Ekan Faozi

The Tumor-Stroma Ratio (TSR) reflects a higher ratio of stromal cells compared to cancer cells in a tumor, which is associated with a worse prognosis, increased risk of recurrence, and reduced overall survival in colorectal cancer. This paper reports the application of optimized deep transfer learning for fully automated tumor-stroma ratio estimation and its significance as a prognostic factor in patient survival prediction. Various base models of transfer learning, including ResNet18, DenseNet201, MobileNet_V2, EfficientNet_b2, and Inception_V3, are considered. All base models were trained using the Optuna hyperparameter optimization framework, which is flexible, modular, and easy to combine with transfer learning. After determining the optimal deep transfer learning model, it was implemented to predict the tumor-stroma ratio (TSR). Experimental comparisons indicate that ResNet18 is the most suitable base deep transfer learning model for colorectal cancer (CRC) classification. Consequently, ResNet18 was used to classify whole slide images of colorectal patients. Finally, the Cox proportional hazards model was applied to features including TSR and other clinical parameters of colorectal cancer. The results demonstrate that the Tumor-Stroma Ratio can be considered an important prognostic factor for colorectal cancer based on survival analysis prediction.

Stroke is among the priority health issues frequently reported by the people of Gemolong, Sragen. Strokes do not only affect the elderly; individuals of productive age can also experience them. Strokes can be prevented by reducing risk factors and providing proper treatment for stroke attacks. This community service activity aims to enhance the knowledge and skills of Aisyiyah administrators in promoting stroke prevention and appropriately handling stroke attacks. The methods used in the service activities include training and simulations, encompassing socialization, education on stroke prevention and treatment, as well as stress management using Al-Quran recitations (murottals). The outcome of the service is an increased knowledge and skills of participants in prevention and treatment. This also includes cadre skills in handling stroke sequelae through bed transfer and Range of Motion (RoM), as well as implementing murottal therapy for stress management.

Prolanis is a health service system and a proactive approach that is implemented in an integrated manner implemented by the Social Health Facility Implementing Agency (BPJS Kesehatan). The program focuses on BPJS Health participants who suffer from chronic diseases including type 2 diabetes mellitus. Participants who take part in Prolanis are expected to achieve optimal quality of life with cost effective and efficient health services. This study aims to describe the quality of life of people with type 2 diabetes mellitus who joined the prolanis program in Sukoharjo Regency. This research is a quantitative study using descriptive methods and survey approaches. The measuring instrument used was the DQOL questionnaire sheet. The sampling technique used in this study was convenience sampling with 95 samples. The results show that as many as 82 people (86.3%) sufferers of type 2 DM who participated in the prolanis program in Sukoharjo regency have a good quality of life with the characteristics of most women, around 61-70 years old, graduated high school, housewife, and suffer from type 2 DM for less than 5 years.

Faozi E, et al. Korean J Adult Nurs. 2021 Dec;33(6):649-656. https://doi.org/10.7475/kjan.2021.33.6.649