Development of cyclopeptide inhibitors of cGAS targeting protein-DNA interaction and phase separation.

Cyclic GMP-AMP synthase (cGAS) is an essential sensor of aberrant cytosolic DNA for initiating innate immunity upon invading pathogens and cellular stress, which is considered as a potential drug target for autoimmune and autoinflammatory diseases. Here, we report the discovery of a class of cyclopeptide inhibitors of cGAS identified by an in vitro screening assay from a focused library of cyclic peptides. These cyclopeptides specifically bind to the DNA binding site of cGAS and block the binding of dsDNA with cGAS, subsequently inhibit dsDNA-induced liquid phase condensation and activation of cGAS. The specificity and potency of one optimal lead XQ2B were characterized in cellular assays. Concordantly, XQ2B inhibited herpes simplex virus-1 (HSV-1)-induced antiviral immune responses and enhanced HSV-1 infection in vitro and in vivo. Furthermore, XQ2B significantly suppressed the elevated levels of type I interferon and proinflammatory cytokines in primary macrophages from Trex1-/- mice and systemic inflammation in Trex1-/- mice. XQ2B represents the specific cGAS inhibitor targeting protein-DNA interaction and phase separation and serves as a scaffold for the development of therapies in the treatment of cGAS-dependent inflammatory diseases.

Leveraging the Adolescent Brain Cognitive Development Study to improve behavioral prediction from neuroimaging in smaller replication samples.

Magnetic resonance imaging (MRI) is a popular and useful non-invasive method to map patterns of brain structure and function to complex human traits. Recently published observations in multiple large scale studies cast doubt upon these prospects, particularly for prediction of cognitive traits from structural and resting state functional MRI, which seems to account for little behavioral variability. We leverage baseline data from thousands of children in the Adolescent Brain Cognitive DevelopmentSM (ABCD®) Study to inform the replication sample size required with both univariate and multivariate methods across different imaging modalities to detect reproducible brain-behavior associations. We demonstrate that by applying multivariate methods to high-dimensional brain imaging data, we can capture lower dimensional patterns of structural and functional brain architecture that correlate robustly with cognitive phenotypes and are reproducible with only 41 individuals in the replication sample for working memory-related functional MRI, and ~100 subjects for structural MRI. Even with 100 random re-samplings of 50 subjects in the discovery sample, prediction can be adequately powered with 98 subjects in the replication sample for multivariate prediction of cognition with working memory task functional MRI. These results point to an important role for neuroimaging in translational neurodevelopmental research and showcase how findings in large samples can inform reproducible brain-behavior associations in small sample sizes that are at the heart of many investigators' research programs and grants.

Computer Vision-Based Artificial Intelligence-Mediated Encoding-Decoding for Multiplexed Microfluidic Digital Immunoassay.

Digital immunoassays with multiplexed capacity, ultrahigh sensitivity, and broad affordability are urgently required in clinical diagnosis, food safety, and environmental monitoring. In this work, a multidimensional digital immunoassay has been developed through microparticle-based encoding and artificial intelligence-based decoding, enabling multiplexed detection with high sensitivity and convenient operation. The information encoded in the features of microspheres, including their size, number, and color, allows for the simultaneous identification and accurate quantification of multiple targets. Computer vision-based artificial intelligence can analyze the microscopy images for information decoding and output identification results visually. Moreover, the optical microscopy imaging can be well integrated with the microfluidic platform, allowing for encoding-decoding through the computer vision-based artificial intelligence. This microfluidic digital immunoassay can simultaneously analyze multiple inflammatory markers and antibiotics within 30 min with high sensitivity and a broad detection range from pg/mL to µg/mL, which holds great promise as an intelligent bioassay for next-generation multiplexed biosensing.

Magnetic relaxation switching immunoassay based on "limited-magnitude" particles for sensitive quantification of aflatoxin B1.

Aflatoxin B1 (AFB1) is a highly toxic and carcinogenic chemical substance that endangers food safety and human health. Magnetic relaxation switching (MRS) immunosensors are utilized in a variety of applications in food analysis due to its resistance to matrix interferences, but they often suffer from magnetic separation-based multi-washing steps and low sensitivity. Herein, we propose novel MRS strategy for the sensitive detection of AFB1 using "Limited-Magnitude" size particles: a single millimeter sized polystyrene spheres (PSmm) and 150 nm superparamagnetic nanoparticles (MNP150). Only a single PSmm is used as the microreactor to enhance all of the magnetic signal on its surface in high concentration by an immune competitive response, successfully preventing signal dilution, which can be transferred by pipette, simplifying the process of separation and washing. The established single polystyrene sphere magnetic relaxation switch biosensor (SMRS) was able to quantify AFB1 from 0.02 to 200 ng/mL with a detection limit of 14.3 pg/mL. SMRS biosensor has been successfully used for the detection of AFB1 in wheat and maize samples, and the results in agreement with high performance liquid chromatography-tandem mass spectrometry (HPLC-MS). Benefiting from high sensitivity and convenient operation, the simple and enzyme-free method is promising in trace small molecules applications.

Effects of superficial gas velocity on the performance of an air-lift internal circulation partial nitrification-anammox granular sludge reactor.

The airlift internal circulation reactor for partial nitrification-anammox (PNA-ALR) has the advantages of a small footprint, high mass transfer efficiency, and the ease of formation of granular sludge, thus making it an effective biological treatment for ammonia-containing wastewater. Although superficial gas velocity (SGV) is an essential parameter for PNA-ALR, it is unclear how the magnitude of SGV impacts nitrogen removal performance. In this study, the nitrogen removal efficiencies of five PNA-ALRs with different SGV were measured during feeding with synthetic municipal wastewater. At an optimal SGV of 2.35 cm s-1, the PNA-ALR consistently maintained the total inorganic nitrogen (TIN) removal efficiency at 76.31% and the effluent TIN concentration was less than 10 mg L-1. By increasing or decreasing the SGV, the nitrogen removal efficiency decreased to a range between 30% and 50%. At lower SGV, the dead space in the PNA-ALR was increased by 21.15%, and the feast/famine ratio of sludge increased to greater than 0.5, which caused a disruption in the structure, and a large loss of, granular sludge. Computational fluid dynamics (CFD) simulations showed operation at a higher SGV, resulting in excessive shear stress of 3.25 N m-2 being generated from bubble rupture in the degassing section. Fluorescent staining determined a decrease of 26.5% in viable bacteria. These results have improved our understanding of the effects of SGV on a PNA-ALR during mainstream wastewater treatment.

Task fMRI paradigms may capture more behaviorally relevant information than resting-state functional connectivity.

Characterizing the optimal fMRI paradigms for detecting behaviorally relevant functional connectivity (FC) patterns is a critical step to furthering our knowledge of the neural basis of behavior. Previous studies suggested that FC patterns derived from task fMRI paradigms, which we refer to as task-based FC, are better correlated with individual differences in behavior than resting-state FC, but the consistency and generalizability of this advantage across task conditions was not fully explored. Using data from resting-state fMRI and three fMRI tasks from the Adolescent Brain Cognitive Development Study ® (ABCD), we tested whether the observed improvement in behavioral prediction power of task-based FC can be attributed to changes in brain activity induced by the task design. We decomposed the task fMRI time course of each task into the task model fit (the fitted time course of the task condition regressors from the single-subject general linear model) and the task model residuals, calculated their respective FC, and compared the behavioral prediction performance of these FC estimates to resting-state FC and the original task-based FC. The FC of the task model fit was better than the FC of the task model residual and resting-state FC at predicting a measure of general cognitive ability or two measures of performance on the fMRI tasks. The superior behavioral prediction performance of the FC of the task model fit was content-specific insofar as it was only observed for fMRI tasks that probed similar cognitive constructs to the predicted behavior of interest. To our surprise, the task model parameters, the beta estimates of the task condition regressors, were equally if not more predictive of behavioral differences than all FC measures. These results showed that the observed improvement of behavioral prediction afforded by task-based FC was largely driven by the FC patterns associated with the task design. Together with previous studies, our findings highlighted the importance of task design in eliciting behaviorally meaningful brain activation and FC patterns.

Magnetic Relaxation Switching Immunosensors via a Click Chemistry-Mediated Controllable Aggregation Strategy for Direct Detection of Chlorpyrifos.

Chlorpyrifos (CPF) is the most frequently found organophosphate pesticide residue in solid food samples and can cause increasing public concerns about potential risks to human health. Traditional detection signals of such small molecules are mostly generated by target-mediated indirect conversion, which tends to be detrimental to sensitivity and accuracy. Herein, a novel magnetic relaxation switching detection platform was developed for target-mediated direct and sensitive detection of CPF with a controllable aggregation strategy based on a bioorthogonal ligation reaction between tetrazine (Tz) and trans-cyclooctene (TCO) ligands. Under optimal conditions, this sensor can achieve a detection limit of 37 pg/mL with a broad linear range of 0.1-500 ng/mL in 45 min, which is approximately 51-fold lower than that of the gas chromatography analysis and 13-fold lower than that of the enzyme-linked immunosorbent assay. The proposed click chemistry-mediated controllable aggregation strategy is direct, rapid, and sensitive, indicating great potential for residue screening in food matrices.

Artificial Intelligence-Assisted Digital Immunoassay Based on a Programmable-Particle-Decoding Technique for Multitarget Ultrasensitive Detection.

The development of a multitarget ultrasensitive immunoassay is significant to fields such as medical research, clinical diagnosis, and food safety inspection. In this study, an artificial intelligence (AI)-assisted programmable-particle-decoding technique (APT)-based digital immunoassay system was developed to perform multitarget ultrasensitive detection. Multitarget was encoded by programmable polystyrene (PS) microspheres with different characteristics (particle size and number), and subsequent visible signals were recorded under an optical microscope after the immune reaction. The resultant images were further analyzed using a customized, AI-based computer vision technique to decode the intrinsic properties of polystyrene microspheres and to reveal the types and concentrations of targets. Our strategy has successfully detected multiple inflammatory markers in clinical serum and antibiotics with a broad detection range from pg/mL to µg/mL without extra signal amplification and conversion. An AI-based digital immunoassay system exhibits great potential to be used for the next generation of multitarget detection in disease screening for candidate patients.

The ZSWIM8 ubiquitin ligase regulates neurodevelopment by guarding the protein quality of intrinsically disordered Dab1.

Protein quality control (PQC) is essential for maintaining protein homeostasis and guarding the accuracy of neurodevelopment. Previously, we found that a conserved EBAX-type CRL regulates the protein quality of SAX-3/ROBO guidance receptors in Caenorhabditis elegans. Here, we report that ZSWIM8, the mammalian homolog of EBAX-1, is essential for developmental stability of mammalian brains. Conditional deletion of Zswim8 in the embryonic nervous system causes global cellular stress, partial perinatal lethality and defective migration of neural progenitor cells. CRISPR-mediated knockout of ZSWIM8 impairs spine formation and synaptogenesis in hippocampal neurons. Mechanistic studies reveal that ZSWIM8 controls protein quality of Disabled 1 (Dab1), a key signal molecule for brain development, thus protecting the signaling strength of Dab1. As a ubiquitin ligase enriched with intrinsically disordered regions (IDRs), ZSWIM8 specifically recognizes IDRs of Dab1 through a "disorder targets misorder" mechanism and eliminates misfolded Dab1 that cannot be properly phosphorylated. Adult survivors of ZSWIM8 CKO show permanent hippocampal abnormality and display severely impaired learning and memory behaviors. Altogether, our results demonstrate that ZSWIM8-mediated PQC is critical for the stability of mammalian brain development.

Fine-tuning of mTOR signaling by the UBE4B-KLHL22 E3 ubiquitin ligase cascade in brain development.

Spatiotemporal regulation of the mechanistic target of rapamycin (mTOR) pathway is pivotal for establishment of brain architecture. Dysregulation of mTOR signaling is associated with a variety of neurodevelopmental disorders. Here, we demonstrate that the UBE4B-KLHL22 E3 ubiquitin ligase cascade regulates mTOR activity in neurodevelopment. In a mouse model with UBE4B conditionally deleted in the nervous system, animals display severe growth defects, spontaneous seizures and premature death. Loss of UBE4B in the brains of mutant mice results in depletion of neural precursor cells and impairment of neurogenesis. Mechanistically, UBE4B polyubiquitylates and degrades KLHL22, an E3 ligase previously shown to degrade the GATOR1 component DEPDC5. Deletion of UBE4B causes upregulation of KLHL22 and hyperactivation of mTOR, leading to defective proliferation and differentiation of neural precursor cells. Suppression of KLHL22 expression reverses the elevated activity of mTOR caused by acute local deletion of UBE4B. Prenatal treatment with the mTOR inhibitor rapamycin rescues neurogenesis defects in Ube4b mutant mice. Taken together, these findings demonstrate that UBE4B and KLHL22 are essential for maintenance and differentiation of the precursor pool through fine-tuning of mTOR activity.

Generalization of cortical MOSTest genome-wide associations within and across samples.

Genome-Wide Association studies have typically been limited to univariate analysis in which a single outcome measure is tested against millions of variants. Recent work demonstrates that a Multivariate Omnibus Statistic Test (MOSTest) is well powered to discover genomic effects distributed across multiple phenotypes. Applied to cortical brain MRI morphology measures, MOSTest has resulted in a drastic improvement in power to discover loci when compared to established approaches (min-P). One question that arises is how well these discovered loci replicate in independent data. Here we perform 10 times cross validation within 34,973 individuals from UK Biobank for imaging measures of cortical area, thickness and sulcal depth (>1,000 dimensionality for each). By deploying a replication method that aggregates discovered effects distributed across multiple phenotypes, termed PolyVertex Score (MOSTest-PVS), we demonstrate a higher replication yield and comparable replication rate of discovered loci for MOSTest (# replicated loci: 242-496, replication rate: 96-97%) in independent data when compared with the established min-P approach (# replicated loci: 26-55, replication rate: 91-93%). An out-of-sample replication of discovered loci was conducted with a sample of 4,069 individuals from the Adolescent Brain Cognitive Development® (ABCD) study, who are on average 50 years younger than UK Biobank individuals. We observe a higher replication yield and comparable replication rate of MOSTest-PVS compared to min-P. This finding underscores the importance of using well-powered multivariate techniques for both discovery and replication of high dimensional phenotypes in Genome-Wide Association studies.

The philanthropic response of substantive and symbolic corporate social responsibility strategies to COVID-19 crisis: Evidence from China.

The COVID-19 epidemic broke out in China in January 2020, which triggered the largest wave of corporate philanthropic donations since the 2008 Sichuan earthquake. Based on A-share listed firms in the Shanghai and Shenzhen stock exchanges in 2020, we study whether substantive and symbolic corporate social responsibility (CSR) strategies affect corporate philanthropic responses during the COVID-19 crisis. We use the lagged annual donation and technical dimension scores (T scores) of rankins ratings (RKS) as proxies of CSR performance and CSR disclosure and then define the CSR gap as the gap between the two. The results show that substantive and symbolic strategies cause firms to have material differential responses in the COVID-19 crisis. Specifically, the CSR gap is negatively related to the possibility and the level of crisis donation. In addition, (1) this difference is more pronounced in the earlier period of the COVID-19 crisis; (2) the negative correlation is more pronounced in private firms; and (3) the crisis donation of firms with either strategy obtains no different response from the capital market. Our evidence suggests that the established CSR strategy influences the substantive response of Chinese firms to public emergencies, but their substantive response does not result in different reactions in the capital market.

Board Network and CSR Decoupling: Evidence From China.

This paper investigates the influence of board network centrality on corporate social responsibility (CSR) decoupling. CSR decoupling refers to the gap between corporate internal and external actions in CSR practices. Specifically, we measure CSR decoupling as the difference between corporate social disclosure (CSD) and corporate social performance (CSP). This paper uses a sample of Chinese A-share listed firms during 2009-2018, takes the technical dimension score (T-score) and content dimension score (C-score) of RKS ratings as proxies of CSD and CSP, and obtains CSR decoupling as the difference between CSD and CSP. Our results show that (1) board network centrality is positively related to over-decoupling in the pre-adoption period (2009-2014) of the new environmental law but negatively related to over-decoupling in the post-adoption period (2015-2018) and (2) centrality is not related to under-decoupling in the pre-adoption period but a significantly positive related in the post-adoption period. Our finding reveals a complex role of the board network in CSR practices in China.

The anammox coupled partial-denitrification process in an integrated granular sludge and fixed-biofilm reactor developed for mainstream wastewater treatment: Performance and community structure.

This study describes an integrated granular sludge and fixed-biofilm (iGB) reactor innovatively designed to carry out the anammox/partial-denitrification (A/PD) process for nitrogen removal with mainstream municipal wastewater. The iGB-A/PD reactor consists of anammox granules inoculated in the lower region of reactor and an acclimated fixed-biofilm positioned in the upper region. Compared to the other reported A/PD systems for mainstream wastewater treatment, this iGB-A/PD reactor is notable due to its higher quality effluent with a total inorganic nitrogen (TIN) of ∼3 mg•L-1 and operation at a high nitrogen removal rate (NRR) of 0.8 ± 0.1 kg-N•m-3•d-1. Reads-based metatranscriptomic analysis found that the expression values of hzsA and hdh, key genes associated with anammox, were much higher than other functional genes on nitrogen conversion, confirming the major roles of the anammox bacteria in nitrogen bio-removal. In both regions of the reactor, the nitrate reduction genes (napA/narG) had expression values of 56-99 RPM, which were similar to that of the nitrite reduction genes (nirS/nirK). The expression reads from genes for dissimilatory nitrate reduction to ammonium (DNRA), nrfA and nirB, were unexpectedly high, and were over the half of the levels of reads from genes required for nitrate reduction. Kinetic assays confirmed that the granules had an anammox activity of 16.2 g-NH4+-N•kg-1-VSS•d-1 and a nitrate reduction activity of 4.1 g-N•kg-1-VSS•d-1. While these values were changed to be 4.9 g- NH4+-N•kg-1-VSS•d-1and 4.3 g-N•kg-1-VSS•d-1 respectively in the fixed-biofilm. Mass flux determination found that PD and DNRA was responsible for ∼50% and ∼25% of nitrate reduction, respectively, in the whole reactor, consistent with high effluent quality and treatment efficiency via a nitrite loop. Metagenomic binning analysis revealed that new and unidentified anammox species, affiliated with Candidatus Brocadia, were the dominant anammox organisms. Myxococcota and Planctomycetota were the principal organisms associated with the PD and DNRA processes, respectively.

Exploring the Influencing Factors of the Recreational Utilization and Evaluation of Urban Ecological Protection Green Belts for Urban Renewal: A Case Study in Shanghai.

With the continuous expansion of urban construction land, the green belts aiming for ecological protection have ensured a sustainable and effective function of regional ecosystem services. At the same time, these ecological green belts are expected to develop their compound service potentials with the development of cities. In order to meet the increasing demand of urban residents for the recreational utilization of urban green space, the primary function of the ecological green belts has transformed from being purely ecological to a combination of being ecological and recreational. Based on social media data, which has the characteristics of a large amount of accessible geographic information, this study used multiple regression models to analyze the recreational utilization intensity of ecological protection green belts with a case study in the green belt of Shanghai, China. The research results showed that the internal elements (total external area, water area, etc.) of the Shanghai green belt have positive correlations with its recreational utilization. The impact of external factors was inconclusive on the recreational utilization of the outer forest belt (the number of subway stations in accessibility factors was negatively correlated; the number of cultural facilities and the number of restaurants in the surrounding service facilities were positively related). Combined with the "Shanghai City Master Plan (2017-2035)", this study suggests potential zones for the recreational transformation of the Shanghai green belt, provides a theoretical and practical basis for improving the recreational utilization of an urban ecological protection green belt and contributes to the sustainable development of ecological protection green belts in high-density cities.

Individual Differences in Cognitive Performance Are Better Predicted by Global Rather Than Localized BOLD Activity Patterns Across the Cortex.

Despite its central role in revealing the neurobiological mechanisms of behavior, neuroimaging research faces the challenge of producing reliable biomarkers for cognitive processes and clinical outcomes. Statistically significant brain regions, identified by mass univariate statistical models commonly used in neuroimaging studies, explain minimal phenotypic variation, limiting the translational utility of neuroimaging phenotypes. This is potentially due to the observation that behavioral traits are influenced by variations in neuroimaging phenotypes that are globally distributed across the cortex and are therefore not captured by thresholded, statistical parametric maps commonly reported in neuroimaging studies. Here, we developed a novel multivariate prediction method, the Bayesian polyvertex score, that turns a unthresholded statistical parametric map into a summary score that aggregates the many but small effects across the cortex for behavioral prediction. By explicitly assuming a globally distributed effect size pattern and operating on the mass univariate summary statistics, it was able to achieve higher out-of-sample variance explained than mass univariate and popular multivariate methods while still preserving the interpretability of a generative model. Our findings suggest that similar to the polygenicity observed in the field of genetics, the neural basis of complex behaviors may rest in the global patterning of effect size variation of neuroimaging phenotypes, rather than in localized, candidate brain regions and networks.

Inter-character Orthographic Similarity Effects on the Recognition of Chinese Coordinative Compound Words.

Chinese coordinative compound words are common and unique in inter-character semantic and orthographic relationships. This study explored the inter-character orthographic similarity effects on the recognition of transparent two-morpheme coordinative compound words. Seventy-two native Chinese readers participated in a lexical decision task. The findings demonstrated robust inhibitory inter-character orthographic similarity effects, intra-word character reversal effects, and inter-character semantic similarity effects. These results were compared to those of previous studies on coordinative compound word recognition and on the orthographic similarity phenomenon at both character and word levels. The findings were explained with the multi-level representational model of morphological processing of Chinese compound words (Zhou and Marslen-Wilson in Psychologia 43:47-66, 2000). The model was further extended by adding the activation of morpho-orthographic relationships and the mapping of morphemic orthographic information onto the semantic information of both morphemes and whole words.

Artificial intelligence on diabetic retinopathy diagnosis: an automatic classification method based on grey level co-occurrence matrix and naive Bayesian model.

AIM: To develop an automatic tool on screening diabetic retinopathy (DR) from diabetic patients. METHODS: We extracted textures from eye fundus images of each diabetes subject using grey level co-occurrence matrix method and trained a Bayesian model based on these textures. The receiver operating characteristic (ROC) curve was used to estimate the sensitivity and specificity of the Bayesian model. RESULTS: A total of 1000 eyes fundus images from diabetic patients in which 298 eyes were diagnosed as DR by two ophthalmologists. The Bayesian model was trained using four extracted textures including contrast, entropy, angular second moment and correlation using a training dataset. The Bayesian model achieved a sensitivity of 0.949 and a specificity of 0.928 in the validation dataset. The area under the ROC curve was 0.938, and the 10-fold cross validation method showed that the average accuracy rate is 93.5%. CONCLUSION: Textures extracted by grey level co-occurrence can be useful information for DR diagnosis, and a trained Bayesian model based on these textures can be an effective tool for DR screening among diabetic patients.

Comparison of ocular biometric measurements between a new swept-source optical coherence tomography and a common optical low coherence reflectometry.

The purpose of the current study was to compare the measurements between a new optical biometer based on swept-source optical coherence tomography (SS-OCT), the OA-2000 (Tomey, Japan), and an optical biometer based on optical low coherence reflectometry (OLCR), the Lenstar (Haag-Streit, Switzerland). Ninety-nine eyes of 99 healthy subjects were included. The axial length (AL), central corneal thickness (CCT), anterior chamber depth (ACD), aqueous depth (AD), lens thickness (LT), keratometry (K) readings, including flat K (Kf), steep K (Ks), mean K (Km), astigmatism vectors J0, J45 at diameters of 2.5 and 3.0 mm, and white-to-white diameter (WTW) were measured three times each using both biometer in normal eyes by random sequence. Bland-Altman analysis showed good agreement between the SS-OCT and OLCR devices for AL, AD, ACD, LT, with narrow 95% LoA (-0.05 to 0.07 mm, -0.09 to 0.10 mm, -0.10 to 0.09 mm, and -0.06 to 0.22 mm, respectively), and the P values of ACD were both >0.05. The CCT, Kf, Ks, Km, J0, J45 and WTW values provided by the OA-2000 were in good agreement with the Lenstar, and statistically significant differences were detected for some of them but not clinical differences. The agreement was excellent especially for AL.

Axial Length Measurement Failure Rates With Biometers Using Swept-Source Optical Coherence Tomography Compared to Partial-Coherence Interferometry and Optical Low-Coherence Interferometry.

PURPOSE: To compare a new swept-source optical coherence tomography (SSOCT)-based biometer (OA-2000) with the IOLMaster v5.4 (partial-coherence interferometry) and Aladdin (optical low-coherence interferometry) biometers in terms of axial length measurement and failure rate in eyes with cataract. DESIGN: Reliability study. METHODS: A total of 377 eyes of 210 patients were scanned with the 3 biometers in a random order. For each biometer, the number of unobtainable axial length measurements was recorded and grouped as per the type and severity of cataract based on the Lens Opacities Classification System III by the same experienced ophthalmologist. The Bland-Altman limits-of-agreement (LoA) method was used to assess the agreement in axial length measurements between the 3 biometers. RESULTS: The failure rate was 0 eyes (0%) with the OA-2000, 136 eyes (36.07%) with the IOLMaster, and 51 eyes (13.53%) with the Aladdin. χ2 analyses indicated a significant difference in failure rate between all 3 devices (P < .001). Logistic regression analysis highlighted a statistically significant trend of higher failure rates with increasing severity of nuclear, cortical, and posterior subcapsular cataracts. Bland-Altman statistics indicated small mean differences and narrow LoA (OA-2000 vs IOLMaster -0.09 to 0.08 mm; OA-2000 vs Aladdin -0.10 to 0.07 mm; IOLMaster vs Aladdin -0.05 to 0.04 mm). CONCLUSIONS: The OA-2000, a new SSOCT-based biometer, outperformed both the IOLMaster and Aladdin biometers in very advanced cataracts of various morphologies. The use of SSOCT technology may be the reason for the improved performance of the OA-2000 and may lead to this technology becoming the gold standard for the measurement of axial length.