Multiple system atrophy (MSA) is a progressive neurological disorder that affects movement and automatic body functions such as blood pressure control. This study followed 17 people with MSA for one year to examine phosphorylated alpha-synuclein, an abnormal protein found in skin nerves. Levels of this protein increased over time and were associated with greater problems in blood pressure regulation and other autonomic symptoms. Skin biopsy results also detected the abnormal protein in two participants whose cerebrospinal fluid tests were negative. These findings suggest that skin biopsies could provide a minimally invasive way to help diagnose MSA, monitor its progression, and identify participants for clinical trials.

Fig.Ìý1

Participant enrollment and classification flow diagram. A total of 23 participants were screened for study inclusion, among whom five were excluded during screening due to advanced multiple system atrophy (MSA) or incidental magnetic resonance imaging findings. Eighteen participants were enrolled, of whom one withdrew prior to follow-up. Seventeen participants completed the 12-month follow-up visit. Final diagnostic classifications based on longitudinal clinical assessments and biomarker data included MSA (nÌý = 10 patients), Parkinson’s disease or dementia with Lewy bodies (PD/DLB,Ìýn = 5), and inconclusive diagnosis (n = 2)

]]> /valiant/2026/09/26/patient-reported-continuity-of-glp-1-receptor-agonist-therapy/ Sat, 26 Sep 2026 21:35:00 +0000 /valiant/?p=7531 Chen, You; Piantek, Gracie; Zhang, Xinmeng; Feng, Yubo; Yan, Chao; Samuels, Jason M.; Rosenbloom, S. Trent; Yu, Danxia; Novak, Laurie L.; Srivastava, Gitanjali. (2026).Ìý.ÌýObesity Science & Practice, 12(5), e70189.Ìý

GLP-1 receptor agonists can produce substantial weight loss, but stopping treatment often leads to weight regain. This study surveyed 261 adults who had experienced a gap in GLP-1 prescriptions to understand challenges in maintaining treatment. Common barriers included insurance approval delays, high out-of-pocket costs, medication shortages, pharmacy delays, and poor coordination between clinics. 91³Ô¹ÏÍø one-third of current medication users reported a treatment gap of at least two weeks. Patients also wanted clearer information about next steps and better digital tools for tracking prescriptions, insurance approvals, and appointments. Overall, the findings suggest that improving care coordination and simplifying medication access could help patients maintain continuous obesity treatment.

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FIGURE 1

Item-level continuity and discontinuity findings. (A)ÌýPercentage of respondents agreeing or strongly agreeing with each continuity item (NÌý=Ìý261). (B)ÌýPercentage of current medication users reporting at least one occurrence of each discontinuity event in the past 6Ìýmonths (nÌý=Ìý143).

]]> /valiant/2026/09/26/current-practices-and-considerations-for-benchmarking-human-gastrointestinal-organoids/ Sat, 26 Sep 2026 21:33:16 +0000 /valiant/?p=7528 Brown, Monica E.; Gleizes, Antoine R.; Yu, Qianhui; Adhikari, Upasana Das; Nagarajan, Rekha; Rout, Prateeksha; Camp, J. Gray; Yilmaz, Omer H.; Tape, Christopher J.; Tyska, Matthew J.; Verzi, Michael P.; Lau, Ken S.; Gracz, Adam D. (2026).Ìý.ÌýCellular and Molecular Gastroenterology and Hepatology, 20(10), 101832.Ìý

Organoids are miniature, laboratory-grown versions of organs that can mimic important features of the human intestine. Since intestinal organoids were first developed from human tissue in 2011, advances in laboratory methods have made them increasingly useful for studying gastrointestinal biology and disease. However, it remains important to determine how accurately and consistently these models represent the real human intestine. This commentary reviews current methods for comparing small intestinal and colon organoids with native tissues, discusses their strengths and limitations, and proposes approaches for improving their accuracy and reproducibility. These efforts will help establish organoids as reliable models for studying human gastrointestinal health and disease.

Figure 1Ìý

Benchmarking human GI organoids across scales.ÌýComparing native GI tissue with organoid models requires multiple approaches that capture different aspects of small intestinal and colonic function. The crypt–villus axis of the small intestine, with a lack of villi in the colon not represented here, as compared with organoids, shows differences in architecture or cell types that can be ascertained using classical imaging techniques. Beyond morphology, assessments of DNA accessibility, epigenomics, gene expression, proteomics, and metabolomics probe distinct aspects of GI and organoid functionality (A). Many of these biological scales have methods that can be performed at either bulk or single-cell resolution: bulk analyses capture overall tissue or organoids characteristics, whereas single-cell approaches reveal cell-specific changes that can be masked by bulk measurements (B).

Immune checkpoint blockade (ICB), a treatment that helps the immune system attack cancer, works well in some colorectal cancers but is largely ineffective in microsatellite-stable (MSS) tumors because they contain few cancer-killing CD8+ T cells. This study investigated DDR1, a protein linked to the exclusion of these immune cells. The researchers found that DDR1 promotes the alignment of collagen fibers around tumors, creating conditions that limit CD8+ T-cell access. A cleaved form of DDR1 was also found in tiny secreted particles called supermeres, which caused fibroblasts—cells that help form tumor-supporting tissue—to contract and reorganize collagen. These findings suggest that DDR1 helps create a physical barrier that keeps immune cells out of MSS colorectal tumors and could be a promising therapeutic target.

Figure 1. High DDR1 immunoreactivity is associated with decreased CD8⁺ÌýT-cell infiltration in MSS colorectal cancer.ÌýA,ÌýRepresentative DDR1, CD8, PanCK, and DAPI immunofluorescence images of three 1-mm tumor cores from an MSS colorectal cancer TMA binned into DDR1 low and high based on epithelial DDR1 fluorescence intensity. Epithelial area outlined with dashed white line. Intraepithelial CD8⁺ÌýT cells are marked by white arrowheads. Scale bars, 200 μm.ÌýBÌýandÌýC,ÌýPirate plots of the relative number of CD8⁺Ìý(B) and CD4⁺Ìý(C) T cells per epithelial area in each DDR1 low and high MSS tumor sample. Each point represents the average of 1 tumor.ÌýNÌý= 58 DDR1-low and 13 DDR1-high patients. ns, not statistically significant; *,ÌýPÌý< 0.05.

Animals depend on coordinated activity among cells and organs to respond to challenges such as danger, food, and infection. However, researchers have lacked a way to observe cellular activity across the whole body in real time. Here, the researchers developed WHOLISTIC, an imaging method that combines fluorescence microscopy, machine learning, and calcium sensors to track cellular activity throughout a living vertebrate body within seconds. Using zebrafish, WHOLISTIC revealed previously unknown responses in cartilage and brain-protective tissues, rhythmic activity in the kidneys, coordination between muscles and organs, and brainstem-controlled changes in blood flow. Combined with optogenetics, which uses light to control specific cells, the method also allowed researchers to investigate cause-and-effect relationships between the brain and body. WHOLISTIC therefore provides a powerful way to study how cells and organs work together and may help identify new biological mechanisms and therapeutic targets.

Fig. 1: WHOLISTIC imaging of intracellular calcium dynamics across the body.

a, Schematic of WHOLISTIC.Ìýb, Transgenic zebrafish (7 days post-fertilization) expressing GCaMP7f pancellularly (Tg(ubi:tTA;TRE:GCaMP7f)), imaged with spinning-disk confocal microscopy; samples were mounted laterally for visceral access.Ìýc, Annotated 3D anatomical model of young zebrafish derived from whole-body expansion microscopyÌý(WB-ExM)Ìýdata.Ìýd, Single fluorescence channel enables identification of organs and tissues via distinct visual textures (enlarged examples).Ìýe, Volumetric imaging acquisition paradigm with typical acquisition parameters. Fluorescence intensity variations reflect changes in intracellular calcium. Images covering an area of 800 μmÌý× 600 μm are acquired in steps of approximately 9 μm, spanning a depth of approximately 130 μm using a ×20 0.75 NA objective.Ìýf, Registration workflow using multiscale iterative estimation of local motion fields.Ìýg, Registration results. Overlay of recording at the start (green) and 1 h into the experiment (magenta; top). The insets show myocytes and gut epithelium before (bottom left) and after (bottom right) registration.Ìýh, Dual-colour imaging (pancellular GCaMP plus membrane-targeted RFP; left), and example tissue (right), showing a 3D model and an enlarged view of the anterior part of pronephric kidney (glomerulus (G) and proximal tubules: descending limb (DL) and ascending limb (AL)).Ìýi, Cellular-scale segmentation (constrained non-negative matrix factorization). The inset shows an enlarged view of the segmentation results for the kidney nephron (1,265 cellular fragments).Ìýj, Coherence-based spectral clustering. The inset shows an enlarged view of clustering results for the kidney nephron (11 functional tissue ensembles).Ìýk, Nephron clusters ordered by time to first calcium activity peak within a burst (left), and raster of cellular activity within a single nephric burst, grouped by functional compartment and ordered within compartments using Rastermap (right). a.u., arbitrary units.Ìýl, Body-wide cellular activity raster ordered using Rastermap. On the right, a spectral cluster assignment is shown for some of the major organ groups. For visualization purposes, a random subset of 50 cells per cluster was included in the plot to reduce density of the figure, which was then ordered by Rastermap.Ìým, Population-level dynamics. Mean activity of example functional tissue ensembles showing distinct dynamic profiles: high-frequency (for example, neurons and muscles), slower-evolving (for example, nephric tissue), tonic bistable (for example, sympathetic ganglion) and oscillatory (for example, enteric tissue) activity.

Temporal lobe epilepsy (TLE) can disrupt brain networks involved in attention, alertness, and thinking. This study examined whether these networks recover after epilepsy surgery, particularly in patients who become seizure-free. Brain scans from 75 patients with TLE and 106 healthy controls showed reduced connectivity involving the nucleus basalis of Meynert (NBM), a brain region important for attention and cognitive function. After surgery, this connectivity improved and became similar to that of healthy controls, with greater recovery seen in patients who were seizure-free. These findings suggest that stopping seizures may allow some disrupted brain networks to recover and that NBM connectivity could help track recovery after epilepsy surgery.

FIGURE 1

Nucleus basalis of Meynert (NBM) exhibits reduced connectivity in patients with temporal lobe epilepsy compared to controls. Violin plots show FisherÌýz-transformed Pearson correlations between ipsilateral NBM node time series (A, B) and the contralateral node time series (C, D) with the frontoparietal association cortex (FPAC) as well as all other nodes in the brain (whole-brain). Data include 75 patients with temporal lobe epilepsy and 106 healthy controls. Voxelwise connectivity maps show threshold-free cluster enhancement (TFCE)-correctedÌýt-statistics, illustrating reductions in ipsilateral (E) and contralateral (F) NBM connectivity in preoperative patients compared to controls. Negative contrasts (patients < controls) are shown, and no positive differences (patients > controls) survived correction. One patient in panel A had functional connectivity (FC) = −7.60, which falls outside the plotted axis range and thus was not shown for visualization purposes. Group differences were assessed using independent samplesÌýt-tests with false discovery rate correction. *p < .05, **p < .01, ***p < .001. C, contralateral; I, ipsilateral.

Prenatal alcohol exposure (PAE) can affect how the brain develops, but its effects on the shape and structure of the brain’s outer layer, or cortex, are not fully understood. Using MRI scans from 121 people aged 7–21 years, including 56 with PAE, we examined several features of cortical structure and how they changed with age. Youth with PAE showed differences in brain folding, groove depth, and structural complexity in specific brain regions compared with unexposed youth. Some of these features also developed differently with age in the PAE group. Overall, the findings suggest that measures of brain folding, particularly gyrification, may be more sensitive to the effects of prenatal alcohol exposure than commonly studied measures such as cortical thickness.

Fig. 1. A: Distribution of ages for participants with PAE (blue) or unexposed (salmon). MRI scans are represented by dots with horizontal lines connecting scans of the same participant. There are 163 scans (73 PAE) from 123 participants (58 PAE). 84 participants had 1 scan, 38 participants had 2, and 1 participant had 3 scans. Figure was created using ggplot2 in R (). B: Flowchart depicting the number of scans (and participants) at each stage of data acquisition and processing. Note that there were no significant group differences in the quality of T1-weighted data or MaCRUISE segmentations.

Electronic health records (EHRs) contain large amounts of patient information that can reveal important patterns in health and disease. Using data from more than 100,000 patients, we applied an automated clustering method to group patients based on similarities in their medical conditions, identifying 70 distinct groups. Age and sex strongly influenced these patterns, while the timing of disease onset was closely related to age and risk of death. Genetic differences also contributed to patterns of disease, although their influence appeared to decrease with age. Several patient groups had particularly high levels of multiple health conditions, and these findings were confirmed in an independent dataset. Overall, this study shows how large-scale EHR data can be used to identify meaningful patterns of co-occurring diseases, aging, and health outcomes.

Fig. 1: Illustration of the study framework using 100,272 eMERGE participants EHR data.

aÌýIllustration of extracting numerical embeddings from a pre-trained patient embedding model as a representation of digital phenotypes.ÌýbÌýIllustration of patient clustering using a Gaussian Mixture model yielding 70 clusters of patients and visualized using T-SNE in two dimensional.ÌýcÌýTopic analysis to characterize patient clusters with distinct comorbidity patterns.ÌýdÌýAnalyzing clusters and phenotype associations with age and sex.ÌýeÌýAnalyzing cross-phenotype association in cluster groups.ÌýfÌýInvestigating mortality rate differences in cluster groups and identifying phenotypes associated with premature aging.ÌýgÌýInvestigation of survival selection and characterizing aging using longitudinal data.

Cancer researchers increasingly rely on high-performance computing (HPC) to run powerful but complex computational tools. However, researchers without specialized computing expertise can face difficulties setting up software, managing data, submitting jobs, and monitoring results. To address these barriers, Memorial Sloan Kettering Cancer Center developed Iris AI, a chatbot that allows researchers to interact with its HPC systems using everyday language. Using AlphaFold3, a tool for predicting protein structures, as an example, Iris AI enables researchers to prepare inputs, launch and monitor GPU-intensive analyses, and view results through conversational commands. A preliminary evaluation with five researchers compared Iris AI with the standard command-line workflow. The system simplified several technical steps, making advanced computational tools more accessible and potentially improving the efficiency and reproducibility of cancer research.

Figure 1.

Iris AI provides a unified tool that integrates the Claude Sonnet (version 4.6), Open OnDemand user interface, and agents that interface with MSKCC’s HPC cluster and an AlphaFold3 container. It supports queries for navigating the on-premises cluster, creating and formatting files, submitting AlphaFold3 jobs, and rendering CIF files in an embedded interactive viewer.

Learning through language requires the brain to rapidly transform words into meaningful concepts, but how this process unfolds over time remains unclear. Using a combination of functional MRI and electroencephalography (EEG), we tracked brain activity in healthy adults as they processed language. We identified a sequence of brain networks that became active within about 700 milliseconds, progressing from visual word recognition to retrieving meaning, interpreting context, integrating sentence structure and meaning, and ultimately forming a coherent concept. Transitions between these stages involved shared brain regions, particularly the left temporoparietal junction. People with stronger language comprehension showed greater involvement of later processes related to interpreting and integrating information. These findings reveal how different brain networks work together over fractions of a second to transform language into conceptual knowledge.

Figure 1

Spatial and temporal perspectives on language processing. Current neuroimaging approaches examine language processing through separate methodological silos, limiting our understanding of how distributed brain networks coordinate dynamically in real time. a) Spatial perspective: brain systems supporting language comprehension, afterÌýFedorenko etÌýal. (2024), showing the distributed architecture of perception of linguistic input (green), core language processing (red), motor planning (navy), and higher-order cognition (purple). Regions are rendered on an inflated left-hemisphere cortical surface from probabilistic functional atlases for the language, multiple demand (light purple), and default mode (dark purple) networks (Lipkin etÌýal. 2022;ÌýYeo etÌýal. 2011), and Harvard-Oxford for the perception and motor areas. While fMRI reveals the spatial organization of these networks, it lacks the temporal resolution to capture their millisecond-scale coordination. b) Temporal perspective: a schematic ERP waveform illustrating the N400 (semantic processing,Ìý~400 ms) and P600 (syntactic/integrative processing,Ìý~600 ms) components that reflect the rapid timecourse of language comprehension. c) Topographical maps illustrating the spatial distribution of ERP effects, reproduced from Schacht etÌýal. (2014, Fig 1B and 1C): the main effect of semantics in the N400 window (top) and the grammaticality-violation effect in the P600 window (bottom). Licensed under CC BY; cropped from the original. While EEG provides precise temporal information about language processing stages, it offers limited spatial resolution for localizing these processes within the distributed brain networks shown in (a). This methodological divide has hindered our understanding of how spatially distributed language networks achieve their rapid, coordinated function during real-world comprehension.

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