Episodic Memory Loss: When Alzheimer's Disease Is Not the Answer.

A 60-year-old man presented to a Neurology Clinic specialized in cognitive disorders to evaluate memory complaints. A comprehensive neuropsychological examination detected an isolated and severe hippocampal memory deficit. Laboratory tests, brain magnetic resonance imaging (MRI), and cerebrospinal fluid (CSF) tests, including Alzheimer's disease (AD) biomarkers, did not show remarkable results. Due to family history of cognitive impairment, we extended the study to non-Alzheimer monogenic mutations (Next Generation Sequencing) detecting a pathogenic variant of the progranulin (PGRN) gene (c.1414-1 G > T) which has been previously associated with the same phenotype. These results should be considered in patients with an Alzheimer-like presentation, negative AD biomarkers' results, and family history of dementia.

Surveying biomedical relation extraction: a critical examination of current datasets and the proposal of a new resource.

Natural language processing (NLP) has become an essential technique in various fields, offering a wide range of possibilities for analyzing data and developing diverse NLP tasks. In the biomedical domain, understanding the complex relationships between compounds and proteins is critical, especially in the context of signal transduction and biochemical pathways. Among these relationships, protein-protein interactions (PPIs) are of particular interest, given their potential to trigger a variety of biological reactions. To improve the ability to predict PPI events, we propose the protein event detection dataset (PEDD), which comprises 6823 abstracts, 39 488 sentences and 182 937 gene pairs. Our PEDD dataset has been utilized in the AI CUP Biomedical Paper Analysis competition, where systems are challenged to predict 12 different relation types. In this paper, we review the state-of-the-art relation extraction research and provide an overview of the PEDD's compilation process. Furthermore, we present the results of the PPI extraction competition and evaluate several language models' performances on the PEDD. This paper's outcomes will provide a valuable roadmap for future studies on protein event detection in NLP. By addressing this critical challenge, we hope to enable breakthroughs in drug discovery and enhance our understanding of the molecular mechanisms underlying various diseases.

CT and MR Enterography in the Evaluation of Celiac Disease.

Celiac disease is a common inflammatory disease of the small bowel that induces mucosal intestinal lesions. The disease is mediated by an immune response and triggered by the ingestion of gluten in genetically predisposed individuals. Gluten contains gliadin, a component found mostly in wheat, barley, and rye. This process leads to gastrointestinal malabsorption with symptoms such as diarrhea, constipation, abdominal pain, and distention. It has a prevalence of 1%-2% in the general adult population, who present with symptoms at any age, but is more frequently found in adult women in the 3rd or 4th decade of life. Recognition of the disease has increased, but it remains a challenge to diagnose. CT and MR enterography are noninvasive studies used for evaluation of small bowel neoplasms and inflammatory small bowel pathologic conditions such as celiac disease. The authors review the spectrum of intestinal and extraintestinal findings of celiac disease at CT and MR enterography, as well as its complications, and the importance of recognizing certain imaging features that help in the diagnosis of celiac disease. More common and specific findings of celiac disease such as inversion of the jejunoileal fold pattern and mesenteric lymphadenopathy are reviewed. More uncommon entities that are more frequently associated with refractory or untreated celiac disease, such as ulcerative jejunoileitis, cavitary mesenteric lymph node syndrome, and malignancies including small bowel adenocarcinoma and lymphoma, are described. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material. The slide presentation from the RSNA Annual Meeting is available for this article.

Alterations in Lysosomal, Glial and Neurodegenerative Biomarkers in Patients with Sporadic and Genetic Forms of Frontotemporal Dementia.

Background: Frontotemporal dementia (FTD) is the most common cause of early-onset dementia with 10-20% of cases caused by mutations in one of three genes: GRN, C9orf72, or MAPT. To effectively develop therapeutics for FTD, the identification and characterization of biomarkers to understand disease pathogenesis and evaluate the impact of specific therapeutic strategies on the target biology as well as the underlying disease pathology are essential. Moreover, tracking the longitudinal changes of these biomarkers throughout disease progression is crucial to discern their correlation with clinical manifestations for potential prognostic usage. Methods: We conducted a comprehensive investigation of biomarkers indicative of lysosomal biology, glial cell activation, synaptic and neuronal health in cerebrospinal fluid (CSF) and plasma from non-carrier controls, sporadic FTD (symptomatic non-carriers) and symptomatic carriers of mutations in GRN, C9orf72, or MAPT, as well as asymptomatic GRN mutation carriers. We also assessed the longitudinal changes of biomarkers in GRN mutation carriers. Furthermore, we examined biomarker levels in disease impacted brain regions including middle temporal gyrus (MTG) and superior frontal gyrus (SFG) and disease-unaffected inferior occipital gyrus (IOG) from sporadic FTD and symptomatic GRN carriers. Results: We confirmed glucosylsphingosine (GlcSph), a lysosomal biomarker regulated by progranulin, was elevated in the plasma from GRN mutation carriers, both symptomatic and asymptomatic. GlcSph and other lysosomal biomarkers such as ganglioside GM2 and globoside GB3 were increased in the disease affected SFG and MTG regions from sporadic FTD and symptomatic GRN mutation carriers, but not in the IOG, compared to the same brain regions from controls. The glial biomarkers GFAP in plasma and YKL40 in CSF were elevated in asymptomatic GRN carriers, and all symptomatic groups, except the symptomatic C9orf72 mutation group. YKL40 was also increased in SFG and MTG regions from sporadic FTD and symptomatic GRN mutation carriers. Neuronal injury and degeneration biomarkers NfL in CSF and plasma, and UCHL1 in CSF were elevated in patients with all forms of FTD. Synaptic biomarkers NPTXR, NPTX1/2, and VGF were reduced in CSF from patients with all forms of FTD, with the most pronounced reductions observed in symptomatic MAPT mutation carriers. Furthermore, we demonstrated plasma NfL was significantly positively correlated with disease severity as measured by CDR+NACC FTLD SB in genetic forms of FTD and CSF NPTXR was significantly negatively correlated with CDR+NACC FTLD SB in symptomatic GRN and MAPT mutation carriers. Conclusions: In conclusion, our comprehensive investigation replicated alterations in biofluid biomarkers indicative of lysosomal function, glial activation, synaptic and neuronal health across sporadic and genetic forms of FTD and unveiled novel insights into the dysregulation of these biomarkers within brain tissues from patients with GRN mutations. The observed correlations between biomarkers and disease severity open promising avenues for prognostic applications and for indicators of drug efficacy in clinical trials. Our data also implicated a complicated relationship between biofluid and tissue biomarker changes and future investigations should delve into the mechanistic underpinnings of these biomarkers, which will serve as a foundation for the development of targeted therapeutics for FTD.

Machine learning-based prediction of acute mortality in emergency department patients using twelve-lead electrocardiogram.

Background: The risk of mortality is relatively high among patients who visit the emergency department (ED), and stratifying patients at high risk can help improve medical care. This study aimed to create a machine-learning model that utilizes the standard 12-lead ECG to forecast acute mortality risk in ED patients. Methods: The database included patients who visited the EDs and underwent standard 12-lead ECG between October 2007 and December 2017. A convolutional neural network (CNN) ECG model was developed to classify survival and mortality using 12-lead ECG tracings acquired from 345,593 ED patients. For machine learning model development, the patients were randomly divided into training, validation and testing datasets. The performance of the mortality risk prediction in this model was evaluated for various causes of death. Results: Patients who visited the ED and underwent one or more ECG examinations experienced a high incidence of 30-day mortality [18,734 (5.42%)]. The developed CNN model demonstrated high accuracy in predicting acute mortality (hazard ratio 8.50, 95% confidence interval 8.20-8.80) with areas under the receiver operating characteristic (ROC) curve of 0.84 for the 30-day mortality risk prediction models. This CNN model also demonstrated good performance in predicting one-year mortality (hazard ratio 3.34, 95% confidence interval 3.30-3.39). This model exhibited good predictive performance for 30-day mortality not only for cardiovascular diseases but also across various diseases. Conclusions: The machine learning-based ECG model utilizing CNN screens the risks for 30-day mortality. This model can complement traditional early warning scoring indexes as a useful screening tool for mortality prediction.

Erasure conversion in a high-fidelity Rydberg quantum simulator.

Minimizing and understanding errors is critical for quantum science, both in noisy intermediate scale quantum (NISQ) devices1 and for the quest towards fault-tolerant quantum computation2,3. Rydberg arrays have emerged as a prominent platform in this context4 with impressive system sizes5,6 and proposals suggesting how error-correction thresholds could be significantly improved by detecting leakage errors with single-atom resolution7,8, a form of erasure error conversion9-12. However, two-qubit entanglement fidelities in Rydberg atom arrays13,14 have lagged behind competitors15,16 and this type of erasure conversion is yet to be realized for matter-based qubits in general. Here we demonstrate both erasure conversion and high-fidelity Bell state generation using a Rydberg quantum simulator5,6,17,18. When excising data with erasure errors observed via fast imaging of alkaline-earth atoms19-22, we achieve a Bell state fidelity of [Formula: see text], which improves to [Formula: see text] when correcting for remaining state-preparation errors. We further apply erasure conversion in a quantum simulation experiment for quasi-adiabatic preparation of long-range order across a quantum phase transition, and reveal the otherwise hidden impact of these errors on the simulation outcome. Our work demonstrates the capability for Rydberg-based entanglement to reach fidelities in the 0.999 regime, with higher fidelities a question of technical improvements, and shows how erasure conversion can be utilized in NISQ devices. These techniques could be translated directly to quantum-error-correction codes with the addition of long-lived qubits7,22-24.

Artificial intelligence-enabled electrocardiographic screening for left ventricular systolic dysfunction and mortality risk prediction.

Background: Left ventricular systolic dysfunction (LVSD) characterized by a reduced left ventricular ejection fraction (LVEF) is associated with adverse patient outcomes. We aimed to build a deep neural network (DNN)-based model using standard 12-lead electrocardiogram (ECG) to screen for LVSD and stratify patient prognosis. Methods: This retrospective chart review study was conducted using data from consecutive adults who underwent ECG examinations at Chang Gung Memorial Hospital in Taiwan between October 2007 and December 2019. DNN models were developed to recognize LVSD, defined as LVEF <40%, using original ECG signals or transformed images from 190,359 patients with paired ECG and echocardiogram within 14 days. The 190,359 patients were divided into a training set of 133,225 and a validation set of 57,134. The accuracy of recognizing LVSD and subsequent mortality predictions were tested using ECGs from 190,316 patients with paired data. Of these 190,316 patients, we further selected 49,564 patients with multiple echocardiographic data to predict LVSD incidence. We additionally used data from 1,194,982 patients who underwent ECG only to assess mortality prognostication. External validation was performed using data of 91,425 patients from Tri-Service General Hospital, Taiwan. Results: The mean age of patients in the testing dataset was 63.7 ± 16.3 years (46.3% women), and 8,216 patients (4.3%) had LVSD. The median follow-up period was 3.9 years (interquartile range 1.5-7.9 years). The area under the receiver-operating characteristic curve (AUROC), sensitivity, and specificity of the signal-based DNN (DNN-signal) to identify LVSD were 0.95, 0.91, and 0.86, respectively. DNN signal-predicted LVSD was associated with age- and sex-adjusted hazard ratios (HRs) of 2.57 (95% confidence interval [CI], 2.53-2.62) for all-cause mortality and 6.09 (5.83-6.37) for cardiovascular mortality. In patients with multiple echocardiograms, a positive DNN prediction in patients with preserved LVEF was associated with an adjusted HR (95% CI) of 8.33 (7.71 to 9.00) for incident LVSD. Signal- and image-based DNNs performed equally well in the primary and additional datasets. Conclusion: Using DNNs, ECG becomes a low-cost, clinically feasible tool to screen LVSD and facilitate accurate prognostication.

Automatic Extraction of Medication Mentions from Tweets-Overview of the BioCreative VII Shared Task 3 Competition.

This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user's publicly available tweets (the user's 'timeline'). In general, detecting health-related tweets is notoriously challenging for natural language processing tools. The main challenge, aside from the informality of the language used, is that people tweet about any and all topics, and most of their tweets are not related to health. Thus, finding those tweets in a user's timeline that mention specific health-related concepts such as medications requires addressing extreme imbalance. Task 3 called for detecting tweets in a user's timeline that mentions a medication name and, for each detected mention, extracting its span. The organizers made available a corpus consisting of 182 049 tweets publicly posted by 212 Twitter users with all medication mentions manually annotated. The corpus exhibits the natural distribution of positive tweets, with only 442 tweets (0.2%) mentioning a medication. This task was an opportunity for participants to evaluate methods that are robust to class imbalance beyond the simple lexical match. A total of 65 teams registered, and 16 teams submitted a system run. This study summarizes the corpus created by the organizers and the approaches taken by the participating teams for this challenge. The corpus is freely available at https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-3/. The methods and the results of the competing systems are analyzed with a focus on the approaches taken for learning from class-imbalanced data.

Semi-supervised learning for topographic map analysis over time: a study of bridge segmentation.

Geographical research using historical maps has progressed considerably as the digitalization of topological maps across years provides valuable data and the advancement of AI machine learning models provides powerful analytic tools. Nevertheless, analysis of historical maps based on supervised learning can be limited by the laborious manual map annotations. In this work, we propose a semi-supervised learning method that can transfer the annotation of maps across years and allow map comparison and anthropogenic studies across time. Our novel two-stage framework first performs style transfer of topographic map across years and versions, and then supervised learning can be applied on the synthesized maps with annotations. We investigate the proposed semi-supervised training with the style-transferred maps and annotations on four widely-used deep neural networks (DNN), namely U-Net, fully-convolutional network (FCN), DeepLabV3, and MobileNetV3. The best performing network of U-Net achieves [Formula: see text] and [Formula: see text] trained on style-transfer synthesized maps, which indicates that the proposed framework is capable of detecting target features (bridges) on historical maps without annotations. In a comprehensive comparison, the [Formula: see text] of U-Net trained on Contrastive Unpaired Translation (CUT) generated dataset ([Formula: see text]) achieves 57.3 % than the comparative score ([Formula: see text]) of the least valid configuration (MobileNetV3 trained on CycleGAN synthesized dataset). We also discuss the remaining challenges and future research directions.

Increased Online Aggression During COVID-19 Lockdowns: Two-Stage Study of Deep Text Mining and Difference-in-Differences Analysis.

BACKGROUND: The COVID-19 pandemic caused a critical public health crisis worldwide, and policymakers are using lockdowns to control the virus. However, there has been a noticeable increase in aggressive social behaviors that threaten social stability. Lockdown measures might negatively affect mental health and lead to an increase in aggressive emotions. Discovering the relationship between lockdown and increased aggression is crucial for formulating appropriate policies that address these adverse societal effects. We applied natural language processing (NLP) technology to internet data, so as to investigate the social and emotional impacts of lockdowns. OBJECTIVE: This research aimed to understand the relationship between lockdown and increased aggression using NLP technology to analyze the following 3 kinds of aggressive emotions: anger, offensive language, and hate speech, in spatiotemporal ranges of tweets in the United States. METHODS: We conducted a longitudinal internet study of 11,455 Twitter users by analyzing aggressive emotions in 1,281,362 tweets they posted from 2019 to 2020. We selected 3 common aggressive emotions (anger, offensive language, and hate speech) on the internet as the subject of analysis. To detect the emotions in the tweets, we trained a Bidirectional Encoder Representations from Transformers (BERT) model to analyze the percentage of aggressive tweets in every state and every week. Then, we used the difference-in-differences estimation to measure the impact of lockdown status on increasing aggressive tweets. Since most other independent factors that might affect the results, such as seasonal and regional factors, have been ruled out by time and state fixed effects, a significant result in this difference-in-differences analysis can not only indicate a concrete positive correlation but also point to a causal relationship. RESULTS: In the first 6 months of lockdown in 2020, aggression levels in all users increased compared to the same period in 2019. Notably, users under lockdown demonstrated greater levels of aggression than those not under lockdown. Our difference-in-differences estimation discovered a statistically significant positive correlation between lockdown and increased aggression (anger: P=.002, offensive language: P<.001, hate speech: P=.005). It can be inferred from such results that there exist causal relations. CONCLUSIONS: Understanding the relationship between lockdown and aggression can help policymakers address the personal and societal impacts of lockdown. Applying NLP technology and using big data on social media can provide crucial and timely information for this effort.

Task reformulation and data-centric approach for Twitter medication name extraction.

Automatically extracting medication names from tweets is challenging in the real world. There are many tweets; however, only a small proportion mentions medications. Thus, datasets are usually highly imbalanced. Moreover, the length of tweets is very short, which makes it hard to recognize medication names from the limited context. This paper proposes a data-centric approach for extracting medications in the BioCreative VII Track 3 (Automatic Extraction of Medication Names in Tweets). Our approach formulates the sequence labeling problem as text entailment and question-answer tasks. As a result, without using the dictionary and ensemble method, our single model achieved a Strict F1 of 0.77 (the official baseline system is 0.758, and the average performance of participants is 0.696). Moreover, combining the dictionary filtering and ensemble method achieved a Strict F1 of 0.804 and had the highest performance for all participants. Furthermore, domain-specific and task-specific pretrained language models, as well as data-centric approaches, are proposed for further improvements. Database URL https://competitions.codalab.org/competitions/23925 and https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-3/.

Safety, pharmacokinetics and target engagement of novel RIPK1 inhibitor SAR443060 (DNL747) for neurodegenerative disorders: Randomized, placebo-controlled, double-blind phase I/Ib studies in healthy subjects and patients.

RIPK1 is a master regulator of inflammatory signaling and cell death and increased RIPK1 activity is observed in human diseases, including Alzheimer's disease (AD) and amyotrophic lateral sclerosis (ALS). RIPK1 inhibition has been shown to protect against cell death in a range of preclinical cellular and animal models of diseases. SAR443060 (previously DNL747) is a selective, orally bioavailable, central nervous system (CNS)-penetrant, small-molecule, reversible inhibitor of RIPK1. In three early-stage clinical trials in healthy subjects and patients with AD or ALS (NCT03757325 and NCT03757351), SAR443060 distributed into the cerebrospinal fluid (CSF) after oral administration and demonstrated robust peripheral target engagement as measured by a reduction in phosphorylation of RIPK1 at serine 166 (pRIPK1) in human peripheral blood mononuclear cells compared to baseline. RIPK1 inhibition was generally safe and well-tolerated in healthy volunteers and patients with AD or ALS. Taken together, the distribution into the CSF after oral administration, the peripheral proof-of-mechanism, and the safety profile of RIPK1 inhibition to date, suggest that therapeutic modulation of RIPK1 in the CNS is possible, conferring potential therapeutic promise for AD and ALS, as well as other neurodegenerative conditions. However, SAR443060 development was discontinued due to long-term nonclinical toxicology findings, although these nonclinical toxicology signals were not observed in the short duration dosing in any of the three early-stage clinical trials. The dose-limiting toxicities observed for SAR443060 preclinically have not been reported for other RIPK1-inhibitors, suggesting that these toxicities are compound-specific (related to SAR443060) rather than RIPK1 pathway-specific.

COVID-19 Vaccine Hesitancy and Acceptance Among Individuals With Cancer, Autoimmune Diseases, or Other Serious Comorbid Conditions: Cross-sectional, Internet-Based Survey.

BACKGROUND: Individuals with comorbid conditions have been disproportionately affected by COVID-19. Since regulatory trials of COVID-19 vaccines excluded those with immunocompromising conditions, few patients with cancer and autoimmune diseases were enrolled. With limited vaccine safety data available, vulnerable populations may have conflicted vaccine attitudes. OBJECTIVE: We assessed the prevalence and independent predictors of COVID-19 vaccine hesitancy and acceptance among individuals with serious comorbidities and assessed self-reported side effects among those who had been vaccinated. METHODS: We conducted a cross-sectional, 55-item, online survey, fielded January 15, 2021 through February 22, 2021, among a random sample of members of Inspire, an online health community of over 2.2 million individuals with comorbid conditions. Multivariable regression analysis was utilized to determine factors independently associated with vaccine hesitancy and acceptance. RESULTS: Of the 996,500 members of the Inspire health community invited to participate, responses were received from 21,943 individuals (2.2%). Respondents resided in 123 countries (United States: 16,277/21,943, 74.2%), had a median age range of 56-65 years, were highly educated (college or postgraduate degree: 10,198/17,298, 58.9%), and had diverse political leanings. All respondents self-reported at least one comorbidity: cancer, 27.3% (5459/19,980); autoimmune diseases, 23.2% (4946/21,294); chronic lung diseases: 35.4% (7544/21,294). COVID-19 vaccine hesitancy was identified in 18.6% (3960/21,294), with 10.3% (2190/21,294) declaring that they would not, 3.5% (742/21,294) stating that they probably would not, and 4.8% (1028/21,294) not sure whether they would agree to be vaccinated. Hesitancy was expressed by the following patients: cancer, 13.4% (731/5459); autoimmune diseases, 19.4% (962/4947); chronic lung diseases: 17.8% (1344/7544). Positive predictors of vaccine acceptance included routine influenza vaccination (odds ratio [OR] 1.53), trust in responsible vaccine development (OR 14.04), residing in the United States (OR 1.31), and never smoked (OR 1.06). Hesitancy increased with a history of prior COVID-19 (OR 0.86), conservative political leaning (OR 0.93), younger age (OR 0.83), and lower education level (OR 0.90). One-quarter (5501/21,294, 25.8%) had received at least one COVID-19 vaccine injection, and 6.5% (1390/21,294) completed a 2-dose series. Following the first injection, 69.0% (3796/5501) self-reported local reactions, and 40.0% (2200/5501) self-reported systemic reactions, which increased following the second injection to 77.0% (1070/1390) and 67.0% (931/1390), respectively. CONCLUSIONS: In this survey of individuals with serious comorbid conditions, significant vaccine hesitancy remained. Assumptions that the most vulnerable would automatically accept COVID-19 vaccination are erroneous and thus call for health care team members to initiate discussions focusing on the impact of the vaccine on an individual's underlying condition. Early self-reported side effect experiences among those who had already been vaccinated, as expressed by our population, should be reassuring and might be utilized to alleviate vaccine fears. Health care-related social media forums that rapidly disseminate accurate information about the COVID-19 vaccine may play an important role.

Cultural competence of Australian dental students.

BACKGROUND: Australia possesses a highly multicultural demographic, and thus dental practitioners are likely to regularly encounter culturally and linguistically diverse individuals. It is important for dental practitioners to be culturally competent, however, cultural competency education is highly variable in the curricula of dentistry and oral health courses in Australia, and research is largely limited to dentistry students. This study aims to investigate and compare perceived attitudes, beliefs and practices of cultural competence amongst first and final year Doctor of Dental Surgery (DDS) and Bachelor of Oral Health (BOH) students at the University of Melbourne Dental School. METHODS: Following ethics approval, anonymous questionnaires were completed by 213 participants. The questionnaire was adapted from Schwarz's Healthcare Provider Cultural Competence Instrument and consisted of five scales. Data was analysed using SPSS V 24.0 software. RESULTS: A total of 213 students participated in this study (response rate = 88%) The majority of participants were female (n = 114, 53.5%) and the mean age of 23.5 years (range 18-40). The majority of participants were Australian born (n = 110) with 74.6% (n = 159) first generation Australians. Participants who identified as Australian represented 35.7% (n = 76) with 66.1% (n = 141) identified as partly Australian. Multivariate analysis indicated that, after controlling for other independent variables in the model, those who had the highest cultural competence score were female, who self-identify as "Australian", who were in the final year. Furthermore, those who were in the final BOH year scored significatively higher than final year DDS students. CONCLUSION: The findings of this study suggest that there is a significant difference in students self-reported cultural competence at different stages of their education. This may be attributed to differences in cultural competence education, scope of practice and the type of patient encounters and role modelling that students may experience. Future research should involve follow up to create longitudinal data, as well as research at other dental schools in Australia and overseas.

American Society of Emergency Radiology Multicenter Blunt Splenic Trauma Study: CT and Clinical Findings.

Background Treatment of blunt splenic trauma (BST) continues to evolve with improved imaging for detection of splenic vascular injuries. Purpose To report on treatments for BST from 11 trauma centers, the frequency and clinical impact of splenic vascular injuries, and factors influencing treatment. Materials and Methods Patients were retrospectively identified as having BST between January 2011 and December 2018, and clinical, imaging, and outcome data were recorded. Patient data were summarized descriptively, both overall and stratified by initial treatment received (nonoperative management [NOM], angiography, or surgery). Regression analyses were used to examine the primary outcomes of interest, which were initial treatment received and length of stay (LOS). Results This study evaluated 1373 patients (mean age, 42 years ± 18; 845 men). Initial treatments included NOM in 849 patients, interventional radiology (IR) in 240 patients, and surgery in 284 patients. Rates from CT reporting were 22% (304 of 1373) for active splenic hemorrhage (ASH) and 20% (276 of 1373) for contained vascular injury (CVI). IR management of high-grade injuries increased 15.6%, from 28.6% (eight of 28) to 44.2% (57 of 129) (2011-2012 vs 2017-2018). Patients who were treated invasively had a higher injury severity score (odds ratio [OR], 1.04; 95% CI: 1.02, 1.05; P < .001), lower temperature (OR, 0.97; 95% CI: 0.97, 1.00; P = .03), and a lower hematocrit (OR, 0.96; 95% CI: 0.93, 0.99; P = .003) and were more likely to show ASH (OR, 8.05; 95% CI: 5.35, 12.26; P < .001) or CVI (OR, 2.70; 95% CI: 1.64, 4.44; P < .001) on CT images, have spleen-only injures (OR, 2.35; 95% CI: 1.45, 3.8; P < .001), and have been administered blood product for fewer than 24 hours (OR, 2.35; 95% CI: 1.58, 3.51; P < .001) compared with those chosen for NOM, after adjusting for key demographic and clinical variables. After adjustment, factors associated with a shorter LOS were female sex (OR, 0.84; 95% CI: 0.73, 0.96; P = .009), spleen-only injury (OR, 0.72; 95% CI: 0.6, 0.86; P < .001), higher admission hematocrit (OR, 0.98; 95% CI: 0.6, 0.86; P < .001), and presence of ASH at CT (OR, 0.74; 95% CI: 0.62, 0.88; P < .001). Conclusion Contained vascular injury and active splenic hemorrhage (ASH) were frequently reported, and rates of interventional radiologic management increased during the study period. ASH was associated with a shorter length of stay, and patients with ASH had eight times the odds of undergoing invasive treatment compared with undergoing nonoperative management. © RSNA, 2021 See also the editorial by Patlas in this issue.

Biomedical named entity recognition and linking datasets: survey and our recent development.

Natural language processing (NLP) is widely applied in biological domains to retrieve information from publications. Systems to address numerous applications exist, such as biomedical named entity recognition (BNER), named entity normalization (NEN) and protein-protein interaction extraction (PPIE). High-quality datasets can assist the development of robust and reliable systems; however, due to the endless applications and evolving techniques, the annotations of benchmark datasets may become outdated and inappropriate. In this study, we first review commonlyused BNER datasets and their potential annotation problems such as inconsistency and low portability. Then, we introduce a revised version of the JNLPBA dataset that solves potential problems in the original and use state-of-the-art named entity recognition systems to evaluate its portability to different kinds of biomedical literature, including protein-protein interaction and biology events. Lastly, we introduce an ensembled biomedical entity dataset (EBED) by extending the revised JNLPBA dataset with PubMed Central full-text paragraphs, figure captions and patent abstracts. This EBED is a multi-task dataset that covers annotations including gene, disease and chemical entities. In total, it contains 85000 entity mentions, 25000 entity mentions with database identifiers and 5000 attribute tags. To demonstrate the usage of the EBED, we review the BNER track from the AI CUP Biomedical Paper Analysis challenge. Availability: The revised JNLPBA dataset is available at https://iasl-btm.iis.sinica.edu.tw/BNER/Content/Re vised_JNLPBA.zip. The EBED dataset is available at https://iasl-btm.iis.sinica.edu.tw/BNER/Content/AICUP _EBED_dataset.rar. Contact: Email: thtsai@g.ncu.edu.tw, Tel. 886-3-4227151 ext. 35203, Fax: 886-3-422-2681 Email: hsu@iis.sinica.edu.tw, Tel. 886-2-2788-3799 ext. 2211, Fax: 886-2-2782-4814 Supplementary information: Supplementary data are available at Briefings in Bioinformatics online.

Open-Label Phase 1 Futility Studies of Salsalate and Young Plasma in Progressive Supranuclear Palsy.

BACKGROUND: Progressive supranuclear palsy (PSP) is a neurodegenerative disease without approved therapies, and therapeutics are often tried off-label in the hope of slowing disease progression. Results from these experiences are seldom shared, which limits evidence-based knowledge to guide future treatment decisions. OBJECTIVES: To describe an open-label experience, including safety/tolerability, and longitudinal changes in biomarkers of disease progression in PSP-Richardson's syndrome (PSP-RS) patients treated with either salsalate or young plasma and compare to natural history data from previous multicenter studies. METHODS: For 6 months, 10 PSP-RS patients received daily salsalate 2,250 mg, and 5 patients received monthly infusions of four units of young plasma. Every 3 months, clinical severity was assessed with the Progressive Supranuclear Palsy Rating Scale (PSPRS), and MRI was obtained for volumetric measurement of midbrain. A range of exploratory biomarkers, including cerebrospinal fluid levels of neurofilament light chain, were collected at baseline and 6 months. Interventional data were compared to historical PSP-RS patients from the davunetide clinical trial and the 4-Repeat Tauopathy Neuroimaging Initiative. RESULTS: Salsalate and young plasma were safe and well tolerated. PSPRS change from baseline (mean ± standard deviation [SD]) was similar in salsalate (+5.6 ± 9.6), young plasma (+5.0 ± 7.1), and historical controls (+5.6 ± 7.1), and change in midbrain volume (cm3 ± SD) did not differ between salsalate (-0.07 ± 0.03), young plasma (-0.06 ± 0.03), and historical controls (-0.06 ± 0.04). No differences were observed between groups on any exploratory endpoint. CONCLUSIONS: Neither salsalate nor young plasma had a detectable effect on disease progression in PSP-RS. Focused open-label clinical trials incorporating historical clinical, neuropsychological, fluid, and imaging biomarkers provide useful preliminary data about the promise of novel PSP-directed therapies.

Diagnostic Approach to Benign and Malignant Calcifications in the Abdomen and Pelvis.

Intra-abdominal calcifications are common. Multiple pathologic processes manifest within the abdomen and pelvis in association with calcifications, which can be benign, premalignant, or malignant. Although calcium deposition in the abdomen can occur secondary to various mechanisms, the most common cau se is cellular injury that leads to dystrophic calcifications. The authors provide a summary of various common and uncommon calcifications in the abdomen and pelvis, primarily using location to illuminate diagnostic significance. Six broad categories of calcifications in the abdomen and pelvis are recognized: mesenteric, peritoneal, retroperitoneal, organ-based, vascular, and musculoskeletal. In addition to site, the various patterns and morphology of calcifications encountered in various conditions can be helpful for diagnosis, especially those depicted on radiographs. For example, some patterns diagnostic for various conditions include round or oval stones in the biliary or urothelial tracts, curvilinear calcifications associated with cysts or neoplasms, and sheetlike calcifications along peritoneal surfaces in the setting of chronic peritoneal dialysis or metastatic disease. Organ encrustation with calcium may be a premalignant finding (eg, porcelain gallbladder). In addition, the development of calcium after initiation of treatment can be used as an indicator of response in conditions such as tuberculosis, lymphoma, and hydatid disease. As calcifications are almost invariably detected at imaging, it is imperative for radiologists to be aware of their diagnostic implications and use the presence of calcification in an organ, mass, or other anatomic location for problem solving. ©RSNA, 2020.

Outcomes of Yttrium-90 Radioembolization for Unresectable Combined Biphenotypic Hepatocellular-Cholangiocarcinoma.

PURPOSE: To evaluate outcomes of yttrium-90 radioembolization in patients with combined biphenotypic hepatocellular-cholangiocarcinoma (cHCC-CC). MATERIALS AND METHODS: A retrospective review of patients with biopsy-confirmed cHCC-CC treated with yttrium-90 radioembolization between 2012 and 2018 was performed. Twenty-two patients with cHCC-CC (mean age 65.6 y, 17 men, 5 women) underwent 29 radioembolization treatments (5 resin, 24 glass microspheres). Survival data were available in 21 patients, and hepatic imaging response data were available in 20 patients. Hepatic imaging response to radioembolization was assessed on follow-up CT or MR imaging using modified Response Evaluation Criteria In Solid Tumours criteria. Univariate stepwise Cox regression analysis was used to evaluate the association between demographic and clinical factors and survival. Logistic regression evaluated associations between clinical factors and response to treatment, overall response, and disease control. RESULTS: Hepatic imaging response was as follows: 15% complete response, 40% partial response, 10% stable disease, and 35% progressive disease (55% response rate, 65% disease control rate). Two patients were downstaged or bridged to transplant, and 1 patient was downstaged to resection. Median overall survival was 9.3 mo (range, 2.5-31.0 mo) from time of radioembolization. Nonreponse to treatment, bilobar disease, presence of multiple tumors, and elevated carbohydrate antigen 19-9 before treatment were associated with reduced survival after radioembolization. CONCLUSIONS: Radioembolization is a viable option for locoregional control of cHCC-CC with good response and disease control rates.