bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Meta-Review about Medical 3D Printing
Authors: Meister Melissa , Luijten Gijs , Gsaxner Christina , Xie Kunpeng , Gruber Lennart J. , Li Jianning , Pepe Antonio , Li Yao , Rashad Ashkan , Seibold Constantin , Bahnsen Fin H. , Kim Moon , Fijačko Nino , Hölzle Frank , Sallam Malik , Röhrig Rainer , Štiglic Gregor , Keyl Julius , Kleesiek Jens , Alves Victor , Chen Xiaojun , Puladi Behrus , Egger Jan 
Published: April 2024

AbstractIn recent years, 3D printing (3DP) has gained importance in various fields. This technology has numerous applications, particularly in medicine. This contribution provides an overview on the state of the art of 3DP in medicine and showcases its current use in different medical disciplines and for medical education. In this meta-review, we provide a detailed listing of systematic reviews on this topic as this technology has become increasingly applied in modern medicine. We identified 134 relevant systematic reviews on medical 3DP in the medical search engine PubMed until 2023. 3DP has applications in various medical specialties, but is mainly used in orthopedics, oral and maxillofacial surgery, dentistry, cardiology and neurosurgery. In surgical contexts, the adoption of 3DP contributes to a reduction in operation time, reduced blood loss, minimized fluoroscopy time and an overall improved surgical outcome. Nevertheless, the primary use of 3DP is observed in non-invasive applications, particularly in the creation of patient-specific models (PSM). These PSMs enhance the visualization of patients’ anatomy and pathology, thereby facilitating surgical planning and execution, medical education and patient counseling. The current significance of 3DP in medicine offers a compelling perspective on the potential for more individualized and personalized medical treatments in the future.
Diagnostic Performance of Claude 3 from Patient History and Key Images in Diagnosis Please Cases
Authors: Kurokawa Ryo , Ohizumi Yuji , Kanzawa Jun , Kurokawa Mariko , Kiguchi Takao , Gonoi Wataru , Abe Osamu 
Published: April 2024

AbstractBackgroundsLarge language artificial intelligence models have showed its diagnostic performance based solely on textual information from clinical history and imaging findings. However, the extent of their performance when utilizing radiological images and providing differential diagnoses has yet to be investigated.PurposeWe employed the latest version of Claude 3, Opus, released on March 4, 2024, to investigate its diagnostic performance in answering Radiology’s Diagnosis Please quiz questions under three conditions: (1) when provided with clinical history alone; (2) when given clinical history along with imaging findings; and (3) when supplied with clinical history and key images.Furthermore, we evaluated the diagnostic performance of the model when instructed to list differential diagnoses.Materials and MethodsClaude 3 Opus was tasked with listing the primary diagnosis and two differential diagnoses for 322 quiz questions from Radiology’s “Diagnosis Please” cases, which included cases 1 to 322, published from 1998 to 2023. The analyses were carried out under the following input conditions:Condition 1: Submitter-provided clinical history (text) aloneCondition 2: Submitter-provided clinical history and imaging findings (text) Condition 3: Submitter-provided clinical history (text) and key images (PDF files)We applied McNemar’s tests to evaluate differences in correct response rates for primary diagnoses across Conditions 1, 2, and 3.ResultsThe correct primary diagnoses rates were 62/322 (19.3%), 178/322 (55.3%), and 93/322 (28.8%) for Conditions 1, 2, and 3, respectively. Additionally, Claude 3 Opus accurately provided the correct answer as a differential diagnosis in up to 22/322 (6.8%) of cases. There were statistically significant differences in correct response rates for primary diagnoses between all combinations of Conditions 1, 2, and 3 (p<0.001).ConclusionClaude 3 Opus demonstrated significantly improved diagnostic performance by inputting key images in addition to clinical history. The ability to list important differential diagnoses was also confirmed.Key ResultsThis study investigated Claude 3 Opus’s performance in Radiology Diagnosis Please Cases using clinical history, key images, and imaging findings.Key images or imaging findings inputs significantly improved correct primary diagnoses from 19.3% to 28.8% or 55.5%, respectively.By having two additional differential diagnoses presented, total correct responses improved by 3.1–6.8%.Summary statementLarge language AI model Claude 3 Opus demonstrated significantly improved diagnostic accuracy by adding key images with clinical history compared with clinical history alone.
Contactless metabolism estimation of small animals using high-frequency millimeter-wave radar
Authors: Ono Hiroaki , Ishikawa Kiyomi , Wataki Ayaka , Fujino Shoko , Sunagawa Genshiro A. 
Published: April 2024

SUMMARYAnimals flexibly adapt to internal and external environmental changes by utilizing energy produced from oxygen as fuel. By non-invasively monitoring an animal’s oxygen consumption, it becomes possible to understand an individual’s metabolic state. Calorimeters are known for directly measuring oxygen consumption but come with the issue of high initial costs. Despite the development of non-invasive techniques for measuring vital signs—including respiration rate (RR), heart rate (HR), and body temperature (Tb)—as indicators of metabolism, conventional methods encounter difficulties in estimating oxygen consumption rate (VO2). In this study, we developed a system that estimates the oxygen consumption of small animals using signals obtained from millimeter wave (mm-wave) radar technology processed through machine learning. By identifying frequency bands within the mm-wave signals contributing to VO2, our system is capable of predicting oxygen consumption several minutes in advance. Our system enables contactless, low-cost and multiplexed measurements of oxygen consumption, presenting a significant advancement in the field.
Bioinformatics Copilot 1.0: A Large Language Model-powered Software for the Analysis of Transcriptomic Data
Authors: Wang Yongheng , Zhang Weidi , Lin Siyu , Farruggio Matthew S. , Wang Aijun 
Published: April 2024

AbstractThe field of single-cell transcriptomics has been producing extensive datasets, advancing our understanding of cellular functions in various tissues, and empowering diagnosis, prognosis, and drug development. However, parsing through this data has been a monumental task, often stretching weeks to months. This bottleneck arises due to the sheer volume of data generated—ranging from hundreds of gigabytes to tens of terabytes—that demands extensive time for analysis. Moreover, the data analysis involves an intricate series of steps utilizing various software packages, creating a steep learning curve for biologists. Additionally, the iterative nature of data analysis in this domain necessitates a deep biological insight to formulate relevant questions, conduct analysis, interpret results, and refine hypotheses. This iterative loop has required close collaboration between biologists and bioinformaticians, which is hampered by protracted communication cycles. To address these challenges, we present a large language model-powered software, Bioinformatics Copilot 1.0. It allows users to analyze data through an intuitive natural language interface, without requiring proficiency in programming languages such as Python or R. It is engineered for cross-platform functionality, with support for Mac, Windows, and Linux. Importantly, it facilitates local data analysis, ensuring adherence to stringent data management regulations that govern the use of patient samples in medical and research institutions. We anticipate that this tool will expedite the data analysis workflow in numerous research endeavors, thereby accelerating advancements in the biomedical sciences.
Enhancing predictions of protein stability changes induced by single mutations using MSA-based Language Models
Authors: Cuturello Francesca , Celoria Marco , Ansuini Alessio , Cazzaniga Alberto 
Published: April 2024

AbstractProtein Language Models offer a new perspective for addressing challenges in structural biology, while relying solely on sequence information. Recent studies have investigated their effectiveness in forecasting shifts in thermodynamic stability caused by single amino acid mutations, a task known for its complexity due to the sparse availability of data, constrained by experimental limitations. To tackle this problem, we introduce two key novelties: leveraging a Protein Language Model that incorporates Multiple Sequence Alignments to capture evolutionary information, and using a recently released mega-scale dataset with rigorous data pre-processing to mitigate overfitting. We ensure comprehensive comparisons by fine-tuning various pre-trained models, taking advantage of analyses such as ablation studies and baselines evaluation. Our methodology introduces a stringent policy to reduce the widespread issue of data leakage, rigorously removing sequences from the training set when they exhibit significant similarity with the test set. The MSA Transformer emerges as the most accurate among the models under investigation, given its capability to leverage co-evolution signals encoded in aligned homologous sequences. Moreover, the optimized MSA Transformer outperforms existing methods and exhibits enhanced generalization power, leading to a notable improvement in predicting changes in protein stability resulting from point mutations. Code and data are available athttps://github.com/RitAreaSciencePark/PLM4Muts.
Axo-axonic synaptic input drives homeostatic plasticity by tuning the axon initial segment structurally and functionally
Authors: Zhao Rui , Ren Baihui , Xiao Yujie , Tian Jifeng , Zou Yi , Wei Jiafan , Qi Yanqing , Hu Ankang , Xie Xiaoying , Huang Z. Josh , Shu Yousheng , He Miao , Lu Jiangteng , Tai Yilin 
Published: April 2024

AbstractThe stability of functional brain network is maintained by homeostatic plasticity, which restores equilibrium following perturbation. As the initiation site of action potentials, the axon initial segment (AIS) of glutamatergic projection neurons (PyNs) undergoes dynamic adjustment that exerts powerful control over neuronal firing properties in response to changes in network states. Although AIS plasticity has been reported to be coupled with the changes of network activity, it is poorly understood whether it involves direct synaptic input to the AIS. Here we show that changes of GABAergic synaptic input to the AIS of cortical PyNs, specifically from chandelier cells (ChCs), are sufficient to drive homeostatic tuning of the AIS within 1-2 weeks, while those from parvalbumin-positive basket cells do not. This tuning is reflected in the morphology of the AIS, the expression level of voltage-gated sodium channels, and the intrinsic neuronal excitability of PyNs. Interestingly, the timing of AIS tuning in PyNs of the prefrontal cortex corresponds to the recovery of changes in social behavior caused by alterations of ChC synaptic transmission. Thus, homeostatic plasticity of the AIS at postsynaptic PyNs may counteract deficits elicited by imbalanced ChC presynaptic input.TeaserAxon initial segment dynamically responds to changes in local input from chandelier cells to prevent abnormal neuronal functions.
psudo: Exploring Multi-Channel Biomedical Image Data with Spatially and Perceptually Optimized Pseudocoloring
Authors: Warchol Simon , Troidl Jakob , Muhlich Jeremy , Krueger Robert , Hoffer John , Lin Tica , Beyer Johanna , Glassman Elena , Sorger Peter K. , Pfister Hanspeter 
Published: April 2024

AbstractOver the past century, multichannel fluorescence imaging has been pivotal in myriad scientific breakthroughs by enabling the spatial visualization of proteins within a biological sample. With the shift to digital methods and visualization software, experts can now flexibly pseudocolor and combine image channels, each corresponding to a different protein, to explore their spatial relationships. We thus proposepsudo, an interactive system that allows users to create optimal color palettes for multichannel spatial data. Inpsudo, a novel optimization method generates palettes that maximize the perceptual differences between channels while mitigating confusing color blending in overlapping channels. We integrate this method into a system that allows users to explore multi-channel image data and compare and evaluate color palettes for their data. An interactive lensing approach provides on-demand feedback on channel overlap and a color confusion metric while giving context to the underlying channel values. Color palettes can be applied globally or, using the lens, to local regions of interest. We evaluate our palette optimization approach using three graphical perception tasks in a crowdsourced user study with 150 participants, showing that users are more accurate at discerning and comparing the underlying data using our approach. Additionally, we showcasepsudoin a case study exploring the complex immune responses in cancer tissue data with a biologist.CCS ConceptsHuman-centered computing→Visualization systems and tools;
Simultaneous cortical, subcortical, and brainstem mapping of sensory activation
Authors: Reddy Neha A. , Clements Rebecca G. , Brooks Jonathan C. W. , Bright Molly G. 
Published: April 2024

AbstractNon-painful tactile sensory stimuli are processed in the cortex, subcortex, and brainstem. Recent functional magnetic resonance imaging (fMRI) studies have highlighted the value of whole-brain, systems-level investigation for examining pain processing. However, whole-brain fMRI studies are uncommon, in part due to challenges with signal to noise when studying the brainstem. Furthermore, the differentiation of small sensory brainstem structures such as the cuneate and gracile nuclei necessitates high resolution imaging. To address this gap in systems-level sensory investigation, we employed a whole-brain, multi-echo fMRI acquisition at 3T with multi-echo independent component analysis (ME-ICA) denoising and brainstem-specific modeling to enable detection of activation across the entire sensory system. In healthy participants, we examined patterns of activity in response to non-painful brushing of the right hand, left hand, and right foot, and found the expected lateralization, with distinct cortical and subcortical responses for upper and lower limb stimulation. At the brainstem level, we were able to differentiate the small, adjacent cuneate and gracile nuclei, corresponding to hand and foot stimulation respectively. Our findings demonstrate that simultaneous cortical, subcortical, and brainstem mapping at 3T could be a key tool to understand the sensory system in both healthy individuals and clinical cohorts with sensory deficits.
MAPK-mediated PHGDH induction is essential for melanoma formation and represents an actionable vulnerability
Authors: Jasani Neel , Xu Xiaonan , Posorske Benjamin , Kim Yumi , Vera Olga , Tsai Kenneth Y. , DeNicola Gina M. , Karreth Florian A. 
Published: April 2024

ABSTRACTOverexpression of PHGDH, the rate-limiting enzyme in the serine synthesis pathway, promotes melanomagenesis, melanoma cell proliferation, and survival of metastases in serine-low environments such as the brain. WhilePHGDHamplification explains PHGDH overexpression in a subset of melanomas, we find that PHGDH levels are universally increased in melanoma cells due to oncogenic BRAFV600EpromotingPHGDHtranscription through mTORC1-mediated translation of ATF4. Importantly, PHGDH expression was critical for melanomagenesis as depletion ofPHGDHin genetic mouse models blocked melanoma formation. Despite BRAFV600E- mediated upregulation, PHGDH was further induced by exogenous serine restriction. Surprisingly, BRAFV600Einhibition diminished serine restriction-mediated PHGDH expression by preventing ATF4 induction, creating a potential vulnerability whereby melanoma cells could be specifically starved of serine by combining BRAFV600Einhibition with exogenous serine restriction. Indeed, we show that this combination promoted cell death in vitro and attenuated melanoma growth in vivo. This study identified a melanoma cell-specific PHGDH-dependent vulnerability.
Demixer: A probabilistic generative model to delineate different strains of a microbial species in a mixed infection sample
Authors: Brintha VP , Narayanan Manikandan 
Published: April 2024

AbstractMotivationMulti-drug resistant or hetero-resistant Tuberculosis (TB) hinders the successful treatment of TB. Hetero-resistant TB occurs when multiple strains of the TB-causing bacterium with varying degrees of drug susceptibility are present in an individual. Existing studies predicting the proportion and identity of strains in a mixed infection sample rely on a reference database of known strains. A main challenge then is to identifyde novostrains not present in the reference database, while quantifying the proportion of known strains.ResultsWe present Demixer, a probabilistic generative model that uses a combination of reference-based and reference-free techniques to delineate mixed infection strains in whole genome sequencing (WGS) data. Demixer extends a topic model widely used in text mining to represent known mutations and discover novel ones. Parallelization and other heuristics enabled Demixer to process large datasets like CRyPTIC (Comprehensive Resistance Prediction for Tuberculosis: an International Consortium). In both synthetic and experimental benchmark datasets, our proposed method precisely detected the identity (e.g., 91.67% accuracy on the experimentalin vitrodataset) as well as the proportions of the mixed strains. In real-world applications, Demixer revealed novel high confidence mixed infections (101 out of 1,963 Malawi samples analyzed), and new insights into the global frequency of mixed infection (2% at the most stringent threshold in the CRyPTIC dataset) and its significant association to drug resistance. Our approach is generalizable and hence applicable to any bacterial and viral WGS data.AvailabilityAll code relevant to Demixer is available athttps://github.com/BIRDSgroup/Demixer.Contactnmanik@cse.iitm.ac.inSupplementary informationThe Supplemental Data/Result Files related to Demixer are available at this link:https://drive.google.com/drive/folders/13WFACrn2EpeVTO7533-YwlAGjgF4UH3k?usp=drive_link.
Detection of Microsatellite Instability in Colorectal Cancer Tissue and Plasma samples using a new Multiplex Droplet Digital PCR kit
Authors: Evrard Camille , Chaba Kariman , Abdelli Justine , Garinet Simon , Blons Helene , Zaanan Aziz , Bachet Jean-Baptiste , Benhaim Leonor , Mulot Claire , Didelot Audrey , Corre Delphine Le , Lukban Jerianne , Yin Jennifer , Corner Adam , Ho Mai , Taly Valérie , Tougeron David , Laurent-Puig Pierre 
Published: April 2024

ABSTRACTManagement of colorectal cancer (CRC) patients relies on the accurate determination of microsatellite instability (MSI) status. MSI status can have an influence on therapy decisions centered on immune checkpoint inhibitors. In this study a novel droplet digital PCR (ddPCR) kit for MSI status determination was validated across 2 separate CRC patient cohorts: 102 tumor samples from the ALGECOLS cohort and 129 plasma samples from the RASANC cohort. Each cohort was assessed for MSI status using the novel ddPCR kit and compared to historical and/or newly obtained results, (either immunohistochemistry analysis or PCR amplification). Concordance between ddPCR and conventional MSI determination methods for the analysis of tissue samples was 97.1% for ALGECOLS. When looking at positive ctDNA samples, a strong concordance was observed for the RASANC cohort. This study illustrates that ddPCR MSI testing represents a rapid, sensitive and accurate method with a strong correlation to established methods. Moreover, the ability of the described approach to monitor MSI status in both tumor and plasma enhances the potential for the use of MSI status in longitudinal monitoring of CRC patients.
Deaths averted by COVID-19 vaccination in select Latin American and Caribbean Countries: a modelling study
Authors: Savinkina Alexandra , Weinberger Daniel M. , Toscano Cristiana M. , De Oliveira Lucia H. 
Published: April 2024

ABSTRACTBackgroundThe COVID-19 pandemic has had a significant impact on global health, with millions of lives lost worldwide. Vaccination has emerged as a crucial strategy in mitigating the impact of the disease. This study aims to estimate the number of deaths averted through vaccination in LAC during the first year and a half of vaccination rollout (January 2021 - May 2022).MethodsPublicly available data on COVID-19 deaths and vaccination rates were used to estimate the total number of deaths averted via vaccination in LAC. Using estimates for number of deaths, number of vaccinated, and vaccine effectiveness, a counterfactual estimated number of deaths observed without vaccination was calculated. Vaccine effectiveness estimates were obtained from published studies. The analysis focused on 17 countries in LAC and considered adults aged 18 years and above.FindingsAfter accounting for underreporting, the analysis estimated that over 1.49 million deaths were caused by COVID-19 in the selected countries during the study period. Without vaccination, the model estimated that between 2.10 and 4.11 million COVID-19 deaths would have occurred. Consequently, vaccination efforts resulted in approximately 610,000 to 2.61 million deaths averted.InterpretationThis study represents the first large-scale, multi-center estimate of population-level vaccine impact on COVID-19 mortality in LAC. The findings underscore the substantial impact of timely and widespread vaccination in averting COVID-19 deaths. These results provide crucial support for vaccination programs aimed at combating epidemic infectious diseases in the region and future pandemics.FundingThis study was funded by the Pan-American Health Organization (PAHO).