Purpose: Claudin 18.2 (CLDN18.2) has emerged as a promising therapeutic target for CLDN18.2-expressing gastric cancer (GC). We sought to examine the heterogeneity of CLDN18.2 expression between primary GC (PGC) and metastatic GC (MGC) using various scoring methods. Materials and Methods: We retrospectively analyzed data from 102 patients with pathologically confirmed paired primary and metastatic gastric or gastroesophageal junction adenocarcinomas. CLDN18.2 expression was evaluated through immunohistochemistry on formalin-fixed paraffin-embedded tissue samples. We assessed CLDN18.2 positivity using multiple scoring approaches, including the immunoreactivity score, H-score, and the percentage of tumor cells showing moderate-to-strong staining intensity. We analyzed the concordance rates between PGC and MGC and the association of CLDN18.2 positivity with clinicopathological features. Results: CLDN18.2 positivity varied from 25% to 65% depending on the scoring method, with PGC consistently showing higher expression levels than MGC. Intratumoral heterogeneity was noted in 25.5% of PGCs and 19.6% of MGCs. Intertumoral heterogeneity, manifesting as discordance in CLDN18.2 positivity between PGC and MGC, was observed in about 20% of cases, with moderate agreement across scoring methods (κ=0.47 to 0.60).In PGC, higher CLDN18.2 positivity correlated with synchronous metastasis, presence of peritoneal metastasis, poorly differentiated grade, and biopsy specimens. In MGC, positivity was associated with synchronous metastasis, presence of peritoneal metastasis, and metastatic peritoneal tissues. Conclusions CLDN18.2 expression demonstrates significant heterogeneity between PGC and MGC, with a 20% discordance rate. Comprehensive tissue sampling and reassessment of CLDN18.2 status are crucial, especially before initiating CLDN18.2-targeted therapies.

Purpose: Claudin 18.2 (CLDN18.2) has emerged as a promising therapeutic target for CLDN18.2-expressing gastric cancer (GC). We sought to examine the heterogeneity of CLDN18.2 expression between primary GC (PGC) and metastatic GC (MGC) using various scoring methods. Materials and Methods: We retrospectively analyzed data from 102 patients with pathologically confirmed paired primary and metastatic gastric or gastroesophageal junction adenocarcinomas. CLDN18.2 expression was evaluated through immunohistochemistry on formalin-fixed paraffin-embedded tissue samples. We assessed CLDN18.2 positivity using multiple scoring approaches, including the immunoreactivity score, H-score, and the percentage of tumor cells showing moderate-to-strong staining intensity. We analyzed the concordance rates between PGC and MGC and the association of CLDN18.2 positivity with clinicopathological features. Results: CLDN18.2 positivity varied from 25% to 65% depending on the scoring method, with PGC consistently showing higher expression levels than MGC. Intratumoral heterogeneity was noted in 25.5% of PGCs and 19.6% of MGCs. Intertumoral heterogeneity, manifesting as discordance in CLDN18.2 positivity between PGC and MGC, was observed in about 20% of cases, with moderate agreement across scoring methods (κ=0.47 to 0.60).In PGC, higher CLDN18.2 positivity correlated with synchronous metastasis, presence of peritoneal metastasis, poorly differentiated grade, and biopsy specimens. In MGC, positivity was associated with synchronous metastasis, presence of peritoneal metastasis, and metastatic peritoneal tissues. Conclusions CLDN18.2 expression demonstrates significant heterogeneity between PGC and MGC, with a 20% discordance rate. Comprehensive tissue sampling and reassessment of CLDN18.2 status are crucial, especially before initiating CLDN18.2-targeted therapies.

Purpose: Claudin 18.2 (CLDN18.2) has emerged as a promising therapeutic target for CLDN18.2-expressing gastric cancer (GC). We sought to examine the heterogeneity of CLDN18.2 expression between primary GC (PGC) and metastatic GC (MGC) using various scoring methods. Materials and Methods: We retrospectively analyzed data from 102 patients with pathologically confirmed paired primary and metastatic gastric or gastroesophageal junction adenocarcinomas. CLDN18.2 expression was evaluated through immunohistochemistry on formalin-fixed paraffin-embedded tissue samples. We assessed CLDN18.2 positivity using multiple scoring approaches, including the immunoreactivity score, H-score, and the percentage of tumor cells showing moderate-to-strong staining intensity. We analyzed the concordance rates between PGC and MGC and the association of CLDN18.2 positivity with clinicopathological features. Results: CLDN18.2 positivity varied from 25% to 65% depending on the scoring method, with PGC consistently showing higher expression levels than MGC. Intratumoral heterogeneity was noted in 25.5% of PGCs and 19.6% of MGCs. Intertumoral heterogeneity, manifesting as discordance in CLDN18.2 positivity between PGC and MGC, was observed in about 20% of cases, with moderate agreement across scoring methods (κ=0.47 to 0.60).In PGC, higher CLDN18.2 positivity correlated with synchronous metastasis, presence of peritoneal metastasis, poorly differentiated grade, and biopsy specimens. In MGC, positivity was associated with synchronous metastasis, presence of peritoneal metastasis, and metastatic peritoneal tissues. Conclusions CLDN18.2 expression demonstrates significant heterogeneity between PGC and MGC, with a 20% discordance rate. Comprehensive tissue sampling and reassessment of CLDN18.2 status are crucial, especially before initiating CLDN18.2-targeted therapies.

Purpose: This study compared the diagnostic performance of quantitative ultrasonography (QUS) with that of conventional ultrasonography (US) in assessing hepatic steatosis among individuals undergoing health screening using magnetic resonance imaging–derived proton density fat fraction (MRI-PDFF) as the reference standard. Methods: This single-center prospective study enrolled 427 participants who underwent abdominal MRI and US. Measurements included the attenuation coefficient in tissue attenuation imaging (TAI) and the scatter-distribution coefficient in tissue scatter-distribution imaging (TSI). The correlation between QUS and MRI-PDFF was evaluated. The diagnostic capabilities of QUS, conventional B-mode US, and their combined models for detecting hepatic fat content of ≥5% (MRI-PDFF ≥5%) and ≥10% (MRI-PDFF ≥10%) were compared by analyzing the areas under the receiver operating characteristic curves. Additionally, clinical risk factors influencing the diagnostic performance of QUS were identified using multivariate linear regression analyses. Results: TAI and TSI were strongly correlated with MRI-PDFF (r=0.759 and r=0.802, respectively; both P<0.001) and demonstrated good diagnostic performance in detecting and grading hepatic steatosis. The combination of QUS and B-mode US resulted in the highest areas under the ROC curve (AUCs) (0.947 and 0.975 for detecting hepatic fat content of ≥5% and ≥10%, respectively; both P<0.05), compared to TAI, TSI, or B-mode US alone (AUCs: 0.887, 0.910, 0.878 for ≥5% and 0.951, 0.922, 0.875 for ≥10%, respectively). The independent determinants of QUS included skinliver capsule distance (β=7.134), hepatic fibrosis (β=4.808), alanine aminotransferase (β=0.202), triglyceride levels (β=0.027), and diabetes mellitus (β=3.710). Conclusion QUS is a useful and effective screening tool for detecting and grading hepatic steatosis during health checkups.

Purpose: This study compared the diagnostic performance of quantitative ultrasonography (QUS) with that of conventional ultrasonography (US) in assessing hepatic steatosis among individuals undergoing health screening using magnetic resonance imaging–derived proton density fat fraction (MRI-PDFF) as the reference standard. Methods: This single-center prospective study enrolled 427 participants who underwent abdominal MRI and US. Measurements included the attenuation coefficient in tissue attenuation imaging (TAI) and the scatter-distribution coefficient in tissue scatter-distribution imaging (TSI). The correlation between QUS and MRI-PDFF was evaluated. The diagnostic capabilities of QUS, conventional B-mode US, and their combined models for detecting hepatic fat content of ≥5% (MRI-PDFF ≥5%) and ≥10% (MRI-PDFF ≥10%) were compared by analyzing the areas under the receiver operating characteristic curves. Additionally, clinical risk factors influencing the diagnostic performance of QUS were identified using multivariate linear regression analyses. Results: TAI and TSI were strongly correlated with MRI-PDFF (r=0.759 and r=0.802, respectively; both P<0.001) and demonstrated good diagnostic performance in detecting and grading hepatic steatosis. The combination of QUS and B-mode US resulted in the highest areas under the ROC curve (AUCs) (0.947 and 0.975 for detecting hepatic fat content of ≥5% and ≥10%, respectively; both P<0.05), compared to TAI, TSI, or B-mode US alone (AUCs: 0.887, 0.910, 0.878 for ≥5% and 0.951, 0.922, 0.875 for ≥10%, respectively). The independent determinants of QUS included skinliver capsule distance (β=7.134), hepatic fibrosis (β=4.808), alanine aminotransferase (β=0.202), triglyceride levels (β=0.027), and diabetes mellitus (β=3.710). Conclusion QUS is a useful and effective screening tool for detecting and grading hepatic steatosis during health checkups.

Purpose: This study compared the diagnostic performance of quantitative ultrasonography (QUS) with that of conventional ultrasonography (US) in assessing hepatic steatosis among individuals undergoing health screening using magnetic resonance imaging–derived proton density fat fraction (MRI-PDFF) as the reference standard. Methods: This single-center prospective study enrolled 427 participants who underwent abdominal MRI and US. Measurements included the attenuation coefficient in tissue attenuation imaging (TAI) and the scatter-distribution coefficient in tissue scatter-distribution imaging (TSI). The correlation between QUS and MRI-PDFF was evaluated. The diagnostic capabilities of QUS, conventional B-mode US, and their combined models for detecting hepatic fat content of ≥5% (MRI-PDFF ≥5%) and ≥10% (MRI-PDFF ≥10%) were compared by analyzing the areas under the receiver operating characteristic curves. Additionally, clinical risk factors influencing the diagnostic performance of QUS were identified using multivariate linear regression analyses. Results: TAI and TSI were strongly correlated with MRI-PDFF (r=0.759 and r=0.802, respectively; both P<0.001) and demonstrated good diagnostic performance in detecting and grading hepatic steatosis. The combination of QUS and B-mode US resulted in the highest areas under the ROC curve (AUCs) (0.947 and 0.975 for detecting hepatic fat content of ≥5% and ≥10%, respectively; both P<0.05), compared to TAI, TSI, or B-mode US alone (AUCs: 0.887, 0.910, 0.878 for ≥5% and 0.951, 0.922, 0.875 for ≥10%, respectively). The independent determinants of QUS included skinliver capsule distance (β=7.134), hepatic fibrosis (β=4.808), alanine aminotransferase (β=0.202), triglyceride levels (β=0.027), and diabetes mellitus (β=3.710). Conclusion QUS is a useful and effective screening tool for detecting and grading hepatic steatosis during health checkups.

Purpose: This study compared the diagnostic performance of quantitative ultrasonography (QUS) with that of conventional ultrasonography (US) in assessing hepatic steatosis among individuals undergoing health screening using magnetic resonance imaging–derived proton density fat fraction (MRI-PDFF) as the reference standard. Methods: This single-center prospective study enrolled 427 participants who underwent abdominal MRI and US. Measurements included the attenuation coefficient in tissue attenuation imaging (TAI) and the scatter-distribution coefficient in tissue scatter-distribution imaging (TSI). The correlation between QUS and MRI-PDFF was evaluated. The diagnostic capabilities of QUS, conventional B-mode US, and their combined models for detecting hepatic fat content of ≥5% (MRI-PDFF ≥5%) and ≥10% (MRI-PDFF ≥10%) were compared by analyzing the areas under the receiver operating characteristic curves. Additionally, clinical risk factors influencing the diagnostic performance of QUS were identified using multivariate linear regression analyses. Results: TAI and TSI were strongly correlated with MRI-PDFF (r=0.759 and r=0.802, respectively; both P<0.001) and demonstrated good diagnostic performance in detecting and grading hepatic steatosis. The combination of QUS and B-mode US resulted in the highest areas under the ROC curve (AUCs) (0.947 and 0.975 for detecting hepatic fat content of ≥5% and ≥10%, respectively; both P<0.05), compared to TAI, TSI, or B-mode US alone (AUCs: 0.887, 0.910, 0.878 for ≥5% and 0.951, 0.922, 0.875 for ≥10%, respectively). The independent determinants of QUS included skinliver capsule distance (β=7.134), hepatic fibrosis (β=4.808), alanine aminotransferase (β=0.202), triglyceride levels (β=0.027), and diabetes mellitus (β=3.710). Conclusion QUS is a useful and effective screening tool for detecting and grading hepatic steatosis during health checkups.

Purpose: This study compared the diagnostic performance of quantitative ultrasonography (QUS) with that of conventional ultrasonography (US) in assessing hepatic steatosis among individuals undergoing health screening using magnetic resonance imaging–derived proton density fat fraction (MRI-PDFF) as the reference standard. Methods: This single-center prospective study enrolled 427 participants who underwent abdominal MRI and US. Measurements included the attenuation coefficient in tissue attenuation imaging (TAI) and the scatter-distribution coefficient in tissue scatter-distribution imaging (TSI). The correlation between QUS and MRI-PDFF was evaluated. The diagnostic capabilities of QUS, conventional B-mode US, and their combined models for detecting hepatic fat content of ≥5% (MRI-PDFF ≥5%) and ≥10% (MRI-PDFF ≥10%) were compared by analyzing the areas under the receiver operating characteristic curves. Additionally, clinical risk factors influencing the diagnostic performance of QUS were identified using multivariate linear regression analyses. Results: TAI and TSI were strongly correlated with MRI-PDFF (r=0.759 and r=0.802, respectively; both P<0.001) and demonstrated good diagnostic performance in detecting and grading hepatic steatosis. The combination of QUS and B-mode US resulted in the highest areas under the ROC curve (AUCs) (0.947 and 0.975 for detecting hepatic fat content of ≥5% and ≥10%, respectively; both P<0.05), compared to TAI, TSI, or B-mode US alone (AUCs: 0.887, 0.910, 0.878 for ≥5% and 0.951, 0.922, 0.875 for ≥10%, respectively). The independent determinants of QUS included skinliver capsule distance (β=7.134), hepatic fibrosis (β=4.808), alanine aminotransferase (β=0.202), triglyceride levels (β=0.027), and diabetes mellitus (β=3.710). Conclusion QUS is a useful and effective screening tool for detecting and grading hepatic steatosis during health checkups.

The objective of this study was to evaluate the beneficial effects of the short-term application of insect-based diet in canine allergic dermatitis. Total 19 atopic dogs with concurrent cutaneous adverse food reactions were enrolled and classified into 3 groups. The treatment group (n = 7) was fed insect-based diet, the positive control group (n = 6) was fed salmon-based diet, and the negative control group (n = 6) was fed commercial or homemade diet for 12 weeks. The degree of skin lesions was evaluated based on canine atopic dermatitis extent and severity index (CADESI-4). Additionally, transepidermal water loss (TEWL) and pruritus visual analog scale were evaluated. All indices were evaluated every 4 weeks after the initial administration of hypoallergenic diets. In the treatment group, significant decrease in the CADESI-4 score was observed at 8 weeks compared to the baseline score (p = 0.031). There were significant differences in the CADESI-4 score between the groups at 8 weeks (p = 0.008), 12 weeks (p = 0.012), and TEWL at 12 weeks (p = 0.022). This preliminary result demonstrates the potential hypoallergenicity of an insect-based diet through features that diminish cutaneous lesions and skin barrier dysfunction.

Background: The effect of each health-related quality of life (HRQOL) component on hemodialysis prognosis has not been well studied. We aimed to investigate the clinical factors associated with HRQOL and the effect of HRQOL after dialysis initiation on long-term survival in an Asian population. Methods: A total of 568 hemodialysis patients were included from a nationwide prospective cohort study. HRQOL was evaluated using the Kidney Disease Quality of Life (KDQOL) Short FormTM 1.3 at 3 months after dialysis initiation. The effect of each KDQOL item score on mortality was analyzed. Multivariable Cox analysis was performed after adjusting for age, sex, modified Charlson comorbidity index, and causes of primary kidney disease. Results: Old age, diabetes mellitus, high comorbidities, and low serum albumin levels were associated with poor physical health status. Decreased urine output was associated with both poor physical and mental health status.The scores of 3 indices in the kidney disease domain (effect of kidney disease, social support, and dialysis staff encouragement) showed significant associations with mortality, as did the 3 indices (physical function, physical role limitation, and body pain) in the physical health domain. Neither the 4 indices in the mental health domain nor the mental composite score showed a significant association with mortality. However, a high physical composite score was associated with decreased overall patient mortality (P = 0.003). The effect of physical composite score on survival was prominent among young or middle-aged groups. Conclusion Poor physical health status 3 months after hemodialysis start correlates significantly with overall mortality.