bluuebunny/crossref_metadata_2025_split huggingface dataset:
Prediction of MYCN Gene Amplification in Pediatric Neuroblastomas: Development of a Deep Learning–Based Tool for Automatic Tumor Segmentation and Comparative Analysis of Computed Tomography–Based Radiomics Features Harmonization
Authors: Yeow Ling Yun , Teh Yu Xuan , Lu Xinyu , Srinivasa Arvind Channarayapatna , Tan Eelin , Tan Timothy Shao Ern , Tang Phua Hwee , KN Bhanu Prakash 
Published: June 2023

Objective

MYCN oncogene amplification is closely linked to high-grade neuroblastoma with poor prognosis. Accurate quantification is essential for risk assessment, which guides clinical decision making and disease management. This study proposes an end-to-end deep-learning framework for automatic tumor segmentation of pediatric neuroblastomas and radiomics features-based classification of MYCN gene amplification.

Methods
Data from pretreatment contrast-enhanced computed tomography scans and MYCN status from 47 cases of pediatric neuroblastomas treated at a tertiary children's hospital from 2009 to 2020 were reviewed. Automated tumor segmentation and grading pipeline includes (1) a modified U-Net for tumor segmentation; (2) extraction of radiomic textural features; (3) feature-based ComBat harmonization for removal of variabilities across scanners; (4) feature selection using 2 approaches, namely, (a) an ensemble approach and (b) stepwise forward-and-backward selection method using logistic regression classifier; and (5) radiomics features-based classification of MYCN gene amplification using machine learning classifiers.

Results
Median train/test Dice score for modified U-Net was 0.728/0.680. The top 3 features from the ensemble approach were neighborhood gray-tone difference matrix (NGTDM) busyness, NGTDM strength, and gray-level run-length matrix (GLRLM) low gray-level run emphasis, whereas those from the stepwise approach were GLRLM low gray-level run emphasis, GLRLM high gray-level run emphasis, and NGTDM coarseness. The top-performing tumor classification algorithm achieved a weighted F1 score of 97%, an area under the receiver operating characteristic curve of 96.9%, an accuracy of 96.97%, and a negative predictive value of 100%. Harmonization-based tumor classification improved the accuracy by 2% to 3% for all classifiers.

Conclusion
The proposed end-to-end framework achieved high accuracy for MYCN gene amplification status classification.
FFRCT and Static Computed Tomography Myocardial Perfusion Imaging for Therapeutic Decision-making and Prognosis in Patients With Coronary Artery Disease
Authors: Li Su Yu , Zhong Jian , Qiao Hong Yan , Schoepf U. Joseph , Emrich Tilman , Butler W. Nicholas , Zuo Rui , Xue Yi , Liu Ya , Dai Li Yan , Zhou Chang Sheng , Lu Guang Ming , Tang Chun Xiang , Zhang Long Jiang 
Published: June 2023

Purpose:
The purpose of this study was to investigate the effect of integrated evaluation of resting static computed tomography perfusion (CTP) and coronary computed tomography angiography (CCTA)–derived fractional flow reserve (FFRCT) on therapeutic decision-making and predicting major adverse cardiovascular events (MACEs) in patients with suspected coronary artery disease.

Materials and Methods:
In this post hoc analysis of a prospective trial of CCTA in patients assigned to either CCTA or CCTA plus FFRCT arms, 500 patients in the CCTA plus FFRCT arm were analyzed. Both resting static CTP and FFRCT were evaluated by using the conventional CCTA. Perfusion defects in the myocardial segments with ≥50% degree of stenosis in the supplying vessels were defined as resting static CTP positive, and any vessel with an FFRCT value of ≤0.80 was considered positive. Patients were divided into 3 groups: (1) negative CTP-FFRCT match group (resting static CTP-negative and FFRCT-negative group); (2) mismatch CTP-FFRCT group (resting static CTP-positive and FFRCT-negative or resting static CTP-negative and FFRCT-positive group); and (3) positive CTP-FFRCT match group (resting static CTP-positive and FFRCT-positive group). We compared the revascularization-to-invasive coronary angiography ratio and the MACE rate among 3 subgroups at 1- and 3-year follow-ups. The adjusted Cox hazard proportional model was used to assess the prognostic value of FFRCT and resting static CTP to determine patients at risk of MACE.

Results:
Patients in the positive CTP-FFRCT match group were more likely to undergo revascularization at the time of invasive coronary angiography compared with those in the mismatch CTP-FFRCT group (81.4% vs 57.7%, P=0.033) and the negative CTP-FFRCT match group (81.4% vs 33.3%, P=0.001). At 1- and 3-year follow-ups, patients in the positive CTP-FFRCT match group were more likely to have MACE than those in the mismatch CTP-FFRCT group (10.5% vs 4.2%, P=0.046; 35.6% vs 9.4%, P<0.001) and the negative CTP-FFRCT match group (10.5% vs 0.9%, P<0.001; 35.6% vs 5.4%, P<0.001). A positive CTP-FFRCT match was strongly related to MACE at 1-year (hazard ratio=8.06, P=0.003) and 3-year (hazard ratio=6.23, P<0.001) follow-ups.

Conclusion:
In patients with suspected coronary artery disease, the combination of FFRCT with resting static CTP could guide therapeutic decisions and have a better prognosis with fewer MACE in a real-world scenario.
CLINICAL CHARACTERISTICS AND PREDICTORS OF MORTALITY DIFFER BETWEEN PULMONARY AND ABDOMINAL SEPSIS
Authors: Ren Enfeng , Xiao Hongli , Li Jiebin , Yu Han , Liu Bo , Wang Guoxing , Sun Xuelian , Duan Meili , Hang Chenchen , Zhang Guoqiang , Wu Caijun , Li Fengjie , Zhang Haiyan , Zhang Yun , Guo Wei , Qi Wenjie , Yin Qin , Zhao Yunzhou , Xie Miaorong , Li Chunsheng 
Published: June 2023

ABSTRACT

Background: Pulmonary sepsis and abdominal sepsis have pathophysiologically distinct phenotypes. This study aimed to compare their clinical characteristics and predictors of mortality. Methods: In this multicenter retrospective trial, 1,359 adult patients who fulfilled the Sepsis-3 criteria were enrolled and classified into the pulmonary sepsis or abdominal sepsis groups. Plasma presepsin was measured, and the scores of Acute Physiology and Chronic Health Evaluation (APACHE) II, Mortality in Emergency Department Sepsis (MEDS), and Simplified Acute Physiology Score (SAPS) II were calculated at enrollment. Data on 28-day mortality were collected for all patients. Results: Compared with patients with abdominal sepsis (n = 464), patients with pulmonary sepsis (n = 895) had higher 28-day mortality rate, illness severity scores, incidence of shock and acute kidney injury, and hospitalization costs. Lactate level and APACHE II and MEDS scores were independently associated with 28-day mortality in both sepsis types. Independent predictors of 28-day mortality included Pao
2/Fio
2 ratio (hazard ratio [HR], 0.998; P < 0.001) and acute kidney injury (HR, 1.312; P = 0.039) in pulmonary sepsis, and SAPS II (HR, 1.037; P = 0.017) in abdominal sepsis. A model that combined APACHE II score, lactate, and MEDS score or SAPS II score had the best area under the receiver operating characteristic curve in predicting mortality in patients with pulmonary sepsis or abdominal sepsis, respectively. Interaction term analysis confirmed the association between 28-day mortality and lactate, APACHE II score, MEDS score, SAPS II score, and shock according to the sepsis subgroups. The mortality of patients with pulmonary sepsis was higher than that of patients with abdominal sepsis among patients without shock (32.9% vs. 8.8%; P < 0.001) but not among patients with shock (63.7 vs. 48.4%; P = 0.118). Conclusions: Patients with pulmonary sepsis had higher 28-day mortality than patients with abdominal sepsis. The study identified sepsis subgroup-specific mortality predictors. Shock had a larger effect on mortality in patients with abdominal sepsis than in those with pulmonary sepsis.
Development, Deployment, and Implementation of a Machine Learning Surgical Case Length Prediction Model and Prospective Evaluation
Authors: Zaribafzadeh Hamed , Webster Wendy L. , Vail Christopher J. , Daigle Thomas , Kirk Allan D. , Allen Peter J. , Henao Ricardo , Buckland Daniel M. 
Published: June 2023

Objective:
Implement a machine learning model using only the restricted data available at case creation time to predict surgical case length for multiple services at different locations.

Background:
The Operating Room (OR) is one of the most expensive resources in a health system, estimated to cost $22-133 per minute and generate about 40% of the hospital revenue. Accurate prediction of surgical case length is necessary for efficient scheduling and cost-effective utilization of the OR and other resources.

Methods:
We introduced a similarity cascade to capture the complexity of cases and surgeon influence on the case length and incorporated that into a gradient boosting machine learning model. The model loss function was customized to improve the balance between over- and under-prediction of the case length. A production pipeline was created to seamlessly deploy and implement the model across our institution.

Results:
The prospective analysis showed that the model output was gradually adopted by the schedulers and outperformed the scheduler-predicted case length in Aug-Dec 2022. In 33,815 surgical cases across outpatient and inpatient platforms, the operational implementation predicted 11.2% fewer under-predicted cases and 5.9% more cases within 20% of the actual case length compared with the schedulers and only over-predicted 5.3% more. The model assisted schedulers to predict 3.4% more cases within 20% of the actual case length and 4.3% fewer under-predicted cases.

Conclusions:
We created a unique framework that is being leveraged every day to predict surgical case length more accurately at case posting time and could be potentially utilized to deploy future machine learning models.
minimap2-fpga: Integrating hardware-accelerated chaining for efficient end-to-end long-read sequence mapping
Authors: Liyanage Kisaru , Samarakoon Hiruna , Parameswaran Sri , Gamaarachchi Hasindu 
Published: June 2023

ABSTRACTminimap2is the gold-standard software for reference-based sequence mapping in third-generation long-read sequencing. Whileminimap2is relatively fast, further speedup is desirable, especially when processing a multitude of large datasets. In this work, we presentminimap2-fpga, a hardware-accelerated version ofminimap2that speeds up the mapping process by integrating an FPGA kernel optimised for chaining. We demonstrate speed-ups in end-to-end run-time for data from both Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio).minimap2-fpgais up to 79% and 53% faster thanminimap2for ∼ 30× ONT and ∼ 50× PacBio datasets respectively, when mapping without base-level alignment. When mapping with base-level alignment,minimap2-fpgais up to 62% and 10% faster thanminimap2for ∼ 30× ONT and ∼ 50× PacBio datasets respectively. The accuracy is near-identical to that of originalminimap2for both ONT and PacBio data, when mapping both with and without base-level alignment.minimap2-fpgais supported on Intel FPGA-based systems (evaluations performed on an on-premise system) and Xilinx FPGA-based systems (evaluations performed on a cloud system). We also provide a well-documented library for the FPGA-accelerated chaining kernel to be used by future researchers developing sequence alignment software with limited hardware background.
Stem cell memory EBV-specific T cells control post-transplant lymphoproliferative disease and persistin vivo
Authors: Palianina Darya , Mietz Juliane , Stühler Claudia , Arnold Brice , Bantug Glenn , Münz Christian , Chijioke Obinna , Khanna Nina 
Published: June 2023

ABSTRACTAdoptive T cell therapy (ACT), the therapeutic transfer of defined T cell immunity to patients, offers great potential in the fight against different human diseases including difficult-to-treat viral infections but response rates are still suboptimal. Very early differentiated stem cell memory T cells (TSCM) have superior self-renewal, engraftment, persistence, and anti-cancer efficacy, but their potential for anti-viral ACT remains unknown. Here, we developed a clinically-scalable protocol for expanding Epstein-Barr virus (EBV)-specific TSCM-enriched T cells with high proportions of CD4+T cells and broad EBV antigen coverage. These cells showed tumor control in a xenograft model of post-transplant lymphoproliferative disorder (PTLD) and were superior to previous ACT protocols in terms of tumor infiltration,in vivoproliferation, persistence, proportion of functional CD4+T cells, and diversity of EBV antigen specificity. Thus, our new protocol may pave the way for the next generation of potent unmodified antigen-specific cell therapies for EBV-associated diseases, including tumors, and other indications.
Secundary Structure of Physicochemical Clustered Proteins
Authors: Nelson Igor 
Published: June 2023

AbstractDiverse methods have been proposed for protein secondary structure prediction. However, such task still presents a challenge in bioinformatics. In this article various of these methods are implemented and analysed. First, a baseline using Support Vector Machine. Then a convolutional neural network (CNN), a Long Short-Term Memory (LSTM) and a strategy of Ensembling both of these methods. Lastly, a novel technique Secundary Structure of Physicochemical Clustered Proteins (SSPCP) is proposed, which combines multiple CNNs trained accordingly to a protein feature clustering and combined using a neural network. The rationale behind SSPCP is that amino acids from proteins which have similar physicochemical characteristics should have the same secondary structure prediction for similar amino acids, but amino acids from differing proteins might have different structures. All of these methods use as features PSSM matrices extracted from PSIBLAST. For performance evaluation, 25pdb dataset was split into training and validation and the same subsets were used on all these methods achieving the Q3 score of CNN: 70.11%, LSTM: 69.25%, Ensemble: 70.71%, SSPCP: 70.91%.The experimental results show that the features extracted from clustering of physicochemical properties of proteins seem to improve the accuracy of highly specific CNN models for accurate protein secondary structure prediction.
Computational Assessment on Catalytic Activity of PET Hydrolase
Authors: Nelson Igor , Ramos Rafael 
Published: June 2023

AbstractBackgroundPET hydrolase fromIdeonella sakaiensismight provide a response for PET accumulation in the environment. In this project some previously studied mutations were implemented and their performance was evaluated via computational methods with tools such as Modeller, HADDOCK, PyMOL and Gromacs. One possible mutation that could lead to improved catalytic activity was proposed.ResultsPET hydrolase DM S209F W130H and I179 provide interesting binding results with studied ligands, however a solution that combines both mutations does not seem viable, since the binding cleft becomes occluded. Following the same rationale, the triple mutant S209F W130H I179Q is proposed but instead leaves space in the binding cleft for ligand to enter and might bond with the oxygen at the ester group. The experiments conducted with triple mutant S209F W130H I179Q failed to beat HADDOCK score for DM, however its experimental results could still increase PET degradation. Results from surface charge may indicate an increase in stability and binding affinity for the protein.ConclusionsAmong models implemented, DM S209F W130H seems the best model studied regarding BHET or PET binding. Despite Protein Engineering is a complex process, computational tools might provide a way of studying binding sites of hypothetical proteins.Supplementary informationSupplementary data is available in annexes.
Genetic determinants of plasma protein levels in the Estonian population
Authors: Kalnapenkis Anette , Jõeloo Maarja , Lepik Kaido , Kukuškina Viktorija , Kals Mart , Alasoo Kaur ,   Estonian Biobank Research Team, Mägi Reedik , Esko Tõnu , Võsa Urmo 
Published: June 2023

AbstractThe proteome holds great potential as an intermediate layer between the genome and phenome. Previous protein quantitative trait locus studies have focused mainly on describing the effects of common genetic variations on the proteome. Here, we assessed the impact of the common and rare genetic variations as well as the copy number variants (CNVs) on 326 plasma proteins measured in up to 500 individuals. We identified 184cisand 94transsignals for 157 protein traits, which were further fine-mapped to credible sets for 101cisand 87transsignals for 151 proteins. Rare genetic variation contributed to the levels of 7 proteins, with 5cisand 14transassociations. CNVs were associated with the levels of 11 proteins (7cisand 5trans), examples including a 3q12.1 deletion acting as a hub for multipletransassociations; and a CNV overlappingNAIP, a sensor component of the NAIP-NLRC4 inflammasome which is affecting pro-inflammatory cytokine interleukin 18 levels. In summary, this work presents a comprehensive resource of genetic variation affecting the plasma protein levels and provides the interpretation of identified effects.
Solvent based fractional biosynthesis, phytochemical analysis and biological activity of silver nanoparticles obtained from extract ofSalvia moorcroftiana
Authors: Khan Maham , Khan Tariq , Aasim Muhammad , Sherazi Tauqir A. , Wahab Shahid , Zahoor Muhammad 
Published: June 2023

AbstractMulti-drug resistant bacteria sometimes known as “superbugs” developed through overuse and misuse of antibiotics are determined to be sensitive to small concentrations of silver nanoparticles. Various methods and sources are under investigation for the safe and efficient synthesis of silver nanoparticles having effective antibacterial activity even at low concentrations. We used a medicinal plant namedSalvia moorcroftianato extract phytochemicals with antibacterial, antioxidant, and reducing properties. Three types of solvents; from polar to nonpolar, i.e., water, dimethyl sulfoxide (DMSO), and hexane, were used to extract the plant as a whole and as well as in fractions. The biosynthesized silver nanoparticles in all extracts (except hexane-based extract) were spherical, smaller than 20 nm, polydispersed (PDI ranging between 0.2 and 0.5), and stable with repulsive force of action (average zeta value = −18.55±1.17). The tested bacterial strains i.e.,Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus, and Enterococcus faecalis were found to be sensitive to even small concentrations of AgNPs, especiallyP. aeruginosa.The antibacterial effect of these AgNPs was associated with their ability to generate reactive oxygen species. DMSO (in fraction) could efficiently extract antibacterial phytochemicals and showed activity against MDR bacteria (inhibition zone = 11-12 mm). Thus, the antibacterial activity of fractionated DMSO extract was comparable to that of AgNPs because it contained phytochemicals having solid antibacterial potential. Furthermore, AgNPs synthesized from this extract owned superior antibacterial activity. However, whole aqueous extract based AgNPs MIC was least (7-32 µg/mL) as compared to others.
Separate Gut Plasma Cell Populations Produce Autoantibodies Against Transglutaminase 2 and Transglutaminase 3 in Dermatitis Herpetiformis
Authors: Das Saykat , Stamnaes Jorunn , Kemppainen Esko , Hervonen Kaisa , Lundin Knut E.A. , Parmar Naveen , Jahnsen Frode L. , Jahnsen Jørgen , Lindfors Katri , Salmi Teea , Iversen Rasmus , Sollid Ludvig M. 
Published: June 2023

AbstractDermatitis herpetiformis (DH) is an inflammatory skin disorder often considered as an extraintestinal manifestation of celiac disease (CeD). Hallmarks of CeD and DH are autoantibodies to transglutaminase 2 (TG2) and transglutaminase 3 (TG3), respectively. DH patients have autoantibodies reactive with both transglutaminase enzymes. We here report that in DH both gut plasma cells and serum autoantibodies are specific for either TG2 or TG3 with no TG2-TG3 cross-reactivity. By generating monoclonal antibodies from TG3-specific duodenal plasma cells of DH patients, we define three conformational epitope groups. Both TG2-specific and TG3-specific gut plasma cells have few immunoglobulin mutations, and the two transglutaminase-reactive populations show distinct selection of certain heavy and light chain V-genes. Mass spectrometry analysis of TG3-specific serum IgA corroborates preferential usage ofIGHV2-5in combination withIGKV4-1. Collectively, our results demonstrate parallel induction of anti-TG2 and anti-TG3 autoantibody responses involving separate B-cell populations in DH patients.
A Spatial Transcriptomics based Label-Free Method for Assessment of Human Stem Cell Distribution and Effects in a Mouse Model of Lung Fibrosis
Authors: Park Jeongbin , Lee Dongjoo , Lee Jae Eun , Lee Daeseung , Song In Ho , Park Hyun Soo , Choi Hongyoon , Im Hyung-Jun 
Published: June 2023

AbstractRecently, cell therapy has emerged as a promising treatment option for various disorders. Given the intricate mechanisms of action (MOA) and heterogenous distribution in target tissues inherent to cell therapy, it is necessary to develop more sophisticated, unbiased approaches to evaluate the distribution of administered cells and the molecular changes at a microscopic level. In this study, we present a label-free approach for assessing the tissue distribution of administered human mesenchymal stem cells (hMSCs) and their MOA, leveraging spatially resolved transcriptomics (ST) analysis. We administered hMSCs to mouse model of lung fibrosis and utilized ST to visualize the spatial distribution of hMSCs within the tissue. This was achieved by capitalizing on interspecies transcript differences between human and mouse. Furthermore, we could examine molecular changes associated with the spatial distribution of hMSCs. We suggest that our method has the potential to serve as an effective tool for various cell-based therapeutic agents.