No abstract available.
Hemangioma, Cavernous, Central Nervous System

The global spread of flaviviruses has triggered major outbreaks worldwide, significantly impacting public health, society, and economies. This has intensified research efforts to understand how flaviviruses interact with their hosts and manipulate the immune system, underscoring the need for advanced research tools.RNA-sequencing (RNA-seq) technologies have revolutionized our understanding of flavivirus infections by offering transcriptome analysis to dissect the intricate dynamics of virus-host interactions. Bulk RNA-seq provides a macroscopic overview of gene expression changes in virus-infected cells, offering insights into infection mechanisms and host responses at the molecular level. Single-cell RNA sequencing (scRNAseq) provides unprecedented resolution by analyzing individual infected cells, revealing remarkable cellular heterogeneity within the host response. A particularly innovative advancement, virus-inclusive single-cell RNA sequencing (viscRNA-seq), addresses the challenges posed by non-polyadenylated flavivirus genomes, unveiling intricate details of virus-host interactions. In this review, we discuss the contributions of bulk RNA-seq, scRNA-seq, and viscRNA-seq to the field, exploring their implications in cell line experiments and studies on patients infected with various flavivirus species. Comprehensive transcriptome analyses from RNA-seq technologies are pivotal in accelerating the development of effective diagnostics and therapeutics, paving the way for innovative treatments and enhancing our preparedness for future outbreaks.

The global spread of flaviviruses has triggered major outbreaks worldwide, significantly impacting public health, society, and economies. This has intensified research efforts to understand how flaviviruses interact with their hosts and manipulate the immune system, underscoring the need for advanced research tools.RNA-sequencing (RNA-seq) technologies have revolutionized our understanding of flavivirus infections by offering transcriptome analysis to dissect the intricate dynamics of virus-host interactions. Bulk RNA-seq provides a macroscopic overview of gene expression changes in virus-infected cells, offering insights into infection mechanisms and host responses at the molecular level. Single-cell RNA sequencing (scRNAseq) provides unprecedented resolution by analyzing individual infected cells, revealing remarkable cellular heterogeneity within the host response. A particularly innovative advancement, virus-inclusive single-cell RNA sequencing (viscRNA-seq), addresses the challenges posed by non-polyadenylated flavivirus genomes, unveiling intricate details of virus-host interactions. In this review, we discuss the contributions of bulk RNA-seq, scRNA-seq, and viscRNA-seq to the field, exploring their implications in cell line experiments and studies on patients infected with various flavivirus species. Comprehensive transcriptome analyses from RNA-seq technologies are pivotal in accelerating the development of effective diagnostics and therapeutics, paving the way for innovative treatments and enhancing our preparedness for future outbreaks.

The global spread of flaviviruses has triggered major outbreaks worldwide, significantly impacting public health, society, and economies. This has intensified research efforts to understand how flaviviruses interact with their hosts and manipulate the immune system, underscoring the need for advanced research tools.RNA-sequencing (RNA-seq) technologies have revolutionized our understanding of flavivirus infections by offering transcriptome analysis to dissect the intricate dynamics of virus-host interactions. Bulk RNA-seq provides a macroscopic overview of gene expression changes in virus-infected cells, offering insights into infection mechanisms and host responses at the molecular level. Single-cell RNA sequencing (scRNAseq) provides unprecedented resolution by analyzing individual infected cells, revealing remarkable cellular heterogeneity within the host response. A particularly innovative advancement, virus-inclusive single-cell RNA sequencing (viscRNA-seq), addresses the challenges posed by non-polyadenylated flavivirus genomes, unveiling intricate details of virus-host interactions. In this review, we discuss the contributions of bulk RNA-seq, scRNA-seq, and viscRNA-seq to the field, exploring their implications in cell line experiments and studies on patients infected with various flavivirus species. Comprehensive transcriptome analyses from RNA-seq technologies are pivotal in accelerating the development of effective diagnostics and therapeutics, paving the way for innovative treatments and enhancing our preparedness for future outbreaks.

The global spread of flaviviruses has triggered major outbreaks worldwide, significantly impacting public health, society, and economies. This has intensified research efforts to understand how flaviviruses interact with their hosts and manipulate the immune system, underscoring the need for advanced research tools.RNA-sequencing (RNA-seq) technologies have revolutionized our understanding of flavivirus infections by offering transcriptome analysis to dissect the intricate dynamics of virus-host interactions. Bulk RNA-seq provides a macroscopic overview of gene expression changes in virus-infected cells, offering insights into infection mechanisms and host responses at the molecular level. Single-cell RNA sequencing (scRNAseq) provides unprecedented resolution by analyzing individual infected cells, revealing remarkable cellular heterogeneity within the host response. A particularly innovative advancement, virus-inclusive single-cell RNA sequencing (viscRNA-seq), addresses the challenges posed by non-polyadenylated flavivirus genomes, unveiling intricate details of virus-host interactions. In this review, we discuss the contributions of bulk RNA-seq, scRNA-seq, and viscRNA-seq to the field, exploring their implications in cell line experiments and studies on patients infected with various flavivirus species. Comprehensive transcriptome analyses from RNA-seq technologies are pivotal in accelerating the development of effective diagnostics and therapeutics, paving the way for innovative treatments and enhancing our preparedness for future outbreaks.

The global spread of flaviviruses has triggered major outbreaks worldwide, significantly impacting public health, society, and economies. This has intensified research efforts to understand how flaviviruses interact with their hosts and manipulate the immune system, underscoring the need for advanced research tools.RNA-sequencing (RNA-seq) technologies have revolutionized our understanding of flavivirus infections by offering transcriptome analysis to dissect the intricate dynamics of virus-host interactions. Bulk RNA-seq provides a macroscopic overview of gene expression changes in virus-infected cells, offering insights into infection mechanisms and host responses at the molecular level. Single-cell RNA sequencing (scRNAseq) provides unprecedented resolution by analyzing individual infected cells, revealing remarkable cellular heterogeneity within the host response. A particularly innovative advancement, virus-inclusive single-cell RNA sequencing (viscRNA-seq), addresses the challenges posed by non-polyadenylated flavivirus genomes, unveiling intricate details of virus-host interactions. In this review, we discuss the contributions of bulk RNA-seq, scRNA-seq, and viscRNA-seq to the field, exploring their implications in cell line experiments and studies on patients infected with various flavivirus species. Comprehensive transcriptome analyses from RNA-seq technologies are pivotal in accelerating the development of effective diagnostics and therapeutics, paving the way for innovative treatments and enhancing our preparedness for future outbreaks.

Purpose: This study assessed the impact of hepatic fibrosis on the diagnostic performance of the controlled attenuation parameter (CAP) in quantifying hepatic steatosis in patients with chronic hepatitis B (CHB). Methods: CHB patients who underwent liver stiffness measurement (LSM) and CAP assessment using transient elastography before liver resection between 2019 and 2022 were retrospectively evaluated. Clinical data included body mass index (BMI) and laboratory parameters. The histologically determined hepatic fat fraction (HFF) and fibrosis stages were reviewed by pathologists blinded to clinical and radiologic data. The Pearson correlation coefficient between CAP and HFF was calculated. The diagnostic performance of CAP for significant hepatic steatosis (HFF ≥10%) was assessed using areas under the receiver operating curve (AUCs), stratified by fibrosis stages (F0-1 vs. F2-4). Factors significantly associated with CAP were determined by univariable and multivariable linear regression analyses. Results: Among 399 CHB patients (median age 59 years; 306 men), 16.3% showed significant steatosis. HFF ranged from 0% to 60%. Of these patients, 9.8%, 19.8%, 29.3%, and 41.1% had fibrosis stages F0-1, F2, F3, and F4, respectively. CAP positively correlated with HFF (r=0.445, P<0.001). The AUC of CAP for diagnosing significant steatosis was 0.786 (95% confidence interval [CI], 0.726 to 0.845) overall, and significantly lower in F2-4 (0.772; 95% CI, 0.708 to 0.836) than in F0-1 (0.924; 95% CI, 0.835 to 1.000) (P=0.006). Multivariable analysis showed that BMI (P<0.001) and HFF (P<0.001) significantly affected CAP, whereas LSM and fibrosis stages did not. Conclusion CAP evaluations of significant hepatic steatosis are less reliable in CHB patients with significant or more advanced (F2-4) than with no or mild (F0-1) fibrosis.

Purpose: This study assessed the impact of hepatic fibrosis on the diagnostic performance of the controlled attenuation parameter (CAP) in quantifying hepatic steatosis in patients with chronic hepatitis B (CHB). Methods: CHB patients who underwent liver stiffness measurement (LSM) and CAP assessment using transient elastography before liver resection between 2019 and 2022 were retrospectively evaluated. Clinical data included body mass index (BMI) and laboratory parameters. The histologically determined hepatic fat fraction (HFF) and fibrosis stages were reviewed by pathologists blinded to clinical and radiologic data. The Pearson correlation coefficient between CAP and HFF was calculated. The diagnostic performance of CAP for significant hepatic steatosis (HFF ≥10%) was assessed using areas under the receiver operating curve (AUCs), stratified by fibrosis stages (F0-1 vs. F2-4). Factors significantly associated with CAP were determined by univariable and multivariable linear regression analyses. Results: Among 399 CHB patients (median age 59 years; 306 men), 16.3% showed significant steatosis. HFF ranged from 0% to 60%. Of these patients, 9.8%, 19.8%, 29.3%, and 41.1% had fibrosis stages F0-1, F2, F3, and F4, respectively. CAP positively correlated with HFF (r=0.445, P<0.001). The AUC of CAP for diagnosing significant steatosis was 0.786 (95% confidence interval [CI], 0.726 to 0.845) overall, and significantly lower in F2-4 (0.772; 95% CI, 0.708 to 0.836) than in F0-1 (0.924; 95% CI, 0.835 to 1.000) (P=0.006). Multivariable analysis showed that BMI (P<0.001) and HFF (P<0.001) significantly affected CAP, whereas LSM and fibrosis stages did not. Conclusion CAP evaluations of significant hepatic steatosis are less reliable in CHB patients with significant or more advanced (F2-4) than with no or mild (F0-1) fibrosis.

Purpose: This study assessed the impact of hepatic fibrosis on the diagnostic performance of the controlled attenuation parameter (CAP) in quantifying hepatic steatosis in patients with chronic hepatitis B (CHB). Methods: CHB patients who underwent liver stiffness measurement (LSM) and CAP assessment using transient elastography before liver resection between 2019 and 2022 were retrospectively evaluated. Clinical data included body mass index (BMI) and laboratory parameters. The histologically determined hepatic fat fraction (HFF) and fibrosis stages were reviewed by pathologists blinded to clinical and radiologic data. The Pearson correlation coefficient between CAP and HFF was calculated. The diagnostic performance of CAP for significant hepatic steatosis (HFF ≥10%) was assessed using areas under the receiver operating curve (AUCs), stratified by fibrosis stages (F0-1 vs. F2-4). Factors significantly associated with CAP were determined by univariable and multivariable linear regression analyses. Results: Among 399 CHB patients (median age 59 years; 306 men), 16.3% showed significant steatosis. HFF ranged from 0% to 60%. Of these patients, 9.8%, 19.8%, 29.3%, and 41.1% had fibrosis stages F0-1, F2, F3, and F4, respectively. CAP positively correlated with HFF (r=0.445, P<0.001). The AUC of CAP for diagnosing significant steatosis was 0.786 (95% confidence interval [CI], 0.726 to 0.845) overall, and significantly lower in F2-4 (0.772; 95% CI, 0.708 to 0.836) than in F0-1 (0.924; 95% CI, 0.835 to 1.000) (P=0.006). Multivariable analysis showed that BMI (P<0.001) and HFF (P<0.001) significantly affected CAP, whereas LSM and fibrosis stages did not. Conclusion CAP evaluations of significant hepatic steatosis are less reliable in CHB patients with significant or more advanced (F2-4) than with no or mild (F0-1) fibrosis.

Purpose: This study assessed the impact of hepatic fibrosis on the diagnostic performance of the controlled attenuation parameter (CAP) in quantifying hepatic steatosis in patients with chronic hepatitis B (CHB). Methods: CHB patients who underwent liver stiffness measurement (LSM) and CAP assessment using transient elastography before liver resection between 2019 and 2022 were retrospectively evaluated. Clinical data included body mass index (BMI) and laboratory parameters. The histologically determined hepatic fat fraction (HFF) and fibrosis stages were reviewed by pathologists blinded to clinical and radiologic data. The Pearson correlation coefficient between CAP and HFF was calculated. The diagnostic performance of CAP for significant hepatic steatosis (HFF ≥10%) was assessed using areas under the receiver operating curve (AUCs), stratified by fibrosis stages (F0-1 vs. F2-4). Factors significantly associated with CAP were determined by univariable and multivariable linear regression analyses. Results: Among 399 CHB patients (median age 59 years; 306 men), 16.3% showed significant steatosis. HFF ranged from 0% to 60%. Of these patients, 9.8%, 19.8%, 29.3%, and 41.1% had fibrosis stages F0-1, F2, F3, and F4, respectively. CAP positively correlated with HFF (r=0.445, P<0.001). The AUC of CAP for diagnosing significant steatosis was 0.786 (95% confidence interval [CI], 0.726 to 0.845) overall, and significantly lower in F2-4 (0.772; 95% CI, 0.708 to 0.836) than in F0-1 (0.924; 95% CI, 0.835 to 1.000) (P=0.006). Multivariable analysis showed that BMI (P<0.001) and HFF (P<0.001) significantly affected CAP, whereas LSM and fibrosis stages did not. Conclusion CAP evaluations of significant hepatic steatosis are less reliable in CHB patients with significant or more advanced (F2-4) than with no or mild (F0-1) fibrosis.