Plasma taurine level is linked to symptom burden and clinical outcomes in post-COVID condition.

BACKGROUND: A subset of individuals (10-20%) experience post-COVID condition (PCC) subsequent to initial SARS-CoV-2 infection, which lacks effective treatment. PCC carries a substantial global burden associated with negative economic and health impacts. This study aims to evaluate the association between plasma taurine levels with self-reported symptoms and adverse clinical outcomes in patients with PCC. METHODS AND FINDINGS: We analyzed the plasma proteome and metabolome of 117 individuals during their acute COVID-19 hospitalization and at the convalescence phase six-month post infection. Findings were compared with 28 age and sex-matched healthy controls. Plasma taurine levels were negatively associated with PCC symptoms and correlated with markers of inflammation, tryptophan metabolism, and gut dysbiosis. Stratifying patients based on the trajectories of plasma taurine levels during six-month follow-up revealed a significant association with adverse clinical events. Increase in taurine levels during the transition to convalescence were associated with a reduction in adverse events independent of comorbidities and acute COVID-19 severity. In a multivariate analysis, increased plasma taurine level between acute and convalescence phase was associated with marked protection from adverse clinical events with a hazard ratio of 0.13 (95% CI: 0.05-0.35; p<0.001). CONCLUSIONS: Taurine emerges as a promising predictive biomarker and potential therapeutic target in PCC. Taurine supplementation has already demonstrated clinical benefits in various diseases and warrants exploration in large-scale clinical trials for alleviating PCC.

Transfer learning predicts species-specific drug interactions in emerging pathogens.

Machine learning (ML) algorithms are necessary to efficiently identify potent drug combinations within a large candidate space to combat drug resistance. However, existing ML approaches cannot be applied to emerging and under-studied pathogens with limited training data. To address this, we developed a transfer learning and crowdsourcing framework (TACTIC) to train ML models on data from multiple bacteria. TACTIC was built using 2,965 drug interactions from 12 bacterial strains and outperformed traditional ML models in predicting drug interaction outcomes for species that lack training data. Top TACTIC model features revealed genetic and metabolic factors that influence cross-species and species-specific drug interaction outcomes. Upon analyzing ~600,000 predicted drug interactions across 9 metabolic environments and 18 bacterial strains, we identified a small set of drug interactions that are selectively synergistic against Gram-negative (e.g., A. baumannii) and non-tuberculous mycobacteria (NTM) pathogens. We experimentally validated synergistic drug combinations containing clarithromycin, ampicillin, and mecillinam against M. abscessus, an emerging pathogen with growing levels of antibiotic resistance. Lastly, we leveraged TACTIC to propose selectively synergistic drug combinations to treat bacterial eye infections (endophthalmitis).

Identification of Reaction Network Hypotheses for Complex Feedstocks from Spectroscopic Measurements with Minimal Human Intervention.

In this work, we detail an automated reaction network hypothesis generation protocol for processes involving complex feedstocks where information about the species and reactions involved is unknown. Our methodology is process agnostic and can be utilized in any reactive process with spectroscopic measurements that provide information on the evolution of the components in the mixture. We decompose the mixture spectra to obtain spectroscopic signatures of the individual components and use a 1-D convolutional neural network to automatically identify functional groups indicated by them. We employ atom-atom mapping to automatically recover reaction rules that are applied on candidate molecules identified from chemistry databases through fingerprint similarity. The method is tested on synthetic data and on spectroscopic measurements of lab-scale batch hydrothermal liquefaction (HTL) of biomass to determine the accuracy of prediction across datasets of varying complexities. Our methodology is able to identify reaction network hypotheses containing reaction networks close to the ground truth in the case of synthetic data, and we are also able to recover candidate molecules and reaction networks close to the ones reported in the previous literature studies for biomass pyrolysis.

Protocol to identify biomarkers in patients with post-COVID condition using multi-omics and machine learning analysis of human plasma.

Here, we present a workflow for analyzing multi-omics data of plasma samples in patients with post-COVID condition (PCC). Applicable to various diseases, we outline steps for data preprocessing and integrating diverse assay datasets. Then, we detail statistical analysis to unveil plasma profile changes and identify biomarker-clinical variable associations. The last two steps discuss machine learning techniques for unsupervised clustering of patients based on their inherent molecular similarities and feature selection to identify predictive biomarkers. For complete details on the use and execution of this protocol, please refer to Wang et al.1.

Comparative analysis of outcomes of two (popular) techniques of haptic exteriorization in scleral fixation intra-ocular lens surgery - A retrospective study.

PURPOSE: To compare the visual outcomes and complication rates between the extra-ocular needle-guided haptic insertion technique (XNIT) and the conventional handshake (HS) technique of scleral fixation intra-ocular lens (SFIOL). METHODS: In this retrospective study, we retrieved data of those patients who had undergone SFIOL surgery from January 2018 to May 2022 at our institute for aphakia following either a complicated cataract surgery or an ocular trauma and had a minimum follow-up of 3 months. RESULTS: Of the 156 eyes, the HS technique was done in 80 eyes and the remaining 76 eyes with XNIT. At 3 months follow-up visit, there was no significant difference in the median best corrected visual acuity (BCVA) ( P = 0.988) and uncorrected visual acuity (UCVA) ( P = 0.765) between the two techniques. There was no statistically significant difference between pre-operative median BCVA and post-operative UCVA in XNIT ( P = 0.961) and the HS technique ( P = 0.831) at 3 months follow-up visit. The complication rates between the two techniques were minimal and comparable. The most common post-operative complication was corneal edema. The incidence of cystoid macular edema was slightly more in the XNIT group but not statistically significant ( P = 0.05). Two patients in the HS group developed retinal detachment, which settled after repeat surgery. CONCLUSION: The newer XNIT technique was found to be as safe and effective as compared to the conventional HS technique.

Adaptive Tensor-Based Feature Extraction for Pupil Segmentation in Cataract Surgery.

Cataract surgery remains the only definitive treatment for visually significant cataracts, which are a major cause of preventable blindness worldwide. Successful performance of cataract surgery relies on stable dilation of the pupil. Automated pupil segmentation from surgical videos can assist surgeons in detecting risk factors for pupillary instability prior to the development of surgical complications. However, surgical illumination variations, surgical instrument obstruction, and lens material hydration during cataract surgery can limit pupil segmentation accuracy. To address these problems, we propose a novel method named adaptive wavelet tensor feature extraction (AWTFE). AWTFE is designed to enhance the accuracy of deep learning-powered pupil recognition systems. First, we represent the correlations among spatial information, color channels, and wavelet subbands by constructing a third-order tensor. We then utilize higher-order singular value decomposition to eliminate redundant information adaptively and estimate pupil feature information. We evaluated the proposed method by conducting experiments with state-of-the-art deep learning segmentation models on our BigCat dataset consisting of 5,700 annotated intraoperative images from 190 cataract surgeries and a public CaDIS dataset. The experimental results reveal that the AWTFE method effectively identifies features relevant to the pupil region and improved the overall performance of segmentation models by up to 2.26% (BigCat) and 3.31% (CaDIS). Incorporation of the AWTFE method led to statistically significant improvements in segmentation performance (P < 1.29 × 10-10 for each model) and yielded the highest-performing model overall (Dice coefficients of 94.74% and 96.71% for the BigCat and CaDIS datasets, respectively). In performance comparisons, the AWTFE consistently outperformed other feature extraction methods in enhancing model performance. In addition, the proposed AWTFE method significantly improved pupil recognition performance by up to 2.87% in particularly challenging phases of cataract surgery.

Tensor-based Feature Extraction for Pupil Recognition in Cataract Surgery.

Cataract surgery remains the definitive treatment for cataracts, which are a major cause of preventable blindness worldwide. Adequate and stable dilation of the pupil are necessary for the successful performance of cataract surgery. Pupillary instability is a known risk factor for cataract surgery complications, and the accurate segmentation of the pupil from surgical video streams can enable the analysis of intraoperative pupil changes in cataract surgery. However, pupil segmentation performance can suffer due to variations in surgical illumination, obscuration of the pupil with surgical instruments, and hydration of the lens material intraoperatively. To overcome these challenges, we present a novel method called tensor-based pupil feature extraction (TPFE) to improve the accuracy of pupil recognition systems. We analyzed the efficacy of this approach with experiments performed on a dataset of 4,560 intraoperative annotated images from 190 cataract surgeries in human patients. Our results indicate that TPFE can identify features relevant to pupil segmentation and that pupil segmentation with state-of-the-art deep learning models can be significantly improved with the TPFE method.

Sequential multi-omics analysis identifies clinical phenotypes and predictive biomarkers for long COVID.

The post-acute sequelae of COVID-19 (PASC), also known as long COVID, is often associated with debilitating symptoms and adverse multisystem consequences. We obtain plasma samples from 117 individuals during and 6 months following their acute phase of infection to comprehensively profile and assess changes in cytokines, proteome, and metabolome. Network analysis reveals sustained inflammatory response, platelet degranulation, and cellular activation during convalescence accompanied by dysregulation in arginine biosynthesis, methionine metabolism, taurine metabolism, and tricarboxylic acid (TCA) cycle processes. Furthermore, we develop a prognostic model composed of 20 molecules involved in regulating T cell exhaustion and energy metabolism that can reliably predict adverse clinical outcomes following discharge from acute infection with 83% accuracy and an area under the curve (AUC) of 0.96. Our study reveals pertinent biological processes during convalescence that differ from acute infection, and it supports the development of specific therapies and biomarkers for patients suffering from long COVID.

Stimulus representations in visual cortex shaped by spatial attention and microsaccades.

Microsaccades (MSs) are commonly associated with spatially directed attention, but how they affect visual processing is still not clear. We studied MSs in a task in which the animal was randomly cued to attend to a target stimulus and ignore distractors, and it was rewarded for detecting a color change in the target. We found that the enhancement of firing rates normally found with attention to a cued stimulus was delayed until the first MS directed towards that stimulus. Once that MS occurred, attention to the target was engaged and there were persistent effects of attention on firing rates for the remainder of the trial. These effects were found in the superficial and deep layers of V4 as well as the lateral pulvinar and IT cortex. Although the tuning curves of V4 cells do not change depending on the locus of spatial attention, we found pronounced effects of MS direction on stimulus representations that persisted for the length of the trial in V4. In intervals following a MS towards the target in the RF, stimulus decoding from population activity was substantially better than in intervals following a MS away from the target. Likewise, turning curves of cells were substantially sharper following a MS towards the target in the RF. This sharpening appeared to result from both a "refreshing" of the initial transient sensory response to stimulus onset, and a magnification of the effects of attention in this condition. MSs to the target also enhanced the neuronal response to the behaviorally relevant target color change and led to faster reaction times. These results thus reveal a major link between spatial attention, object processing and its coordination with eye movements.

Characterization of synthetic antigen binding fragments targeting Toc75 for the isolation of TOC in A. thaliana and P. sativum.

Roughly 95% of the proteins that make up the chloroplast must be imported from the cytoplasm. The machinery responsible for the translocation of these cargo proteins is called the translocon at the outer membrane of chloroplast (TOC). The TOC core consists of three proteins, Toc34, Toc75, and Toc159; no high-resolution structure has been solved of fully assembled TOC from plants. Efforts toward determining the structure of the TOC have been hindered almost entirely by difficulties in producing sufficient yields for structural studies. In this study, we introduce an innovative method that utilizes synthetic antigen binding fragments (sABs) to isolate TOC directly from wild-type plant biomass including A. thaliana and P. sativum. Binding between the sABs and the POTRA domains was characterized by size-exclusion chromatography coupled with small-angle X-ray scattering (SEC-SAXS), X-ray crystallography, and isothermal titration calorimetry. We also demonstrate the isolation of the TOC from P. sativum, laying the framework for large-scale isolation and purification of TOC for functional and structural studies.

Impact of public sentiments on the transmission of COVID-19 across a geographical gradient.

COVID-19 is a respiratory disease caused by a recently discovered, novel coronavirus, SARS-COV-2. The disease has led to over 81 million confirmed cases of COVID-19, with close to two million deaths. In the current social climate, the risk of COVID-19 infection is driven by individual and public perception of risk and sentiments. A number of factors influences public perception, including an individual's belief system, prior knowledge about a disease and information about a disease. In this article, we develop a model for COVID-19 using a system of ordinary differential equations following the natural history of the infection. The model uniquely incorporates social behavioral aspects such as quarantine and quarantine violation. The model is further driven by people's sentiments (positive and negative) which accounts for the influence of disinformation. People's sentiments were obtained by parsing through and analyzing COVID-19 related tweets from Twitter, a social media platform across six countries. Our results show that our model incorporating public sentiments is able to capture the trend in the trajectory of the epidemic curve of the reported cases. Furthermore, our results show that positive public sentiments reduce disease burden in the community. Our results also show that quarantine violation and early discharge of the infected population amplifies the disease burden on the community. Hence, it is important to account for public sentiment and individual social behavior in epidemic models developed to study diseases like COVID-19.

Human Fecal Bile Acid Analysis after Investigational Microbiota-Based Live Biotherapeutic Delivery for Recurrent Clostridioides difficile Infection.

Microbiome-based therapeutics are increasingly evaluated as a strategy to reduce recurrent Clostridioides difficile infection (rCDI), with proposed mechanisms including restoration of the microbiota and microbiota-mediated functions, such as bile acid (BA) metabolism. This study reports a quantitative and sensitive assay for targeted metabolomic assessment, and the application of the assay to profile BA composition in a Phase 2 trial of the investigational microbiota-based live biotherapeutic RBX2660 for reduction of rCDI. A liquid chromatography tandem mass spectrometry method was developed to extract and quantify 35 BAs from 113 participant stool samples from 27 RBX2660-treated rCDI participants in the double-blinded, placebo-controlled clinical trial. The results demonstrate a high-confidence assay as represented by sensitivity, linearity, accuracy, and precision. Furthermore, the assay enabled the observation of primary BAs as the dominant BA species at baseline in stool samples from clinical trial participants, consistent with the expected loss of commensals after broad-spectrum antibiotic treatment. After RBX2660 administration, there was a significant drop in primary BAs concurrent with increased secondary BAs that sustained through 24 months post-RBX2660. Taken together, we describe a robust assay that demonstrates altered BA metabolism in rCDI patients treated with RBX2660 administration.

Discovery of associative patterns between workplace sound level and physiological wellbeing using wearable devices and empirical Bayes modeling.

We conducted a field study using multiple wearable devices on 231 federal office workers to assess the impact of the indoor environment on individual wellbeing. Past research has established that the workplace environment is closely tied to an individual's wellbeing. Since sound is the most-reported environmental factor causing stress and discomfort, we focus on quantifying its association with physiological wellbeing. Physiological wellbeing is represented as a latent variable in an empirical Bayes model with heart rate variability measures-SDNN and normalized-HF as the observed outcomes and with exogenous factors including sound level as inputs. We find that an individual's physiological wellbeing is optimal when sound level in the workplace is at 50 dBA. At lower (<50dBA) and higher (>50dBA) amplitude ranges, a 10 dBA increase in sound level is related to a 5.4% increase and 1.9% decrease in physiological wellbeing respectively. Age, body-mass-index, high blood pressure, anxiety, and computer use intensive work are person-level factors contributing to heterogeneity in the sound-wellbeing association.

It's about time: Linking dynamical systems with human neuroimaging to understand the brain.

Most human neuroscience research to date has focused on statistical approaches that describe stationary patterns of localized neural activity or blood flow. While these patterns are often interpreted in light of dynamic, information-processing concepts, the static, local, and inferential nature of the statistical approach makes it challenging to directly link neuroimaging results to plausible underlying neural mechanisms. Here, we argue that dynamical systems theory provides the crucial mechanistic framework for characterizing both the brain's time-varying quality and its partial stability in the face of perturbations, and hence, that this perspective can have a profound impact on the interpretation of human neuroimaging results and their relationship with behavior. After briefly reviewing some key terminology, we identify three key ways in which neuroimaging analyses can embrace a dynamical systems perspective: by shifting from a local to a more global perspective, by focusing on dynamics instead of static snapshots of neural activity, and by embracing modeling approaches that map neural dynamics using "forward" models. Through this approach, we envisage ample opportunities for neuroimaging researchers to enrich their understanding of the dynamic neural mechanisms that support a wide array of brain functions, both in health and in the setting of psychopathology.

Pharmacological, Pharmacokinetic, Pharmacodynamic and Physicochemical Characterization of FE 205030: A Potent, Fast Acting, Injectable CGRP Receptor Antagonist for the Treatment of Acute Episodic Migraine.

The neuropeptide calcitonin gene-related peptide (CGRP) is known to play a central role in the underlying pathophysiology of migraine. In comparison to the effective triptan class of antimigraine treatments, the CGRP antagonists possess a comparable efficacy but a superior cardiovascular safety profile in patients. This paper describes the development of selective and potent peptidic CGRP antagonist, FE 205030, that has a fast onset of action and an optimal half-life (subcutaneous Tmax ~ 60 min, and t1/2 ~ 4.4 h in 80 kg pigs, respectively), which is key to prevention of the progression of debilitating migraine symptoms. The in vivo efficacy of this agent has been established a translational pharmacodynamic model (inhibition of capsaicin-induced increase in skin blood flow) in cynomolgus monkeys and shows maximal inhibitory activity at circulating concentrations of 30-100 nM. Antagonist activity of FE 205030 was characterized on CGRP-induced vasodilation in isolated human mesenteric resistance arteries in an ex vivo isometric myograph study, and FE 205030 effectively blocked CGRP-induced vasodilation with a pA2 of 9.3 ± 0.1, mean ± standard error. Multispecies allometric scaling and modeling of subcutaneous (SC) effective concentrations indicates that a dose of 10-30 mg/day is sufficient to achieve a drug exposure/target coverage of 8h, which is useful to prevent migraine recurrence in patients. The molecule also possesses appropriate physicochemical properties that allows for a convenient dosing form factor of 1 ml injection volume with a sufficient solubility and acceptable short-term stability, optimal for treatment of acute migraine episodes in patients. Hence, FE 205030 may provide an important fast-acting injectable option for patients suffering from frequent acute migraine episodes, complementary to preventative monoclonal antibodies and oral small molecule CGRP-R antagonist therapies.

A descriptive analysis of medical abortion commodity availability and pricing at retail outlets in 44 countries across four regions globally.

Pharmacies in low- and middle-income countries play an important role in increasing the availability of medical abortion to individuals for self-use. We aimed to document the costs to users of medical abortion products at outlets across geographies and understand the diversity of available products, primarily in low- and middle-income countries or in places where access to abortion is restricted. A descriptive analysis of price data was completed for identified medical abortion products at retail outlets visited in 44 countries from November 2017 to February 2018. Median prices and ranges are reported in $US for mifepristone 200 mg tablets, misoprostol 200 mcg tablets, and combipacks. Misoprostol, mifepristone, and combipacks were found in 44, 19, and 16 countries, respectively. Nearly two-thirds of products (321/508) required a prescription. The median price of misoprostol was $0.63 per tablet (range $0.09-$27.63) based on 304 price points. Mifepristone and combipacks had fewer price points available (n = 59 and n = 44, respectively). Median prices were $11.78 per mifepristone tablet (range $1.77-$37.83) and $11.18 per combipack (range $3.50-$35.86). Overall, prices were highest in Latin America and lowest in South/Southeast Asia. Only 11.5% (7/61) of the total unique misoprostol brands were quality-assured (i.e. approved by a stringent regulatory authority or pre-qualified by the World Health Organization), compared to 25.0% (4/16) of unique combipack products. There was wide variation in product pricing and availability across settings. The infrequent availability of mifepristone and combipacks, in addition to the limited availability of quality-assured medicines and high cost of abortion medications, are important factors affecting access to high-quality abortion care.

Structure Determination of Membrane Proteins Using X-Ray Crystallography.

Membrane proteins serve essential roles in all aspects of life and make up roughly one-third of all genomes from prokaryotes to eukaryotes. Their responsibilities include mediating cell signaling, nutrient import, waste export, cellular communication, trafficking, and immunity. For their critical role in many cellular processes, membrane proteins serve as targets for up to 50% of drugs currently on the market and remain primary targets in new therapeutics being developed. Despite their importance and abundance in nature, only ~1% of structures in the Protein Data Bank are of transmembrane proteins. This discrepancy can be directly attributed to the biochemical properties of membrane proteins and the difficulty in producing sufficient yields for structural studies or the difficulty in growing well-ordered crystals. Here, we present methods from our work that outline our general pipeline from cloning to structure determination of membrane proteins, with a focus on using X-ray crystallography, which still yields ~90% of all structures being deposited into the Protein Data Bank.

Novel High-Throughput Strategy for the Aqueous Solubility Assessment of Peptides and Proteins Exhibiting a Propensity for Gelation: Application to the Discovery of Novel Antibacterial Teixobactin Analogues.

A novel high-throughput aqueous solubility assay was developed for peptides and proteins exhibiting a high gelling propensity (in this case, antibacterial teixobactin analogues). By integrating the assessment of gel formation, as indicated by an increase in the solution viscosity, into the peptide equilibrium solubility screening assay, we were able to estimate the "free-flowing solubility", which is defined as the concentration at which the peptide solution not only is fully dissolved but also is a liquid exhibiting ideal flowing characteristics. In this workflow, peptide solutions passing the turbidity assessment were further screened by viscosity measurements based on nanobead-assisted dynamic light scattering analysis in a 96-well plate. The method is able to effectively detect the initiation of peptide gelation and facilitate compound ranking based on their aqueous solubility. The application of such an approach helped confirm that the substitution of Ser3 in teixobactin led to desired physicochemical improvements and provided a focal point for further chemistry structure-activity relationship exploration.

Automated fall risk assessment of elderly using wearable devices.

INTRODUCTION: Falls cause major expenses in the healthcare sector. We investigate the ability of supporting a fall risk assessment by introducing algorithms for automated assessments of standardized fall risk-related tests via wearable devices. METHODS: In a study, 13 participants conducted the standardized 6-Minutes Walk Test, the Timed-Up-and-Go Test, the 30-Second Sit-to-Stand Test, and the 4-Stage Balance Test repeatedly, producing 226 tests in total. Automatedalgorithms computed by wearable devices, as well as a visual analysis of the recorded data streams, were compared to the observational results conducted by physiotherapists. RESULTS: There was a high congruence between automated assessments and the ground truth for all four test types (ranging from 78.15% to 96.55%), with deviations ranging all well within one standard deviation of the ground truth. Fall risk (assessed by questionnaire) correlated with the individual tests. CONCLUSIONS: The automated fall risk assessment using wearable devices and algorithms matches the validity of the ground truth, thus providing a resourceful alternative to the effortful observational assessment, while minimizing the risk of human error. No single test can predict overall fall risk; instead, a much more complex model with additional input parameters (e.g., fall history, medication etc.) is needed.