Leveraging Large Language Models in Conversational Recommender Systems

A Conversational Recommender System (CRS) offers increased transparency and control to users by enabling them to engage with the system through a real-time multi-turn dialogue. Recently, Large Language Models (LLMs) have exhibited an unprecedented ability to converse naturally and incorporate world knowledge and common-sense reasoning into language understanding, unlocking the potential of this paradigm. However, effectively leveraging LLMs within a CRS introduces new technical challenges, including properly understanding and controlling a complex conversation and retrieving from external sources of information. These issues are exacerbated by a large, evolving item corpus and a lack of conversational data for training. In this paper, we provide a roadmap for building an end-to-end large-scale CRS using LLMs. In particular, we propose new implementations for user preference understanding, flexible dialogue management and explainable recommendations as part of an integrated architecture powered by LLMs. For improved personalization, we describe how an LLM can consume interpretable natural language user profiles and use them to modulate session-level context. To overcome conversational data limitations in the absence of an existing production CRS, we propose techniques for building a controllable LLM-based user simulator to generate synthetic conversations. As a proof of concept we introduce RecLLM, a large-scale CRS for YouTube videos built on LaMDA, and demonstrate its fluency and diverse functionality through some illustrative example conversations

A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection

The response to respiratory viruses varies substantially between individuals, and there are currently no known molecular predictors from the early stages of infection. Here we conduct a community-based analysis to determine whether pre- or early post-exposure molecular factors could predict physiologic responses to viral exposure. Using peripheral blood gene expression profiles collected from healthy subjects prior to exposure to one of four respiratory viruses (H1N1, H3N2, Rhinovirus, and RSV), as well as up to 24 h following exposure, we find that it is possible to construct models predictive of symptomatic response using profiles even prior to viral exposure. Analysis of predictive gene features reveal little overlap among models; however, in aggregate, these genes are enriched for common pathways. Heme metabolism, the most significantly enriched pathway, is associated with a higher risk of developing symptoms following viral exposure. This study demonstrates that pre-exposure molecular predictors can be identified and improves our understanding of the mechanisms of response to respiratory viruses

Computed tomography guided sizing for transcatheter pulmonary valve replacement.

ObjectiveTo evaluate the predictive value of Computed Tomography Angiography (CTA) measurements of the RVOT for transcatheter valve sizing.BackgroundTranscatheter pulmonary valve replacement (TPVR) provides an alternative to surgery in patients with right ventricular outflow tract (RVOT) dysfunction. We studied 18 patients who underwent catheterization for potential TPVR to determine whether CT imaging can be used to accurately predict implant size.MethodsCases were grouped by RVOT characteristics: native or transannular patch (n = 8), conduit (n = 5) or bioprosthetic valve (n = 5). TPVR was undertaken in 14/18 cases, after balloon-sizing was used to confirm suitability and select implant size. Retrospective CT measurements of the RVOT (circumference-derived (Dcirc) and area-derived (Darea) diameters) were obtained at the level of the annulus, bioprosthesis or conduit. Using manufacturer sizing guidance, a valve size was generated and a predicted valve category assigned: (1) <18 mm, (2) 18-20 mm, (3) 22-23 mm, (4) 26-29 mm and (5) >29 mm. Predicted and implanted valves were compared for inter-rater agreement using Cohen's kappa coefficient.ResultsThe median age of patients was 37 years old (IQR: 30-49); 55% were male. Diagnoses included: Tetralogy of Fallot (12/18), d-Transposition repair (3/18), congenital pulmonary stenosis (2/18) and carcinoid heart disease (1/18). Measurements of Darea (κ = 0.697, p < 0.01) and Dcirc (κ = 0.540, p < 0.01) were good predictors of implanted valve size. When patients with RVOT conduits were excluded, the predictive accuracy improved for Darea (κ = 0.882, p < 0.01) and Dcirc (κ = 0.882, p < 0.01).ConclusionsCT measurement of the RVOT, using Darea or Dcirc, can predict prosthetic valve sizing in TPVR. These measurements are less predictive in patients with conduits, compared to those with a native RVOT or pulmonic bioprosthesis.Condensed abstractWe studied 18 patients who underwent catheterization for TPVR to determine whether CT imaging could be used to accurately predict implant size. Retrospective RVOT measurements were used to generate a predicted valve size, which was compared with implanted valve size for inter-rater agreement. Measurements of Darea (κ = 0.697, p < 0.01) and Dcirc (κ = 0.540, p < 0.01) were good predictors of implanted valve size. When cases with RVOT conduits were excluded, the predictive accuracy improved for Darea (κ = 0.882, p < 0.01) and Dcirc (κ = 0.882, p < 0.01). CT measurement of the RVOT can accurately predict prosthetic valve sizing in TPVR. These measurements are less predictive in patients with conduits

Computed tomography guided sizing for transcatheter pulmonary valve replacement.

ObjectiveTo evaluate the predictive value of Computed Tomography Angiography (CTA) measurements of the RVOT for transcatheter valve sizing.BackgroundTranscatheter pulmonary valve replacement (TPVR) provides an alternative to surgery in patients with right ventricular outflow tract (RVOT) dysfunction. We studied 18 patients who underwent catheterization for potential TPVR to determine whether CT imaging can be used to accurately predict implant size.MethodsCases were grouped by RVOT characteristics: native or transannular patch (n = 8), conduit (n = 5) or bioprosthetic valve (n = 5). TPVR was undertaken in 14/18 cases, after balloon-sizing was used to confirm suitability and select implant size. Retrospective CT measurements of the RVOT (circumference-derived (Dcirc) and area-derived (Darea) diameters) were obtained at the level of the annulus, bioprosthesis or conduit. Using manufacturer sizing guidance, a valve size was generated and a predicted valve category assigned: (1) <18 mm, (2) 18-20 mm, (3) 22-23 mm, (4) 26-29 mm and (5) >29 mm. Predicted and implanted valves were compared for inter-rater agreement using Cohen's kappa coefficient.ResultsThe median age of patients was 37 years old (IQR: 30-49); 55% were male. Diagnoses included: Tetralogy of Fallot (12/18), d-Transposition repair (3/18), congenital pulmonary stenosis (2/18) and carcinoid heart disease (1/18). Measurements of Darea (κ = 0.697, p < 0.01) and Dcirc (κ = 0.540, p < 0.01) were good predictors of implanted valve size. When patients with RVOT conduits were excluded, the predictive accuracy improved for Darea (κ = 0.882, p < 0.01) and Dcirc (κ = 0.882, p < 0.01).ConclusionsCT measurement of the RVOT, using Darea or Dcirc, can predict prosthetic valve sizing in TPVR. These measurements are less predictive in patients with conduits, compared to those with a native RVOT or pulmonic bioprosthesis.Condensed abstractWe studied 18 patients who underwent catheterization for TPVR to determine whether CT imaging could be used to accurately predict implant size. Retrospective RVOT measurements were used to generate a predicted valve size, which was compared with implanted valve size for inter-rater agreement. Measurements of Darea (κ = 0.697, p < 0.01) and Dcirc (κ = 0.540, p < 0.01) were good predictors of implanted valve size. When cases with RVOT conduits were excluded, the predictive accuracy improved for Darea (κ = 0.882, p < 0.01) and Dcirc (κ = 0.882, p < 0.01). CT measurement of the RVOT can accurately predict prosthetic valve sizing in TPVR. These measurements are less predictive in patients with conduits

Digestive Manifestations in Patients Hospitalized With Coronavirus Disease 2019.

BACKGROUND & AIMS: The prevalence and significance of digestive manifestations in coronavirus disease 2019 (COVID-19) remain uncertain. We aimed to assess the prevalence, spectrum, severity, and significance of digestive manifestations in patients hospitalized with COVID-19. METHODS: Consecutive patients hospitalized with COVID-19 were identified across a geographically diverse alliance of medical centers in North America. Data pertaining to baseline characteristics, symptomatology, laboratory assessment, imaging, and endoscopic findings from the time of symptom onset until discharge or death were abstracted manually from electronic health records to characterize the prevalence, spectrum, and severity of digestive manifestations. Regression analyses were performed to evaluate the association between digestive manifestations and severe outcomes related to COVID-19. RESULTS: A total of 1992 patients across 36 centers met eligibility criteria and were included. Overall, 53% of patients experienced at least 1 gastrointestinal symptom at any time during their illness, most commonly diarrhea (34%), nausea (27%), vomiting (16%), and abdominal pain (11%). In 74% of cases, gastrointestinal symptoms were judged to be mild. In total, 35% of patients developed an abnormal alanine aminotransferase or total bilirubin level; these were increased to less than 5 times the upper limit of normal in 77% of cases. After adjusting for potential confounders, the presence of gastrointestinal symptoms at any time (odds ratio, 0.93; 95% CI, 0.76-1.15) or liver test abnormalities on admission (odds ratio, 1.31; 95% CI, 0.80-2.12) were not associated independently with mechanical ventilation or death. CONCLUSIONS: Among patients hospitalized with COVID-19, gastrointestinal symptoms and liver test abnormalities were common, but the majority were mild and their presence was not associated with a more severe clinical course

Digestive Manifestations in Patients Hospitalized With Coronavirus Disease 2019

This article is made available for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.BACKGROUND & AIMS: The prevalence and significance of digestive manifestations in coronavirus disease 2019 (COVID-19) remain uncertain. We aimed to assess the prevalence, spectrum, severity, and significance of digestive manifestations in patients hospitalized with COVID-19. METHODS: Consecutive patients hospitalized with COVID-19 were identified across a geographically diverse alliance of medical centers in North America. Data pertaining to baseline characteristics, symptomatology, laboratory assessment, imaging, and endoscopic findings from the time of symptom onset until discharge or death were abstracted manually from electronic health records to characterize the prevalence, spectrum, and severity of digestive manifestations. Regression analyses were performed to evaluate the association between digestive manifestations and severe outcomes related to COVID-19. RESULTS: A total of 1992 patients across 36 centers met eligibility criteria and were included. Overall, 53% of patients experienced at least 1 gastrointestinal symptom at any time during their illness, most commonly diarrhea (34%), nausea (27%), vomiting (16%), and abdominal pain (11%). In 74% of cases, gastrointestinal symptoms were judged to be mild. In total, 35% of patients developed an abnormal alanine aminotransferase or total bilirubin level; these were increased to less than 5 times the upper limit of normal in 77% of cases. After adjusting for potential confounders, the presence of gastrointestinal symptoms at any time (odds ratio, 0.93; 95% CI, 0.76-1.15) or liver test abnormalities on admission (odds ratio, 1.31; 95% CI, 0.80-2.12) were not associated independently with mechanical ventilation or death. CONCLUSIONS: Among patients hospitalized with COVID-19, gastrointestinal symptoms and liver test abnormalities were common, but the majority were mild and their presence was not associated with a more severe clinical course

Treatments for intracranial hypertension in acute brain-injured patients: grading, timing, and association with outcome. Data from the SYNAPSE-ICU study

Purpose: Uncertainties remain about the safety and efficacy of therapies for managing intracranial hypertension in acute brain injured (ABI) patients. This study aims to describe the therapeutical approaches used in ABI, with/without intracranial pressure (ICP) monitoring, among different pathologies and across different countries, and their association with six months mortality and neurological outcome. Methods: A preplanned subanalysis of the SYNAPSE-ICU study, a multicentre, prospective, international, observational cohort study, describing the ICP treatment, graded according to Therapy Intensity Level (TIL) scale, in patients with ABI during the first week of intensive care unit (ICU) admission. Results: 2320 patients were included in the analysis. The median age was 55 (I-III quartiles = 39-69) years, and 800 (34.5%) were female. During the first week from ICU admission, no-basic TIL was used in 382 (16.5%) patients, mild-moderate in 1643 (70.8%), and extreme in 295 cases (eTIL, 12.7%). Patients who received eTIL were younger (median age 49 (I-III quartiles = 35-62) vs 56 (40-69) years, p < 0.001), with less cardiovascular pre-injury comorbidities (859 (44%) vs 90 (31.4%), p < 0.001), with more episodes of neuroworsening (160 (56.1%) vs 653 (33.3%), p < 0.001), and were more frequently monitored with an ICP device (221 (74.9%) vs 1037 (51.2%), p < 0.001). Considerable variability in the frequency of use and type of eTIL adopted was observed between centres and countries. At six months, patients who received no-basic TIL had an increased risk of mortality (Hazard ratio, HR = 1.612, 95% Confidence Interval, CI = 1.243-2.091, p < 0.001) compared to patients who received eTIL. No difference was observed when comparing mild-moderate TIL with eTIL (HR = 1.017, 95% CI = 0.823-1.257, p = 0.873). No significant association between the use of TIL and neurological outcome was observed. Conclusions: During the first week of ICU admission, therapies to control high ICP are frequently used, especially mild-moderate TIL. In selected patients, the use of aggressive strategies can have a beneficial effect on six months mortality but not on neurological outcome

Reproducibility of fluorescent expression from engineered biological constructs in E. coli

We present results of the first large-scale interlaboratory study carried out in synthetic biology, as part of the 2014 and 2015 International Genetically Engineered Machine (iGEM) competitions. Participants at 88 institutions around the world measured fluorescence from three engineered constitutive constructs in E. coli. Few participants were able to measure absolute fluorescence, so data was analyzed in terms of ratios. Precision was strongly related to fluorescent strength, ranging from 1.54-fold standard deviation for the ratio between strong promoters to 5.75-fold for the ratio between the strongest and weakest promoter, and while host strain did not affect expression ratios, choice of instrument did. This result shows that high quantitative precision and reproducibility of results is possible, while at the same time indicating areas needing improved laboratory practices.Peer reviewe