Sedation is an essential component of the anesthesia process. Inadequate sedation during anesthesia increases the risk of patient discomfort, intraoperative awareness, and psychological trauma. Conventional electroencephalography (EEG) based depth of anesthesia monitoring is often impractical in out-of-hospital settings due to equipment limitations and signal artifacts. Alternative non-EEG-based approaches are therefore required. In this study, we developed a machine learning model to detect inadequate sedation using 27 feature parameters, including demographics, vital signs, and heart rate variability metrics, from the open-access VitalDB database. Patient states were defined as inadequate sedation when the bispectral index (BIS) > 60. We systematically evaluated four temporal windows and four algorithms, and assessed model interpretability using Shapley Additive Explanations (SHAP). The Light Gradient Boosting Machine (LGBM) achieved the best performance, with an area under the receiver operating characteristic curve (AUC) of 0.825 and an accuracy (ACC) of 0.741 using a 2 s time window. Extending the time window to 20 s improved both metrics by approximately 0.012. Feature selection identified 12 key parameters that maintained comparable accuracy, confirming robustness with reduced complexity. These findings demonstrate the feasibility of using non-EEG-based physiological data for real-time detection of inadequate sedation. The developed model is interpretable, resource-efficient, scalable, and shows strong potential for integration into portable monitoring systems in prehospital, emergency, and low-resource surgical settings.

To explore the value of fluorescence in situ hybridization (FISH) technique in diagnosis of variant Ph chromosome translocation (VT) and Ph chromosome-negative chronic myelocytic leukemia (CML). Nine CML patients with VT and 2 Ph chromosome-negative CML patients confirmed by R banding were assayed with dual color-dual fusion BCR/ABL probe by FISH. The 9 patients with VT involved chromosomes 1, 3, 5, 12, 13, 15, 17 and 21 besides chromosomes 9 and 22, and some of them showed recurrent aberrations; FISH results were positive and the signal feature was 2R2G1Y. The 2 Ph-negative CML patients had normal karyotypes; FISH was positive and the signal feature was 1R1G2Y and 1R1G1Y respectively. FISH can provide better diagnosis for CML with VT and Ph-negative CML. Abnormal karyotype and marker gene changes can be assessed based on the signal feature of the positive cell. So FISH is a complementary method to banding technique in diagnosis of CML.