CD4+ T cell calibration of antigen-presenting cells optimizes antiviral CD8+ T cell immunity.

Antiviral CD8+ T cell immunity depends on the integration of various contextual cues, but how antigen-presenting cells (APCs) consolidate these signals for decoding by T cells remains unclear. Here, we describe gradual interferon-α/interferon-ß (IFNα/ß)-induced transcriptional adaptations that endow APCs with the capacity to rapidly activate the transcriptional regulators p65, IRF1 and FOS after CD4+ T cell-mediated CD40 stimulation. While these responses operate through broadly used signaling components, they induce a unique set of co-stimulatory molecules and soluble mediators that cannot be elicited by IFNα/ß or CD40 alone. These responses are critical for the acquisition of antiviral CD8+ T cell effector function, and their activity in APCs from individuals infected with severe acute respiratory syndrome coronavirus 2 correlates with milder disease. These observations uncover a sequential integration process whereby APCs rely on CD4+ T cells to select the innate circuits that guide antiviral CD8+ T cell responses.

Renal NF-κB activation impairs uric acid homeostasis to promote tumor-associated mortality independent of wasting.

Tumor-induced host wasting and mortality are general phenomena across species. Many groups have previously demonstrated endocrinal impacts of malignant tumors on host wasting in rodents and Drosophila. Whether and how environmental factors and host immune response contribute to tumor-associated host wasting and survival, however, are largely unknown. Here, we report that flies bearing malignant yki3SA-gut tumors exhibited the exponential increase of commensal bacteria, which were mostly acquired from the environment, and systemic IMD-NF-κB activation due to suppression of a gut antibacterial amidase PGRP-SC2. Either gut microbial elimination or specific IMD-NF-κB blockade in the renal-like Malpighian tubules potently improved mortality of yki3SA-tumor-bearing flies in a manner independent of host wasting. We further indicate that renal IMD-NF-κB activation caused uric acid (UA) overload to reduce survival of tumor-bearing flies. Therefore, our results uncover a fundamental mechanism whereby gut commensal dysbiosis, renal immune activation, and UA imbalance potentiate tumor-associated host death.

FL-circAS: an integrative resource and analysis for full-length sequences and alternative splicing of circular RNAs with nanopore sequencing.

Circular RNAs (circRNAs) are RNA molecules with a continuous loop structure characterized by back-splice junctions (BSJs). While analyses of short-read RNA sequencing have identified millions of BSJ events, it is inherently challenging to determine exact full-length sequences and alternatively spliced (AS) isoforms of circRNAs. Recent advances in nanopore long-read sequencing with circRNA enrichment bring an unprecedented opportunity for investigating the issues. Here, we developed FL-circAS (https://cosbi.ee.ncku.edu.tw/FL-circAS/), which collected such long-read sequencing data of 20 cell lines/tissues and thereby identified 884 636 BSJs with 1 853 692 full-length circRNA isoforms in human and 115 173 BSJs with 135 617 full-length circRNA isoforms in mouse. FL-circAS also provides multiple circRNA features. For circRNA expression, FL-circAS calculates expression levels for each circRNA isoform, cell line/tissue specificity at both the BSJ and isoform levels, and AS entropy for each BSJ across samples. For circRNA biogenesis, FL-circAS identifies reverse complementary sequences and RNA binding protein (RBP) binding sites residing in flanking sequences of BSJs. For functional patterns, FL-circAS identifies potential microRNA/RBP binding sites and several types of evidence for circRNA translation on each full-length circRNA isoform. FL-circAS provides user-friendly interfaces for browsing, searching, analyzing, and downloading data, serving as the first resource for discovering full-length circRNAs at the isoform level.

An antioxidant feedforward cycle coordinated by linker histone variant H1.2 and NRF2 that drives nonsmall cell lung cancer progression.

Nonsmall cell lung cancer (NSCLC) is highly malignant with limited treatment options, platinum-based chemotherapy is a standard treatment for NSCLC with resistance commonly seen. NSCLC cells exploit enhanced antioxidant defense system to counteract excessive reactive oxygen species (ROS), which contributes largely to tumor progression and resistance to chemotherapy, yet the mechanisms are not fully understood. Recent studies have suggested the involvement of histones in tumor progression and cellular antioxidant response; however, whether a major histone variant H1.2 (H1C) plays roles in the development of NSCLC remains unclear. Herein, we demonstrated that H1.2 was increasingly expressed in NSCLC tumors, and its expression was correlated with worse survival. When crossing the H1c knockout allele with a mouse NSCLC model (KrasLSL-G12D/+), H1.2 deletion suppressed NSCLC progression and enhanced oxidative stress and significantly decreased the levels of key antioxidant glutathione (GSH) and GCLC, the catalytic subunit of rate-limiting enzyme for GSH synthesis. Moreover, high H1.2 was correlated with the IC50 of multiple chemotherapeutic drugs and with worse prognosis in NSCLC patients receiving chemotherapy; H1.2-deficient NSCLC cells presented reduced survival and increased ROS levels upon cisplatin treatment, while ROS scavenger eliminated the survival inhibition. Mechanistically, H1.2 interacted with NRF2, a master regulator of antioxidative response; H1.2 enhanced the nuclear level and stability of NRF2 and, thus, promoted NRF2 binding to GCLC promoter and the consequent transcription; while NRF2 also transcriptionally up-regulated H1.2. Collectively, these results uncovered a tumor-driving role of H1.2 in NSCLC and indicate an "H1.2-NRF2" antioxidant feedforward cycle that promotes tumor progression and chemoresistance.

Loss of renal tubular G9a benefits acute kidney injury by lowering focal lipid accumulation via CES1.

Surgery-induced renal ischemia and reperfusion (I/R) injury and nephrotoxic drugs like cisplatin can cause acute kidney injury (AKI), for which there is no effective therapy. Lipid accumulation is evident following AKI in renal tubules although the mechanisms and pathological effects are unclear. Here, we report that Ehmt2-encoded histone methyltransferase G9a is upregulated in patients and mouse kidneys after AKI. Renal tubular specific knockout of G9a (Ehmt2Ksp ) or pharmacological inhibition of G9a alleviates lipid accumulation associated with AKI. Mechanistically, G9a suppresses transcription of the lipolytic enzyme Ces1; moreover, G9a and farnesoid X receptor (FXR) competitively bind to the same promoter regions of Ces1. Ces1 is consistently observed to be downregulated in the kidney of AKI patients. Pharmacological inhibition of Ces1 increases lipid accumulation, exacerbates renal I/R-injury and eliminates the beneficial effects on AKI observed in Ehmt2Ksp mice. Furthermore, lipid-lowering atorvastatin and an FXR agonist alleviate AKI by activating Ces1 and reducing renal lipid accumulation. Together, our results reveal a G9a/FXR-Ces1 axis that affects the AKI outcome via regulating renal lipid accumulation.

Detecting intragenic trans-splicing events from non-co-linearly spliced junctions by hybrid sequencing.

Trans-spliced RNAs (ts-RNAs) are a type of non-co-linear (NCL) transcripts that consist of exons in an order topologically inconsistent with the corresponding DNA template. Detecting ts-RNAs is often interfered by experimental artifacts, circular RNAs (circRNAs) and genetic rearrangements. Particularly, intragenic ts-RNAs, which are derived from separate precursor mRNA molecules of the same gene, are often mistaken for circRNAs through analyses of RNA-seq data. Here we developed a bioinformatics pipeline (NCLscan-hybrid), which integrated short and long RNA-seq reads to minimize false positives and proposed out-of-circle and rolling-circle long reads to distinguish between intragenic ts-RNAs and circRNAs. Combining NCLscan-hybrid screening and multiple experimental validation steps successfully confirmed that four NCL events, which were previously regarded as circRNAs in databases, originated from trans-splicing. CRISPR-based endogenous genome modification experiments further showed that flanking intronic complementary sequences can significantly contribute to ts-RNA formation, providing an efficient/specific method to deplete ts-RNAs. We also experimentally validated that one ts-RNA (ts-ARFGEF1) played an important role for p53-mediated apoptosis through affecting the PERK/eIF2a/ATF4/CHOP signaling pathway in breast cancer cells. This study thus described both bioinformatics procedures and experimental validation steps for rigorous characterization of ts-RNAs, expanding future studies for identification, biogenesis, and function of these important but understudied transcripts.

Tinnitus classification based on resting-state functional connectivity using a convolutional neural network architecture.

OBJECTIVES: Many studies have investigated aberrant functional connectivity (FC) using resting-state functional MRI (rs-fMRI) in subjective tinnitus patients. However, no studies have verified the efficacy of resting-state FC as a diagnostic imaging marker. We established a convolutional neural network (CNN) model based on rs-fMRI FC to distinguish tinnitus patients from healthy controls, providing guidance and fast diagnostic tools for the clinical diagnosis of subjective tinnitus. METHODS: A CNN architecture was trained on rs-fMRI data from 100 tinnitus patients and 100 healthy controls using an asymmetric convolutional layer. Additionally, a traditional machine learning model and a transfer learning model were included for comparison with the CNN, and each of the three models was tested on three different brain atlases. RESULTS: Of the three models, the CNN model outperformed the other two models with the highest area under the curve, especially on the Dos_160 atlas (AUC = 0.944). Meanwhile, the model with the best classification performance highlights the crucial role of the default mode network, salience network, and sensorimotor network in distinguishing between normal controls and patients with subjective tinnitus. CONCLUSION: Our CNN model could appropriately tackle the diagnosis of tinnitus patients using rs-fMRI and confirmed the diagnostic value of FC as measured by rs-fMRI.

Abnormal causal connectivity of anterior cingulate cortex-visual cortex circuit related to nonsteroidal anti-inflammatory drug efficacy in migraine.

The anterior cingulate cortex (ACC) and visual cortex are integral components of the neurophysiological mechanisms underlying migraine, yet the impact of altered connectivity patterns between these regions on migraine treatment remains unknown. To elucidate this issue, we investigated the abnormal causal connectivity between the ACC and visual cortex in patients with migraine without aura (MwoA), based on the resting-state functional magnetic resonance imaging data, and its predictive ability for the efficacy of nonsteroidal anti-inflammatory drugs (NSAIDs). The results revealed increased causal connectivity from the bilateral ACC to the lingual gyrus (LG) and decreased connectivity in the opposite direction in nonresponders compared with the responders. Moreover, compared with the healthy controls, nonresponders exhibited heightened causal connectivity from the ACC to the LG, right inferior occipital gyrus (IOG) and left superior occipital gyrus, while connectivity patterns from the LG and right IOG to the ACC were diminished. Based on the observed abnormal connectivity patterns, the support vector machine (SVM) models showed that the area under the receiver operator characteristic curves for the ACC to LG, LG to ACC and bidirectional models were 0.857, 0.898, and 0.939, respectively. These findings indicate that neuroimaging markers of abnormal causal connectivity in the ACC-visual cortex circuit may facilitate clinical decision-making regarding NSAIDs administration for migraine management.

Single-etched fiber-chip coupler with a metal mirror on a 220-nm silicon-on-insulator platform for perfectly vertical coupling.

Vertical couplers play a pivotal role as essential components supporting interconnections between fibers and photonic integrated circuits (PICs). In this study, we propose and demonstrate a high-performance perfectly vertical coupler based on a three-stage inverse design method, realized through a single full etching process on a 220-nm silicon-on-insulator (SOI) platform with a backside metal mirror. Under surface-normal fiber placement, experimental results indicate a remarkable 3-dB bandwidth of 99 nm with a peak coupling efficiency of -1.44 dB at the wavelength of 1549 nm. This achievement represents the best record to date, to the best of our knowledge, for a perfectly vertical coupler fabricated under similar process conditions.

Paternal care plasticity: males care more for early- than late-developing embryos in an arboreal breeding treefrog.

BACKGROUND: Parental care benefits offspring but comes with costs. To optimize the trade-off of costs and benefits, parents should adjust care based on intrinsic and/or extrinsic conditions. The harm to offspring hypothesis suggests that parents should invest more in younger offspring than older offspring because younger offspring are more vulnerable. However, this hypothesis has rarely been comprehensively tested, as many studies only reveal an inverse correlation between parental care and offspring age, without directly testing the effects of offspring age on their vulnerability. To test this hypothesis, we studied Kurixalus eiffingeri, an arboreal treefrog with paternal care. We first performed a field survey by monitoring paternal care during embryonic development. Subsequently, we conducted a field experiment to assess the prevalence of egg predators (a semi-slug, Parmarion martensi) and the plasticity of male care. Finally, we conducted a laboratory experiment to assess how embryo age affects predation by P. martensi. RESULTS: Our results showed that (1) male attendance and brooding frequency affected embryo survival, and (2) males attended and brooded eggs more frequently in the early stage than in the late stage. The experimental results showed that (3) males increased attendance frequency when the predators were present, and (4) the embryonic predation by the semi-slug during the early was significantly higher than in the late stage. CONCLUSIONS: Our findings highlight the importance of paternal care to embryo survival, and the care behavior is plastic. Moreover, our results provide evidence consistent with the predictions of the harm to offspring hypothesis, as males tend to care more for younger offspring which are more vulnerable.

Machine learning models for screening clinically significant nephrolithiasis in overweight and obese populations.

PURPOSES: Our aim is to build and evaluate models to screen for clinically significant nephrolithiasis in overweight and obesity populations using machine learning (ML) methodologies and simple health checkup clinical and urine parameters easily obtained in clinics. METHODS: We developed ML models to screen for clinically significant nephrolithiasis (kidney stone > 2 mm) in overweight and obese populations (body mass index, BMI ≥ 25 kg/m2) using gender, age, BMI, gout, diabetes mellitus, estimated glomerular filtration rate, bacteriuria, urine pH, urine red blood cell counts, and urine specific gravity. The data were collected from hospitals in Kaohsiung, Taiwan between 2012 and 2021. RESULTS: Of the 2928 subjects we enrolled, 1148 (39.21%) had clinically significant nephrolithiasis and 1780 (60.79%) did not. The testing dataset consisted of data collected from 574 subjects, 235 (40.94%) with clinically significant nephrolithiasis and 339 (59.06%) without. One model had a testing area under curve of 0.965 (95% CI, 0.9506-0.9794), a sensitivity of 0.860 (95% CI, 0.8152-0.9040), a specificity of 0.947 (95% CI, 0.9230-0.9708), a positive predictive value of 0.918 (95% CI, 0.8820-0.9544), and negative predictive value of 0.907 (95% CI, 0.8756-0.9371). CONCLUSION: This ML-based model was found able to effectively distinguish the overweight and obese subjects with clinically significant nephrolithiasis from those without. We believe that such a model can serve as an easily accessible and reliable screening tool for nephrolithiasis in overweight and obesity populations and make possible early intervention such as lifestyle modifications and medication for prevention stone complications.

Diffusion-/perfusion-weighted imaging fusion to automatically identify stroke within 4.5 h.

OBJECTIVES: We aimed to develop machine learning (ML) models based on diffusion- and perfusion-weighted imaging fusion (DP fusion) for identifying stroke within 4.5 h, to compare them with DWI- and/or PWI-based ML models, and to construct an automatic segmentation-classification model and compare with manual labeling methods. METHODS: ML models were developed from multimodal MRI datasets of acute stroke patients within 24 h of clear symptom onset from two centers. The processes included manual segmentation, registration, DP fusion, feature extraction, and model establishment (logistic regression (LR) and support vector machine (SVM)). A segmentation-classification model (X-Net) was proposed for automatically identifying stroke within 4.5 h. The area under the receiver operating characteristic curve (AUC), sensitivity, Dice coefficients, decision curve analysis, and calibration curves were used to evaluate model performance. RESULTS: A total of 418 patients (≤ 4.5 h: 214; > 4.5 h: 204) were evaluated. The DP fusion model achieved the highest AUC in identifying the onset time in the training (LR: 0.95; SVM: 0.92) and test sets (LR: 0.91; SVM: 0.90). The DP fusion-LR model displayed consistent positive and greater net benefits than other models across a broad range of risk thresholds. The calibration curve demonstrated the good calibration of the DP fusion-LR model (average absolute error: 0.049). The X-Net model obtained the highest Dice coefficients (DWI: 0.81; Tmax: 0.83) and achieved similar performance to manual labeling (AUC: 0.84). CONCLUSIONS: The automatic segmentation-classification models based on DWI and PWI fusion images had high performance in identifying stroke within 4.5 h. CLINICAL RELEVANCE STATEMENT: Perfusion-weighted imaging (PWI) fusion images had high performance in identifying stroke within 4.5 h. The automatic segmentation-classification models based on DWI and PWI fusion images could provide clinicians with decision-making guidance for acute stroke patients with unknown onset time. KEY POINTS: • The diffusion/perfusion-weighted imaging fusion model had the best performance in identifying stroke within 4.5 h. • The X-Net model had the highest Dice and achieved performance close to manual labeling in segmenting lesions of acute stroke. • The automatic segmentation-classification model based on DP fusion images performed well in identifying stroke within 4.5 h.

Preliminary study on AI-assisted diagnosis of bone remodeling in chronic maxillary sinusitis.

OBJECTIVE: To construct the deep learning convolution neural network (CNN) model and machine learning support vector machine (SVM) model of bone remodeling of chronic maxillary sinusitis (CMS) based on CT image data to improve the accuracy of image diagnosis. METHODS: Maxillary sinus CT data of 1000 samples in 500 patients from January 2018 to December 2021 in our hospital was collected. The first part is the establishment and testing of chronic maxillary sinusitis detection model by 461 images. The second part is the establishment and testing of the detection model of chronic maxillary sinusitis with bone remodeling by 802 images. The sensitivity, specificity and accuracy and area under the curve (AUC) value of the test set were recorded, respectively. RESULTS: Preliminary application results of CT based AI in the diagnosis of chronic maxillary sinusitis and bone remodeling. The sensitivity, specificity and accuracy of the test set of 93 samples of CMS, were 0.9796, 0.8636 and 0.9247, respectively. Simultaneously, the value of AUC was 0.94. And the sensitivity, specificity and accuracy of the test set of 161 samples of CMS with bone remodeling were 0.7353, 0.9685 and 0.9193, respectively. Simultaneously, the value of AUC was 0.89. CONCLUSION: It is feasible to use artificial intelligence research methods such as deep learning and machine learning to automatically identify CMS and bone remodeling in MSCT images of paranasal sinuses, which is helpful to standardize imaging diagnosis and meet the needs of clinical application.

Multipathway regulation induced by 4-(phenylsulfonyl)morpholine derivatives against triple-negative breast cancer.

Phenotypic drug discovery (PDD) is an effective drug discovery approach by observation of therapeutic effects on disease phenotypes, especially in complex disease systems. Triple-negative breast cancer (TNBC) is composed of several complex disease features, including high tumor heterogeneity, high invasive and metastatic potential, and a lack of effective therapeutic targets. Therefore, identifying effective and novel agents through PDD is a current trend in TNBC drug development. In this study, 23 novel small molecules were synthesized using 4-(phenylsulfonyl)morpholine as a pharmacophore. Among these derivatives, GL24 (4m) exhibited the lowest half-maximal inhibitory concentration value (0.90 µM) in MDA-MB-231 cells. To investigate the tumor-suppressive mechanisms of GL24, transcriptomic analyses were used to detect the perturbation for gene expression upon GL24 treatment. Followed by gene ontology (GO) analysis, gene set enrichment analysis (GSEA), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, multiple ER stress-dependent tumor suppressive signals were identified, such as unfolded protein response (UPR), p53 pathway, G2/M checkpoint, and E2F targets. Most of the identified pathways triggered by GL24 eventually led to cell-cycle arrest and then to apoptosis. In summary, we developed a novel 4-(phenylsulfonyl)morpholine derivative GL24 with a strong potential for inhibiting TNBC cell growth through ER stress-dependent tumor suppressive signals.

Lamin A/C and Vimentin as a Coordinated Regulator during Amoeboid Migration in Microscale Confined Microenvironments.

Cell migration occurs in confined microenvironments, which plays a vital role in the process of tumor metastasis. However, it is challenging to study their behaviors in vivo. Here we developed a cell squeeze system that can be scaled down to micrometers to mimic native physical confined microenvironments, wherein degrees of surface adhesion and mechanical constraints could be manipulated in order to investigate cell-migrating behaviors. Based on the microscale cell squeeze system, we found the synergistic role of lamin A/C and vimentin in cell transition and migration under strong confinement. The dynamic variations in lamin A/C and vimentin expression establish a positive feedback loop in response to confinement, effectively promoting amoeboid migration by modulating nuclear deformability while ensuring cell viability. This work shed light on modulating cell response to microenvironments by altering the expression of lamin A/C and/or vimentin, which may be a more efficient way of inhibiting cancer metastasis.

An optimization framework for basin-scale water environmental carrying capacity.

The interaction between water environment and social economy at a basin scale is complex and challenging to quantify. To address this issue, this study proposes an integrated framework that builds parametric connections among water, contaminants, administrative regions, and social activities. The framework, known as the water environmental carrying capacity (WECC) optimization framework, effectively captures the intricacy of the interaction and integrates socio-economic parameter structure relationships, a water environmental model, a WECC optimization model, and a sensitivity analysis of regulatory parameters. Applied to the Anhui-Huaihe Basin in mid-eastern China, the framework considers nine administrative regions and three economic factors: industry, agriculture, and GDP per capita (pGDP). Results show that the current water environmental carrying capacity of the watershed is insufficient to meet socio-economic development requirements. After optimization, the WECC for industry, agriculture, and pGDP in the region increased by 22.40%, 26.59%, and 15.08% respectively. Overall COD and NH4-N discharge decreased by 13.6% and 14.7% respectively, effectively reducing pollution loads in rivers and enhancing sustainable development potential. At the regional scale, optimization for industry, agriculture, and pGDP exhibited different characteristics, but all aimed to improve efficiency by reducing the K value (pollution discharge/output value ratio). Regions with industrial treatment rates (αwt) below 0.8 should prioritize increasing treatment rates, while those above 0.8 should consider industrial upgrading for enhanced efficiency. For agriculture, important sensitive parameters for farming and livestock breeding are the proportion of high standard farmland (αs) and the scale breeding ratio (αb), which should be increased to above 0.15 and 0.83 respectively for all regions to achieve agricultural optimization. For pGDP optimization, the focus is on improving living environments and reducing pollution discharge, with crucial measures including collecting and treating rural domestic sewage, where the rural toilet improvement rate (αt) in each region should be increased to 0.78 or above. The results emphasize the need for both interregional allocation and intraregional planning to achieve comprehensive basin optimization and a harmonious balance between regional development and water environment.

Radiomics Analysis of Diffusion-Weighted Imaging and Long-Term Unfavorable Outcomes Risk for Acute Stroke.

BACKGROUND: Diffusion-weighted imaging radiomics could be used as prognostic biomarkers in acute ischemic stroke. We aimed to identify a clinical and diffusion-weighted imaging radiomics model for individual unfavorable outcomes risk assessment in acute ischemic stroke. METHODS: A total of 1716 patients with acute ischemic stroke from 2 centers were divided into a training cohort and a validation cohort. Patient outcomes were measured with the modified Rankin Scale score. An unfavorable outcome was defined as a modified Rankin Scale score greater than 2. The primary end point was all-cause mortality or outcomes 1 year after stroke. The MRI-DRAGON score was calculated based on previous publications. We extracted and selected the infarct features on diffusion-weighted imaging to construct a radiomic signature. The clinic-radiomics signature was built by measuring the Cox proportional risk regression score (CrrScore) and compared with the MRI-DRAGON score and the ClinicScore. CrrScore model performance was estimated by 1-year unfavorable outcomes prediction. RESULTS: A high radiomic signature predicted a higher probability of unfavorable outcomes than a low radiomic signature in the training (hazard ratio, 3.19 [95% CI, 2.51-4.05]; P<0.0001) and validation (hazard ratio, 3.25 [95% CI, 2.20-4.80]; P<0.0001) cohorts. The diffusion-weighted imaging Alberta Stroke Program Early CT Score, age, glucose level before therapy, National Institutes of Health Stroke Scale score on admission, glycated hemoglobin' radiomic signature, hemorrhagic infarction, and malignant cerebral edema were associated with an unfavorable outcomes risk after multivariable adjustment. A CrrScore nomogram was developed to predict outcomes and had the best performance in the training (area under the curve, 0.862) and validation cohorts (area under the curve, 0.858). The CrrScore model time-dependent areas under the curve of the probability of unfavorable outcomes at 1 year in the training and validation cohorts were 0.811 and 0.801, respectively. CONCLUSIONS: The CrrScore model allows the accurate prediction of patients with acute ischemic stroke outcomes and can potentially guide rehabilitation therapies for patients with different risks of unfavorable outcomes.

Reorganization of the cortical connectome functional gradient in age-related hearing loss.

Age-related hearing loss (ARHL), one of the most common sensory deficits in elderly individuals, is a risk factor for dementia; however, it is unclear how ARHL affects the decline in cognitive function. To address this issue, a connectome gradient framework was used to identify critical features of information integration between sensory and cognitive processing centers using resting-state functional magnetic resonance imaging (rs-fMRI) data from 40 individuals with ARHL and 36 healthy controls (HCs). The first three functional gradient alterations associated with ARHL were investigated at the global, network and regional levels. Using a support vector machine (SVM) model, our analysis distinguished individuals with ARHL with normal cognitive function from those with cognitive decline. Compared to HCs, individuals with ARHL had a contracted principal primary-to-transmodal gradient axis, especially in the visual and default mode networks, with an altered gradient explained ratio and variance. Among individuals with ARHL, cognitive decline was detected in the visual network in the principal gradient as well as in the limbic, salience and default mode networks in the third gradient (salience to frontoparietal/default mode). These results suggest that ARHL is associated with disrupted information processing from the primary sensory networks to higher-order cognitive networks and highlight the key nodes closely associated with cognitive decline during cognitive processing in ARHL, providing new insights into the mechanism of cognitive impairment and suggesting potential treatments related to ARHL.

Optimization of structural connectomes and scaled patterns of structural-functional decoupling in Parkinson's disease.

Parkinson's disease (PD) is manifested with disrupted topology of the structural connection network (SCN) and the functional connection network (FCN). However, the SCN and its interactions with the FCN remain to be further investigated. This multimodality study attempted to precisely characterize the SCN using diffusion kurtosis imaging (DKI) and further identify the neuropathological pattern of SCN-FCN decoupling, underscoring the neurodegeneration of PD. Diffusion-weighted imaging and resting-state functional imaging were available for network constructions among sixty-nine patients with PD and seventy demographically matched healthy control (HC) participants. The classification performance and topological prosperities of both the SCN and the FCN were analyzed, followed by quantification of the SCN-FCN couplings across scales. The SCN constructed by kurtosis metrics achieved optimal classification performance (area under the curve 0.89, accuracy 80.55 %, sensitivity 78.40 %, and specificity 80.65 %). Along with diverse alterations of structural and functional network topology, the PD group exhibited decoupling across scales including: reduced global coupling; increased nodal coupling within the sensorimotor network (SMN) and subcortical network (SN); higher intramodular coupling within the SMN and SN and lower intramodular coupling of the default mode network (DMN); decreased coupling between the modules of DMN-fronto-parietal network and DMN-visual network, but increased coupling between the SMN-SN module. Several associations between the coupling coefficient and topological properties of the SCN, as well as between network values and clinical scores, were observed. These findings validated the clinical implementation of DKI for structural network construction with better differentiation ability and characterized the SCN-FCN decoupling as supplementary insight into the pathological process underlying PD.

Alteration in resting-state effective connectivity within the Papez circuit in Presbycusis.

Previous studies have suggested that the Papez circuit may be involved in the cognitive impairment observed after hearing loss in presbycusis patients, yet relatively little is known about the pattern of changes in effective connectivity within the circuit. The aim of this study was to investigate abnormal alterations in resting-state effective connectivity within the Papez circuit and their association with cognitive decline in presbycusis patients. The spectral dynamic causal modelling (spDCM) approach was used for resting-state effective connectivity analysis in 61 presbycusis patients and 52 healthy controls (HCs) within the Papez circuit. The hippocampus (HPC), mamillary body (MB), anterior thalamic nuclei (ATN), anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), entorhinal cortex (ERC), subiculum (Sub) and parahippocampal gyrus (PHG) were selected as the regions of interest (ROIs). The fully connected model difference in effective connectivity between the two groups was assessed, and the correlation between effective connectivity alteration and cognitive scale was analysed. We found that presbycusis patients demonstrated decreased effective connectivity from MB, PCC, and Sub to ACC relative to HCs, whereas higher effective connectivity strength was shown from HPC to MB, from ATN to PHG and from PHG to Sub. The effective connectivity from PHG to Sub was significantly negatively correlated with the complex figure test (CFT)-delay score (rho = -0.259, p = 0.044). The results support and reinforce the role of abnormal effective connectivity within the Papez circuit in the pathophysiology of presbycusis-related cognitive impairment and reveal its potential as a novel imaging marker.