Histamine-releasing factor in severe asthma and rhinovirus-associated asthma exacerbation.

BACKGROUND: Histamine-releasing factor (HRF) is implicated in allergic diseases. We previously showed its pathogenic role in murine models of asthma. OBJECTIVE: We aim to present data analysis from 3 separate human samples (sera samples from asthmatic patients, nasal washings from rhinovirus [RV]-infected individuals, and sera samples from patients with RV-induced asthma exacerbation) and 1 mouse sample to investigate correlates of HRF function in asthma and virus-induced asthma exacerbations. METHODS: Total IgE and HRF-reactive IgE/IgG as well as HRF in sera from patients with mild/moderate asthma or severe asthma (SA) and healthy controls (HCs) were quantified by ELISA. HRF secretion in culture media from RV-infected adenovirus-12 SV40 hybrid virus transformed human bronchial epithelial cells and in nasal washings from experimentally RV-infected subjects was analyzed by Western blotting. HRF-reactive IgE/IgG levels in longitudinal serum samples from patients with asthma exacerbations were also quantified. RESULTS: HRF-reactive IgE and total IgE levels were higher in patients with SA than in HCs, whereas HRF-reactive IgG (and IgG1) level was lower in asthmatic patients versus HCs. In comparison with HRF-reactive IgElow asthmatic patients, HRF-reactive IgEhigh asthmatic patients had a tendency to release more tryptase and prostaglandin D2 on anti-IgE stimulation of bronchoalveolar lavage cells. RV infection induced HRF secretion from adenovirus-12 SV40 hybrid virus transformed bronchial epithelial cells, and intranasal RV infection of human subjects induced increased HRF secretion in nasal washes. Asthmatic patients had higher levels of HRF-reactive IgE at the time of asthma exacerbations associated with RV infection, compared with those after the resolution. This phenomenon was not seen in asthma exacerbations without viral infections. CONCLUSIONS: HRF-reactive IgE is higher in patients with SA. RV infection induces HRF secretion from respiratory epithelial cells both in vitro and in vivo. These results suggest the role of HRF in asthma severity and RV-induced asthma exacerbation.

Hyper-reflective dots in optical coherence tomography imaging and inflammation markers in diabetic retinopathy.

The aim of this study is to correlate small dot hyper-reflective foci (HRF) observed in spectral domain optical coherence tomography (SD-OCT) scans of an animal model of hyperglycaemia with focal electroretinography (fERG) response and immunolabelling of retinal markers. The eyes of an animal model of hyperglycaemia showing signs of diabetic retinopathy (DR) were imaged using SD-OCT. Areas showing dot HRF were further evaluated using fERG. Retinal areas enclosing the HRF were dissected and serially sectioned, stained and labelled for glial fibrillary acidic protein (GFAP) and a microglial marker (Iba-1). Small dot HRF were frequently seen in OCT scans in all retinal quadrants in the inner nuclear layer or outer nuclear layer in the DR rat model. Retinal function in the HRF and adjacent areas was reduced compared with normal control rats. Microglial activation was detected by Iba-1 labelling and retinal stress identified by GFAP expression in Müller cells observed in discrete areas around small dot HRF. Small dot HRF seen in OCT images of the retina are associated with a local microglial response. This study provides the first evidence of dot HRF correlating with microglial activation, which may allow clinicians to better evaluate the microglia-mediated inflammatory component of progressive diseases showing HRF.

Across the adult lifespan the ipsilateral sensorimotor cortex negative BOLD response exhibits decreases in magnitude and spatial extent suggesting declining inhibitory control.

Ipsilateral sensorimotor (iSM1) cortex negative BOLD responses (NBR) are observed to unilateral tasks and are thought to reflect a functionally relevant component of sensorimotor inhibition. Evidence suggests that sensorimotor inhibitory mechanisms degrade with age, along with aspects of motor ability and dexterity. However, understanding of age-related changes to NBR is restricted by limited comparisons between young vs old adults groups with relatively small samples sizes. Here we analysed a BOLD fMRI dataset (obtained from the CamCAN repository) of 581 healthy subjects, gender-balanced, sampled from the whole adult lifespan performing a motor response task to an audiovisual stimulus. We aimed to investigate how sensorimotor and default-mode NBR characteristics of magnitude, spatial extent and response shape alter at every decade of the aging process. We observed a linear decrease in iSM1 NBR magnitude across the whole lifespan, whereas the contralateral sensorimotor (cSM1) PBR magnitude was unchanged. An age-related decrease in the spatial extent of NBR and an increase in the ipsilateral positive BOLD response (PBR) was observed. This occurred alongside an increasing negative correlation between subject's iSM1 NBR and cSM1 PBR magnitude, reflecting a change in the balance between cortical excitation and inhibition. Conventional GLM analysis, using a canonical haemodynamic response (HR) function, showed disappearance of iSM1 NBR in subjects over 50 years of age. However, a deconvolution analysis showed that the shape of the iSM1 HR altered throughout the lifespan, with significantly delayed time-to-peak and decreased magnitude. The most significant decreases in iSM1 HR magnitude occurred in older age (>60 years) but the first changes in HR shape and timing occurred as early as 30 years, suggesting the possibility of separate mechanisms underlying these alterations. Reanalysis using data-driven HRs for each decade detected significant sensorimotor NBR into late older age, showing the importance of taking changes in HR morphology into account in fMRI aging studies. These results may reflect fMRI measures of the age-related decreases in transcollosal inhibition exerted upon ipsilateral sensorimotor cortex and alterations to the excitatory-inhibitory balance in the sensorimotor network.

Event-recurring multiband SWIFT functional MRI with 200-ms temporal resolution during deep brain stimulation and isoflurane-induced burst suppression in rat.

PURPOSE: To develop a high temporal resolution functional MRI method for tracking repeating events in the brain. METHODS: We developed a novel functional MRI method using multiband sweep imaging with Fourier transformation (SWIFT), termed event-recurring SWIFT (EVER-SWIFT). The method is able to image similar repeating events with subsecond temporal resolution. Here, we demonstrate the use of EVER-SWIFT for detecting functional MRI responses during deep brain stimulation of the medial septal nucleus and during spontaneous isoflurane-induced burst suppression in the rat brain at 9.4 T with 200-ms temporal resolution. RESULTS: The EVER-SWIFT approach showed that the shapes and time-to-peak values of the response curves to deep brain stimulation significantly differed between downstream brain regions connected to the medial septal nucleus, resembling findings obtained with traditional 2-second temporal resolution. In contrast, EVER-SWIFT allowed for detailed temporal measurement of a spontaneous isoflurane-induced bursting activity pattern, which was not achieved with traditional temporal resolution. CONCLUSION: The EVER-SWIFT technique enables subsecond 3D imaging of both stimulated and spontaneously recurring brain activities, and thus holds great potential for studying the mechanisms of neuromodulation and spontaneous brain activity.

Tailored haemodynamic response function increases detection power of fMRI in awake dogs (Canis familiaris).

Functional magnetic resonance imaging (fMRI) of awake and unrestrained dogs (Canis familiaris) has been established as a novel opportunity for comparative neuroimaging, promising important insights into the evolutionary roots of human brain function and cognition. However, data processing and analysis pipelines are often derivatives of methodological standards developed for human neuroimaging, which may be problematic due to profound neurophysiological and anatomical differences between humans and dogs. Here, we explore whether dog fMRI studies would benefit from a tailored dog haemodynamic response function (HRF). In two independent experiments, dogs were presented with different visual stimuli. BOLD signal changes in the visual cortex during these experiments were used for (a) the identification and estimation of a tailored dog HRF, and (b) the independent validation of the resulting dog HRF estimate. Time course analyses revealed that the BOLD signal in the primary visual cortex peaked significantly earlier in dogs compared to humans, while being comparable in shape. Deriving a tailored dog HRF significantly improved the model fit in both experiments, compared to the canonical HRF used in human fMRI. Using the dog HRF yielded significantly increased activation during visual stimulation, extending from the occipital lobe to the caudal parietal cortex, the bilateral temporal cortex, into bilateral hippocampal and thalamic regions. In sum, our findings provide robust evidence for an earlier onset of the dog HRF in two visual stimulation paradigms, and suggest that using such an HRF will be important to increase fMRI detection power in canine neuroimaging. By providing the parameters of the tailored dog HRF and related code, we encourage and enable other researchers to validate whether our findings generalize to other sensory modalities and experimental paradigms.

rsHRF: A toolbox for resting-state HRF estimation and deconvolution.

The hemodynamic response function (HRF) greatly influences the intra- and inter-subject variability of brain activation and connectivity, and might confound the estimation of temporal precedence in connectivity analyses, making its estimation necessary for a correct interpretation of neuroimaging studies. Additionally, the HRF shape itself is a useful local measure. However, most algorithms for HRF estimation are specific for task-related fMRI data, and only a few can be directly applied to resting-state protocols. Here we introduce rsHRF, a Matlab and Python toolbox that implements HRF estimation and deconvolution from the resting-state BOLD signal. We first provide an overview of the main algorithm, practical implementations, and then demonstrate the feasibility and usefulness of rsHRF by validation experiments with a publicly available resting-state fMRI dataset. We also provide tools for statistical analyses and visualization. We believe that this toolbox may significantly contribute to a better analysis and understanding of the components and variability of BOLD signals.

Multivariate semi-blind deconvolution of fMRI time series.

Whole brain estimation of the haemodynamic response function (HRF) in functional magnetic resonance imaging (fMRI) is critical to get insight on the global status of the neurovascular coupling of an individual in healthy or pathological condition. Most of existing approaches in the literature works on task-fMRI data and relies on the experimental paradigm as a surrogate of neural activity, hence remaining inoperative on resting-stage fMRI (rs-fMRI) data. To cope with this issue, recent works have performed either a two-step analysis to detect large neural events and then characterize the HRF shape or a joint estimation of both the neural and haemodynamic components in an univariate fashion. In this work, we express the neural activity signals as a combination of piece-wise constant temporal atoms associated with sparse spatial maps and introduce an haemodynamic parcellation of the brain featuring a temporally dilated version of a given HRF model in each parcel with unknown dilation parameters. We formulate the joint estimation of the HRF shapes and spatio-temporal neural representations as a multivariate semi-blind deconvolution problem in a paradigm-free setting and introduce constraints inspired from the dictionary learning literature to ease its identifiability. A fast alternating minimization algorithm, along with its efficient implementation, is proposed and validated on both synthetic and real rs-fMRI data at the subject level. To demonstrate its significance at the population level, we apply this new framework to the UK Biobank data set, first for the discrimination of haemodynamic territories between balanced groups (n=24 individuals in each) patients with an history of stroke and healthy controls and second, for the analysis of normal aging on the neurovascular coupling. Overall, we statistically demonstrate that a pathology like stroke or a condition like normal brain aging induce longer haemodynamic delays in certain brain areas (e.g. Willis polygon, occipital, temporal and frontal cortices) and that this haemodynamic feature may be predictive with an accuracy of 74 % of the individual's age in a supervised classification task performed on n=459 subjects.

Simultaneous fMRI and fast-scan cyclic voltammetry bridges evoked oxygen and neurotransmitter dynamics across spatiotemporal scales.

The vascular contributions of neurotransmitters to the hemodynamic response are gaining more attention in neuroimaging studies, as many neurotransmitters are vasomodulatory. To date, well-established electrochemical techniques that detect neurotransmission in high magnetic field environments are limited. Here, we propose an experimental setting enabling simultaneous fast-scan cyclic voltammetry (FSCV) and blood oxygenation level-dependent functional magnetic imaging (BOLD fMRI) to measure both local tissue oxygen and dopamine responses, and global BOLD changes, respectively. By using MR-compatible materials and the proposed data acquisition schemes, FSCV detected physiological analyte concentrations with high temporal resolution and spatial specificity inside of a 9.4 T MRI bore. We found that tissue oxygen and BOLD correlate strongly, and brain regions that encode dopamine amplitude differences can be identified via modeling simultaneously acquired dopamine FSCV and BOLD fMRI time-courses. This technique provides complementary neurochemical and hemodynamic information and expands the scope of studying the influence of local neurotransmitter release over the entire brain.

Changes in Hemodynamic Response Function Resulting From Chronic Alcohol Consumption.

BACKGROUND: Functional MRI (fMRI) task-related analyses rely on an estimate of the brain's hemodynamic response function (HRF) to model the brain's response to events. Although changes in the HRF have been found after acute alcohol administration, the effects of heavy chronic alcohol consumption on the HRF have not been explored, and the potential benefits or pitfalls of estimating each individual's HRF on fMRI analyses of chronic alcohol use disorder (AUD) are not known. METHODS: Participants with AUD and controls (CTL) received structural, functional, and vascular scans. During fMRI, participants were cued to tap their fingers, and averaged responses were extracted from the motor cortex. Curve fitting on these HRFs modeled them as a difference between 2 gamma distributions, and the temporal occurrence of the main peak and undershoot of the HRF was computed from the mean of the first and second gamma distributions, respectively. RESULTS: ANOVA and regression analyses found that the timing of the HRF undershoot increased significantly as a function of total lifetime drinking. Although gray matter volume in the motor cortex decreased with lifetime drinking, this was not sufficient to explain undershoot timing shifts, and vascular factors measured in the motor cortex did not differ among groups. Comparison of random-effects analyses using custom-fitted and canonical HRFs for CTL and AUD groups showed better results throughout the brain for custom-fitted versus canonical HRFs for CTL subjects. For AUD subjects, the same was true except for the basal ganglia. CONCLUSIONS: These findings suggest that excessive alcohol consumption is associated with changes in the HRF undershoot. HRF changes could provide a possible biomarker for the effects of lifetime drinking on brain function. Changes in HRF topography affect fMRI activation measures, and subject-specific HRFs generally improve fMRI activation results.

Introduction of the harpinXooc-encoding gene hrf2 in soybean enhances resistance against the oomycete pathogen Phytophthora sojae.

Phytophthora root and stem rot (PRR) caused by an oomycete pathogen Phytophthora sojae is one of the most devastating and widespread diseases throughout soybean-producing regions worldwide. The diversity and variability of P. sojae races make effective control of the pathogen challenging. Here, we introduced an elicitor of plant defense response, the harpinXooc-encoding hrf2 gene from the rice bacterial pathogen Xanthomonas oryzae pv. oryzicola into soybean and evaluated resistance to P. sojae infection. Molecular analysis confirmed the integration and expression of hrf2 in the transgenic soybean. After inoculation with P. sojae, non-transformed control (NC) plants exhibited typical PRR symptoms, including necrotic and wilting leaves, and plant death, whereas most of the transgenic plants showed slightly chlorotic leaves and developed normally. Through T3 to T5 generations, the transgenic events displayed milder disease symptoms and had higher survival rates compared to NC plants, indicating enhanced and stable resistance to P. sojae infection, whereas without P. sojae inoculation, no significant differences in agronomic traits were observed between the transgenic and non-transformed plants. Moreover, after inoculation with P. sojae, significant upregulation of a set of plant defense-related genes, including salicylic acid- and jasmonic acid-dependent and hypersensitive response-related genes was observed in the transgenic plants. Our results indicate that hrf2 expression in transgenic soybean significantly enhanced resistance to P. sojae by eliciting multiple defense responses mediated by different signaling pathways. The potential functional role of the hrf2 gene in plant defense against P. sojae and other pathogens makes it a promising tool for broadening disease resistance in soybean.

Macroscale variation in resting-state neuronal activity and connectivity assessed by simultaneous calcium imaging, hemodynamic imaging and electrophysiology.

Functional imaging of spontaneous activity continues to play an important role in the field of connectomics. The most common imaging signal used for these experiments is the blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) signal, but how this signal relates to spontaneous neuronal activity remains incompletely understood. Genetically encoded calcium indicators represent a promising tool to study this problem, as they can provide brain-wide measurements of neuronal activity compared to point measurements afforded by electrophysiological recordings. However, the relationship between the calcium signal and neurophysiological parameters at the mesoscopic scale requires further systematic characterization. Therefore, we collected simultaneous resting-state measurements of electrophysiology, along with calcium and hemodynamic imaging, in lightly anesthetized mice to investigate two aims. First, we examined the relationship between each imaging signal and the simultaneously recorded electrophysiological signal in a single brain region, finding that both signals are better correlated with multi-unit activity compared to local field potentials, with the calcium signal possessing greater signal-to-noise ratio and regional specificity. Second, we used the resting-state imaging data to model the relationship between the calcium and hemodynamic signals across the brain. We found that this relationship varied across brain regions in a way that is consistent across animals, with delays increasing by600 ms towards posterior cortical regions. Furthermore, while overall functional connectivity (FC) measured by the hemodynamic signal is significantly correlated with FC measured by calcium, the two estimates were found to be significantly different. We hypothesize that these differences arise at least in part from the observed regional variation in the hemodynamic response. In total, this work highlights some of the caveats needed in interpreting hemodynamic-based measurements of FC, as well as the need for improved modeling methods to reduce this potential source of bias.

Hemodynamic response function (HRF) variability confounds resting-state fMRI functional connectivity.

PURPOSE: fMRI is the convolution of the hemodynamic response function (HRF) and unmeasured neural activity. HRF variability (HRFv) across the brain could, in principle, alter functional connectivity (FC) estimates from resting-state fMRI (rs-fMRI). Given that HRFv is driven by both neural and non-neural factors, it is problematic when it confounds FC. However, this aspect has remained largely unexplored even though FC studies have grown exponentially. We hypothesized that HRFv confounds FC estimates in the brain's default-mode-network. METHODS: We tested this hypothesis using both simulations (where the ground truth is known and modulated) as well as rs-fMRI data obtained in a 7T MRI scanner (N = 47, healthy). FC was obtained using 2 pipelines: data with hemodynamic deconvolution (DC) to estimate the HRF and minimize HRFv, and data with no deconvolution (NDC, HRFv-ignored). DC and NDC FC networks were compared, along with regional HRF differences, revealing potential false connectivities that resulted from HRFv. RESULTS: We found evidence supporting our hypothesis using both simulations and experimental data. With simulations, we found that HRFv could cause a change of up to 50% in FC. With rs-fMRI, several potential false connectivities attributable to HRFv, with majority connections being between different lobes, were identified. We found a double exponential relationship between the magnitude of HRFv and its impact on FC, with a mean/median error of 30.5/11.5% caused in FC by HRF confounds. CONCLUSION: HRFv, if ignored, could cause identification of false FC. FC findings from HRFv-ignored data should be interpreted cautiously. We suggest deconvolution to minimize HRFv.

BOLD neurovascular coupling does not change significantly with normal aging.

Studies of cognitive function that compare the blood oxygenation level dependent (BOLD) signal across age groups often require the assumption that neurovascular coupling does not change with age. Tests of this assumption have produced mixed results regarding the strength of the coupling and its relative time course. Using deconvolution, we found that age does not have a significant effect on the time course of the hemodynamic impulse response function or on the slope of the BOLD versus stimulus duration relationship. These results suggest that in cognitive studies of healthy aging, group differences in BOLD activation are likely due to age-related changes in cognitive-neural interactions and information processing rather than to impairments in neurovascular coupling. Hum Brain Mapp 38:3538-3551, 2017. © 2017 Wiley Periodicals, Inc.

Genes influence the amplitude and timing of brain hemodynamic responses.

In functional magnetic resonance imaging (fMRI), the hemodynamic response function (HRF) reflects regulation of regional cerebral blood flow in response to neuronal activation. The HRF varies significantly between individuals. This study investigated the genetic contribution to individual variation in HRF using fMRI data from 125 monozygotic (MZ) and 149 dizygotic (DZ) twin pairs. The resemblance in amplitude, latency, and duration of the HRF in six regions in the frontal and parietal lobes was compared between MZ and DZ twin pairs. Heritability was estimated using an ACE (Additive genetic, Common environmental, and unique Environmental factors) model. The genetic influence on the temporal profile and amplitude of HRF was moderate to strong (24%-51%). The HRF may be used in the genetic analysis of diseases with a cerebrovascular etiology.

Data-driven HRF estimation for encoding and decoding models.

Despite the common usage of a canonical, data-independent, hemodynamic response function (HRF), it is known that the shape of the HRF varies across brain regions and subjects. This suggests that a data-driven estimation of this function could lead to more statistical power when modeling BOLD fMRI data. However, unconstrained estimation of the HRF can yield highly unstable results when the number of free parameters is large. We develop a method for the joint estimation of activation and HRF by means of a rank constraint, forcing the estimated HRF to be equal across events or experimental conditions, yet permitting it to differ across voxels. Model estimation leads to an optimization problem that we propose to solve with an efficient quasi-Newton method, exploiting fast gradient computations. This model, called GLM with Rank-1 constraint (R1-GLM), can be extended to the setting of GLM with separate designs which has been shown to improve decoding accuracy in brain activity decoding experiments. We compare 10 different HRF modeling methods in terms of encoding and decoding scores on two different datasets. Our results show that the R1-GLM model outperforms competing methods in both encoding and decoding settings, positioning it as an attractive method both from the points of view of accuracy and computational efficiency.

Deconvolution of neural dynamics from fMRI data using a spatiotemporal hemodynamic response function.

Functional magnetic resonance imaging (fMRI) is a powerful and broadly used means of non-invasively mapping human brain activity. However fMRI is an indirect measure that rests upon a mapping from neuronal activity to the blood oxygen level dependent (BOLD) signal via hemodynamic effects. The quality of estimated neuronal activity hinges on the validity of the hemodynamic model employed. Recent work has demonstrated that the hemodynamic response has non-separable spatiotemporal dynamics, a key property that is not implemented in existing fMRI analysis frameworks. Here both simulated and empirical data are used to demonstrate that using a physiologically based model of the spatiotemporal hemodynamic response function (stHRF) results in a quantitative improvement of the estimated neuronal response relative to unphysical space-time separable forms. To achieve this, an integrated spatial and temporal deconvolution is established using a recently developed stHRF. Simulated data allows the variation of key parameters such as noise and the spatial complexity of the neuronal drive, while knowing the neuronal input. The results demonstrate that the use of a spatiotemporally integrated HRF can avoid "ghost" neuronal responses that can otherwise be falsely inferred. Applying the spatiotemporal deconvolution to high resolution fMRI data allows the recovery of neuronal responses that are consistent with independent electrophysiological measures.

Richness of information about novel words influences how episodic and semantic memory networks interact during lexicalization.

The complementary learning systems account of declarative memory suggests two distinct memory networks, a fast-mapping, episodic system involving the hippocampus, and a slower semantic memory system distributed across the neocortex in which new information is gradually integrated with existing representations. In this study, we investigated the extent to which these two networks are involved in the integration of novel words into the lexicon after extensive learning, and how the involvement of these networks changes after 24h. In particular, we explored whether having richer information at encoding influences the lexicalization trajectory. We trained participants with two sets of novel words, one where exposure was only to the words' phonological forms (the form-only condition), and one where pictures of unfamiliar objects were associated with the words' phonological forms (the picture-associated condition). A behavioral measure of lexical competition (indexing lexicalization) indicated stronger competition effects for the form-only words. Imaging (fMRI) results revealed greater involvement of phonological lexical processing areas immediately after training in the form-only condition, suggesting that tight connections were formed between novel words and existing lexical entries already at encoding. Retrieval of picture-associated novel words involved the episodic/hippocampal memory system more extensively. Although lexicalization was weaker in the picture-associated condition, overall memory strength was greater when tested after a 24hour delay, probably due to the availability of both episodic and lexical memory networks to aid retrieval. It appears that, during lexicalization of a novel word, the relative involvement of different memory networks differs according to the richness of the information about that word available at encoding.

Depth-compensated diffuse optical tomography enhanced by general linear model analysis and an anatomical atlas of human head.

One of the main challenges in functional diffuse optical tomography (DOT) is to accurately recover the depth of brain activation, which is even more essential when differentiating true brain signals from task-evoked artifacts in the scalp. Recently, we developed a depth-compensated algorithm (DCA) to minimize the depth localization error in DOT. However, the semi-infinite model that was used in DCA deviated significantly from the realistic human head anatomy. In the present work, we incorporated depth-compensated DOT (DC-DOT) with a standard anatomical atlas of human head. Computer simulations and human measurements of sensorimotor activation were conducted to examine and prove the depth specificity and quantification accuracy of brain atlas-based DC-DOT. In addition, node-wise statistical analysis based on the general linear model (GLM) was also implemented and performed in this study, showing the robustness of DC-DOT that can accurately identify brain activation at the correct depth for functional brain imaging, even when co-existing with superficial artifacts.

Human relevance framework for rodent liver tumors induced by the insecticide sulfoxaflor.

Sulfoxaflor, a novel active substance that targets sap-feeding insects, induced rodent hepatotoxicity when administered at high dietary doses. Specifically, hepatocellular adenomas and carcinomas increased after 18 months in male and female CD-1 mice at 750 and 1250 ppm, respectively, and hepatocellular adenomas increased after 2 years in male F344 rats at 500 ppm. Studies to determine the mode of action (MoA) for these liver tumors were performed in an integrated and prospective manner as part of the standard battery of toxicology studies such that the MoA data were available prior to, or by the time of, the completion of the carcinogenicity studies. Sulfoxaflor is not genotoxic and the MoA data support the following key events in the etiology of the rodent liver tumors: (1) CAR nuclear receptor activation and (2) hepatocellular proliferation. The MoA data were evaluated in a weight of evidence approach using the Bradford Hill criteria for causation and were found to align with dose and temporal concordance, biological plausibility, coherence, strength, consistency, and specificity for a CAR-mediated MoA while excluding other alternate MoAs. The available data include: activation of CAR, Cyp2b induction, hepatocellular hypertrophy and hyperplasia, absence of liver effects in KO mice, absence of proliferation in humanized mice, and exclusion of other possible mechanisms (e.g., genotoxicity, cytotoxicity, AhR, or PPAR activation), and indicate that the identified rodent liver tumor MoA for sulfoxaflor would not occur in humans. In this case, sulfoxaflor is considered not to be a potential human liver carcinogen.

Novel Artificial Intelligence-Based Assessment of Imaging Biomarkers in Full-Thickness Macular Holes: Preliminary Data from a Pivotal Trial.

Artificial intelligence (AI)- and deep learning (DL)-based systems have shown significant progress in the field of macular disorders, demonstrating high performance in detecting retinal fluid and assessing anatomical changes during disease progression. This study aimed to validate an AI algorithm for identifying and quantifying prognostic factors in visual recovery after macular hole (MH) surgery by analyzing major optical coherence tomography (OCT) biomarkers. This study included 20 patients who underwent vitrectomy for a full-thickness macular hole (FTMH). The mean diameter of the FTMH was measured at 285.36 ± 97.4 µm. The preoperative best-corrected visual acuity (BCVA) was 0.76 ± 0.06 logMAR, improving to 0.38 ± 0.16 postoperatively, with a statistically significant difference (p = 0.001). AI software was utilized to assess biomarkers, such as intraretinal fluid (IRF) and subretinal fluid (SRF) volume, external limiting membrane (ELM) and ellipsoid zone (EZ) integrity, and retinal hyperreflective foci (HRF). The AI analysis showed a significant decrease in IRF volume, from 0.08 ± 0.12 mm3 preoperatively to 0.01 ± 0.01 mm3 postoperatively. ELM interruption improved from 79% ± 18% to 34% ± 37% after surgery (p = 0.006), whereas EZ interruption improved from 80% ± 22% to 40% ± 36% (p = 0.007) postoperatively. Additionally, the study revealed a negative correlation between preoperative IRF volume and postoperative BCVA recovery, suggesting that greater preoperative fluid volumes may hinder visual improvement. The integrity of the ELM and EZ was found to be essential for postoperative visual acuity improvement, with their disruption negatively impacting visual recovery. The study highlights the potential of AI in quantifying OCT biomarkers for managing MHs and improving patient care.