Temporal Alterations Research Articles

PMCT findings can vary considerably with the postmortem interval (PMI), potentially complicating the assessment of pulmonary thromboembolism (PTE). Although attenuation changes in antemortem thrombi have been investigated, the postmortem evolution of thrombus imaging characteristics remains insufficiently characterized. We present the case of a man in his 70s who died of PTE. Serial non-contrast PMCT was performed at 1h and 96h postmortem. On the initial scan, the thrombus in the left pulmonary artery measured 45 Hounsfield units (HU), while the adjacent vascular lumen measured 58 HU. On the delayed scan, thrombus attenuation increased to 70 HU, while vascular lumen attenuation decreased to 30 HU. Consequently, the contrast ratio between thrombus and lumen increased from 13% to 40%, thereby enhancing thrombus conspicuity. At autopsy, bilateral pulmonary artery thromboemboli were confirmed. The progressive enhancement in thrombus conspicuity over time may reflect postmortem physiological processes such as plasma separation and hypostasis, contrasting with the attenuation decline typically described in antemortem thrombi. Despite potential variability of CT acquisition parameters, relative attenuation differences may provide more reliable diagnostic information. To our knowledge, this is the first report to demonstrate time-dependent imaging evolution of pulmonary thromboemboli using serial non-contrast PMCT. Recognition of such temporal imaging alterations may enhance the diagnostic utility of PMCT and guide the development of time-sensitive postmortem imaging protocols in forensic radiology.

Temporal and spatial patterns of secondary motor cortex calcium activity in cocaine self-administration: A study using miniScope imaging and machine learning.

BACKGROUNDSepsis is a life-threatening condition defined by organ dysfunction, triggered by a dysregulated host response to infection. there is limited published literature combining cognitive impairment with topological property alterations in brain networks in sepsis survivors. Therefore, we employed graph theory and Granger causality analysis (GCA) methods to analyze resting-state functional magnetic resonance imaging (rs-fMRI) data, aiming to explore the topological alterations in the brain networks of intensive care unit (ICU) sepsis survivors. Using correlation analysis, the interplay between topological property alterations and cognitive impairment was also investigated.AIMTo explore the topological alterations of the brain networks of sepsis survivors and their correlation with cognitive impairment.METHODSSixteen sepsis survivors and nineteen healthy controls from the community were recruited. Within one month after discharge, neurocognitive tests were administered to assess cognitive performance. Rs-fMRI was acquired and the topological properties of brain networks were measured based on graph theory approaches. GCA was conducted to quantify effective connectivity (EC) between brain regions showing positive topological alterations and other regions in the brain. The correlations between topological properties and cognitive were analyzed.RESULTSSepsis survivors exhibited significant cognitive impairment. At the global level, sepsis survivors showed lower normalized clustering coefficient (γ) and small-worldness (σ) than healthy controls. At the local level, degree centrality (DC) and nodal efficiency (NE) decreased in the right orbital part of inferior frontal gyrus (ORBinf.R), NE decreased in the left temporal pole of superior temporal gyrus (TPOsup.L) whereas DC and NE increased in the right cerebellum Crus 2 (CRBLCrus2.R). Regarding directional connection alterations, EC from left cerebellum 6 (CRBL6.L) to ORBinf.R and EC from TPOsup.L to right cerebellum 1 (CRBLCrus1.R) decreased, whereas EC from right lingual gyrus (LING.R) to TPOsup.L increased. The implementation of correlation analysis revealed a negative correlation between DC in CRBLCrus2.R and both Mini-mental state examination (r = -0.572, P = 0.041) and Montreal cognitive assessment (MoCA) scores (r = -0.629, P = 0.021) at the local level. In the CRBLCrus2.R cohort, a negative correlation was identified between NE and MoCA scores, with a statistically significant result of r = -0.633 and P = 0.020.CONCLUSIONFrontal, temporal and cerebellar topological property alterations are possibly associated with cognitive impairment of ICU sepsis survivors and may serve as biomarkers for early diagnosis.

Gaining insight into the spectral and temporal alterations in brain connectivity associated with Alzheimer’s disease (AD) may offer pathways toward more informative biomarkers and a deeper understanding of disease mechanisms. We propose FINE (Frequency-aware Interpretable Neural Encoder), a novel deep learning model designed to capture multi-scale temporal and frequency-specific patterns in dynamic functional network connectivity (dFNC) derived from resting-state fMRI. FINE integrates multiple expert branches, including convolutional layers, learnable wavelet layers, transformers, and static encoders, enabling the joint modeling of temporal evolution and spectral content of brain networks in an end-to-end framework. Beyond classification, FINE supports frequency-wise interpretability by aligning gradient-based saliency maps with statistical group differences, revealing potential robust, biologically meaningful biomarkers of AD. Evaluated on the large OASIS-3 dataset (856 subjects), FINE achieves AD classification performance (ROC-AUC 0.769) and provides insights into frequency-specific connectivity disruptions, particularly within subcortical, sensorimotor, and cerebellar networks. Our results demonstrate that incorporating frequency-aware modeling and interpretable architectures can advance both disease classification and underlying functional disruption of AD-related brain dynamics.

Herniation, rotation, looping, and retraction of the midgut occur sequentially during midgut morphogenesis. Recent studies have demonstrated the importance of mechanical forces arising from the differential growth between the midgut and mesentery in the formation of small intestinal loops. However, the roles of mechanics and differential growth in the overall process remain unclear. In this study, we developed a computational model of midgut morphogenesis based on continuum mechanics. We showed that the protrusion, rotation, and retraction of the midgut can emerge sequentially because of temporal changes in differential growth. The midgut was modeled as a hyperelastic tube with a Gaussian shape. The differential growth of the midgut and mesentery was modeled by the spatial variation in spontaneous plastic deformation. The hyperelastic tube developed a protrusion by compression-induced deformation, suggesting that other external forces are not necessary for midgut herniation prior to rotation. Appropriate differential growth induced a rotation of the tube. A less-growing mesentery attempts to face inward to minimize the tensile forces, which causes tube twisting and results in midgut rotation. Excess differential growth may cause the retraction of the midgut before the formation of small intestinal loops. The results of this study will serve as reference in future studies on embryology and tissue engineering.

Depressive cognitive impairment is seen in a significant number of patients with depression. However, it remains challenging to differentiate between patients with amnestic (those with subjective cognitive impairment complaints) and non-amnestic major depressive disorder, highlighting the urgent need for additional objective tools to help classify these patients more accurately. We analyzed cognitive state, alterations in regional entropy and functional connectivity measures of the brain between patients with major depression and healthy controls. The depressed cohort was categorized as either “amnestic” or “non-amnestic,” depending on self-reported experiences of forgetfulness. The superior temporal region and insula exhibited altered entropy and connectivity measures in individuals with depression and subjective cognitive impairment, which was correlated with impaired executive functions, a pattern not being evident in the control group. Our findings support the notion that insular and superior temporal entropic alterations are linked to subjective cognitive changes in the pathology of depression. These regions also hold potential as biomarkers for determining the underlying objective cognitive deficits in subjective cognitive complaints in patients with major depressive disorder (MDD). This underscores the need for improved diagnostic approaches and the implementation of practical dynamic neuroimaging modalities capable of addressing the current challenges in diagnosing subjective cognitive impairment in MDD, offering promise for the future management of patients with depression.

Myotonic dystrophy type 1 (DM1) is an inherited neuromuscular disorder characterized by muscle weakness, atrophy and myotonia, with multi-system involvement. Recent studies have highlighted the pathological heterogeneity within the CNS of DM1 patients, particularly significant changes in spinal transcriptome expression and alternative splicing. In this study, we conducted a comprehensive transcriptome analysis of the spinal cord in the muscle-specific DM1 mouse model and their wild-type controls across different life stages: young, adult and old age. Our results revealed an age-dependent increase in differential gene expression between DM1 and wild-type mice with a predominance of downregulated genes. Notably, five genes (Adgre1, Ccl3, Fcrls, Ogfrl1 and Reg3b) were consistently differentially expressed across all age groups. We also generated a temporal profile of cell-type proportions and observed reductions in microglia and astrocytes, along with a trend towards increased ventral neuron populations. Additionally, we characterized the temporal splicing alterations in the spinal cord of DM1 mice and compared with homologous exon skipping events in the CNS of DM1 patients. Our RNA sequencing data elucidate the molecular and cellular adaptations of the spinal cord to muscle defects over time, underscoring that splicing abnormalities observed in the CNS of DM1 patients may reflect contributions from muscle pathology. These findings highlight the necessity of a holistic approach to comprehensively understand the complexity of DM1.

Schizophrenia is a complex mental disorder whose pathophysiological mechanisms remain yet unclear. Various lines of evidence converge on a temporal disorder with temporal imprecision occurring in the millisecond range of the ongoing phase cycles. However, the intertrial phase coherence (ITPC) often used to index such temporal imprecision in EEG, is by itself not able to capture temporal irregularities in the range of around 10 milliseconds. This is due to its static calculation with the averaging over trials. To obtain a more dynamic measures in the millisecond range, we introduce 1. The precision index (PI) as temporally more precise measure, and 2. a novel more dynamic method to calculate the ITPC in temporally resolved way, i.e., dITPC. We show that schizophrenia subjects show decreased PI during deviant tones in an auditory oddball task which shows strong but not one to one correlation with the ITPC. Moreover, we demonstrate that schizophrenia subjects showed higher latencies and frequencies over the course of time in the dITPC. Finally, employing multiple regression models, we show that the latency of the dITPC, as calculated dynamically across both standard and deviant tones, predicts the PI deficits in the deviant tones. Together, our findings demonstrate temporal alterations in the phase dynamics of schizophrenia with temporal irregularities in the dynamic background predicting temporal imprecision in the lower millisecond range in the more cognitive foreground.

Temporal alterations in coastlines depict the significant changes in coastal areas, driven by both natural processes and human activities. For island nations, monitoring of the coastline is essential due to their vulnerability to such impacts. In this study, Funafuti Atoll, an archipelago of small and scattered islands around the capital of Tuvalu, is selected as the study region, and the aim is to extract coastlines of different islands and investigate coastal area changes between 2019 and 2023 using Sentinel-2 imagery. A simple linear iterative clustering-based superpixel segmentation and adaptive thresholding approach is employed for coastline extraction. Initially, superpixel segmentation is conducted to cluster 3-band image pixels into coherent regions, excluding the sea area. Subsequently, the normalized difference vegetation index (NDVI) is calculated, and the superpixels are used to obtain corresponding NDVI regions, on which adaptive Gaussian thresholding is applied to extract coastlines. Finally, the areas enclosed by the extracted coastline boundaries are utilized for change analysis. The results indicate that islands along the western rim of Funafuti exhibited significant alteration (an average decrease of −14.48%), whereas those along the eastern rim remained relatively stable due to the presence of coral rubble ridges and steep slopes. The change analysis revealed that from 2019 to 2020, approximately 15.1 hectares (ha) were eroded, resulting in a net area change rate of −4.14%. Between 2020 and 2021, erosion increased to 20.2 ha, yielding a net change of −7.75%. From 2021 to 2022, 13.2 ha were eroded, corresponding to a −1.74% change. From 2022 to 2023, a net gain of 10.3 ha occurred (+0.25%), primarily due to land reclamation along the lagoon-facing coast of Fongafale Island. Overall, all islands showed a decreasing area trend between 2019 and 2023, with an average net change of −12.97%. The coastal changes occurred along the sand-dominated coast with gentle slopes, possibly driven by the impact of tropical cyclones, prolonged swells, and coastal flooding, which act as the primary driving forces for the study region.

The phenomenon of biological invasions represents one of the most significant threats to biodiversity. A fundamental aspect of combating invasive plant species is the comprehension of the spatial and temporal alterations in their population dynamics. One of the important habitats of the European Union is the Pannon sand grasslands in Hungary, which are primarily threatened by the invasive common milkweed (Asclepias syriaca). The objective of this study was to ascertain the efficacy of drone imaging in examining the spatial patterns of milkweed shoots in comparison to ground survey data. To facilitate comparison, a survey was conducted on 12 milkweed populations in the Fülöpháza area of Kiskunság National Park. In each population, a 12-meter transect (comprising six contiguous 2m × 2m quadrats) was designated within which the positions of the shoots were recorded with centimeter accuracy through ground surveys. The individual shoots were marked on images captured from an altitude of 20m using a drone. The results indicated that the number of shoots identified in the drone images was slightly lower than in the ground surveys; however, a positive correlation was observed between the two datasets (r = 0.9594). A strong positive correlation was evident between the ground and drone surveys in terms of both the average distance between shoots and the observed pattern (r = 0.933 and r = 0.9146). In light of these findings, it can be concluded that drone imaging represents an effective method for examining the size and pattern of populations. Consequently, it may prove to be a valuable tool for the accurate planning of invasive species management in conservation efforts and the monitoring of the effectiveness of treatments.

Atlas of temporal molecular pathological alterations after traumatic brain injury based on RNA-Seq.

<p dir="ltr"><span>The Provincial Government of Special Capital Region of DKI Jakarta has designated Transit Oriented Development (TOD) areas at several Mass Rapid Transit (MRT) station locations through Regional Regulation Number 31 of 2022 concerning the Detailed Spatial Planning (RDTR) of DKI Jakarta. This designation aims to enhance spatial utilization within these areas. However, there are concerns that the intensified spatial use may not fully adhere to the minimum planning criteria stipulated in the regulation. This study aimed to identify land-cover changes in Jakarta’s TOD areas between 2012 and 2024. Although the official TOD implementation commenced in 2022, the selection of 2012 as a baseline provides insight into historical land cover conditions before significant TOD influence, thereby allowing an assessment of long-term trends. Satellite imagery from Google Earth for the years 2012 and 2024 were analysed using GIS tools and manually digitized classifications, with a Sankey diagram used to visually represent land cover transitions, particularly in the Lebak Bulus area. The findings revealed that while most TOD areas show limited changes, Lebak Bulus experienced significant conversion of vegetation and open spaces into built-up areas. </span></p><div><span><br /></span></div>

The prediction of delayed cerebral ischemia (DCI) and poor clinical outcome following aneurysmal subarachnoid hemorrhage (aSAH) is an unmet clinical need to improve on stratification of patients. DCI and poor clinical outcome following aSAH have been associated with hypercoagulability as detected by viscoelastic testing. This study assesses temporal alterations in rotational thromboelastography (ROTEM) coagulation profiles and the discriminative ability of ROTEM parameters for DCI and poor clinical outcome following aSAH. ROTEM parameters were measured on admission, days 3-5, and days 9-11 after aSAH and compared between patients with and without DCI, radiological DCI, and poor 6-month clinical outcome as per modified Rankin Scale scores 4-6. Receiver operating characteristic curve analyses were used to calculate areas under the curve (AUCs) and determine cutoff values with a sensitivity > 90% for (radiological) DCI and with a specificity > 90% for poor outcome. Of 160 included patients with aSAH, 31 (19%) had DCI, 16 (10%) had radiological DCI, and 68 (44%) had poor outcome at 6 months. DCI, radiological DCI, and poor clinical outcome were associated with hypercoagulability. The ROTEM parameter with the best discriminative ability for radiological DCI was INTEM clotting time (AUC 0.75) on admission day, with an optimal cutoff value of < 146s (sensitivity 92%, specificity 47%). For poor outcome, this was increased clot strength byFIBTEMamplitude at10 minutes (A10, AUC 0.85) on days 3-5, with an optimal cutoff value > 27mm (specificity 94%, sensitivity 49%). In this study, ROTEM parameters indicative of increased coagulation had good predictive ability for poor clinical outcome. If independently validated, ROTEM parameters might have the potential to stratify patients with aSAH who may benefit from anticoagulant treatment in future trials with the aim to improve clinical outcome.

Spinal cord injury (SCI) triggers both local and systemic pathological responses that evolve over time and differ with injury severity. Small extracellular vesicles (sEVs), known mediators of intercellular communication, may serve as biomarkers reflecting these complex dynamics. In this study, we investigated whether SCI severity modulates the composition and abundance of circulating plasma-derived sEVs across subacute and chronic phases. Using a graded thoracic contusion model in mice, plasma was collected at defined timepoints post-injury. sEVs were isolated via size-exclusion chromatography and characterized using nanoparticle tracking analysis (NTA), transmission electron microscopy (TEM), and MACSPlex surface marker profiling. We observed an SCI-dependent increase in sEVs during the subacute (7 days) phase, most notably in moderate injuries (50 kdyne), with overall vesicle counts lower chronically (3 months). CD9 emerged as the predominant tetraspanin sEV marker, while CD63 and CD81 were generally present at low levels across all injury severities and timepoints. Surface sEV analysis revealed dynamic regulation of CD41+, CD44+, and CD61+ in the CD9+ sEV subset, suggesting persistent systemic signaling activity. These markers, traditionally associated with platelet function, may also reflect immune or reparative responses following SCI. Our findings highlight the evolving nature of sEV profiles after SCI and support their potential as non-invasive biomarkers for monitoring injury progression.

Time-restricted feeding (TRF) can improve metabolic outcomes. Rodents experiencing TRF exhibit an increase in spontaneous locomotor activity before mealtime and show a phase shift in the rhythm of clock gene expression in peripheral organs, particularly in the liver. Because activation of the transient receptor potential vanilloid-1 (TRPV1) channel produces similar beneficial effects on metabolism as TRF, we hypothesized that this channel mediates the metabolic changes induced by TRF. To assess the role of TRPV1 in metabolism and circadian responses, we utilized the agonist resiniferatoxin (RTX), which at a dosage of 20 µg/kg desensitizes TRPV1. After treatment with RTX or its vehicle, adult male rats were exposed to 21 days of TRF during the light phase. RTX-treated rats show some effects of TRF similar to vehicle-treated controls, with increased locomotor activity and body temperature at the beginning of the light phase, decreased body weight gain and food intake relative to ad-libitum-fed controls. However, RTX-treated rats did not show a decrease in VO2 consumption or an improvement in glucose tolerance induced by TRF. In addition, RTX treatment eliminated the temporal changes in the expression of clock genes Per1 and Rev-Erba in the liver as well as leptin blood levels. In addition, RTX abolished the temporal alterations of the Trb3 gene in the liver, which encodes a protein that negatively modulates insulin signaling without affecting the expression of insulin, Pparα, Pck1, G6pc, or other clock genes in the liver. In conclusion, TRPV1 may participate in the TRF-induced alterations in metabolism, most likely through its regulation of the temporal changes in Per1, Rev-Erba, and Trb3 expressions in the liver, along with leptin secretion.

Introduction and Objective: Previous studies have reported aberrations of the gut microbiota of individuals with prediabetes. Our objective was to explore changes over four years in gut microbiota and metabolic phenotypes and their mutual relationships in individuals with prediabetes. Methods: The study included 486 Europeans with prediabetes. Phenotyping at baseline and four years later used consistent protocols and core lab analyses, including anthropometrics, fasting and mean plasma glucose/insulin during an oral glucose tolerance test (OGTT), 2-hour OGTT glucose, insulin sensitivity, BMI, waist circumference, and blood pressure. Gut microbiota profiling employed shotgun metagenomic sequencing and identical bioinformatics pipelines at both timepoints. Temporal changes of bacterial and viral microbiota, and microbial functional pathways were related to concomitant changes in markers of host metabolism. Results: Over four years, individuals with prediabetes exhibited a significant increase in glycemia and insulinemia alongside a significant decline in insulin sensitivity. Concurrently, significant declines were observed in the richness of gut bacterial and viral species and microbial pathways accompanied by significant changes in the relative abundance and the genetic composition of multiple bacterial species. Additionally, bacterial-viral interactions diminished over time. The temporal gut microbial alterations were significantly correlated with temporal impairments in host metabolic health. Conclusion: In this 4-year prospective study of Europeans with prediabetes, the gut microbiota exhibited major changes in taxonomic composition, bacterial species genetics and microbial functional potentials, many of which paralleled an aggravation of host metabolism. Whether gut microbiota changes in prediabetes adapt to or drive metabolic abnormalities is the scope of future studies. Disclosure L. Lyu: None. O. Pedersen: Board Member; GutCRINE.

Autoregulation and neurovascular coupling are key mechanisms that modulate myogenic tone (MT) in vessels to regulate cerebral blood flow (CBF) during resting state and periods of increased neural activity, respectively. To determine relative contributions of distinct vascular zones across different cortical depths in CBF regulation, we developed a simplified yet detailed and computationally efficient model of the mouse cerebrovasculature. The model integrates multiple simplifications and generalizations regarding vascular morphology, the hierarchical organization of mural cells, and potentiation/inhibition of MT in vessels. Our analysis showed that autoregulation is the result of the synergy between these factors, but achieving an optimal balance across all cortical depths and throughout the autoregulation range is a complex task. This complexity explains the non-uniformity observed experimentally in capillary blood flow at different cortical depths. In silico simulations of cerebral autoregulation support the idea that the cerebral vasculature does not maintain a plateau of blood flow throughout the autoregulatory range and consists of both flat and sloped phases. We learned that small-diameter vessels with large contractility, such as penetrating arterioles and precapillary arterioles, have major control over intravascular pressure at the entry points of capillaries and play a significant role in CBF regulation. However, temporal alterations in capillary diameter contribute moderately to cerebral autoregulation and minimally to functional hyperemia. In addition, hemodynamic analysis shows that while hemodynamics within capillaries remain relatively stable across all cortical depths throughout the entire autoregulation range, significant variability in hemodynamics can be observed within the first few branch orders of precapillary arterioles or transitional zone vessels. The computationally efficient cerebrovasculature model, proposed in this study, provides a novel framework for analyzing dynamics of the CBF regulation where hemodynamic and vasodynamic interactions are the foundation on which more sophisticated models can be developed.

To better understand the control of cerebral blood flow (CBF) and the effect of different CNS pathologies such as seizures on brain hemodynamics, several computational models have been developed. However, many of these models do not recapitulate spatiotemporal CBF variations or pathology-related CBF changes. Recent technological advances in in vivo imaging have enabled us to more accurately account for the microstructural and functional features of the cerebral vasculature in such models and validate their hemodynamic predictions. Therefore, here we present a novel “image-informed model” of temporal CBF changes in healthy and seizure-afflicted murine brains. To calibrate and validate our model, we collected multicontrast image data from four mice under normal and seizure conditions using a multicontrast miniscope developed by our group1. Seizures were induced with intraperitoneal injection of Pentylenetetrazole and the intrinsic optical signal and the laser speckle contrast (LSC) channels of the miniscope to estimate segment-specific diameter and relative CBF every 5 seconds. Additionally, we used time-to-peak (TTP) fluorescence derived from injections of FITC-labeled dextran to identify arterioles and venules. Next, we adapted an existing hemodynamics model2 to incorporate time-varying changes in segment-specific microvascular diameter. This model incorporated Poiseuille’s law and hematocrit-dependent viscosity calculations to iteratively derive nodal pressures and hematocrit distributions using MATLAB®2. Boundary conditions (BC) were assigned using transit time maps, and arteriolar/venular segments identified from TTP data. Inlet boundary pressures were optimized using a diameter-ranked Gaussian distribution, and temporal diameter changes updated with in vivo data to simulate time-varying changes in cerebral hemodynamics (i.e. CBF and CBV). We validated model predictions by comparing CBF velocity distributions with published data and relative CBF changes with LSC-derived measurements. We observed significant correlations between simulated and observed rCBF time-series data for healthy (r ~ 0.75, p &lt; 0.05) and seizure conditions (r ~ 0.5, p &lt; 0.05). Finally, using our validated model we compared the hemodynamics before, during and after a seizure. The predictions showed a decrease in overall vessel diameter, an increase in overall viscosity, and a decrease in CBF during the peak seizure event, compared to the minutes preceding the seizure. We observed the reverse trend for the two minutes after the seizure. Our “image-based-modeling” strategy enabled us to predict time-dependent hemodynamic changes within microvascular networks of the mouse brain by incorporating vessel diameter changes measured in vivo. Our model can be applied to investigate region- and time-dependent hemodynamic variations caused by pathologies such as seizures, strokes and brain tumors. Our model advances the field by employing simulations informed by measurements of individual segments' diameters, and by optimizing BC (i.e. inlet boundary nodes identification and pressure assignment) to limit the number of segments with non-physiological blood flow velocities. In summary, ”image-based models” provide a new platform for investigating temporal alterations in cerebral hemodynamics, enabling the assessment of more physiologically relevant changes in brain function. 1.Senarathna, J. et al. A miniature multi-contrast microscope for functional imaging in freely behaving animals. Nat Commun 10, 99 (2019). 2. Bhargava, A. et al. VascuViz: a multimodality and multiscale imaging and visualization pipeline for vascular systems biology. Nat Methods 19, 242–254 (2022). This work was supported by NIH grant nos. 2R01CA196701-06A1, 5R01CA237597-04 and 5R01DE027957-05 This abstract was presented at the American Physiology Summit 2025 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.

Introduction This study aimed to compare autophagic activity in peripheral blood mononuclear cells (PBMCs) between intensive care unit-acquired weakness (ICU-AW) and non-ICU-AW patients, and to evaluate phase-specific differences and their associations with immune and inflammatory profiles. Methods This single-center, cross-sectional observational study included 42 patients who required mechanical ventilation for more than 48 hours between April 2020 and March 2022. PBMCs were collected within 48 hours of ICU admission (early phase) and on day 7 (late phase). Autophagic activity, assessed by mean fluorescence intensity (MFI), was evaluated via flow cytometry using DAPGreen (Dojindo, Kumamoto, Japan). ICU-AW was diagnosed based on a Medical Research Council sum score of less than 48 points. Results Among the 42 patients, 14 (33.3%) developed ICU-AW. PBMCs from ICU-AW patients demonstrated significantly lower autophagic activity in the early phase compared to non-ICU-AW patients (non-ICU-AW vs. ICU-AW patients: MFI of granulocytes, 30.9 (22.6, 51.7) vs. 20.4 (18.0, 22.6), p < 0.001; and lymphocytes, 94.6 (64.9, 123.0) vs. 65.2 (58.0, 77.5), p = 0.011). In contrast, excessive autophagic activity was observed in some ICU-AW cases during the late phase (MFI of granulocytes, 21.0 (17.9, 22.9) vs. 33.8 (22.9, 56.0), p < 0.001; and lymphocytes, 67.5 (54.4, 93.5) vs. 106.2 (64.6, 124.5), p = 0.012). The proportion of monocytes was also significantly reduced in the ICU-AW group. These findings suggest that impaired early-phase autophagy may contribute to ICU-AW pathogenesis, whereas delayed overactivation could be associated with persistent inflammation and impaired muscle recovery. Conclusion Autophagic activity in PBMCs exhibited temporal alterations in patients with ICU-AW. These findings suggest a potential association between dysregulated autophagy and muscle dysfunction in critically ill patients. Further research is needed to explore whether modulation of autophagy could inform future preventive strategies.

Background: Sepsis-associated encephalopathy (SAE) is a severe complication of sepsis, affecting approximately 70% of patients, leading to increased mortality and long-term cognitive impairments among survivors. However, there is a lack of comprehensive studies on the development of SAE, especially related to the cellular communication networks in the brain microenvironment. Methods: We evaluated the impact of myeloid cells on the brain's immune microenvironment through glial cell alterations using bulk and single-cell transcriptomics data from human and mouse models and validated this with correlative experiments. We also developed the DeconvCellLink R package to study neuroinflammation-associated cellular interaction networks. A dynamic brain immune microenvironment map showing temporal alterations in brain cellular network during systemic inflammatory reactions was constructed using time-series data. Results: While brain cellular alterations differed between human and animal models, a highly conserved set of sepsis-associated genes regulating immune microenvironment signalling was identified. The dynamic alterations in cellular interaction networks and cytokines revealed brain immune cells' temporal response to systemic inflammation. We also found that valproic acid could mitigate sepsis-induced neuroinflammation by regulating glial cell balance and modulating the neuroimmune microenvironment. Conclusion: Through dynamic cellular communication networks, the study revealed that, immune dysregulation in the inflamed brain in SAE involves overactivation of innate immunity, with neutrophils playing a crucial role, providing a scientific framework for developing novel therapeutic strategies and offering new insights into the mechanisms underlying sepsis-induced brain dysfunction.