Objective To investigate the relationship between K+ concentration in rabbit vitreous humor and postmortem interval (PMI) under different ambient temperature. Methods Rabbit corpses were stored at 5℃ , 15℃ , 25℃ , and 35℃ after execution, and 80~100μL vitreous humor was extracted from each eye of the rabbit in turn every 12 hours. The concentrations of K+ were examined by Modular DPPI automatic biochemistry analyzer. The Interpolation Functions were used to analyze the statistical relationship between PMI and K+ concentration under different temperature. Results In each animal group, K+ concentration increased with PMI. Equation was obtained after interpolation analysis on range of temperature 5℃ ~30℃ . The three-variable quintic surface equation was f(x,y)=-1.998e14+1.345e12x+5.902e13y+0.005585x2-4.509e11xy-3.876e12y2-0.0002868x3+0.003545x2y+4.406e10xy2-1.746e10y3+2.669e-6x4-1.568e-5x3y-0.0001771x2y2-1.64e9xy3+6.669e9y4-8.672e-9x5+4.467e-8x4y+2.354e-7x3y2+2.459e-6x2y3+2.05e7xy4-1.214e8y5(R2=0.9956), x stands for temperature, y stands for K+ concentration, f(x,y) stands for PMI. Conclusion The rule of K+ concentration changes at ambient temperature complied with three-variable quintic surface equation distribution. Measurement of interpolation function may be used for PMI estimation at different ambient temperature.

Stroke ranks as the third leading cause of death and the fourth leading cause of disability worldwide. Its high disability rate and prolonged recovery period not only severely impact patients' quality of life but also impose a significant burden on families and society. Primary prevention is the cornerstone of stroke control, as early intervention on risk factors can effectively reduce its incidence. Therefore, the development of predictive models for first-ever stroke risk holds substantial clinical value. In recent years, advancements in big data and artificial intelligence technologies have opened new avenues for stroke risk prediction. This article reviews the current research status of traditional methods and machine learning models in predicting first-ever stroke risk and outlines future development trends from three perspectives: First, emphasis should be placed on technological innovation by incorporating advanced algorithms such as deep learning and large models to further enhance the accuracy of predictive models. Second, there is a need to diversify data types and optimize model architectures to construct more comprehensive and precise predictive models. Lastly, particular attention should be given to the clinical validation of models in real-world settings. This not only enhances the robustness and generalizability of the models but also promotes physicians' understanding of predictive models, which is crucial for their application and dissemination.