Abstract. in order to evaluate the relationship between clinical markers of glycemia and glucose excursion, we performed 48-hour continuous glucose monitoring (Cgm) in 43 diabetic patients. For the clinical markers, hba1c, glycoalbumin (ga), and 1,5-anhydroglucitol (1,5-ag) were measured, and for the parameters of glucose excursion from Cgm, average glucose (ag), standard deviation of glucose (SD), the area under the curve for glucose levels >180 mg/dL (auC>180), and the difference between the maximum and minimum glucose levels during 48 hours (ΔG48hr) were analyzed. all patients were treated without any changes of the dosages of oral anti-diabetic agents or insulin for at least the previous 6 months with coefficient of variation (CV) of HbA1c less than 4 %. in results, while hba1c did not show any single correlation with ag, SD, auC>180, or ΔG48hr, both GA and 1,5-AG were significantly related to all these parameters. Furthermore, GA significantly correlated to all CGM parameters, and SD significantly correlated to GA in multiple regression analyses. These results suggest that ga may be a different marker from hba1c for diabetic complications, because ga, but not hba1c, may reflect not only short-term average glucose but also fluctuation of glucose.Key words: glycoalbumin, glycated hemoglobin, 1,5-anhydroglucitol, Continuous glucose monitoringThe sTAble frAcTion of glycated hemoglobin (hba 1c ) is routinely measured in the majority of patients with diabetes around the world, since hba 1c reflects the mean glucose level over the preceding 3 months [1]. hba 1c is not only used to determine whether a patient's metabolic control has been maintained within the target range, but also to estimate the risk of chronic diabetic complications in each patient. Previous the large-scale prospective studies of diabetic patients have used hba 1c as a marker of glycemic control to evaluate the association of consistent hyperglycemia with the development or progression of chronic diabetic complications [2,3]. however, recent studies have indicated that postprandial hyperglycemia or fluctuations of the glucose level may be an independent risk factor for macrovascular complications in diabetic patients, which cannot be evaluated by measuring hba 1c alone [4]. Since hba 1c is a marker of the average level of glycemia, it does not reflect acute glucose fluctuations and is poorly correlated with glucose excursions [5]. Therefore, in order to assess the risk of diabetic complications, especially macrovascular complications, it may be necessary to evaluate not only the mean level of glycemic control, but also the extent of glucose excursions such as glucose fluctuations or postprandial elevation of glucose.To assess daily blood glucose excursions, portable devices for self-monitoring of blood glucose (Smbg) are now widely used by insulin-treated diabetic patients. although such devices are helpful, the number of measuring times is limited. Recently, a continuous subcutaneous glucose monitoring (Cgm) device was developed to evaluate the...
Abstract. To investigate the role of ghrelin, an endogenous ligand of the growth hormone secretagogue receptor, in diabetic gastroparesis, we evaluated the plasma ghrelin profile during the oral glucose tolerance test in 55 patients with diabetes (men/women: 36/19, mean ± SE of age: 55.1 ± 1.7 years) with or without gastroparesis (diagnosed by the 13 Cacetate breath test). We also further examined cardiac autonomic neuropathy by assessing 24-hour variation of the R-R interval in randomly selected 32 patients with diabetes (men/women: 23/9, mean ± SE of age: 54.2 ± 2.5 years), and evaluated the influence of autonomic neuropathy on ghrelin. The fasting plasma ghrelin level was significantly lower in diabetes mellitus with gastroparesis than in healthy controls (7.9 ± 0.7 fmol/ml versus 16.6 ± 5.3 fmol/ml, p = 0.006). Patients with diabetes with gastroparesis showed no decrease of plasma ghrelin after glucose loading, unlike patients without gastroparesis or healthy controls. Diabetes mellitus with autonomic neuropathy, but not those without it, also showed no decrease of plasma ghrelin after glucose loading. Diabetic gastroparesis may be related to ghrelin-associated neurohormonal abnormalities, but the pathophysiological meaning of this abnormal ghrelin response needs further clarification.
Persistent infections with two types of human papillomaviruses (HPV), HPV16 and HPV18, are the most common cause of cervical cancer (CC). Two viral early genes, E6 and E7, are associated with tumor development, and expressions of E6 and E7 are primarily regulated by a single viral promoter: P97 in HPV16 and P105 in HPV18. We previously demonstrated that the homeobox D9 (HOXD9) transcription factor is responsible for the malignancy of HPV16-positive CC cell lines via binding to the P97 promoter. Here, we investigated whether HOXD9 is also involved in the regulation of the P105 promoter using two HPV18-positive CC cell lines, SKG-I and HeLa. Following the HOXD9 knockdown, cell viability was significantly reduced, and E6 expression was suppressed and was accompanied by increased protein levels of P53, while mRNA levels of TP53 did not change. E7 expression was also downregulated and, while mRNA levels of RB1 and E2F were unchanged, mRNA levels of E2F-target genes, MCM2 and PCNA, were decreased, which indicates that the HOXD9 knockdown downregulates E7 expression, thus leading to an inactivation of E2F and the cell-cycle arrest. Chromatin immunoprecipitation and promoter reporter assays confirmed that HOXD9 is directly associated with the P105 promoter. Collectively, our results reveal that HOXD9 drives the HPV18 early promoter activity to promote proliferation and immortalization of the CC cells.
Objective: To analyse the efficacy of low-dose rosuvastatin for treating hypo high-density lipoprotein (HDL) cholesterolaemia in patients with type 2 diabetes and dyslipidaemia. Methods: Patients with HDL-cholesterol (C) <40 mg/dl and triglycerides (TG) <400 mg/dl who were receiving treatment with lipid-lowering drugs other than rosuvastatin (or previously untreated with lipid-lowering drugs) and with low-density lipoprotein [LDL]-C !120 mg/dl were included. Patients were treated with 2.5 or 5 mg rosuvastatin orally, once daily, to achieve the target LDL-C level specified in Japanese guidelines. Changes in total cholesterol, HDL-C, TG, LDL-C, LDL-C/HDL-C and non-HDL-C at 3 and 6 months were prospectively analysed. Safety was evaluated by examining changes in hepatorenal function, glucose metabolism and creatine kinase. Results: Out of 49 patients, all lipid parameters other than TG were significantly improved at 3 and 6 months. At 3 months, 83.3% of patients had achieved the target LDL-C level. Among nonlipid parameters, no changes were observed except for estimated glomerular filtration rate, which was improved by þ 5.2% and þ 9.6% at 3 and 6 months, respectively. Conclusions: Low-dose rosuvastatin was effective in improving hypo-HDL cholesterolaemia and may have renoprotective effects.
Best Oral Presentation ContentsObjective: Adoptive cell therapy using tumor-infiltrating lymphocytes (TIL-ACT) is a promising immunotherapy using autologous lymphocytes ex vivo expanded from patient's tumor. We are performing a phase I study for malignant melanoma and then perform a phase II study for recurrent cervical cancer as to evaluate the safety and efficacy of TIL-ACT. Methods: Three patients with malignant melanoma received TIL-ACT. Tumor fragments were cultured in outgrowth medium to produce TIL. Then T cell populations with tumor reactivity were selected for rapid expansion, generating over 1,000-fold TILs within 2 weeks and finally reinfused into the patient who received preparative lymphodepleting. IL-2 regimens after cell transfer were conducted to promote TIL growth and antitumor activity. The primary endpoint was to define treatment feasibility as completion of TIL-ACT without early cessation due to unacceptable adverse events. The secondary endpoints were safety assessed using CTCAE v. 4.0, clinical response; objective response rate based on the RECIST v.1.1. Results: Three cases of TIL-ACT treated melanoma patients were successfully completed without unacceptable adverse events including one partial response, one stable disease and one progression disease case. Next, we manufactured a TIL products from specimens of three cervical cancer patient, and succeeded in producing TIL that meets the standards of TIL-ACT. Based on this result, TIL-ACT for cervical cancer was approved by the Japanese Ministry of Health, Labour and Welfare as advanced medical treatment. Conclusion: TIL-ACT could be safely performed for Japanese patients with malignant melanoma. Currently, we are implementing TIL-ACT for recurrent cervical cancer.
1549 Background: The presence of genetic mutations is a vital prognostic in many types of cancer. However, genomic testing is expensive and challenging to perform. In contrast, hematoxylin and eosin (H&E) staining is relatively inexpensive and straightforward. Thus, in this study, we propose a method of predicting the presence of genetic mutations using H&E-stained whole-slide images (WSIs). Methods: We divided each H&E–stained WSI into small pieces or “patches.” We used a deep learning model to classify each patch based on the presence of tumor-containing regions. We then extracted image features from each tumor-containing patch using a deep learning-based feature extractor. We created image features for the entire WSI by concatenating the features of the patches. We then trained genetic mutation classification models using the WSI features as the input and the presence or absence of genetic mutations as the output. Finally, we evaluated the performance of these models using the area under the receiver operating characteristic curve (AUC). Results: First, we evaluated our methods using The Cancer Genome Atlas (TCGA) colorectal cancer dataset. We used H&E–stained WSIs and data associated with Microsatellite Instability ( MSI) and BRAF gene mutations, which are directly relevant to therapeutic strategies, obtained from an independent clinical cohort of 566 patients with TCGA colon and rectum adenocarcinoma. We divided the data into training, validation, and test splits, comprising 367, 90, and 109 patients, respectively. We used the training and validation splits for model training and selection, and the test split for model evaluation. The AUC values of the classification models and associated 95% confidence intervals (CIs) were 0.721 (CI = 0.572–0.870) for MSI and 0.712 (CI = 0.547–0.877) for BRAF gene mutations. We also applied our approach to MUC16, KRAS, and ALK mutations using the TCGA lung cancer dataset. We divided 909 TCGA lung adenocarcinoma and lung squamous cell carcinoma patients into training, validation, and test splits, comprising 582, 146, and 181 patients, respectively. In contrast with those of the colorectal dataset, WSI image features were generated using all patches. The AUC values on the test splits were 0.897 (CI = 0.85–0.95) for MUC16, 0.845 (CI = 0.75–0.94) for KRAS, and 0.756 (CI = 0.57–0.94) for ALK mutations. Conclusions: We proposed an approach to predict the presence of genetic mutations using only H&E–stained WSIs and evaluated its performance using colorectal and lung cancer datasets. Our model has the potential to predict the presence of certain genetic mutations with superior performance. These predictions can be used to improve the accuracy of prognostic prediction using WSIs alone.
3119 Background: Previous studies have shown that the presence or absence of genetic mutations is critical for colorectal cancer prognosis. However, genomic testing can be expensive and difficult to perform on all samples. In contrast, hematoxylin and eosin (H&E) staining is relatively inexpensive and can be performed on all tissue specimens. In this study, we designed a novel prognostic method using spatial image features extracted from H&E–stained whole slide images (WSIs) and genetic mutation prediction neural networks. Methods: We obtained H&E–stained WSIs and data on Microsatellite Instability ( MSI), BRAF, TTN and APC gene mutations from a clinical cohort of 548 patients with The Cancer Genome Atlas (TCGA) Colon adenocarcinoma and rectum adenocarcinoma. We divided them into training (n=361), validation (n=90), and test (n=115) groups. Classification models were trained to predict the presence or absence of MSI, BRAF, TTN, and APC mutations. The model input comprised features of the H&E–stained WSIs, as obtained via a deep learning–based feature extractor. All resultant models were incorporated into a prognostic model (overall survival: > 60 months (low risk)/< 60 months (high risk)). Our prognostic model’s performance was evaluated against TCGA colorectal dataset, and a survival analysis was performed on the model using the Kaplan–Meier method. Finally, we compared our model’s performance with the end–to–end prognostic prediction of a convolutional neural network (CNN) that also used H&E–stained WSIs as input and provided prognostic prediction as output. Results: Our deep learning–based prognostic prediction model achieved an AUC score of 0.834 with a 95% confidence interval (CI) of 0.734–1.000 alongside TCGA dataset; the survival analysis compared the survival distributions of low–risk and high–risk groups, as predicted by our model; a p–value < 0.01 was obtained. The model could classify low– and high–risk patients and accurately predict patient status as alive (low risk) or deceased (high risk) at 60 months. In contrast, the CNN–based model achieved an AUC score of only 0.502 (95% CI: 0.315–0.690) on the same TCGA dataset, and the p–value obtained for it under the Kaplan–Meier log–rank test was greater than 0.5. The CNN–based method was unable to distinguish between low– and high–risk patients, confirming that our method using spatial imaging features extracted from WSIs was a more effective approach. Conclusions: We developed a novel prognostic prediction method using spatial image features extracted from WSIs and genetic mutation prediction neural networks. Our results demonstrated the advantage of using image features over gene mutation data for prognostic prediction in colorectal cancer patients.
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