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Pathogenic large inversions are rarely reported on DMD gene due to the lack of effective detection methods. Here we report two DMD pedigrees and proposed a reliable pipeline to define large inversions in DMD patients. In the first pedigree, conventional approaches including multiplex ligation-dependent probe amplification, and whole-exome sequencing by next generation sequencing were failed to detect any pathologic variant. Then an advanced analysis pipeline which consists of RNA-seq, cDNA array capture sequencing, optical mapping, long-read sequencing was built. RNA-seq and cDNA capture sequencing showed a complete absence of transcripts of exons 3-55. Optical mapping identified a 55 Mb pericentric inversion between Xp21 and Xq21. Subsequently, long-read sequencing and Sanger sequencing determined the inversion breakpoints at 32,915,769 and 87,989,324 of the X chromosomes. In the second pedigree, long-read sequencing was directly conducted and Sanger sequencing was performed to verify the mutation. Long-read sequencing and Sanger sequencing found breakpoints at 32,581,576 and 127,797,236 on DMD gene directly. In conclusion, large inversion might be a rare but important mutation type in DMD gene. An effective pipeline was built in detecting large inversion mutations based on long-read sequencing platforms.

Background Differentiating between Crohn’s disease (CD) and intestinal tuberculosis (ITB) with endoscopy is challenging. We aim to perform more accurate endoscopic diagnosis between CD and ITB by building a trustworthy AI differential diagnosis application. Methods A total of 1271 electronic health record (EHR) patients who had undergone colonoscopies at Peking Union Medical College Hospital (PUMCH) and were clinically diagnosed with CD (n = 875) or ITB (n = 396) were used in this study. We build a workflow to make diagnoses with EHRs and mine differential diagnosis features; this involves finetuning the pretrained language models, distilling them into a light and efficient TextCNN model, interpreting the neural network and selecting differential attribution features, and then adopting manual feature checking and carrying out debias training. Results The accuracy of debiased TextCNN on differential diagnosis between CD and ITB is 0.83 (CR F1: 0.87, ITB F1: 0.77), which is the best among the baselines. On the noisy validation set, its accuracy was 0.70 (CR F1: 0.87, ITB: 0.69), which was significantly higher than that of models without debias. We also find that the debiased model more easily mines the diagnostically significant features. The debiased TextCNN unearthed 39 diagnostic features in the form of phrases, 17 of which were key diagnostic features recognized by the guidelines. Conclusion We build a trustworthy AI differential diagnosis application for differentiating between CD and ITB focusing on accuracy, interpretability and robustness. The classifiers perform well, and the features which had statistical significance were in agreement with clinical guidelines.

Glioblastoma (GBM) is regarded as an incurable disease due to its poor prognosis and limited treatment options. Virotherapies were once utilized on cancers for their oncolytic effects. And they are being revived on GBM treatment, as accumulating evidence presents the immunogenic effects of virotherapies in remodeling immunosuppressive GBM microenvironment. In this review, we focus on the immune responses induced by oncolytic virotherapies and viral vectors in GBM. The current developments of GBM virotherapies are briefly summarized, followed by a detailed depiction of their immune response. Limitations and lessons inferred from earlier experiments and trials are discussed. Moreover, we highlight the importance of engaging the immune responses induced by virotherapies into the multidisciplinary management of GBM.

BACKGROUND Differentiating between Crohn’s disease (CD) and intestinal tuberculosis (ITB) has long been an important and challenging problem in clinical practice. Endoscopy is an essential examination for a timely and accurate diagnosis but the results can be confusing and rely heavily on the experience of the clinician. OBJECTIVE We aim to perform more accurate endoscopic diagnosis between CD and ITB by building a trustworthy AI differential diagnosis application with high interpretability. METHODS A total of 1271 electronic health record (EHR) patients who had undergone colonoscopies at Peking Union Medical College Hospital (PUMCH) and were clinically diagnosed with CD (n=875) or ITB (n=396) were used in this study. We build a workflow to make diagnoses with EHRs and mine differential diagnosis features; this involves finetuning the pretrained language models, distilling them into a light and efficient TextCNN model, interpreting the neural network and selecting differential attribution features, and then adopting manual feature checking and carrying out debias training. RESULTS The accuracy of debiased TextCNN on differential diagnosis between CD and ITB is 0.83 (CR F1: 0.87, ITB F1: 0.77), which is the best among the baselines. On the noisy validation set, its accuracy was 0.70 (CR F1: 0.87, ITB: 0.69), which was significantly higher than that of models without debias. We also interpret all of the models and find that the debiased model more easily mines the diagnostically significant features. The debiased TextCNN unearthed 39 diagnostic features in the form of phrases, 17 of which were key diagnostic features recognized by the guidelines. CONCLUSIONS We build a trustworthy AI differential diagnosis application for differentiating between CD and ITB focusing on accuracy, interpretability and robustness. The classifiers perform well, and the features that were found to have statistical significance are in agreement with clinical guidelines.

Diabetes mellitus (DM) is a common chronic disease worldwide. Ambient air pollution has long been proven to be associated with type 2 diabetes mellitus (T2DM) progression, but the underlying mechanism is not clear yet. In addition, previous studies mainly focused on the prevention of healthy people against the incidence of T2DM. We designed a panel study including two follow-ups and enrolled 39 patients with T2DM living in Beijing. Linear mixed model was fitted to assess the association between two pairs of variables (ambient air pollution exposure and C3 levels, ambient air pollution exposures and T2DM index). Mediation analysis of C3 between ambient air pollution exposure and indicators of T2DM progression was conducted. We found that PM2.5 exposures is are negatively associated with serum complement C3. Given that C3 might act as a protector of pancreas β cell, PM2.5 exposures could accelerate disease in T2DM populations. No mediation effects were found. This study reveals that exposures to PM2.5 can cause progression of diseases among T2DM populations.

Purpose：We focus on adverse events in colorectal cancer patients with advanced age, high Eastern Cooperative Oncology Group scores and comorbidities. Methods：408 Chinese colorectal cancer patients undergoing adjuvant chemotherapy at Peking Union Medical College Hospital were included. The cumulative incidences of different adverse events and mediation analyses were analyzed with respect to age, Eastern Cooperative Oncology Group status and comorbidities. Results：Young patients and patients with Eastern Cooperative Oncology Group score 1 had a significantly higher incidence of digestive adverse events related to adjuvant therapy. There were no significant mediation effects of adverse events on survival. Conclusion：Advanced age did not directly lead to poor survival outcomes and adverse events. Although higher Eastern Cooperative Oncology Group scores and comorbidity led to impaired survival and higher incidence of adverse events, the latter had no mediation effects on survival outcomes.

Background Differentiating between ulcerative colitis (UC), Crohn’s disease (CD) and intestinal tuberculosis (ITB) using endoscopy is challenging. We aimed to realize automatic differential diagnosis among these diseases through machine learning algorithms. Methods A total of 6399 consecutive patients (5128 UC, 875 CD and 396 ITB) who had undergone colonoscopy examinations in the Peking Union Medical College Hospital from January 2008 to November 2018 were enrolled. The input was the description of the endoscopic image in the form of free text. Word segmentation and key word filtering were conducted as data preprocessing. Random forest (RF) and convolutional neural network (CNN) approaches were applied to different disease entities. Three two-class classifiers (UC and CD, UC and ITB, and CD and ITB) and a three-class classifier (UC, CD and ITB) were built. Results The classifiers built in this research performed well, and the CNN had better performance in general. The RF sensitivities/specificities of UC-CD, UC-ITB, and CD-ITB were 0.89/0.84, 0.83/0.82, and 0.72/0.77, respectively, while the values for the CNN of CD-ITB were 0.90/0.77. The precisions/recalls of UC-CD-ITB when employing RF were 0.97/0.97, 0.65/0.53, and 0.68/0.76, respectively, and when employing the CNN were 0.99/0.97, 0.87/0.83, and 0.52/0.81, respectively. Conclusions Classifiers built by RF and CNN approaches had excellent performance when classifying UC with CD or ITB. For the differentiation of CD and ITB, high specificity and sensitivity were achieved as well. Artificial intelligence through machine learning is very promising in helping unexperienced endoscopists differentiate inflammatory intestinal diseases. Conference The abstract of this article has won the first prize of the Young Investigator Award during the Asian Pacific Digestive Week (APDW) 2019 held in Kolkata, India.

Evidence of combined mortality effects of multi-pollutant on cardiovascular diseases (CVD) and the corresponding exposure-response (ER) relationship is limited. In this paper, we examined the association between four ambient air pollutants (i.e., fine particulate matter, PM2.5, particulate matter, PM10, nitrogen dioxide, NO2, and sulfur dioxide, SO2.) and CVD mortality and the corresponding ER relationship after incorporating the potential interaction among the multiple pollutants. Bayesian kernel machine regression (BKMR) was used to evaluate the ER relationship and to explore the interactions between pollutants. The results showed that PM10 and SO2 were dominant pollutants from 0 to 2 days, while PM2.5 and NO2 had strong effect on CVD mortality from 3 to 4 days. Generally, PM2.5 and NO2 had the similar ER relationship across different moving average concerning the CVD mortality. For the interaction among the multiple pollutants, we found that there is no interaction between particle pollutants (i.e. PM2.5 and PM10) and gaseous pollutants (i.e.NO2 and SO2). On the contrary, there might be an interaction between PM2.5 and PM10 though this interaction was detected by visually comparing the slopes of ER curves of a given particle pollutant at different levels of the other particle pollutant. But there is a lack of statistical significance test for this interaction. This study suggests that different ambient air pollutants might have the dominant effect on CVD deaths during different moving average, though there might not be statistical significant interactions among the ambient air pollutants in present study.

Additional file 1: Supplementary Table 1. Parameters applied in the RF model. Supplementary Table 2. Parameters applied in the CNN model. Supplementary Figure. 1. The convergence of loss and accuracy of CNN.

Diabetes mellitus (DM) is a common chronic disease worldwide. Ambient air pollution has long been proven to be associated with type 2 diabetes mellitus (T2DM) progression, but the underlying mechanism is not clear yet. In addition, previous studies mainly focused on the prevention of healthy people against the incidence of T2DM. We designed a panel study including two follow-ups and enrolled 39 patients with T2DM living in Beijing. Linear mixed model was fitted to assess the association between two pairs of variables (ambient air pollution exposure and C3 levels, ambient air pollution exposures and T2DM index). Mediation analysis of C3 between ambient air pollution exposure and indicators of T2DM progression was conducted. We found that PM2.5 exposures is are negatively associated with serum complement C3. Given that C3 might act as a protector of pancreas β cell, PM2.5 exposures could accelerate disease in T2DM populations. No mediation effects were found. This study reveals that exposures to PM2.5 can cause progression of diseases among T2DM populations.