Background: The potassium voltage-gated channel subfamily Q member 1 (KCNQ1) gene has recently received much attention as a candidate susceptibility gene for type 2 diabetes mellitus, especially in Asian populations. We previously reported that Kcnq1 mutant mice exhibit reduced insulin secretion and hyperglycemia due to a decrease in pancreatic β-cell mass. Through in vivo and in vitro analyses, we ascertained that this mechanism is the result of the downregulation of the non-coding RNA ‘Kcnq1ot1,’ which is expressed in the paternal allele of the Kcnq1 gene region, causing an increase in the expression of the cell cycle inhibitor cyclin dependent kinase inhibitor 1C (Cdkn1c). It was found that decreased Kcnq1ot1 expression resulted in pancreatic β-cell failure; however, the degree of pancreatic β-cell volume reduction was not severe. Methods: We induced obesity in Kcnq1ot1 truncation mice by feeding them a high-fat diet and evaluated pancreatic β-cell mass. Results: In the present study, we reveal that CCAAT/enhancer binding protein beta (C/EBPβ), which is expressed at higher levels in pancreatic β-cells in obese individuals, further increases the expression of Cdkn1c, which is upregulated by the Kcnq1 gene mutation. We found that simultaneous Cdkn1c hypomethylation and C/EBPβ overexpression in pancreatic β-cells causes a synergistic decrease in pancreatic β-cell mass. Conclusion This finding suggests that the synergistic effect of genetic factors such as Kcnq1 gene mutations and environmental factors such as obesity and overeating, which lead to increased expression of C/EBPβ, contribute to the regulation of pancreatic β-cell mass. This study is the first to show that the Kcnq1 gene is related to pancreatic β-cell mass through genetic-environment interactions.

Purpose: For several decades, bone scintigraphy (BS) has been used as a diagnostic tool for arthritis in patients with synovitis, acne, pustulosis, hyperostosis, and osteitis (SAPHO) syndrome. Artificial intelligence (AI) diagnostic supporting systems are effective in BS. The bone scan index (BSI) on BS with AI diagnostic support systems has been used for bone tumors.However, its application in arthritis has not been validated. The current study aimed to evaluate the usefulness of BSI using an AI diagnostic supporting system for arthritis in patients with SAPHO syndrome. Methods: The regional BSI (rBSI) of arthritis uptake around the sternoclavicular and sternocostal joints on BS in patients with SAPHO syndrome was calculated using an AI diagnostic supporting system (VSBONE BSI®). For comparison, patients with degenerative changes on BS in the same region were evaluated. rBSI was calculated using the same process. Results: This study included 43 patients with SAPHO syndrome and 48 with degenerative changes. The rBSIs with the diagnostic supporting system were 0.19 ± 0.19 in patients with SAPHO syndrome and 0.043 ± 0.056 in those with degenerative changes. Patients with SAPHO syndrome had significantly higher rBSIs than those with degenerative changes (P < 0.001).A cutoff value of 0.030 for rBSI in the region of interest had a sensitivity of 0.98 and specificity of 0.63 for differentiating arthritis from degenerative changes (area under the curve: 0.87, 95% confidence interval: 0.81–0.92). Conclusion The objective evaluation of arthritis using rBSI calculated with an AI diagnostic supporting system may be useful.

Purpose: For several decades, bone scintigraphy (BS) has been used as a diagnostic tool for arthritis in patients with synovitis, acne, pustulosis, hyperostosis, and osteitis (SAPHO) syndrome. Artificial intelligence (AI) diagnostic supporting systems are effective in BS. The bone scan index (BSI) on BS with AI diagnostic support systems has been used for bone tumors.However, its application in arthritis has not been validated. The current study aimed to evaluate the usefulness of BSI using an AI diagnostic supporting system for arthritis in patients with SAPHO syndrome. Methods: The regional BSI (rBSI) of arthritis uptake around the sternoclavicular and sternocostal joints on BS in patients with SAPHO syndrome was calculated using an AI diagnostic supporting system (VSBONE BSI®). For comparison, patients with degenerative changes on BS in the same region were evaluated. rBSI was calculated using the same process. Results: This study included 43 patients with SAPHO syndrome and 48 with degenerative changes. The rBSIs with the diagnostic supporting system were 0.19 ± 0.19 in patients with SAPHO syndrome and 0.043 ± 0.056 in those with degenerative changes. Patients with SAPHO syndrome had significantly higher rBSIs than those with degenerative changes (P < 0.001).A cutoff value of 0.030 for rBSI in the region of interest had a sensitivity of 0.98 and specificity of 0.63 for differentiating arthritis from degenerative changes (area under the curve: 0.87, 95% confidence interval: 0.81–0.92). Conclusion The objective evaluation of arthritis using rBSI calculated with an AI diagnostic supporting system may be useful.

Purpose: For several decades, bone scintigraphy (BS) has been used as a diagnostic tool for arthritis in patients with synovitis, acne, pustulosis, hyperostosis, and osteitis (SAPHO) syndrome. Artificial intelligence (AI) diagnostic supporting systems are effective in BS. The bone scan index (BSI) on BS with AI diagnostic support systems has been used for bone tumors.However, its application in arthritis has not been validated. The current study aimed to evaluate the usefulness of BSI using an AI diagnostic supporting system for arthritis in patients with SAPHO syndrome. Methods: The regional BSI (rBSI) of arthritis uptake around the sternoclavicular and sternocostal joints on BS in patients with SAPHO syndrome was calculated using an AI diagnostic supporting system (VSBONE BSI®). For comparison, patients with degenerative changes on BS in the same region were evaluated. rBSI was calculated using the same process. Results: This study included 43 patients with SAPHO syndrome and 48 with degenerative changes. The rBSIs with the diagnostic supporting system were 0.19 ± 0.19 in patients with SAPHO syndrome and 0.043 ± 0.056 in those with degenerative changes. Patients with SAPHO syndrome had significantly higher rBSIs than those with degenerative changes (P < 0.001).A cutoff value of 0.030 for rBSI in the region of interest had a sensitivity of 0.98 and specificity of 0.63 for differentiating arthritis from degenerative changes (area under the curve: 0.87, 95% confidence interval: 0.81–0.92). Conclusion The objective evaluation of arthritis using rBSI calculated with an AI diagnostic supporting system may be useful.

Purpose: For several decades, bone scintigraphy (BS) has been used as a diagnostic tool for arthritis in patients with synovitis, acne, pustulosis, hyperostosis, and osteitis (SAPHO) syndrome. Artificial intelligence (AI) diagnostic supporting systems are effective in BS. The bone scan index (BSI) on BS with AI diagnostic support systems has been used for bone tumors.However, its application in arthritis has not been validated. The current study aimed to evaluate the usefulness of BSI using an AI diagnostic supporting system for arthritis in patients with SAPHO syndrome. Methods: The regional BSI (rBSI) of arthritis uptake around the sternoclavicular and sternocostal joints on BS in patients with SAPHO syndrome was calculated using an AI diagnostic supporting system (VSBONE BSI®). For comparison, patients with degenerative changes on BS in the same region were evaluated. rBSI was calculated using the same process. Results: This study included 43 patients with SAPHO syndrome and 48 with degenerative changes. The rBSIs with the diagnostic supporting system were 0.19 ± 0.19 in patients with SAPHO syndrome and 0.043 ± 0.056 in those with degenerative changes. Patients with SAPHO syndrome had significantly higher rBSIs than those with degenerative changes (P < 0.001).A cutoff value of 0.030 for rBSI in the region of interest had a sensitivity of 0.98 and specificity of 0.63 for differentiating arthritis from degenerative changes (area under the curve: 0.87, 95% confidence interval: 0.81–0.92). Conclusion The objective evaluation of arthritis using rBSI calculated with an AI diagnostic supporting system may be useful.

Purpose: For several decades, bone scintigraphy (BS) has been used as a diagnostic tool for arthritis in patients with synovitis, acne, pustulosis, hyperostosis, and osteitis (SAPHO) syndrome. Artificial intelligence (AI) diagnostic supporting systems are effective in BS. The bone scan index (BSI) on BS with AI diagnostic support systems has been used for bone tumors.However, its application in arthritis has not been validated. The current study aimed to evaluate the usefulness of BSI using an AI diagnostic supporting system for arthritis in patients with SAPHO syndrome. Methods: The regional BSI (rBSI) of arthritis uptake around the sternoclavicular and sternocostal joints on BS in patients with SAPHO syndrome was calculated using an AI diagnostic supporting system (VSBONE BSI®). For comparison, patients with degenerative changes on BS in the same region were evaluated. rBSI was calculated using the same process. Results: This study included 43 patients with SAPHO syndrome and 48 with degenerative changes. The rBSIs with the diagnostic supporting system were 0.19 ± 0.19 in patients with SAPHO syndrome and 0.043 ± 0.056 in those with degenerative changes. Patients with SAPHO syndrome had significantly higher rBSIs than those with degenerative changes (P < 0.001).A cutoff value of 0.030 for rBSI in the region of interest had a sensitivity of 0.98 and specificity of 0.63 for differentiating arthritis from degenerative changes (area under the curve: 0.87, 95% confidence interval: 0.81–0.92). Conclusion The objective evaluation of arthritis using rBSI calculated with an AI diagnostic supporting system may be useful.

BACKGROUND: Low-dose exposure to organophosphate (OP) insecticides during pregnancy may adversely affect neurodevelopment in children. To evaluate the OP exposure levels, single urine sampling is commonly adopted to measure the levels of dialkylphosphates (DAPs), common OP metabolites. However, the inter-day variations of urinary DAP concentrations within subjects are supposed to be large due to the short biological half-lives of the metabolites, and it is thus considered difficult to accurately assess OP exposure during pregnancy with single sampling. This study aimed to assess intra-individual variations of DAP concentrations and the reproducibility of the exposure dose categorization of OPs according to DAP concentration ranges in pregnant women in Japan. METHODS: Urine samples were collected from 62 non-smoking pregnant women (12-22 weeks of gestation) living in Aichi Prefecture, Japan. First morning void (FMV) and spot urine samples taken between lunch and dinner on the same day were collected on five different days during 2 weeks. The concentrations of DAP and creatinine in urine samples were measured using an ultra performance liquid chromatography with tandem mass spectrometry. Creatinine-adjusted and unadjusted concentrations were used for the intraclass correlation coefficient (ICC) calculations and surrogate category analyses. RESULTS: For all DAP metabolites, the creatinine-adjusted single ICCs exceeded 0.4, indicating moderate reliability. Overall, ICCs of spot urine samples taken in the afternoon were better than those taken as FMV. Surrogate category analyses showed that participants were categorized accurately into four exposure dose groups according to the quartile points. CONCLUSION This study indicated that a single urine sample taken in the afternoon may be useful in assessing OP exposure as long as the exposure is categorized into quartiles when conducting epidemiological studies in early to mid-pregnant women in Japan.
Adult ; Chromatography, Liquid ; Creatinine ; urine ; Environmental Exposure ; analysis ; Environmental Monitoring ; methods ; Environmental Pollutants ; urine ; Female ; Humans ; Japan ; Mass Spectrometry ; Organophosphates ; urine ; Pesticides ; urine ; Pregnancy ; Pregnant Women ; Young Adult