Journal of Health Sciences Sukhothai hospital

Effects of a low carbohydrate diet on Obesity among Healthcare workers at Sukhothai hospital

2026-03-17T09:32:54+07:00

Background: from the annual health examination in 2023 at Sukhothai Hospital, healthcare workers with a body mass index (BMI) greater than 25 kg/m² about 43%. Low carbohydrate diet Program help to lose weight, decrease HbA1C and lipid level.

Objective: This quasi-experimental study with a two-group pretest–posttest design aimed to examine the effects of a low-carbohydrate diet on body weight, waist circumference, body fat composition, and lipid profiles among obese healthcare personnel at Sukhothai Hospital.

Methods: The participants consisted of 95 healthcare workers with a body mass index (BMI) greater than 25 kg/m², identified from the annual health examination in 2023. Participants were voluntarily recruited without randomization and subsequently allocated into an experimental group (n=49) and a control group (n= 46) based on preference. Baseline assessments, including body weight, anthropometric measurements, and lipid profiles, were conducted during the first week following obesity education for all participants. The experimental group received a low-carbohydrate dietary program (60–130 g/day), combined with multidisciplinary support, application-based monitoring, and continuous health education. The control group maintained their usual dietary patterns. Outcome measures were reassessed after 12 weeks. Statistical analyses included Chi-square test and independent-sample t test for between-group comparisons, and paired-sample t test for within-group comparisons.

Results: After 12 weeks, the experimental group demonstrated significantly lower triglyceride levels compared to the control group (p < 0.05). Significant reductions were observed in body weight, waist circumference, visceral fat, and triglyceride levels compared to baseline (p < 0.05). However, no difference in body weight was found between the two groups (p > 0.05), which may reflect suboptimal adherence to dietary recommendations among healthcare personnel with high workload demands.

Conclusion: Future health promotion programs should adopt a multidisciplinary approach to enhance adherence and achieve sustainable weight management outcomes.

The effect of using Clinical nursing practice on the knowledge and self-care practice of patients receiving intravitreal injection

2026-03-10T10:46:39+07:00

Background: Intravitreal injection of bevacizumab (Avastin) is commonly used in the treatment of retinal diseases. Appropriate patient education and self-care are essential for preventing complications, particularly endophthalmitis. Nursing practice guidelines may improve patient knowledge and promote appropriate self-care behaviors after treatment.

Objective: aimed to compare mean knowledge and self-care practice scores between patients receiving a nursing practice guideline for intravitreal Avastin injection and those receiving routine nursing care.

Methods: Incidence of endophthalmitis within one month after injection. The sample included patients with retinal diseases attending the ophthalmology outpatient department at Sukhothai Hospital. The control group (n = 30) received routine care during, and the experimental group (n = 30) received care based on the nursing practice guideline during. The instruments demonstrated good content validity (IOC > 0.50) and acceptable reliability. Data were analyzed using descriptive statistics, independent t-test, and Fisher’s exact test.

Results: showed that the experimental group had significantly higher mean knowledge and self-care practice scores than the control group (p < 0.05). No cases of endophthalmitis were found in either group after intravitreal Avastin injection.

Conclusion: the nursing practice guideline improved patients’ knowledge and self-care practices. It should be applied in clinical practice to promote patient safety and effective self-care following treatment.

Results The study revealed that the mean knowledge score of the experimental group (patients who received nursing care based on the guidelines for patients receiving intravitreal Avastin injection) was significantly higher than that of the control group (patients who received standard nursing care) (p < 0.05). This indicates that the nursing guidelines had a positive impact on the knowledge of patients receiving intravitreal Avastin injection. Similarly, the mean behavior score of the experimental group was significantly higher than that of the control group (p < 0.05). This suggests that the nursing guidelines also had a positive effect on the behavior of patients receiving intravitreal Avastin injection.

However, the incidence of endophthalmitis (eye infection) within one month after the injection did not differ significantly between the experimental and control groups (p > 0.05). This implies that the nursing guidelines did not influence the infection rate.

Conclusion The nursing guidelines for patients receiving intravitreal Avastin injection were found to have a positive impact on the knowledge and behavior of patients. Patients who received nursing care based on these guidelines demonstrated significantly higher levels of knowledge and better adherence to recommended self-care practices compared to those who received standard nursing care. However, the nursing guidelines did not influence the incidence of endophthalmitis within one month after the injection.

Deep Learning Classification of Urine Crystal Images using Convolutional Neural Networks

2026-03-31T15:49:36+07:00

Background: Urinalysis is a fundamental component of routine clinical laboratory screening. Despite the widespread adoption of automated analyzers, manual microscopic classification of urinary sediment remains the gold standard. However, this manual process is time-consuming and susceptible to inter-observer variability among laboratory personnel. This research addresses these limitations by developing an automated classification system for urine crystals leveraging Deep Learning techniques.

Objectives: The primary objective of this study is to develop a high-precision automated classification system for urine crystals using a custom Convolutional Neural Network (CNN). The secondary objective is to deploy the model as a real-time web application to minimize diagnostic errors and enhance the operational efficiency of laboratory technologists.

Method: The study utilized a dataset of 4,539 images categorized into 13 distinct types of urine crystals: Acyclovir, Bilirubin, Calcium Carbonate, Leucine, Calcium Phosphate, Uric Acid, Triple Phosphate, Calcium Oxalate, Ammonium Biurate, Hippuric Acid, Tyrosine, Cystine, and Cholesterol. The dataset was partitioned into training and testing sets. Development was conducted using Python within the Google Colab environment, employing a Sequential CNN architecture with three convolutional layers. Furthermore, a web application was developed using the FastAPI framework to facilitate real-time image prediction.

Results: The experimental results demonstrate that the model achieved an overall accuracy of 92% on the test dataset. Exceptional performance was observed in several classes; notably, Acyclovir, Hippuric Acid, and Leucine achieved perfect F1-scores of 1.00. Other high-performing classes included Ammonium Biurate (F1-score: 0.98) and Cystine (F1-score: 0.92). Training monitored over 10 epochs showed consistent improvement, with both training and validation accuracy exceeding 90%, while the validation loss decreased in tandem with the training loss, indicating a well-generalized model without overfitting.

Conclusion: This research successfully demonstrates the effectiveness of a custom CNN model for high-precision automated urine crystal classification. Deployment via a FastAPI-based interface provides a practical and efficient tool for clinical laboratories. The system effectively reduces workload, saves time, and minimizes subjectivity in diagnosis, serving as a rapid screening tool to enhance the overall standards of urinalysis.