Clinic information, pathological, and imaging characteristics in 2 058 surgical patients with lung cancer from a single center. / 单中心2 058ä¾‹è‚ºç™Œæ‰‹æœ¯æ‚£è€ çš„ä¸´åºŠä¿¡æ¯ã€ç— ç†åŠå½±åƒå­¦ç‰¹å¾.

OBJECTIVES: Lung cancer is characterized by its high incidence and case fatality rate. Factors related to population composition and cancer prevention programme policy have an effect on the incidence and diagnosis of lung cancer. This study aims to provide scientific support for early diagnosis and treatment of lung cancer by investigating the clinic information, pathological, and imaging characteristics of surgical patients with lung cancer. METHODS: The data of 2 058 patients, who underwent surgery for lung cancer in the Department of Thoracic Surgery of Xiangya Hospital of Central South University from 2016 to 2019, were retrospectively collected to analyze changes in clinic information, pathological, and imaging characteristics. RESULTS: From 2016 to 2019, the number of patients per year was 280, 376, 524, and 878, respectively. Adenocarcinoma (68.1%) was the most common pathological type of surgical patients with lung cancer. From 2016 to 2019, the proportion of adenocarcinoma was increased from 55.5% to 74.1%. The proportion lung cancer patients in stage IA was increased from 38.9% to 62.3%, and the proportion of patients who underwent sublobar resection was increased from 1.8% to 8.6%. The proportion of lymph node sampling was increased in 2019. Compared with the rate in 2016, the detection rate of nodules with diameter≤1 cm detected by CT before surgery in 2019 was significantly improved (2.0% vs 18.2%), and the detection rate of nodules with diameter>3 cm was decreased (34.7% vs 18.3%). From 2016 to 2019, the proportion of lesions with pure ground-glass density and partial solid density detected by CT was increased from 2.0% and 16.6% to 20.0% and 37.3%, respectively. The proportion of solid density was decreased from 81.4% to 42.7%. CONCLUSIONS: The number of lung cancer surgery patients is rapidly increasing year by year, the proportion of CT-detected purely ground-glass density and partially solid density lesions are increasing, the proportion of patients with adenocarcinoma is rising, the proportion of early-stage lung cancer is increasing, smaller lung cancers are detected in earlier clinical stage leading to a more minimally invasive approach to the surgical methods.

Cross-ancestry genome-wide association studies of brain imaging phenotypes.

Genome-wide association studies of brain imaging phenotypes are mainly performed in European populations, but other populations are severely under-represented. Here, we conducted Chinese-alone and cross-ancestry genome-wide association studies of 3,414 brain imaging phenotypes in 7,058 Chinese Han and 33,224 white British participants. We identified 38 new associations in Chinese-alone analyses and 486 additional new associations in cross-ancestry meta-analyses at P < 1.46 × 10-11 for discovery and P < 0.05 for replication. We pooled significant autosomal associations identified by single- or cross-ancestry analyses into 6,443 independent associations, which showed uneven distribution in the genome and the phenotype subgroups. We further divided them into 44 associations with different effect sizes and 3,557 associations with similar effect sizes between ancestries. Loci of these associations were shared with 15 brain-related non-imaging traits including cognition and neuropsychiatric disorders. Our results provide a valuable catalog of genetic associations for brain imaging phenotypes in more diverse populations.

Active Learning in Brain Tumor Segmentation with Uncertainty Sampling and Annotation Redundancy Restriction.

Deep learning models have demonstrated great potential in medical imaging but are limited by the expensive, large volume of annotations required. To address this, we compared different active learning strategies by training models on subsets of the most informative images using real-world clinical datasets for brain tumor segmentation and proposing a framework that minimizes the data needed while maintaining performance. Then, 638 multi-institutional brain tumor magnetic resonance imaging scans were used to train three-dimensional U-net models and compare active learning strategies. Uncertainty estimation techniques including Bayesian estimation with dropout, bootstrapping, and margins sampling were compared to random query. Strategies to avoid annotating similar images were also considered. We determined the minimum data necessary to achieve performance equivalent to the model trained on the full dataset (α = 0.05). Bayesian approximation with dropout at training and testing showed results equivalent to that of the full data model (target) with around 30% of the training data needed by random query to achieve target performance (p = 0.018). Annotation redundancy restriction techniques can reduce the training data needed by random query to achieve target performance by 20%. We investigated various active learning strategies to minimize the annotation burden for three-dimensional brain tumor segmentation. Dropout uncertainty estimation achieved target performance with the least annotated data.

Villin headpiece unfolding upon binding to boridene mediated by the "anchoring-perturbation" mechanism.

We employ molecular dynamics (MD) simulations to investigate the influence of boridene on the behavior of a protein model, HP35, with the aim of assessing the potential biotoxicity of boridene. Our MD results reveal that HP35 can undergo unfolding via an "anchoring-perturbation" mechanism upon adsorption onto the boridene surface. Specifically, the third helix of HP35 becomes tightly anchored to the boridene surface through strong electrostatic interactions between the abundant molybdenum atoms on the boridene surface and the oxygen atoms on the HP35 backbone. Meanwhile, the first helix, experiencing continuous perturbation from the surrounding water solution over an extended period, suffers from potential breakage of hydrogen bonds, ultimately resulting in its unfolding. Our findings not only propose, for the first time to our knowledge, the "anchoring-perturbation" mechanism as a guiding principle for protein unfolding but also reveal the potential toxicity of boridene on protein structures.

Eco-environmental response to land cover change in ASEAN countries from 2001 to 2020 based on spatial granular association.

As one of the major forms of terrestrial ecosystem change, land cover change (LCC) alters the structure of surface landscape patterns, thereby causing regional eco-environmental responses. Due to limitations in research methods, existing studies have focused more on the overall response between LCC and the eco-environment, and cannot calculate the level change response of eco-environmental quality caused by LCC. Based on the method of spatial data information granulation, this study used a remote sensing ecological index to represent the eco-environmental system and divided the complex eco-environmental system and land system into a simple system composed of spatial information granules, thus simplifying the spatial data calculation. The main contributions of this study are as follows: (1) A computing method of eco-environmental response to LCC based on spatial granular association was proposed, which can spatially identify the main response types of regional LCC; (2) three measures, namely, spatial association support degree, spatial association confidence degree, and spatial association cover degree, were proposed to measure the eco-environmental response of regional LCC from different perspectives; and (3) during 2001-2020, the eco-environmental response to l LCC, namely, the response to degradation caused by shrinking forest area, was not very dramatic in ASEAN (Association of Southeast Asian Nations).

Deep learning-based radiomic nomogram to predict risk categorization of thymic epithelial tumors: A multicenter study.

PURPOSE: The study was aimed to develop and evaluate a deep learning-based radiomics to predict the histological risk categorization of thymic epithelial tumors (TETs), which can be highly informative for patient treatment planning and prognostic assessment. METHOD: A total of 681 patients with TETs from three independent hospitals were included and separated into derivation cohort and external test cohort. Handcrafted and deep learning features were extracted from preoperative contrast-enhanced CT images and selected to build three radiomics signatures (radiomics signature [Rad_Sig], deep learning signature [DL_Sig] and deep learning radiomics signature [DLR_Sig]) to predict risk categorization of TETs. A deep learning-based radiomic nomogram (DLRN) was then depicted to visualize the classification evaluation. The performance of predictive models was compared using the receiver operating characteristic and decision curve analysis (DCA). RESULTS: Among three radiomics signatures, DLR_Sig demonstrated optimum performance with an AUC of 0.883 for the derivation cohort and 0.749 for the external test cohort. Combining DLR_Sig with age and gender, DLRN was depict and exhibited optimum performance among all radiomics models with an AUC of 0.965, accuracy of 0.911, sensitivity of 0.921 and specificity of 0.902 in the derivation cohort, and an AUC of 0.786, accuracy of 0.774, sensitivity of 0.778 and specificity of 0.771 in the external test cohort. The DCA showed that DLRN had greater clinical benefit than other radiomics signatures. CONCLUSIONS: Our study developed and validated a DLRN to accurately predict the risk categorization of TETs, which has potential to facilitate individualized treatment and improve patient prognosis evaluation.

The role of circadian clock genes in colorectal carcinoma: Novel insights into regulatory mechanism and implications in clinical therapy.

Colorectal cancer (CRC) is a lethal malignancy with limited treatment strategies. Accumulating evidence indicates that CRC tumorigenesis, progression and metastasis are intimately associated with circadian clock, an inherent 24-h cycle oscillation of biochemical, physiological functions in almost every eukaryote. In the present review, we summarize the altered expression level of circadian genes in CRC and the prognosis associated with gene abundance switch. We illustrate the function and potential mechanisms of circadian genes in CRC pathogenesis and progression. Moreover, circadian based-therapeutic strategies including chronotherapy, therapeutics targeting potential circadian components, and melatonin treatment in CRC are also highlighted.

A Novel Nomogram Based on Quantitative MRI and Clinical Features for the Prediction of Neonatal Intracranial Hypertension.

Intracranial hypertension (ICH) is a serious threat to the health of neonates. However, early and accurate diagnosis of neonatal intracranial hypertension remains a major challenge in clinical practice. In this study, a predictive model based on quantitative magnetic resonance imaging (MRI) data and clinical parameters was developed to identify neonates with a high risk of ICH. Newborns who were suspected of having intracranial lesions were included in our study. We utilized quantitative MRI to obtain the volumetric data of gray matter, white matter, and cerebrospinal fluid. After the MRI examination, a lumbar puncture was performed. The nomogram was constructed by incorporating the volumetric data and clinical features by multivariable logistic regression. The performance of the nomogram was evaluated by discrimination, calibration curve, and decision curve. Clinical parameters and volumetric quantitative MRI data, including postmenstrual age (p = 0.06), weight (p = 0.02), mode of delivery (p = 0.01), and gray matter volume (p = 0.003), were included in and significantly associated with neonatal intracranial hypertension risk. The nomogram showed satisfactory discrimination, with an area under the curve of 0.761. Our results demonstrated that decision curve analysis had promising clinical utility of the nomogram. The nomogram, incorporating clinical and quantitative MRI features, provided an individualized prediction of neonatal intracranial hypertension risk and facilitated decision making guidance for the early diagnosis and treatment for neonatal ICH. External validation from studies using a larger sample size before implementation in the clinical decision-making process is needed.

Covariation of preadult environmental exposures, adult brain imaging phenotypes, and adult personality traits.

Exposure to preadult environmental exposures may have long-lasting effects on mental health by affecting the maturation of the brain and personality, two traits that interact throughout the developmental process. However, environment-brain-personality covariation patterns and their mediation relationships remain unclear. In 4297 healthy participants (aged 18-30 years), we combined sparse multiple canonical correlation analysis with independent component analysis to identify the three-way covariation patterns of 59 preadult environmental exposures, 760 adult brain imaging phenotypes, and five personality traits, and found two robust environment-brain-personality covariation models with sex specificity. One model linked greater stress and less support to weaker functional connectivity and activity in the default mode network, stronger activity in subcortical nuclei, greater thickness and volume in the occipital, parietal and temporal cortices, and lower agreeableness, consciousness and extraversion as well as higher neuroticism. The other model linked higher urbanicity and better socioeconomic status to stronger functional connectivity and activity in the sensorimotor network, smaller volume and surface area and weaker functional connectivity and activity in the medial prefrontal cortex, lower white matter integrity, and higher openness to experience. We also conducted mediation analyses to explore the potential bidirectional mediation relationships between adult brain imaging phenotypes and personality traits with the influence of preadult environmental exposures and found both environment-brain-personality and environment-personality-brain pathways. We finally performed moderated mediation analyses to test the potential interactions between macro- and microenvironmental exposures and found that one category of exposure moderated the mediation pathways of another category of exposure. These results improve our understanding of the effects of preadult environmental exposures on the adult brain and personality traits and may facilitate the design of targeted interventions to improve mental health by reducing the impact of adverse environmental exposures.

Moderate binding of villin headpiece protein to C3N3 nanosheet reveals the suitable biocompatibility of this nanomaterial.

Since its recent successful synthesis and due to its promising physical and chemical properties, the carbon nitrite nanomaterial, C3N3, has attracted considerable attention in various scientific areas. However, thus far, little effort has been devoted to investigating the structural influence of the direct interaction of this 2D nanomaterial and biomolecules, including proteins and biomembranes so as to understand the physical origin of its bio-effect, particularly from the molecular landscape. Such information is fundamental to correlate to the potential nanotoxicology of the C3N3 nanomaterial. In this work, we explored the potential structural influence of a C3N3 nanosheet on the prototypical globular protein, villin headpiece (HP35) using all-atom molecular dynamics (MD) simulations. We found that HP35 could maintain its native conformations upon adsorption onto the C3N3 nanosheet regardless of the diversity in the binding sites, implying the potential advantage of C3N3 in protecting the biomolecular structure. The adsorption was mediated primarily by vdW interactions. Moreover, once adsorbed on the C3N3 surface, HP35 remains relatively fixed on the nanostructure without a distinct lateral translation, which may aid in keeping the structural integrity of the protein. In addition, the porous topological structure of C3N3 and the special water layer present on the C3N3 holes conjointly contributed to the restricted motion of HP35 via the formation of a high free energy barrier and a steric hindrance to prevent the surface displacement. This work revealed for the first time the potential influence of the 2D C3N3 nanomaterial in the protein structure and provided the corresponding in-depth molecular-level mechanism, which is valuable for future applications of C3N3 in bionanomedicine.

The role of circadian clocks in cancer: Mechanisms and clinical implications.

Circadian rhythm refers to the inherent 24-h cycle oscillation of biochemical, physiological and behavioral functions, which is almost universal in eukaryotes. At least 14 core clock genes have been reported to form multiple chain feedback loops that confer intrinsic circadian rhythmicity onto the molecular clock. Accumulating evidence has shown that the circadian gene dysfunction resulted from single nucleotide polymorphisms (SNPs), deletions, epigenetic modification, and deregulation is strongly associated with cancer risk. In the present review, we describe the composition of circadian rhythm system. We highlight the function and mechanism of clock genes in cancer pathogenesis and progression. Moreover, their potential clinical implications as prognostic biomarkers and therapeutic targets have been addressed.

Cross-ancestry genome-wide association meta-analyses of hippocampal and subfield volumes.

The hippocampus is critical for memory and cognition and neuropsychiatric disorders, and its subfields differ in architecture and function. Genome-wide association studies on hippocampal and subfield volumes are mainly conducted in European populations; however, other ancestral populations are under-represented. Here we conduct cross-ancestry genome-wide association meta-analyses in 65,791 individuals for hippocampal volume and 38,977 for subfield volumes, including 7,009 individuals of East Asian ancestry. We identify 339 variant-trait associations at P < 1.13 × 10-9 for 44 hippocampal traits, including 23 new associations. Common genetic variants have similar effects on hippocampal traits across ancestries, although ancestry-specific associations exist. Cross-ancestry analysis improves the fine-mapping precision and the prediction performance of polygenic scores in under-represented populations. These genetic variants are enriched for Wnt signaling and neuron differentiation and affect cognition, emotion and neuropsychiatric disorders. These findings may provide insight into the genetic architectures of hippocampal and subfield volumes.

Beyond antioxidation: Harnessing the CeO2 nanoparticles as a renoprotective contrast agent for in vivo spectral CT angiography.

It is a challenging task to develop a contrast agent that not only provides excellent image contrast but also protects impaired kidneys from oxidative-related stress during angiography. Clinically approved iodinated CT contrast media are associated with potential renal toxicity, making it necessary to develop a renoprotective contrast agent. Here, we develop a CeO2 nanoparticles (NPs)-mediated three-in-one renoprotective imaging strategy, namely, i) renal clearable CeO2 NPs serve as a one-stone-two-birds antioxidative contrast agent, ii) low contrast media dose, and iii) spectral CT, for in vivo CT angiography (CTA). Benefiting from the merits of advanced sensitivity of spectral CT and K-edge energy of Cerium (Ce, 40.4 keV), an improved image quality of in vivo CTA is successfully achieved with a 10 times reduction of contrast agent dosage. In parallel, the sizes of CeO2 NPs and broad catalytic activities are suitable to be filtered via glomerulus thus directly alleviating the oxidative stress and the accompanying inflammatory injury of the kidney tubules. In addition, the low dosage of CeO2 NPs reduces the hypoperfusion stress of renal tubules induced by concentrated contrast agents used in angiography. This three-in-one renoprotective imaging strategy helps prevent kidney injury from being worsened during the CTA examination.

A Phase 1/2 Multicenter Randomized Trial of Local Ablation plus Toripalimab versus Toripalimab Alone for Previously Treated Unresectable Hepatocellular Carcinoma.

PURPOSE: To assess the safety and efficacy of local ablation plus PD-1 inhibitor toripalimab in previously treated unresectable hepatocellular carcinoma (HCC). PATIENTS AND METHODS: In the multicenter, two-stage, and randomized phase 1/2 trial, patients were randomly assigned to receive toripalimab alone (240 mg, every 3 weeks), subtotal local ablation followed by toripalimab starting on post-ablation day 3 (Schedule D3), or on post-ablation day 14 (Schedule D14). The first endpoint of stage 1 was to determine which combination schedule could continue and progression-free survival (PFS) as the primary endpoint for stage 1/2. RESULTS: A total of 146 patients were recruited. During stage 1, Schedule D3 achieved numerically higher objective response rate (ORR) than Schedule D14 for non-ablation lesions (37.5% vs. 31.3%), and was chosen for stage 2 evaluation. For the entire cohort of both stages, patients with Schedule D3 had a significantly higher ORR than with toripalimab alone (33.8% vs. 16.9%; P = 0.027). Moreover, patients with Schedule D3 had improved median PFS (7.1 vs. 3.8 months; P < 0.001) and median overall survival (18.4 vs. 13.2 months; P = 0.005), as compared with toripalimab alone. In addition, six (9%) patients with toripalimab, eight (12%) with Schedule D3, and 4 (25%) with Schedule D14 developed grade 3 or 4 adverse events, and one patient (2%) with Schedule D3 manifested grade 5 treatment-related pneumonitis. CONCLUSIONS: In patients with previously treated unresectable HCC, subtotal ablation plus toripalimab improved the clinical efficacy as compared with toripalimab alone, with an acceptable safety profile.

Prognostic value of autophagy-related genes based on single-cell RNA-sequencing in colorectal cancer.

Background: Colorectal cancer (CRC) is the second most common cancer in China. Autophagy plays an important role in the initiation and development of CRC. Here, we assessed the prognostic value and potential functions of autophagy-related genes (ARGs) using integrated analysis using single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) and RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA). Methods: We analyzed GEO-scRNA-seq data from GEO using various single-cell technologies, including cell clustering, and identification of differentially expressed genes (DEGs) in different cell types. Additionally, we performed gene set variation analysis (GSVA). The differentially expressed ARGs among different cell types and those between CRC and normal tissues were identified using TCGA-RNA-seq data, and the hub ARGs were screened. Finally, a prognostic model based on the hub ARGs was constructed and validated, and patients with CRC in TCGA datasets were divided into high- and low-risk groups based on their risk-score, and immune cells infiltration and drug sensitivity analyses between the two groups were performed. Results: We obtained single-cell expression profiles of 16,270 cells, and clustered them into seven types of cells. GSVA revealed that the DEGs among the seven types of cells were enriched in many signaling pathways associated with cancer development. We screened 55 differentially expressed ARGs, and identified 11 hub ARGs. Our prognostic model revealed that the 11 hub ARGs including CTSB, ITGA6, and S100A8, had a good predictive ability. Moreover, the immune cell infiltrations in CRC tissues were different between the two groups, and the hub ARGs were significantly correlated with the enrichment of immune cell infiltration. The drug sensitivity analysis revealed that the patients in the two risk groups had difference in their response to anti-cancer drugs. Conclusion: We developed a novel prognostic 11-hub ARG risk model, and these hubs may act as potential therapeutic targets for CRC.

The combination of radiomics features and VASARI standard to predict glioma grade.

Background and Purpose: Radiomics features and The Visually AcceSAble Rembrandt Images (VASARI) standard appear to be quantitative and qualitative evaluations utilized to determine glioma grade. This study developed a preoperative model to predict glioma grade and improve the efficacy of clinical strategies by combining these two assessment methods. Materials and Methods: Patients diagnosed with glioma between March 2017 and September 2018 who underwent surgery and histopathology were enrolled in this study. A total of 3840 radiomic features were calculated; however, using the least absolute shrinkage and selection operator (LASSO) method, only 16 features were chosen to generate a radiomic signature. Three predictive models were developed using radiomic features and VASARI standard. The performance and validity of models were evaluated using decision curve analysis and 10-fold nested cross-validation. Results: Our study included 102 patients: 35 with low-grade glioma (LGG) and 67 with high-grade glioma (HGG). Model 1 utilized both radiomics and the VASARI standard, which included radiomic signatures, proportion of edema, and deep white matter invasion. Models 2 and 3 were constructed with radiomics or VASARI, respectively, with an area under the receiver operating characteristic curve (AUC) of 0.937 and 0.831, respectively, which was less than that of Model 1, with an AUC of 0.966. Conclusion: The combination of radiomics features and the VASARI standard is a robust model for predicting glioma grades.

The impact of pre-adulthood urbanicity on hippocampal subfield volumes and neurocognitive abilities in young adults.

BACKGROUND: Urbanicity refers to the conditions that are particular to urban areas and is a growing environmental challenge that may affect hippocampus and neurocognition. This study aimed to investigate the effects of the average pre-adulthood urbanicity on hippocampal subfield volumes and neurocognitive abilities as well as the sensitive age windows of the urbanicity effects. PARTICIPANTS AND METHODS: We included 5,390 CHIMGEN participants (3,538 females; age: 23.69 ± 2.26 years, range: 18-30 years). Pre-adulthood urbanicity of each participant was defined as the average value of annual night-time light (NL) or built-up% from age 0-18, which were extracted from remote-sensing satellite data based on annual residential coordinates of the participants. The hippocampal subfield volumes were calculated based on structural MRI and eight neurocognitive measures were assessed. The linear regression was applied to investigate the associations of pre-adulthood NL with hippocampal subfield volumes and neurocognitive abilities, mediation models were used to find the underlying pathways among urbanicity, hippocampus and neurocognition, and distributed lag models were used to identify sensitive age windows of urbanicity effect. RESULTS: Higher pre-adulthood NL was associated with greater volumes in the left (ß = 0.100, 95%CI: [0.075, 0.125]) and right (0.078, [0.052, 0.103]) fimbria and left subiculum body (0.045, [0.020, 0.070]) and better neurocognitive abilities in information processing speed (-0.212, [-0.240, -0.183]), working memory (0.085, [0.057, 0.114]), episodic memory (0.107, [0.080, 0.135]), and immediate (0.094, [0.065, 0.123]) and delayed (0.087, [0.058, 0.116]) visuospatial recall, and hippocampal subfield volumes and visuospatial memory showed bilateral mediations for the urbanicity effects. Urbanicity effects were greatest on the fimbria in preschool and adolescence, on visuospatial memory and information processing from childhood to adolescence and on working memory after 14 years. CONCLUSION: These findings improve our understanding of the impact of urbanicity on hippocampus and neurocognitive abilities and will benefit for designing more targeted intervention for neurocognitive improvement.

Head and neck tumor segmentation convolutional neural network robust to missing PET/CT modalities using channel dropout.

Objective. Radiation therapy for head and neck (H&N) cancer relies on accurate segmentation of the primary tumor. A robust, accurate, and automated gross tumor volume segmentation method is warranted for H&N cancer therapeutic management. The purpose of this study is to develop a novel deep learning segmentation model for H&N cancer based on independent and combined CT and FDG-PET modalities.Approach. In this study, we developed a robust deep learning-based model leveraging information from both CT and PET. We implemented a 3D U-Net architecture with 5 levels of encoding and decoding, computing model loss through deep supervision. We used a channel dropout technique to emulate different combinations of input modalities. This technique prevents potential performance issues when only one modality is available, increasing model robustness. We implemented ensemble modeling by combining two types of convolutions with differing receptive fields, conventional and dilated, to improve capture of both fine details and global information.Main Results. Our proposed methods yielded promising results, with a Dice similarity coefficient (DSC) of 0.802 when deployed on combined CT and PET, DSC of 0.610 when deployed on CT, and DSC of 0.750 when deployed on PET.Significance. Application of a channel dropout method allowed for a single model to achieve high performance when deployed on either single modality images (CT or PET) or combined modality images (CT and PET). The presented segmentation techniques are clinically relevant to applications where images from a certain modality might not always be available.

Neuroimaging uncovers distinct relationships of glymphatic dysfunction and motor symptoms in Parkinson's disease.

BACKGROUND: Studies of glymphatic dysfunction in Parkinson's disease (PD) patients have attracted much attention in recent years. However, the relationships between glymphatic dysfunction and clinical symptoms remains unclear. OBJECTIVES: To determine whether the diffusion tensor image analysis along the perivascular space (DTI-ALPS) affect the severity and types of motor and non-motor symptoms in PD patients. METHODS: De novo PD patients and controls who performed both DTI and 123I-DaTscan single photon emission computed tomography (SPECT) scanning were retrieved from the international multicenter Parkinson's Progression Marker Initiative (PPMI) cohort. Glymphatic system was evaluated by the DTI-ALPS. Motor symptoms were assessed by Movement Disorders Society Unified Parkinson's Disease Rating Scale III (MDS-UPDRS-III). The influence of glymphatic activity on motor and non-motor symptoms was explored by multivariate linear regression models. RESULTS: A total of 153 PD patients (mean age 60.97 ± 9.47 years; 99 male) and 67 normal controls (mean age 60.10 ± 10.562 years; 43 male) were included. The DTI-ALPS index of PD patients was significantly lower than normal controls (Z = - 2.160, p = 0.031). MDS-UPDRS III score (r = - 0.213, p = 0.008) and subscore for rigidity (r = - 0.177, p = 0.029) were negatively correlated with DTI-ALPS index. The DTI-ALPS index was significantly associated with MDS-UPDRS-III score (ß = - 0.160, p = 0.048) and subscore for rigidity (ß = - 0.170, p = 0.041) after adjusting for putamen dopamine transporter availability and clinical factors. CONCLUSIONS: Our results showed distinct relationships between glymphatic dysfunction and the severity and types of PD motor symptoms, suggesting the potential of DTI-ALPS index as a biomarker for PD motor symptoms.

Correlation between retinal structure and brain multimodal magnetic resonance imaging in patients with Alzheimer's disease.

Background: The retina imaging and brain magnetic resonance imaging (MRI) can both reflect early changes in Alzheimer's disease (AD) and may serve as potential biomarker for early diagnosis, but their correlation and the internal mechanism of retinal structural changes remain unclear. This study aimed to explore the possible correlation between retinal structure and visual pathway, brain structure, intrinsic activity changes in AD patients, as well as to build a classification model to identify AD patients. Methods: In the study, 49 AD patients and 48 healthy controls (HCs) were enrolled. Retinal images were obtained by optical coherence tomography (OCT). Multimodal MRI sequences of all subjects were collected. Spearman correlation analysis and multiple linear regression models were used to assess the correlation between OCT parameters and multimodal MRI findings. The diagnostic value of combination of retinal imaging and brain multimodal MRI was assessed by performing a receiver operating characteristic (ROC) curve. Results: Compared with HCs, retinal thickness and multimodal MRI findings of AD patients were significantly altered (p < 0.05). Significant correlations were presented between the fractional anisotropy (FA) value of optic tract and mean retinal thickness, macular volume, macular ganglion cell layer (GCL) thickness, inner plexiform layer (IPL) thickness in AD patients (p < 0.01). The fractional amplitude of low frequency fluctuations (fALFF) value of primary visual cortex (V1) was correlated with temporal quadrant peripapillary retinal nerve fiber layer (pRNFL) thickness (p < 0.05). The model combining thickness of GCL and temporal quadrant pRNFL, volume of hippocampus and lateral geniculate nucleus, and age showed the best performance to identify AD patients [area under the curve (AUC) = 0.936, sensitivity = 89.1%, specificity = 87.0%]. Conclusion: Our study demonstrated that retinal structure change was related to the loss of integrity of white matter fiber tracts in the visual pathway and the decreased LGN volume and functional metabolism of V1 in AD patients. Trans-synaptic axonal retrograde lesions may be the underlying mechanism. Combining retinal imaging and multimodal MRI may provide new insight into the mechanism of retinal structural changes in AD and may serve as new target for early auxiliary diagnosis of AD.