Gut microbiota compositional profile in patients with posner-schlossman syndrome.

The cause of Posner-Schlossman syndrome (PSS) remains unknown and its frequent recurrence may eventually lead to irreversible damage of the optic nerve. The influence of immune factors in the pathophysiology of PSS is gaining more and more interest. Increasing evidence suggests that gut dysbiosis plays vital roles in a variety of neurodegenerative and immune-related diseases. However, alterations of the gut microbiota in PSS patients have not been well defined yet. In this study, 16S rRNA sequencing was used to explore the difference of gut microbiota between PSS patients and healthy controls, and the correlation between the microbiota profile and clinical features was also analyzed. Our data demonstrated a significant increase of Prevotella and Prevotellaceae, and a significant reduction of Bacteroides and Bacteroidaceae in PSS patients, and KEGG analysis showed dysfunction of gut microbiota between PSS patients and healthy controls. Interestingly, further analysis showed that the alteration of gut microbiota was correlated with the PSS attack frequency of PSS. This study demonstrated the gut microbiota compositional profile of PSS patients and speculated the risk microbiota of PSS, which is expected to provide new insights for the diagnosis and treatment of PSS.

Perfecting Liquid-State Theories with Machine Intelligence.

Recent years have seen a significant increase in the use of machine intelligence for predicting the electronic structure, molecular force fields, and physicochemical properties of various condensed systems. However, substantial challenges remain in developing a comprehensive framework capable of handling a wide range of atomic compositions and thermodynamic conditions. This perspective discusses potential future developments in liquid-state theories leveraging recent advancements in functional machine learning. By harnessing the strengths of theoretical analysis and machine learning techniques including surrogate models, dimension reduction, and uncertainty quantification, we envision that liquid-state theories will gain significant improvements in accuracy, scalability, and computational efficiency, enabling their broader applications across diverse materials and chemical systems.

Predicting SARS-CoV-2 infection among hemodialysis patients using multimodal data.

Background: The coronavirus disease 2019 (COVID-19) pandemic has created more devastation among dialysis patients than among the general population. Patient-level prediction models for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are crucial for the early identification of patients to prevent and mitigate outbreaks within dialysis clinics. As the COVID-19 pandemic evolves, it is unclear whether or not previously built prediction models are still sufficiently effective. Methods: We developed a machine learning (XGBoost) model to predict during the incubation period a SARS-CoV-2 infection that is subsequently diagnosed after 3 or more days. We used data from multiple sources, including demographic, clinical, treatment, laboratory, and vaccination information from a national network of hemodialysis clinics, socioeconomic information from the Census Bureau, and county-level COVID-19 infection and mortality information from state and local health agencies. We created prediction models and evaluated their performances on a rolling basis to investigate the evolution of prediction power and risk factors. Result: From April 2020 to August 2020, our machine learning model achieved an area under the receiver operating characteristic curve (AUROC) of 0.75, an improvement of over 0.07 from a previously developed machine learning model published by Kidney360 in 2021. As the pandemic evolved, the prediction performance deteriorated and fluctuated more, with the lowest AUROC of 0.6 in December 2021 and January 2022. Over the whole study period, that is, from April 2020 to February 2022, fixing the false-positive rate at 20%, our model was able to detect 40% of the positive patients. We found that features derived from local infection information reported by the Centers for Disease Control and Prevention (CDC) were the most important predictors, and vaccination status was a useful predictor as well. Whether or not a patient lives in a nursing home was an effective predictor before vaccination, but became less predictive after vaccination. Conclusion: As found in our study, the dynamics of the prediction model are frequently changing as the pandemic evolves. County-level infection information and vaccination information are crucial for the success of early COVID-19 prediction models. Our results show that the proposed model can effectively identify SARS-CoV-2 infections during the incubation period. Prospective studies are warranted to explore the application of such prediction models in daily clinical practice.

Molecular-scale substrate anisotropy, crowding and division drive collective behaviours in cell monolayers.

The ability of cells to reorganize in response to external stimuli is important in areas ranging from morphogenesis to tissue engineering. While nematic order is common in biological tissues, it typically only extends to small regions of cells interacting via steric repulsion. On isotropic substrates, elongated cells can co-align due to steric effects, forming ordered but randomly oriented finite-size domains. However, we have discovered that flat substrates with nematic order can induce global nematic alignment of dense, spindle-like cells, thereby influencing cell organization and collective motion and driving alignment on the scale of the entire tissue. Remarkably, single cells are not sensitive to the substrate's anisotropy. Rather, the emergence of global nematic order is a collective phenomenon that requires both steric effects and molecular-scale anisotropy of the substrate. To quantify the rich set of behaviours afforded by this system, we analyse velocity, positional and orientational correlations for several thousand cells over days. The establishment of global order is facilitated by enhanced cell division along the substrate's nematic axis, and associated extensile stresses that restructure the cells' actomyosin networks. Our work provides a new understanding of the dynamics of cellular remodelling and organization among weakly interacting cells.

Combined Trabeculotomy-Non-Penetrating Deep Sclerectomy for Glaucoma in Sturge-Weber Syndrome.

INTRODUCTION: The aim of the study was to evaluate the efficacy and safety of combined trabeculotomy-non-penetrating deep sclerectomy (CTNS) in the treatment of Sturge-Weber syndrome (SWS) secondary glaucoma. METHODS: This retrospective study reviewed cases that underwent CTNS as initial surgery for SWS secondary glaucoma at our Ophthalmology Department center from April 2019 to August 2020. Surgical success was defined as an intraocular pressure (IOP) ≤ 21 mm Hg with (qualified success) or without (complete success) the use of anti-glaucoma medications. IOP >21 mm Hg or <5 mm Hg despite 3 or more applications of anti-glaucoma medications on 2 consecutive follow-up visits or at the last follow-up, performance of additional glaucoma (IOP-lowering) surgery, or with vision-threatening complications were classified as failure. RESULTS: A total of 22 eyes of 21 patients were included. Twenty-one eyes were of early-onset type and 1 eye was of adulthood onset. For Kaplan-Meier survival analysis, the overall success rates at 1st and 2nd years were 95.2% and 84.9%, while the complete success rates at 1st and 2nd years were 42.9% and 36.7%. At the last follow-up (22.3 ± 4.0 months, range: 11.2∼31.2), overall success was achieved in 19 (85.7%) eyes and complete success in 12 (52.4%) eyes. Postoperative complications included transient hyphema (11/22, 50.0%) and transient Ⅰ degree shallow anterior chamber (1/22, 4.5%), and retinal detachment (1/22, 4.5%). No other severe com plications were detected during the follow-up. CONCLUSION: CTNS significantly reduces IOP in SWS secondary glaucoma patients who have serious episcleral vascular malformation. CTNS in SWS secondary glaucoma patients is safe and effective for short and medium periods. A randomized controlled study comparing the long-term prognosis of SWS early-onset and late-onset glaucoma underwent CTNS is worth conducting.

Evaluation of Schlemm's canal with swept-source optical coherence tomography in primary angle-closure disease.

PURPOSE: To perform an in vivo evaluation of the changes in Schlemm's canal (SC) among patients with primary angle-closure disease (PACD) using swept-source optical coherence tomography (SS-OCT). METHODS: Patients diagnosed with PACD who had not undergone surgery were recruited. The SS-OCT quadrants scanned herein included the nasal and temporal sections at 3 and 9 o'clock, respectively. The diameter and cross-sectional area of the SC were measured. A linear mixed-effects model was performed to analyze the effects of parameters on the SC changes. The hypothesis of interest was related to the angle status (iridotrabecular contact, ITC/open angle, OPN), which was further explored with pairwise comparisons of the estimated marginal means (EMMs) of the SC diameter and SC area. In the ITC regions, the relationship between the trabecular-iris contact length (TICL) percentage and SC parameters was also studied by a mixed model. RESULTS: A total of 49 eyes of 35 patients were included for measurements and analysis. The percentage of observable SCs in the ITC regions was only 58.5% (24/41), whereas it was 86.0% (49/57) in the OPN regions (χ2 = 9.44, p = 0.002). ITC was significantly associated with a decreasing SC size. The EMMs for the diameter and cross-sectional area of SC at the ITC and OPN regions were 203.34 µm versus 261.41 µm (p = 0.006) and 3174.43 µm2 versus 5347.63 µm2 (p = 0.022), respectively. Sex, age, spherical equivalent refraction, intraocular pressure, axial length, extent of angle closure, history of acute attack and treatment with LPI were not significantly associated with SC parameters. In the ITC regions, a larger TICL percentage was significantly associated with a decrease in SC diameter and area (p = 0.003 and 0.019, respectively). CONCLUSIONS: The morphologies of SC could be affected by the angle status (ITC/OPN) in patients with PACD, and ITC was significantly associated with a decreasing SC size. These changes in SC as described by OCT scans might help to elucidate the progression mechanisms of PACD.

Aryl hydrocarbon receptor dependent anti-inflammation and neuroprotective effects of tryptophan metabolites on retinal ischemia/reperfusion injury.

Glaucoma is the major cause of irreversible blindness in the world characterized by progressive retinal neurodegeneration, in which local inflammation in retina is involved in persistent loss of retinal ganglion cells (RGCs). In order to explore whether aryl hydrocarbon receptor (AhR) and its agonists tryptophan metabolites are involved in the development of glaucoma, we collected serum and retinas from non-glaucoma controls and patients with glaucoma. Results showed altered serum tryptophan metabolism and reduced retinal AhR expression in glaucoma patients. We also showed intraperitoneally injection of tryptophan metabolite 2-(1'H-indole-3'-carbonyl)-thiazole-4-carboxylic acid methyl ester (ITE) down-regulated retinal local inflammation and protected RGC apoptosis from retinal ischemia/reperfusion (IR) injury via AhR activation. We further revealed that ITE could inhibit inflammation in BV2 microglia and alleviate the neurotoxicity of microglial conditioned medium to RGCs under IR. Finally, we illustrated the possible mechanism that ITE limited ERK and NFκB dependent microglial inflammation. In summary, these findings suggest the critical role of tryptophan metabolism and retinal AhR signaling in modulating local inflammation mediated by microglia in glaucoma, and provide a novel avenue to targeting the intrinsically altered AhR signaling resulted from disturbed tryptophan metabolism for glaucoma treatment.

Reliable emulation of complex functionals by active learning with error control.

A statistical emulator can be used as a surrogate of complex physics-based calculations to drastically reduce the computational cost. Its successful implementation hinges on an accurate representation of the nonlinear response surface with a high-dimensional input space. Conventional "space-filling" designs, including random sampling and Latin hypercube sampling, become inefficient as the dimensionality of the input variables increases, and the predictive accuracy of the emulator can degrade substantially for a test input distant from the training input set. To address this fundamental challenge, we develop a reliable emulator for predicting complex functionals by active learning with error control (ALEC). The algorithm is applicable to infinite-dimensional mapping with high-fidelity predictions and a controlled predictive error. The computational efficiency has been demonstrated by emulating the classical density functional theory (cDFT) calculations, a statistical-mechanical method widely used in modeling the equilibrium properties of complex molecular systems. We show that ALEC is much more accurate than conventional emulators based on the Gaussian processes with "space-filling" designs and alternative active learning methods. In addition, it is computationally more efficient than direct cDFT calculations. ALEC can be a reliable building block for emulating expensive functionals owing to its minimal computational cost, controllable predictive error, and fully automatic features.

Magnolol limits NFκB-dependent inflammation by targeting PPARγ relieving retinal ischemia/reperfusion injury.

BACKGROUND: Glaucoma is the leading cause of irreversible blindness in the world. Elevated intraocular pressure (IOP) is recognized as one of the most critical factors, but the loss of retinal ganglia cells (RGCs) often persists when IOP is controlled. Recently, a large number of studies focus on the inflammatory and immune responses in the occurrence and development of glaucoma. Magnolol (MAG), the principal ingredient of magnoliae officinalis cortex, has anti-inflammatory effects, but its role and mechanism in retinal protection need to be further studied. METHODS: The neurodegeneration of retina in mice model following ischemia/reperfusion (IR) injury was evaluated by immunohistochemistry, hematoxylin and eosin (H&E) staining and electroretinography (ERG). The inflammation-regulatory effect of MAG was detected by quantitative RT-PCR, western blot, and immunohistochemistry. Peroxisome proliferator-activated receptor-γ (PPARγ) inhibitor assays by H&E staining and western blot were used to test the target and mechanism pathway of MAG. RESULTS: We found MAG relieved IR-induced retinal damages and inflammation. Further studies revealed MAG alleviated nuclear factor kappa B (NFκB)-dependent inflammatory process by preserving the expression of NFκB inhibitor alpha (IκBα), and it modulated microglia polarization after IR injury. PPARγ was a primary target of MAG, and treatment with PPARγ inhibitor GW9662 attenuated the neuroprotective and anti-inflammatory effects of MAG. CONCLUSIONS: Our findings revealed that MAG inhibits NFκB-dependent inflammatory processes by elevating PPARγ in mice retinas to achieve its neuroprotective role following IR, which suggesting that MAG could be developed to a novel anti-inflammatory therapeutic agent for relieving the progression of glaucoma.

Efficient force field and energy emulation through partition of permutationally equivalent atoms.

Gaussian process (GP) emulator has been used as a surrogate model for predicting force field and molecular potential, to overcome the computational bottleneck of ab initio molecular dynamics simulation. Integrating both atomic force and energy in predictions was found to be more accurate than using energy alone, yet it requires O((NM)3) computational operations for computing the likelihood function and making predictions, where N is the number of atoms and M is the number of simulated configurations in the training sample due to the inversion of a large covariance matrix. The high computational cost limits its applications to the simulation of small molecules. The computational challenge of using both gradient information and function values in GPs was recently noticed in machine learning communities, whereas conventional approximation methods may not work well. Here, we introduce a new approach, the atomized force field model, that integrates both force and energy in the emulator with many fewer computational operations. The drastic reduction in computation is achieved by utilizing the naturally sparse covariance structure that satisfies the constraints of the energy conservation and permutation symmetry of atoms. The efficient machine learning algorithm extends the limits of its applications on larger molecules under the same computational budget, with nearly no loss of predictive accuracy. Furthermore, our approach contains an uncertainty assessment of predictions of atomic forces and energies, useful for developing a sequential design over the chemical input space.

High-throughput microscopy to determine morphology, microrheology, and phase boundaries applied to phase separating coacervates.

Evolution of composition, rheology, and morphology during phase separation in complex fluids is highly coupled to rheological and mass transport processes within the emerging phases, and understanding this coupling is critical for materials design of multiphase complex fluids. Characterizing these dependencies typically requires careful measurement of a large number of equilibrium and transport properties that are difficult to measure in situ as phase separation proceeds. Here, we propose and demonstrate a high-throughput microscopy platform to achieve simultaneous, in situ mapping of time-evolving morphology and microrheology in phase separating complex fluids over a large compositional space. The method was applied to a canonical example of polyelectrolyte complex coacervation, whereby mixing of oppositely charged species leads to liquid-liquid phase separation into distinct solute-dense and dilute phases. Morphology and rheology were measured simultaneously and kinetically after mixing to track the progression of phase separation. Once equilibrated, the dense phase viscosity was determined to high compositional accuracy using passive probe microrheology, and the results were used to derive empirical relationships between the composition and viscosity. These relationships were inverted to reconstruct the dense phase boundary itself, and further extended to other mixture compositions. The resulting predictions were validated by independent equilibrium compositional measurements. This platform paves the way for rapid screening and formulation of complex fluids and (bio)macromolecular materials, and serves as a critical link between formulation and rheology for multi-phase material discovery.

Uncertainty quantification and estimation in differential dynamic microscopy.

Differential dynamic microscopy (DDM) is a form of video image analysis that combines the sensitivity of scattering and the direct visualization benefits of microscopy. DDM is broadly useful in determining dynamical properties including the intermediate scattering function for many spatiotemporally correlated systems. Despite its straightforward analysis, DDM has not been fully adopted as a routine characterization tool, largely due to computational cost and lack of algorithmic robustness. We present statistical analysis that quantifies the noise, reduces the computational order, and enhances the robustness of DDM analysis. We propagate the image noise through the Fourier analysis, which allows us to comprehensively study the bias in different estimators of model parameters, and we derive a different way to detect whether the bias is negligible. Furthermore, through use of Gaussian process regression (GPR), we find that predictive samples of the image structure function require only around 0.5%-5% of the Fourier transforms of the observed quantities. This vastly reduces computational cost, while preserving information of the quantities of interest, such as quantiles of the image scattering function, for subsequent analysis. The approach, which we call DDM with uncertainty quantification (DDM-UQ), is validated using both simulations and experiments with respect to accuracy and computational efficiency, as compared with conventional DDM and multiple particle tracking. Overall, we propose that DDM-UQ lays the foundation for important new applications of DDM, as well as to high-throughput characterization.

Robust estimation of SARS-CoV-2 epidemic in US counties.

The COVID-19 outbreak is asynchronous in US counties. Mitigating the COVID-19 transmission requires not only the state and federal level order of protective measures such as social distancing and testing, but also public awareness of time-dependent risk and reactions at county and community levels. We propose a robust approach to estimate the heterogeneous progression of SARS-CoV-2 at all US counties having no less than 2 COVID-19 associated deaths, and we use the daily probability of contracting (PoC) SARS-CoV-2 for a susceptible individual to quantify the risk of SARS-CoV-2 transmission in a community. We found that shortening by [Formula: see text] of the infectious period of SARS-CoV-2 can reduce around [Formula: see text] (or 78 K, [Formula: see text] CI: [66 K , 89 K ]) of the COVID-19 associated deaths in the US as of 20 September 2020. Our findings also indicate that reducing infection and deaths by a shortened infectious period is more pronounced for areas with the effective reproduction number close to 1, suggesting that testing should be used along with other mitigation measures, such as social distancing and facial mask-wearing, to reduce the transmission rate. Our deliverable includes a dynamic county-level map for local officials to determine optimal policy responses and for the public to better understand the risk of contracting SARS-CoV-2 on each day.

Robust estimation of SARS-CoV-2 epidemic in US counties.

The COVID-19 outbreak is asynchronous in US counties. Mitigating the COVID-19 transmission requires not only the state and federal level order of protective measures such as social distancing and testing, but also public awareness of time-dependent risk and reactions at county and community levels. We propose a robust approach to estimate the heterogeneous progression of SARS-CoV-2 at all US counties having no less than 2 COVID-19 associated deaths, and we use the daily probability of contracting (PoC) SARS-CoV-2 for a susceptible individual to quantify the risk of SARS-CoV-2 transmission in a community. We found that shortening by $5\%$ of the infectious period of SARS-CoV-2 can reduce around $39\%$ (or $78$K, $95\%$ CI: $[66$K $, 89$K $]$) of the COVID-19 associated deaths in the US as of 20 September 2020. Our findings also indicate that reducing infection and deaths by a shortened infectious period is more pronounced for areas with the effective reproduction number close to 1, suggesting that testing should be used along with other mitigation measures, such as social distancing and facial mask-wearing, to reduce the transmission rate. Our deliverable includes a dynamic county-level map for local officials to determine optimal policy responses and for the public to better understand the risk of contracting SARS-CoV-2 on each day.

Magma reservoir failure and the onset of caldera collapse at Kilauea Volcano in 2018.

Caldera-forming eruptions are among Earth's most hazardous natural phenomena, yet the architecture of subcaldera magma reservoirs and the conditions that trigger collapse are poorly understood. Observations from the formation of a 0.8-cubic kilometer basaltic caldera at Kilauea Volcano in 2018 included the draining of an active lava lake, which provided a window into pressure decrease in the reservoir. We show that failure began after <4% of magma was withdrawn from a shallow reservoir beneath the volcano's summit, reducing its internal pressure by ~17 megapascals. Several cubic kilometers of magma were stored in the reservoir, and only a fraction was withdrawn before the end of the eruption. Thus, caldera formation may begin after withdrawal of only small amounts of magma and may end before source reservoirs are completely evacuated.

Whole-Brain Mapping of Direct Inputs to and Axonal Projections from GABAergic Neurons in the Parafacial Zone.

The GABAergic neurons in the parafacial zone (PZ) play an important role in sleep-wake regulation and have been identified as part of a sleep-promoting center in the brainstem, but the long-range connections mediating this function remain poorly characterized. Here, we performed whole-brain mapping of both the inputs and outputs of the GABAergic neurons in the PZ of the mouse brain. We used the modified rabies virus EnvA-ΔG-DsRed combined with a Cre/loxP gene-expression strategy to map the direct monosynaptic inputs to the GABAergic neurons in the PZ, and found that they receive inputs mainly from the hypothalamic area, zona incerta, and parasubthalamic nucleus in the hypothalamus; the substantia nigra, pars reticulata and deep mesencephalic nucleus in the midbrain; and the intermediate reticular nucleus and medial vestibular nucleus (parvocellular part) in the pons and medulla. We also mapped the axonal projections of the PZ GABAergic neurons with adeno-associated virus, and defined the reciprocal connections of the PZ GABAergic neurons with their input and output nuclei. The newly-found inputs and outputs of the PZ were also listed compared with the literature. This cell-type-specific neuronal whole-brain mapping of the PZ GABAergic neurons may reveal the circuits underlying various functions such as sleep-wake regulation.