bluuebunny/crossref_metadata_2025_split huggingface dataset:
DeepLION2: deep multi-instance contrastive learning framework enhancing the prediction of cancer-associated T cell receptors by attention strategy on motifs
Authors: Qian Xinyang , Yang Guang , Li Fan , Zhang Xuanping , Zhu Xiaoyan , Lai Xin , Xiao Xiao , Wang Tao , Wang Jiayin 
Published: March 2024

IntroductionT cell receptor (TCR) repertoires provide valuable insights into complex human diseases, including cancers. Recent advancements in immune sequencing technology have significantly improved our understanding of TCR repertoire. Some computational methods have been devised to identify cancer-associated TCRs and enable cancer detection using TCR sequencing data. However, the existing methods are often limited by their inadequate consideration of the correlations among TCRs within a repertoire, hindering the identification of crucial TCRs. Additionally, the sparsity of cancer-associated TCR distribution presents a challenge in accurate prediction.MethodsTo address these issues, we presented DeepLION2, an innovative deep multi-instance contrastive learning framework specifically designed to enhance cancer-associated TCR prediction. DeepLION2 leveraged content-based sparse self-attention, focusing on the top k related TCRs for each TCR, to effectively model inter-TCR correlations. Furthermore, it adopted a contrastive learning strategy for bootstrapping parameter updates of the attention matrix, preventing the model from fixating on non-cancer-associated TCRs.ResultsExtensive experimentation on diverse patient cohorts, encompassing over ten cancer types, demonstrated that DeepLION2 significantly outperformed current state-of-the-art methods in terms of accuracy, sensitivity, specificity, Matthews correlation coefficient, and area under the curve (AUC). Notably, DeepLION2 achieved impressive AUC values of 0.933, 0.880, and 0.763 on thyroid, lung, and gastrointestinal cancer cohorts, respectively. Furthermore, it effectively identified cancer-associated TCRs along with their key motifs, highlighting the amino acids that play a crucial role in TCR-peptide binding.ConclusionThese compelling results underscore DeepLION2's potential for enhancing cancer detection and facilitating personalized cancer immunotherapy. DeepLION2 is publicly available on GitHub, at https://github.com/Bioinformatics7181/DeepLION2, for academic use only.
Mycobacterium tuberculosis-dependent monocyte expression quantitative trait loci, cytokine production, and TB pathogenesis
Authors: Hong Hyejeong , Dill-McFarland Kimberly A. , Simmons Jason D. , Peterson Glenna J. , Benchek Penelope , Mayanja-Kizza Harriet , Boom W. Henry , Stein Catherine M. , Hawn Thomas R. 
Published: March 2024

IntroductionThe heterogeneity of outcomes after Mycobacterium tuberculosis (Mtb) exposure is a conundrum associated with millennia of host-pathogen co-evolution. We hypothesized that human myeloid cells contain genetically encoded, Mtb-specific responses that regulate critical steps in tuberculosis (TB) pathogenesis.MethodsWe mapped genome-wide expression quantitative trait loci (eQTLs) in Mtb-infected monocytes with RNAseq from 80 Ugandan household contacts of pulmonary TB cases to identify monocyte-specific, Mtb-dependent eQTLs and their association with cytokine expression and clinical resistance to tuberculin skin test (TST) and interferon-γ release assay (IGRA) conversion.Resultscis-eQTLs (n=1,567) were identified in Mtb-infected monocytes (FDR&lt;0.01), including 29 eQTLs in 16 genes which were Mtb-dependent (significant for Mtb:genotype interaction [FDR&lt;0.1], but not classified as eQTL in uninfected condition [FDR≥0.01]). A subset of eQTLs were associated with Mtb-induced cytokine expression (n=8) and/or clinical resistance to TST/IGRA conversion (n=1). Expression of BMP6, an Mtb-dependent eQTL gene, was associated with IFNB1 induction in Mtb-infected and DNA ligand-induced cells. Network and enrichment analyses identified fatty acid metabolism as a pathway associated with eQTL genes.DiscussionThese findings suggest that monocyte genes contain Mtb-dependent eQTLs, including a subset associated with cytokine expression and/or clinical resistance to TST/IGRA conversion, providing insight into immunogenetic pathways regulating susceptibility to Mtb infection and TB pathogenesis.
Group A streptococcal collagen-like protein 1 restricts tumor growth in murine pancreatic adenocarcinoma and inhibits cancer-promoting neutrophil extracellular traps
Authors: Henderson Emily A. , Ivey Abby , Choi Soo Jeon , Santiago Stell , McNitt Dudley , Liu Tracy W. , Lukomski Slawomir , Boone Brian A. 
Published: March 2024

IntroductionPancreatic ductal adenocarcinoma (PDAC) is a lethal cancer associated with an immunosuppressive environment. Neutrophil extracellular traps (NETs) were initially described in the context of infection but have more recently been implicated in contributing to the tolerogenic immune response in PDAC. Thus, NETs are an attractive target for new therapeutic strategies. Group A Streptococcus (GAS) has developed defensive strategies to inhibit NETs.MethodsIn the present work, we propose utilizing intra-tumoral GAS injection to stimulate anti-tumor activity by inhibiting cancer-promoting NETs. Mice harboring Panc02 or KPC subcutaneous tumors injected with three different M-type GAS strains. Tumors and spleens were harvested at the endpoint of the experiments to assess bacterial colonization and systemic spread, while sera were analyzed for humoral responses toward the streptococcal antigens, especially the M1 and Scl1 proteins. Role of the streptococcal collagen-like protein 1 (Scl1) in anti-PDAC activity was assessed in vivo after intratumoral injection with M1 GAS wild-type, an isogenic mutant strain devoid of Scl1, or a complemented mutant strain with restored scl1 expression. In addition, recombinant Scl1 proteins were tested for NET inhibition using in vitro and ex vivo assays assessing NET production and myeloperoxidase activity.ResultsInjection of three different M-type GAS strains reduced subcutaneous pancreatic tumor volume compared to control in two different murine PDAC models. Limitation of tumor growth was dependent on Scl1, as isogenic mutant strain devoid of Scl1 did not reduce tumor size. We further show that Scl1 plays a role in localizing GAS to the tumor site, thereby limiting the systemic spread of bacteria and off-target effects. While mice did elicit a humoral immune response to GAS antigens, tested sera were weakly immunogenic toward Scl1 antigen following intra-tumoral treatment with Scl1-expressing GAS. M1 GAS inhibited NET formation when co-cultured with neutrophils while Scl1-devoid mutant strain did not. Recombinant Scl1 protein inhibited NETs ex vivo in a dose-dependent manner by suppressing myeloperoxidase activity.DiscussionAltogether, we demonstrate that intra-tumoral GAS injections reduce PDAC growth, which is facilitated by Scl1, in part through inhibition of cancer promoting NETs. This work offers a novel strategy by which NETs can be targeted through Scl1 protein and potentiates its use as a cancer therapeutic.
Health-related quality of life in patients with inborn errors of immunity: a bibliometric analysis
Authors: Xiao Ningkun , Huang Xinlin , Zang Wanli , Kiselev Sergey , Bolkov Mikhail A. , Tuzankina Irina A. , Chereshnev Valery A. 
Published: March 2024

BackgroundInborn Errors of Immunity (IEI) are characterized by a heightened susceptibility to infections, allergies, and various other health complications. Health-Related Quality of Life (HRQOL) in patients with IEI is a critical area of research that demands attention due to the impact of IEI on patients’ lives. This study utilized bibliometric methods, aiming to comprehensively explore the research content and hotspots in the field of HRQOL in patients with IEI.MethodsThis bibliometric analysis utilized data from the Science Citation Index Expanded (SCIE) and Social Sciences Citation Index (SSCI) within the Web of Science core datasets up to January 1, 2024. The study focused on literature that addressed HRQOL in IEI patients, involving a total of 1,807 authors and 309 articles published across 112 journals. The analysis included publication volume and growth trends, country and institutional contributions, authorship, and journal analysis.ResultsThe research found that despite the importance of HRQOL in IEI, the volume of publications in this field remains consistently low, with no significant increase in trend. The USA leads in publication and citation volumes, reflecting a geographical imbalance in research contributions. Key journals in this field include the Journal of Clinical Immunology, Frontiers in Immunology, and the Journal of Allergy and Clinical Immunology. The study highlights that while treatments like hematopoietic stem cell transplants and gene therapy have improved patient IEI survival rates, they still often come with significant side effects impacting HRQOL. The analysis underlines the need for comprehensive HRQOL assessments in IEI, considering the physical and psychological impacts of treatments.ConclusionsThis study represents a bibliometric analysis focusing on HRQOL in patients with. It underscores the need for more extensive and systematic research in this area, emphasizing the importance of a multidisciplinary approach. Despite advancements in medical treatments for IEI, there is a crucial need to focus on HRQOL to enhance patient satisfaction and overall well-being. The findings advocate for more personalized treatment plans and a better understanding of the psychosocial needs of patients with IEI to improve their quality of life.
Detection of the infective Plasmodium falciparum gametocytes by RT-qPCR assay from a malaria-endemic region of Northeastern India
Authors: Das Ram , Vashisht Kapil , Kori Lokesh , Singh Kuldeep , Kumar Gaurav , Hasan Izazul , Gam Jugal , Pandey Kailash C. 
Published: March 2024

IntroductionThe diagnosis of infectious reservoirs in malaria (gametocytes) is necessary, especially in low-density infections and asymptomatic malaria patients. The gametocyte stage is a surrogate marker for infection of P. falciparum malaria in healthy individuals. The early detection of infectious gametocytes and treatment will strengthen our efforts in curbing transmission. The nested PCR and real-time quantitative PCR (RT-qPCR) methods have been demonstrated for the diagnosis of infectious gametocyte reservoirs. In this study, RDT, blood smear microscopy, and nested-PCR were used for the detection of P. falciparum and P. vivax, and compared with RT-qPCR detection of Pfg27 gametocyte biomarker gene.MethodsIn the present cross-sectional study, 356 human blood samples were collected from endemic areas of Kokrajhar Assam (asymptomatic and symptomatic malaria patients) for malaria diagnosis.ResultsA total of 8.42%(30/356) incidence of malaria was observed. Malaria patients were observed to be both symptomatic, 80%(24/30; 13Pf+11Pv), and asymptomatic, 20%(6 (4Pf +2Pv)). More than 64%(11/17) of Pf and 92.3%(12/13) of Pv infections were observed in children and the adolescent population (age &lt;20 years) by RDT, microscopy, nested PCR, and RT-qPCR methods. The prevalence of Pf infection was 4.77%(17/356) by RT-qPCR method. Of 16 the Pf positive samples 81.25%(13/16) were symptomatic and 18.75%(3/16) were asymptomatic. One asymptomatic individual was found positive for Pf infection by the RT-qPCR method.ConclusionThe findings from this research study revealed that the routine microscopy and RDT methods are insufficient for detecting all asymptomatic malaria and gametocyte infectious reservoirs. The early detection of infectious P. falciparum gametocytes and the treatment of patients will be helpful in preventing the transmission of malaria.
Data model-based toolpath generation techniques for CNC milling machines
Authors: Liao Jianbin , Huang Zeng 
Published: March 2024

Introduction: With the development of computer technology and data modeling, the use of point cloud models to generate tool paths is particularly important for improving productivity and accuracy.Methods: This study proposes a new method that first preprocesses the point cloud data using four-point denoising and octree methods to improve processing efficiency. Subsequently, roughing tool paths were analyzed using the layer slicing method and finishing paths using the residual height method.Results and Discussion: The experimental results show that the layer slicing method has a minimum error close to 10% on the roughing path generation and the computation time is reduced to 35 s, while the residual height method has an error rate of 10.17% on the finishing path and the computation time is only 11.82 s, which reflects a high trajectory smoothness and accuracy. The above results show that the study not only optimizes the tool path generation process and improves the machining efficiency and accuracy, but also demonstrates the potential application of point cloud models in the machining of complex parts.Conclusion: The novel tool roughing and finishing methods provide more reliable path planning for actual machining operations, and future research will be devoted to further improving the performance of the data processing algorithms and exploring more efficient path planning strategies to facilitate automated production.
AGRAMP: machine learning models for predicting antimicrobial peptides against phytopathogenic bacteria
Authors: Shao Jonathan , Zhao Yan , Wei Wei , Vaisman Iosif I. 
Published: March 2024

IntroductionAntimicrobial peptides (AMPs) are promising alternatives to traditional antibiotics for combating plant pathogenic bacteria in agriculture and the environment. However, identifying potent AMPs through laborious experimental assays is resource-intensive and time-consuming. To address these limitations, this study presents a bioinformatics approach utilizing machine learning models for predicting and selecting AMPs active against plant pathogenic bacteria.MethodsN-gram representations of peptide sequences with 3-letter and 9-letter reduced amino acid alphabets were used to capture the sequence patterns and motifs that contribute to the antimicrobial activity of AMPs. A 5-fold cross-validation technique was used to train the machine learning models and to evaluate their predictive accuracy and robustness.ResultsThe models were applied to predict putative AMPs encoded by intergenic regions and small open reading frames (ORFs) of the citrus genome. Approximately 7% of the 10,000-peptide dataset from the intergenic region and 7% of the 685,924-peptide dataset from the whole genome were predicted as probable AMPs. The prediction accuracy of the reported models range from 0.72 to 0.91. A subset of the predicted AMPs was selected for experimental test against Spiroplasma citri, the causative agent of citrus stubborn disease. The experimental results confirm the antimicrobial activity of the selected AMPs against the target bacterium, demonstrating the predictive capability of the machine learning models.DiscussionHydrophobic amino acid residues and positively charged amino acid residues are among the key features in predicting AMPs by the Random Forest Algorithm. Aggregation propensity appears to be correlated with the effectiveness of the AMPs. The described models would contribute to the development of effective AMP-based strategies for plant disease management in agricultural and environmental settings. To facilitate broader accessibility, our model is publicly available on the AGRAMP (Agricultural Ngrams Antimicrobial Peptides) server.
MCSS: microbial community simulator based on structure
Authors: Hui Xingqi , Yang Jinbao , Sun Jinhuan , Liu Fang , Pan Weihua 
Published: March 2024

De novo assembly plays a pivotal role in metagenomic analysis, and the incorporation of third-generation sequencing technology can significantly improve the integrity and accuracy of assembly results. Recently, with advancements in sequencing technology (Hi-Fi, ultra-long), several long-read-based bioinformatic tools have been developed. However, the validation of the performance and reliability of these tools is a crucial concern. To address this gap, we present MCSS (microbial community simulator based on structure), which has the capability to generate simulated microbial community and sequencing datasets based on the structure attributes of real microbiome communities. The evaluation results indicate that it can generate simulated communities that exhibit both diversity and similarity to actual community structures. Additionally, MCSS generates synthetic PacBio Hi-Fi and Oxford Nanopore Technologies (ONT) long reads for the species within the simulated community. This innovative tool provides a valuable resource for benchmarking and refining metagenomic analysis methods.Code available at:https://github.com/panlab-bio/mcss
Assessing interstitial fluid dynamics in type 2 diabetes mellitus and prediabetes cases through diffusion tensor imaging analysis along the perivascular space
Authors: Tuerxun Rukeye , Kamagata Koji , Saito Yuya , Andica Christina , Takabayashi Kaito , Uchida Wataru , Yoshida Seina , Kikuta Junko , Tabata Hiroki , Naito Hitoshi , Someya Yuki , Kaga Hideyoshi , Miyata Mari , Akashi Toshiaki , Wada Akihiko , Taoka Toshiaki , Naganawa Shinji , Tamura Yoshifumi , Watada Hirotaka , Kawamori Ryuzo , Aoki Shigeki 
Published: March 2024

Background and purposeGlymphatic system in type 2 diabetes mellitus (T2DM) but not in the prodrome, prediabetes (Pre-DM) was investigated using diffusion tensor image analysis along the perivascular space (DTI-ALPS). Association between glymphatic system and insulin resistance of prominent characteristic in T2DM and Pre-DM between is yet elucidated. Therefore, this study delves into the interstitial fluid dynamics using the DTI-ALPS in both Pre-DM and T2DM and association with insulin resistance.Materials and methodsIn our cross-sectional study, we assessed 70 elderly individuals from the Bunkyo Health Study, which included 22 with Pre-DM, 18 with T2DM, and 33 healthy controls with normal glucose metabolism (NGM). We utilized the general linear model (GLM) to evaluate the ALPS index based on DTI-ALPS across these groups, considering variables like sex, age, intracranial volume, years of education, anamnesis of hypertension and hyperlipidemia, and the total Fazekas scale. Furthermore, we have explored the relationship between the ALPS index and insulin resistance, as measured by the homeostasis model assessment of insulin resistance (HOMA-IR) using GLM and the same set of covariates.ResultsIn the T2DM group, the ALPS index demonstrated a reduction compared with the NGM group [family-wise error (FWE)-corrected p &lt; 0.001; Cohen's d = −1.32]. Similarly, the Pre-DM group had a lower ALPS index than the NGM group (FWE-corrected p &lt; 0.001; Cohen's d = −1.04). However, there was no significant disparity between the T2DM and Pre-DM groups (FWE-corrected p = 1.00; Cohen's d = −0.63). A negative correlation was observed between the ALPS index and HOMA-IR in the combined T2DM and Pre-DM groups (partial correlation coefficient r = −0.35, p &lt; 0.005).ConclusionThe ALPS index significantly decreased in both the pre-DM and T2DM groups and showed a correlated with insulin resistance. This indicated that changes in interstitial fluid dynamics are associated with insulin resistance.
Prediction of tumor lysis syndrome in childhood acute lymphoblastic leukemia based on machine learning models: a retrospective study
Authors: Xiao Yao , Xiao Li , Zhang Yang , Xu Ximing , Guan Xianmin , Guo Yuxia , Shen Yali , Lei XiaoYing , Dou Ying , Yu Jie 
Published: March 2024

BackgroundTumor lysis syndrome (TLS) often occurs early after induction chemotherapy for acute lymphoblastic leukemia (ALL) and can rapidly progress. This study aimed to construct a machine learning model to predict the risk of TLS using clinical indicators at the time of ALL diagnosis.MethodsThis observational cohort study was conducted at the National Clinical Research Center for Child Health and Disease. Data were collected from pediatric ALL patients diagnosed between December 2008 and December 2021. Four machine learning models were constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) to select key clinical indicators for model construction.ResultsThe study included 2,243 pediatric ALL patients, and the occurrence of TLS was 8.87%. A total of 33 indicators with missing values ≤30% were collected, and 12 risk factors were selected through LASSO regression analysis. The CatBoost model with the best performance after feature screening was selected to predict the TLS of ALL patients. The CatBoost model had an AUC of 0.832 and an accuracy of 0.758. The risk factors most associated with TLS were the absence of potassium, phosphorus, aspartate transaminase (AST), white blood cell count (WBC), and urea levels.ConclusionWe developed the first TLS prediction model for pediatric ALL to assist clinicians in risk stratification at diagnosis and in developing personalized treatment protocols. This study is registered on the China Clinical Trials Registry platform (ChiCTR2200060616).Clinical trial registrationhttps://www.chictr.org.cn/, identifier ChiCTR2200060616.
Immune checkpoint inhibitor-related acquired amegakaryocytosis thrombocytopenia: a case report and literature review
Authors: Rivet Valérian , Sibaud Vincent , Dion Jérémie , Volosov Thibaut , Biteau Mélanie , Pastissier Andréa , Delavigne Karen , Cougoul Pierre , Rauzy Odile , Comont Thibault 
Published: March 2024

IntroductionImmune checkpoint inhibitors (ICIs) are used in several advanced malignancies and may cause various immune-related adverse events (irAEs). Among them, hematological irAEs are less described. Acquired amegakaryocytic thrombocytopenia (AAT) is a rare immune hematologic disorder characterized by severe thrombocytopenia and complete absence of megakaryocytes in bone marrow.Case presentationHerein, we present the case of a patient in their 40s with metastatic melanoma who developed an AAT after 12 cycles of nivolumab (anti-PD1). His platelet count decreased by ≤5 × 109/l without other cytopenia. Bone marrow biopsy showed normal cellularity with a complete absence of megakaryocyte and T-CD8+ lymphocyte infiltration. Given the failure of systemic steroids, eltrombopag was started, an oral thrombopoietin receptor agonist (TPO-RA), and his platelet count subsequently increased with complete response.DiscussionFour other cases are described on literature with the same features than non-ICI-related AAT. All cases occurred after anti-PD/PD-L1 treatment with a median onset of 5 weeks. The presentation of our case is quite different with delayed cytopenia. Both ciclosporin and TPO-RA seem to be efficient therapies.ConclusionTPO-RA could be preferred in oncologic patients, but safety data are still missing to define clear guidelines for immune-related AAT management.