Adding Hyponatremia to the "Rule-of-6" Prediction Tool Improves Performance in Identifying Hospitalised Patients with COVID-19 at Risk of Adverse Clinical Outcomes.

The 'rule-of-6' prediction tool was shown to be able to identify COVID-19 patients at risk of adverse outcomes. During the pandemic, we frequently observed hyponatremia at presentation. We sought to evaluate if adding hyponatremia at presentation could improve the 'rule-of-6' prediction tool. We retrospectively analysed 1781 consecutive patients admitted to a single tertiary academic institution in Singapore with COVID-19 infection from February 2020 to October 2021. A total of 161 (9.0%) patients had hyponatremia. These patients were significantly older, with more co-morbidities and more likely to be admitted during the Delta wave (2021). They were more likely to have radiographic evidence of pneumonia (46.0% versus 13.0%, p < 0.001) and more adverse outcomes (25.5% vs. 4.1%, p < 0.001). Hyponatremia remained independently associated with adverse outcomes after adjusting for age, lack of medical co-morbidities, vaccination status, year of admission, CRP, LDH, and ferritin. The optimised cut-off for serum sodium in predicting adverse outcomes was approximately <135 mmol/L as determined by the Youden index. Although derived in early 2020, the 'rule-of-6' prediction tool continued to perform well in our later cohort (AUC: 0.72, 95%CI: 0.66-0.78). Adding hyponatremia to the 'rule-of-6' improved its performance (AUC: 0.76, 95%CI: 0.71-0.82). Patients with hyponatremia at presentation for COVID-19 had poorer outcomes even as new variants emerged.

Referring wisely: knowing when and how to make subspecialty consultations in hospital medicine.

Subspecialty consultations are becoming highly prevalent in hospital medicine, due to an ageing population with multimorbid conditions and increasingly complex care needs, as well as medicolegal fears that lead to widespread defensive medical practices. Although timely subspecialty consultations in the appropriate clinical context have been found to improve clinical outcomes, there remains a significant proportion of specialty referrals in hospital medicine which are inappropriate, excessive, or do not add value to patient care. In this article, we sought to provide an overview of the common problems pertaining to excessive quantity and suboptimal quality of inpatient subspecialty consultations made in real-world practice and highlight their implications for healthcare financing and patient care. In addition, we discuss the underlying contributing factors that predispose to inappropriate use of the specialist referral system. Finally, we offer a practical, multitiered approach to help rationalize subspecialty consultations, through (i) a systematic model ('WISE' template) for individual referral-making, (ii) development of standardized healthcare institutional referral guidelines with routine clinical audits for quality control, (iii) adopting an integrated generalist care model, and (iv) incorporating training on effective referral-making in medical education.

Trends in electrocardiographic and cardiovascular manifestations of patients hospitalised with COVID-19.

INTRODUCTION: Early in the coronavirus disease 2019 (COVID-19) pandemic, a low incidence of cardiovascular complications was reported in Singapore. Little was known about the trend of cardiovascular complications as the pandemic progressed. In this study, we examined the evolving trends in electrocardiographic and cardiovascular manifestations in patients hospitalised with COVID-19. METHODS: We examined the first 1781 consecutive hospitalised patients with polymerase chain reaction-confirmed COVID-19. We divided the population based on whether they had abnormal heart rate (HR) or electrocardiography (ECG) or normal HR and ECG, comparing the baseline characteristics and outcomes. Cardiovascular complications were defined as acute myocardial infarction, stroke, pulmonary embolism, myocarditis and mortality. RESULTS: The 253 (14.2%) patients who had abnormal HR/ECG at presentation were more likely to be symptomatic. Sinus tachycardia was commonly observed. Troponin I levels (97.0 ± 482.9 vs. 19.7 ± 68.4 ng/L, P = 0.047) and C-reactive protein levels (20.1 ± 50.7 vs. 13.9 ± 24.1 µmol/L, P = 0.003) were significantly higher among those with abnormal HR/ECGs, with a higher prevalence of myocarditis (2.0% vs. 0.5%, P = 0.019), pulmonary embolism (2.0% vs. 0.3%, P = 0.008) and acute myocardial infarction (1.2% vs. 0.1%, P = 0.023). After adjusting for age and comorbidities, abnormal HR/ECG (adjusted odds ratio 4.41, 95% confidence interval 2.21-8.77; P < 0.001) remained independently associated with adverse cardiovascular complications. Over time, there was a trend towards a higher proportion of hospitalised patients with cardiovascular complications. CONCLUSION: Cardiovascular complications appear to be increasing in proportion over time among hospitalised patients with COVID-19. A baseline ECG and HR measurement may be helpful for predicting these complications.

Clinical reasoning in real-world practice: a primer for medical trainees and practitioners.

Clinical reasoning is a crucial skill and defining characteristic of the medical profession, which relates to intricate cognitive and decision-making processes that are needed to solve real-world clinical problems. However, much of our current competency-based medical education systems have focused on imparting swathes of content knowledge and skills to our medical trainees, without an adequate emphasis on strengthening the cognitive schema and psychological processes that govern actual decision-making in clinical environments. Nonetheless, flawed clinical reasoning has serious repercussions on patient care, as it is associated with diagnostic errors, inappropriate investigations, and incongruent or suboptimal management plans that can result in significant morbidity and even mortality. In this article, we discuss the psychological constructs of clinical reasoning in the form of cognitive 'thought processing' models and real-world contextual or emotional influences on clinical decision-making. In addition, we propose practical strategies, including pedagogical development of a personal cognitive schema, mitigating strategies to combat cognitive bias and flawed reasoning, and emotional regulation and self-care techniques, which can be adopted in medical training to optimize physicians' clinical reasoning in real-world practice that effectively translates learnt knowledge and skill sets into good decisions and outcomes.

OLB-AC: toward optimizing ligand bioactivities through deep graph learning and activity cliffs.

MOTIVATION: Deep graph learning (DGL) has been widely employed in the realm of ligand-based virtual screening. Within this field, a key hurdle is the existence of activity cliffs (ACs), where minor chemical alterations can lead to significant changes in bioactivity. In response, several DGL models have been developed to enhance ligand bioactivity prediction in the presence of ACs. Yet, there remains a largely unexplored opportunity within ACs for optimizing ligand bioactivity, making it an area ripe for further investigation. RESULTS: We present a novel approach to simultaneously predict and optimize ligand bioactivities through DGL and ACs (OLB-AC). OLB-AC possesses the capability to optimize ligand molecules located near ACs, providing a direct reference for optimizing ligand bioactivities with the matching of original ligands. To accomplish this, a novel attentive graph reconstruction neural network and ligand optimization scheme are proposed. Attentive graph reconstruction neural network reconstructs original ligands and optimizes them through adversarial representations derived from their bioactivity prediction process. Experimental results on nine drug targets reveal that out of the 667 molecules generated through OLB-AC optimization on datasets comprising 974 low-activity, noninhibitor, or highly toxic ligands, 49 are recognized as known highly active, inhibitor, or nontoxic ligands beyond the datasets' scope. The 27 out of 49 matched molecular pairs generated by OLB-AC reveal novel transformations not present in their training sets. The adversarial representations employed for ligand optimization originate from the gradients of bioactivity predictions. Therefore, we also assess OLB-AC's prediction accuracy across 33 different bioactivity datasets. Results show that OLB-AC achieves the best Pearson correlation coefficient (r2) on 27/33 datasets, with an average improvement of 7.2%-22.9% against the state-of-the-art bioactivity prediction methods. AVAILABILITY AND IMPLEMENTATION: The code and dataset developed in this work are available at github.com/Yueming-Yin/OLB-AC.

Risk Perception, Acceptance, and Trust of Using AI in Gastroenterology Practice in the Asia-Pacific Region: Web-Based Survey Study.

BACKGROUND: The use of artificial intelligence (AI) can revolutionize health care, but this raises risk concerns. It is therefore crucial to understand how clinicians trust and accept AI technology. Gastroenterology, by its nature of being an image-based and intervention-heavy specialty, is an area where AI-assisted diagnosis and management can be applied extensively. OBJECTIVE: This study aimed to study how gastroenterologists or gastrointestinal surgeons accept and trust the use of AI in computer-aided detection (CADe), computer-aided characterization (CADx), and computer-aided intervention (CADi) of colorectal polyps in colonoscopy. METHODS: We conducted a web-based questionnaire from November 2022 to January 2023, involving 5 countries or areas in the Asia-Pacific region. The questionnaire included variables such as background and demography of users; intention to use AI, perceived risk; acceptance; and trust in AI-assisted detection, characterization, and intervention. We presented participants with 3 AI scenarios related to colonoscopy and the management of colorectal polyps. These scenarios reflect existing AI applications in colonoscopy, namely the detection of polyps (CADe), characterization of polyps (CADx), and AI-assisted polypectomy (CADi). RESULTS: In total, 165 gastroenterologists and gastrointestinal surgeons responded to a web-based survey using the structured questionnaire designed by experts in medical communications. Participants had a mean age of 44 (SD 9.65) years, were mostly male (n=116, 70.3%), and mostly worked in publicly funded hospitals (n=110, 66.67%). Participants reported relatively high exposure to AI, with 111 (67.27%) reporting having used AI for clinical diagnosis or treatment of digestive diseases. Gastroenterologists are highly interested to use AI in diagnosis but show different levels of reservations in risk prediction and acceptance of AI. Most participants (n=112, 72.72%) also expressed interest to use AI in their future practice. CADe was accepted by 83.03% (n=137) of respondents, CADx was accepted by 78.79% (n=130), and CADi was accepted by 72.12% (n=119). CADe and CADx were trusted by 85.45% (n=141) of respondents and CADi was trusted by 72.12% (n=119). There were no application-specific differences in risk perceptions, but more experienced clinicians gave lesser risk ratings. CONCLUSIONS: Gastroenterologists reported overall high acceptance and trust levels of using AI-assisted colonoscopy in the management of colorectal polyps. However, this level of trust depends on the application scenario. Moreover, the relationship among risk perception, acceptance, and trust in using AI in gastroenterology practice is not straightforward.

Optimizing differential expression analysis for proteomics data via high-performing rules and ensemble inference.

Identification of differentially expressed proteins in a proteomics workflow typically encompasses five key steps: raw data quantification, expression matrix construction, matrix normalization, missing value imputation (MVI), and differential expression analysis. The plethora of options in each step makes it challenging to identify optimal workflows that maximize the identification of differentially expressed proteins. To identify optimal workflows and their common properties, we conduct an extensive study involving 34,576 combinatoric experiments on 24 gold standard spike-in datasets. Applying frequent pattern mining techniques to top-ranked workflows, we uncover high-performing rules that demonstrate optimality has conserved properties. Via machine learning, we confirm optimal workflows are indeed predictable, with average cross-validation F1 scores and Matthew's correlation coefficients surpassing 0.84. We introduce an ensemble inference to integrate results from individual top-performing workflows for expanding differential proteome coverage and resolve inconsistencies. Ensemble inference provides gains in pAUC (up to 4.61%) and G-mean (up to 11.14%) and facilitates effective aggregation of information across varied quantification approaches such as topN, directLFQ, MaxLFQ intensities, and spectral counts. However, further development and evaluation are needed to establish acceptable frameworks for conducting ensemble inference on multiple proteomics workflows.

Providing family updates: a primer for the medical trainee.

Providing family updates is a common clinical task for medical trainees and practitioners working in hospital settings. Good clinical communication skills are essential in clinical care as it is associated with improved patient satisfaction, understanding of condition, treatment adherence, and better overall clinical outcomes. Moreover, poor communications are often the source of medical complaints. However, while patient-centred communication skills training has generally been incorporated into clinical education, there hitherto remains inadequate training on clinical communications with patients' families, which carry different nuances. In recent years, it is increasingly recognized that familial involvement in the care of hospitalized patients leads to better clinical and psychological outcomes. In fact, in Asian populations with more collectivistic cultures, families are generally highly involved in patient care and decision-making. Therefore, effective clinical communications and regular provision of family updates are essential to build therapeutic rapport, facilitate familial involvement in patient care, and also provide a more holistic understanding of the patient's background and psychosocial set-up. In this article, we herein describe a seven-step understand the clinical context, gather perspectives, deliver medical information, address questions, concerns and expectations, provide tentative plans, demonstrate empathy, postcommunication reflections model as a practical guide for medical trainees and practitioners in provision of structured and effective family updates in their clinical practice.

Optimizing the PROTREC network-based missing protein prediction algorithm.

This article summarizes the PROTREC method and investigates the impact that the different hyper-parameters have on the task of missing protein prediction using PROTREC. We evaluate missing protein recovery rates using different PROTREC score selection approaches (MAX, MIN, MEDIAN, and MEAN), different PROTREC score thresholds, as well as different complex size thresholds. In addition, we included two additional cancer datasets in our analysis and introduced a new validation method to check both the robustness of the PROTREC method as well as the correctness of our analysis. Our analysis showed that the missing protein recovery rate can be improved by adopting PROTREC score selection operations of MIN, MEDIAN, and MEAN instead of the default MAX. However, this may come at a cost of reduced numbers of proteins predicted and validated. The users should therefore choose their hyper-parameters carefully to find a balance in the accuracy-quantity trade-off. We also explored the possibility of combining PROTREC with a p-value-based method (FCS) and demonstrated that PROTREC is able to perform well independently without any help from a p-value-based method. Furthermore, we conducted a downstream enrichment analysis to understand the biological pathways and protein networks within the cancerous tissues using the recovered proteins. Missing protein recovery rate using PROTREC can be improved by selecting a different PROTREC score selection method. Different PROTREC score selection methods and other hyper-parameters such as PROTREC score threshold and complex size threshold introduce accuracy-quantity trade-off. PROTREC is able to perform well independently of any filtering using a p-value-based method. Verification of the PROTREC method on additional cancer datasets. Downstream Enrichment Analysis to understand the biological pathways and protein networks in cancerous tissues.

'Insight' in medical training: what, why, and how?

The term 'insight' is generically defined in English language as the ability to perceive deeper truths about people and situations. In clinical practice, patient insight is known to have important implications in treatment compliance and clinical outcomes, and can be assessed clinically by looking for the presence of illness awareness, correct attribution of symptoms to underlying condition, and acceptance of treatment. In this article, we suggest that cultivating insight is actually a highly important, yet often overlooked, component of medical training, which may explain why some consistently learn well, communicate effectively, and quickly attain clinical competency, while others struggle throughout their clinical training and may even be difficult to remediate. We herein define 'insight' in the context of medical training as having an astute perception of personal cognitive processes, motivations, emotions, and ability (strengths, weaknesses, and limitations) that should drive self-improvement and effective behavioural regulation. We then describe the utility of cultivating 'insight' in medical training through three lenses of (i) promoting self-regulated, lifelong clinical learning, (ii) improving clinical competencies and person-centred care, and (iii) enhancing physician mental health and well-being. In addition, we review educational pedagogies that are helpful to create a medical eco-system that promotes the cultivation of insight among its trainees and practitioners. Finally, we highlight several tell-tale signs of poor insight and discuss psychological and non-psychological interventions that may help those severely lacking in insight to become more amenable to change and remediation.

Modeling the influence of attitudes, trust, and beliefs on endoscopists' acceptance of artificial intelligence applications in medical practice.

Introduction: The potential for deployment of Artificial Intelligence (AI) technologies in various fields of medicine is vast, yet acceptance of AI amongst clinicians has been patchy. This research therefore examines the role of antecedents, namely trust, attitude, and beliefs in driving AI acceptance in clinical practice. Methods: We utilized online surveys to gather data from clinicians in the field of gastroenterology. Results: A total of 164 participants responded to the survey. Participants had a mean age of 44.49 (SD = 9.65). Most participants were male (n = 116, 70.30%) and specialized in gastroenterology (n = 153, 92.73%). Based on the results collected, we proposed and tested a model of AI acceptance in medical practice. Our findings showed that while the proposed drivers had a positive impact on AI tools' acceptance, not all effects were direct. Trust and belief were found to fully mediate the effects of attitude on AI acceptance by clinicians. Discussion: The role of trust and beliefs as primary mediators of the acceptance of AI in medical practice suggest that these should be areas of focus in AI education, engagement and training. This has implications for how AI systems can gain greater clinician acceptance to engender greater trust and adoption amongst public health systems and professional networks which in turn would impact how populations interface with AI. Implications for policy and practice, as well as future research in this nascent field, are discussed.

MultiPro: DDA-PASEF and diaPASEF acquired cell line proteomic datasets with deliberate batch effects.

Mass spectrometry-based proteomics plays a critical role in current biological and clinical research. Technical issues like data integration, missing value imputation, batch effect correction and the exploration of inter-connections amongst these technical issues, can produce errors but are not well studied. Although proteomic technologies have improved significantly in recent years, this alone cannot resolve these issues. What is needed are better algorithms and data processing knowledge. But to obtain these, we need appropriate proteomics datasets for exploration, investigation, and benchmarking. To meet this need, we developed MultiPro (Multi-purpose Proteome Resource), a resource comprising four comprehensive large-scale proteomics datasets with deliberate batch effects using the latest parallel accumulation-serial fragmentation in both Data-Dependent Acquisition (DDA) and Data Independent Acquisition (DIA) modes. Each dataset contains a balanced two-class design based on well-characterized and widely studied cell lines (A549 vs K562 or HCC1806 vs HS578T) with 48 or 36 biological and technical replicates altogether, allowing for investigation of a multitude of technical issues. These datasets allow for investigation of inter-connections between class and batch factors, or to develop approaches to compare and integrate data from DDA and DIA platforms.

Filter and Wrapper Stacking Ensemble (FWSE): a robust approach for reliable biomarker discovery in high-dimensional omics data.

Selecting informative features, such as accurate biomarkers for disease diagnosis, prognosis and response to treatment, is an essential task in the field of bioinformatics. Medical data often contain thousands of features and identifying potential biomarkers is challenging due to small number of samples in the data, method dependence and non-reproducibility. This paper proposes a novel ensemble feature selection method, named Filter and Wrapper Stacking Ensemble (FWSE), to identify reproducible biomarkers from high-dimensional omics data. In FWSE, filter feature selection methods are run on numerous subsets of the data to eliminate irrelevant features, and then wrapper feature selection methods are applied to rank the top features. The method was validated on four high-dimensional medical datasets related to mental illnesses and cancer. The results indicate that the features selected by FWSE are stable and statistically more significant than the ones obtained by existing methods while also demonstrating biological relevance. Furthermore, FWSE is a generic method, applicable to various high-dimensional datasets in the fields of machine intelligence and bioinformatics.

Data pre-processing for analyzing microbiome data - A mini review.

The human microbiome is an emerging research frontier due to its profound impacts on health. High-throughput microbiome sequencing enables studying microbial communities but suffers from analytical challenges. In particular, the lack of dedicated preprocessing methods to improve data quality impedes effective minimization of biases prior to downstream analysis. This review aims to address this gap by providing a comprehensive overview of preprocessing techniques relevant to microbiome research. We outline a typical workflow for microbiome data analysis. Preprocessing methods discussed include quality filtering, batch effect correction, imputation of missing values, normalization, and data transformation. We highlight strengths and limitations of each technique to serve as a practical guide for researchers and identify areas needing further methodological development. Establishing robust, standardized preprocessing will be essential for drawing valid biological conclusions from microbiome studies.

RNA-sequencing of peripheral whole blood of individuals at ultra-high-risk for psychosis - A longitudinal perspective.

BACKGROUND: The peripheral blood is an attractive source of prognostic biomarkers for psychosis conversion. There is limited research on the transcriptomic changes associated with psychosis conversion in the peripheral whole blood. STUDY DESIGN: We performed RNA-sequencing of peripheral whole blood from 65 ultra-high-risk (UHR) participants and 70 healthy control participants recruited in the Longitudinal Youth-at-Risk Study (LYRIKS) cohort. 13 UHR participants converted in the study duration. Samples were collected at 3 timepoints, at 12-months interval across a 2-year period. We examined whether the genes differential with psychosis conversion contain schizophrenia risk loci. We then examined the functional ontologies and GWAS associations of the differential genes. We also identified the overlap between differentially expressed genes across different comparisons. STUDY RESULTS: Genes containing schizophrenia risk loci were not differentially expressed in the peripheral whole blood in psychosis conversion. The differentially expressed genes in psychosis conversion are enriched for ontologies associated with cellular replication. The differentially expressed genes in psychosis conversion are associated with non-neurological GWAS phenotypes reported to be perturbed in schizophrenia and psychosis but not schizophrenia and psychosis phenotypes themselves. We found minimal overlap between the genes differential with psychosis conversion and the genes that are differential between pre-conversion and non-conversion samples. CONCLUSION: The associations between psychosis conversion and peripheral blood-based biomarkers are likely to be indirect. Further studies to elucidate the mechanism behind potential indirect associations are needed.