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Background: In proteomics, batch effects are technical sources of variation that confounds proper analysis, preventing effective deployment in clinical and translational research. Results: Using simulated and real data, we demonstrate existing batch effect-correction methods do not always eradicate all batch effects. Worse still, they may alter data integrity, and introduce false positives. Moreover, although Principal component analysis (PCA) is commonly used for detecting batch effects. The principal components (PCs) themselves may be used as differential features, from which relevant differential proteins may be effectively traced. Batch effect are removable by identifying PCs highly correlated with batch but not class effect. However, neither PC-based nor existing batch effect-correction methods address well subtle batch effects, which are difficult to eradicate, and involve data transformation and/or projection which is error-prone. To address this, we introduce the concept of batch-effect resistant methods and demonstrate how such methods incorporating protein complexes are particularly resistant to batch effect without compromising data integrity. Conclusions: Protein complex-based analyses are powerful, offering unparalleled differential protein-selection reproducibility and high prediction accuracy. We demonstrate for the first time their innate resistance against batch effects, even subtle ones. As complex-based analyses require no prior data transformation (e.g. batch-effect correction), data integrity is protected. Individual checks on top-ranked protein complexes confirm strong association with phenotype classes and not batch. Therefore, the constituent proteins of these complexes are more likely to be clinically relevant. [ABSTRACT FROM AUTHOR]

In mass spectrometry (MS)-based proteomics, protein inference from identified peptides (protein fragments) is a critical step. We present ProInfer (Protein Inference), a novel protein assembly method that takes advantage of information in biological networks. ProInfer assists recovery of proteins supported only by ambiguous peptides (a peptide which maps to more than one candidate protein) and enhances the statistical confidence for proteins supported by both unique and ambiguous peptides. Consequently, ProInfer rescues weakly supported proteins thereby improving proteome coverage. Evaluated across THP1 cell line, lung cancer and RAW267.4 datasets, ProInfer always infers the most numbers of true positives, in comparison to mainstream protein inference tools Fido, EPIFANY and PIA. ProInfer is also adept at retrieving differentially expressed proteins, signifying its usefulness for functional analysis and phenotype profiling. Source codes of ProInfer are available at https://github.com/PennHui2016/ProInfer. Author summary: Protein inference is a key step in proteomics data analysis. However, this procedure suffers from coverage issues due to high statistical stringency requirement and noise. Integration of prior knowledge to guide protein assembly can be a powerful approach. Hence, we developed a novel protein inference tool ProInfer that incorporates a length-adjusted and weighted-accumulated posterior error probability score with protein-complex networks. Compared against existing tools, ProInfer achieves the highest recall and F1 score in protein inference and also identifies novel differentially expressed proteins not reported by any other tool. [ABSTRACT FROM AUTHOR]

• In 2020, COVID-19 predominantly affected young migrant workers in Singapore. • Therefore, a low incidence of severe complications was observed in 2020. • In 2021, COVID-19 affected Singapore's more elderly and vulnerable local population. • Consequently, there was a greater strain on intensive care facilities in 2021. • Monitoring COVID-19 demographic shifts help guide healthcare resource allocation. Objectives: In the early months of the COVID-19 pandemic in Singapore, the vast majority of infected persons were migrant workers living in dormitories who had few medical comorbidities. In 2021, with the Delta and Omicron waves, this shifted to the more vulnerable, elderly population within the local community. We examined evolving trends among the hospitalised cases of COVID-19. Methods: All patients with polymerase chain reaction-positive SARS-CoV-2 admitted from February 2020 to October 2021 were included and subsequently stratified by their year of admission (2020 or 2021). We compared the baseline clinical characteristics, clinical course, and outcomes. Results: A majority of cases were seen in 2020 (n = 1359), compared with 2021 (n = 422), due to the large outbreaks in migrant worker dormitories. Nevertheless, the greater proportion of locally transmitted cases outside of dormitories in 2021 (78.7% vs 12.3%) meant a significantly older population with more medical comorbidities had COVID-19. This led to an observably higher proportion of patients with severe disease presenting with raised inflammatory markers, need for therapeutics, supplemental oxygenation, and higher mortality. Conclusion: Changing demographics and the characteristics of the exposed populations are associated with distinct differences in clinical presentation and outcomes. Older age remained consistently associated with adverse outcomes. [ABSTRACT FROM AUTHOR]

Clinical evidence links arterial calcification and cardiovascular risk. Finite-element modelling of the stress distribution within atherosclerotic plaques has suggested that subcellular microcalcifications in the fibrous cap may promote material failure of the plaque, but that large calcifications can stabilize it. Yet the physicochemical mechanisms underlying such mineral formation and growth in atheromata remain unknown. Here, by using three-dimensional collagen hydrogels that mimic structural features of the atherosclerotic fibrous cap, and high-resolution microscopic and spectroscopic analyses of both the hydrogels and of calcified human plaques, we demonstrate that calcific mineral formation and maturation results from a series of events involving the aggregation of calcifying extracellular vesicles, and the formation of microcalcifications and ultimately large calcification zones. We also show that calcification morphology and the plaque’s collagen content – two determinants of atherosclerotic plaque stability - are interlinked.

Quantile normalization is an important normalization technique commonly used in high-dimensional data analysis. However, it is susceptible to class-effect proportion effects (the proportion of class-correlated variables in a dataset) and batch effects (the presence of potentially confounding technical variation) when applied blindly on whole data sets, resulting in higher false-positive and false-negative rates. We evaluate five strategies for performing quantile normalization, and demonstrate that good performance in terms of batch-effect correction and statistical feature selection can be readily achieved by first splitting data by sample class-labels before performing quantile normalization independently on each split ("Class-specific"). Via simulations with both real and simulated batch effects, we demonstrate that the "Class-specific" strategy (and others relying on similar principles) readily outperform whole-data quantile normalization, and is robust-preserving useful signals even during the combined analysis of separately-normalized datasets. Quantile normalization is a commonly used procedure. But when carelessly applied on whole datasets without first considering class-effect proportion and batch effects, can result in poor performance. If quantile normalization must be used, then we recommend using the "Class-specific" strategy. [ABSTRACT FROM AUTHOR]

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 (r 2) 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. [ABSTRACT FROM AUTHOR]

Cancer is a heterogeneous disease, confounding the identification of relevant markers and drug targets. Network-based analysis is robust against noise, potentially offering a promising approach towards biomarker identification. We describe here the application of two network-based methods, qPSP (Quantitative Proteomics Signature Profiling) and PFSNet (Paired Fuzzy SubNetworks), in an intra-tissue proteome data set of prostate tissue samples. Despite high basal variation, we find that traditional statistical analysis may exaggerate the extent of heterogeneity. We also report that network-based analysis outperforms protein-based feature selection with concomitantly higher cross-validation accuracy. Overall, network-based analysis provides emergent signal that boosts sensitivity while retaining good precision. It is a potential means of circumventing heterogeneity for stable biomarker discovery. Unlabelled Image • Traditional analytical approaches typically exaggerate tumor heterogeneity. • Network-based analysis suggests heterogeneity is over-estimated. • Network-based analysis leads towards good cross-validation accuracy. • Network-based analysis is a useful means of circumventing heterogeneity. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A missing protein (MP) is an unconfirmed genetic sequence for which a protein product is not yet detected. Currently, MPs are tiered based on supporting evidence mainly in the form of protein existence (PE) classification. As we discuss here, this definition is overly restrictive because proteins go missing in day-to-day proteomics as a result of low abundance, lack of sequence specificity, splice variants, and so on. Thus, we propose a broader functional classification of MPs that complements PE classification, discuss major causes, and examine three corresponding solution tiers: biological, technical, and informatics. We assert that informatics-driven solutions would have a major role in resolving the MP problem (MPP). [ABSTRACT FROM AUTHOR]

Missing values (MVs) can adversely impact data analysis and machine-learning model development. We propose a novel mixed-model method for missing value imputation (MVI). This method, ProJect (short for Protein inJection), is a powerful and meaningful improvement over existing MVI methods such as Bayesian principal component analysis (PCA), probabilistic PCA, local least squares and quantile regression imputation of left-censored data. We rigorously tested ProJect on various high-throughput data types, including genomics and mass spectrometry (MS)-based proteomics. Specifically, we utilized renal cancer (RC) data acquired using DIA-SWATH, ovarian cancer (OC) data acquired using DIA-MS, bladder (BladderBatch) and glioblastoma (GBM) microarray gene expression dataset. Our results demonstrate that ProJect consistently performs better than other referenced MVI methods. It achieves the lowest normalized root mean square error (on average, scoring 45.92% less error in RC_C, 27.37% in RC_full, 29.22% in OC, 23.65% in BladderBatch and 20.20% in GBM relative to the closest competing method) and the Procrustes sum of squared error (Procrustes SS) (exhibits 79.71% less error in RC_C, 38.36% in RC full, 18.13% in OC, 74.74% in BladderBatch and 30.79% in GBM compared to the next best method). ProJect also leads with the highest correlation coefficient among all types of MV combinations (0.64% higher in RC_C, 0.24% in RC full, 0.55% in OC, 0.39% in BladderBatch and 0.27% in GBM versus the second-best performing method). ProJect's key strength is its ability to handle different types of MVs commonly found in real-world data. Unlike most MVI methods that are designed to handle only one type of MV, ProJect employs a decision-making algorithm that first determines if an MV is missing at random or missing not at random. It then employs targeted imputation strategies for each MV type, resulting in more accurate and reliable imputation outcomes. An R implementation of ProJect is available at https://github.com/miaomiao6606/ProJect. [ABSTRACT FROM AUTHOR]

In clinical proteomics, reproducible feature selection is unattainable given the standard statistical hypothesis-testing framework. This leads to irreproducible signatures with no diagnostic power. Instability stems from high P -value variability ( p _var), which is inevitable and insolvable. The impact of p _var can be reduced via power increment, for example increasing sample size and measurement accuracy. However, these are not realistic solutions in practice. Instead, workarounds using existing data such as signal boosting transformation techniques and network-based statistical testing is more practical. Furthermore, it is useful to consider other metrics alongside P -values including confidence intervals, effect sizes and cross-validation accuracies to make informed inferences. [ABSTRACT FROM AUTHOR]

Proteomic studies characterize the protein composition of complex biological samples. Despite recent advancements in mass spectrometry instrumentation and computational tools, low proteome coverage and interpretability remains a challenge. To address this, we developed Proteome Support Vector Enrichment (PROSE), a fast, scalable and lightweight pipeline for scoring proteins based on orthogonal gene co-expression network matrices. PROSE utilizes simple protein lists as input, generating a standard enrichment score for all proteins, including undetected ones. In our benchmark with 7 other candidate prioritization techniques, PROSE shows high accuracy in missing protein prediction, with scores correlating strongly to corresponding gene expression data. As a further proof-of-concept, we applied PROSE to a reanalysis of the Cancer Cell Line Encyclopedia proteomics dataset, where it captures key phenotypic features, including gene dependency. We lastly demonstrated its applicability on a breast cancer clinical dataset, showing clustering by annotated molecular subtype and identification of putative drivers of triple-negative breast cancer. PROSE is available as a user-friendly Python module from https://github.com/bwbio/PROSE. [ABSTRACT FROM AUTHOR]

Data analysis is complex due to a myriad of technical problems. Amongst these, missing values and batch effects are endemic. Although many methods have been developed for missing value imputation (MVI) and batch correction respectively, no study has directly considered the confounding impact of MVI on downstream batch correction. This is surprising as missing values are imputed during early pre-processing while batch effects are mitigated during late pre-processing, prior to functional analysis. Unless actively managed, MVI approaches generally ignore the batch covariate, with unknown consequences. We examine this problem by modelling three simple imputation strategies: global (M1), self-batch (M2) and cross-batch (M3) first via simulations, and then corroborated on real proteomics and genomics data. We report that explicit consideration of batch covariates (M2) is important for good outcomes, resulting in enhanced batch correction and lower statistical errors. However, M1 and M3 are error-generating: global and cross-batch averaging may result in batch-effect dilution, with concomitant and irreversible increase in intra-sample noise. This noise is unremovable via batch correction algorithms and produces false positives and negatives. Hence, careless imputation in the presence of non-negligible covariates such as batch effects should be avoided. [ABSTRACT FROM AUTHOR]

Interpretable machine learning models for gene expression datasets are important for understanding the decision-making process of a classifier and gaining insights on the underlying molecular processes of genetic conditions. Interpretable models can potentially support early diagnosis before full disease manifestation. This is particularly important yet, challenging for mental health. We hypothesise this is due to extreme heterogeneity issues which may be overcome and explained by personalised modelling techniques. Thus far, most machine learning methods applied to gene expression datasets, including deep neural networks, lack personalised interpretability. This paper proposes a new methodology named personalised constrained neuro fuzzy inference (PCNFI) for learning personalised rules from high dimensional datasets which are structurally and semantically interpretable. Case studies on two mental health related datasets (schizophrenia and bipolar disorders) have shown that the relatively short and simple personalised fuzzy rules provided enhanced interpretability as well as better classification performance compared to other commonly used machine learning methods. Performance test on a cancer dataset also showed that PCNFI matches previous benchmarks. Insights from our approach also indicated the importance of two genes (ATRX and TSPAN2) as possible biomarkers for early differentiation of ultra-high risk, bipolar and healthy individuals. These genes are linked to cognitive ability and impulsive behaviour. Our findings suggest a significant starting point for further research into the biological role of cognitive and impulsivity-related differences. With potential applications across bio-medical research, the proposed PCNFI method is promising for diagnosis, prognosis, and the design of personalised treatment plans for better outcomes in the future. [ABSTRACT FROM AUTHOR]

Background: Progesterone receptor (PGR) is a master regulator of uterine function through antagonistic and synergistic interplays with oestrogen receptors. PGR action is primarily mediated by activation functions AF1 and AF2, but their physiological significance is unknown. Results: We report the first study of AF1 function in mice. The AF1 mutant mice are infertile with impaired implantation and decidualization. This is associated with a delay in the cessation of epithelial proliferation and in the initiation of stromal proliferation at preimplantation. Despite tissue selective effect on PGR target genes, AF1 mutations caused global loss of the antioestrogenic activity of progesterone in both pregnant and ovariectomized models. Importantly, the study provides evidence that PGR can exert an antioestrogenic effect by genomic inhibition of Esr1 and Greb1 expression. ChIP-Seq data mining reveals intermingled PGR and ESR1 binding on Esr1 and Greb1 gene enhancers. Chromatin conformation analysis shows reduced interactions in these genes' loci in the mutant, coinciding with their upregulations. Conclusion: AF1 mediates genomic inhibition of ESR1 action globally whilst it also has tissue-selective effect on PGR target genes. [ABSTRACT FROM AUTHOR]

Despite technological advances in proteomics, incomplete coverage and inconsistency issues persist, resulting in "data holes". These data holes cause the missing protein problem (MPP), where relevant proteins are persistently unobserved, or sporadically observed across samples, hindering biomarker discovery and proper functional characterization. Network-based approaches can provide powerful solutions for resolving these issues. Functional Class Scoring (FCS) is one such method that uses protein complex information to recover missing proteins with weak support. However, FCS has not been evaluated on more recent proteomic technologies with higher coverage, and there is no clear way to evaluate its performance. To address these issues, we devised a more rigorous evaluation schema based on cross-verification between technical replicates and evaluated its performance on data acquired under recent Data-Independent Acquisition (DIA) technologies (viz. SWATH). Although cross-replicate examination reveals some inconsistencies amongst same-class samples, tissue-differentiating signal is nonetheless strongly conserved, confirming that FCS selects for biologically meaningful networks. We also report that predicted missing proteins are statistically significant based on FCS p values. Despite limited cross-replicate verification rates, the predicted missing proteins as a whole have higher peptide support than non-predicted proteins. FCS also predicts missing proteins that are often lost due to weak specific peptide support. [ABSTRACT FROM AUTHOR]

The robustness of a breast cancer gene signature, the super-proliferation set (SPS), is initially tested and investigated on breast cancer cell lines from the Cancer Cell Line Encyclopaedia (CCLE). Previously, SPS was derived via a meta-analysis of 47 independent breast cancer gene signatures, benchmarked on survival information from clinical data in the NKI dataset. Here, relying on the stability of cell line data and associative prior knowledge, we first demonstrate through Principal Component Analysis (PCA) that SPS prioritizes survival information over secondary subtype information, surpassing both PAM50 and Boruta, an artificial intelligence-based feature-selection algorithm, in this regard. We can also extract higher resolution 'progression' information using SPS, dividing survival outcomes into several clinically relevant stages ('good', 'intermediate', and 'bad) based on different quadrants of the PCA scatterplot. Furthermore, by transferring these 'progression' annotations onto independent clinical datasets, we demonstrate the generalisability of our method on actual patient data. Finally, via the characteristic genetic profiles of each quadrant/stage, we identified efficacious drugs using their gene reversal scores that can shift signatures across quadrants/stages, in a process known as gene signature reversal. This confirms the power of meta-analytical approaches for gene signature inference in breast cancer, as well as the clinical benefit in translating these inferences onto real-world patient data for more targeted therapies. [Display omitted] • Super-proliferation set (SPS) carries robust information about breast cancer survival. • Naïve application of Boruta cannot outperform SPS. • A meta-analytical approach to gene signature inference is effective. • When tested on clinical datasets, SPS can guide drug discovery and repositioning. [ABSTRACT FROM AUTHOR]

Activation function 1 of progesterone receptor is required for progesterone antagonism of oestrogen action in the uterus. B Author Correction: BMC Biol 20, 222 (2022) b B https://doi.org/10.1186/s12915-022-01410-3 b The original article [[1]] contains two incorrect terms in the following sentence: "We propose that the antioestrogenic effect of PGR at certain developmental stage (e.g. GD 2-3) is achieved by genomic upregulation of Esr1 and its coactivator Greb1 which in turn enhance the expression of other oestrogen target genes.". [Extracted from the article]

A novel network-based approach for predicting missing proteins (MPs) is proposed here. This approach, PROTREC (short for PROtein RECovery), dominates existing network-based methods – such as Functional Class Scoring (FCS), Hypergeometric Enrichment (HE), and Gene Set Enrichment Analysis (GSEA) – across a variety of proteomics datasets derived from different proteomics data acquisition paradigms: Higher PROTREC scores are much more closely correlated with higher recovery rates of MPs across sample replicates. The PROTREC score, unlike methods reporting p -values, can be directly interpreted as the probability that an unreported protein in a proteomic screen is actually present in the sample being screened. Mass spectrometry (MS) has developed rapidly in recent years; however, an obvious proportion of proteins is still undetected, leading to missing protein problems. A few existing protein recovery methods are based on biological networks, but the performance is not satisfactory. We propose a new protein recovery method, PROTREC, a Bayesian-inspired approach based on biological networks, which shows exceptional performance across multiple validation strategies. It does not rely on peptide information, so it avoids the ambiguity issue that most protein assembly methods face. [Display omitted] • PROTREC is a novel protein recovery method based on protein complexes. • PROTREC performs well across different proteomic acquisition methods and validation strategies. • PROTREC protects the data from information loss. • PROTREC is not affected by protein ambiguity. [ABSTRACT FROM AUTHOR]

Distinguishing stable and fluctuating psychopathological features in young individuals at Ultra High Risk (UHR) for psychosis is challenging, but critical for building robust, accurate, early clinical detection and prevention capabilities. Over a 24-month period, 159 UHR individuals were assessed using the Positive and Negative Symptom Scale (PANSS). Generalisability Theory was used to validate the PANSS with this population and to investigate stable and fluctuating features, by estimating the reliability and generalisability of three factor (Positive, Negative, and General) and five factor (Positive, Negative, Cognitive, Depression, and Hostility) symptom models. Acceptable reliability and generalisability of scores across occasions and sample population were demonstrated by the total PANSS scale (Gr = 0.85). Fluctuating symptoms (delusions, hallucinatory behaviour, lack of spontaneity, flow in conversation, emotional withdrawal, and somatic concern) showed high variability over time, with 50-68% of the variance explained by individual transient states. In contrast, more stable symptoms included excitement, poor rapport, anxiety, guilt feeling, uncooperativeness, and poor impulse control. The 3-factor model of PANSS and its subscales showed robust reliability and generalisability of their assessment scores across the UHR population and evaluation periods (G = 0.77-0.93), offering a suitable means to assess psychosis risk. Certain subscales within the 5-factor PANSS model showed comparatively lower reliability and generalisability (G = 0.33-0.66). The identified and investigated fluctuating symptoms in UHR individuals are more amendable by means of intervention, which could have significant implications for preventing and addressing psychosis. Prioritising the treatment of fluctuating symptoms could enhance intervention efficacy, offering a sharper focus in clinical trials. At the same time, using more reliable total scale and 3 subscales can contribute to more accurate assessment of enduring psychosis patterns in clinical and experimental settings., (© 2024. The Author(s).)

Artificial Intelligence (AI) and Machine Learning (ML) models are increasingly deployed on biomedical and health data to shed insights on biological mechanism, predict disease outcomes, and support clinical decision-making. However, ensuring model validity is challenging. The 10 quick tips described here discuss useful practices on how to check AI/ML models from 2 perspectives-the user and the developer., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Goh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Kikuchi-Fujimoto lymphadenitis (or histiocytic necrotising lymphadenitis) is a rare disease that is usually benign and self-limiting. A higher prevalence is reported amongst East Asian populations. No clear etiology has been identified although it has been associated with some viruses, rarely the Human Immunodeficiency Virus (HIV) and autoimmune pathologies. To date, there has only been a handful of cases reported globally in association with HIV, and this association is even rarer in the Asian context., Case Presentation: A 20-year-old Asian ethnic Malay male, with no past medical history, presented with daily fevers and chills for 2 weeks associated with constitutional symptoms and bilateral non-tender cervical, axillary and inguinal lymphadenopathy. Full blood count showed lymphocytosis with large granular lymphocytes. HIV viral load returned positive at >10 million copies/mL. His absolute CD4 T helper cell count was 375 cells/uL (7%). The rest of the infective and autoimmune workup were negative. Excision biopsy of an enlarged left cervical lymph node revealed Kikuchi lymphadenitis in the proliferative phase, with no evidence of lymphoproliferative disease. He was started on anti-retroviral therapy with resolution of the lymphadenopathy in 3 months., Conclusion: We present a case of Kikuchi lymphadenitis associated with HIV. This highlights that Kikuchi lymphadenitis may mimic sinister pathologies (such as tuberculosis and lymphoma) and that it needs to be considered in the differential diagnosis before empirical treatment for tuberculosis or invasive investigations for lymphoma are done., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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., (© The Author(s) 2024. Published by Oxford University Press on behalf of Fellowship of Postgraduate Medicine. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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.

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., (Copyright © 2024 Copyright: © 2024 Singapore Medical Journal.)

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., (© The Author(s) 2024. Published by Oxford University Press on behalf of Fellowship of Postgraduate Medicine. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Competing Interests: Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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., (© 2024. The Author(s).)

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., (© The Author(s) 2024. Published by Oxford University Press on behalf of Fellowship of Postgraduate Medicine. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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., (©Wilson WB Goh, Kendrick YA Chia, Max FK Cheung, Kalya M Kee, May O Lwin, Peter J Schulz, Minhu Chen, Kaichun Wu, Simon SM Ng, Rashid Lui, Tiing Leong Ang, Khay Guan Yeoh, Han-mo Chiu, Deng-chyang Wu, Joseph JY Sung. Originally published in JMIR AI (https://ai.jmir.org), 07.03.2024.)