Characteristics of disinfection byproducts from dissolved organic matter during chlor(am)ination of source water in Tibetan Plateau, China.

The Tibetan Plateau, a typical high-altitude area, is less affected by human activities such as industrial development, and the external pollution to water sources is extremely low. Then it is also an important source of water samples for exploring the molecular characteristics of precursors in the dissolved organic matter (DOM) of disinfection byproducts (DBPs) in drinking water. Research data on DBPs in drinking water on the Tibet Plateau remains insufficient, leading to uncertainty about DBP contamination in the area. This study explores the formation potential of 35 typical DBPs, including 6 trihalomethanes (THMs), 9 haloacetic acids (HAAs), 2 halogenated ketones (HKs), 9 nitrosamines (NAs), and 9 aromatic DBPs, during chlorination and chloramination of typical source water samples in the Tibet Plateau of China. Moreover, in order to further investigate the characteristics of the generation of DBPs, the molecular composition of DOM in the collected water samples was characterized by Fourier transform ion cyclotron resonance mass spectrometry. The findings reveal that, for chlorination and chloramination, the average concentration of the five classes of DBPs was ranked as follows (chlorination, chloramination): HAAs (268.1 µg/L, 54.2 µg/L) > THMs (44.0 µg/L, 2.0 µg/L) > HKs (0.7 µg/L, 1.8 µg/L) > NAs (26.5 ng/L, 74.6 ng/L) > Aromatics (20.4 ng/L, 19.5 ng/L). The dominant compounds in THMs, HAAs, and NAs are trichloromethane, dichloroacetic acid, trichloroacetic acid, and nitrosopyrrolidine, respectively. This study highlights a significant positive correlation between DBP generation and UV254, SUV254, and the double bond equivalents of DOM in the source water. It systematically elucidates DOM molecular composition characteristics and DBP formation potential in high-altitude water sources, shedding light on key factors influencing DBP generation at the molecular level in high-altitude areas.

Foundation models in ophthalmology.

Foundation models represent a paradigm shift in artificial intelligence (AI), evolving from narrow models designed for specific tasks to versatile, generalisable models adaptable to a myriad of diverse applications. Ophthalmology as a specialty has the potential to act as an exemplar for other medical specialties, offering a blueprint for integrating foundation models broadly into clinical practice. This review hopes to serve as a roadmap for eyecare professionals seeking to better understand foundation models, while equipping readers with the tools to explore the use of foundation models in their own research and practice. We begin by outlining the key concepts and technological advances which have enabled the development of these models, providing an overview of novel training approaches and modern AI architectures. Next, we summarise existing literature on the topic of foundation models in ophthalmology, encompassing progress in vision foundation models, large language models and large multimodal models. Finally, we outline major challenges relating to privacy, bias and clinical validation, and propose key steps forward to maximise the benefit of this powerful technology.

Expectation maximisation pseudo labels.

In this paper, we study pseudo-labelling. Pseudo-labelling employs raw inferences on unlabelled data as pseudo-labels for self-training. We elucidate the empirical successes of pseudo-labelling by establishing a link between this technique and the Expectation Maximisation algorithm. Through this, we realise that the original pseudo-labelling serves as an empirical estimation of its more comprehensive underlying formulation. Following this insight, we present a full generalisation of pseudo-labels under Bayes' theorem, termed Bayesian Pseudo Labels. Subsequently, we introduce a variational approach to generate these Bayesian Pseudo Labels, involving the learning of a threshold to automatically select high-quality pseudo labels. In the remainder of the paper, we showcase the applications of pseudo-labelling and its generalised form, Bayesian Pseudo-Labelling, in the semi-supervised segmentation of medical images. Specifically, we focus on: (1) 3D binary segmentation of lung vessels from CT volumes; (2) 2D multi-class segmentation of brain tumours from MRI volumes; (3) 3D binary segmentation of whole brain tumours from MRI volumes; and (4) 3D binary segmentation of prostate from MRI volumes. We further demonstrate that pseudo-labels can enhance the robustness of the learned representations. The code is released in the following GitHub repository: https://github.com/moucheng2017/EMSSL.

Cross-modality Labeling Enables Noninvasive Capillary Quantification as a Sensitive Biomarker for Assessing Cardiovascular Risk.

Purpose: We aim to use fundus fluorescein angiography (FFA) to label the capillaries on color fundus (CF) photographs and train a deep learning model to quantify retinal capillaries noninvasively from CF and apply it to cardiovascular disease (CVD) risk assessment. Design: Cross-sectional and longitudinal study. Participants: A total of 90732 pairs of CF-FFA images from 3893 participants for segmentation model development, and 49229 participants in the UK Biobank for association analysis. Methods: We matched the vessels extracted from FFA and CF, and used vessels from FFA as labels to train a deep learning model (RMHAS-FA) to segment retinal capillaries using CF. We tested the model's accuracy on a manually labeled internal test set (FundusCapi). For external validation, we tested the segmentation model on 7 vessel segmentation datasets, and investigated the clinical value of the segmented vessels in predicting CVD events in the UK Biobank. Main Outcome Measures: Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity for segmentation. Hazard ratio (HR; 95% confidence interval [CI]) for Cox regression analysis. Results: On the FundusCapi dataset, the segmentation performance was AUC = 0.95, accuracy = 0.94, sensitivity = 0.90, and specificity = 0.93. Smaller vessel skeleton density had a stronger correlation with CVD risk factors and incidence (P < 0.01). Reduced density of small vessel skeletons was strongly associated with an increased risk of CVD incidence and mortality for women (HR [95% CI] = 0.91 [0.84-0.98] and 0.68 [0.54-0.86], respectively). Conclusions: Using paired CF-FFA images, we automated the laborious manual labeling process and enabled noninvasive capillary quantification from CF, supporting its potential as a sensitive screening method for identifying individuals at high risk of future CVD events. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

CF-Loss: Clinically-relevant feature optimised loss function for retinal multi-class vessel segmentation and vascular feature measurement.

Characterising clinically-relevant vascular features, such as vessel density and fractal dimension, can benefit biomarker discovery and disease diagnosis for both ophthalmic and systemic diseases. In this work, we explicitly encode vascular features into an end-to-end loss function for multi-class vessel segmentation, categorising pixels into artery, vein, uncertain pixels, and background. This clinically-relevant feature optimised loss function (CF-Loss) regulates networks to segment accurate multi-class vessel maps that produce precise vascular features. Our experiments first verify that CF-Loss significantly improves both multi-class vessel segmentation and vascular feature estimation, with two standard segmentation networks, on three publicly available datasets. We reveal that pixel-based segmentation performance is not always positively correlated with accuracy of vascular features, thus highlighting the importance of optimising vascular features directly via CF-Loss. Finally, we show that improved vascular features from CF-Loss, as biomarkers, can yield quantitative improvements in the prediction of ischaemic stroke, a real-world clinical downstream task. The code is available at https://github.com/rmaphoh/feature-loss.

Development and validation of a diagnostic nomogram model for predicting monoclonal gammopathy of renal significance.

In patients with kidney disease, the presence of monoclonal gammopathy necessitates the exploration of potential causal relationships. Therefore, in this study, we aimed to address this concern by developing a nomogram model for the early diagnosis of monoclonal gammopathy of renal significance (MGRS). Univariate and multivariate logistic regression analyses were employed to identify risk factors for MGRS. Verification and evaluation of the nomogram model's differentiation, calibration, and clinical value were conducted using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis. The study encompassed 347 patients who underwent kidney biopsy, among whom 116 patients (33.4%) were diagnosed with MGRS and 231 (66.6%) with monoclonal gammopathy of undetermined significance. Monoclonal Ig-related amyloidosis (n = 86) and membranous nephropathy (n = 86) was the most common renal pathological type in each group. Notably, older age, abnormal serum-free light chain ratio, and the absence of microscopic hematuria were identified as independent prognostic factors for MGRS. The areas under the ROC curves for the training and verification sets were 0.848 and 0.880, respectively. In conclusion, the nomogram model demonstrated high accuracy and clinical applicability for diagnosing MGRS, potentially serving as a valuable tool for noninvasive early MGRS diagnosis.

Aurora kinase B disruption suppresses pathological retinal angiogenesis by affecting cell cycle progression.

PURPOSE: The detrimental effects of pathological angiogenesis on the visual function are indisputable. Within a prominent role in chromosome segregation and tumor progression, aurora kinase B (AURKB) assumes a prominent role. However, its role in pathological retinal angiogenesis remains unclear. This study explores this latent mechanism. METHODS: To inhibit AURKB expression, we designed specific small interfering RNAs targeting AURKB and transfected them into vascular endothelial cells. Barasertib was selected as the AURKB inhibitor. The anti-angiogenic effects of both AURKB siRNA and barasertib were assessed in vitro by cell proliferation, transwell migration, and tube formation. To evaluate the angiogentic effects of AURKB in vivo, neonatal mice were exposed to 75% oxygen followed by normoxic repositioning to establish an oxygen-induced retinopathy (OIR) model. Subsequently, phosphate-buffered saline and barasertib were administered into OIR mice via intravitreal injection. The effects of AURKB on cell cycle proteins were determined by western blot analysis. RESULTS: We found that AURKB was overexpressed during pathological angiogenesis. AURKB siRNA and barasertib significantly inhibited endothelial cell proliferation, migration, and tube formation in vitro. Furthermore, AURKB inhibition attenuated retinal angiogenesis in the OIR model. A possible mechanism is the disruption of cell cycle by AURKB inhibition. CONCLUSION: In conclusion, AURKB significantly influenced pathological retinal angiogenesis, thereby presenting a promising therapeutic target in ocular neovascular diseases.