Expanding the clinical application of OPM-MEG using an effective automatic suppression method for the dental brace metal artifact.

Optically pumped magnetometer magnetoencephalography (OPM-MEG) holds significant promise for clinical functional brain imaging due to its superior spatiotemporal resolution. However, effectively suppressing metallic artifacts, particularly from devices such as orthodontic braces and vagal nerve stimulators remains a major challenge, hindering the wider clinical application of wearable OPM-MEG devices. A comprehensive analysis of metal artifact characteristics from time, frequency, and time-frequency perspectives was conducted for the first time using an OPM-MEG device in clinical medicine. This study focused on patients with metal orthodontics, examining the modulation of metal artifacts by breath and head movement, the incomplete regular sub-Gaussian distribution, and the high absolute power ratio in the 0.5-8 Hz band. The existing metal artifact suppression algorithms applied to SQUID-MEG, such as fast independent component analysis (FastICA), information maximization (Infomax), and algorithms for multiple unknown signal extraction (AMUSE), exhibit limited efficacy. Consequently, this study introduced the second-order blind identification (SOBI) algorithm, which utilized multiple time delays for the component separation of OPM-MEG measurement signals. We modified the time delays of the SOBI method to improve its efficacy in separating artifact components, particularly those in the ultralow frequency range. This approach employs the frequency-domain absolute power ratio, root mean square (RMS) value, and mutual information methods to automate the artifact component screening process. The effectiveness of this method was validated through simulation experiments involving four subjects in both resting and evoked experiments. In addition, the proposed method was also validated by the actual OPM-MEG evoked experiments of three subjects. Comparative analyses were conducted against the FastICA, Infomax, and AMUSE algorithms. Evaluation metrics included normalized mean square error, normalized delta band power error, RMS error, and signal-to-noise ratio, demonstrating that the proposed method provides optimal suppression of metal artifacts. This advancement holds promise for enhancing data quality and expanding the clinical applications of OPM-MEG.

How Ohio public library systems respond to opioid-related substance use: a descriptive analysis of survey results.

BACKGROUND: Public libraries in the United States have experienced increases in opioid-related substance use in their communities and on their premises. This includes fatal and non-fatal overdose events. Some libraries have adopted response measures in their branches to deter substance use or prevent overdose. A small number of libraries around the nation have decided to stock the opioid antagonist naloxone (Narcan) for staff to administer to patrons who experience overdose. This response measure has generated extensive media attention. Although Ohio ranks fourth in age-adjusted drug mortality rate in the United States, there has been no investigation of whether Ohio libraries are observing opioid-related transactions, consumption, and/or overdose events, or which measures they have adopted in response to these activities. We conducted a multimethod survey with Ohio public library directors to identify the response measures they have adopted. We present descriptive findings from the quantitative and qualitative items in our survey. METHODS: We conducted a cross-sectional 54-item multimethod survey of public library system directors (one per system) in Ohio. Directors of each of Ohio's public library systems were invited to participate via email. RESULTS: Of 251 library systems, 56 responded (22.3% response rate), with 34 respondents (60.7%) indicating awareness of opioid-related transactions, consumption, and/or overdose on their premises. Most (n = 43, 76.8%) did not stock naloxone in their buildings. Over half (n = 34, 60.7%) reported implementing one or more non-naloxone response measures. These measures focus on improving security for staff and patrons, deterring opioid-related transactions (purchases and exchanges) and consumption, and providing educational events on substance use. Nearly half (n = 25, 47.2%) partner with community organizations to provide opioid response measures. A similar proportion reported adequate funding to respond to opioid-related substance use (n = 23, 45.1%), and most (n = 38, 74.5%) reported adequate support from their boards and communities. Few respondents have implemented evaluations of their response measures. CONCLUSIONS: Ohio public libraries are responding to evidence of opioid-related transactions, consumption, and/or overdose on their premises with a range of measures that focus on substance use prevention and deterrence. Most Ohio library systems do not stock naloxone. Respondents indicated they prefer to call 911 and let first responders handle overdose events. The majority of respondents indicated their library systems have political capacity to respond to evidence of opioid-related substance use on their premises, but have limited operational and functional capacity. Findings suggest the need to revisit assumptions that public libraries are willing to stock naloxone to respond to overdose events, and that libraries have the resources to respond robustly to opioid-related transactions, consumption, and/or overdose on their premises.

Flexible template matching for observational study design.

Matching is a popular design for inferring causal effect with observational data. Unlike model-based approaches, it is a nonparametric method to group treated and control subjects with similar characteristics together, hence to re-create a randomization-like scenario. The application of matched design for real world data may be limited by: (1) the causal estimand of interest; (2) the sample size of different treatment arms. We propose a flexible design of matching, based on the idea of template matching, to overcome these challenges. It first identifies the template group which is representative of the target population, then match subjects from the original data to this template group and make inference. We provide theoretical justification on how it unbiasedly estimates the average treatment effect using matched pairs and the average treatment effect on the treated when the treatment group has a bigger sample size. We also propose using the triplet matching algorithm to improve matching quality and devise a practical strategy to select the template size. One major advantage of matched design is that it allows both randomization-based or model-based inference, with the former being more robust. For the commonly used binary outcome in medical research, we adopt a randomization inference framework of attributable effects in matched data, which allows heterogeneous effects and can incorporate sensitivity analysis for unmeasured confounding. We apply our design and analytical strategy to a trauma care evaluation study.

Automatic Estimation of the Interference Subspace Dimension Threshold in the Subspace Projection Algorithms of Magnetoencephalography Based on Evoked State Data.

A class of algorithms based on subspace projection is widely used in the denoising of magnetoencephalography (MEG) signals. Setting the dimension of the interference (external) subspace matrix of these algorithms is the key to balancing the denoising effect and the degree of signal distortion. However, most current methods for estimating the dimension threshold rely on experience, such as observing the signal waveforms and spectrum, which may render the results too subjective and lacking in quantitative accuracy. Therefore, this study proposes a method to automatically estimate a suitable threshold. Time-frequency transformations are performed on the evoked state data to obtain the neural signal of interest and the noise signal in a specific time-frequency band, which are then used to construct the objective function describing the degree of noise suppression and signal distortion. The optimal value of the threshold in the selected range is obtained using the weighted-sum method. Our method was tested on two classical subspace projection algorithms using simulation and two sensory stimulation experiments. The thresholds estimated by the proposed method enabled the algorithms to achieve the best waveform recovery and source location error. Therefore, the threshold selected in this method enables subspace projection algorithms to achieve the best balance between noise removal and neural signal preservation in subsequent MEG analyses.

iTRAQ-based quantitative proteomic analysis of the liver regeneration termination phase after partial hepatectomy in mice.

Liver regeneration (LR) is an important biological process after liver injury. As the "brake" in the process of LR, the termination phase of LR not only suppresses the continuous increase in liver volume but also effectively promotes the recovery of liver function. However, the mechanisms underlying the termination phase of LR are still not clear. In our study, we used isobaric tags for relative and absolute quantification (iTRAQ)-based quantitative proteomic analysis to determine the protein expression profiles of livers in the termination phase of mouse LR after partial hepatectomy (PH). We found that the expression of 197 proteins increased gradually during LR; in addition, 187 proteins were upregulated and 264 proteins were downregulated specifically in the termination phase of LR. The GO analysis of the proteins revealed the upregulation of "cell-cell adhesion" and "translation" and the downregulation of the "oxidation-reduction process". The KEGG pathway analysis showed that "biosynthesis of antibiotics" and "ribosomes" were significantly upregulated, while "metabolic pathways" were significantly downregulated. These analyses indicated that the termination phase of LR mainly focuses on restoring cellular structure and function. Differentially expressed proteins such as SNX5 were also screened out from biological processes. SIGNIFICANCE: The key regulatory factors in the termination phase of LR were studied by iTRAQ-based proteomics to lay a foundation for further study of the molecular mechanism and biomarkers of the termination phase of LR. This study will guide the clinical perioperative management of patients after hepatectomy.

MCM2 and NUSAP1 Are Potential Biomarkers for the Diagnosis and Prognosis of Pancreatic Cancer.

Pancreatic cancer (PC) is one of the most malignant tumors. Despite considerable progress in the treatment of PC, the prognosis of patients with PC is poor. The aim of this study was to identify potential biomarkers for the diagnosis and prognosis of PC. First, the original data of three independent mRNA expression datasets were downloaded from the Gene Expression Omnibus and The Cancer Genome Atlas databases and screened for differentially expressed genes (DEGs) using the R software. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of the DEGs were performed, and a protein-protein interaction (PPI) network was constructed to screen for hub genes. The hub genes were analyzed for genetic variations, as well as for survival, prognostic, and diagnostic value, using the cBioPortal and Gene Expression Profiling Interactive Analysis (GEPIA) databases and the pROC package. After screening for potential biomarkers, the mRNA and protein levels of the biomarkers were verified at the tissue and cellular levels using the Cancer Cell Line Encyclopedia, GEPIA, and the Human Protein Atlas. As a result, a total of 248 DEGs were identified. The GO terms enriched in DEGs were related to the separation of mitotic sister chromatids and the binding of the spindle to the extracellular matrix. The enriched pathways were associated with focal adhesion, ECM-receptor interaction, and phosphatidylinositol 3-kinase (PI3K)/AKT signaling. The top 20 genes were selected from the PPI network as hub genes, and based on the analysis of multiple databases, MCM2 and NUSAP1 were identified as potential biomarkers for the diagnosis and prognosis of PC. In conclusion, our results show that MCM2 and NUSAP1 can be used as potential biomarkers for the diagnosis and prognosis of PC. The study also provides new insights into the underlying molecular mechanisms of PC.