Lgr5 marks stem/progenitor cells contributing to epithelial and muscle development in the mouse esophagus.

The existence and function of Lgr5+ cells within the developing esophagus remains unknown. Here, we document multiple discrete Lgr5+ populations in the developing mouse esophagus, predominantly within nascent epithelial and external muscle layers. Lgr5 expression initially emerges in the developing proximal embryonic epithelium, but progressively extends distally and persists within the distal epithelium of neonates. Fate mapping and ablation analyses reveal a long-term contribution of epithelial Lgr5+ cells to esophageal organogenesis. Additionally, Lgr5-expressing cells are present in the developing external muscle layer, particularly during the development of the striated component. Fate mapping reveals a significant contribution of these embryonic Lgr5+ cells to the adult muscle layer. Embryonic Lgr5+ epithelial cells are also found to be important for regulating epithelial development, serving as a key source of Wnt6, among other ligands, to promote epithelial cell proliferation and formation of epithelial layers. These findings significantly enhance our understanding of esophageal development and shed light on the involvement of Lgr5+ stem/progenitor cells during organogenesis. Importantly, this study lays the foundation for investigating esophageal diseases related to the Lgr5+ stem/progenitor cell pool.

Association between alcohol flushing syndrome and cancer: A systematic review and meta-analysis.

Introduction: Alcohol flushing syndrome (AFS) is experienced by up to 46% of East Asians. This study aimed to review the risk of cancers in AFS patients, elucidate an exposure-response relationship, and understand risk associated with alcohol intake and cancer. Method: An electronic database search of PubMed, Embase, Scopus and Cochrane Library was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines. Observational studies on AFS' effects and all cancers risk were included. Studies including patients with existing malignancy were excluded. Dichotomous variables were pooled using the Mantel-Haenszel method with a random effects model. Sensitivity and subgroup analyses were performed. PROSPERO (CRD42023392916) protocol was followed. Results: A total of 18 articles were included in the final analysis with a total of 387,521 participants. AFS was associated with an increased risk of all cancers (odds ratio [OR] 1.19, 95% confidence interval [CI] 1.06-1.34), esophageal squamous cell carcinoma (OR 1.47, 95% CI 1.05-2.05) and gastric adenocarci-noma (OR 1.40, 95% CI 1.14-1.72). Men with AFS exhibited an increased risk of all cancers (OR 1.34, 95% CI 1.13-1.59). However, this was not observed in women. All cancers risk was associated with AFS in those who consumed drink (i.e. consumed alcohol) more than 200 g of pure ethanol/week (OR 1.68, 95% CI 1.20-2.37) but not those who consumed less than 200 g of pure ethanol/week (OR 1.27, 95% CI 0.90-1.79) or non-drinkers (OR 0.99, 95% CI 0.67-1.47). Conclusion: AFS is associated with an increased risk of all cancers, particularly esophageal squamous cell carcinoma and gastric adenocarcinoma.

Splenic marginal zone B cells restrict Mycobacterium tuberculosis infection by shaping the cytokine pattern and cell-mediated immunity.

Understanding the role of B cells in tuberculosis (TB) is crucial for developing new TB vaccines. However, the changes in B cell immune landscapes during TB and their functional implications remain incompletely explored. Using high-dimensional flow cytometry to map the immune landscape in response to Mycobacterium tuberculosis (Mtb) infection, our results show an accumulation of marginal zone B (MZB) cells and other unconventional B cell subsets in the lungs and spleen, shaping an unconventional B cell landscape. These MZB cells exhibit activated and memory-like phenotypes, distinguishing their functional profiles from those of conventional B cells. Notably, functional studies show that MZB cells produce multiple cytokines and contribute to systemic protection against TB by shaping cytokine patterns and cell-mediated immunity. These changes in the immune landscape are reversible upon successful TB chemotherapy. Our study suggests that, beyond antibody production, targeting the regulatory function of B cells may be a valuable strategy for TB vaccine development.

Proteomics profiling and association with cardiorenal complications in type 2 diabetes subtypes in Asian population.

AIM: Among multi-ethnic Asians, type 2 diabetes (T2D) clustered in three subtypes; mild obesity-related diabetes (MOD), mild age-related diabetes with insulin insufficiency (MARD-II) and severe insulin-resistant diabetes with relative insulin insufficiency (SIRD-RII) had differential cardio-renal complication risk. We assessed the proteomic profiles to identify subtype specific biomarkers and its association with diabetes complications. METHODS: 1448 plasma proteins at baseline were measured and compared across the T2D subtypes. Multivariable cox regression was used to assess associations between significant proteomics features and cardio-renal complications. RESULTS: Among 645 T2D participants (SIRD-RII [19%], MOD [45%], MARD-II [36%]), 295 proteins expression differed significantly across the groups. These proteins were enriched in cell adhesion, neurogenesis and inflammatory response processes. In SIRD-RII group, ADH4, ACY1, THOP1, IGFBP2, NEFL, ENTPD2, CALB1, HAO1, CTSV, ITGAV, SCLY, EDA2R, ERBB2 proteins significantly associated with progressive CKD and LILRA5 protein with incident heart failure (HF). In MOD group, TAFA5, RSPO3, EDA2R proteins significantly associated with incident HF. In MARD-II group, FABP4 protein significantly associated with progressive CKD and PTPRN2 protein with major adverse cardiovascular events. Genetically determined NEFL and CALB1 were associated with kidney function decline. CONCLUSIONS: Each T2D subtype has unique proteomics signature and association with clinical outcomes and underlying mechanisms.

Natural Language Processing in medicine and ophthalmology: A review for the 21st-century clinician.

Natural Language Processing (NLP) is a subfield of artificial intelligence that focuses on the interaction between computers and human language, enabling computers to understand, generate, and derive meaning from human language. NLP's potential applications in the medical field are extensive and vary from extracting data from Electronic Health Records -one of its most well-known and frequently exploited uses- to investigating relationships among genetics, biomarkers, drugs, and diseases for the proposal of new medications. NLP can be useful for clinical decision support, patient monitoring, or medical image analysis. Despite its vast potential, the real-world application of NLP is still limited due to various challenges and constraints, meaning that its evolution predominantly continues within the research domain. However, with the increasingly widespread use of NLP, particularly with the availability of large language models, such as ChatGPT, it is crucial for medical professionals to be aware of the status, uses, and limitations of these technologies.

Managing a patient with uveitis in the era of artificial intelligence: Current approaches, emerging trends, and future perspectives.

The integration of artificial intelligence (AI) with healthcare has opened new avenues for diagnosing, treating, and managing medical conditions with remarkable precision. Uveitis, a diverse group of rare eye conditions characterized by inflammation of the uveal tract, exemplifies the complexities in ophthalmology due to its varied causes, clinical presentations, and responses to treatments. Uveitis, if not managed promptly and effectively, can lead to significant visual impairment. However, its management requires specialized knowledge, which is often lacking, particularly in regions with limited access to health services. AI's capabilities in pattern recognition, data analysis, and predictive modelling offer significant potential to revolutionize uveitis management. AI can classify disease etiologies, analyze multimodal imaging data, predict outcomes, and identify new therapeutic targets. However, transforming these AI models into clinical applications and meeting patient expectations involves overcoming challenges like acquiring extensive, annotated datasets, ensuring algorithmic transparency, and validating these models in real-world settings. This review delves into the complexities of uveitis and the current AI landscape, discussing the development, opportunities, and challenges of AI from theoretical models to bedside application. It also examines the epidemiology of uveitis, the global shortage of uveitis specialists, and the disease's socioeconomic impacts, underlining the critical need for AI-driven approaches. Furthermore, it explores the integration of AI in diagnostic imaging and future directions in ophthalmology, aiming to highlight emerging trends that could transform management of a patient with uveitis and suggesting collaborative efforts to enhance AI applications in clinical practice.

Visualising cancer in 3D: 3-Dimensional Tissue Imaging for management of cutaneous basal cell carcinoma.

Surgical management of basal cell carcinoma (BCC) typically involves surgical excision with post-operative margin assessment using the bread-loafing technique; or gold-standard Mohs micrographic surgery (MMS), where margins are iteratively examined for residual cancer after tumour removal, with additional excisions performed upon detecting residual tumour at margins. There is limited sampling of resection margins with bread loafing, with detection of positive margins 44% of the time using 2 mm intervals. To resolve this, we have developed three-dimensional (3D) Tissue Imaging for: (1) complete examination of cancer margins and (2) detection of tumour proximity to nerves and blood vessels. 3D Tissue optical clearing with a light sheet imaging protocol was developed for margin assessment in two datasets assessed by two independent evaluators: (1) 48 samples from 29 patients with varied BCC subtypes, sizes and pigmentation levels; (2) 32 samples with matching Mohs' surgeon reading of tumour margins using two-dimensional haematoxylin & eosin-stained sections. The 3D Tissue Imaging protocol permits a complete examination of deeper and peripheral margins. Two independent evaluators achieved negative predictive values of 92.3% and 88.24% with 3D Tissue Imaging. Images obtained from 3D Tissue Imaging recapitulates histological features of BCC, such as nuclear crowding, palisading and retraction clefting and provides a 3D context for recognising normal skin adnexal structures. Concurrent immunofluorescence labelling of nerves and blood vessels allows visualisation of structures closer to tumour-positive regions, which may have a higher risk for neural and vascular infiltration. Together, this method provides more information in a 3D spatial context, enabling better cancer management by clinicians.

The role of perfusion, grey matter volume and behavioural phenotypes in the data-driven classification of cognitive syndromes.

BACKGROUND: The use of structural and perfusion brain imaging in combination with behavioural information in the prediction of cognitive syndromes using a data-driven approach remains to be explored. Here, we thus examined the contribution of brain structural and perfusion imaging and behavioural features to the existing classification of cognitive syndromes using a data-driven approach. METHODS: Study participants belonged to the community-based Biomarker and Cognition Cohort Study in Singapore who underwent neuropsychological assessments, structural-functional MRI and blood biomarkers. Participants had a diagnosis of cognitively normal (CN), subjective cognitive impairment (SCI), mild cognitive impairment (MCI) and dementia. Cross-sectional structural and cerebral perfusion imaging, behavioural scale data including mild behaviour impairment checklist, Pittsburgh Sleep Quality Index and Depression, Anxiety and Stress scale data were obtained. RESULTS: Three hundred seventy-three participants (mean age 60.7 years; 56% female sex) with complete data were included. Principal component analyses demonstrated that no single modality was informative for the classification of cognitive syndromes. However, multivariate glmnet analyses revealed a specific combination of frontal perfusion and temporo-frontal grey matter volume were key protective factors while the severity of mild behaviour impairment interest sub-domain and poor sleep quality were key at-risk factors contributing to the classification of CN, SCI, MCI and dementia (p < 0.0001). Moreover, the glmnet model showed best classification accuracy in differentiating between CN and MCI cognitive syndromes (AUC = 0.704; sensitivity = 0.698; specificity = 0.637). CONCLUSIONS: Brain structure, perfusion and behavioural features are important in the classification of cognitive syndromes and should be incorporated by clinicians and researchers. These findings illustrate the value of using multimodal data when examining syndrome severity and provide new insights into how cerebral perfusion and behavioural impairment influence classification of cognitive syndromes.

High-density lipoprotein cholesterol subfraction HDL2 is associated with improved endothelial function in systemic lupus erythematosus.

OBJECTIVE: Patients with systemic lupus erythematosus (SLE) have increased risk of premature atherosclerosis but the exact mechanisms remains unclear. Flow-mediated dilatation (FMD) is an established non-invasive assessment of vascular endothelial function. Lipoprotein subfractions may be better predictors of FMD than conventional cholesterol measurements. We tested the hypothesis that lipoprotein subfractions are independently associated with FMD. METHODS: Forty-one consecutive adult patients with SLE without known cardiovascular risk factors or disease were recruited in this cross-sectional study. Endothelial function and early atherosclerosis were assessed by brachial FMD and common carotid artery (CCA) intima-media thickness (IMT). High-density lipoprotein (HDL)/low-density lipoprotein (LDL) subfractions were measured. Machine learning models were also constructed to predict FMD and CCA IMT. RESULTS: Median FMD was 4.48% (IQR 5.00%) while median IMT was 0.54 mm (IQR 0.12 mm). Univariate analysis showed lower LDL1 (r=-0.313, p<0.05) and higher HDL2 subfractions (r=0.313, p<0.05) were significantly associated with higher log-transformed FMD. In a multiple linear regression model, HDL2 (ß=0.024, SE=0.012, p<0.05) remained an independent predictor of higher FMD after adjusting for age, body mass index, LDL1 and systolic blood pressure. The machine learning model included parameters such as HDL2 (positive association), prednisolone dose, LDL cholesterol and LDL1 for prediction of FMD (r=0.433, p<0.01). Age, LDL cholesterol and systolic blood pressure were independently associated with higher CCA IMT after adjusting for body mass index and HDL2. CONCLUSIONS: HDL 2, a large HDL particle, was independently associated with greater FMD and may be a biomarker of vascular health in SLE.

Longitudinal single cell atlas identifies complex temporal relationship between type I interferon response and COVID-19 severity.

Due to the paucity of longitudinal molecular studies of COVID-19, particularly those covering the early stages of infection (Days 1-8 symptom onset), our understanding of host response over the disease course is limited. We perform longitudinal single cell RNA-seq on 286 blood samples from 108 age- and sex-matched COVID-19 patients, including 73 with early samples. We examine discrete cell subtypes and continuous cell states longitudinally, and we identify upregulation of type I IFN-stimulated genes (ISGs) as the predominant early signature of subsequent worsening of symptoms, which we validate in an independent cohort and corroborate by plasma markers. However, ISG expression is dynamic in progressors, spiking early and then rapidly receding to the level of severity-matched non-progressors. In contrast, cross-sectional analysis shows that ISG expression is deficient and IFN suppressors such as SOCS3 are upregulated in severe and critical COVID-19. We validate the latter in four independent cohorts, and SOCS3 inhibition reduces SARS-CoV-2 replication in vitro. In summary, we identify complexity in type I IFN response to COVID-19, as well as a potential avenue for host-directed therapy.