Biology subjects

Xu, I.

Publications and source records attributed to Xu, I..

6 recordsLinked to original sources

bioRxiv2026

Noninvasive Laser SCOS Monitoring of Rat Brain Hemodynamics During Intracerebral Injection

Cerebral blood volume (CBV) and blood flow (CBF) constitute key metrics for cerebrovascular monitoring, enabling assessment of stroke severity and risk-prediction, aging-related changes, and neurological diseases. CBF and CBV monitoring are key aspects in diagnosis, treatment triage, and clinical outcome of ischemic and hemorrhagic strokes. In recent years, there have been ongoing efforts toward the development of optical devices for noninvasive monitoring of CBV and CBF. Speckle contrast optical spectroscopy (SCOS) has recently emerged as a strong candidate for clinical translation in monitoring CBF and CBV, due to its affordability, compact and wearable design, and noninvasive nature. However, experimental demonstrations that SCOS can effectively monitor brain hemodynamics remain sparse. This is primarily due to challenges in design experiments that isolate cerebral blood dynamics from those in the scalp and skull. In this paper, we report experiments using SCOS to monitor cerebral hemodynamics in rats during intracerebral blood flow modulation. To modify cerebral blood dynamics, a surgical procedure was performed to insert a catheter for direct injection of flow modulation fluids into the brain. Using the SCOS device, we monitored changes in CBV during deliberate CBF interventions into the brains of five rats. A saline solution was also injected as a sham control of the flow intervention. The results show a significant decrease in CBV during injection, followed by a return to baseline. This behavior is consistent with physiological expectations, as the injected fluids dilute the blood, leading to a transient reduction in blood volume. Notably, the CBV decrease induced by the flow modulation fluid solution required more than twice as long to recover to baseline compared with the saline solution, which is consistent with the delayed clearance of the flow modulation fluid by design. These experimental results demonstrate the effectiveness of SCOS for monitoring cerebral hemodynamics in animal models and highlight its potential for translation to human studies. Moreover, this work paves the way for the testing and characterization of cerebral therapeutic agents intended for blood flow modulation in animal models.

biophysics↗

bioRxiv2024

Cilia dysfunction in the lateral ventricles after neonatal intraventricular hemorrhage does not lead to functional changes in cilia-based CSF flow networks

Intraventricular hemorrhage (IVH) has long been thought to lead to motile cilia dysfunction whereby intraventricular blood breakdown products damage and slough cilia from the ependymal wall. However, specifically how IVH may affect cilia development, structure, and transcriptional activation is not well-understood. Moreover, the impact of blood breakdown product-mediated cilia damage on the functional organization of cilia-based CSF flow networks is unknown. Here, we show hemoglobin exposure affects the number of ciliated ependymal cells in the lateral ventricle (LV) but does not impact in vitro beat frequency of the remaining cilia. Ultrastructurally, IVH decreases the total number of ciliary tufts without impacting axoneme structure. IVH does not result in changes in the expression of cilia-related genes and instead leads to downregulation of neurogenesis markers in parallel with innate immune upregulation. Functionally, we identify three previously uncharacterized cilia-mediated CSF flow domains in the LV lateral wall and show that IVH does not result in widespread disruption of their functional organization. These data de-emphasize cilia as a major contributor to global CSF dysfunction after IVH, and instead call attention to preserving the neurodevelopmental environment and preventing runaway innate immune system activation, as considerations to developing treatment strategies to prevent posthemorrhagic hydrocephalus and other neurodevelopmental sequelae.

neuroscience↗

bioRxiv2023

Testing SIPA1L2 as a modifier of CMT1A using mouse models

Charcot-Marie-Tooth 1A is a demyelinating peripheral neuropathy caused by the duplication of peripheral myelin protein 22 (PMP22), which produces muscle weakness and loss of sensation in the hands and feet. A recent case-only genome wide association study by the Inherited Neuropathy Consortium identified a strong association between variants in signal induced proliferation associated 1 like 2 (SIPA1L2) and strength of foot dorsiflexion. To validate SIPA1L2 as a candidate modifier, and to assess its potential as a therapeutic target, we engineered mice with a deletion in SIPA1L2 and crossed them to the C3-PMP22 mouse model of CMT1A. We performed neuromuscular phenotyping and identified an interaction between Sipa1l2 deletion and muscular endurance decrements assayed by wire-hang duration in C3-PMP22 mice, as well as several interactions in femoral nerve axon morphometrics such as myelin thickness. Gene expression changes suggested an involvement of Sipa1l2 in cholesterol biosynthesis, which was also implicated in C3-PMP22 mice. Though several interactions between Sipa1l2 deletion and CMT1A-associated phenotypes were identified, validating a genetic interaction, the overall effect on neuropathy was small.

genetics↗

bioRxiv2023

RExPRT: a machine learning tool to predict pathogenicity of tandem repeat loci

Tandem repeats (TRs) are polymorphic sequences of DNA that are composed of repeating units of motifs, whose lengths can vary depending on the type of TR. Expansions of TRs are responsible for approximately 50 monogenic diseases, compared to over 4,300 disease causing genes disrupted by single nucleotide variants and small indels. It appears thus reasonable to expect the discovery of additional pathogenic repeat expansions, which has the potential of significantly narrowing the current diagnostic gap in many diseases. Recently, short and long-read whole genome sequencing with the use of advanced bioinformatics tools, have identified a growing number of TR expansions in the human population. The majority of these loci are expanded in <1% of genomes. Categorizing and prioritizing such TR loci is a growing challenge to human genomic studies. We present a first-in-class machine learning tool, RExPRT (Repeat EXpansion Pathogenicity pRediction Tool), which is designed to distinguish pathogenic from benign TR expansions. Leave-one-out cross validation results demonstrated that an ensemble approach comprised of support vector machines (SVM) and extreme gradient boosted decision tree (XGB) classify TRs with a precision of 92% and a recall of 90%. Further validation of RExPRT on unseen test data demonstrate a similar precision of 86%, and a recall of 60%. RExPRTs high precision in particular, will be of significant value to large-scale discovery studies, which require the prioritization of promising candidate loci for time-consuming and costly functional follow-up studies. Application of RExPRT to ~800,000 TRs in the reference genome identified ~30,000 TRs that would be likely pathogenic upon expansion. Thus, RExPRT establishes a foundation for the application of machine learning approaches to categorize the pathogenicity of tandem repeat loci.

bioinformatics↗

bioRxiv2022

Generative Adversarial Networks Can Create High Quality Artificial Prostate Cancer Magnetic Resonance Images

PurposeRecent integration of open-source data to machine learning models, especially in the medical field, has opened new doors to study disease progression and/or regression. However, the limitation of using medical data for machine learning approaches is the specificity of data to a particular medical condition. In this context, most recent technologies like generative adversarial networks (GAN) could be used to generate high quality synthetic data that preserves the clinical variability. Materials and MethodsIn this study, we used 139 T2-weighted prostate magnetic resonant images (MRI) from various sources as training data for Single Natural Image GAN (SinGAN), to make a generative model. A deep learning semantic segmentation pipeline trained the model to segment the prostate boundary on 2D MRI slices. Synthetic images with a high-level segmentation boundary of the prostate were filtered and used in the quality control assessment by participating scientists with varying degree of experience (more than 10 years, 1 year, or no experience) to work with MRI images. ResultsThe most experienced participating group correctly identified conventional vs synthetic images with 67% accuracy, the group with 1 year of experience correctly identified the images with 58% accuracy, and group with no prior experience reached 50% accuracy. Nearly half (47%) of the synthetic images were mistakenly evaluated as conventional images. Interestingly, a blinded quality assessment by a board-certified radiologist to differentiate conventional and synthetic images was not significantly different in context of the mean quality of synthetic and conventional images. ConclusionsThis study shows promise that high quality synthetic images from MRI can be generated using GAN. Such an AI model may contribute significantly to various clinical applications which involves supervised machine learning approaches.

bioinformatics↗

bioRxiv2020

DIAFree enables untargeted open-search identification for Data-Independent Acquisition data

As a reliable and high-throughput proteomics strategy, data-independent acquisition (DIA) has shown great potential for protein analysis. However, DIA also imposes stress on the data processing algorithm by generating complex multiplexed spectra. Traditionally, DIA data is processed using spectral libraries refined from experiment histories, which requires stable experiment conditions and additional runs. Furthermore, scientists still need to use library-free tools to generate spectral libraries from additional runs. To lessen those burdens, here we present DIAFree(https://github.com/xuesu/DIAFree), a library-free, tag-index-based software suite that enables both restrict search and open search on DIA data using the information of MS1 scans in a precursor-centric and spectrum-centric style. We validate the quality of detection by publicly available data. We further evaluate the quality of spectral libraries produced by DIAFree.

biochemistry↗