tbuckley/cabot-search huggingface dataset:
Machine learning-based clinical decision support system for treatment recommendation and overall survival prediction of hepatocellular carcinoma: a multi-center study
Authors: Kyung Hwa Lee, Gwang Hyeon Choi, Jihye Yun, Jonggi Choi, Myung Ji Goh, Dong Hyun Sinn, Young Joo Jin, Minseok Albert Kim, Su Jong Yu, Sangmi Jang, Soon Kyu Lee, Jeong Won Jang, Jae Seung Lee, Do Young Kim, Young Youn Cho, Hyung Joon Kim, Sehwa Kim, Ji Hoon Kim, Namkug Kim, Kang Mo Kim
Journal: npj Digital Medicine (2024)

Abstract The treatment decisions for patients with hepatocellular carcinoma are determined by a wide range of factors, and there is a significant difference between the recommendations of widely used staging systems and the actual initial treatment choices. Herein, we propose a machine learning-based clinical decision support system suitable for use in multi-center settings. We collected data from nine institutions in South Korea for training and validation datasets. The internal and external datasets included 935 and 1750 patients, respectively. We developed a model with 20 clinical variables consisting of two stages: the first stage which recommends initial treatment using an ensemble voting machine, and the second stage, which predicts post-treatment survival using a random survival forest algorithm. We derived the first and second treatment options from the results with the highest and the second-highest probabilities given by the ensemble model and predicted their post-treatment survival. When only the first treatment option was accepted, the mean accuracy of treatment recommendation in the internal and external datasets was 67.27% and 55.34%, respectively. The accuracy increased to 87.27% and 86.06%, respectively, when the second option was included as the correct answer. Harrell’s C index, integrated time-dependent AUC curve, and integrated Brier score of survival prediction in the internal and external datasets were 0.8381 and 0.7767, 91.89 and 86.48, 0.12, and 0.14, respectively. The proposed system can assist physicians by providing data-driven predictions for reference from other larger institutions or other physicians within the same institution when making treatment decisions.
Latent neural population dynamics underlying breathing, opioid-induced respiratory depression and gasping
Authors: Nicholas Edward Bush, Jan-Marino Ramirez
Journal: Nature Neuroscience (2024)

Abstract Breathing is vital and must be concurrently robust and flexible. This rhythmic behavior is generated and maintained within a rostrocaudally aligned set of medullary nuclei called the ventral respiratory column (VRC). The rhythmic properties of individual VRC nuclei are well known, yet technical challenges have limited the interrogation of the entire VRC population simultaneously. Here we characterize over 15,000 medullary units using high-density electrophysiology, opto-tagging and histological reconstruction. Population dynamics analysis reveals consistent rotational trajectories through a low-dimensional neural manifold. These rotations are robust and maintained even during opioid-induced respiratory depression. During severe hypoxia-induced gasping, the low-dimensional dynamics of the VRC reconfigure from rotational to all-or-none, ballistic efforts. Thus, latent dynamics provide a unifying lens onto the activities of large, heterogeneous populations of neurons involved in the simple, yet vital, behavior of breathing, and well describe how these populations respond to a variety of perturbations.
Antitumor efficacy of a sequence-specific DNA-targeted γPNA-based c-Myc inhibitor
Authors: Shipra Malik, Sai Pallavi Pradeep, Vikas Kumar, Yong Xiao, Yanxiang Deng, Rong Fan, Juan C. Vasquez, Vijender Singh, Raman Bahal
Journal: Cell Reports Medicine (2024)

<h2>Summary</h2> Targeting oncogenes at the genomic DNA level can open new avenues for precision medicine. Significant efforts are ongoing to target oncogenes using RNA-targeted and protein-targeted platforms, but no progress has been made to target genomic DNA for cancer therapy. Here, we introduce a gamma peptide nucleic acid (γPNA)-based genomic DNA-targeted platform to silence oncogenes <i>in vivo</i>. γPNAs efficiently invade the mixed sequences of genomic DNA with high affinity and specificity. As a proof of concept, we establish that γPNA can inhibit <i>c-Myc</i> transcription in multiple cell lines. We evaluate the <i>in vivo</i> efficacy and safety of genomic DNA targeting in three pre-clinical models. We also establish that anti-transcription γPNA in combination with histone deacetylase inhibitors and chemotherapeutic drugs results in robust antitumor activity in cell-line- and patient-derived xenografts. Overall, this strategy offers a unique therapeutic platform to target genomic DNA to inhibit oncogenes for cancer therapy.
Evolutionary proteogenomic landscape from pre-invasive to invasive lung adenocarcinoma
Authors: Yang Zhang, Fangqiu Fu, Qiao Zhang, Lingling Li, Hui Liu, Chaoqiang Deng, Qianqian Xue, Yue Zhao, Wenrui Sun, Han Han, Zhendong Gao, Chunmei Guo, Qiang Zheng, Hong Hu, Yihua Sun, Yuan Li, Chen Ding, Haiquan Chen
Journal: Cell Reports Medicine (2024)

Lung adenocarcinoma follows a stepwise progression from pre-invasive to invasive. However, there remains a knowledge gap regarding molecular events from pre-invasive to invasive. Here, we conduct a comprehensive proteogenomic analysis comprising whole-exon sequencing, RNA sequencing, and proteomic and phosphoproteomic profiling on 98 pre-invasive and 99 invasive lung adenocarcinomas. The deletion of chr4q12 contributes to the progression from pre-invasive to invasive adenocarcinoma by downregulating SPATA18, thus suppressing mitophagy and promoting cell invasion. Proteomics reveals diverse enriched pathways in normal lung tissues and pre-invasive and invasive adenocarcinoma. Proteomic analyses identify three proteomic subtypes, which represent different stages of tumor progression. We also illustrate the molecular characterization of four immune clusters, including endothelial cells, B cells, DCs, and immune depression subtype. In conclusion, this comprehensive proteogenomic study characterizes the molecular architecture and hallmarks from pre-invasive to invasive lung adenocarcinoma, guiding the way to a deeper understanding of the tumorigenesis and progression of this disease.
Novel‐Ink‐Based Direct Ink Writing of Ti6Al4V Scaffolds with Sub‐300 µm Structural Pores for Superior Cell Proliferation and Differentiation
Authors: Chao Xu, Yan Xu, Hao Chen, Qing Han, Wenzheng Wu, Lu Zhang, Qingping Liu, Jincheng Wang, Luquan Ren
Journal: Advanced Healthcare Materials (2024)

Ti6Al4V scaffolds with pore sizes between 300 and 600 µm are deemed suitable for bone tissue engineering. However, a significant proportion of human bone pores are smaller than 300 µm, playing a crucial role in cell proliferation, differentiation, and bone regeneration. Ti6Al4V scaffolds with these small-sized pores are not successfully fabricated, and their cytocompatibility remains unknown. The study presents a novel ink formula specifically tailored for fabricating Ti6Al4V scaffolds featuring precise and unobstructed sub-300 µm structural pores, achieved by investigating the rheological properties and printability of five inks containing 60-77.5 vol% Ti6Al4V powders and bisolvent binders. Ti6Al4V scaffolds with 50-600 µm pores are fabricated via direct ink writing and subjected to in vitro assays with MC3T3-E1 and bone marrow mesenchymal stem cells. The 100 µm pore-sized scaffolds exhibit the highest cell adhesion and proliferation capacity based on live/dead assay, FITC-phalloidin/4',6-diamidino-2-phenylindole staining, and cell count kit 8 assay. The alizarin red staining, real-time quantitative PCR assay, and immunocytochemical staining demonstrate the superior osteogenic differentiation potential of 100 and 200 µm pore-sized scaffolds. The importance of sub-300 µm structrual pores is highlighted, redefining the optimal pore size for Ti6Al4V scaffolds and advancing bone tissue engineering and clinical medicine development.
Macrophage-derived biomimetic nanoparticles enhanced SDT combined with immunotherapy inhibited tumor growth and metastasis
Authors: Sijie Chen, Tianliang Ma, Jiahao Wang, Shuailong Liang, Haiqin Liao, Wanlin Tan, Mingyu Chen, Xiaohui Zhou, Yan Xu, Long Wang, Chengcheng Niu
Journal: Biomaterials (2024)
Molecular and Pathologic Characterization of YAP1-Expressing Small Cell Lung Cancer Cell Lines Leads to Reclassification as SMARCA4-Deficient Malignancies
Authors: Jin Ng, Ling Cai, Luc Girard, Owen W.J. Prall, Neeha Rajan, Christine Khoo, Ahida Batrouney, David J. Byrne, Danielle K. Boyd, Ariena J. Kersbergen, Michael Christie, John D. Minna, Marian L. Burr, Kate D. Sutherland
Journal: Clinical Cancer Research (2023)

The classification of small cell lung cancer (SCLC) into distinct molecular subtypes defined by ASCL1, NEUROD1, POU2F3, or YAP1 (SCLC-A, -N, -P, or -Y) expression, paves the way for a personalized treatment approach. However, the existence of a distinct YAP1-expressing SCLC subtype remains controversial.
An interpretable model based on graph learning for diagnosis of Parkinson’s disease with voice-related EEG
Authors: Shuzhi Zhao, Guangyan Dai, Jingting Li, Xiaoxia Zhu, Xiyan Huang, Yongxue Li, Mingdan Tan, Lan Wang, Peng Fang, Xi Chen, Nan Yan, Hanjun Liu
Journal: npj Digital Medicine (2024)

Abstract Parkinson’s disease (PD) exhibits significant clinical heterogeneity, presenting challenges in the identification of reliable electroencephalogram (EEG) biomarkers. Machine learning techniques have been integrated with resting-state EEG for PD diagnosis, but their practicality is constrained by the interpretable features and the stochastic nature of resting-state EEG. The present study proposes a novel and interpretable deep learning model, graph signal processing-graph convolutional networks (GSP-GCNs), using event-related EEG data obtained from a specific task involving vocal pitch regulation for PD diagnosis. By incorporating both local and global information from single-hop and multi-hop networks, our proposed GSP-GCNs models achieved an averaged classification accuracy of 90.2%, exhibiting a significant improvement of 9.5% over other deep learning models. Moreover, the interpretability analysis revealed discriminative distributions of large-scale EEG networks and topographic map of microstate MS5 learned by our models, primarily located in the left ventral premotor cortex, superior temporal gyrus, and Broca’s area that are implicated in PD-related speech disorders, reflecting our GSP-GCN models’ ability to provide interpretable insights identifying distinctive EEG biomarkers from large-scale networks. These findings demonstrate the potential of interpretable deep learning models coupled with voice-related EEG signals for distinguishing PD patients from healthy controls with accuracy and elucidating the underlying neurobiological mechanisms.
An Immune Gene Expression Risk Score for Distant Metastases after Radiotherapy for Cervical Cancer
Authors: Jelena Lukovic, Melania Pintilie, Kathy Han, Anthony W. Fyles, Jeffrey P. Bruce, Rene Quevedo, Trevor J. Pugh, Christina S. Fjeldbo, Heidi Lyng, Michael F. Milosevic
Journal: Clinical Cancer Research (2024)

Abstract Purpose: To develop an immune-based gene expression risk score to identify patients with cervical cancer at increased risk of distant metastases (DM). Experimental Design: Tumor biopsies were obtained from 81 patients prior to chemoradiotherapy. Whole-transcriptome RNA sequencing was performed (Illumina NextSeq500). Beginning with 4,723 immune-related genes, a 55-gene risk score for DM was derived using Cox modeling and principal component analysis. It was validated in independent cohorts of 274 patients treated at the Norwegian Radium Hospital (NRH) and 206 patients from The Cancer Genome Atlas (TCGA). Results: The risk score was predictive of DM (HR, 2.7; P &amp;lt; 0.0001) and lower cause-specific survival (CSS) by univariate analysis (HR, 2.0; P = 0.0003) and multivariate analysis adjusted for clinical factors (DM HR, 3.0; P &amp;lt; 0.0001; CSS HR, 2.2; P = 0.0004). The risk score predicted DM (HR, 1.4; P = 0.05) and CSS (HR, 1.48; P = 0.013) in the NRH cohort and CSS (HR, 1.4; P = 0.03) in TCGA cohort. Higher risk scores were associated with lower CIBERSORT estimates of tumor-infiltrating immune cells, including CD8 T cells and M1 and M2 macrophages (all P &amp;lt; 0.001). Higher risk scores were associated with lower expression (all P &amp;lt; 0.001) of important chemokines (CXCL12, CXCR4), IFN-regulated genes (IRF1, STAT1, IDO1), and immune checkpoint regulators (PD-1, PD-L1, CTLA-4). Conclusions: The immune metastatic risk score addresses important challenges in the treatment of cervical cancer—identifying patients at high risk of DM after radiotherapy. The findings of this study indicate that high tumor mutational burden and a “cold,” immune-excluded tumor microenvironment influence distant metastatic recurrence. Further validation of the risk score is needed.
Antiracist training programs for mental health professionals: A scoping review
Authors: Jude Mary Cénat, Cathy Broussard, Grace Jacob, Cary Kogan, Kim Corace, Gloria Ukwu, Olivia Onesi, Sarah Elisabeth Furyk, Farid Mansoub Bekarkhanechi, Monnica Williams, Marie-Hélène Chomienne, Jean Grenier, Patrick R. Labelle
Journal: Clinical Psychology Review (2024)

Racism has been shown to directly be deleterious to the mental health care received by minoritized peoples. In response, some mental health institutions have pledged to provide antiracist mental health care, which includes training mental health care professionals in this approach. This scoping review aimed to synthesize the existing published material on antiracist training programs among mental health care professionals. To identify studies, a comprehensive search strategy was developed and executed by a research librarian in October 2022 across seven databases (APA PsycInfo, Education Source, Embase, ERIC, MEDLINE, CINAHL, and Web of Science). Subject headings and keywords relating to antiracist training as well as to mental health professionals were used and combined. There were 7186 studies generated by the initial search and 377 by the update search, 30 were retained and included. Findings revealed four main antiracist competencies to develop in mental health professionals: importance of understanding the cultural, social, and historical context at the root of the mental health problems; developing awareness of individual biases, self-identity and privilege; recognizing oppressive and racism-sustaining behaviors in mental health care settings; and, employing antiracist competencies in therapy. Professionals who have taken trainings having the main components have developed skills on the interconnectedness between racialized groups' mental health and the cultural, religious, social, historical, economic, and political issues surrounding race, necessary for successful clinical practice and for providing anti-racist mental health care. This scoping review presents a summary of the essential antiracist competencies drawn from the literature which must be applied in a mental health care setting, to improve help seeking behaviors, and reduce distrust in mental health care professionals and settings.
IL-23 to see: Gut DCs shine bright in inductive sites
Authors: Isabel Ulmert, Katharina Lahl
Journal: The Journal of Experimental Medicine (2024)

The cytokine IL-23 plays important roles in intestinal barrier protection and integrity, but is also linked to chronic inflammation. In this issue of JEM, Ohara et al. (https://doi.org/10.1084/jem.20230923) provide clarity on the much-debated question of which cells produce IL-23.
Reporting Requirements, Confidentiality, and Legal Immunity for Physicians Who Report Medically Impaired Drivers
Authors: Elaine M. Tran, Jeffrey E. Lee
Journal: JAMA Network Open (2024)

Importance Physicians play an important role in assessing patients’ ability to drive. There is a dearth of peer-reviewed information on policies regarding physician reporting of medically impaired drivers. Objective To investigate state reporting requirements and the availability of confidentiality and legal immunity for physicians who report medically impaired drivers. Design, Setting, and Participants This cross-sectional study was conducted from November 1 to 30, 2022, in 3 rounds. First, all 50 US states’ Department of Motor Vehicle (DMV) websites were systematically reviewed. Second, DMV staff from each state were surveyed via telephone. Third, each state’s legal codes for driver licensing were reviewed. Main Outcomes and Measures Outcome measures included the percentage of states with mandatory and voluntary reporting policies, reporting instructions on DMV websites, anonymous reporting options, and legal immunity for reporting physicians, in addition to characteristics of states’ mandatory reporting policies (ie, types of medical conditions that require reporting) and policies surrounding the confidentiality of reports. The data were analyzed using descriptive statistics. Results One-third of state DMV websites (17 [34%]) lacked instructions regarding physician reporting. Six states had mandatory reporting requirements; 4 of these states only required reporting of conditions characterized by lapses of consciousness. Only 3 states (6%) accepted anonymous reports, and 7 states (14%) deemed physician reports of medically impaired drivers confidential without exception. Nearly one-third of states (15 [30%]) deemed reports by physicians confidential, with the exception that reported drivers could find out who reported them if they asked for a copy of the reporting form. Most states (37 [74%]) had statutes that protected physicians from liability related to reporting medically impaired drivers. Conclusions and Relevance This cross-sectional study of state reporting requirements regarding medically impaired drivers found many differences in state policies regarding mandatory reporting and the conditions that require reporting. There was also limited availability of online reporting instructions, anonymous reporting options, and legal protections for reporting physicians.
Maternal Diabetes and Overweight and Congenital Heart Defects in Offspring
Authors: Riitta Turunen, Anna Pulakka, Johanna Metsälä, Tero Vahlberg, Tiina Ojala, Mika Gissler, Eero Kajantie, Emmi Helle
Journal: JAMA Network Open (2024)

Importance Maternal diabetes and overweight or obesity are known to be associated with increased risk of congenital heart defects (CHDs) in offspring, but there are no large studies analyzing outcomes associated with these factors in 1 model. Objective To investigate the association of maternal diabetes and overweight or obesity with CHDs among offspring in 1 model. Design, Setting, and Participants This nationwide, population-based register study was conducted in a birth cohort from Finland consisting of all children born between 2006 and 2016 (620 751 individuals) and their mothers. Data were analyzed from January 2022 until November 2023. Exposures Maternal prepregnancy body mass index (BMI; calculated as weight in kilograms divided by height in meters squared), categorized as underweight (&amp;amp;lt;18.5), normal (18.5-24.9), overweight (25.0-29.9), and obesity (≥30), was assessed. Maternal diabetes status, classified as no diabetes, type 1 diabetes (T1D), type 2 or other diabetes, and gestational diabetes, was assessed. Main Outcomes and Measures Odds ratios (ORs) of isolated CHDs in children were found. In addition, 9 anatomical CHD subgroups were studied. Results Of 620 751 children (316 802 males [51.0%]; 573 259 mothers aged 20-40 years [92.3%]) born in Finland during the study period, 10 254 children (1.7%) had an isolated CHD. Maternal T1D was associated with increased odds of having a child with any CHD (OR, 3.77 [95% CI, 3.26-4.36]) and 6 of 9 CHD subgroups (OR range, 3.28 [95% CI, 1.55-6.95] for other septal defects to 7.39 [95% CI, 3.00-18.21] for transposition of great arteries) compared with no maternal diabetes. Maternal overweight was associated with left ventricular outflow tract obstruction (OR, 1.28 [95% CI, 1.10-1.49]) and ventricular septal defects (OR, 0.92 [95% CI, 0.86-0.98]), and obesity was associated with complex defects (OR, 2.70 [95% CI, 1.14-6.43]) and right outflow tract obstruction (OR, 1.31 [95% CI, 1.09-1.58]) compared with normal maternal BMI. Conclusions and Relevance This study found that maternal T1D was associated with increased risk for most types of CHD in offspring, while obesity and overweight were associated with increased risk for complex defects and outflow tract obstruction and decreased risk for ventricular septal defects. These different risk profiles of T1D and overweight and obesity may suggest distinct underlying teratogenic mechanisms.
Macromolecular Cargo Encapsulation via In Vitro Assembly of Two‐Component Protein Nanoparticles
Authors: Karla‐Luise Herpoldt, Ciana L. López, Isaac Sappington, Minh N. Pham, Selvi Srinivasan, Jason Netland, Katherine S. Montgomery, Debashish Roy, Alexander N. Prossnitz, Daniel Ellis, Adam J. Wargacki, Marion Pepper, Anthony J. Convertine, Patrick S. Stayton, Neil P. King
Journal: Advanced Healthcare Materials (2024)

Self-assembling protein nanoparticles are a promising class of materials for targeted drug delivery. Here, the use of a computationally designed, two-component, icosahedral protein nanoparticle is reported to encapsulate multiple macromolecular cargoes via simple and controlled self-assembly in vitro. Single-stranded RNA molecules between 200 and 2500 nucleotides in length are encapsulated and protected from enzymatic degradation for up to a month with length-dependent decay rates. Immunogenicity studies of nanoparticles packaging synthetic polymers carrying a small-molecule TLR7/8 agonist show that co-delivery of antigen and adjuvant results in a more than 20-fold increase in humoral immune responses while minimizing systemic cytokine secretion associated with free adjuvant. Coupled with the precise control over nanoparticle structure offered by computational design, robust and versatile encapsulation via in vitro assembly opens the door to a new generation of cargo-loaded protein nanoparticles that can combine the therapeutic effects of multiple drug classes.