Search

Electrophysiological research has been widely utilized to study brain responses to acoustic stimuli. The frequency-following response (FFR), a non-invasive reflection of how the brain encodes acoustic stimuli, is a particularly propitious electrophysiologic measure. While the FFR has been studied extensively, there are limitations in obtaining and analyzing FFR recordings that recent machine learning algorithms may address. In this study, we aimed to investigate whether FFRs can be enhanced using an “improved” source-separation machine learning algorithm. For this study, we recruited 28 native speakers of American English with normal hearing. We obtained two separate FFRs from each participant while they listened to two stimulus tokens /i/ and /da/. Electroencephalographic signals were pre-processed and analyzed using a source-separation non-negative matrix factorization (SSNMF) machine learning algorithm. The algorithm was trained using individual, grand-averaged, or stimulus token spectrograms as a reference. A repeated measures analysis of variance revealed that FFRs were significantly enhanced (p< .001) when the “improved” SSNMF algorithm was trained using both individual and grand-averaged spectrograms, but not when utilizing the stimulus token spectrogram. Similar results were observed when extracting FFRs elicited by using either stimulus token, /i/ or /da/. This demonstration shows how the SSNMF machine learning algorithm, using individual and grand-averaged spectrograms as references in training the algorithm, significantly enhanced FFRs. This improvement has important implications for the obtainment and analytical processes of FFR, which may lead to advancements in clinical applications of FFR testing.

In this study, we explore the feasibility and performance of detecting scalp-recorded frequency-following responses (FFRs) with a specialized machine learning (ML) model. By leveraging the strengths of feature extraction of the source separation non-negative matrix factorization (SSNMF) algorithm and its adeptness in handling limited training data, we adapted the SSNMF algorithm into a specialized ML model with a hybrid architecture to enhance FFR detection amidst background noise. We recruited 40 adults with normal hearing and evoked their scalp recorded FFRs using the English vowel/i/with a rising pitch contour. The model was trained on FFR-present and FFR-absent conditions, and its performance was evaluated using sensitivity, specificity, efficiency, false-positive rate, and false-negative rate metrics. This study revealed that the specialized SSNMF model achieved heightened sensitivity, specificity, and efficiency in detecting FFRs as the number of recording sweeps increased. Sensitivity exceeded 80% at 500 sweeps and maintained over 89% from 1000 sweeps onwards. Similarly, specificity and efficiency also improved rapidly with increasing sweeps. The progressively enhanced sensitivity, specificity, and efficiency of this specialized ML model underscore its practicality and potential for broader applications. These findings have immediate implications for FFR research and clinical use, while paving the way for further advancements in the assessment of auditory processing.

Purpose: The uncertainty on the management of small adrenal incidentalomas (AIs) still represents a challenge in real clinical practice. Considering the lack of knowledge on risk factors implicated in tumour enlargement, the aim of this study was to identify risk factors for morphological changes during follow-up of adrenal incidentalomas (AIs). Methods: We retrospectively evaluated demographic, clinical, radiological and biochemical parameters of 153 AIs (2007–2021). Patients with histological diagnosis of metastases or pheochromocytoma were excluded. To detect risk factors for tumor enlargement, diseases associated with AIs were included if their prevalence was higher than 2%. Patients were divided into two groups (A: radiological stability; B: tumor enlargement defined as > 5 mm/year in the main diameter). Results: Group A: 89.5% and group B: 10.5%, mean follow-up 38.6 ± 6.9 months (range 6–240). Tumor enlargement when occurred was within 36 months of follow-up. In group B high body weight (p&lt; 0.03), dehydroepiandrosterone sulfate (DHEAS) (p&lt; 0.05) and direct renin concentration (DRC) (p&lt; 0.04) were higher than group A, while aldosterone levels were lower; moreover, considering comorbidities, glaucoma and dysglycemia (p&lt; 0.01 for both) had higher prevalence in group B. Glaucoma and dysglycemia were independent predictors of enlargement. Patients affected by glaucoma, atrial fibrillation, dysglycemia had a lower dimensional change-free survival than non-affected. Conclusions: Glaucoma might be a novel risk factor for AI enlargement. If subtle undetectable cortisol hypersecretion has a role is a topic for further research.

Abstract: Purpose: This study was designed to assess the safety and outcomes achieved with stapled transanal rectal resection vs. biofeedback training in obstructed defecation patients. Methods: A total of 119 women patients who suffered from obstructed defecation with associated rectocele and rectal intussusception were randomized to stapled transanal rectal resection or biofeedback training. Stapled transanal rectal resection was performed by using two circular staplers to produce transanal full-thickness rectal resection. Primary outcome was symptoms of obstructed defecation resolution at 12 months; secondary outcomes included safety, change in quality of life score, and anatomic correction of rectocele and rectal intussusception. Results: Fourteen percent (8/59) stapled transanal rectal resection and 50 percent (30/60) biofeedback training patients withdrew early. Eight (15 percent) patients treated with stapled transanal rectal resection and 1 (2 percent) biofeedback patient experienced adverse events. One serious adverse event (bleeding) occurred after stapled transanal rectal resection. Scores of obstructed defecation improved significantly in both groups as did quality of life (both P?

Introduction:Kallmann syndrome (KS) is a genetically heterogeneous developmental disorder that most often manifests hypogonadotropic hypogonadism (HH) and hypo-/anosmia due to early embryonic impairment in the migration of gonadotropin-releasing hormone neurons. SOX10(SRY-Box 10; MIM*602229), a key transcriptional activator involved in the development of neural crest cells, has been associated with KS and is identified as one of the causative genes of Waardenburg syndrome (WS). Case Presentation:A 28-year-old female patient, who was clinically diagnosed with KS in her childhood, presented with HH and anosmia, mild bilateral sensorineural hearing loss (SNHL), and pigmentation abnormalities. Next-generation sequencing analysis detected a missense heterozygous SOX10pathogenic variant (NM_006941.4:c.506C>T) in the proposita and in her mother, whose phenotype included exclusively anosmia and hypopigmented skin patches. The same variant has been described by Pingault et al. [Clin Genet. 2015;88(4):352–9] in a patient with apparently isolated bilateral severe SNHL. Conclusion:Our finding substantiates the extreme phenotypic variability of SOX10-related disorders, which range from classical KS and/or WS to contracted endophenotypes that could share a common pathway in the development of neural crest cells and highlights the need for careful evaluation and long-term follow-up of SOX10patients, with special focus on atypical/additional and/or late-onset phenotypic traits.

The ontology underlying the Wikidata knowledge graph (KG) has not been formalized. Instead, its semantics emerges bottom-up from the use of its classes and properties. Flexible guidelines and rules have been defined by the Wikidata project for the use of its ontology, however, it is still often difficult to reuse the ontology’s constructs. Based on the assumption that identifying ontology design patterns from a knowledge graph contributes to making its (possibly) implicit ontology emerge, in this paper we present a method for extracting what we term empiricalontology design patterns (EODPs) from a knowledge graph. This method takes as input a knowledge graph and extracts EODPs as sets of axioms/constraints involving the classes instantiated in the KG. These EODPs include data about the probability of such axioms/constraints happening. We apply our method on two domain-specific portions of Wikidata, addressing the musicand art, architecture, and archaeologydomains, and we compare the empirical ontology design patterns we extract with the current support present in Wikidata. We show how these patterns can provide guidance for the use of the Wikidata ontology and its potential improvement, and can give insight into the content of (domain-specific portions of) the Wikidata knowledge graph.

Source-Separation Non-Negative Matrix Factorization (SSNMF) is a mathematical algorithm recently developed to extract scalp-recorded frequency-following responses (FFRs) from noise. Despite its initial success, the effects of silent intervals on algorithm performance remain undetermined. Our purpose in this study was to determine the effects of silent intervals on the extraction of FFRs, which are electrophysiological responses that are commonly used to evaluate auditory processing and neuroplasticity in the human brain. We used an English vowel /i/ with a rising frequency contour to evoke FFRs in 23 normal-hearing adults. The stimulus had a duration of 150 ms, while the silent interval between the onset of one stimulus and the offset of the next one was also 150 ms. We computed FFR Enhancementand Noise Residueto estimate algorithm performance, while silent intervals were either included (i.e., the WithSIcondition) or excluded (i.e., the WithoutSIcondition) in our analysis. The FFR Enhancementsand Noise Residuesobtained in the WithoutSIcondition were significantly better (p< .05) than those obtained in the WithSIcondition. On average, the exclusion of silent intervals produced a 11.78% increment in FFR Enhancementand a 20.69% decrement in Noise Residue. These results not only quantify the effects of silent intervals on the extraction of human FFRs, but also provide recommendations for designing and improving the SSNMF algorithm in future research.

Multimodal astrocyte–neuron communications govern brain circuitry assembly and function1. For example, through rapid glutamate release, astrocytes can control excitability, plasticity and synchronous activity2,3of synaptic networks, while also contributing to their dysregulation in neuropsychiatric conditions4–7. For astrocytes to communicate through fast focal glutamate release, they should possess an apparatus for Ca2+-dependent exocytosis similar to neurons8–10. However, the existence of this mechanism has been questioned11–13owing to inconsistent data14–17and a lack of direct supporting evidence. Here we revisited the astrocyte glutamate exocytosis hypothesis by considering the emerging molecular heterogeneity of astrocytes18–21and using molecular, bioinformatic and imaging approaches, together with cell-specific genetic tools that interfere with glutamate exocytosis in vivo. By analysing existing single-cell RNA-sequencing databases and our patch-seq data, we identified nine molecularly distinct clusters of hippocampal astrocytes, among which we found a notable subpopulation that selectively expressed synaptic-like glutamate-release machinery and localized to discrete hippocampal sites. Using GluSnFR-based glutamate imaging22in situ and in vivo, we identified a corresponding astrocyte subgroup that responds reliably to astrocyte-selective stimulations with subsecond glutamate release events at spatially precise hotspots, which were suppressed by astrocyte-targeted deletion of vesicular glutamate transporter 1 (VGLUT1). Furthermore, deletion of this transporter or its isoform VGLUT2 revealed specific contributions of glutamatergic astrocytes in cortico-hippocampal and nigrostriatal circuits during normal behaviour and pathological processes. By uncovering this atypical subpopulation of specialized astrocytes in the adult brain, we provide insights into the complex roles of astrocytes in central nervous system (CNS) physiology and diseases, and identify a potential therapeutic target.

Determining the CT-derived muscle status enabled prediction of clinical outcomes (intensive care unit admission and death during hospitalization) in patients with COVID-19.

ABSTRACTThe polymorphism L412F in TLR3 has been associated with several infectious diseases. However, the mechanism underlying this association is still unexplored. Here, we show that the L412F polymorphism in TLR3 is a marker of severity in COVID-19. This association increases in the sub-cohort of males. Impaired macroautophagy/autophagy and reduced TNF/TNFα production was demonstrated in HEK293 cells transfected with TLR3L412F-encoding plasmid and stimulated with specific agonist poly(I:C). A statistically significant reduced survival at 28 days was shown in L412F COVID-19 patients treated with the autophagy-inhibitor hydroxychloroquine (p = 0.038). An increased frequency of autoimmune disorders such as co-morbidity was found in L412F COVID-19 males with specific class II HLA haplotypes prone to autoantigen presentation. Our analyses indicate that L412F polymorphism makes males at risk of severe COVID-19 and provides a rationale for reinterpreting clinical trials considering autophagy pathways.Abbreviations:AP: autophagosome; AUC: area under the curve; BafA1: bafilomycin A1; COVID-19: coronavirus disease-2019; HCQ: hydroxychloroquine; RAP: rapamycin; ROC: receiver operating characteristic; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2; TLR: toll like receptor; TNF/TNF-α: tumor necrosis factor

ABSTRACTIntroductionInterleukin 23 (IL-23) is a pro-inflammatory cytokine that plays a protective role against bacterial and fungal infections. However, the dysregulation of the IL-23/IL-17 axis provides a solid substrate for the development of various inflammatory diseases, such as psoriatic arthritis (PsA) and ankylosing spondylitis (AS).Areas coveredIn different clinical trials, several drugs against IL-23 have shown efficacy and safety toward PsA, with excellent results on skin and joint scores. However, the same drugs did not show the same efficacy in AS, suggesting that IL-23 may not be a relevant driver of the pathobiology and clinical symptoms of active axial spondyloarthritis (axSpA).Expert opinionThese drugs have shown an excellent efficacy and a good safety profile toward PsA, while in AS the efficacy of the IL-23 blockade is lacking for reasons not yet known. Several hypotheses have been reported, but further studies will be needed for a greater understanding. This suggests the involvement of pathways or mechanisms for the development of SpA that remain unknown. In order to allow a wide use of IL-23 inhibitors, further clinical trials and long-term prospective studies are necessary.

Purpose: Hypogonadism is frequent in HIV-infected men and might impact on metabolic and sexual health. Low testosterone results from either primary testicular damage, secondary hypothalamic-pituitary dysfunction, or from liver-derived sex-hormone-binding-globulin (SHBG) elevation, with consequent reduction of free testosterone. The relationship between liver fibrosis and hypogonadism in HIV-infected men is unknown. Aim of our study was to determine the prevalence and type of hypogonadism in a cohort of HIV-infected men and its relationship with liver fibrosis. Methods: We performed a cross-sectional retrospective study including 107 HIV-infected men (median age 54 years) with hypogonadal symptoms. Based on total testosterone (TT), calculated free testosterone, and luteinizing hormone, five categories were identified: eugonadism, primary, secondary, normogonadotropic and compensated hypogonadism. Estimates of liver fibrosis were performed by aspartate aminotransferase (AST)-to-platelet ratio index (APRI) and Fibrosis-4 (FIB-4) scores. Results: Hypogonadism was found in 32/107 patients (30.8%), with normogonadotropic (10/107, 9.3%) and compensated (17/107, 15.8%) being the most frequent forms. Patients with secondary/normogonadotropic hypogonadism had higher body mass index (BMI) (p&lt; 0001). Patients with compensated hypogonadism had longer HIV infection duration (p= 0.031), higher APRI (p= 0.035) and FIB-4 scores (p= 0.008), and higher HCV co-infection. Univariate analysis showed a direct significant correlation between APRI and TT (p= 0.006) and SHBG (p= 0.002), and between FIB-4 and SHBG (p= 0.045). Multivariate analysis showed that SHBG was independently associated with both liver fibrosis scores. Conclusion: Overt and compensated hypogonadism are frequently observed among HIV-infected men. Whereas obesity is related to secondary hypogonadism, high SHBG levels, related to liver fibrosis degree and HCV co-infection, are responsible for compensated forms.

Background: COVID-19 pandemic has currently no vaccines. Thus, the only feasible solution for prevention relies on the detection of COVID-19-positive cases through quick and accurate testing. Since artificial intelligence (AI) offers the powerful mechanism to automatically extract the tissue features and characterise the disease, we therefore hypothesise that AI-based strategies can provide quick detection and classification, especially for radiological computed tomography (CT) lung scans. Methodology: Sixmodels, two traditional machine learning (ML)-based (k-NN and RF), two transfer learning (TL)-based (VGG19 and InceptionV3), and the last two were our custom-designed deep learning (DL) models (CNN and iCNN), were developed for classification between COVID pneumonia (CoP) and non-COVID pneumonia (NCoP). K10 cross-validation (90% training: 10% testing) protocol on an Italian cohort of 100 CoP and 30 NCoP patients was used for performance evaluation and bispectrum analysis for CT lung characterisation. Results: Using K10 protocol, our results showed the accuracy in the order of DL > TL > ML, ranging the six accuracies for k-NN, RF, VGG19, IV3, CNN, iCNN as 74.58 ± 2.44%, 96.84 ± 2.6, 94.84 ± 2.85%, 99.53 ± 0.75%, 99.53 ± 1.05%, and 99.69 ± 0.66%, respectively. The corresponding AUCs were 0.74, 0.94, 0.96, 0.99, 0.99, and 0.99 (p-values &lt; 0.0001), respectively. Our Bispectrum-based characterisation system suggested CoP can be separated against NCoP using AI models. COVID risk severity stratification also showed a high correlation of 0.7270 (p&lt; 0.0001) with clinical scores such as ground-glass opacities (GGO), further validating our AI models. Conclusions: We prove our hypothesis by demonstrating that all the six AI models successfully classified CoP against NCoP due to the strong presence of contrasting features such as ground-glass opacities (GGO), consolidations, and pleural effusion in CoP patients. Further, our online system takes &lt; 2 s for inference.

In patients suspected of having coronavirus disease 2019 (COVID-19) who have two consecutive negative nasopharyngeal swab tests, high-probability CT infection measured with Society of Thoracic Radiology/Radiological Society of North America and COVID-19 Reporting and Data System scores were associated with bronchoalveolar lavage findings.

Ontology Design Patterns (ODPs) have become an established and recognised practice for guaranteeing good quality ontology engineering. There are several ODP repositories where ODPs are shared as well as ontology design methodologies recommending their reuse. Performing rigorous testing is recommended as well for supporting ontology maintenance and validating the resulting resource against its motivating requirements. Nevertheless, it is less than straightforward to find guidelines on how to apply such methodologies for developing domain-specific knowledge graphs. ArCo is the knowledge graph of Italian Cultural Heritage and has been developed by using eXtreme Design (XD), an ODP- and test-driven methodology. During its development, XD has been adapted to the need of the CH domain e.g. gathering requirements from an open, diverse community of consumers, a new ODP has been defined and many have been specialised to address specific CH requirements. This paper presents ArCo and describes howto apply XD to the development and validation of a CH knowledge graph, also detailing the (intellectual) process implemented for matching the encountered modelling problems to ODPs. Relevant contributions also include a novel web tool for supporting unit-testing of knowledge graphs, a rigorous evaluation of ArCo, and a discussion of methodological lessons learned during ArCo’s development.

Understanding the changes in diverse molecular pathways underlying the development of breast tumors is critical for improving diagnosis, treatment, and drug development. Here, we used RNA-profiling of canine mammary tumors (CMTs) coupled with a robust analysis framework to model molecular changes in human breast cancer. Our study leveraged a key advantage of the canine model, the frequent presence of multiple naturally occurring tumors at diagnosis, thus providing samples spanning normal tissue and benign and malignant tumors from each patient. We showed human breast cancer signals, at both expression and mutation level, are evident in CMTs. Profiling multiple tumors per patient enabled by the CMT model allowed us to resolve statistically robust transcription patterns and biological pathways specific to malignant tumors versus those arising in benign tumors or shared with normal tissues. We showed that multiple histological samples per patient is necessary to effectively capture these progression-related signatures, and that carcinoma-specific signatures are predictive of survival for human breast cancer patients. To catalyze and support similar analyses and use of the CMT model by other biomedical researchers, we provide FREYA, a robust data processing pipeline and statistical analyses framework.

ObjectiveThe University of California Los Angeles Scleroderma Clinical Trials Consortium Gastrointestinal Tract 2.0 (GIT 2.0) instrument is a self-report tool measuring gastrointestinal (GI) quality of life in patients with systemic sclerosis (SSc). Scarce data are available on the correlation between patient-reported GI symptoms and motility dysfunction as assessed by esophageal transit scintigraphy (ETS).MethodsWe evaluated the GIT 2.0 reflux scale in patients with SSc admitted to our clinic and undergoing ETS, and correlated their findings.ResultsThirty-one patients with SSc undergoing ETS were included. Twenty-seven were female, and 9 had diffuse cutaneous SSc. Twenty-six of 31 (84%) patients had a delayed transit and an abnormal esophageal emptying activity (EA); they also had a higher GIT 2.0 reflux score (P= 0.04). Mean EA percentage was higher in patients with none to mild GIT 2.0 reflux score (81.1 [SD 11.5]) than in those with moderate (55.7 [SD 17.8], P= 0.003) and severe to very severe scores (55.8 [SD 19.7], P= 0.002). The percentage of esophageal EA negatively correlated with the GIT 2.0 reflux score (r = –0.68, P< 0.0001), but it did not correlate with the other GIT 2.0 scales and the total GIT 2.0 score.ConclusionSSc patients with impaired ETS findings have a higher GIT 2.0 reflux score. The GIT 2.0 is a complementary tool for objective measurement of esophageal involvement that can be easily administered in day-to-day clinical assessment.

Automated, low-speed shuttles are being deployed to help solve the first-mile/last-mile problem in several cities worldwide. To achieve full automation, each of the roles and responsibilities of the operator must be considered. This research aimed to address how increased ridership, abrupt emergency stops, and the operator influenced the development of trust in riders. Surveys and video footage were collected from riders, as well as the operator on-board. Results suggested that increased ridership with the shuttle predicts more positive experiences and confidence in the technology. However, riders that experienced one or more unexpected emergency stops during shuttle operation were less trusting of the technology. In addition, we found that the backup operator actively worked to foster rider trust. These findings suggest that several challenges will need to be addressed in order to develop and maintain rider trust in low speed automated shuttles when an operator is no longer present.

Semi-automated vehicles (Level-2) provide driving assistance, but they still require driver supervision to maintain safe driving. However, little is known about potential differences in drivers’ cognitive states during manual vs. Level-2 automated driving. The current study systematically examined the effects of manual and Level-2 driving on drivers’ arousal and workload during on-road driving. No differences between the two driving modes were found for the five outcomes that assessed cognitive arousal and workload (i.e., heart rate, root mean square of successive heart period differences, EEG alpha power, and hit rate and reaction time on a secondary task). A Bayes Factor analysis suggested that there is strong evidence that cognitive arousal and workload during Level-2 driving did not differ from manual driving. These novel and theoretically meaningful findings provide strong evidence of similar cognitive arousal and workload states in Level-2 automation and manual driving.

Of 725 consecutive hospitalized patients with coronavirus disease 2019, 108 (15%) had acute neurologic symptoms necessitating neurologic imaging.

Variations in the Forkhead Box G1(FOXG1)gene cause FOXG1syndrome spectrum, including the congenital variant of Rett syndrome, characterized by early onset of regression, Rett-like and jerky movements, and cortical visual impairment. Due to the largely unknown pathophysiological mechanisms downstream the impairment of this transcriptional regulator, a specific treatment is not yet available. Since both haploinsufficiency and hyper-expression of FOXG1cause diseases in humans, we reasoned that adding a gene under nonnative regulatory sequences would be a risky strategy as opposed to a genome editing approach where the mutated gene is reversed into wild-type. Here, we demonstrate that an adeno-associated viruses (AAVs)-coupled CRISPR/Cas9 system is able to target and correct FOXG1variants in patient-derived fibroblasts, induced Pluripotent Stem Cells (iPSCs) and iPSC-derived neurons. Variant-specific single-guide RNAs (sgRNAs) and donor DNAs have been selected and cloned together with a mCherry/EGFP reporter system. Specific sgRNA recognition sequences were inserted upstream and downstream Cas9 CDS to allow self-cleavage and inactivation. We demonstrated that AAV serotypes vary in transduction efficiency depending on the target cell type, the best being AAV9 in fibroblasts and iPSC-derived neurons, and AAV2 in iPSCs. Next-generation sequencing (NGS) of mCherry+/EGFP+transfected cells demonstrated that the mutated alleles were repaired with high efficiency (20–35% reversion) and precision both in terms of allelic discrimination and off-target activity. The genome editing strategy tested in this study has proven to precisely repair FOXG1and delivery through an AAV9-based system represents a step forward toward the development of a therapy for Rett syndrome.

Background The use of flow diverter stent (FDS) has limitations in cases of subarachnoid haemorrhage caused by ruptured aneurysm, due to the need for double antiplatelet therapy and the delay in the aneurysm occluding. The p48 MW and the p64 MW (Phenox) are available with Hydrophilic Polymer Coating (HPC), that reduces the risk of thrombus formation. Purpose of this study is to evaluate the safety and efficacy of p48 and p64 MW HPC with single antiplatelet therapy for the acute treatment of ruptured aneurysm.Methods We retrospectively evaluated all patients treated for acutely ruptured aneurysms with a p48 MW HPC or p64 MW HPC from October 2019 to April 2020 using single antiplatelet therapy. For each patient, we considered demographic and aneurysm-related data, clinical presentation, size and location of the implanted flow diverter stent, intra- and post-procedural complications, aneurysm occlusion.Results Seven patients were included. The ruptured aneurysms were four saccular, two blister-like and one dissecting, six in the anterior and one in posterior circulation. No intraprocedural stent thrombosis and rebleeding was observed. In two cases the aneurysm is completely excluded, in one patient it was found only neck perfusion, in three cases there were mild reduction of the sac and in one case there was a persistent perfusion. No patients needed retreatment in this series.Conclusion In our experience, FDS HPC appears a potential treatment option in selected cases. Our study is limited by small population and short-term follow-up. We report our preliminary data, but further investigations are necessary.

Weight stigma is reportedly globalizing, but there is limited evidence of how concerns around weight change are impacting those in many places. Additionally, few studies to date have considered how weight stigma operates in the context of interventions that purposefully eschew from a focus on weight itself. We have used qualitative analysis to examine body acceptance and responses to weight stigma among urban Brazilian gordawomen participating in two forms of Health at Every Size®(HAES®) interventions: an intensive HAES®program (I-HAES®, n=26), which directly addressed weight stigma and a traditional, less-intensive HAES®program (CTRL, n=13). Individual, semi-structured interviews regarding how participants felt about their bodies were conducted post-intervention. Exploratory content analysis followed an inductive approach. The I-HAES®-group was more prone to accept their bodies, to feel well-being, to do new things, and to give proactive responses to weight stigma, while the CTRL-group internalized and accepted stigma, reported a lack of body acceptance, and indicated they were less able to challenge stigma in their lives. Interdisciplinary, intensive HAES®interventions appear able to meaningfully tackle responses to weight stigma and promote body acceptance, important in contexts where stigma is a major component of how women experience their weight.