Background: and Purpose Although amyloid positron emission tomography (PET) might provide a molecular diagnosis for cerebral amyloid angiopathy (CAA), it does not have sufficient specificity for this condition relative to incipient Alzheimer’s disease (AD). To identify a regional amyloid uptake pattern specific to CAA, we attempted to reduce this overlap by selecting “pure CAA” (i.e., fulfilling the criteria for probable CAA but without tau PET AD signature) and “pure AD” (i.e., positive amyloid PET and presence of tau PET AD signature, but without lobar hemorrhagic lesions). We hypothesized that occipital tracer uptake relative to the whole cortex (WC) would be higher in patients with pure CAA and may serve as a specific diagnostic marker. Methods: Patients who fulfilled these criteria were identified. In addition to the occipital region of interest (ROI), we assessed the frontal and posterior cingulate cortex (PCC) ROIs that are sensitive to AD. Amyloid PET uptake was expressed as the absolute standardized uptake value ratio (SUVR) and ROI/WC ratio. The diagnostic utility of amyloid PET was assessed using the Youden index cutoff. Results: Eighteen patients with AD and 42 patients with CAAs of comparable age were eligible. The occipital/WC was significantly higher in CAA than AD (1.02 [0.97–1.06] vs. 0.95 [0.87–1.01], P=0.001), with an area under curve of 0.762 (95% confidence interval [CI] 0.635–0.889) and a specificity of 72.2% (95% CI 46.5–90.3) at Youden cutoff (0.98). The occipital lobe, frontal lobe, PCC and WC SUVRs were significantly lower in CAA than in AD. The frontal/WC and PCC/WC ratios did not differ significantly between the groups. Conclusion Using stringent patient selection to minimize between-condition overlap, this study demonstrated the specificity of higher relative occipital amyloid uptake in CAA than in AD.

Background: and Purpose Although amyloid positron emission tomography (PET) might provide a molecular diagnosis for cerebral amyloid angiopathy (CAA), it does not have sufficient specificity for this condition relative to incipient Alzheimer’s disease (AD). To identify a regional amyloid uptake pattern specific to CAA, we attempted to reduce this overlap by selecting “pure CAA” (i.e., fulfilling the criteria for probable CAA but without tau PET AD signature) and “pure AD” (i.e., positive amyloid PET and presence of tau PET AD signature, but without lobar hemorrhagic lesions). We hypothesized that occipital tracer uptake relative to the whole cortex (WC) would be higher in patients with pure CAA and may serve as a specific diagnostic marker. Methods: Patients who fulfilled these criteria were identified. In addition to the occipital region of interest (ROI), we assessed the frontal and posterior cingulate cortex (PCC) ROIs that are sensitive to AD. Amyloid PET uptake was expressed as the absolute standardized uptake value ratio (SUVR) and ROI/WC ratio. The diagnostic utility of amyloid PET was assessed using the Youden index cutoff. Results: Eighteen patients with AD and 42 patients with CAAs of comparable age were eligible. The occipital/WC was significantly higher in CAA than AD (1.02 [0.97–1.06] vs. 0.95 [0.87–1.01], P=0.001), with an area under curve of 0.762 (95% confidence interval [CI] 0.635–0.889) and a specificity of 72.2% (95% CI 46.5–90.3) at Youden cutoff (0.98). The occipital lobe, frontal lobe, PCC and WC SUVRs were significantly lower in CAA than in AD. The frontal/WC and PCC/WC ratios did not differ significantly between the groups. Conclusion Using stringent patient selection to minimize between-condition overlap, this study demonstrated the specificity of higher relative occipital amyloid uptake in CAA than in AD.

Background: and Purpose Although amyloid positron emission tomography (PET) might provide a molecular diagnosis for cerebral amyloid angiopathy (CAA), it does not have sufficient specificity for this condition relative to incipient Alzheimer’s disease (AD). To identify a regional amyloid uptake pattern specific to CAA, we attempted to reduce this overlap by selecting “pure CAA” (i.e., fulfilling the criteria for probable CAA but without tau PET AD signature) and “pure AD” (i.e., positive amyloid PET and presence of tau PET AD signature, but without lobar hemorrhagic lesions). We hypothesized that occipital tracer uptake relative to the whole cortex (WC) would be higher in patients with pure CAA and may serve as a specific diagnostic marker. Methods: Patients who fulfilled these criteria were identified. In addition to the occipital region of interest (ROI), we assessed the frontal and posterior cingulate cortex (PCC) ROIs that are sensitive to AD. Amyloid PET uptake was expressed as the absolute standardized uptake value ratio (SUVR) and ROI/WC ratio. The diagnostic utility of amyloid PET was assessed using the Youden index cutoff. Results: Eighteen patients with AD and 42 patients with CAAs of comparable age were eligible. The occipital/WC was significantly higher in CAA than AD (1.02 [0.97–1.06] vs. 0.95 [0.87–1.01], P=0.001), with an area under curve of 0.762 (95% confidence interval [CI] 0.635–0.889) and a specificity of 72.2% (95% CI 46.5–90.3) at Youden cutoff (0.98). The occipital lobe, frontal lobe, PCC and WC SUVRs were significantly lower in CAA than in AD. The frontal/WC and PCC/WC ratios did not differ significantly between the groups. Conclusion Using stringent patient selection to minimize between-condition overlap, this study demonstrated the specificity of higher relative occipital amyloid uptake in CAA than in AD.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Lung cancer, particularly non-small cell lung cancer (NSCLC) which contributes more than 80% to totally lung cancer cases, remains the leading cause of cancer death and the 5-year survival is less than 20%. Continuous understanding on the mechanisms underlying the pathogenesis of this disease and identification of biomarkers for therapeutic application and response to treatment will help to improve patient survival. Here we found that a molecule known as DUSP10 (also known as MAPK phosphatase 5) is oncogenic in NSCLC.Overexpression of DUSP10 in NSCLC cells resulted in reduced activation of ERK and JNK, but increased activation of p38, which was associated with increased cellular growth and migration. When inoculated in immunodeficient mice, the DUSP10-overexpression NSCLC cells formed larger tumors compared to control cells. The increased growth of DUSP10-overexpression NSCLC cells was associated with increased expression of tumor-promoting cytokines including IL-6 and TGFβ. Importantly, higher DUSP10 expression was associated with poorer prognosis of NSCLC patients. Therefore, DUSP10 could severe as a biomarker for NSCLC prognosis and could be a target for development of therapeutic method for lung cancer treatment.

Klebsiella pneumoniae(K.pneumoniae)is an important pathogen that can cause severe hospital-and community-acquired infections.To systematically investigate its methylation features,we determined the whole-genome sequences of 14 K.pneumoniae strains covering varying serotypes,multilocus sequence types,clonal groups,viscosity/virulence,and drug resistance.Their methy-lomes were further characterized using Pacific Biosciences single-molecule real-time and bisulfite technologies.We identified 15 methylation motifs[13 N6-methyladenine(6mA)and two 5-methylcytosine(5mC)motifs],among which eight were novel.Their corresponding DNA methyl-transferases were also validated.Additionally,we analyzed the genomic distribution of GATC and CCWGG methylation motifs shared by all strains,and identified differential distribution pat-terns of some hemi-/un-methylated GATC motifs,which tend to be located within intergenic regions(IGRs).Specifically,we characterized the in vivo methylation kinetics at single-base resolu-tion on a genome-wide scale by simulating the dynamic processes of replication-mediated passive demethylation and MTase-catalyzed re-methylation.The slow methylation of the GATC motifs in the replication origin(oriC)regions and IGRs implicates the epigenetic regulation of replication initiation and transcription.Our findings illustrate the first comprehensive dynamic methylome map of K.pneumoniae at single-base resolution,and provide a useful reference to better understand epigenetic regulation in this and other bacterial species.