bluuebunny/crossref_metadata_2025_split huggingface dataset:
InTraSeq: A Multimodal Assay that Uncovers New Single-Cell Biology and Regulatory Mechanisms
Authors: Beausoleil Sean , Ariss Majd , Huang Linglin , Ding Xiaokai , Sheth Shivani , Levy Tyler , Fisher Jeremy , Loebelenz Jean , Arlotta Keith , Dixon Karen , Polakiewicz Roberto , Kuchroo Vijay 
Published: December 2024

Abstract

Single-cell RNA sequencing (scRNA-seq) has revolutionized cell biology by enabling the profiling of transcriptomes at a single-cell resolution, leading to important discoveries that have advanced our understanding of cellular and tissue heterogeneity, developmental trajectories, and disease progression. Despite these important advances, scRNA-seq is limited to measuring the transcriptome providing a partial view of cellular function. To address this limitation, multimodal scRNA-seq assays have emerged, allowing for the simultaneous measurement of RNA expression and protein. Intracellular Transcriptomic and Protein Sequencing (InTraSeq), a novel multimodal scRNA-seq technology described here, enables the concurrent measurement of mRNA, surface markers, cytoplasmic proteins, and nuclear proteins within individual cells through oligo-barcoded antibodies. This method offers a comprehensive approach to studying cellular function by combining RNA and protein profiling from the same sample and utilizing a relatively simple protocol. The InTraSeq method enables researchers to expand their view of critical intracellular protein expression including post-translational modifications (PTMs) and transcription factors, allowing for the identification of novel cellular subtypes and states that may be obscured by RNA-based analyses alone. This is particularly valuable in understanding the heterogeneity of cell populations and identifying distinct functional states. In this report, we used InTraSeq to characterize the complex cellular states and regulatory mechanisms during Th17 cell differentiation. We simultaneously profiled RNA and protein expression in over 85,000 cells, capturing transcriptional changes, changes in protein expression and the dynamics of signaling pathways at a high resolution. Our results revealed novel insights into Th17 cell differentiation, including the identification of key regulatory factors and their target genes. By simultaneously measuring mRNA, extra and intra-cellular proteins, signaling proteins, and PTMs, InTraSeq offers a comprehensive understanding of cellular processes and enables the identification of novel regulatory mechanisms.
Selection is more important than complementarity in global grasslands
Authors: Wang Yonghui , Ma Wenhong , Miao Bailing , Zhang Bin , Yang Huping , Dai Xinxin , Jia Xinhui , Jiang Lin 
Published: December 2024

Abstract

A central question concerning biodiversity loss is how it impacts ecosystem functions and services. Experiments manipulating species diversity often show the complementarity effect, stemming from niche differentiation or facilitation among species, contributes dominantly to ecosystem functions. The selection effect, resulting from the increased likelihood of species-diverse communities containing high-performance species, plays a limited role. However, the applicability of these findings to natural ecosystems remains unclear. By partitioning the two effects and further separating them into dominant-species and subordinate-species components, we found that the selection effect was significant in enhancing natural grassland functions (plant biomass production and community coverage), and better predicted functions than the complementarity effect. In natural grasslands, the selection effect was largely driven by dominant species and independent from community-wide species diversity, while the complementarity effect was largely driven by subordinate species and positively associated with species diversity. Our results suggest that the selection effect may play a more important role in driving the functioning of natural ecosystems, which may be underestimated in biodiversity experiments.
Single-cell RNA landscape of the intratumoral heterogeneity and expression of angiogenesis-related genes in osteosarcoma
Authors: Li Hao , Ma Tian , Yi Zhiqian , Gao Fang , Li Xiaojuan , Li Mi 
Published: December 2024

Abstract

Background: Osteosarcoma is an aggressive malignancy of bone that poses significant treatment challenges and has been a focal point of extensive research due to its complex pathogenesis. Despite advances in traditional therapeutic approaches, the intricate genetic and cellular landscape of osteosarcoma remains inadequately understood, emphasizing the need for innovative research methodologies to unravel its underlying mechanisms.
Objective: This study aims to leverage the power of single-cell transcriptome sequencing technology to elucidate the cellular heterogeneity, gene expression patterns, intercellular communication networks, and critical genetic pathways implicated in osteosarcoma. By doing so, we intend to contribute valuable insights into the biogenesis of this malignancy, which may ultimately inform precision treatment strategies.
Methods: Utilizing single-cell sequencing, we conducted a comprehensive analysis of osteosarcoma samples to identify diverse cellular subpopulations within the tumor microenvironment. Our focus on gene expression profiles revealed significant differences across these subpopulations. Moreover, we employed bioinformatics approaches to explore the intercellular communication networks and identified key ligand-receptor pairings, substantiating the role of angiogenesis-related genes prominently expressed in osteoblasts and their proliferative counterparts.
Results: Our findings underscore the critical involvement of angiogenesis in osteosarcoma pathogenesis, with notable pathway activity variations among distinct cellular subpopulations. Additionally, protein interaction network mapping has unveiled significant discrepancies in pathway activities and highlighted the potential functional roles of key genes involved in tumor progression.
Conclusion: This study offers a comprehensive exploration of the biological characteristics of osteosarcoma through single-cell sequencing technology, thereby establishing a robust theoretical foundation that may facilitate the development of targeted and effective therapeutic strategies. However, it is essential to recognize that these findings are preliminary, necessitating further validation through expanded sample sizes and integration of multi-omics data. Future research will delve deeper into the mechanisms of the identified key pathways and genes, with the aspiration of enhancing the prognostic outcomes and quality of life for patients with osteosarcoma.
Longitudinal Trajectories of Cognitive Function, Functional Ability, and Neuropsychiatric Symptoms in Alzheimer's Disease: The Mediating Role of IADL and the Impact of COVID-19 Lockdown in South Korean Older Adults
Authors: Lee Hahyun , Shin Soojin , Nam Gieun , Kwak Seyul , Cho Yoon Soo , Hwang Eunji , Oh Sohee , Kim So Yoon , Yoo So Young , Lee Jun-Young 
Published: December 2024

Abstract

Background:
The progression of Alzheimer's Disease (AD) and its interaction with COVID-19-induced social isolation remains poorly understood. This study investigated the longitudinal trajectories of AD severity on cognitive function, functional ability, and neuropsychiatric symptoms, and examined the impact of COVID-19 lockdown on AD patients in South Korea.
Methods:
In this retrospective longitudinal study, data from 253 adults (aged ≥ 55) diagnosed with mild cognitive impairment (MCI) or AD were analyzed, collected between 2018 and 2022. Participants were classified into four groups based on Clinical Dementia Rating (CDR) scores: MCI, AD-CDR0.5, AD-CDR1, and AD-CDR2. Cognitive function, functional abilities, neuropsychiatric symptoms, depressive symptoms, and overall dementia severity were assessed. Linear mixed-effects models, along with mediation and moderation analyses were employed to analyze the data.
Results:
Significant trajectories of decline in cognitive function and functional abilities were observed over time, with more pronounced declines in higher AD severity groups. The COVID-19 lockdown exacerbated cognitive decline and impairment in Activities of Daily Living (ADL) specifically in the most severe AD group (AD-CDR2). Instrumental Activities of Daily Living (IADL) mediated the relationship between Mini-Mental State Examination (MMSE) scores and CDR Sum of Boxes (CDR-SB) in the MCI, AD-CDR0.5, and AD-CDR1 groups. A significant interaction between MMSE scores and neuropsychiatric symptoms was observed in the moderate AD group (AD-CDR1), indicating that worsening neuropsychiatric symptoms intensified cognitive decline. Neuropsychiatric Inventory (NPI) scores increased over time, indicating worsening neuropsychiatric symptoms, whereas depressive symptoms, measured by the Short Geriatric Depression Scale (SGDS), remained stable over the study period.
Conclusions:
This study highlights the impact of AD severity on cognitive and functional decline, further exacerbated by the COVID-19 lockdown. The mediating role of IADL suggests that maintaining complex daily activities may be crucial in slowing disease progression in AD patients. Additionally, the worsening of neuropsychiatric symptoms underscores the need for targeted mental health support, especially during periods of social isolation, to mitigate adverse effects on patients and caregivers.
Stereotactic body radiotherapy versus laparoscopic liver resection for single hepatocellular carcinoma ≤ 5 cm: a retrospective, multicenter, cohort study
Authors: Zhou Bin , Ma Zihui , Cheng Xi , Yan Maolin , Duan Yunfei , Ye Jiazhou , Guo Lei , Xue Jie , Meng Yan , Cheng Shuqun , Guo Weixing 
Published: December 2024

Abstract

Objectives
To compare the efficacy of stereotactic body radiotherapy (SBRT) and laparoscopic liver resection (LLR) for patients with single hepatocellular carcinoma (HCC) ≤ 5 cm.
Materials and methods
This retrospective cohort study included 383 patients from four hospitals between January 2017 and December 2019, with 268 patients in the LLR group and 115 in the SBRT group. Propensity score matching (PSM) and subgroup analyses were performed.
Results
After PSM adjustment, 98 patients were selected from each group for further analysis. The 1-, 3- and 5-year OS rates were 94.9%, 88.8%, and 85.7% in the LLR group and 94.9%, 74.5% and 67.3% in the SBRT group, respectively (p = 0.005). The 1-, 3- and 5-year RFS rates were 86.7%, 71.4%, and 70.4% in the LLR group and 83.7%, 61.2% and 53.1% in the SBRT group, respectively (p = 0.011). LLR provided better OS and RFS than SBRT. However, SBRT resulted in comparable OS and RFS times to LLR for tumors ≤ 3 cm ((p = 0.158; p = 0.418)), and perivascular tumors (p = 0.082; p = 0.166).
Conclusion
LLR is the first choice for treating single HCC ≤ 5 cm, while SBRT offers advantages in treating small and perivascular lesions.
Enrichment of pSer129 alpha-synuclein in synaptic terminals precedes synaptic loss in Parkinson’s disease
Authors: Frigerio Irene , Morella Martino , Winkelaar Amber B , Vorgia Danai T , Brevé John JJP , Boiten Walter A , Gaag Bram L van der , Calandri Ismael L , Jonker Allert J , Berendse Henk W , Mona Daniel , Britschgi Markus , Jonkman Laura E , de Berg Wilma DJ van 
Published: December 2024

Abstract

Background At early stages in Parkinson’s disease (PD), α-synuclein aggregation in pre-synaptic terminals in the striatum is hypothesized to set off a cascade of events leading to synaptic loss and widespread cortical Lewy body (LB) pathology. Here, we tested whether α-synuclein proteoforms are enriched in putaminal pre- and post-synaptic terminals in relation to synaptic degeneration and cortical LB pathology in anatomically connected regions across PD stages.Methods We employed multiplex-immunofluorescence in combination with high-resolution confocal microscopy to visualize and quantify total α-synuclein (Syn1), phosphorylated at Serine129 (pSer129), and C-terminal-truncated at residue 122 (CTT122) in dopaminergic (DAT+), cortical glutamatergic (VGLUT1+) pre-synapses and post-synapses (PSD95+), in the putamen of 32 incidental LB disease (iLBD), 27 PD, and 10 control brain donors. LB density was quantified in cingulate and frontal regions. In a subset of the same donors (n = 38), total, pSer129, and CTT122 α-synuclein protein levels were quantified in putaminal synaptosomes using AlphaLISA immunoassays.Results In PD, severe DAT+ terminal loss was observed in the putamen (-86%, p < 0.001), which associated with lower Syn1+ DAT+ synaptic density (rho = 0.61, p < 0.001). VGLUT1+ and PSD95+ synaptic density did not change across disease stages (p > 0.05). pSer129 was highly increased in DAT+ synapses in iLBD cases with Braak stage 3 (+ 2000%, p = 0.007) and in VGLUT1+ and PSD95+ synapses in later stages (Braak stages 4–5; +1296–1613%, p < 0.05). CTT122 was enriched in DAT+, VGLUT1+, PSD95+ synapses in late PD stages (Braak stages 4–6; +294–1332%, p < 0.05). DAT+ synaptic loss was associated with pSer129 (rho=-0.54, p < 0.001) and CTT122 (rho=-0.47, p < 0.001) enrichment in remaining DAT+ synapses. pSer129 and CTT122 were enriched in VGLUT1+ synapses prior to LB pathology in cingulate and frontal regions. Synaptosome fractions showed increased pSer129 protein levels in PD (+ 134%, p = 0.043) but not in iLBD (p = 0.392). We did not detect changes in total or CTT122 protein levels in PD or iLBD (p > 0.05).Conclusions Our results strongly suggest that synaptic enrichment of pSer129 α-synuclein is already increased in early PD stages, possibly contributing to dopaminergic denervation and cortical LB pathology, while CTT122 α-synuclein enrichment in synaptic terminals accompanies pSer129 in later stages.
Enhancing Software Development Effort Estimation: A Cloud-based Data Framework Utilizing Use Case Points, Fuzzy Logic, and Machine Learning
Authors: . Ritu , Bhambri Pankaj 
Published: December 2024

Abstract

Effort estimation is a crucial task in the software development process, as it determines the type of resources needed, the number of required resources, and the project duration. Nevertheless, client requirement could alter during the development process, reducing the accuracy and dependability of early estimates. By offering a methodology, this study seeks to improve effort estimation. A framework that uses the Use Case Point method to estimate the project’s effort has been developed and made available as software as a service on the cloud. The framework determines how long the project will take, based on the input values for the project’s domain, technical factors, environmental factors, and information. The framework maintains a database called the use case repository, which developers and project managers may use to compare and estimate the length of future projects. To increase accuracy and dependability, fuzzy rules and a triangular membership function are employed in fuzzy logic to predict the time. The estimation’s accuracy is demonstrated by cross-checking the findings against popular datasets. Regression analysis is used to anticipate the project’s duration using the Gaussian Process Regression technique in combination with a machine learning system. The training and testing data, collected from friends, classmates, and other software businesses, are validated against the widely available De-shranais dataset. Ultimately, a business intelligence tool is used to produce reports and do comparative analysis based on various features in the repository data. Developers and administrators may now use a range of criteria to analyze the repository data according to specific project and domain-specific requirements.
Predicting 5-Year Survival in Gastric Cancer Patients Using Iliopsoas Muscle CT Radiomics and Machine Learning Techniques
Authors: Hong Yuan , Li Yifan , Zhang Peng , Chen Haosong , Chen Yixian , Yu Yang , Zhang Zimo , Cheng Kang , Xiong Maoming , Chen Bo 
Published: December 2024

Abstract

Objectives
Sarcopenia, linked to postoperative survival in cancer patients, was investigated in this study. The research explored the relationship between CT imaging features of muscles in gastric cancer patients and their survival. Additionally, the study aimed to create a quantifiable survival prediction model using artificial intelligence.
Methods
In a retrospective study, 100 patients who underwent radical gastrectomy for gastric cancer were analyzed. After identifying sarcopenia using the psoas muscle index, clinical factors related to patient survival were investigated. Imaging features were extracted from manually delineated iliopsoas muscles and used in 11 machine learning algorithms. After completing the model training, we used a dataset comprising 34 patients from a secondary center as an external validation set to evaluate the model’s classification performance. After identifying the optimal model, we further explored the fusion methods of clinical omics and radiomics. Based on this, we constructed a predictive model for estimating the five-year survival rate of patients.
Results
Clinical survival analysis highlighted age and tumor M stage as relevant factors. For the task of predicting five-year survival, we found that the Logistic Regression (LR) model without clinical feature fusion exhibited the most balanced and superior performance. Specifically, the AUC (Area Under Curve) values of this model on the training set, internal validation set, and external validation set were 0.82, 0.72, and 0.69, respectively. Additionally, the model’s accuracy remained relatively stable, approximately around 70%.
Conclusions
In this study, we developed a machine learning model based on preoperative CT imaging data of gastric cancer patients to predict their five-year survival rate. The model can achieve about 70% accuracy. Additionally, we explored the necessity and rationale of incorporating clinical independent factors into this predictive model. The results indicated a significant correlation between muscle imaging features and overall patient survival, highlighting the importance of sarcopenia in the clinical management of gastric cancer patients.
Accurate Prediction of Disease-Free and Overall Survival in Non-Small Cell Lung Cancer Using Patient-Level Multimodal Weakly Supervised Learning
Authors: Che Nanying , Li Yongmeng , Chai Xiaodong , Yang Moxuan , Xiong Jiahang , Zeng Junyang , Chen Yun , Xu Gang , Lin Haifeng , Wang Wei , Wang Shuhao 
Published: December 2024

Abstract

With the rapid progress in artificial intelligence (AI) and digital pathology, prognosis prediction for non-small cell lung cancer (NSCLC) patients has become a critical component of personalized medicine. In this study, we developed a multimodal AI model that integrates whole-slide images and dense clinical data to predict disease-free survival (DFS) and overall survival (OS) with high accuracy for NSCLC patients undergoing surgery. Utilizing data from 618 patients at Beijing Chest Hospital, the model achieved outstanding performance, with areas under the curve of 0.8084 for predicting progression and 0.8021 for predicting death in the test set. Importantly, the model demonstrated accurate prediction of 5-year DFS and OS, achieving accuracies of 0.7680 for DFS and 0.7760 for OS. By categorizing patients into high-risk and low-risk groups, the model identified significant differences in survival outcomes, with hazard ratios of 4.85 for progression and 4.57 for death, both with p-values below 0.0001. Additionally, it uncovered novel digital biomarkers associated with poor prognosis, offering further insights into NSCLC treatment. This model has the potential to revolutionize postoperative decision-making by providing clinicians with a precise tool for predicting DFS and OS, thereby improving patient outcomes.
The Degree of Pre-Operative Osteoarthritis is Associated with Outcome Following THA
Authors: Kashanian Koorosh , Hinde Yoshio , Austin Roman , Mavromatis Alexander , Bingham Joshua , Grammatopoulos George 
Published: December 2024

Abstract

Introduction: Total hip arthroplasty (THA) is increasingly used in less severe cases of hip osteoarthritis (OA).This paper aimed to determine 1) The prevalence of different degrees of OA before THA, 2) The association of pre-operative OA severity with the severity of baseline symptoms and 3) The association of pre-operative OA severity with outcomes post-THA.
Methods: A retrospective study of a prospective database of all THAs at two tertiary academic centers between 2020 and 2021 was conducted. There were 803 patients in the database, with 109 being excluded due to incomplete data. There were 694 patients included, and the mean age and body mass index were 64.50±12.80 years and 28.45±5.90 kg/m2, respectively. Most patients were women (n=361; 52.0%).
Radiographic analysis was performed to assess the degree of hip OA with the Tönnis grade (0 – 3; no OA – complete degradation). Patients had pre-operative patient-reported outcome measures (PROMs) of hip function via the Oxford Hip Score (OHS) and their overall well-being via the EQ-5D-5L, with follow-up and post-operative PROMs 12 months after surgery. Data analysis was performed using SPSS (IBM, Armonk, NY).
Results: There were no differences in pre-operative OHS between Tönnis groups (18±6.26, n=14 in Tönnis 1, 21.19±8.27, n=260 in Tönnis 2 and 18.18±7.09, n=420 in Tönnis 3, p=0.14) After one year, the increase in OHS was 18.03±11.95, 18.32±10.96, and 24.32±9.98, respectively for Tönnis 1,2, and 3 (p<0.001). Thus, the percentage meeting the patient acceptable symptom state (PASS) was 57.1%, 60.8% and 72.6%. One year EQ-5D-5L was also highest in Tönnis 3 (47.5 ± 4.03, 79.6 ± 13.25 and 82.12 ± 11.67, p<0.001).
Conclusion: The highest-grade OA before THA was correlated with the greatest improvements in hip function and quality of life. This study demonstrates that a conservative approach and re-education should be attempted in patients who have an absence of advanced radiographic OA.
Osteosarcopenia as a Predictor of Histopathologic Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer
Authors: Hirase Yuki , Sasaki Ken , Tsuruda Yusuke , Shimonosono Masataka , Uchikado Yasuto , Matsushita Daisuke , Arigami Takaaki , Tada Nobuhiro , Baba Kenji , Kawasaki Yota , Ohtsuka Takao 
Published: December 2024

Abstract

Purpose: Predicting chemoradiotherapy (CRT) response in esophageal cancer is essential as outcomes vary among patients. This study aimed to evaluate the impact of osteosarcopenia on the effectiveness of neoadjuvant CRT (NACRT).
Methods: Ninety-five patients with advanced esophageal cancer who underwent surgical resection post-NACRT were included. Sarcopenia and osteopenia were determined using pre-NACRT skeletal muscle index and bone density at L3 and Th11 levels. Patients were categorized based on osteosarcopenia status.
Results: Thirty-seven patients (39%) had osteosarcopenia. Among tumors, 49 (51.6%) were grade 1 (non-responders), 12 grade 2 (12.6%), and 34 (35.8%) grade 3 (responders). NACRT was significantly more effective in patients with above-median body mass index, shallow tumor depth, low squamous cell carcinoma antigen levels, and without osteosarcopenia. Osteosarcopenia was independently correlated with the histopathologic response to NACRT. No significant differences in overall- or relapse-free survival were observed based on osteosarcopenia status.
Conclusion: Osteosarcopenia may predict NACRT response in esophageal cancer.