This article focuses on inclusive education for deaf and hard-of-hearing students in Rochester, New York, which prepares them to become healthcare professionals or researchers in health science fields. We highlight the unique programs for deaf and hard-of-hearing students to develop their careers at the Rochester Institute of Technology (RIT), the National Technical Institute for the Deaf (NTID), and the University of Rochester (UR). These universities also foster an inclusive work environment that caters to the needs of deaf and hard-of-hearing clinical professionals and faculty specialized in health research, enabling them to play leadership roles in their fields. Notably, Strong Memorial Hospital at UR supports deaf staff by providing interpreter services, allowing them to actively engage in their clinical work as professionals. Such seamless support, spanning from higher education to professional careers in Rochester, demonstrates a compelling model for enabling deaf and hard-of-hearing individuals to pursue and thrive in their chosen professions.

Objective: Magnetic resonance imaging (MRI) is efficient for the diagnosis of preoperative uterine sarcoma; however, misdiagnoses may occur. In this study, we developed a new artificial intelligence (AI) system to overcome the limitations of requiring specialists to manually process datasets and a large amount of computer resources. Methods: The AI system comprises a tumor image filter, which extracts MRI slices containing tumors, and sarcoma evaluator, which diagnoses uterine sarcomas. We used 15 types of MRI patient sequences to train deep neural network (DNN) models used by tumor filter and sarcoma evaluator with 8 cross-validation sets. We implemented tumor filter and sarcoma evaluator using ensemble prediction technique with 9 DNN models. Ten tumor filters and sarcoma evaluator sets were developed to evaluate fluctuation accuracy. Finally, AutoDiag-AI was used to evaluate the new validation dataset, including 8 cases of sarcomas and 24 leiomyomas. Results: Tumor image filter and sarcoma evaluator accuracies were 92.68% and 90.50%, respectively. AutoDiag-AI with the original dataset accuracy was 89.32%, with 90.47% sensitivity and 88.95% specificity, whereas AutoDiag-AI with the new validation dataset accuracy was 92.44%, with 92.25% sensitivity and 92.50% specificity. Conclusion Our newly established AI system automatically extracts tumor sites from MRI images and diagnoses them as uterine sarcomas without human intervention. Its accuracy is comparable to that of a radiologist. With further validation, the system could be applied for diagnosis of other diseases. Further improvement of the system's accuracy may enable its clinical application in the future.

Objective: Magnetic resonance imaging (MRI) is efficient for the diagnosis of preoperative uterine sarcoma; however, misdiagnoses may occur. In this study, we developed a new artificial intelligence (AI) system to overcome the limitations of requiring specialists to manually process datasets and a large amount of computer resources. Methods: The AI system comprises a tumor image filter, which extracts MRI slices containing tumors, and sarcoma evaluator, which diagnoses uterine sarcomas. We used 15 types of MRI patient sequences to train deep neural network (DNN) models used by tumor filter and sarcoma evaluator with 8 cross-validation sets. We implemented tumor filter and sarcoma evaluator using ensemble prediction technique with 9 DNN models. Ten tumor filters and sarcoma evaluator sets were developed to evaluate fluctuation accuracy. Finally, AutoDiag-AI was used to evaluate the new validation dataset, including 8 cases of sarcomas and 24 leiomyomas. Results: Tumor image filter and sarcoma evaluator accuracies were 92.68% and 90.50%, respectively. AutoDiag-AI with the original dataset accuracy was 89.32%, with 90.47% sensitivity and 88.95% specificity, whereas AutoDiag-AI with the new validation dataset accuracy was 92.44%, with 92.25% sensitivity and 92.50% specificity. Conclusion Our newly established AI system automatically extracts tumor sites from MRI images and diagnoses them as uterine sarcomas without human intervention. Its accuracy is comparable to that of a radiologist. With further validation, the system could be applied for diagnosis of other diseases. Further improvement of the system's accuracy may enable its clinical application in the future.

Objective: Magnetic resonance imaging (MRI) is efficient for the diagnosis of preoperative uterine sarcoma; however, misdiagnoses may occur. In this study, we developed a new artificial intelligence (AI) system to overcome the limitations of requiring specialists to manually process datasets and a large amount of computer resources. Methods: The AI system comprises a tumor image filter, which extracts MRI slices containing tumors, and sarcoma evaluator, which diagnoses uterine sarcomas. We used 15 types of MRI patient sequences to train deep neural network (DNN) models used by tumor filter and sarcoma evaluator with 8 cross-validation sets. We implemented tumor filter and sarcoma evaluator using ensemble prediction technique with 9 DNN models. Ten tumor filters and sarcoma evaluator sets were developed to evaluate fluctuation accuracy. Finally, AutoDiag-AI was used to evaluate the new validation dataset, including 8 cases of sarcomas and 24 leiomyomas. Results: Tumor image filter and sarcoma evaluator accuracies were 92.68% and 90.50%, respectively. AutoDiag-AI with the original dataset accuracy was 89.32%, with 90.47% sensitivity and 88.95% specificity, whereas AutoDiag-AI with the new validation dataset accuracy was 92.44%, with 92.25% sensitivity and 92.50% specificity. Conclusion Our newly established AI system automatically extracts tumor sites from MRI images and diagnoses them as uterine sarcomas without human intervention. Its accuracy is comparable to that of a radiologist. With further validation, the system could be applied for diagnosis of other diseases. Further improvement of the system's accuracy may enable its clinical application in the future.

Background: In Japan, neonates have typically been bathed in a bathtub immediately after birth because bathing is a custom for cleansing impurities. However, dry technique has been introduced into many institutions since 2000. There is little scientific evidence on the benefit or harmfulness of either method to neonatal skin, and consequently, opinion remains split on which method is superior. Objective: The purpose of the present study was to determine whether bathing or the dry technique of cleaning is better in maintaining skin health in the early neonatal period. Methods: Transepidermal water loss (TEWL) and skin pH, considered an index of skin barrier function, were measured in each group. Tumor necrosis factor (TNF)-alpha and interleukin (IL)-6, which are inflammatory cytokines released by keratinocytes, were measured by skin blotting. Results: TEWL and skin pH of neonates were lower with the dry technique than with bathing. The expression level of IL-6 and TNF-α in chest skin of neonates was higher with bathing than with the dry technique. Conclusion These results suggest that the dry technique may maintain skin health better than bathing in the early neonatal period.

BACKGROUND: The burden of dementia is growing rapidly and has become a medical and social problem in Japan. Prospective cohort studies have been considered an effective methodology to clarify the risk factors and the etiology of dementia. We aimed to perform a large-scale dementia cohort study to elucidate environmental and genetic risk factors for dementia, as well as their interaction. METHODS: The Japan Prospective Studies Collaboration for Aging and Dementia (JPSC-AD) is a multisite, population-based prospective cohort study of dementia, which was designed to enroll approximately 10,000 community-dwelling residents aged 65 years or older from 8 sites in Japan and to follow them up prospectively for at least 5 years. Baseline exposure data, including lifestyles, medical information, diets, physical activities, blood pressure, cognitive function, blood test, brain magnetic resonance imaging (MRI), and DNA samples, were collected with a pre-specified protocol and standardized measurement methods. The primary outcome was the development of dementia and its subtypes. The diagnosis of dementia was adjudicated by an endpoint adjudication committee using standard criteria and clinical information according to the Diagnostic and Statistical Manual of Mental Disorders, 3rd Revised Edition. For brain MRI, three-dimensional acquisition of T1-weighted images was performed. Individual participant data were pooled for data analyses. RESULTS: The baseline survey was conducted from 2016 to 2018. The follow-up surveys are ongoing. A total of 11,410 individuals aged 65 years or older participated in the study. The mean age was 74.4 years, and 41.9% were male. The prevalence of dementia at baseline was 8.5% in overall participants. However, it was 16.4% among three sites where additional home visit and/or nursing home visit surveys were performed. Approximately two-thirds of dementia cases at baseline were Alzheimer's disease. CONCLUSIONS The prospective cohort data from the JPSC-AD will provide valuable insights regarding the risk factors and etiology of dementia as well as for the development of predictive models and diagnostic markers for the future onset of dementia. The findings of this study will improve our understanding of dementia and provide helpful information to establish effective preventive strategies for dementia in Japan.
Aged ; Alzheimer Disease/genetics* ; Dementia/genetics* ; Environment ; Female ; Humans ; Incidence ; Japan/epidemiology* ; Male ; Middle Aged ; Prevalence ; Prospective Studies ; Risk Factors

Background/Aims: The adenoma detection rate (ADR) of screening colonoscopies performed by trainees is often lower than that of colonoscopies performed by experts. The effcacy of cap-assisted colonoscopy (CAC) in adenoma detection is well documented, especially that of CACs performed by trainees. Endocuff, a new endoscopic cap, is reportedly useful for adenoma detection; however, no trials have compared the effcacy of Endocuff-assisted colonoscopy (EAC) and CAC conducted by trainees. Therefore, the present study retrospectively compared the effcacy between EAC and CAC in trainees. Methods: This was a single-center, retrospective study involving 305 patients who underwent either EAC or CAC performed by three trainees between January and December 2018. We evaluated the ADR, mean number of adenomas detected per patient (MAP), cecal intubation rate, cecal intubation time, and occurrence of complications between the EAC and CAC groups. Results: The ADR was significantly higher in the EAC group than in the CAC group (54.3% vs. 37.3%, p=0.019), as was the MAP (1.36 vs. 0.74, p=0.003). No significant differences were found between the groups with respect to the cecal intubation rate or cecal intubation time. No major complications occurred in either group. Conclusions Our results suggest that EAC exhibits increased ADR and MAP compared to CAC when performed by trainees.

OBJECTIVE: Gynecologists occasionally encounter synchronous endometrial and ovarian endometrioid carcinoma (SEO-EC) patients who show favorable prognosis than locally advanced or metastatic disease patients. This study aimed to elucidate prognostic factors of SEO-EC and identify patients who have a sufficiently low risk of recurrence without receiving adjuvant chemotherapy. METHODS: We retrospectively reviewed 46 patients with pathologically confirmed SEO-EC who underwent surgery at the National Cancer Center Hospital between 1997 and 2016. Immunohistochemical evaluation of DNA mismatch repair (MMR) protein expression were performed for both endometrial and ovarian tumors. Patient outcomes were analyzed according to clinicopathologic factors. RESULTS: From the multivariate analysis, cervical stromal invasion indicated a worse prognosis for progression-free survival (hazard ratio [HR]=6.85; 95% confidence interval [CI]=1.50–31.1) and overall survival (HR=6.95; 95% CI=1.15–41.8). Lymph node metastasis and peritoneal dissemination did not significantly affect survival. MMR deficiency was observed in 13 patients (28.3%), with both endometrial and ovarian tumors showing the same MMR expression status. MMR deficiency was not significantly associated with survival. Of 23 patients with lesions confined to only the uterine body and adnexa, only 2 had recurrence in the group receiving adjuvant therapy, while none of the 10 patients who did not receive adjuvant therapy had recurrence. CONCLUSION: SEO-EC patients with tumors localized to the uterine body and adnexa lesions had a low risk for recurrence and may not require adjuvant therapy. SEO-EC may have prognostic factors different from those of endometrial and ovarian cancer.
Carcinoma, Endometrioid* ; Chemotherapy, Adjuvant ; Disease-Free Survival ; DNA Mismatch Repair ; Humans ; Immunohistochemistry ; Lymph Nodes ; Multivariate Analysis ; Neoplasm Metastasis ; Neoplasms, Multiple Primary ; Ovarian Neoplasms ; Prognosis ; Recurrence ; Retrospective Studies

A laser endoscopy system was developed in 2012. The system allows blue laser imaging (BLI), BLI-bright, and linked color imaging (LCI) to be performed as modes of narrow-band light observation; these modes have been reported to be useful for tumor detection and characterization. Furthermore, an innovative endoscopy system using four-light emitting diode (LED) multilight technology was released in 2016 to 2017 in some areas in which laser endoscopes have not been approved for use, including the United States and Europe. This system enables blue light imaging (this is also known as BLI) and LCI with an LED light source instead of a laser light source. Several reports have shown that these modes have improved tumor detection. In this paper, we review the efficacy of BLI and LCI with laser and LED endoscopes in tumor detection and characterization.
Colorectal Neoplasms ; Endoscopes ; Endoscopy ; Europe ; United States

　　Pancreatic neuroendocrine tumors, relatively rare cancers, mostly arise in the pancreatic parenchyma with infrequent involvement of the main pancreatic duct. Now and then, however, case reports have been published on pancreatic neuroendocrine carcinoma in which the main pancreatic duct is obstructed by tumor cells with severely fibrous stromal cells. Here, in this paper, we report a case of pancreatic neuroendocrine carcinoma with obstruction of the main pancreatic duct. A 59-year-old man complained of right upper abdominal pain. Magnetic resonance cholangiopancreatography and fat-suppressed T1-weighted magnetic resonance imaging showed gallbladder stones, a low-intensity-area measuring 8 mm in diameter in the pancreatic body, and club-shaped dilatation at the distal end of the pancreatic duct. The patient was thus diagnosed with a tumor in the pancreatic body and cholecystolithiasis, and underwent distal pancreatectomy and cholecystectomy. HE-staining showed tumor cells with eosinophilic cytoplasm and nuclear atypia. The infiltrative growth of the cells with severe fibrosis caused stenosis of the pancreatic duct. Based on the positive results of immunohistochemical staining for chromogranin A and synaptophysin and the Ki-67 index, the tumor was finally identified as pancreatic neuroendocrine carcinoma. The patient has been under follow-up with no additional treatment for >3 years since the surgery, without evidence of tumor recurrence.