Kenneth Hon Kim Ban

Atopic dermatitis (AD) is a prevalent inflammatory skin disease and a major source of disease burden in children. Biomarker studies in childhood AD span genetic, immune, microbial and metabolic domains, but prior reviews have often focused on single molecular layers, specific sample sites or clinical classification. As a result, the field lacks an integrated, systems-level synthesis that compares and contextualizes biomarkers across domains while clearly distinguishing evidence strength. The rapid growth of literature in this field also poses practical challenges for traditional manual review workflows. To address these gaps, we conducted an AI-augmented, multi-domain review of childhood AD biomarkers. ASReview supported title and abstract screening, while ChatGPT assisted structured data extraction with human validation. Across 526 studies, we identified 141 genome, 95 immunome, 57 microbiome and 75 metabolome childhood AD biomarkers. The most frequently reported biomarkers included Filaggrin, IgE, CCL17, Staphylococcus, Bifidobacterium and vitamin D. Using a structured evidence-grading framework, eight biomarkers were categorized as having strong evidence: IgE, CCL17, CCL27, eosinophil cationic protein, eosinophil, IL-18, IL-31 and Escherichia. By synthesizing evidence across biomarker domains, we developed a systems-level, conceptual AD model in which barrier defects, Th2 inflammation, microbial dysbiosis and metabolic imbalance drive a self-perpetuating cycle of inflammation and barrier dysfunction. We also developed a web app for exploration of the biomarker findings: https://leejw.shinyapps.io/eczema_review_526/. This review provides a broad synthesis of childhood AD biomarkers and frames the evidence within an integrated, multi-domain conceptual model. The findings support the rationale for approaches that consider multiple biological nodes, including barrier repair, immune modulation, microbiome-directed strategies and metabolic factors, while underscoring the need for further validation before clinical implementation. Methodologically, the study illustrates how a hybrid human-AI review workflow can support scalable biomedical evidence synthesis without replacing human oversight.

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.

The systematic identification of genetic events driving cellular transformation and tumor progression in the absence of a highly recurrent oncogenic driver mutation is a challenge in cutaneous oncology. In cutaneous squamous cell carcinoma (cuSCC), the high UV-induced mutational burden poses a hurdle to achieve a complete molecular landscape of this disease. Here, we utilized the Sleeping Beauty transposon mutagenesis system to statistically define drivers of keratinocyte transformation and cuSCC progression in vivo in the absence of UV-IR, and identified both known tumor suppressor genes and novel oncogenic drivers of cuSCC. Functional analysis confirms an oncogenic role for the ZMIZ genes, and tumor suppressive roles for KMT2C, CREBBP and NCOA2, in the initiation or progression of human cuSCC. Taken together, our in vivo screen demonstrates an extremely heterogeneous genetic landscape of cuSCC initiation and progression, which can be harnessed to better understand skin oncogenic etiology and prioritize therapeutic candidates.

Background: Taxonomic assignment is a key step in the identification of human viral pathogens. Current tools for taxonomic assignment from sequencing reads based on alignment or alignment-free k-mer approaches may not perform optimally in cases where the sequences diverge significantly from the reference sequences. Furthermore, many tools may not incorporate the genomic coverage of assigned reads as part of overall likelihood of a correct taxonomic assignment for a sample. Results: In this paper, we describe the development of a pipeline that incorporates a multi-task learning model based on convolutional neural network (MT-CNN) and a Bayesian ranking approach to identify and rank the most likely human virus from sequence reads. For taxonomic assignment of reads, the MT-CNN model outperformed Kraken 2, Centrifuge, and Bowtie 2 on reads generated from simulated divergent HIV-1 genomes and was more sensitive in identifying SARS as the closest relation in four RNA sequencing datasets for SARS-CoV-2 virus. For genomic region assignment of assigned reads, the MT-CNN model performed competitively compared with Bowtie 2 and the region assignments were used for estimation of genomic coverage that was incorporated into a naïve Bayesian network together with the proportion of taxonomic assignments to rank the likelihood of candidate human viruses from sequence data. Conclusions: We have developed a pipeline that combines a novel MT-CNN model that is able to identify viruses with divergent sequences together with assignment of the genomic region, with a Bayesian approach to ranking of taxonomic assignments by taking into account both the number of assigned reads and genomic coverage. The pipeline is available at GitHub via https://github.com/MaHaoran627/CNN_Virus.

Background: While hand, foot and mouth disease (HFMD) is primarily self-resolving—soaring incidence rate of symptomatic HFMD effectuates economic burden in the Asia-Pacific region. Singapore has seen a conspicuous rise in the number of HFMD cases from 2010s. Here, we aims to identify the serology and genotypes responsible for such outbreaks in hospitals and childcare facilities. Methods: We studied symptomatic paediatric HFMD cases from 2013 to 2018 in Singapore. Surveillance for subclinical enterovirus infections was also performed in childcares at the same time period. Results: Genotyping 101 symptomatic HFMD samples revealed CV-A6 as the major etiological agent for recent outbreaks. We detected infections with CV-A6 (41.0%), EV-A71 (7%), CV-A16 (3.0%), coxsackievirus A2, CV-A2 (1.0%) and coxsackievirus A10, CV-A10 (1.0%). Phylogenetic analysis of local CV-A6 strains revealed a high level of heterogeneity compared against others worldwide, dissimilar to other HFMD causative enteroviruses for which the dominant strains and genotypes are highly region specific. We detected sub-clinical enterovirus infections in childcare centres; 17.1% (n = 245) tested positive for enterovirus in saliva, without HFMD indicative symptoms at the point of sample collection. Conclusions: CV-A6 remained as the dominant HFMD causative strain in Singapore. Silent subclinical enteroviral infections were detected and warrant further investigations.

// <![CDATA[ $('.header-date').hide();$('#titleAuthors').hide();$('#abstractHeader').hide(); // ]]> Allen Chong1, Jing Xian Teo2, Kenneth H.K. Ban3, 4, 5 1Department of Pathology, National University of Singapore, 119074 Singapore 2Cancer Science Institute, National University of Singapore, 117599 Singapore 3Department of Biochemistry, National University of Singapore, 117596 Singapore 4Institute of Molecular and Cell Biology, 138673 Singapore 5Present address: Shanxi Guoxin Caregeno Medical Laboratories, Taiyuan, Shanxi Province, China Correspondence to: Allen Chong, e-mail: chongallen@yahoo.com Kenneth H.K. Ban, e-mail: kenneth_ban@nuhs.edu.sg Keywords: DNA methylation, enhancers, epigenetics, bioinformatics, colon cancer Received: December 10, 2015 Accepted: March 14, 2016 Published: March 30, 2016

For many years, increasing demands for fossil fuels have met with limited supply. As a potential substitute and renewable source of biofuel feedstock, microalgae have received significant attention. However, few of the current algal species produce high lipid yields to be commercially viable. To discover more high yielding strains, next-generation sequencing technology is used to elucidate lipid synthetic pathways and energy metabolism involved in lipid yield. When subjected to manipulation by genetic and metabolic engineering, enhancement of such pathways may further enhance lipid yield. In this study, transcriptome profiling of a random insertional mutant with enhanced lipid production generated from a non-model marine microalga Dunaliella tertiolecta is presented. D9 mutant has a lipid yield that is 2- to 4-fold higher than that of wild type. Using novel Bag2D-workflow scripts developed and reported here, the non-redundant transcripts from de novo assembly were annotated based on the best hits in five model microalgae, namely Chlamydomonas reinhardtii, Coccomyxa subellipsoidea C-169, Ostreococcus lucimarinus, Volvox carteri, Chlorella variabilis NC64A and a high plant species Arabidopsis thaliana. The assembled contigs (~181 Mb) includes 481,381 contigs, covering 10,185 genes. Pathway analysis showed that a pathway from inositol phosphate metabolism to fatty acid biosynthesis is the most significantly correlated with higher lipid yield in this mutant. Herein, we described a pipeline to analyze RNA-Seq data without pre-existing transcriptomic information. The draft transcriptome of D. tertiolecta was constructed and annotated, which offered useful information for characterizing high lipid-producing mutants. D. tertiolecta mutant was generated with an enhanced photosynthetic efficiency and lipid production. RNA-Seq data of the mutant and wild type were compared, providing biological insights into the expression patterns of contigs associated with energy metabolism and carbon flow pathways. Comparison of D. tertiolecta genes with homologs of five other green algae and a model high plant species can facilitate the annotation of D. tertiolecta and lead to a more complete annotation of its sequence database, thus laying the groundwork for optimization of lipid production pathways based on genetic manipulation.

The Sleeping Beauty (SB) transposon mutagenesis screen is a powerful tool to facilitate the discovery of cancer genes that drive tumorigenesis in mouse models. In this study, we sought to identify genes that functionally cooperate with sonic hedgehog signaling to initiate medulloblastoma (MB), a tumor of the cerebellum. By combining SB mutagenesis with Patched1 heterozygous mice (Ptch1^lacZ/+), we observed an increased frequency of MB and decreased tumor-free survival compared with Ptch1^lacZ/+ controls. From an analysis of 85 tumors, we identified 77 common insertion sites that map to 56 genes potentially driving increased tumorigenesis. The common insertion site genes identified in the mutagenesis screen were mapped to human orthologs, which were used to select probes and corresponding expression data from an independent set of previously described human MB samples, and surprisingly were capable of accurately clustering known molecular subgroups of MB, thereby defining common regulatory networks underlying all forms of MB irrespective of subgroup. We performed a network analysis to discover the likely mechanisms of action of subnetworks and used an in vivo model to confirm a role for a highly ranked candidate gene, Nfia, in promoting MB formation. Our analysis implicates candidate cancer genes in the deregulation of apoptosis and translational elongation, and reveals a strong signature of transcriptional regulation that will have broad impact on expression programs in MB. These networks provide functional insights into the complex biology of human MB and identify potential avenues for intervention common to all clinical subgroups. Significance Medulloblastoma is a common malignant pediatric brain tumor. Gene expression data have indicated that the tumors fall into four molecular subgroups, “Wnt,” “Hedgehog,” “group 3,” and “group 4.” With the exception of the Hedgehog subgroup, few functional data exist defining key molecular pathways driving tumor growth. Using a transposon mutagenesis approach, we identified genes that functionally cooperate with Hedgehog signalling to promote tumorigenesis in a Ptch1 mouse model of medulloblastoma. Surprisingly, the genes we identified were able to accurately define all four human molecular subtypes, not just Hedgehog, when used to interrogate published expression data. Thus, we have functionally defined key regulatory networks that illustrate both the differences and commonalities between tumor subgroups indicating a number of therapeutic strategies.

In early mitosis, the END (Emi1/NuMA/Dynein-dynactin) network anchors the anaphase-promoting complex/cyclosome (APC/C) to the mitotic spindle and poles. Spindle anchoring restricts APC/C activity, thereby limiting the destruction of spindle-associated cyclin B and ensuring maintenance of spindle integrity. Emi1 binds directly to hypophosphorylated APC/C, linking the APC/C to the spindle via NuMA. However, whether the phosphorylation state of the APC/C is important for its association with the spindle and what kinases and phosphatases are necessary for regulating this event remain unknown. Here, we describe the regulation of APC/C-mitotic spindle pole association by phosphorylation. We find that only hypophosphorylated APC/C associates with microtubule asters, suggesting that phosphatases are important. Indeed, a specific form of PPP2 (CA/R1A/R2B) binds APC/C, and PPP2 activity is necessary for Cdc27 dephosphorylation. Screening by RNA interference, we find that inactivation of CA, R1A, or R2B leads to delocalization of APC/C from spindle poles, early mitotic spindle defects, a failure to congress chromosomes, and decreased levels of cyclin B on the spindle. Consistently, inhibition of cyclin B/Cdk1 activity increased APC/C binding to microtubules. Thus, cyclin B/Cdk1 and PPP2 regulate the dynamic association of APC/C with spindle poles in early mitosis, a step necessary for proper spindle formation.