Molecular cloning and functional characterization of 2,3-oxidosqualene cyclases from Artemisia argyi.

Artemisia argyi is a traditional medicinal and edible plant, generating various triterpenoids with pharmacological activities, such as anti-virus, anti-cancer, and anti-oxidant. The 2,3-oxidosqualene cyclase family of A. argyi offers novel insights into the triterpenoid pathway, which might contribute to the medicinal value of its tissue extracts. Nevertheless, the biosynthesis of active triterpenoids in Artemisia argyi is still uncertain. In this study, four putative OSC (2,3-oxidosqualene cyclase) genes (AaOSC1-4) were first isolated and identified from A. argyi. Through the yeast heterologous expression system, three AaOSCs were characterized for the biosynthesis of diverse triterpenoids including cycloartenol, ß-amyrin, (3S,13R)-malabarica-14(27),17,21-trien-3ß-ol, and dammara-20,24-dien-3ß-ol. AaOSC1 was a multifunctional dammara-20,24-dien-3ß-ol synthase, which yielded 8 different triterpenoids, including tricyclic, and tetracyclic products. AaOSC2 and AaOSC3 were cycloartenol, and ß-amyrin synthases, respectively. As a result, these findings provide a deeper understanding of the biosynthesis pathway of triterpenes in A. argyi.

Adjusting Catalytic Activity of ß-Amyrin Synthase GgBAS by Utilizing the Plasticity Residues of an Active Site.

ß-Amyrin synthase (bAS) is a representative plant oxidosqualene cyclase (OSC), and previous studies have identified many functional residues and mutants that can alter its catalytic activity. However, the regulatory mechanism of the active site architecture for adjusting the catalytic activity remains unclear. In this study, we investigate the function of key residues and their regulatory effects on the catalytic activity of Glycyrrhiza glabra ß-amyrin synthase (GgbAS) through molecular dynamics simulations and site-directed mutagenesis experiments. We identified the plasticity residues located in two active site regions and explored the interactions between these residues and tetracyclic/pentacyclic intermediates. Based on computational and experimental results, we further categorize these plasticity residues into three types: effector, adjuster, and supporter residues, according to their functions in the catalytic process. This study provides valuable insights into the catalytic mechanism and active site plasticity of GgbAS, offering important references for the rational enzyme engineering of other OSC enzyme.

Metabolic engineering of Saccharomyces cerevisiae for high-level production of (+)-ambrein from glucose.

(+)-Ambrein is the primary component of ambergris, a rare product found in sperm whales (Physeter microcephalus). Microbial production using sustainable resources is a promising way to replace animal extraction and chemical synthesis. We constructed an engineered yeast strain to produce (+)-ambrein de novo. Squalene is a substrate for the biosynthesis of (+)-ambrein. Firstly, strain LQ2, with a squalene yield of 384.4 mg/L was obtained by optimizing the mevalonate pathway. Then we engineered a method for the de novo production of (+)-ambrein using glucose as a carbon source by overexpressing codon-optimized tetraprenyl-ß-curcumene cyclase (BmeTC) and its double mutant enzyme (BmeTCY167A/D373C), evaluating different promoters, knocking out GAL80, and fusing the protein with BmeTC and squalene synthase (AtSQS2). Nevertheless, the synthesis of (+)-ambrein is still limited, causing low catalytic activity in BmeTC. We carried out a protein surface amino acid modification of BmeTC. The dominant mutant BmeTCK6A/Q9E/N454A for the first step was obtained to improve its catalytic activity. The yield of (+)-ambrein increased from 35.2 to 59.0 mg/L in the shake flask and finally reached 457.4 mg/L in the 2 L fermenter, the highest titer currently available for yeast. Efficiently engineered strains and inexpensive fermentation conditions for the industrial production of (+)-ambrein. The metabolic engineering tools provide directions for optimizing the biosynthesis of other high-value triterpenes.

Molecular recognition between volatile molecules and odorant binding proteins 7 by homology modeling, molecular docking and molecular dynamics simulation.

BACKGROUND: Odorant-binding proteins (OBPs) in insects are key to detection and recognition of external chemical signals associated with survival. OBP7 in Spodoptera frugiperda's larval stage (SfruOBP7) may search for host plants by sensing plant volatiles, which are important sources of pest attractants and repellents. However, the atomic-level basis of binding modes remains elusive. RESULTS: SfruOBP7 structure was constructed through homology modeling, and complex models of six plant volatiles ((E)-2-hexenol, α-pinene, (Z)-3-hexenyl acetate, lauric acid, O-cymene and 1-octanol) and SfruOBP7 were obtained through molecular docking. To study the detailed interactions between the six plant volatile molecules and SfruOBP7, we conducted three 300 ns molecular dynamics simulations for each study object. The correlation coefficients between binding free energy obtained by molecular mechanics/generalized Born surface area together with solvated interaction energy methods and experimental values are 0.90 and 0.88, respectively, showing a good correlation. By comparing binding free energy along with interaction patterns between SfruOBP7 and the six volatile molecules, hotspot residues of SfruOBP7 when binding with different volatile molecules were determined. Hydrophobic interactions stemming from van der Waals interactions play a significant role in SfruOBP7 and these plant volatile systems. CONCLUSION: The optimized three-dimensional structure of SfruOBP7 and its binding modes with six plant volatiles revealed their interactions, thus providing a means for estimating the binding energies of other plant volatiles. Our study will help to guide the rational design of effective and selective insect attractants. © 2024 Society of Chemical Industry.

Rational Reprogramming of O-Methylation Regioselectivity for Combinatorial Biosynthetic Tailoring of Benzenediol Lactone Scaffolds.

O-Methylation modulates the pharmacokinetic and pharmacodynamic (PK/PD) properties of small-molecule natural products, affecting their bioavailability, stability, and binding to targets. Diversity-oriented combinatorial biosynthesis of new chemical entities for drug discovery and optimization of known bioactive scaffolds during drug development both demand efficient O-methyltransferase (OMT) biocatalysts with considerable substrate promiscuity and tunable regioselectivity that can be deployed in a scalable and sustainable manner. Here we demonstrate efficient total biosynthetic and biocatalytic platforms that use a pair of fungal OMTs with orthogonal regiospecificity to produce unnatural O-methylated benzenediol lactone polyketides. We show that rational, structure-guided active-site cavity engineering can reprogram the regioselectivity of these enzymes. We also characterize the interplay of engineered regioselectivity with substrate plasticity. These findings will guide combinatorial biosynthetic tailoring of unnatural products toward the generation of diverse chemical matter for drug discovery and the PK/PD optimization of bioactive scaffolds for drug development.

CsMYB60 is a key regulator of flavonols and proanthocyanidans that determine the colour of fruit spines in cucumber.

Spine colour is an important fruit quality trait that influences the commercial value of cucumber (Cucumis sativus). However, little is known about the metabolites and the regulatory mechanisms of their biosynthesis in black spine varieties. In this study, we determined that the pigments of black spines are flavonoids, including flavonols and proanthocyanidins (PAs). We identified CsMYB60 as the best candidate for the previously identified B (Black spine) locus. Expression levels of CsMYB60 and the key genes involved in flavonoid biosynthesis were higher in black-spine inbred lines than that in white-spine lines at different developmental stages. The insertion of a Mutator-like element (CsMULE) in the second intron of CsMYB60 decreased its expression in a white-spine line. Transient overexpression assays indicated that CsMYB60 is a key regulatory gene and Cs4CL is a key structural gene in the pigmentation of black spines. In addition, the DNA methylation level in the CsMYB60 promoter was much lower in the black-spine line compared with white-spine line. The CsMULE insert may decrease the expression level of CsMYB60, causing hindered synthesis of flavonols and PAs in cucumber fruit spines.

Identification of RoCYP01 (CYP716A155) enables construction of engineered yeast for high-yield production of betulinic acid.

Betulinic acid (BA) and its derivatives possess potent pharmacological activity against cancer and HIV. As with many phytochemicals, access to BA is limited by the requirement for laborious extraction from plant biomass where it is found in low amounts. This might be alleviated by metabolically engineering production of BA into an industrially relevant microbe such as Saccharomyces cerevisiae (yeast), which requires complete elucidation of the corresponding biosynthetic pathway. However, while cytochrome P450 enzymes (CYPs) that can oxidize lupeol into BA have been previously identified from the CYP716A subfamily, these generally do not seem to be specific to such biosynthesis and, in any case, have not been shown to enable high-yielding metabolic engineering. Here RoCYP01 (CYP716A155) was identified from the BA-producing plant Rosmarinus officinalis (rosemary) and demonstrated to effectively convert lupeol into BA, with strong correlation of its expression and BA accumulation. This was further utilized to construct a yeast strain that yields > 1 g/L of BA, providing a viable route for biotechnological production of this valuable triterpenoid.

An experimental study on breast lesion detection and classification from ultrasound images using deep learning architectures.

BACKGROUND: Computer-aided diagnosis (CAD) in the medical field has received more and more attention in recent years. One important CAD application is to detect and classify breast lesions in ultrasound images. Traditionally, the process of CAD for breast lesions classification is mainly composed of two separated steps: i) locate the lesion region of interests (ROI); ii) classify the located region of interests (ROI) to see if they are benign or not. However, due to the complex structure of breast and the existence of noise in the ultrasound images, traditional handcrafted feature based methods usually can not achieve satisfactory result. METHODS: With the recent advance of deep learning, the performance of object detection and classification has been boosted to a great extent. In this paper, we aim to systematically evaluate the performance of several existing state-of-the-art object detection and classification methods for breast lesions CAD. To achieve that, we have collected a new dataset consisting of 579 benign and 464 malignant lesion cases with the corresponding ultrasound images manually annotated by experienced clinicians. We evaluate different deep learning architectures and conduct comprehensive experiments on our newly collected dataset. RESULTS: For the lesion regions detecting task, Single Shot MultiBox Detector with the input size as 300×300 (SSD300) achieves the best performance in terms of average precision rate (APR), average recall rate (ARR) and F1 score. For the classification task, DenseNet is more suitable for our problems. CONCLUSIONS: Our experiments reveal that better and more efficient detection and convolutional neural network (CNN) frameworks is one important factor for better performance of detecting and classification task of the breast lesion. Another significant factor for improving the performance of detecting and classification task, which is transfer learning from the large-scale annotated ImageNet to classify breast lesion.

Functional Diversification of Kaurene Synthase-Like Genes in Isodon rubescens.

Ent-kaurene diterpenoids are the largest group of known Isodon diterpenoids. Among them, oridonin is accumulated in the leaves, and is the most frequently studied compound because of its antitumor and antibacterial activities. We have identified five copalyl diphosphate synthase (CPS) and six kaurene synthase-like (KSL) genes by transcriptome profiling of Isodon rubescens leaves. An in vitro assay assigns ten of them to five different diterpene biosynthesis pathways, except IrCPS3 that has a mutation in the catalytic motif. The Lamiaceae-specific clade genes (IrCPS1 and IrCPS2) synthesize the intermediate copalyl diphosphate (normal-CPP), while IrCPS4 and IrCPS5 synthesize the intermediate ent-copalyl diphosphate (ent-CPP). IrKSL2, IrKSL4, and IrKSL5 react with ent-CPP to produce an ent-isopimaradiene-like compound, ent-atiserene and ent-kaurene, respectively. Correspondingly, the Lamiaceae-specific clade genes IrKSL1 or IrKSL3 combined with normal-CPP led to the formation of miltiradiene. The compound then underwent aromatization and oxidization with a cytochrome P450 forming two related compounds, abietatriene and ferruginol, which were detected in the root bark. IrKSL6 reacts with normal-CPP to produce isopimaradiene. IrKSL3 and IrKSL6 have the γßα tridomain structure, as these proteins tend to possess the bidomain structure of IrKSL1, highlighting the evolutionary history of KSL gene domain loss and further elucidating chemical diversity evolution from a macroevolutionary stance in Lamiaceae.

Functional Divergence of Diterpene Syntheses in the Medicinal Plant Salvia miltiorrhiza.

The medicinal plant Salvia miltiorrhiza produces various tanshinone diterpenoids that have pharmacological activities such as vasorelaxation against ischemia reperfusion injury and antiarrhythmic effects. Their biosynthesis is initiated from the general diterpenoid precursor (E,E,E)-geranylgeranyl diphosphate by sequential reactions catalyzed by copalyl diphosphate synthase (CPS) and kaurene synthase-like cyclases. Here, we report characterization of these enzymatic families from S. miltiorrhiza, which has led to the identification of unique pathways, including roles for separate CPSs in tanshinone production in roots versus aerial tissues (SmCPS1 and SmCPS2, respectively) as well as the unique production of ent-13-epi-manoyl oxide by SmCPS4 and S. miltiorrhiza kaurene synthase-like2 in floral sepals. The conserved SmCPS5 is involved in gibberellin plant hormone biosynthesis. Down-regulation of SmCPS1 by RNA interference resulted in substantial reduction of tanshinones, and metabolomics analysis revealed 21 potential intermediates, indicating a complex network for tanshinone metabolism defined by certain key biosynthetic steps. Notably, the correlation between conservation pattern and stereochemical product outcome of the CPSs observed here suggests a degree of correlation that, especially when combined with the identity of certain key residues, may be predictive. Accordingly, this study provides molecular insights into the evolutionary diversification of functional diterpenoids in plants.

[Metabolomics research of medicinal plants].

Metabolomics is the comprehensively study of chemical processes involving small molecule metabolites. It is an important part of systems biology, and is widely applied in complex traditional Chinese medicine（TCM）system. Metabolites biosynthesized by medicinal plants are the effective basis for TCM. Metabolomics studies of medicinal plants will usher in a new period of vigorous development with the implementation of Herb Genome Program and the development of TCM synthetic biology. This manuscript introduces the recent research progresses of metabolomics technology and the main research contents of metabolomics studies for medicinal plants, including identification and quality evaluation for medicinal plants, cultivars breeding, stress resistance, metabolic pathways, metabolic network, metabolic engineering and synthetic biology researches. The integration of genomics, transcriptomics and metabolomics approaches will finally lay foundation for breeding of medicinal plants, R&D, quality and safety evaluation of innovative drug.

Diagnostic accuracy of intracellular mycobacterium tuberculosis detection for tuberculous meningitis.

RATIONALE: Early diagnosis and treatment of tuberculous meningitis saves lives, but current laboratory diagnostic tests lack sensitivity. OBJECTIVES: We investigated whether the detection of intracellular bacteria by a modified Ziehl-Neelsen stain and early secretory antigen target (ESAT)-6 in cerebrospinal fluid leukocytes improves tuberculous meningitis diagnosis. METHODS: Cerebrospinal fluid specimens from patients with suspected tuberculous meningitis were stained by conventional Ziehl-Neelsen stain, a modified Ziehl-Neelsen stain involving cytospin slides with Triton processing, and an ESAT-6 immunocytochemical stain. Acid-fast bacteria and ESAT-6-expressing leukocytes were detected by microscopy. All tests were performed prospectively in a central laboratory by experienced technicians masked to the patients' final diagnosis. MEASUREMENTS AND MAIN RESULTS: Two hundred and eighty patients with suspected tuberculous meningitis were enrolled. Thirty-seven had Mycobacterium tuberculosis cultured from cerebrospinal fluid; 40 had a microbiologically confirmed alternative diagnosis; the rest had probable or possible tuberculous meningitis according to published criteria. Against a clinical diagnostic gold standard the sensitivity of conventional Ziehl-Neelsen stain was 3.3% (95% confidence interval, 1.6-6.7%), compared with 82.9% (95% confidence interval, 77.4-87.3%) for modified Ziehl-Neelsen stain and 75.1% (95% confidence interval, 68.8-80.6%) for ESAT-6 immunostain. Intracellular bacteria were seen in 87.8% of the slides positive by the modified Ziehl-Neelsen stain. The specificity of modified Ziehl-Neelsen and ESAT-6 stain was 85.0% (95% confidence interval, 69.4-93.8%) and 90.0% (95% confidence interval, 75.4-96.7%), respectively. CONCLUSIONS: Enhanced bacterial detection by simple modification of the Ziehl-Neelsen stain and an ESAT-6 intracellular stain improve the laboratory diagnosis of tuberculous meningitis.

A survey on computer aided diagnosis for ocular diseases.

BACKGROUND: Computer Aided Diagnosis (CAD), which can automate the detection process for ocular diseases, has attracted extensive attention from clinicians and researchers alike. It not only alleviates the burden on the clinicians by providing objective opinion with valuable insights, but also offers early detection and easy access for patients. METHOD: We review ocular CAD methodologies for various data types. For each data type, we investigate the databases and the algorithms to detect different ocular diseases. Their advantages and shortcomings are analyzed and discussed. RESULT: We have studied three types of data (i.e., clinical, genetic and imaging) that have been commonly used in existing methods for CAD. The recent developments in methods used in CAD of ocular diseases (such as Diabetic Retinopathy, Glaucoma, Age-related Macular Degeneration and Pathological Myopia) are investigated and summarized comprehensively. CONCLUSION: While CAD for ocular diseases has shown considerable progress over the past years, the clinical importance of fully automatic CAD systems which are able to embed clinical knowledge and integrate heterogeneous data sources still show great potential for future breakthrough.

Calibrate the Inter-observer Segmentation Uncertainty via Diagnosis-first Principle.

Many of the tissues/lesions in the medical images may be ambiguous. Therefore, medical segmentation is typically annotated by a group of clinical experts to mitigate personal bias. A common solution to fuse different annotations is the majority vote, e.g., taking the average of multiple labels. However, such a strategy ignores the difference between the grader expertness. Inspired by the observation that medical image segmentation is usually used to assist the disease diagnosis in clinical practice, we propose the diagnosis-first principle, which is to take disease diagnosis as the criterion to calibrate the inter-observer segmentation uncertainty. Following this idea, a framework named Diagnosis-First segmentation Framework (DiFF) is proposed. Specifically, DiFF will first learn to fuse the multi-rater segmentation labels to a single ground-truth which could maximize the disease diagnosis performance. We dubbed the fused ground-truth as Diagnosis-First Ground-truth (DF-GT). Then, the Take and Give Model (T&G Model) to segment DF-GT from the raw image is proposed. With the T&G Model, DiFF can learn the segmentation with the calibrated uncertainty that facilitate the disease diagnosis. We verify the effectiveness of DiFF on three different medical segmentation tasks: optic-disc/optic-cup (OD/OC) segmentation on fundus images, thyroid nodule segmentation on ultrasound images, and skin lesion segmentation on dermoscopic images. Experimental results show that the proposed DiFF can effectively calibrate the segmentation uncertainty, and thus significantly facilitate the corresponding disease diagnosis, which outperforms previous state-of-the-art multi-rater learning methods.

Integration of artificial intelligence and multi-omics in kidney diseases.

Kidney disease is a leading cause of death worldwide. Currently, the diagnosis of kidney diseases and the grading of their severity are mainly based on clinical features, which do not reveal the underlying molecular pathways. More recent surge of ∼omics studies has greatly catalyzed disease research. The advent of artificial intelligence (AI) has opened the avenue for the efficient integration and interpretation of big datasets for discovering clinically actionable knowledge. This review discusses how AI and multi-omics can be applied and integrated, to offer opportunities to develop novel diagnostic and therapeutic means in kidney diseases. The combination of new technology and novel analysis pipelines can lead to breakthroughs in expanding our understanding of disease pathogenesis, shedding new light on biomarkers and disease classification, as well as providing possibilities of precise treatment.

Open Set Domain Adaptation With Soft Unknown-Class Rejection.

The goal of domain adaptation (DA) is to train a good model for a target domain, with a large amount of labeled data in a source domain but only limited labeled data in the target domain. Conventional closed set domain adaptation (CSDA) assumes source and target label spaces are the same. However, this is not quite practical in real-world applications. In this work, we study the problem of open set domain adaptation (OSDA), which only requires the target label space to partially overlap with the source label space. Consequently, the solution to OSDA requires unknown classes detection and separation, which is normally achieved by introducing a threshold for the prediction of target unknown classes; however, the performance can be quite sensitive to that threshold. In this article, we tackle the above issues by proposing a novel OSDA method to perform soft rejection of unknown target classes and simultaneously match the source and target domains. Extensive experiments on three standard datasets validate the effectiveness of the proposed method over the state-of-the-art competitors.

Noisy Label Learning With Provable Consistency for a Wider Family of Losses.

Deep models have achieved state-of-the-art performance on a broad range of visual recognition tasks. Nevertheless, the generalization ability of deep models is seriously affected by noisy labels. Though deep learning packages have different losses, this is not transparent for users to choose consistent losses. This paper addresses the problem of how to use abundant loss functions designed for the traditional classification problem in the presence of label noise. We present a dynamic label learning (DLL) algorithm for noisy label learning and then prove that any surrogate loss function can be used for classification with noisy labels by using our proposed algorithm, with a consistency guarantee that the label noise does not ultimately hinder the search for the optimal classifier of the noise-free sample. In addition, we provide a depth theoretical analysis of our algorithm to verify the justifies' correctness and explain the powerful robustness. Finally, experimental results on synthetic and real datasets confirm the efficiency of our algorithm and the correctness of our justifies and show that our proposed algorithm significantly outperforms or is comparable to current state-of-the-art counterparts.

Characterization of a Group of 2,3-Oxidosqualene Cyclase Genes Involved in the Biosynthesis of Diverse Triterpenoids of Perilla frutescens.

Perilla frutescens (L.), a traditional edible and medicinal crop, contains diverse triterpenes with multiple pharmacological properties. However, the biosynthesis of triterpenes in perilla remains rarely revelation. In this study, nine putative 2,3-oxidosqualene cyclase (OSC) genes (PfOSC1-9) were screened from the P. frutescens genome and functionally characterized by heterologous expression. Camelliol C, a triterpenol with pharmacological effect, was first identified as abundant in perilla seeds, and the camelliol C synthase (PfOSC7) was first identified in P. frutescens utilizing a yeast system. In addition, PfOSC2, PfOSC4, and PfOSC9 were identified as cycloartenol, lupeol, and ß-amyrin synthase, respectively. Molecular docking and site-directed mutagenesis revealed that changes in Leu253 of PfOSC4, Ala480 of PfOSC7, and Trp257 of PfOSC9 might lead to variations of catalytic specificity or efficiency. These results will provide key insights into the biosynthetic pathways of triterpenoids and have great significance for germplasm breeding in P. frutescens.

Functional characterization of triterpene synthases in Cibotium barometz.

Cibotium barometz (Linn.) J. Sm., a tree fern in the Dicksoniaceae family, is an economically important industrial exported plant in China and widely used in Traditional Chinese Medicine. C. barometz produces a range of bioactive triterpenes and their metabolites. However, the biosynthetic pathway of triterpenes in C. barometz remains unknown. To clarify the origin of diverse triterpenes in C. barometz, we conducted de novo transcriptome sequencing and analysis of C. barometz rhizomes and leaves to identify the candidate genes involved in C. barometz triterpene biosynthesis. Three C. barometz triterpene synthases (CbTSs) candidate genes were obtained. All of them were highly expressed in C. barometz rhizomes, consisting of the accumulation pattern of triterpenes in C. barometz. To characterize the function of these CbTSs, we constructed a squalene- and oxidosqualene-overproducing yeast chassis by overexpressing all the enzymes in the MVA pathway under the control of GAL-regulated promoter and disrupted the GAL80 gene in Saccharomyces cerevisiae simultaneously. Heterologous expressing CbTS1, CbTS2, and CbTS3 in engineering yeast strain produced cycloartenol, dammaradiene, and diploptene, respectively. Phylogenetic analysis revealed that CbTS1 belongs to oxidosqualene cyclase, while CbTS2 and CbTS3 belong to squalene cyclase. These results decipher enzymatic mechanisms underlying the origin of diverse triterpene in C. barometz.

Reproducibility of deep learning based scleral spur localisation and anterior chamber angle measurements from anterior segment optical coherence tomography images.

AIMS: To apply a deep learning model for automatic localisation of the scleral spur (SS) in anterior segment optical coherence tomography (AS-OCT) images and compare the reproducibility of anterior chamber angle (ACA) width between deep learning located SS (DLLSS) and manually plotted SS (MPSS). METHODS: In this multicentre, cross-sectional study, a test dataset comprising 5166 AS-OCT images from 287 eyes (116 healthy eyes with open angles and 171 eyes with primary angle-closure disease (PACD)) of 287 subjects were recruited from four ophthalmology clinics. Each eye was imaged twice by a swept-source AS-OCT (CASIA2, Tomey, Nagoya, Japan) in the same visit and one eye of each patient was randomly selected for measurements of ACA. The agreement between DLLSS and MPSS was assessed using the Euclidean distance (ED). The angle opening distance (AOD) of 750 µm (AOD750) and trabecular-iris space area (TISA) of 750 µm (TISA750) were calculated using the CASIA2 embedded software. The repeatability of ACA width was measured. RESULTS: The mean age was 60.8±12.3 years (range: 30-85 years) for the normal group and 63.4±10.6 years (range: 40-91 years) for the PACD group. The mean difference in ED for SS localisation between DLLSS and MPSS was 66.50±20.54 µm and 84.78±28.33 µm for the normal group and the PACD group, respectively. The span of 95% limits of agreement between DLLSS and MPSS was 0.064 mm for AOD750 and 0.034 mm2 for TISA750. The respective repeatability coefficients of AOD750 and TISA750 were 0.049 mm and 0.026 mm2 for DLLSS, and 0.058 mm and 0.030 mm2 for MPSS. CONCLUSION: DLLSS achieved comparable repeatability compared with MPSS for measurement of ACA.