tbuckley/cabot-search huggingface dataset:
Health Care Workers and War in the Middle East
Authors: Aaron E. Glatt
Journal: JAMA (2023)

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Optimization of base editors for the functional correction of SMN2 as a treatment for spinal muscular atrophy
Authors: Christiano R. R. Alves, Leillani L. Ha, Rebecca Yaworski, Emma R. Sutton, Cicera R. Lazzarotto, Kathleen A. Christie, Aoife Reilly, Ariane Beauvais, Roman M. Doll, Demitri de la Cruz, Casey A. Maguire, Kathryn J. Swoboda, Shengdar Q. Tsai, Rashmi Kothary, Benjamin P. Kleinstiver
Journal: Nature Biomedical Engineering (2023)
Week-long normoglycaemia in diabetic mice and minipigs via a subcutaneous dose of a glucose-responsive insulin complex
Authors: Juan Zhang, Xiangqian Wei, Wei Liu, Yanfang Wang, Anna R. Kahkoska, Xianchi Zhou, Huimin Zheng, Wentao Zhang, Tao Sheng, Yang Zhang, Yun Liu, Kangfan Ji, Yichen Xu, Peng Zhang, Jianchang Xu, John. B. Buse, Jinqiang Wang, Zhen Gu
Journal: Nature Biomedical Engineering (2023)
Multifocal fluorescence video-rate imaging of centimetre-wide arbitrarily shaped brain surfaces at micrometric resolution
Authors: Hao Xie, Xiaofei Han, Guihua Xiao, Hanyun Xu, Yuanlong Zhang, Guoxun Zhang, Qingwei Li, Jing He, Dan Zhu, Xinguang Yu, Qionghai Dai
Journal: Nature Biomedical Engineering (2023)

Fluorescence microscopy allows for the high-throughput imaging of cellular activity across brain areas in mammals. However, capturing rapid cellular dynamics across the curved cortical surface is challenging, owing to trade-offs in image resolution, speed, field of view and depth of field. Here we report a technique for wide-field fluorescence imaging that leverages selective illumination and the integration of focal areas at different depths via a spinning disc with varying thickness to enable video-rate imaging of previously reconstructed centimetre-scale arbitrarily shaped surfaces at micrometre-scale resolution and at a depth of field of millimetres. By implementing the technique in a microscope capable of acquiring images at 1.68 billion pixels per second and resolving 16.8 billion voxels per second, we recorded neural activities and the trajectories of neutrophils in real time on curved cortical surfaces in live mice. The technique can be integrated into many microscopes and macroscopes, in both reflective and fluorescence modes, for the study of multiscale cellular interactions on arbitrarily shaped surfaces.
Repeated mRNA vaccination sequentially boosts SARS-CoV-2-specific CD8+ T cells in persons with previous COVID-19
Authors: Emily S. Ford, Koshlan Mayer-Blackwell, Lichen Jing, Kerry J. Laing, Anton M. Sholukh, Russell St. Germain, Emily L. Bossard, Hong Xie, Thomas H. Pulliam, Saumya Jani, Stacy Selke, Carlissa J. Burrow, Christopher L. McClurkan, Anna Wald, Alexander L. Greninger, Michael R. Holbrook, Brett Eaton, Elizabeth Eudy, Michael Murphy, Elena Postnikova, Harlan S. Robins, Rebecca Elyanow, Rachel M. Gittelman, Matyas Ecsedi, Elise Wilcox, Aude G. Chapuis, Andrew Fiore-Gartland, David M. Koelle
Journal: Nature Immunology (2023)
Early life changes in histone landscape protect against age-associated amyloid toxicities through HSF-1-dependent regulation of lipid metabolism
Authors: Bryndon J. Oleson, Janakraj Bhattrai, Sarah L. Zalubas, Tessa R. Kravchenko, Yuanyuan Ji, Emily L. Jiang, Christine C. Lu, Ciara R. Madden, Julia G. Coffman, Daphne Bazopoulou, Jace W. Jones, Ursula Jakob
Journal: Nature Aging (2023)
CXCL10 and Nrf2-upregulated mesenchymal stem cells reinvigorate T lymphocytes for combating glioblastoma
Authors: Jiaji Mao, Jianing Li, Junwei Chen, Qin Wen, Minghui Cao, Fang Zhang, Baoxun Li, Qinyuan Zhang, Zhe Wang, Jingzhong Zhang, Jun Shen
Journal: Journal for ImmunoTherapy of Cancer (2023)

Lack of tumor-infiltrating T lymphocytes and concurrent T-cell dysfunction have been identified as major contributors to glioblastoma (GBM) immunotherapy resistance. Upregulating CXCL10 in the tumor microenvironment (TME) is a promising immunotherapeutic approach that potentially increases tumor-infiltrating T cells and boosts T-cell activity but is lacking effective delivery methods.
Comprehensive assessment methods are key to progress in deep learning
Authors: Michael W. Spratling
Journal: Behavioral and Brain Sciences (2023)

Bowers et al. eloquently describe issues with current deep neural network (DNN) models of vision, claiming that there are deficits both with the methods of assessment, and with the models themselves. I am in agreement with both these claims, but propose a different recipe to the one outlined in the target article for overcoming these issues.
Going after the bigger picture: Using high-capacity models to understand mind and brain
Authors: Hans Op de Beeck, Stefania Bracci
Journal: Behavioral and Brain Sciences (2023)

Abstract Deep neural networks (DNNs) provide a unique opportunity to move towards a generic modelling framework in psychology. The high representational capacity of these models combined with the possibility for further extensions has already allowed us to investigate the forest, namely the complex landscape of representations and processes that underlie human cognition, without forgetting about the trees, which include individual psychological phenomena.
You can't play 20 questions with nature and win redux
Authors: Bradley C. Love, Robert M. Mok
Journal: Behavioral and Brain Sciences (2023)

Abstract An incomplete science begets imperfect models. Nevertheless, the target article advocates for jettisoning deep-learning models with some competency in object recognition for toy models evaluated against a checklist of laboratory findings; an approach which evokes Alan Newell's 20 questions critique. We believe their approach risks incoherency and neglects the most basic test; can the model perform its intended task.
Thinking beyond the ventral stream: Comment on Bowers et al.
Authors: Christopher Summerfield, Jessica A. F. Thompson
Journal: Behavioral and Brain Sciences (2023)

Abstract Bowers et al. rightly emphasise that deep learning models often fail to capture constraints on visual perception that have been discovered by previous research. However, the solution is not to discard deep learning altogether, but to design stimuli and tasks that more closely reflect the problems that biological vision evolved to solve, such as understanding scenes and preparing skilled action.
Neural networks need real-world behavior
Authors: Aedan Y. Li, Marieke Mur
Journal: Behavioral and Brain Sciences (2023)

Abstract Bowers et al. propose to use controlled behavioral experiments when evaluating deep neural networks as models of biological vision. We agree with the sentiment and draw parallels to the notion that “neuroscience needs behavior.” As a promising path forward, we suggest complementing image recognition tasks with increasingly realistic and well-controlled task environments that engage real-world object recognition behavior.
<i>Explananda</i> and <i>explanantia</i> in deep neural network models of neurological network functions
Authors: Mihnea Moldoveanu
Journal: Behavioral and Brain Sciences (2023)

Abstract Depending on what we mean by “explanation,” challenges to the explanatory depth and reach of deep neural network models of visual and other forms of intelligent behavior may need revisions to both the elementary building blocks of neural nets (the explananda) and to the ways in which experimental environments and training protocols are engineered (the explanantia). The two paths assume and imply sharply different conceptions of how an explanation explains and of the explanatory function of models.
Modelling human vision needs to account for subjective experience
Authors: Marcin Koculak, Michał Wierzchoń
Journal: Behavioral and Brain Sciences (2023)

Vision is inseparably connected to perceptual awareness which can be seen as the culmination of sensory processing. Studies on conscious vision reveal that object recognition is just one of the means through which our representation of the world is built. We propose an operationalization of subjective experience in the context of deep neural networks (DNNs) that could encourage a more thorough comparison of human and artificial vision.
Beyond the limitations of any imaginable mechanism: Large language models and psycholinguistics
Authors: Conor Houghton, Nina Kazanina, Priyanka Sukumaran
Journal: Behavioral and Brain Sciences (2023)

Large language models (LLMs) are not detailed models of human linguistic processing. They are, however, extremely successful at their primary task: Providing a model for language. For this reason LLMs are important in psycholinguistics: They are useful as a practical tool, as an illustrative comparative, and philosophically, as a basis for recasting the relationship between language and thought.
The model-resistant richness of human visual experience
Authors: Jianghao Liu, Paolo Bartolomeo
Journal: Behavioral and Brain Sciences (2023)

Abstract Current deep neural networks (DNNs) are far from being able to model the rich landscape of human visual experience. Beyond visual recognition, we explore the neural substrates of visual mental imagery and other visual experiences. Rather than shared visual representations, temporal dynamics and functional connectivity of the process are essential. Generative adversarial networks may drive future developments in simulating human visual experience.
Statistical prediction alone cannot identify good models of behavior
Authors: Nisheeth Srivastava, Anjali Sifar, Narayanan Srinivasan
Journal: Behavioral and Brain Sciences (2023)

Abstract The dissociation between statistical prediction and scientific explanation advanced by Bowers et al. for studies of vision using deep neural networks is also observed in several other domains of behavior research, and is in fact unavoidable when fitting large models such as deep nets and other supervised learners, with weak theoretical commitments, to restricted samples of highly stochastic behavioral phenomena.
There is a fundamental, unbridgeable gap between DNNs and the visual cortex
Authors: Moshe Gur
Journal: Behavioral and Brain Sciences (2023)

Abstract Deep neural networks (DNNs) are not just inadequate models of the visual system but are so different in their structure and functionality that they are not even on the same playing field. DNN units have almost nothing in common with neurons, and, unlike visual neurons, they are often fully connected. At best, DNNs can label inputs, while our object perception is both holistic and detail preserving. A feat that no computational system can achieve.
Perceptual learning in humans: An active, top-down-guided process
Authors: Heleen A. Slagter
Journal: Behavioral and Brain Sciences (2023)

Abstract Deep neural network (DNN) models of human-like vision are typically built by feeding blank slate DNN visual images as training data. However, the literature on human perception and perceptual learning suggests that developing DNNs that truly model human vision requires a shift in approach in which perception is not treated as a largely bottom-up process, but as an active, top-down-guided process.
Once upon a time, the Amyloid Cascade Hypothesis
Authors: Alberto Granzotto, Stefano L. Sensi
Journal: Ageing Research Reviews (2023)

Recent trials with monoclonal antibodies targeting amyloid-β (Aβ) in Alzheimer's disease (AD) have sparked a renewed interest in disease-modifying therapies. Despite their promise, these trials leave the issue open and posit some doubts about the validity of the Amyloid Cascade Hypothesis (ACH). While some scores of neurocognitive tests improved upon treatment, real-world clinical benefits were minimal. This Viewpoint discusses additional, often overlooked findings from these trials. We also emphasize the multifactorial nature of AD and the need for a broader research perspective beyond the simplistic disease model provided by the ACH.
Weight Loss Induced by Antiobesity Medications and <scp>All‐Cause</scp> Mortality Among Patients With Knee or Hip Osteoarthritis
Authors: Jie Wei, David Hunter, Nancy E. Lane, Jing Wu, Chao Zeng, Guanghua Lei, Yuqing Zhang
Journal: Arthritis & Rheumatology (2023)

Objective The current guidelines recommend weight loss for patients with overweight or obesity and knee or hip osteoarthritis (OA); however, there is a paucity of data on the relation of weight loss to death among patients with OA. We aimed to examine the relation of the rate of weight loss induced by antiobesity medications over one year to all‐cause mortality among patients with overweight or obesity and knee or hip OA. Methods Using the IQVIA Medical Research Database, we identified people with overweight or obesity and knee or hip OA. We emulated analyses of a hypothetical target trial to assess the effect of slow‐to‐moderate (2%–10%) or fast (≥10%) weight loss induced by the initiation of antiobesity medications within one year on all‐cause mortality and secondary outcomes over five years’ follow‐up. Results Among 6,524 participants, the five‐year all‐cause mortality rates were 5.3%, 4.0%, and 5.4% for weight gain or stable, slow‐to‐moderate weight loss, and fast weight loss arms, respectively. Compared with the weight gain or stable arm, hazard ratios of all‐cause mortality were 0.72 (95% confidence interval [CI] 0.56–0.92) for the slow‐to‐moderate weight loss arm and 0.99 (95% CI 0.67–1.44) for the fast weight loss arm. We found dose–response protective effects of weight loss on incident hypertension, type 2 diabetes, and venous thromboembolism but a slightly higher risk of cardiovascular disease, albeit not statistically significant, in the fast rate of weight loss arm than in the weight gain or stable arm and no significant relations of weight loss to the risk of cancer. Conclusion In this population‐based study, a slow‐to‐moderate, but not fast, rate of weight loss induced by antiobesity medications is associated with a lower risk of all‐cause mortality in people with overweight or obesity and knee or hip OA. image
Prognostic Biomarkers in Kidney Transplantation: A Systematic Review and Critical Appraisal
Authors: Marc Raynaud, Solaf Al-Awadhi, Kevin Louis, Huanxi Zhang, Xiaojun Su, Valentin Goutaudier, Jiali Wang, Zeynep Demir, Yongcheng Wei, Agathe Truchot, Antoine Bouquegneau, Arnaud Del Bello, Élodie Bailly, Yannis Lombardi, Mehdi Maanaoui, Alessia Giarraputo, Sofia Naser, Gillian Divard, Olivier Aubert, Mohammad Hassan Murad, Changxi Wang, Longshan Liu, Oriol Bestard, Maarten Naesens, John J. Friedewald, Carmen Lefaucheur, Leonardo Riella, Gary Collins, John P.A. Ioannidis, Alexandre Loupy
Journal: Journal of the American Society of Nephrology (2023)

Why are there so few biomarkers accepted by health authorities and implemented in clinical practice, despite the high and growing number of biomaker studies in medical research ? In this meta-epidemiological study, including 804 studies that were critically appraised by expert reviewers, the authors have identified all prognostic kidney transplant biomarkers and showed overall suboptimal study designs, methods, results, interpretation, reproducible research standards, and transparency. The authors also demonstrated for the first time that the limited number of studies challenged the added value of their candidate biomarkers against standard-of-care routine patient monitoring parameters. Most biomarker studies tended to be single-center, retrospective studies with a small number of patients and clinical events. Less than 5% of the studies performed an external validation. The authors also showed the poor transparency reporting and identified a data beautification phenomenon. These findings suggest that there is much wasted research effort in transplant biomarker medical research and highlight the need to produce more rigorous studies so that more biomarkers may be validated and successfully implemented in clinical practice.
Expert consensus on digital guided therapy for endodontic diseases
Authors: Xi Wei, Yu Du, Xuedong Zhou, Lin Yue, Qing Yu, Benxiang Hou, Zhi Chen, Jingping Liang, Wenxia Chen, Lihong Qiu, Xiangya Huang, Liuyan Meng, Dingming Huang, Xiaoyan Wang, Yu Tian, Zisheng Tang, Qi Zhang, Leiying Miao, Jin Zhao, Deqin Yang, Jian Yang, Junqi Ling
Journal: International Journal of Oral Science (2023)

Abstract Digital guided therapy (DGT) has been advocated as a contemporary computer-aided technique for treating endodontic diseases in recent decades. The concept of DGT for endodontic diseases is categorized into static guided endodontics (SGE), necessitating a meticulously designed template, and dynamic guided endodontics (DGE), which utilizes an optical triangulation tracking system. Based on cone-beam computed tomography (CBCT) images superimposed with or without oral scan (OS) data, a virtual template is crafted through software and subsequently translated into a 3-dimensional (3D) printing for SGE, while the system guides the drilling path with a real-time navigation in DGE. DGT was reported to resolve a series of challenging endodontic cases, including teeth with pulp obliteration, teeth with anatomical abnormalities, teeth requiring retreatment, posterior teeth needing endodontic microsurgery, and tooth autotransplantation. Case reports and basic researches all demonstrate that DGT stand as a precise, time-saving, and minimally invasive approach in contrast to conventional freehand method. This expert consensus mainly introduces the case selection, general workflow, evaluation, and impact factor of DGT, which could provide an alternative working strategy in endodontic treatment.
Dental impact of anti-fibroblast growth factor 23 therapy in X-linked hypophosphatemia
Authors: Elis J. Lira dos Santos, Kenta Nakajima, Julien Po, Ayako Hanai, Volha Zhukouskaya, Martin Biosse Duplan, Agnès Linglart, Takashi Shimada, Catherine Chaussain, Claire Bardet
Journal: International Journal of Oral Science (2023)

Abstract Elevated fibroblast growth factor 23 (FGF23) in X-linked hypophosphatemia (XLH) results in rickets and phosphate wasting, manifesting by severe bone and dental abnormalities. Burosumab, a FGF23-neutralizing antibody, an alternative to conventional treatment (phosphorus and active vitamin D analogs), showed significant improvement in the long bone phenotype. Here, we examined whether FGF23 antibody (FGF23-mAb) also improved the dentoalveolar features associated with XLH. Four-week-old male Hyp mice were injected weekly with 4 or 16 mg·kg −1 of FGF23-mAb for 2 months and compared to wild-type (WT) and vehicle (PBS) treated Hyp mice ( n = 3–7 mice). Micro-CT analyses showed that both doses of FGF23-mAb restored dentin/cementum volume and corrected the enlarged pulp volume in Hyp mice, the higher concentration resulting in a rescue similar to WT levels. FGF23-mAb treatment also improved alveolar bone volume fraction and mineral density compared to vehicle-treated ones. Histology revealed improved mineralization of the dentoalveolar tissues, with a decreased amount of osteoid, predentin and cementoid. Better periodontal ligament attachment was also observed, evidenced by restoration of the acellular cementum. These preclinical data were consistent with the retrospective analysis of two patients with XLH showing that burosumab treatment improved oral features. Taken together, our data show that the dentoalveolar tissues are greatly improved by FGF23-mAb treatment, heralding its benefit in clinics for dental abnormalities.
Effects of RNA methylation on Tumor angiogenesis and cancer progression
Authors: Mingyu Han, Haifeng Sun, Quanbo Zhou, Jinbo Liu, Junhong Hu, Weitang Yuan, Zhenqiang Sun
Journal: Molecular Cancer (2023)

Abstract Tumor angiogenesis plays vital roles in the growth and metastasis of cancer. RNA methylation is one of the most common modifications and is widely observed in eukaryotes and prokaryotes. Accumulating studies have revealed that RNA methylation affects the occurrence and development of various tumors. In recent years, RNA methylation has been shown to play an important role in regulating tumor angiogenesis. In this review, we mainly elucidate the mechanisms and functions of RNA methylation on angiogenesis and progression in several cancers. We then shed light on the role of RNA methylation-associated factors and pathways in tumor angiogenesis. Finally, we describe the role of RNA methylation as potential biomarker and novel therapeutic target.
Compound Absorption in Polymer Devices Impairs the Translatability of Preclinical Safety Assessments
Authors: Aurino M. Kemas, Reza Zandi Shafagh, Nayere Taebnia, Maurice Michel, Lena Preiss, Ute Hofmann, Volker M. Lauschke
Journal: Advanced Healthcare Materials (2023)

Organotypic and microphysiological systems (MPS) that can emulate the molecular phenotype and function of human tissues, such as liver, are increasingly used in preclinical drug development. However, despite their improved predictivity, drug development success rates have remained low with most compounds failing in clinical phases despite promising preclinical data. Here, it is tested whether absorption of small molecules to polymers commonly used for MPS fabrication can impact preclinical pharmacological and toxicological assessments and contribute to the high clinical failure rates. To this end, identical devices are fabricated from eight different MPS polymers and absorption of prototypic compounds with different physicochemical properties are analyzed. It is found that overall absorption is primarily driven by compound hydrophobicity and the number of rotatable bonds. However, absorption can differ by >1000-fold between polymers with polydimethyl siloxane (PDMS) being most absorptive, whereas polytetrafluoroethylene (PTFE) and thiol-ene epoxy (TEE) absorbed the least. Strikingly, organotypic primary human liver cultures successfully flagged hydrophobic hepatotoxins in lowly absorbing TEE devices at therapeutically relevant concentrations, whereas isogenic cultures in PDMS devices are resistant, resulting in false negative safety signals. Combined, these results can guide the selection of MPS materials and facilitate the development of preclinical assays with improved translatability.
A deep new look at color
Authors: Jelmer Philip de Vries, Alban Flachot, Takuma Morimoto, Karl R. Gegenfurtner
Journal: Behavioral and Brain Sciences (2023)

Abstract Bowers et al. counter deep neural networks (DNNs) as good models of human visual perception. From our color perspective we feel their view is based on three misconceptions: A misrepresentation of the state-of-the-art of color perception; the type of model required to move the field forward; and the attribution of shortcomings to DNN research that are already being resolved.