bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cancer Microenvironment Defines Tumor-Infiltrating Lymphocyte Density and Tertiary Lymphoid Structure Formation in Laryngeal Cancer
Authors: Gkegka Anastasia G. , Koukourakis Michael I. , Katotomichelakis Michael , Giatromanolaki Alexandra 
Published: December 2022

Abstract
Background
The presence and activity of tumor-infiltrating lymphocytes (TILs) is a key parameter related to the antitumor immune response. A large number of studies reveal TIL density as a prognostic marker and predictor of response to radiotherapy, chemotherapy, and immunotherapy.

Methods
We examined the TIL and tertiary lymphoid structure TLS density in the invading front and inner tumor stroma, in a 33 squamous cell laryngeal carcinomas (LSCC) treated with laryngectomy. TIL and TLS densities were in parallel examined with markers of anaerobic metabolism, vascular density (VD), vascular survival ability (VSA), and histopathological parameters.

Results
TIL and TLS densities significantly decreased in inner tumor areas (p < 0.0001). TIL density in the invading tumor front was inversely related with lymph node involvement (p = 0.03), HIF1α expression (p = 0.008), vessel density (p = 0.02), and MIB1 (p = 0.006). TIL density in inner stroma was inversely linked to local invasion (marginal p = 0.05), tumor budding (TB) (p = 0.005), MIB1 (p = 0.02), and HIF1α expression (p = 0.02). Low-TLS density in the invading front and in inner tumor areas was related to high TB (p = 0.02 and 0.002, respectively), HIF1α (p = 0.003 and 0.01, respectively), and LDH5 expression (p = 0.003 and 0.007, respectively). CD4+, FOXP3+ TIL density, and FOXP3+/CD8+ ratio were directly associated with VSA (p = 0.008, 0.02, and 0.05, respectively).

Conclusion
Poor immune response is related to hypoxic background and anaerobic metabolism, as well as increased invasive and metastatic ability. Regulatory TIL markers are linked with increased angiogenic potential. The prognostic, predictive, and therapy-guiding value of TILs in clinical practice demands thorough investigation.
Productivity Enhancement in Directed Energy Deposition: The Oscillating Scanning Strategy Approach
Authors: Aversa Alberta , Carrozza Alessandro , Marchese Giulio , Felicioni Stefano , De Chirico Michele , Lombardi Mariangela , Bondioli Federica , Fino Paolo 
Published: December 2022

AbstractDirected Energy Deposition (DED) is an additive manufacturing process that enables the production of large metal components by melting the feedstock material while being deposited. An improvement of the production speed of this process would further increase its applicability in many industrial fields. The DED building rate is strictly related to the building parameters adopted, in particular to the laser spot diameter, which also affects the build accuracy and the surface quality of the components. The possibility of using a variable laser spot would result in a significant increase in the production rate in bulky zones, while also providing a good surface quality where needed. In the present work, an oscillating scanning strategy was used to create a large apparent laser spot (+ 170% of the nominal value) to produce 316L stainless steel samples via DED. The optimisation of the DED parameters with the oscillating strategy was performed using the single scan tracks (SSTs) approach. The morphologies of the SSTs obtained with different process parameters were assessed and the geometrical features related to the melt pools were analysed in order to select the most suitable X and Z displacements for the production of the cubic samples. The analyses of the cubes revealed that, if the correct overlap among nearby scans is selected, it is possible to obtain dense samples with all the oscillating diameters tested. Finally, comparing the building rate and powder efficiency values confirmed that this method can accelerate the building process and improve its overall performance.

Graphical Abstract
Construction and validation of a prognostic model for osteosarcoma patients based on autophagy-related genes
Authors: Ning Biao , Liu Yixin , Xu Tianzi , Li Yi , Wei Dongyi , Huang Tianhe , Wei Yongchang 
Published: December 2022

Abstract
Background
Osteosarcoma is the most frequent primary bone malignancy with a poor prognosis because of pulmonary metastasis. Autophagy is strongly associated with tumor metastasis, and it is valuable to construct an autophagy-related gene risk model for predicting the prognosis of osteosarcoma.

Methods
We obtained ARGs from the Human Autophagy Database and RNA-sequencing data of osteosarcoma patients from the Gene Expression Omnibus (GEO) database. Subsequently, univariate and multivariate cox regression analyses were performed to construct a three-gene prognostic model and its accuracy was further confirmed in the Therapeutic Applications Research to Generate Effective Treatments (TARGET) database. Afterward, we detected the expression levels and effects on osteosarcoma cells metastasis of MYC and MBTPS2, which were involved in the model.

Results
In both training and verification cohorts, patients with lower risk scores had longer OS, and the model was identified as an independent prognostic factor in osteosarcoma. Besides, the ROC curve demonstrated the reliability of the model. Furthermore, RT-qPCR, Western Blotting and IHC results indicated that MYC and MBTPS2 were differently expressed in osteosarcoma tissues and cell lines. MYC knockdown or MBTPS2 overexpression prevented the capacity of migration and invasion in osteosarcoma cell lines through inhibiting cellular autophagy.

Conclusion
The risk model based on three ARGs had a strong ability to predict the prognosis of osteosarcoma patients. Our findings also suggested that MYC and MBTPS2 were two major factors regulating autophagy in osteosarcoma, and could serve as potential therapeutic targets for osteosarcoma.
Unraveling the nature of spin excitations disentangled from charge contributions in a doped cuprate superconductor
Authors: Zhang Wenliang , Agrapidis Cliò Efthimia , Tseng Yi , Asmara Teguh Citra , Paris Eugenio , Strocov Vladimir N. , Giannini Enrico , Nishimoto Satoshi , Wohlfeld Krzysztof , Schmitt Thorsten 
Published: December 2022

AbstractThe nature of the spin excitations in superconducting cuprates is a key question toward a unified understanding of the cuprate physics from long-range antiferromagnetism to superconductivity. The intense spin excitations up to the over-doped regime revealed by resonant inelastic X-ray scattering bring new insights as well as questions like how to understand their persistence or their relation to the collective excitations in ordered magnets (magnons). Here, we study the evolution of the spin excitations upon hole-doping the superconducting cuprate Bi2Sr2CaCu2O8+δby disentangling the spin from the charge excitations in the experimental cross section. We compare our experimental results against density matrix renormalization group calculations for at-J-like model on a square lattice. Our results unambiguously confirm the persistence of the spin excitations, which are closely connected to the persistence of short-range magnetic correlations up to high doping. This suggests that the spin excitations in hole-doped cuprates are related to magnons—albeit short-ranged.
Outcome of multi-staged induced membrane technique based on post-debridement cultures for the management of critical-sized bone defect following fracture-related infection
Authors: Cho Jae-Woo , Kent William T. , Kim Jin-Kak , Jeong Seong-Ho , Sakong Seungyeob , Kim Hanju , Son Whee Sung , Lim Eic Ju , Choi Wonseok , Oh Jong-Keon 
Published: December 2022

AbstractThe authors’ institution utilizes multi-staged induced membrane technique protocol based on post-debridement culture in treating patients with critical-sized bone defect in lower extremity due to infected nonunion or post-traumatic osteomyelitis. This study aimed to evaluate the success rate of this limb reconstruction method and which risk factors are associated with recurrence of infection. 140 patients were treated with multi-staged induced membrane technique from 2013 to 2018 and followed up more than 24 months after bone grafting. The primary success rate of limb reconstruction was 75% with a mean follow-up of 45.3 months. The mean Lower Extremity Functional Scale in success group improved from 12.1 ± 8.5 to 56.6 ± 9.9 after the treatment. There were 35 cases of recurrence of infection at a mean of 18.5 months after bone grafting. Independent risk factors for recurrence of infection were infected free flap, surprise positive culture, deviation from our surgical protocol, and elevated ESR before final bone graft procedure. In conclusion, this study showed that multi-staged induced membrane technique protocol based on post-debridement culture resulted in 75% success rate and revealed a number of risk factors for recurrence of infection.
Cardiac magnetic resonance feature tracking global and segmental strain in acute and chronic ST-elevation myocardial infarction
Authors: Erley Jennifer , Starekova Jitka , Sinn Martin , Muellerleile Kai , Chen Hang , Harms Phillip , Naimi Lieda , Meyer Mathias , Cavus Ersin , Schneider Jan , Blankenberg Stefan , Lund Gunnar K. , Adam Gerhard , Tahir Enver 
Published: December 2022

AbstractStrain is an important imaging parameter to determine myocardial deformation. This study sought to 1) assess changes in left ventricular strain and ejection fraction (LVEF) from acute to chronic ST-elevation myocardial infarction (STEMI) and 2) analyze strain as a predictor of late gadolinium enhancement (LGE). 32 patients with STEMI and 18 controls prospectively underwent cardiac magnetic resonance imaging. Patients were scanned 8 $$\pm$$
±
5 days and six months after infarction (± 1.4 months). Feature tracking was performed and LVEF was calculated. LGE was determined visually and quantitatively on short-axis images and myocardial segments were grouped according to the LGE pattern (negative, non-transmural and transmural). Global strain was impaired in patients compared to controls, but improved within six months after STEMI (longitudinal strain from −14 ± 4 to −16 ± 4%, p < 0.001; radial strain from 38 ± 11 to 42 ± 13%, p = 0.006; circumferential strain from −15 ± 4 to −16 ± 4%, p = 0.023). Patients with microvascular obstruction showed especially attenuated strain results. Regional strain persisted impaired in LGE-positive segments. Circumferential strain could best distinguish between LGE-negative and -positive segments (AUC 0.73- 0.77). Strain improves within six months after STEMI, but remains impaired in LGE-positive segments. Strain may serve as an imaging biomarker to analyze myocardial viability. Especially circumferential strain could predict LGE.
Managing Oromandibular Hardware Failure after Free Flap Surgery
Authors: Domack Aaron M. , Silverman Dustin A. , Tang Alice L. , Zender Chad A. , Patil Yash J. 
Published: December 2022

AbstractHardware failure after oromandibular reconstruction using free tissue transfer can delay additional therapies directed at cancer treatment and prevent patients from returning to normal oral function. Understanding and strict adherence to principles of rigid fixation is critical in preventing complications. Early surgical intervention for hardware exposure as well as utilization of locoregional flaps may prevent the need for more extensive revision surgery.
A comprehensive investigation of statistical and machine learning approaches for predicting complex human diseases on genomic variants
Authors: Wang Chonghao , Zhang Jing , Veldsman Werner Pieter , Zhou Xin , Zhang Lu 
Published: December 2022

AbstractQuantifying an individual’s risk for common diseases is an important goal of precision health. The polygenic risk score (PRS), which aggregates multiple risk alleles of candidate diseases, has emerged as a standard approach for identifying high-risk individuals. Although several studies have been performed to benchmark the PRS calculation tools and assess their potential to guide future clinical applications, some issues remain to be further investigated, such as lacking (i) various simulated data with different genetic effects; (ii) evaluation of machine learning models and (iii) evaluation on multiple ancestries studies. In this study, we systematically validated and compared 13 statistical methods, 5 machine learning models and 2 ensemble models using simulated data with additive and genetic interaction models, 22 common diseases with internal training sets, 4 common diseases with external summary statistics and 3 common diseases for trans-ancestry studies in UK Biobank. The statistical methods were better in simulated data from additive models and machine learning models have edges for data that include genetic interactions. Ensemble models are generally the best choice by integrating various statistical methods. LDpred2 outperformed the other standalone tools, whereas PRS-CS, lassosum and DBSLMM showed comparable performance. We also identified that disease heritability strongly affected the predictive performance of all methods. Both the number and effect sizes of risk SNPs are important; and sample size strongly influences the performance of all methods. For the trans-ancestry studies, we found that the performance of most methods became worse when training and testing sets were from different populations.
Inferring gene regulatory networks from single-cell gene expression data via deep multi-view contrastive learning
Authors: Lin Zerun , Ou-Yang Le 
Published: December 2022

Abstract
The inference of gene regulatory networks (GRNs) is of great importance for understanding the complex regulatory mechanisms within cells. The emergence of single-cell RNA-sequencing (scRNA-seq) technologies enables the measure of gene expression levels for individual cells, which promotes the reconstruction of GRNs at single-cell resolution. However, existing network inference methods are mainly designed for data collected from a single data source, which ignores the information provided by multiple related data sources. In this paper, we propose a multi-view contrastive learning (DeepMCL) model to infer GRNs from scRNA-seq data collected from multiple data sources or time points. We first represent each gene pair as a set of histogram images, and then introduce a deep Siamese convolutional neural network with contrastive loss to learn the low-dimensional embedding for each gene pair. Moreover, an attention mechanism is introduced to integrate the embeddings extracted from different data sources and different neighbor gene pairs. Experimental results on synthetic and real-world datasets validate the effectiveness of our contrastive learning and attention mechanisms, demonstrating the effectiveness of our model in integrating multiple data sources for GRN inference.
The Standard Genetic Code Lacks Redundancy For Amino Acid Codons
Authors: Laibelman Alan M. 
Published: December 2022

ABSTRACTThe Standard Genetic Code contains sixty-one codons used to translate twenty amino acids. A 3:1 ratio is presumed to mean all of them except methionine and tryptophan exhibit redundancy (also known as degeneracy) such that multiple codons are interchangeable with respect to cognate amino acids. Since the process of translation converting codon triplets into amino acids involves a 1:1 relationship between mRNA-containing codons and tRNA-containing anticodons, any physicochemical variability in tRNA molecules associated with the same amino acid must break redundancy among codons. By studying the characteristics of 186 tRNA sequences from Archaea, it is demonstrated that multiple features lead to the conclusion that every anticodon triplet is unique, and therefore that every codon triplet must likewise be unlike all others, even if coding for the same amino acid. Many implications necessarily accrue from this result related to the mechanism for translation, codon usage, and the evolution of the genetic code, all of which are discussed.
First-line arteriovenous access and risks of hospitalization and death in patients starting hemodialysis – a nationwide cohort study
Authors: de Pinho Natalia Alencar , Prezelin-Reydit Mathilde , Harambat Jerome , Couchoud Cécile , Glaudet Florence , Combe Christian , Rondeau Virginie , Leffondré Karen 
Published: December 2022

AbstractArteriovenous (AV) access choice has sparked controversy with recent evidence suggesting overestimation of benefits associated with AV fistula versus graft in certain populations. We assessed outcomes associated with first-line AV access type in patients who started hemodialysis with a catheter in France, overall and by subgroups of age, sex, and comorbidities. In this retrospective cohort study, we included incident patients who initiated hemodialysis with a catheter from 2010 through 2018, followed by the French REIN Registry. Our main exposure was the first-line (first-created) AV graft versus fistula, ascertained through the linkage with the French national health-administrative database. Outcomes were all-cause and cause-specific hospitalization, and all-cause mortality. We used joint frailty models to deal with recurrent hospitalizations and informative censoring by death, Cox proportional hazard (PH) models, and inverse probability weighting. From the 18,625 patients included (mean age was 68±15 years, 35% were women), 5% had a first-line AV graft. Patients with AV graft had an 11%-higher weighted hazard of all-cause hospitalization (95% CI 1.09 to 1.13), 16% higher weighted hazard of cardiovascular (95% CI 1.05 to 1.29) and infection-related (95% CI 1.01 to 1.33) hospitalization, 34% higher weighted hazard of vascular access-related hospitalization, and a 9%-higher weighted hazard of all-cause death (95% CI 0.97 to 1.23). Results were consistent for most subgroups, except that the highest hazard of hospitalization with AV graft was blunted in patients with comorbidities (i.e. diabetes, weighted HR of all-cause hospitalization 1.03, 95% CI 0.95-1.12).- To conclude, in patients starting hemodialysis with a catheter, first-line AV graft is associated with increased hazard of hospitalization vs. patients with AV fistula. This may, however, not be the case for patients with a poor vascular condition, i.e., those with diabetes, who have a similar hospitalization and mortality rates with either graft or fistula.
Automated assessment of chest CT severity scores in patients suspected of COVID-19 infection
Authors: Ajmera Pranav , Rathi Snehal , Dosi Udayan , Kalli Suvarna Lakshmi , Luthra Avinav , Khaladkar Sanjay , Pant Richa , Seth Jitesh , Mishra Pranshu , Gawali Manish , Pargaonkar Yash , Kulkarni Viraj , Kharat Amit 
Published: December 2022

ABSTRACTBackgroundThe COVID-19 pandemic has claimed numerous lives in the last three years. With new variants emerging every now and then, the world is still battling with the management of COVID-19.PurposeTo utilize a deep learning model for the automatic detection of severity scores from chest CT scans of COVID-19 patients and compare its diagnostic performance with experienced human readers.MethodsA deep learning model capable of identifying consolidations and ground-glass opacities from the chest CT images of COVID-19 patients was used to provide CT severity scores on a 25-point scale for definitive pathogen diagnosis. The model was tested on a dataset of 469 confirmed COVID-19 cases from a tertiary care hospital. The quantitative diagnostic performance of the model was compared with three experienced human readers.ResultsThe test dataset consisted of 469 CT scans from 292 male (average age: 52.30 ± 15.90 years) and 177 female (average age: 53.47 ± 15.24) patients. The standalone model had an MAE of 3.192, which was lower than the average radiologists’ MAE of 3.471. The model achieved a precision of 0.69 [0.65, 0.74] and an F1 score of 0.67 [0.62, 0.71], which was significantly superior to the average reader precision of 0.68 [0.65, 0.71] and F1 score of 0.65 [0.63, 0.67]. The model demonstrated a sensitivity of 0.69 [95% CI: 0.65, 0.73] and specificity of 0.83 [95% CI: 0.81, 0.85], which was comparable to the performance of the three human readers, who had an average sensitivity of 0.71 [95% CI: 0.69, 0.73] and specificity of 0.84 [95% CI: 0.83, 0.85].ConclusionThe AI model provided explainable results and performed at par with human readers in calculating CT severity scores from the chest CT scans of patients affected with COVID-19. The model had a lower MAE than that of the radiologists, indicating that the CTSS calculated by the AI was very close in absolute value to the CTSS determined by the reference standard.
Using passive acoustic monitoring to estimate the abundance of a critically endangered parrot, the great green macaw (Ara ambiguus)
Authors: Lewis Thomas C. , Vargas Ignacio Gutiérrez , Williams Sam , Beckerman Andrew P. , Childs Dylan Z. 
Published: December 2022

AbstractMost conservation relies on being able to estimate population size accurately. The development, implementation and adaptation of effective conservation strategies rely on quantifying the impacts of different threats on population dynamics, identifying species that need conservation management, and providing feedback on the effectiveness of any management actions. However, current approaches are not suitable for wide-ranging species that reside in tropical ecosystems. Here we use the great green macawAra ambiguusas a case study to show that passive acoustic monitoring is an effective tool for collecting data that can then estimate abundance. We estimate a population of 485.65 ± 61 SE great green macaws in Costa Rica during the breeding, suggesting the population here is larger than previously estimated. We have also highlighted potentially important areas for the species in regions that had not previously been studied. We have demonstrated at a population scale that passive acoustic monitoring (PAM) offers conservationists an efficient and effective way to understand population dynamics. With a high proportion of parrot species threatened globally, passive acoustic monitoring will enable effective monitoring and become an essential tool in conservation planning and evaluation. PAM technology has enormous potential to facilitate such assessments because it is easily scalable, recordings can be stored and re-analysed as machine learning, and abundance estimation techniques become more advanced.
Residue communities reveal evolutionary signatures ofγδT-Cell receptor
Authors: Cheung Ngaam J. , Huang Si-Yu 
Published: December 2022

ABSTRACTNaturally co-occurring amino acids, term coevolution, in a protein family play a significant role in both protein engineering and folding, and it is expanding in recent years from the studies of the effects of single-site mutations to the complete re-design of a protein and its folding, especially in three-dimensional structure prediction. Here, to better characterize such coevolving interactions, wein silicodecipher evolutionary couplings from massive homologous sequences using spectral analysis to capture signatures that are important for specific molecular interactions and binding activities. We implement the present approach on the G7 gamma delta T-cell receptor to identify functionally important residues that contribute to its highly distinct binding mode. The analysis indicates the evolutionary signatures (highly ordered networks of coupled amino acids, termed residue communities) of the protein confirm previously identified functional sites that are relevant to dock the receptor underneath the major histocompatibility complex class I-related protein-1 (MR1) antigen presenting groove. Moreover, we analyze the correlation of inter-residue contacts with the activation states of receptors and show that contact patterns closely correlating with activation indeed coincide with these sites. The theoretical results demonstrate our method provides an alternative path towards bridging protein sequence with its function at residue-level without requiring its tertiary structure or highly accurate measurement of its biological activitiesin vivo/vitro.