Objective To develop a PDA-based wireless electrocardiographic monitoring system meeting the requirements of doctors in mobile work.Methods ECG signals were received from hospital monitor center through mobile network,and then were analyzed with digital signal processing technology and electrocardiographic information processing technology.The results were sent to patients as soon as the data were diagnosed by doctors,thus realizing the real-time monitoring.Results PDA-based wireless electrocardiographic monitoring system applied many advanced technologies such as mobile communication technology,blue-tooth technology,embedded database technology,etc.so that doctors could examine patients' records and electrocardiogram at any place and in any time.Conclusion Clinical experimental results show that the system fulfils doctors' requirements and improves their work greatly.

Objective To propose a classification method for ultrasound images of pedicle screw canals based on radiomics analysis,and to evaluate the integrity of the screw canal.Methods With thoracolumbar spine specimens from 4 fresh cadavers,50 pedicle screw canals were pre-established and ultrasound images of the canals were acquired.A total of 2 000 images(1 000 intact and 1 000 damaged canal samples)were selected.The dataset was randomly divided in a 4∶1 ratio using 5-fold cross-validation to form training and testing sets(consisting of 1 600 and 400 samples,respectively).Firstly,the optimal radius of the region of interest was identified using the Otsu's thresholding method,followed by feature extraction using pyradiomics.Principal component analysis and the least absolute shrinkage and selection operator algorithm were employed for dimensionality reduction and feature selection,respectively.Subsequently,3 machine learning models(support vector machine[SVM],logistic regression,and random forest)and 3 deep learning models(visual geometry group[VGG],ResNet,and Transformer)were used to classify the ultrasound images.The performance of each model was evaluated using accuracy.Results With a region of interest radius of 230 pixels,the SVM model achieved the highest classification accuracy of 96.25%.The accuracy of the VGG model was only 51.29%,while the accuracies of the logistic regression,random forest,ResNet,and Transformer models were 85.50%,80.75%,80.17%,and 75.18%,respectively.Conclusion For ultrasound images of pedicle screw canals,the machine learning model performs better than the deep learning model as a whole,and the SVM model has the best classification performance,which can be used to assist physicians in diagnosis.

Owing to incurable castration-resistant prostate cancer (CRPC) ultimately developing after treating with androgen deprivation therapy (ADT), it is vital to devise new therapeutic strategies to treat CRPC. Treatments that target programmed cell death protein 1 (PD-1) and programmed death ligand-1 (PD-L1) have been approved for human cancers with clinical benefit. However, many patients, especially prostate cancer, fail to respond to anti-PD-1/PD-L1 treatment, so it is an urgent need to seek a support strategy for improving the traditional PD-1/PD-L1 targeting immunotherapy. In the present study, analyzing the data from our prostate cancer tissue microarray, we found that PD-L1 expression was positively correlated with the expression of heterogeneous nuclear ribonucleoprotein L (HnRNP L). Hence, we further investigated the potential role of HnRNP L on the PD-L1 expression, the sensitivity of cancer cells to T-cell killing and the synergistic effect with anti-PD-1 therapy in CRPC. Indeed, HnRNP L knockdown effectively decreased PD-L1 expression and recovered the sensitivity of cancer cells to T-cell killing in vitro and in vivo, on the contrary, HnRNP L overexpression led to the opposite effect in CRPC cells. In addition, consistent with the previous study, we revealed that ferroptosis played a critical role in T-cell-induced cancer cell death, and HnRNP L promoted the cancer immune escape partly through targeting YY1/PD-L1 axis and inhibiting ferroptosis in CRPC cells. Furthermore, HnRNP L knockdown enhanced antitumor immunity by recruiting infiltrating CD8⁺ T cells and synergized with anti-PD-1 therapy in CRPC tumors. This study provided biological evidence that HnRNP L knockdown might be a novel therapeutic agent in PD-L1/PD-1 blockade strategy that enhanced anti-tumor immune response in CRPC.