Severe Lenalidomide-Associated Hyperbilirubinemia.

Immunomodulatory drugs (IMids), such as thalidomide and lenalidomide, are used to treat plasma cell neoplasms and B-cell malignancies. We present a case of severe direct hyperbilirubinemia in a patient taking lenalidomide-based therapy for plasmacytoma. Imaging was unrevealing, and liver biopsy showed only mild sinusoidal dilation. Roussel Uclaf Causality Assessment (RUCAM) score was 6, indicating lenalidomide was a probable cause of the injury. To our knowledge, this is the highest reported direct bilirubin regarding lenalidomide drug-induced liver injury (DILI), with a peak bilirubin of 41mg/dL. While a clear pathophysiology was not identified, this case provides important considerations regarding lenalidomide safety.

COVID-19-Associated Liver Injury.

This review analyzes data regarding liver injury associated with COVID-19 infection. We discuss reported effects on the liver from both COVID-19 and COVID-19 treatment as well as pathophysiology, review the potential role of drug-induced liver injury as an etiology of COVID-19-associated liver injury, and touch on other reports of significant outcomes including COVID-19 cholangiopathy and autoimmune hepatitis. Finally, we review the implications of COVID-19 infection in liver transplant recipients.

Highlights of the drug-induced liver injury literature for 2021.

INTRODUCTION: In 2021, over 3,000 articles on Drug-Induced Liver Injury (DILI) were published, nearly doubling the annual number compared to 2011. This review selected DILI articles from 2021 we felt held the greatest interest and clinical relevance. AREAS COVERED: A literature search was conducted using PubMed between 1 March 2021 and 28 February 2022. 86 articles were included. This review discusses new and established cases of hepatotoxins, including new FDA approvals and COVID-19 therapeutics. Developments in biomarkers and causality assessment methods are discussed. Updates from registries are also explored. EXPERT OPINION: DILI diagnosis and prognostication remain challenging. Roussel Uclaf Causality Assessment Method (RUCAM) is the best option for determining causality and has been increasingly accepted by clinicians. Revised Electronic Causality Assessment Method (RECAM) may be more user-friendly and accurate but requires further validation. Quantitative systems pharmacology methods, such as DILIsym, are increasingly used to predict hepatotoxicity. Oncotherapeutic agents represent many newly approved and described causes of DILI. Such hepatotoxicity is deemed acceptable relative to the benefit these drugs offer. Drugs developed for non-life-threatening disorders may not show a favorable benefit-to-risk ratio and will be more difficult to approve. As the COVID-19 landscape evolves, its effect on DILI deserves further investigation.

Derivation of a natural language processing algorithm to identify febrile infants.

BACKGROUND: Diagnostic codes can retrospectively identify samples of febrile infants, but sensitivity is low, resulting in many febrile infants eluding detection. To ensure study samples are representative, an improved approach is needed. OBJECTIVE: To derive and internally validate a natural language processing algorithm to identify febrile infants and compare its performance to diagnostic codes. METHODS: This cross-sectional study consisted of infants aged 0-90 days brought to one pediatric emergency department from January 2016 to December 2017. We aimed to identify infants with fever, defined as a documented temperature ≥38°C. We used 2017 clinical notes to develop two rule-based algorithms to identify infants with fever and tested them on data from 2016. Using manual abstraction as the gold standard, we compared performance of the two rule-based algorithms (Models 1, 2) to four previously published diagnostic code groups (Models 5-8) using area under the receiver-operating characteristics curve (AUC), sensitivity, and specificity. RESULTS: For the test set (n = 1190 infants), 184 infants were febrile (15.5%). The AUCs (0.92-0.95) and sensitivities (86%-92%) of Models 1 and 2 were significantly greater than Models 5-8 (0.67-0.74; 20%-74%) with similar specificities (93%-99%). In contrast to Models 5-8, samples from Models 1 and 2 demonstrated similar characteristics to the gold standard, including fever prevalence, median age, and rates of bacterial infections, hospitalizations, and severe outcomes. CONCLUSIONS: Findings suggest rule-based algorithms can accurately identify febrile infants with greater sensitivity while preserving specificity compared to diagnostic codes. If externally validated, rule-based algorithms may be important tools to create representative study samples, thereby improving generalizability of findings.

Bouveret Syndrome in a Cirrhotic Patient.

Here, we present a case of Bouveret syndrome, a rare etiology of gallstone impaction in the setting of chole-enteric fistula, in a cirrhotic patient. This syndrome is most often seen in elderly patients with multiple comorbidities and as such has high morbidity and mortality rates. Because of its prevalence in this patient population and its rarity, there are no established guidelines for the workup and management of this disease. We discuss currently available options for management and thoughts on our comorbid patient and her clinical course.

When Recurring Infections Mask an Atypical Presentation of Inflammatory Bowel Disease (IBD): A Re-Visitation and Literature Review.

Pyoderma gangrenosum (PG) is an uncommon and severe extra-intestinal manifestation (EIM) of inflammatory bowel disease (IBD). Head or scalp involvement in this condition is exceedingly rare. Approximately one-third of presentations can be precipitated by skin trauma or infection, a phenomenon known as pathergy. These ulcers develop acutely, do not necessarily correlate with IBD activity, and can precede IBD diagnosis. Here, we present an atypical presentation of PG that became a cornerstone finding in the subsequent diagnosis of IBD.

Information Density and Syntactic Repetition.

In noun phrase (NP) coordinate constructions (e.g., NP and NP), there is a strong tendency for the syntactic structure of the second conjunct to match that of the first; the second conjunct in such constructions is therefore low in syntactic information. The theory of uniform information density predicts that low-information syntactic constructions will be counterbalanced by high information in other aspects of that part of the sentence, and high-information constructions will be counterbalanced by other low-information components. Three predictions follow: (a) lexical probabilities (measured by N-gram probabilities and head-dependent probabilities) will be lower in second conjuncts than first conjuncts; (b) lexical probabilities will be lower in matching second conjuncts (those whose syntactic expansions match the first conjunct) than nonmatching ones; and (c) syntactic repetition should be especially common for low-frequency NP expansions. Corpus analysis provides support for all three of these predictions.

Bayesian tree substitution grammars as a usage- based approach.

Tree substitution grammar (TSG) is a generalization of context-free grammar (CFG) that permits non-terminals to rewrite as fragments of arbitrary size, instead of just depth-one productions. We discuss connections between the TSG framework and the larger family of usage-based approaches to language, showing how TSG allows us to make some of the claims of these approaches sufficiently concrete for computational modeling. A fundamental difficulty in defining a TSG is to determine the set of fragments for the grammar, because the set of possible fragments is exponential in the size of the parse trees from which TSGs are typically learned. We describe a model-based approach that learns a TSG using Gibbs sampling with a non-parametric prior to control fragment size, yielding grammars that contain mostly small fragments but that include larger ones. as the data permits. We evaluate these grammars on two tasks (parsing accuracy and grammaticality classification), and find that these Bayesian TSGs achieve excellent performance on two tasks relative to a set of heuristically extracted TSGs spanning the spectrum of representations, from a standard depth-one context-free Treebank grammar to explicit approximations of the Data-Oriented Parsing model.

Do grammars minimize dependency length?

A well-established principle of language is that there is a preference for closely related words to be close together in the sentence. This can be expressed as a preference for dependency length minimization (DLM). In this study, we explore quantitatively the degree to which natural languages reflect DLM. We extract the dependencies from natural language text and reorder the words in such a way as to minimize dependency length. Comparing the original text with these optimal linearizations (and also with random linearizations) reveals the degree to which natural language minimizes dependency length. Tests on English data show that English shows a strong effect of DLM, with dependency length much closer to optimal than to random; the optimal English grammar also has many specific features in common with English. In German, too, dependency length is significantly less than random, but the effect is much weaker than in English. We conclude by speculating about some possible reasons for this difference between English and German.

Effects of disfluencies, predictability, and utterance position on word form variation in English conversation.

Function words, especially frequently occurring ones such as (the, that, and, and of), vary widely in pronunciation. Understanding this variation is essential both for cognitive modeling of lexical production and for computer speech recognition and synthesis. This study investigates which factors affect the forms of function words, especially whether they have a fuller pronunciation (e.g., thi, thaet, aend, inverted-v v) or a more reduced or lenited pronunciation (e.g., thax, thixt, n, ax). It is based on over 8000 occurrences of the ten most frequent English function words in a 4-h sample from conversations from the Switchboard corpus. Ordinary linear and logistic regression models were used to examine variation in the length of the words, in the form of their vowel (basic, full, or reduced), and whether final obstruents were present or not. For all these measures, after controlling for segmental context, rate of speech, and other important factors, there are strong independent effects that made high-frequency monosyllabic function words more likely to be longer or have a fuller form (1) when neighboring disfluencies (such as filled pauses uh and um) indicate that the speaker was encountering problems in planning the utterance; (2) when the word is unexpected, i.e., less predictable in context; (3) when the word is either utterance initial or utterance final. Looking at the phenomenon in a different way, frequent function words are more likely to be shorter and to have less-full forms in fluent speech, in predictable positions or multiword collocations, and utterance internally. Also considered are other factors such as sex (women are more likely to use fuller forms, even after controlling for rate of speech, for example), and some of the differences among the ten function words in their response to the factors.