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Objective: Early revascularization of the occluded coronary artery in patients with ST elevation myocardial infarction (STEMI) has been demonstrated to decrease mortality and morbidity. Currently, physicians rely on features of electrocardiograms (ECGs) to identify the most likely location of coronary arteries related to an infarct. We sought to predict these culprit arteries more accurately by using deep learning. Methods: A deep learning model with a convolutional neural network (CNN) that incorporated ECG signals was trained on 384 patients with STEMI who underwent primary percutaneous coronary intervention (PCI) at a medical center. The performances of various signal preprocessing methods (short-time Fourier transform [STFT] and continuous wavelet transform [CWT]) with different lengths of input ECG signals were compared. The sensitivity and specificity for predicting each infarct-related artery and the overall accuracy were evaluated. Results: ECG signal preprocessing with STFT achieved fair overall prediction accuracy (79.3%). The sensitivity and specificity for predicting the left anterior descending artery (LAD) as the culprit vessel were 85.7% and 88.4%, respectively. The sensitivity and specificity for predicting the left circumflex artery (LCX) were 37% and 99%, respectively, and the sensitivity and specificity for predicting the right coronary artery (RCA) were 88.4% and 82.4%, respectively. Using CWT (Morlet wavelet) for signal preprocessing resulted in better overall accuracy (83.7%) compared with STFT preprocessing. The sensitivity and specificity were 93.46% and 80.39% for LAD, 56% and 99.7% for LCX, and 85.9% and 92.9% for RCA, respectively. Conclusion: Our study demonstrated that deep learning with a CNN could facilitate the identification of the culprit coronary artery in patients with STEMI. Preprocessing ECG signals with CWT was demonstrated to be superior to doing so with STFT.

Objective: To present the detailed history of three cervical cancer patients with rectovaginal fistula, who had undergone radiotherapy. Cases report: A 74-year-old patient with end-stage renal disease undergoing hemodialysis had radiotherapy for her advanced cervical cancer. Colonoscopic biopsy showed radiation sigmoid colitis and ulcers. Laparotomy revealed colon perforation and rectovaginal fistula. The second case is a 54-year-old cervical cancer patient, who had received concurrent chemoradiation therapy and further systemic therapy with cisplatin, paclitaxel, and bevacizumab. She suffered from bloody stool and abdominal pain. Rectovaginal fistula was found during exploratory laparotomy. The third case is a 35-year-old cervical cancer patient, who had received concurrent chemoradiation therapy. Systemic therapy was then prescribed with platinum, paclitaxel, and bevacizumab for her lung metastasis, and a rectovaginal fistula was found later. All three patients did not survive later. Conclusions: Fatal rectovaginal fistula may occur in post-radiation advanced cervical cancer patients. Unnecessary colonoscopic biopsy may cause significant sequelae. In patients with high risk for rectovaginal fistulas, chemotherapy without adding bevacizumab might be suggested in patients with low risk of poor response to chemotherapy. In addition, three-dimensional conformal radiation therapy or intensity-modulated radiation therapy should be used for patients with high risk for fistulas.

Ventricular fibrillation (VF) is a type of cardiac arrhythmia. This chaotic cardiac electrical activity results in heart quivering instead of normal pumping. To date, early cardiopulmonary resuscitation (CPR) and defibrillation are the only effective VF treatment. Acute myocardial infarction is the most common cause of VF, and cardiomyopathy, myocarditis, electrolyte imbalance, cardiotoxic medication, and even ion channel abnormality can cause VF. Physicians have attempted to identify specific patterns in electrocardiography (ECG) that might predict VF in the short term. For example, ST segment changes might imply coronary artery occlusion with myocardial ischemia, increasing VF risk. However, in most cases, VF occurs abruptly without any early warning. Machine learning is used to extract information usually neglected by the human brain. In deep learning, a cascade of multiple layers of processing is used to extract features. Machine learning is used to classify different types and outcomes of cardiac arrhythmias that are difficult to recognize directly. In this study, we developed a new deep learning method to predict the onset of VF. ECG from MIT-BIH databases were used as the training and validation data sets; the prediction results showed that the proposed two-dimensional short-time Fourier transform (2D STFT)/continuous wavelet transform (CWT) convolutional neural network (CNN) model can reach a recall of 99% and an accuracy of 97%. We also compared the proposed 2D model with 1D and 2D time-domain CNN models. The results showed that the 1D CNN and 2D time-domain models can achieve an accuracy of 60.5% and 56%, respectively.

Broadcasting schemes, such as the fast broadcasting and harmonic broadcasting schemes, significantly reduce the bandwidth requirement of video-on-demand services. In the real world, some history events are very hot. For example, every year in March, thousands of people connect to Internet to watch the live show of Oscar Night. Such actions easily cause the networks contested. However, the schemes mentioned previously cannot alleviate the problem because they do not support live broadcasting. In this paper, we analyze the requirements for transferring live videos. Based on the requirements, a time skewing approach is proposed to enable the broadcasting schemes to support live broadcasting. However, the improved schemes require extra bandwidth for live broadcasting once the length of live shows exceeds the default. Accordingly, we proposed a scalable binomial broadcasting scheme to transfer live videos using constant bandwidth by increasing clients’ waiting time. When the scheme finds that the length of a video exceeds the default, it doubles the length of to-be-played segments and then its required bandwidth is constant.

Broadcasting Protocols can efficiently transmit videos that simultaneously shared by clients with partitioning the videos into segments. Many studies focus on decreasing clients' waiting time, such as the fixed-delay pagoda broadcasting (FDPB) and the harmonic broadcasting schemes. However, limited-capability client devices such as PDAs and set-top boxes (STBs) suffer from storing a significant fraction of each video while it is being watched. How to reduce clients' buffer demands is thus an important issue. Related works include the staircase broadcasting (SB), the reverse fast broadcasting (RFB), and the hybrid broadcasting (HyB) schemes. This work improves FDPB to save client buffering space as well as waiting time. In comparison with SB, RFB, and HyB, the improved FDPB scheme can yield the smallest waiting time under the same buffer requirements.