C-reactive protein-to-albumin ratio as a biomarker in patients with sepsis: a novel LASSO-COX based prognostic nomogram.

To develop a C-reactive protein-to-albumin ratio (CAR)-based nomogram for predicting the risk of in-hospital death in sepsis patients. Sepsis patients were selected from the MIMIC-IV database. Independent predictors were determined by multiple Cox analysis and then integrated to predict survival. The performance of the model was evaluated using the concordance index (C-index), receiver operating characteristic curve (ROC) analysis, and calibration curve. The risk stratifications analysis and subgroup analysis of the model in overall survival (OS) were assessed by Kaplan-Meier (K-M) curves. A total of 6414 sepsis patients were included. C-index of the CAR-based model was 0.917 [standard error (SE): 0.112] for the training set and 0.935 (SE: 0.010) for the validation set. The ROC curve analysis showed that the area under the curve (AUC) of the nomogram was 0.881 in the training set and 0.801 in the validation set. And the calibration curve showed that the nomogram performs well in both the training and validation sets. K-M curves indicated that patients with high CAR had significantly higher in-hospital mortality than those with low CAR. The CAR-based model has considerably high accuracy for predicting the OS of sepsis patients.

Surgical lighting with contrast enhancement based on spectral reflectance comparison and entropy analysis.

Surgical light is important for helping the surgeon easily identify specific tissues during an operation. We propose a spectral reflectance comparison model to optimize the light-emitting diode light spectrum in the operating room. An entropy evaluation method, meant specifically for surgical situations, was developed to evaluate images of biological samples. White light was mixed to achieve an optimal spectrum, and images of different tissues under the light were captured and analyzed. Results showed that images obtained with light with an optimal spectrum had a higher contrast than those obtained with a commercial white light of different color temperatures. Optimized surgical light obtained using this simple and effective method could replace the traditional surgical illumination systems.

Computational method for correcting complex optical distortion based on FOV division.

We propose a computational method for correcting complex optical distortion in off-axis optical systems, such as the optical systems found in head-mounted and head-up displays. The proposed method divides the wide field of view (FOV) into subsections, thereby allowing the distortion to be calculated for each small FOV. Instead of applying the conventional distortion model, the distortion coefficients for each small FOV can be calculated using a simple linear polynomial. In addition, in contrast to the conventional distortion coefficients that refer to the deviation between the real and paraxial image, the distortion coefficients employed by this method directly characterize the relationship between the object and its image. Thus, using the polynomial in the reverse manner repeatedly for each small FOV with the corresponding distortion coefficients, a pixel lookup table is obtained, which can be used to accurately compensate for the distortion in the optical system. This method avoids complicated computations, and there are no requirements for intrinsic or extrinsic parameters. Our experiments verified the effectiveness of the method where the root-mean-square deviation of the projected distorted straight lines was corrected from 23 to 65 pixels to approximately 1 pixel.