SUGAR: Spherical ultrafast graph attention framework for cortical surface registration.

Cortical surface registration plays a crucial role in aligning cortical functional and anatomical features across individuals. However, conventional registration algorithms are computationally inefficient. Recently, learning-based registration algorithms have emerged as a promising solution, significantly improving processing efficiency. Nonetheless, there remains a gap in the development of a learning-based method that exceeds the state-of-the-art conventional methods simultaneously in computational efficiency, registration accuracy, and distortion control, despite the theoretically greater representational capabilities of deep learning approaches. To address the challenge, we present SUGAR, a unified unsupervised deep-learning framework for both rigid and non-rigid registration. SUGAR incorporates a U-Net-based spherical graph attention network and leverages the Euler angle representation for deformation. In addition to the similarity loss, we introduce fold and multiple distortion losses to preserve topology and minimize various types of distortions. Furthermore, we propose a data augmentation strategy specifically tailored for spherical surface registration to enhance the registration performance. Through extensive evaluation involving over 10,000 scans from 7 diverse datasets, we showed that our framework exhibits comparable or superior registration performance in accuracy, distortion, and test-retest reliability compared to conventional and learning-based methods. Additionally, SUGAR achieves remarkable sub-second processing times, offering a notable speed-up of approximately 12,000 times in registering 9,000 subjects from the UK Biobank dataset in just 32 min. This combination of high registration performance and accelerated processing time may greatly benefit large-scale neuroimaging studies.

Individualized functional connectivity markers associated with motor and mood symptoms of Parkinson's disease.

Parkinson's disease (PD) is a complex neurological disorder characterized by many motor and non-motor symptoms. While most studies focus on the motor symptoms of the disease, it is important to identify markers that underlie different facets of the disease. In this case-control study, we sought to discover reliable, individualized functional connectivity markers associated with both motor and mood symptoms of PD. Using functional MRI, we extensively sampled 166 patients with PD (64 women, 102 men; mean age=61.8 years, SD=7.81) and 51 healthy control participants (32 women, 19 men; mean age=55.68 years, SD=7.62). We found that a model consisting of 44 functional connections predicted both motor (UPDRS-III: Pearson r=0.21, FDR-adjusted p=0.006) and mood symptoms (HAMD: Pearson r=0.23, FDR-adjusted p=0.006; HAMA: Pearson r=0.21, FDR-adjusted p=0.006). Two sets of connections contributed differentially to these predictions. Between-network connections, mainly connecting the sensorimotor and visual large-scale functional networks, substantially contributed to the prediction of motor measures, while within-network connections in the insula and sensorimotor network contributed more so to mood prediction. The middle to posterior insula region played a particularly important role in predicting depression and anxiety scores. We successfully replicated and generalized our findings in two independent PD datasets. Taken together, our findings indicate that sensorimotor and visual network markers are indicative of PD brain pathology, and that distinct subsets of markers are associated with motor and mood symptoms of PD.

Gut microbiome and metabolic profiles of mouse model for MeCP2 duplication syndrome.

The extra copy of the methyl-CpG-binding protein 2 (MeCp2) gene causes MeCP2 duplication syndrome (MDS), a neurodevelopmental disorder characterized by intellectual disability and autistic phenotypes. However, the disturbed microbiome and metabolic profiling underlying the autistic-like behavioral deficits of MDS are rarely investigated. Here we aimed to understand the contributions of microbiome disruption and associated metabolic alterations, especially the disturbed neurotransmitters in MDS employing a transgenic mouse model with MeCP2 overexpression. We analyzed metabolic profiles of plasma, urine, and cecum content and microbiome profiles by both 16 s RNA and shotgun metagenomics sequence technology. We found the decreased levels of Firmicutes and increased levels of Bacteroides in the single MeCP2 gene mutation autism-like mouse model, demonstrating the importance of the host genome in a selection of microbiome, leading to the heterogeneity characteristics of microbiome in MDS. Furthermore, the changed levels of several neurotransmitters (such as dopamine, taurine, and glutamate) implied the excitatory-inhibitory imbalance caused by the single gene mutation. Concurrently, a range of microbial metabolisms of aromatic amino acids (such as tryptophan and phenylalanine) were identified in different biological matrices obtained from MeCP2 transgenic mice. Our investigation revealed the importance of genetic variation in accounting for the differences in microbiomes and confirmed the bidirectional regulatory axis of microbiota-gut-brain in studying the role of microbiome on MDS, which could be useful in deeply understanding the microbiome-based treatment in this autistic-like disease.

Reliable quantification of citrate isomers and isobars with direct-infusion tandem mass spectrometry.

Direct-infusion tandem mass spectrometry (DI-MS/MS) is an excellent tool for large cohort high-throughput quantitative metabolomics, MS imaging and single cell studies but incapable of discriminating isomers/isobars with similar MS spectral features. With experimental and density-functional theory (DFT) approaches, here, we comprehensively investigated the fragmentation pathways and characteristics of differential ion-mobility spectrometry (DMS) for three citrate isomers (citrate, isocitrate, glucaro-1,4-lactone) and an isobar (quinate) co-existing in biological sample such as urine. Results showed that all these compounds gave better MS spectra in negative-ion mode than positive-ion one and had numerous fragment ions under collision-induced dissociation (CID) with sequential losses of H2O and CO2. All observed fragment ions were assignable by combining experimental with DFT calculation results. A DI-DMS-MS/MS method was then developed to simultaneously quantify these four isomers/isobars with m/z 191-87 (CoV, -5.5 V), 191-73 (CoV, -3.5 V), 191-85 (CoV, -29.5 V) and m/z 191-93 (CoV, -41.5 V) for citrate, isocitrate, glucaro-1,4-lactone and quinate, respectively. The low limit-of-quantification was below 5.5 nM whilst accuracy was above 94% for all above compounds. The urinary concentrations of them in human and C57BL/6 mouse samples were further quantified showing clear inter-individual and inter-species level differences with significantly higher levels of isocitrate, glucaro-1,4-lactone and quinate in human urine samples than mouse ones. This provides an approach to understand the detailed fragmentation pathways for organic isomers/isobars and a high-throughput MS strategy to quantify them in complex mixtures for metabolomics, lipidomics, foodomics and exposomics especially when chromatographic separations are not useable.

Hydroxychloroquine protects against autoimmune premature ovarian insufficiency by modulating the Treg/Th17 cell ratio in BALB/c mice.

AIMS: The role of hydroxychloroquine (HCQ) in premature ovarian insufficiency (POI) remains unclear. The purpose of this study was to evaluate the effect of HCQ on ovarian function in mice with POI and to clarify its potential mechanisms. METHODS: POI was induced in mice by injection with zona pellucida 3 peptide (pZP3), and HCQ was administered intragastrically. Stages of the estrous cycle were determined using vaginal cytology. The ovarian structure was observed under a microscope after hematoxylin-eosin staining. The levels of serum hormones and anti-ZP antibody (aZPAb) were measured using enzyme-linked immunosorbent assay (ELISA). The expression levels of CD4, CD45, and ZP2, ZP3 were determined using immunofluorescence and immunohistochemistry, respectively. The T regulatory (Treg)/ T helper 17 (Th17) cell ratio was analyzed using flow cytometry analysis. Western blotting was performed to assess the expression levels of proteins, transcription factors and cytokines. RESULTS: Administration of HCQ to mice with POI greatly restored their estrus cycle. In the treatment group compared to the POI group, estradiol (E2 ) levels were higher, and follicle stimulating hormone (FSH) levels were lower. In addition, following pZP3, HCQ treatment increased ZP2 and ZP3 expression. Additionally, by inhibiting the activation of the TLR7 signaling pathway, HCQ attenuated the infiltration of inflammatory cells and prevented the activated naive CD4+ T cells from developing into Th17 cells. CONCLUSION: Our findings showed that HCQ effectively restored ovarian function by altering the Treg/Th17 cell ratio in mice with POI, indicating that HCQ maybe a promising therapeutic method for patients with POI.

Identification of Veratrum Species in Pimacao Based on ITS2 Sequences and Steroidal Alkaloids by a Pseudo-Targeted Metabolomics Method.

Pimacao is a traditional Chinese folk medicine and is the main component of the famous Chinese herbal remedy "Yunnan Baiyao" for its significant analgesic activity in the treatment of wounds. Due to increases in consumption, its wild population is now difficult to find, and adulterant from the same genus has occurred. However, this is challenging to distinguish the species of Veratrum in Pimacao using dried roots and rhizomes or medicinal powder. ITS2 sequences and steroidal alkaloids by the non-targeted and pseudo-targeted metabolomics methods were taken advantage of establishing an effective identification method. Based on the ITS2 sequence, metabolite profiling of steroidal alkaloids and morphological characteristics, the classification of two distinct subspecies in V. mengzeanum has been reinforced. In addition, the new subspecies V. mengzeanum subsp. phuwae was collected in China for the first time. The ITS2 sequence could be used in the identification of V. taliense, V. mengtzeanum, V. stenophyllum, and V. nigrum, but is insufficient for intraspecific identification. Simultaneously, 147 variables were labeled by non-targeted analysis accomplished utilizing an ultra-high-performance liquid chromatography electrospray ionization orbitrap tandem mass spectrometry (UPLC-ESI-QE-Orbitrap-MS) system consisting of an Orbitrap QE HF-X. Followed by a pseudo-targeted analysis method developed for the Qtrap 6500-plus mass spectrometry system coupled with an ESI source, 29 labeled steroidal alkaloids detected by the MRM mode could distinguish between four species. Notably, 25 labeled steroidal alkaloids could distinguish between three closely related species. These have the potential to be used as markers for identification. Furthermore, there were several variables with statistical differences between two subspecies of V. mengtzeanum and populations of V. taliense, V. mengtzeanum, and V. stenophyllum.

Fast cortical surface reconstruction from MRI using deep learning.

Reconstructing cortical surfaces from structural magnetic resonance imaging (MRI) is a prerequisite for surface-based functional and anatomical image analyses. Conventional algorithms for cortical surface reconstruction are computationally inefficient and typically take several hours for each subject, causing a bottleneck in applications when a fast turnaround time is needed. To address this challenge, we propose a fast cortical surface reconstruction (FastCSR) pipeline by leveraging deep machine learning. We trained our model to learn an implicit representation of the cortical surface in volumetric space, termed the "level set representation". A fast volumetric topology correction method and a topology-preserving surface mesh extraction procedure were employed to reconstruct the cortical surface based on the level set representation. Using 1-mm isotropic T1-weighted images, the FastCSR pipeline was able to reconstruct a subject's cortical surfaces within 5 min with comparable surface quality, which is approximately 47 times faster than the traditional FreeSurfer pipeline. The advantage of FastCSR becomes even more apparent when processing high-resolution images. Importantly, the model demonstrated good generalizability in previously unseen data and showed high test-retest reliability in cortical morphometrics and anatomical parcellations. Finally, FastCSR was robust to images with compromised quality or with distortions caused by lesions. This fast and robust pipeline for cortical surface reconstruction may facilitate large-scale neuroimaging studies and has potential in clinical applications wherein brain images may be compromised.

Quantitative structure-retention relationship for reliable metabolite identification and quantification in metabolomics using ion-pair reversed-phase chromatography coupled with tandem mass spectrometry.

Hydrophilic metabolites are essential for all biological systems with multiple functions and their quantitative analysis forms an important part of metabolomics. However, poor retention of these metabolites on reversed-phase (RP) chromatographic column hinders their effective analysis with RPLC-MS methods. Herein, we developed a method for detecting hydrophilic metabolites using the ion-pair reversed-phase liquid-chromatography coupled with mass spectrometry (IPRP-LC-MS/MS) in scheduled multiple-reaction-monitoring (sMRM) mode. We first developed a hexylamine-based IPRP-UHPLC-QTOFMS method and experimentally measured retention time (tR) for 183 hydrophilic metabolites. We found that tRs of these metabolites were dominated by their electrostatic potential depending upon the numbers and types of their ionizable groups. We then systematically investigated the quantitative structure-retention relationship (QSRR) and constructed QSRR models using the measured tR. Subsequently, we developed a retention time predictive model using the random-forest regression algorithm (r2 = 0.93, q2 = 0.70, MAE = 1.28 min) for predicting metabolite retention time, which was applied in IPRP-UHPLC-MS/MS method in sMRM mode for quantitative metabolomic analysis. Our method can simultaneously quantify more than 260 metabolites. Moreover, we found that this method was applicable for multiple major biological matrices including biofluids and tissues. This approach offers an efficient method for large-scale quantitative hydrophilic metabolomic profiling even when metabolite standards are unavailable.

A comprehensive evaluation of single nucleotide polymorphisms associated with gastric cancer risk: A protocol for systematic review and network meta-analysis.

BACKGROUND: Single nucleotide polymorphisms (SNPs) have been inconsistently associated with gastric cancer (GC) risk. This meta-analysis aimed to synthesize relevant data on SNPs associated with GC. METHODS: Databases were searched to identify association studies of SNPs and GC published through January 2020 from the databases of PubMed, Web of Science, Embase, Cochrane Library, China National Knowledge Infrastructure, the Chinese Science and Technology Periodical Database, and Wan fang databases. Network meta-analysis and Thakkinstian algorithm were used to select the most appropriate genetic model, along with false positive report probability for noteworthy associations. The methodological quality of data was assessed based on the STrengthening the REporting of Genetic Association Studies statement Stata 14.0 will be used for systematic review and meta-analysis. RESULTS: This study will provide a high-quality evidence to find the SNP most associated with GC susceptibility and the best genetic model. CONCLUSIONS: This study will explore which SNP is most associated with GC susceptibility. REGISTRATION: INPLASY202040132.