Structural and functional connectivity of the whole brain and subnetworks in individuals with mild traumatic brain injury:Predictors of patient prognosis

Patients with mild traumatic brain injury have a diverse clinical presentation, and the underlying pathophysiology remains poorly understood. Magnetic resonance imaging is a non-invasive technique that has been widely utilized to investigate neurobiological markers after mild traumatic brain injury. This approach has emerged as a promising tool for investigating the pathogenesis of mild traumatic brain injury. Graph theory is a quantitative method of analyzing complex networks that has been widely used to study changes in brain structure and function. However, most previous mild traumatic brain injury studies using graph theory have focused on specific populations, with limited exploration of simultaneous abnormalities in structural and functional connectivity. Given that mild traumatic brain injury is the most common type of traumatic brain injury encountered in clinical practice, further investigation of the patient characteristics and evolution of structural and functional connectivity is critical. In the present study, we explored whether abnormal structural and functional connectivity in the acute phase could serve as indicators of longitudinal changes in imaging data and cognitive function in patients with mild traumatic brain injury. In this longitudinal study, we enrolled 46 patients with mild traumatic brain injury who were assessed within 2 weeks of injury, as well as 36 healthy controls. Resting-state functional magnetic resonance imaging and diffusion-weighted imaging data were acquired for graph theoretical network analysis. In the acute phase, patients with mild traumatic brain injury demonstrated reduced structural connectivity in the dorsal attention network. More than 3 months of followup data revealed signs of recovery in structural and functional connectivity, as well as cognitive function, in 22 out of the 46 patients. Furthermore, better cognitive function was associated with more efficient networks. Finally, our data indicated that small-worldness in the acute stage could serve as a predictor of longitudinal changes in connectivity in patients with mild traumatic brain injury. These findings highlight the importance of integrating structural and functional connectivity in understanding the occurrence and evolution of mild traumatic brain injury. Additionally, exploratory analysis based on subnetworks could serve a predictive function in the prognosis of patients with mild traumatic brain injury.</p

Using proteomic analysis to investigate uniconazole-induced phytohormone variation and starch accumulation in duckweed (Landoltia punctata)

KEGG pathway analysis of the annotated proteins. The pathway, number of annotated proteins, and pathway ID were listed in the table. (XLSX 15 kb

Metatranscriptomics Reveals the Functions and Enzyme Profiles of the Microbial Community in Chinese Nong-Flavor Liquor Starter

Chinese liquor is one of the world's best-known distilled spirits and is the largest spirit category by sales. The unique and traditional solid-state fermentation technology used to produce Chinese liquor has been in continuous use for several thousand years. The diverse and dynamic microbial community in a liquor starter is the main contributor to liquor brewing. However, little is known about the ecological distribution and functional importance of these community members. In this study, metatranscriptomics was used to comprehensively explore the active microbial community members and key transcripts with significant functions in the liquor starter production process. Fungi were found to be the most abundant and active community members. A total of 932 carbohydrate-active enzymes, including highly expressed auxiliary activity family 9 and 10 proteins, were identified at 62°C under aerobic conditions. Some potential thermostable enzymes were identified at 50, 62, and 25°C (mature stage). Increased content and overexpressed key enzymes involved in glycolysis and starch, pyruvate and ethanol metabolism were detected at 50 and 62°C. The key enzymes of the citrate cycle were up-regulated at 62°C, and their abundant derivatives are crucial for flavor generation. Here, the metabolism and functional enzymes of the active microbial communities in NF liquor starter were studied, which could pave the way to initiate improvements in liquor quality and to discover microbes that produce novel enzymes or high-value added products

Investigating the shared genetic architecture between hypothyroidism and rheumatoid arthritis

BackgroundThere is still controversy regarding the relationship between hypothyroidism and rheumatoid arthritis (RA), and there has been a dearth of studies on this association. The purpose of our study was to explore the shared genetic architecture between hypothyroidism and RA.MethodsUsing public genome-wide association studies summary statistics of hypothyroidism and RA, we explored shared genetics between hypothyroidism and RA using linkage disequilibrium score regression, ρ-HESS, Pleiotropic analysis under a composite null hypothesis (PLACO), colocalization analysis, Multi-Trait Analysis of GWAS (MTAG), and transcriptome-wide association study (TWAS), and investigated causal associations using Mendelian randomization (MR).ResultsWe found a positive genetic association between hypothyroidism and RA, particularly in local genomic regions. Mendelian randomization analysis suggested a potential causal association of hypothyroidism with RA. Incorporating gene expression data, we observed that the genetic associations between hypothyroidism and RA were enriched in various tissues, including the spleen, lung, small intestine, adipose visceral, and blood. A comprehensive approach integrating PLACO, Bayesian colocalization analysis, MTAG, and TWAS, we successfully identified TYK2, IL2RA, and IRF5 as shared risk genes for both hypothyroidism and RA.ConclusionsOur investigation unveiled a shared genetic architecture between these two diseases, providing novel insights into the underlying biological mechanisms and establishing a foundation for more effective interventions

Association Between Serum Uric Acid Levels and Benign Paroxysmal Positional Vertigo: A Systematic Review and Meta-Analysis of Observational Studies

Objective: The objective of the present study was to meta-analyze relevant literature to gain a comprehensive understanding of the potential relationship between serum uric acid levels and risk of benign paroxysmal positional vertigo (BPPV).Methods: The databases of PubMed, Web of Science, Embase, Chinese National Knowledge Infrastructure, Wanfang, and SinoMed were systematically searched for observational case-control studies of the association between BPPV and serum uric acid levels published up to October 2018. Data from eligible studies were meta-analyzed using Stata 12.0.Results: A total of 12 studies were included in the analysis. There was a strong tendency for serum uric acid levels to be associated with risk of BPPV among studies conducted in China (OR 0.69, 95%CI 0.01–1.40, p = 0.053), but not among studies outside China (OR 1.07, 95%CI 1.08–3.22, p = 0.33). Across all studies, serum uric acid level was significantly higher among individuals with BPPV than among controls (OR 0.78, 95%CI 0.15–1.41, p = 0.015), yet it did not independently predict risk of the disorder (OR 1.003, 95%CI 0.995–1.012, p = 0.471).Conclusion: The available evidence suggests that BPPV is associated with elevated levels of serum uric acid, but these levels may not be an independent risk factor of BPPV

Large vestibular schwannomas presenting in the late state of pregnancy: a case report and literature review

Vestibular schwannomas in pregnancy have rarely been reported, and there is a lack of in-depth discussion on the experience of management of massive acoustic neuromas in pregnancy. Herein, we present a pregnant woman with a giant vestibular schwannoma and obstructive hydrocephalus who presented at 30 weeks of gestation. She was initially misdiagnosed as having a pregnancy-related reaction of headache, dizziness, and vomiting that had occurred 2 months earlier. After observation at home, her symptoms progressed at 30 weeks of gestation, and imaging findings revealed a brain tumor in the CPA region with secondary cerebella tonsil herniation and obstructive hydrocephalus, and she was transferred to our center for treatment. Consequently, we relieved her hydrocephalus with a ventriculoperitoneal shunt (V-P shunt) and used corticosteroids to simulate fetal maturation. After 10 days, her mental condition deteriorated, and her right limb muscle strength gradually decreased until grade 0 (MMT Grading). Finally, under a joint consultation with the Department of Neurosurgery, Obstetrics, and Anesthesiology, she underwent a cesarean section under general anesthesia and first-stage tumor removal at 31 weeks of gestation. Upon discharge, the previously observed neurological deficits, which were reversible and had manifested during her gestational period, had been successfully resolved, and the fetus had been conserved. The neuroimaging confirmed the complete tumor removal, while the neuropathologic examination revealed a vestibular schwannoma. Therefore, we recommend early diagnosis and treatment for these patients, especially people with headaches, vomiting, and sudden hearing loss during pregnancy. Herein, we concluded that our cases provide a valuable experience in the latest acceptable time frame for the operation to prevent irreversible neurological impairment and premature delivery in late pregnancy

Predicting pharmacodynamic effects through early drug discovery with artificial intelligence-physiologically based pharmacokinetic (AI-PBPK) modelling

A mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) model links the concentration-time profile of a drug with its therapeutic effects based on the underlying biological or physiological processes. Clinical endpoints play a pivotal role in drug development. Despite the substantial time and effort invested in screening drugs for favourable pharmacokinetic (PK) properties, they may not consistently yield optimal clinical outcomes. Furthermore, in the virtual compound screening phase, researchers cannot observe clinical outcomes in humans directly. These uncertainties prolong the process of drug development. As incorporation of Artificial Intelligence (AI) into the physiologically based pharmacokinetic/pharmacodynamic (PBPK) model can assist in forecasting pharmacodynamic (PD) effects within the human body, we introduce a methodology for utilizing the AI-PBPK platform to predict the PK and PD outcomes of target compounds in the early drug discovery stage. In this integrated platform, machine learning is used to predict the parameters for the model, and the mechanism-based PD model is used to predict the PD outcome through the PK results. This platform enables researchers to align the PK profile of a drug with desired PD effects at the early drug discovery stage. Case studies are presented to assess and compare five potassium-competitive acid blocker (P-CAB) compounds, after calibration and verification using vonoprazan and revaprazan