Purpose
Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy.
Methods
ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers.
Results
CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches.
Conclusion
Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

Objectives

The purpose of this study is to investigate e effects of preischemic hyperglycemia on e alterations of'adenosine triphosphate and lactate concentrations in e acutefocal ischernic brain of the cats.

Methods

Acute focal cerebral ischemia in cats was induced by occlusion of the left middlecerebral artery through the postorbital technique. The experimental animals were divided into 3 goups: sham control, occlusion and recirculation groups. Each group was divided into 2 subgroups: normoglycemic and hyperglycemic groups.

Results

The experimental results are obtained as fo11ows;

1) In normoglycemic subgroups of occlusion and recirculation proups, amount of adenosinetriphosphate in ischmic brain decreased significantly to 3.0% and 13.0% of the sham control,respectively(p < 0.001).

In hyperglycemic subgroups of occlusion and recirculation groups, amount of adenosine trisphosphate decreased a little more an at in normoglycemic subgroups, even so there wasno statistic significancy(p > 0.1).

2) In normoglycemic subgroups of occlusion and recirculation groups, amount of lactate inischemic brain increased signigicantly to 175.7% and 187.9% of the sham control, respectively(p < 0.001).

In hyperglycemic subgroups of occlusion and recirculation groups, amount of lactate increased nore than that in normoglycemic subgroups with statistic significancy(0.001 < p < 0.01).

Conclusion

These results suggest that hyperglycemia before ischemia serves to elevate glucose transport into brain tissue and thereby, to promote profound tissue acidosis throughanaerobic glycolysis caused by a failure of adenosine triphosphate stnthesis during the ischemicperiod.