.4 kg and the average weight of women was 60.0±8.3 kg. The prevalence of obesity (body mass index ≥30.0 kg/m²) of all donors was 14.8%; 14.7% of men were obese, and 15% of women were obese. Among all plasma donors, 88.8% were farmers and 60% were frequent donors with a donation history of at least 5 years. Among all donors, 84.0% did not smoke, 67.3% did not drink, and 95.1% reported good sleep quality. All respondents reported healthy dietary habits. CONCLUSIONS Healthy lifestyle habits considerably affect the health of plasma donors and the quality of source plasma. Chinese plasma donors in this study demonstrated imbalances in terms of characteristics, which became more marked with age.

Iterative feature representations improve N4-methylcytosine site prediction.

MOTIVATION: Accurate identification of N4-methylcytosine (4mC) modifications in a genome wide can provide insights into their biological functions and mechanisms. Machine learning recently have become effective approaches for computational identification of 4mC sites in genome. Unfortunately, existing methods cannot achieve satisfactory performance, owing to the lack of effective DNA feature representations that are capable to capture the characteristics of 4mC modifications. RESULTS: In this work, we developed a new predictor named 4mcPred-IFL, aiming to identify 4mC sites. To represent and capture discriminative features, we proposed an iterative feature representation algorithm that enables to learn informative features from several sequential models in a supervised iterative mode. Our analysis results showed that the feature representations learnt by our algorithm can capture the discriminative distribution characteristics between 4mC sites and non-4mC sites, enlarging the decision margin between the positives and negatives in feature space. Additionally, by evaluating and comparing our predictor with the state-of-the-art predictors on benchmark datasets, we demonstrate that our predictor can identify 4mC sites more accurately. AVAILABILITY AND IMPLEMENTATION: The user-friendly webserver that implements the proposed 4mcPred-IFL is well established, and is freely accessible at http://server.malab.cn/4mcPred-IFL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identification of a gene from Streptomyces rimosus M527 negatively affecting rimocidin biosynthesis and morphological differentiation.

The polyene macrolide rimocidin, produced by Streptomyces rimosus M527, was found to be highly effective against a broad range of fungal plant pathogens. Current understanding of the regulatory mechanism of rimocidin biosynthesis and morphological differentiation in S. rimosus M527 is limited. NsdA is considered a negative regulator involved in morphological differentiation and biosynthesis of secondary metabolites in some Streptomyces species. In this study, nsdAsr was cloned from S. rimosus M527. The role of nsdAsr in rimocidin biosynthesis and morphological differentiation was investigated by gene deletion, complementation, and over-expression. A ΔnsdAsr mutant was obtained using CRISPR/Cas9. The mutant produced more rimocidin (46%) and accelerated morphological differentiation than the wild-type strain. Over-expression of nsdAsr led to a decrease in rimocidin production and impairment of morphological differentiation. Quantitative RT-PCR analysis revealed that transcription of rim genes responsible for rimocidin biosynthesis was upregulated in the ΔnsdAsr mutant but downregulated in the nsdAsr over-expression strain. Similar effects have been described for Streptomyces coelicolor M145 and the industrial toyocamycin-producing strain Streptomyces diastatochromogenes 1628. KEY POINTS: • A negative regulator for sporulation and rimocidin production was identified. • The CRISPR/Cas9 system was used for gene deletion in S. rimosus M527.

Re-sensitization of 5-FU resistance by SPARC through negative regulation of glucose metabolism in hepatocellular carcinoma.

Secreted protein, acidic and rich in cysteine (SPARC), a calcium-binding matricellular glycoprotein, is implicated in the progression of many cancers. Currently, there is growing evidence for important functions of SPARC in a variety of cancers and its role in cancer depends on tumor types. In this study, we reported SPARC negatively regulated glucose metabolism in hepatocellular carcinoma (HCC). Overexpression of SPARC inhibited glucose uptake and lactate product through downregulation of key enzymes of glucose metabolism. On the other hand, knock down of SPARC reversed the phenotypes. Meanwhile, exogenous expression of SPARC in HepG2 cells resulted in tolerance to low glucose and was correlated with AMPK pathway. Interestingly, the 5-fluorouracil (5-FU)-resistant HepG2 cells showed increased glucose metabolism and downregulated SPARC levels. Finally, we reported the overexpression of SPARC re-sensitize 5-FU-resistant cells to 5-FU through inhibition of glycolysis both in vitro and in vivo. Our study proposed a novel function of SPARC in the regulation of glucose metabolism in hepatocellular carcinoma and will facilitate the development of therapeutic strategies for the treatments of liver tumor patients.

Prediction of LncRNA-Protein Interactions Based on Kernel Combinations and Graph Convolutional Networks.

The complexes of long non-coding RNAs bound to proteins can be involved in regulating life activities at various stages of organisms. However, in the face of the growing number of lncRNAs and proteins, verifying LncRNA-Protein Interactions (LPI) based on traditional biological experiments is time-consuming and laborious. Therefore, with the improvement of computing power, predicting LPI has met new development opportunity. In virtue of the state-of-the-art works, a framework called LncRNA-Protein Interactions based on Kernel Combinations and Graph Convolutional Networks (LPI-KCGCN) has been proposed in this article. We first construct kernel matrices by taking advantage of extracting both the lncRNAs and protein concerning the sequence features, sequence similarity features, expression features, and gene ontology. Then reconstruct the existent kernel matrices as the input of the next step. Combined with known LPI interactions, the reconstructed similarity matrices, which can be used as features of the topology map of the LPI network, are exploited in extracting potential representations in the lncRNA and protein space using a two-layer Graph Convolutional Network. The predicted matrix can be finally obtained by training the network to produce scoring matrices w.r.t. lncRNAs and proteins. Different LPI-KCGCN variants are ensemble to derive the final prediction results and testify on balanced and unbalanced datasets. The 5-fold cross-validation shows that the optimal feature information combination on a dataset with 15.5% positive samples has an AUC value of 0.9714 and an AUPR value of 0.9216. On another highly unbalanced dataset with only 5% positive samples, LPI-KCGCN also has outperformed the state-of-the-art works, which achieved an AUC value of 0.9907 and an AUPR value of 0.9267.

Advancing Underlying Cause of Death Inference Through Wide and Deep Model.

Introduction: Accurately filling out death certificates is essential for death surveillance. However, manually determining the underlying cause of death is often imprecise. In this study, we investigate the Wide and Deep framework as a method to improve the accuracy and reliability of inferring the underlying cause of death. Methods: Death report data from national-level cause of death surveillance sites in Fujian Province from 2016 to 2022, involving 403,547 deaths, were analyzed. The Wide and Deep embedded with Convolutional Neural Networks (CNN) was developed. Model performance was assessed using weighted accuracy, weighted precision, weighted recall, and weighted area under the curve (AUC). A comparison was made with XGBoost, CNN, Gated Recurrent Unit (GRU), Transformer, and GRU with Attention. Results: The Wide and Deep achieved strong performance metrics on the test set: precision of 95.75%, recall of 92.08%, F1 Score of 93.78%, and an AUC of 95.99%. The model also displayed specific F1 Scores for different cause-of-death chain lengths: 97.13% for single causes, 95.08% for double causes, 91.24% for triple causes, and 79.50% for quadruple causes. Conclusions: The Wide and Deep significantly enhances the ability to determine the root causes of death, providing a valuable tool for improving cause-of-death surveillance quality. Integrating artificial intelligence (AI) in this field is anticipated to streamline death registration and reporting procedures, thereby boosting the precision of public health data.

Alpha sensory stimulation modulates theta phase during speech-print associative learning.

Applying 10 Hz (α-rate) sensory stimulation, not 5 Hz (θ-rate), prior to introducing novel speech-print pairs can reset the phase of θ oscillations and enhance associative learning. This rapid gain indicates coordinated mechanisms to regulate attentional/cognitive resources (α oscillations) and facilitate memory storage (θ oscillations) early in learning. The present findings may inform educational practices for children with reading difficulties.

Machine learning-based classification of deubiquitinase USP26 and its cell proliferation inhibition through stabilizing KLF6 in cervical cancer.

OBJECTIVE: We aim to accurately distinguish ubiquitin-specific proteases (USPs) from other members within the deubiquitinating enzyme families based on protein sequences. Additionally, we seek to elucidate the specific regulatory mechanisms through which USP26 modulates Krüppel-like factor 6 (KLF6) and assess the subsequent effects of this regulation on both the proliferation and migration of cervical cancer cells. METHODS: All the deubiquitinase (DUB) sequences were classified into USPs and non-USPs. Feature vectors, including 188D, n-gram, and 400D dimensions, were extracted from these sequences and subjected to binary classification via the Weka software. Next, thirty human USPs were also analyzed to identify conserved motifs and ascertained evolutionary relationships. Experimentally, more than 90 unique DUB-encoding plasmids were transfected into HeLa cell lines to assess alterations in KLF6 protein levels and to isolate a specific DUB involved in KLF6 regulation. Subsequent experiments utilized both wild-type (WT) USP26 overexpression and shRNA-mediated USP26 knockdown to examine changes in KLF6 protein levels. The half-life experiment was performed to assess the influence of USP26 on KLF6 protein stability. Immunoprecipitation was applied to confirm the USP26-KLF6 interaction, and ubiquitination assays to explore the role of USP26 in KLF6 deubiquitination. Additional cellular assays were conducted to evaluate the effects of USP26 on HeLa cell proliferation and migration. RESULTS: 1. Among the extracted feature vectors of 188D, 400D, and n-gram, all 12 classifiers demonstrated excellent performance. The RandomForest classifier demonstrated superior performance in this assessment. Phylogenetic analysis of 30 human USPs revealed the presence of nine unique motifs, comprising zinc finger and ubiquitin-specific protease domains. 2. Through a systematic screening of the deubiquitinase library, USP26 was identified as the sole DUB associated with KLF6. 3. USP26 positively regulated the protein level of KLF6, as evidenced by the decrease in KLF6 protein expression upon shUSP26 knockdown in both 293T and Hela cell lines. Additionally, half-life experiments demonstrated that USP26 prolonged the stability of KLF6. 4. Immunoprecipitation experiments revealed a strong interaction between USP26 and KLF6. Notably, the functional interaction domain was mapped to amino acids 285-913 of USP26, as opposed to the 1-295 region. 5. WT USP26 was found to attenuate the ubiquitination levels of KLF6. However, the mutant USP26 abrogated its deubiquitination activity. 6. Functional biological assays demonstrated that overexpression of USP26 inhibited both proliferation and migration of HeLa cells. Conversely, knockdown of USP26 was shown to promote these oncogenic properties. CONCLUSIONS: 1. At the protein sequence level, members of the USP family can be effectively differentiated from non-USP proteins. Furthermore, specific functional motifs have been identified within the sequences of human USPs. 2. The deubiquitinating enzyme USP26 has been shown to target KLF6 for deubiquitination, thereby modulating its stability. Importantly, USP26 plays a pivotal role in the modulation of proliferation and migration in cervical cancer cells.

CYP1A1 MspI polymorphism is associated with prostate cancer susceptibility: evidence from a meta-analysis.

Cytochrome P450 1A1 (CYP1A1), an important phase I xenobiotic metabolizing enzyme, is responsible for metabolizing numerous carcinogens, particularly polycyclic aromatic hydrocarbons. The genetic polymorphism of CYP1A1 at the site of MspI (CYP1A1 MspI) has been implicated in prostate cancer risk, but the results of individual studies remain conflicting and inconclusive. The aim of this meta-analysis was to investigate the association of CYP1A1 MspI polymorphism with prostate cancer risk more precisely. We performed a comprehensive search of the PubMed, Embase, Web of Science, and China National Knowledge Infrastructure databases from their inception up to September 20, 2012 for relevant publications. The pooled odds ratios with the corresponding 95% confidence intervals (95% CIs) were calculated to assess the association of CYP1A1 MspI polymorphism with prostate cancer risk. In addition, stratified analyses by ethnicity and sensitivity analyses were conducted for further estimation. Sixteen eligible publications with 6,411 subjects were finally included into the meta-analysis after checking the retrieved papers. Overall, meta-analysis of total studies suggested that individuals carrying the TC genotype and a combined C genotype (CC + TC) were more susceptible to prostate cancer (OR(TC vs. TT) = 1.33, 95% CI 1.10-1.61, P(OR) = 0.004; OR(CC+TC vs. TT) = 1.27, 95% CI 1.05-1.55, P(OR) = 0.016). Stratified analysis of high quality studies also confirmed the significant association (OR(TC vs. TT) = 1.32, 95% CI 1.04-1.67, P(OR) = 0.024; OR(CC+TC vs. TT) = 1.30, 95% CI 1.02-1.66, P(OR) = 0.035). In subgroup analyses by ethnicity, a significant association between the CYP1A1 MspI polymorphism and risk of prostate cancer was found among Asians (OR(TC vs. TT) = 1.44, 95% CI 1.20-1.72, P(OR) < 0.001; OR(CC+TC vs. TT) = 1.33, 95% CI 1.12-1.58, P(OR) = 0.001), but not in Caucasians or mixed populations. The meta-analysis suggests an important role of the CYP1A1 MspI polymorphism in the risk of developing prostate cancer, especially in Asians.

AIEgens-Doped Photonic Crystals for High Sensitivity Fluorescence Detection of Tumor Markers.

Detection of tumor markers is of great significance to preliminarily judge whether patients have malignant tumors. Fluorescence detection (FD) is an effective means to achieve sensitive detection of tumor markers. Currently, the increased sensitivity of FD has attracted research interest worldwide. Here, we have proposed a method of doping luminogens with aggregation-induced emission (AIEgens) into photonic crystals (PCs), which can significantly enhance the fluorescence intensity to achieve high sensitivity in the detection of tumor markers. PCs are made by scraping and self-assembling, which has the special effect of fluorescence enhancement. The combination of AIEgens and PCs can enhance the fluorescence intensity 4-7 times. These characteristics make it extremely sensitive. The limit of detection (LOD) for the detection of alpha-fetoprotein (AFP) in the AIE10 (Tetraphenyl ethylene-Br) doped PCs with a reflection peak of 520 nm is 0.0377 ng/mL. LOD for the detection of carcinoembryonic antigen (CEA) in the AIE25 (Tetraphenyl ethylene-NH2) doped PCs with a reflection peak of 590 nm is 0.0337 ng/mL. Our concept offers a good solution for highly sensitive detection of tumor markers.

Identification of SH2 domain-containing proteins and motifs prediction by a deep learning method.

The Src Homology 2 (SH2) domain plays an important role in the signal transmission mechanism in organisms. It mediates the protein-protein interactions based on the combination between phosphotyrosine and motifs in SH2 domain. In this study, we designed a method to identify SH2 domain-containing proteins and non-SH2 domain-containing proteins through deep learning technology. Firstly, we collected SH2 and non-SH2 domain-containing protein sequences including multiple species. We built six deep learning models through DeepBIO after data preprocessing and compared their performance. Secondly, we selected the model with the strongest comprehensive ability to conduct training and test separately again, and analyze the results visually. It was found that 288-dimensional (288D) feature could effectively identify two types of proteins. Finally, motifs analysis discovered the specific motif YKIR and revealed its function in signal transduction. In summary, we successfully identified SH2 domain and non-SH2 domain proteins through deep learning method, and obtained 288D features that perform best. In addition, we found a new motif YKIR in SH2 domain, and analyzed its function which helps to further understand the signaling mechanisms within the organism.

Electrochemical Microneedles: Innovative Instruments in Health Care.

As a significant part of drug therapy, the mode of drug transport has attracted worldwide attention. Efficient drug delivery methods not only markedly improve the drug absorption rate, but also reduce the risk of infection. Recently, microneedles have combined the advantages of subcutaneous injection administration and transdermal patch administration, which is not only painless, but also has high drug absorption efficiency. In addition, microneedle-based electrochemical sensors have unique capabilities for continuous health state monitoring, playing a crucial role in the real-time monitoring of various patient physiological indicators. Therefore, they are commonly applied in both laboratories and hospitals. There are a variety of reports regarding electrochemical microneedles; however, the comprehensive introduction of new electrochemical microneedles is still rare. Herein, significant work on electrochemical microneedles over the past two years is summarized, and the main challenges faced by electrochemical microneedles and future development directions are proposed.

Emerging 3D Printing Strategies for Enzyme Immobilization: Materials, Methods, and Applications.

As the strategies of enzyme immobilization possess attractive advantages that contribute to realizing recovery or reuse of enzymes and improving their stability, they have become one of the most desirable techniques in industrial catalysis, biosensing, and biomedicine. Among them, 3D printing is the emerging and most potential enzyme immobilization strategy. The main advantages of 3D printing strategies for enzyme immobilization are that they can directly produce complex channel structures at low cost, and the printed scaffolds with immobilized enzymes can be completely modified just by changing the original design graphics. In this review, a comprehensive set of developments in the fields of 3D printing techniques, materials, and strategies for enzyme immobilization and the potential applications in industry and biomedicine are summarized. In addition, we put forward some challenges and possible solutions for the development of this field and some possible development directions in the future.

BAT-Net: An enhanced RNA Secondary Structure prediction via bidirectional GRU-based network with attention mechanism.

BACKGROUND: RNA Secondary Structure (RSS) has drawn growing concern, both for their pivotal roles in RNA tertiary structures prediction and critical effect in penetrating the mechanism of functional non-coding RNA. Computational techniques that can reduce the in vitro and in vivo experimental costs have become popular in RSS prediction. However, as an NP-hard problem, there is room for improvement that the validity of the prediction RSS with pseudoknots in traditional machine learning predictors. RESULTS: In this essay, by integrating the bidirectional GRU (Gated Recurrent Unit) with the attention, we propose a multilayered neural network called BAT-Net to predict RSS. Different from the state-of-the-art works, BAT-Net can not only make full use of the information about the direct predecessor and direct successor of the predicted base in the RNA sequence but also dynamically adjust the corresponding loss function. The experimental results on five representative datasets extracted from the RNA STRAND database show that the sensitivity, precision, accuracy, and MCC (Matthews Correlation Coefficient) of the BAT-Net have improved by 8.52%, 8.28%, 5.66% and 9.82%, respectively, compared with the benchmark approaches on the best averages. CONCLUSIONS: BAT-Net can provide users with more credible RSS results since it has further utilized the source information of the dataset. Comparative results show that the proposed BAT-Net is superior to the other existing methods on the relevant indicators.

Intranasal oxytocin decreases self-oriented learning.

RATIONALE: Oxytocin has been found to play an important role in human social cognition and social interaction. Over the last two decades, surge studies have been conducted to investigate how oxytocin impacts other-oriented processes, such as trust and generosity (Zak et al. in PLoS ONE 2(11):e1128, 2007); however, the examination of the effect of oxytocin on self-related processes was relatively inadequate. Appropriate and efficient social interactions require both self- and other-related information processing. Recent studies have found that intranasal oxytocin (IN-OT) influences the self-related process, although the results have been mixed. The computational process underlying the effects of IN-OT on self-processing remains unknown. OBJECTIVES: We aim to investigate the effect of IN-OT on self-oriented learning across different contexts (self-other independent vs. self-other dependent) and uncover the process by which IN-OT affects dynamic behavior changes. METHODS: We performed two double-blind, placebo-controlled studies and used reinforcement learning theory to integrate action and related feedback for participants' behaviors. RESULTS: In study 1, IN-OT decreased self-oriented reward learning when self-oriented learning and prosocial (other-oriented) learning were assessed separately. These effects were partially due to the OT-related increase in choice variability during self-oriented learning. In study 2, IN-OT also decreased learning performance during self-oriented reward learning when self-related and other-related rewards were present together. These effects occurred at an early stage of the learning process and could be moderated by the participants' social value orientation. Our findings show that OT attenuates the process of self-oriented learning and provides an underlying computational process. CONCLUSIONS: Our findings shed new light on the dynamics of IN-OT's effects on human self-oriented learning processes. For future studies on OT effects on self-oriented learning, individual factors such as social value orientation should be taken into consideration in research development and analysis.

A retrospective analysis of the risk factors affecting recurrence time in patients with recurrent glioblastoma.

BACKGROUND: This study explored the related factors that influence the recurrence time of glioblastomas (GBM). METHODS: A retrospective study of recurrent GBM patients with surgical resection was performed. Recurrence time was analyzed using Kaplan-Meier survival curves. The Cox regression model was used to investigate the possible factors associated with recurrence time. RESULTS: A total of 176 patients (113 males and 63 females) were enrolled in the study, with a median age of 57 years (range, 19-76 years). From this cohort, 18 patients (10.2%) had gross total resection (GTR), 53 patients (30.1%) had subtotal resection (STR), and 105 patients (59.7%) had partial resection (PR). Postoperatively, all patients received radiotherapy (RT), with 55.1% administered concurrent chemotherapy (CTh) and 59.7% administered adjuvant CTh. The median recurrence time was 10.0 months (range, 1.0-75.0 months). Patients with PR (P=0.004), gliomas that contacted the subventricular zone (SVZ) (P=0.004), isocitrate dehydrogenase 1 (IDH1) wild-type (P=0.048), telomerase reverse transcriptase (TERT) C228T wild-type (P=0.012), and positive glial fibrillary acidic protein (GFAP) expression (P=0.044) had a shortened time to recurrence. Cox regression analysis revealed that PR (P=0.036), SVZ contact (P=0.008), and TERT C228T wild type (P=0.023) were significantly associated with a shortened recurrence time. CONCLUSIONS: PR, tumor contacting the SVZ, and TERT C228T wild type were independent risk factors for tumor recurrence in patients with GBM.