In vivo cerebral metabolic and dopaminergic characteristics in multiple system atrophy with orthostatic hypotension.

PURPOSE: Multiple system atrophy (MSA) is a rare neurodegenerative disease, often presented with orthostatic hypotension (OH), which is a disabling symptom but has not been very explored. Here, we investigated MSA patients with OH by using positron emission tomography (PET) with 18F-fluorodeoxyglucose (18F-FDG) and 11C-N-2-carbomethoxy-3-(4-fluorophenyl)-tropane (11C-CFT) for in vivo evaluation of the glucose metabolism and dopaminergic function of the brain. METHODS: Totally, 51 patients with MSA and 20 healthy controls (HC) who underwent 18F-FDG PET/CT were retrospectively enrolled, among which 24 patients also underwent 11C-CFT PET/CT. All patients were divided into MSA-OH(+) and MSA-OH(-) groups. Then, statistical parametric mapping (SPM) method was used to reveal the regional metabolic and dopaminergic characteristics of MSA-OH(+) compared with MSA-OH(-). Moreover, the metabolic networks of MSA-OH(+), MSA-OH(-) and HC groups were also constructed and analyzed based on graph theory to find possible network-level changes in MSA patients with OH. RESULTS: The SPM results showed significant hypometabolism in the pons and right cerebellar tonsil, as well as hypermetabolism in the left parahippocampal gyrus and left superior temporal gyrus in MSA-OH(+) compared with MSA-OH(-). A reduced 11C-CFT uptake in the left caudate was also shown in MSA-OH(+) compared with MSA-OH(-). In the network analysis, significantly reduced local efficiency and clustering coefficient were shown in MSA-OH(+) compared with HC, and decreased nodal centrality in the frontal gyrus was found in MSA-OH(+) compared with MSA-OH(-). CONCLUSION: In this study, the changes in glucose metabolism in the pons, right cerebellar tonsil, left parahippocampal gyrus and left superior temporal gyrus were found closely related to OH in MSA patients. And the decreased presynaptic dopaminergic function in the left caudate may contribute to OH in MSA. Taken together, this study provided in vivo pathophysiologic information on MSA with OH from neuroimaging approach, which is essential for a better understanding of MSA with OH.

[18F]FDG PET integrated with structural MRI for accurate brain age prediction.

PURPOSE: Brain aging is a complex and heterogeneous process characterized by both structural and functional decline. This study aimed to establish a novel deep learning (DL) method for predicting brain age by utilizing structural and metabolic imaging data. METHODS: The dataset comprised participants from both the Universal Medical Imaging Diagnostic Center (UMIDC) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). The former recruited 395 normal control (NC) subjects, while the latter included 438 NC subjects, 51 mild cognitive impairment (MCI) subjects, and 56 Alzheimer's disease (AD) subjects. We developed a novel dual-pathway, 3D simple fully convolutional network (Dual-SFCNeXt) to estimate brain age using [18F]fluorodeoxyglucose positron emission tomography ([18F]FDG PET) and structural magnetic resonance imaging (sMRI) images of NC subjects as input. Several prevailing DL models were trained and tested using either MRI or PET data for comparison. Model accuracies were evaluated using mean absolute error (MAE) and Pearson's correlation coefficient (r). Brain age gap (BAG), deviations of brain age from chronologic age, was correlated with cognitive assessments in MCI and AD subjects. RESULTS: Both PET- and MRI-based models achieved high prediction accuracy. The leading model was the SFCNeXt (the single-pathway version) for PET (MAE = 2.92, r = 0.96) and MRI (MAE = 3.23, r = 0.95) on all samples. By integrating both PET and MRI images, the Dual-SFCNeXt demonstrated significantly improved accuracy (MAE = 2.37, r = 0.97) compared to all single-modality models. Significantly higher BAG was observed in both the AD (P < 0.0001) and MCI (P < 0.0001) groups compared to the NC group. BAG correlated significantly with Mini-Mental State Examination (MMSE) scores (r=-0.390 for AD, r=-0.436 for MCI) and the Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) scores (r = 0.333 for AD, r = 0.372 for MCI). CONCLUSION: The integration of [18F]FDG PET with structural MRI enhances the accuracy of brain age prediction, potentially introducing a new avenue for related multimodal brain age prediction studies.

Spatial-temporal dynamic evolution of lewy body dementia by metabolic PET imaging.

PURPOSE: Lewy body dementia (LBD) is a neurodegenerative disease with high heterogeneity and complex pathogenesis. Our study aimed to use disease progression modeling to uncover spatial-temporal dynamic evolution of LBD in vivo, and to explore differential profiles of clinical features, glucose metabolism, and dopaminergic function among different evolution-related subtypes. METHODS: A total of 123 participants (31 healthy controls and 92 LBD patients) who underwent 18F-FDG PET scans were retrospectively enrolled. 18F-FDG PET-based Subtype and Stage Inference (SuStaIn) model was established to illustrate spatial-temporal evolutionary patterns and categorize relevant subtypes. Then subtypes and stages were further related to clinical features, glucose metabolism, and dopaminergic function of LBD patients. RESULTS: This 18F-FDG PET imaging-based approach illustrated two distinct patterns of neurodegenerative evolution originating from the neocortex and basal ganglia in LBD and defined them as subtype 1 and subtype 2, respectively. There were obvious differences between subtypes. Compared with subtype 1, subtype 2 exhibited a greater proportion of male patients (P = 0.045) and positive symptoms such as visual hallucinations (P = 0.033) and fluctuating cognitions (P = 0.033). Cognitive impairment, metabolic abnormalities, dopaminergic dysfunction and progression were all more severe in subtype 2 (all P < 0.05). In addition, a strong association was observed between SuStaIn subtypes and two clinical phenotypes (Parkinson's disease dementia and dementia with Lewy bodies) (P = 0.005). CONCLUSIONS: Our findings based on 18F-FDG PET and data-driven model illustrated spatial-temporal dynamic evolution of LBD and categorized novel subtypes with different evolutionary patterns, clinical and imaging features in vivo. The evolution-related subtypes are associated with LBD clinical phenotypes, which supports the perspective of existence of distinct entities in LBD spectrum.

A key O-demethylase in the degradation of guaiacol by Rhodococcus opacus PD630.

Rhodococcus opacus PD630 is a high oil-producing strain with the ability to convert lignin-derived aromatics to high values, but limited research has been done to elucidate its conversion pathway, especially the upper pathways. In this study, we focused on the upper pathways and demethylation mechanism of lignin-derived aromatics metabolism by R. opacus PD630. The results of the aromatic carbon resource utilization screening showed that R. opacus PD630 had a strong degradation capacity to the lignin-derived methoxy-containing aromatics, such as guaiacol, 3,4-veratric acid, anisic acid, isovanillic acid, and vanillic acid. The gene of gcoAR, which encodes cytochrome P450, showed significant up-regulation when R. opacus PD630 grew on diverse aromatics. Deletion mutants of gcoAR and its partner protein gcoBR resulted in the strain losing the ability to grow on guaiacol, but no significant difference to the other aromatics. Only co-complementation alone of gcoAR and gcoBR restored the strain's ability to utilize guaiacol, demonstrating that both genes were equally important in the utilization of guaiacol. In vitro assays further revealed that GcoAR could convert guaiacol and anisole to catechol and phenol, respectively, with the production of formaldehyde as a by-product. The study provided robust evidence to reveal the molecular mechanism of R. opacus PD630 on guaiacol metabolism and offered a promising study model for dissecting the demethylation process of lignin-derived aromatics in microbes.IMPORTANCEAryl-O-demethylation is believed to be the key rate-limiting step in the catabolism of heterogeneous lignin-derived aromatics in both native and engineered microbes. However, the mechanisms of O-demethylation in lignin-derived aromatic catabolism remain unclear. Notably, guaiacol, the primary component unit of lignin, lacks in situ demonstration and illustration of the molecular mechanism of guaiacol O-demethylation in lignin-degrading bacteria. This is the first study to illustrate the mechanism of guaiacol metabolism by R. opacus PD630 in situ as well as characterize the purified key O-demethylase in vitro. This study provided further insight into the lignin metabolic pathway of R. opacus PD630 and could guide the design of an efficient biocatalytic system for lignin valorization.

Characterization of neuroinflammation pattern in anti-LGI1 encephalitis based on TSPO PET and symptom clustering analysis.

PURPOSE: TSPO PET with radioligand [18F]DPA-714 is an emerging molecular imaging technique that reflects cerebral inflammation and microglial activation, and it has been recently used in central nervous system diseases. In this study, we aimed to investigate the neuroinflammation pattern of anti-leucine-rich glioma-inactivated 1 (LGI1) protein autoimmune encephalitis (AIE) and to evaluate its possible correlation with clinical phenotypes. METHODS: Twenty patients with anti-LGI1 encephalitis from the autoimmune encephalitis cohort in Huashan Hospital and ten with other AIE and non-inflammatory diseases that underwent TSPO PET imaging were included in the current study. Increased regional [18F]DPA-714 retention in anti-LGI1 encephalitis was detected on a voxel basis using statistic parametric mapping analysis. Multiple correspondence analysis and hierarchical clustering were conducted for discriminate subgroups in anti-LGI1 encephalitis. Standardized uptake value ratios normalized to the cerebellum (SUVRc) were calculated for semiquantitative analysis of TSPO PET features between different LGI1-AIE subgroups. RESULTS: Increased regional retention of [18F]DPA-714 was identified in the bilateral hippocampus, caudate nucleus, and frontal cortex in LGI1-AIE patients. Two subgroups of LGI1-AIE patients were distinguished based on the top seven common symptoms. Patients in cluster 1 had a high frequency of facio-brachial dystonic seizures than those in cluster 2 (p = 0.004), whereas patients in cluster 2 had a higher frequency of general tonic-clonic (GTC) seizures than those in cluster 1 (p < 0.001). Supplementary motor area and superior frontal gyrus showed higher [18F]DPA-714 retention in cluster 2 patients compared with those in cluster 1 (p = 0.024; p = 0.04, respectively). CONCLUSIONS: Anti-LGI1 encephalitis had a distinctive molecular imaging pattern presented by TSPO PET scan. LGI1-AIE patients with higher retention of [18F]DPA-714 in the frontal cortex were more prone to present with GTC seizures. Further studies are required for verifying its value in clinical application.

[18F]FDG PET/MRI combined with chest HRCT in early cancer detection: a retrospective study of 3020 asymptomatic subjects.

PURPOSE: PET/MRI has become an important medical imaging approach in clinical practice. In this study, we retrospectively investigated the detectability of fluorine-18 (18F)-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging ([18F]FDG PET/MRI) combined with chest computerized tomography (CT) for early cancer in a large cohort of asymptomatic subjects. METHODS: This study included a total of 3020 asymptomatic subjects who underwent whole-body [18F]FDG PET/MRI and chest HRCT examinations. All subjects received a 2-4-year follow-up for cancer development. Cancer detection rate, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the [18F]FDG PET/MRI with or without chest HRCT were calculated and analyzed. RESULTS: Sixty-one subjects were pathologically diagnosed with cancers, among which 59 were correctly detected by [18F]FDG PET/MRI combined with chest HRCT. Of the 59 patients (32 with lung cancer, 9 with breast cancer, 6 with thyroid cancer, 5 with colon cancer, 3 with renal cancer, 1 with prostate cancer, 1 with gastric cancer, 1 with endometrial cancer, and 1 with lymphoma), 54 (91.5%) were at stage 0 or stage I (according to the 8th edition of the tumor-node-metastasis [TNM] staging system), 33 (55.9%) were detected by PET/MRI alone (27 with non-lung cancers and 6 with lung cancer). Cancer detection rate, sensitivity, specificity, PPV, and NPV for PET/MRI combined with chest CT were 2.0%, 96.7%, 99.6%, 83.1%, and 99.9%, respectively. For PET/MRI alone, the metrics were 1.1%, 54.1%, 99.6%, 73.3%, and 99.1%, respectively, and for PET/MRI in non-lung cancers, the metrics were 0.9%, 93.1%, 99.6%, 69.2%, and 99.9%, respectively. CONCLUSIONS: [18F]FDG PET/MRI holds great promise for the early detection of non-lung cancers, while it seems insufficient for detecting early-stage lung cancers. Chest HRCT can be complementary to whole-body PET/MRI for early cancer detection. TRIAL REGISTRATION: ChiCTR2200060041. Registered 16 May 2022. Public site: https://www.chictr.org.cn/index.html.

A deep learning framework for 18F-FDG PET imaging diagnosis in pediatric patients with temporal lobe epilepsy.

PURPOSE: Epilepsy is one of the most disabling neurological disorders, which affects all age groups and often results in severe consequences. Since misdiagnoses are common, many pediatric patients fail to receive the correct treatment. Recently, 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) imaging has been used for the evaluation of pediatric epilepsy. However, the epileptic focus is very difficult to be identified by visual assessment since it may present either hypo- or hyper-metabolic abnormality with unclear boundary. This study aimed to develop a novel symmetricity-driven deep learning framework of PET imaging for the identification of epileptic foci in pediatric patients with temporal lobe epilepsy (TLE). METHODS: We retrospectively included 201 pediatric patients with TLE and 24 age-matched controls who underwent 18F-FDG PET-CT studies. 18F-FDG PET images were quantitatively investigated using 386 symmetricity features, and a pair-of-cube (PoC)-based Siamese convolutional neural network (CNN) was proposed for precise localization of epileptic focus, and then metabolic abnormality level of the predicted focus was calculated automatically by asymmetric index (AI). Performances of the proposed framework were compared with visual assessment, statistical parametric mapping (SPM) software, and Jensen-Shannon divergence-based logistic regression (JS-LR) analysis. RESULTS: The proposed deep learning framework could detect the epileptic foci accurately with the dice coefficient of 0.51, which was significantly higher than that of SPM (0.24, P < 0.01) and significantly (or marginally) higher than that of visual assessment (0.31-0.44, P = 0.005-0.27). The area under the curve (AUC) of the PoC classification was higher than that of the JS-LR (0.93 vs. 0.72). The metabolic level detection accuracy of the proposed method was significantly higher than that of visual assessment blinded or unblinded to clinical information (90% vs. 56% or 68%, P < 0.01). CONCLUSION: The proposed deep learning framework for 18F-FDG PET imaging could identify epileptic foci accurately and efficiently, which might be applied as a computer-assisted approach for the future diagnosis of epilepsy patients. TRIAL REGISTRATION: NCT04169581. Registered November 13, 2019 Public site: https://clinicaltrials.gov/ct2/show/NCT04169581.

Brain metabolic characteristics distinguishing typical and atypical benign epilepsy with centro-temporal spikes.

OBJECTIVES: Atypical benign epilepsy with centro-temporal spikes (BECTS) have less favorable outcomes than typical BECTS, and thus should be accurately identified for adequate treatment. We aimed to investigate the glucose metabolic differences between typical and atypical BECTS using 18F-fluorodeoxyglucose positron emission tomography ([18F]FDG PET) imaging, and explore whether these differences can help distinguish. METHODS: Forty-six patients with typical BECTS, 31 patients with atypical BECTS and 23 controls who underwent [18F]FDG PET examination were retrospectively involved. Absolute asymmetry index (|AI|) was applied to evaluate the severity of metabolic abnormality. Glucose metabolic differences were investigated among typical BECTS, atypical BECTS, and controls by using statistical parametric mapping (SPM). Logistic regression analyses were performed based on clinical, PET, and hybrid features. RESULTS: The |AI| was found significantly higher in atypical BECTS than in typical BECTS (p = 0.040). Atypical BECTS showed more hypo-metabolism regions than typical BECTS, mainly located in the fronto-temporo-parietal cortex. The PET model had significantly higher area under the curve (AUC) than the clinical model (0.91 vs. 0.70, p = 0.006). The hybrid model had the highest sensitivity (0.90), specificity (0.85), and accuracy (0.87) of all three models. CONCLUSIONS: Atypical BECTS showed more widespread and severe hypo-metabolism than typical BECTS, depending on which the two groups can be well distinguished. The combination of metabolic characteristics and clinical variables has the potential to be used clinically to distinguish between typical and atypical BECTS. KEY POINTS: • Distinguishing between typical and atypical BECTS is very important for the formulation of treatment regimens in clinical practice. • Atypical BECTS showed more widespread and severe hypo-metabolism than typical BECTS, mainly located in the fronto-temporo-parietal cortex. • The logistic regression model based on PET outperformed that based on clinical characteristics in classification of typical and atypical BECTS, and the hybrid model achieved the best classification performance.

Increasing L-lysine production in Corynebacterium glutamicum by engineering amino acid transporters.

Corynebacterium glutamicum has a long and successful history in the biotechnological production of L-lysine. Besides the adjustment of metabolic pathways, intracellular and extracellular transport systems are critical for the cellular metabolism of L-lysine or its by-products. Here, three amino acid transmembrane transporters, namely, GluE, BrnE/BrnF, and LysP, which are widely present in C. glutamicum strains, were each investigated by gene knockout. In comparison with that in the wild-type strain, the yield of L-lysine increased by 9.0%, 12.3%, and 10.0% after the deletion of the gluE, brnE/brnF, and lysP genes, respectively, in C. glutamicum 23,604. Moreover, the amount of by-product amino acids decreased significantly when the gluE and brnE/brnF genes were deleted. It was also demonstrated that there was no effect on the growth of the strain when the gluE or lysP gene was deleted, whereas the biomass of C. glutamicum WL1702 (ΔbrnE/ΔbrnF) in the fermentation medium was significantly reduced in comparison with that of the wild type. These results also provide useful information for enhancing the production of L-lysine or other amino acids by C. glutamicum.

Transcriptomic profiling reveals MEP pathway contributing to ginsenoside biosynthesis in Panax ginseng.

BACKGROUND: Panax ginseng C. A. Mey is one of famous medicinal herb plant species. Its major bioactive compounds are various ginsenosides in roots and rhizomes. It is commonly accepted that ginsenosides are synthesized from terpene precursors, IPP and DMAPP, through the cytoplasmic mevalonate (MVA) pathway. Another plastic 2-C-methyl-D-erythritol 4-phosphate (MEP) pathway was proved also contributing to ginsenoside generation in the roots of P. ginseng by using specific chemical inhibitors recently. But their gene expression characteristics are still under reveal in P. ginseng. With the development of the high-throughput next generation sequencing (NGS) technologies, we have opportunities to discover more about the complex ginsenoside biosynthesis pathways in P. ginseng. RESULTS: We carried out deep RNA sequencing and comprehensive analyses on the ginseng root samples of 1-5 years old and five different tissues of 5 years old ginseng plants. The de novo assembly totally generated 48,165 unigenes, including 380 genes related to ginsenoside biosynthesis and all the genes encoding the enzymes of the MEP pathway and the MVA pathway. We further illustrated the gene expression profiles related to ginsenoside biosynthesis among 1-5 year-old roots and different tissues of 5 year-old ginseng plants. Particularly for the first time, we revealed that the gene transcript abundances of the MEP pathway were similar to those of the MVA pathway in ginseng roots but higher in ginseng leaves. The IspD was predicated to be the rate-limiting enzyme in the MEP pathway through both co-expression network and gene expression profile analyses. CONCLUSIONS: At the transcriptional level, the MEP pathway has similar contribution to ginsenoside biosynthesis in ginseng roots, but much higher in ginseng leaves, compared with the MVA pathway. The IspD might be the key enzyme for ginsenoside generation through the MEP pathway. These results provide new information for further synthetic biology study on ginsenoside metabolic regulation.

Magnetoferritin: Process, Prospects, and Their Biomedical Applications.

Ferritin is a spherical iron storage protein composed of 24 subunits and an iron core. Using biomimetic mineralization, magnetic iron oxide can be synthesized in the cavity of ferritin to form magnetoferritin (MFt). MFt, also known as a superparamagnetic protein, is a novel magnetic nanomaterial with good biocompatibility and flexibility for biomedical applications. Recently, it has been demonstrated that MFt had tumor targetability and a peroxidase-like catalytic activity. Thus, MFt, with its many unique properties, provides a powerful platform for tumor diagnosis and therapy. In this review, we discuss the biomimetic synthesis and biomedical applications of MFt.

Molecular cloning and characterization of a novel Y-box gene from planarian Dugesia japonica.

Y-box binding protein (YB protein) is an ancient conserved multifunctional DNA/RNA-binding protein. A novel YB protein DjY2 gene from planarian Dugesia japonica was cloned by RACE method and characterized. This cDNA contains 689 bp with a putative open reading frame of 197 amino acids. It has a predicted molecular mass of 22.14 kDa and an isoelectric point of 9.67. Whole-mount in situ hybridization and relative quantitative real-time PCR were used to study the spatial and temporal expression pattern of DjY2 in the process of planarian regeneration. Results showed that DjY2 was expressed in many parts of the body in intact planarian, but the expression level was low in head and pharynx. The transcripts of DjY2 was significantly increased both at the head parts and the tail parts after amputation, especially at the site of cutting. The spatial expression gradually recovered to the state of intact planarian with the time of regeneration. Our results indicated that DjY2 might participate in the process of regeneration in planarian.

Development of mazF-based markerless genome editing system and metabolic pathway engineering in Candida tropicalis for producing long-chain dicarboxylic acids.

Candida tropicalis can grow with alkanes or plant oils as the sole carbon source, and its industrial application thus has great potential. However, the choice of a suitable genetic operating system can effectively increase the speed of metabolic engineering. MazF functions as an mRNA interferase that preferentially cleaves single-stranded mRNAs at ACA sequences to inhibit protein synthesis, leading to cell growth arrest. Here, we constructed a suicide plasmid named pPICPJ-mazF that uses the mazF gene of Escherichia coli as a counterselectable marker for the markerless editing of C. tropicalis genes to increase the rate of conversion of oils into long-chain dicarboxylic acids. To reduce the ß-oxidation of fatty acids, the carnitine acetyltransferase gene (CART) was deleted using the gene editing system, and the yield of long-chain acids from the strain was increased to 8.27 g/L. By two homologous single exchanges, the promoters of both the cytochrome P450 gene and the NADPH-cytochrome P450 reductase gene were subsequently replaced by the constitutively expressed promoter pGAP, and the production of long-chain dicarboxylic acids by the generated strain (C. tropicalis PJPP1702) reached 11.39 g/L. The results of fed-batch fermentation showed that the yield of long-chain acids from the strain was further increased to 32.84 g/L, which was 11.4 times higher than that from the original strain. The results also showed that the pPICPJ-mazF-based markerless editing system may be more suited for completing the genetic editing of C. tropicalis.

Directed weighted network structure analysis of complex impedance measurements for characterizing oil-in-water bubbly flow.

Characterizing the flow structure underlying the evolution of oil-in-water bubbly flow remains a contemporary challenge of great interests and complexity. In particular, the oil droplets dispersing in a water continuum with diverse size make the study of oil-in-water bubbly flow really difficult. To study this issue, we first design a novel complex impedance sensor and systematically conduct vertical oil-water flow experiments. Based on the multivariate complex impedance measurements, we define modalities associated with the spatial transient flow structures and construct modality transition-based network for each flow condition to study the evolution of flow structures. In order to reveal the unique flow structures underlying the oil-in-water bubbly flow, we filter the inferred modality transition-based network by removing the edges with small weight and resulting isolated nodes. Then, the weighted clustering coefficient entropy and weighted average path length are employed for quantitatively assessing the original network and filtered network. The differences in network measures enable to efficiently characterize the evolution of the oil-in-water bubbly flow structures.

Synthesis, crystal structures, and antibacterial evaluation of metal complexes based on functionalized 2-phenylquinoline derivatives.

A series of dinuclear paddle-wheel like transition metal complexes based on 2-phenylquinoline-4-carboxylic derivative L have been synthesized and characterized by IR, elemental analysis, and X-ray diffraction single crystal analysis. The biological activities of L and its complexes were evaluated as assayed antibacterial activities, including Escherichia coli, Pseudomonas aeruginosa, Bacillus subtilis and Staphylococcus aureus. The results indicated that these complexes showed better antibacterial activities than the free ligand or metal salts alone. Among them, the Zn(II) and Cd(II) complexes with IC50 of 0.57 µg/mL and 0.51 µg/mL, respectively, showed excellent antibacterial activity against Staphylococcus aureus.

Sex-dependent nonlinear Granger connectivity patterns of brain aging in healthy population.

BACKGROUND: Brain aging is a complex process that involves functional alterations in multiple subnetworks and brain regions. However, most previous studies investigating aging-related functional connectivity (FC) changes using resting-state functional magnetic resonance images (rs-fMRIs) have primarily focused on the linear correlation between brain subnetworks, ignoring the nonlinear casual properties of fMRI signals. METHODS: We introduced the neural Granger causality technique to investigate the sex-dependent nonlinear Granger connectivity (NGC) during aging on a publicly available dataset of 227 healthy participants acquired cross-sectionally in Leipzig, Germany. RESULTS: Our findings indicate that brain aging may cause widespread declines in NGC at both regional and subnetwork scales. These findings exhibit high reproducibility across different network sparsities, demonstrating the efficacy of static and dynamic analysis strategies. Females exhibit greater heterogeneity and reduced stability in NGC compared to males during aging, especially the NGC between the visual network and other subnetworks. Besides, NGC strengths can well reflect the individual cognitive function, which may therefore work as a sensitive metric in cognition-related experiments for individual-scale or group-scale mechanism understanding. CONCLUSION: These findings indicate that NGC analysis is a potent tool for identifying sex-dependent brain aging patterns. Our results offer valuable perspectives that could substantially enhance the understanding of sex differences in neurological diseases in the future, especially in degenerative disorders.

A novel spatiotemporal graph convolutional network framework for functional connectivity biomarkers identification of Alzheimer's disease.

BACKGROUND: Functional connectivity (FC) biomarkers play a crucial role in the early diagnosis and mechanistic study of Alzheimer's disease (AD). However, the identification of effective FC biomarkers remains challenging. In this study, we introduce a novel approach, the spatiotemporal graph convolutional network (ST-GCN) combined with the gradient-based class activation mapping (Grad-CAM) model (STGC-GCAM), to effectively identify FC biomarkers for AD. METHODS: This multi-center cross-racial retrospective study involved 2,272 participants, including 1,105 cognitively normal (CN) subjects, 790 mild cognitive impairment (MCI) individuals, and 377 AD patients. All participants underwent functional magnetic resonance imaging (fMRI) and T1-weighted MRI scans. In this study, firstly, we optimized the STGC-GCAM model to enhance classification accuracy. Secondly, we identified novel AD-associated biomarkers using the optimized model. Thirdly, we validated the imaging biomarkers using Kaplan-Meier analysis. Lastly, we performed correlation analysis and causal mediation analysis to confirm the physiological significance of the identified biomarkers. RESULTS: The STGC-GCAM model demonstrated great classification performance (The average area under the curve (AUC) values for different categories were: CN vs MCI = 0.98, CN vs AD = 0.95, MCI vs AD = 0.96, stable MCI vs progressive MCI = 0.79). Notably, the model identified specific brain regions, including the sensorimotor network (SMN), visual network (VN), and default mode network (DMN), as key differentiators between patients and CN individuals. These brain regions exhibited significant associations with the severity of cognitive impairment (p < 0.05). Moreover, the topological features of important brain regions demonstrated excellent predictive capability for the conversion from MCI to AD (Hazard ratio = 3.885, p < 0.001). Additionally, our findings revealed that the topological features of these brain regions mediated the impact of amyloid beta (Aß) deposition (bootstrapped average causal mediation effect: ß = -0.01 [-0.025, 0.00], p < 0.001) and brain glucose metabolism (bootstrapped average causal mediation effect: ß = -0.02 [-0.04, -0.001], p < 0.001) on cognitive status. CONCLUSIONS: This study presents the STGC-GCAM framework, which identifies FC biomarkers using a large multi-site fMRI dataset.

Positron Emission Tomography Imaging of Synaptic Dysfunction in Parkinson's Disease.

Parkinson's disease (PD) is one of the most common neurodegenerative diseases with a complex pathogenesis. Aggregations formed by abnormal deposition of alpha-synuclein (αSyn) lead to synapse dysfunction of the dopamine and non-dopamine systems. The loss of dopaminergic neurons and concomitant alterations in non-dopaminergic function in PD constitute its primary pathological manifestation. Positron emission tomography (PET), as a representative molecular imaging technique, enables the non-invasive visualization, characterization, and quantification of biological processes at cellular and molecular levels. Imaging synaptic function with PET would provide insights into the mechanisms underlying PD and facilitate the optimization of clinical management. In this review, we focus on the synaptic dysfunction associated with the αSyn pathology of PD, summarize various related targets and radiopharmaceuticals, and discuss applications and perspectives of PET imaging of synaptic dysfunction in PD.

Genome-wide association study reveals the genetic basis of rice resistance to three herbicides.

Crop resistance to herbicides is crucial for agricultural productivity and sustainability amidst escalating challenges of weed resistance. Uncovering herbicide resistant genes is particularly important for rice production. In this study, we tested the resistance to three commonly used herbicides: glufosinate, glyphosate and mesotrione of 421 diverse rice cultivars and employed genome-wide association studies (GWAS) to unravel the genetic underpinnings of resistance to these three herbicides in rice. We discovered that cultivated rice exhibited rich variation in resistance to the three herbicides, and the differences among subpopulations were significant. Six identified associations harboring candidate genes for resistance to these herbicides were significant. Among them, RGlu6 and RGly8 were the major QTL for resistance to glufosinate and glyphosate, respectively. The favorable alleles of RGlu6 and RGly8 were primarily present in japonica cultivars that originated from Europe, highlighting the geographic and genetic diversity of herbicide resistance and emphasizing the localized selection pressures in European rice varieties. Moreover, our findings might suggest that traditional target genes may not contain tolerant alleles in nature, and alternative mechanisms with novel loci associated with resistance may work. By mapping the genes for herbicide resistance, our results may help develop new strategies to combat the dual challenges on effective weed management and herbicide sustainability.