Epigenome-augmented eQTL-hotspots reveal genome-wide transcriptional programs in 36 human tissues.

Expression quantitative trait loci (eQTLs) are used to inform the mechanisms of transcriptional regulation in eukaryotic cells. However, the specificity of genome-wide eQTL identification is limited by stringent control for false discoveries. Here, we described a method based on the non-homogeneous Poisson process to identify 125 489 regions with highly frequent, multiple eQTL associations, or 'eQTL-hotspots', from the public database of 59 human tissues or cell types. We stratified the eQTL-hotspots into two classes with their distinct sequence and epigenomic characteristics. Based on these classifications, we developed a machine-learning model, E-SpotFinder, for augmented discovery of tissue- or cell-type-specific eQTL-hotspots. We applied this model to 36 tissues or cell types. Using augmented eQTL-hotspots, we recovered 655 402 eSNPs and reconstructed a comprehensive regulatory network of 2 725 380 cis-interactions among eQTL-hotspots. We further identified 52 012 modules representing transcriptional programs with unique functional backgrounds. In summary, our study provided a framework of epigenome-augmented eQTL analysis and thereby constructed comprehensive genome-wide networks of cis-regulations across diverse human tissues or cell types.

Approved immune checkpoint inhibitors in hepatocellular carcinoma: a large-scale meta-analysis and systematic review.

A meta-analysis was performed to assess the benefits and safety profile of approved immune checkpoint inhibitors in hepatocellular carcinoma patients. Eligible studies were searched from Cochrane, Embase, and PubMed databases based on a well-established strategy. Following the exclusion of ineligible studies, 12 studies were included in this meta-analysis. Compared with control group, immune checkpoint inhibitors were associated with improved ORR (OR 3.03, 95% CI 2.26-4.05, P < 0.00001), SD (OR 0.77, 95% CI 0.62-0.95, P = 0.02), OS (HR 0.75, 95% CI 0.68-0.83, P < 0.00001), and PFS (HR 0.74, 95% CI 0.63-0.87, P < 0.0003). However, no significant differences were observed in DCR (OR 1.33, 95% CI 0.97-1.81, P = 0.07), PD (OR 0.90, 95% CI 0.67-1.21, P = 0.48), and all caused any-grade adverse events (OR 1.22, 95% CI 0.62-2.39, P = 0. 57), all caused ≥ grade 3 adverse events (OR 1.10, 95% CI 0.97-1.25, P = 0.14), treatment-related any-grade adverse events (OR 1.13, 95% CI 0.55-2.32, P = 0.73), and treatment-related ≥ grade 3 events (OR 0.82, 95% CI 0.34-1.97, P = 0.65) between the two groups. After subgroup analysis conducted, patients in the immune checkpoint inhibitor group compared with targeted drug group showed significant improvements in OS (HR 0.74, 95% CI 0.66-0.84, P < 0.00001) and PFS (HR 0.75, 95% CI 0.61-0.91, P = 0.004). Immune checkpoint inhibitors have demonstrated peculiar benefits in the treatment of HCC with an acceptable safety profile. Compared to targeted drugs, immune checkpoint inhibitors still offer advantages in the treatment of hepatocellular carcinoma. However, there is still considerable room for further improvement.

An automatic diagnosis model of otitis media with high accuracy rate using transfer learning.

Introduction: Chronic Suppurative Otitis Media (CSOM) and Middle Ear Cholesteatoma are two common chronic otitis media diseases that often cause confusion among physicians due to their similar location and shape in clinical CT images of the internal auditory canal. In this study, we utilized the transfer learning method combined with CT scans of the internal auditory canal to achieve accurate lesion segmentation and automatic diagnosis for patients with CSOM and middle ear cholesteatoma. Methods: We collected 1019 CT scan images and utilized the nnUnet skeleton model along with coarse grained focal segmentation labeling to pre-train on the above CT images for focal segmentation. We then fine-tuned the pre-training model for the downstream three-classification diagnosis task. Results: Our proposed algorithm model achieved a classification accuracy of 92.33% for CSOM and middle ear cholesteatoma, which is approximately 5% higher than the benchmark model. Moreover, our upstream segmentation task training resulted in a mean Intersection of Union (mIoU) of 0.569. Discussion: Our results demonstrate that using coarse-grained contour boundary labeling can significantly enhance the accuracy of downstream classification tasks. The combination of deep learning and automatic diagnosis of CSOM and internal auditory canal CT images of middle ear cholesteatoma exhibits high sensitivity and specificity.

A review: Music-emotion recognition and analysis based on EEG signals.

Music plays an essential role in human life and can act as an expression to evoke human emotions. The diversity of music makes the listener's experience of music appear diverse. Different music can induce various emotions, and the same theme can also generate other feelings related to the listener's current psychological state. Music emotion recognition (MER) has recently attracted widespread attention in academics and industry. With the development of brain science, MER has been widely used in different fields, e.g., recommendation systems, automatic music composing, psychotherapy, and music visualization. Especially with the rapid development of artificial intelligence, deep learning-based music emotion recognition is gradually becoming mainstream. Besides, electroencephalography (EEG) enables external devices to sense neurophysiological signals in the brain without surgery. This non-invasive brain-computer signal has been used to explore emotions. This paper surveys EEG music emotional analysis, involving the analysis process focused on the music emotion analysis method, e.g., data processing, emotion model, and feature extraction. Then, challenging problems and development trends of EEG-based music emotion recognition is proposed. Finally, the whole paper is summarized.

A large scale meta analysis identifies common adverse events with checkpoint inhibitors vs chemotherapy in melanoma patients.

BACKGROUND: A meta-analysis was performed to assess the risk of common adverse events in melanoma patients treated with checkpoint inhibitors. METHODS: Eligible studies were downloaded from PubMed, Embase, and Cochrane databases based on an established strategy. Review manager version 5.3 was used to analyze data. RESULTS: After exclusion of ineligible studies, six studies were finally included in the meta-analysis, which comprised of 2136 patients in intervention group and 1773 patients in control group. There was a difference in low grade risk of pruritus (OR 5.63, 95% CI 2.92-10.85, P < 0.00001), diarrhea/colitis (OR 1.51, 95% CI 1.09-2.09, P = 0.01), but not fatigue (low grade, OR 0.96, 95% CI 0.72-1.29, P = 0.80; high grade, OR 0.72, 95% CI 0.23-2.24, P = 0.57) and some high grade risk between the intervention group and control group. Subgroups analysis revealed that low grade risk of pruritus (OR 8.17, 95% CI 4.29-15.55, P < 0.00001) and high grade risk of pruritus (OR 7.08, 95% CI 1.25-40.09, P = 0.03) were significantly different between patients treated with chemotherapy and those treated with checkpoint inhibitors. But fatigue and diarrhea/colitis were not different between the two groups. CONCLUSION: Checkpoint inhibitors are associated with a higher risk in some side effects than chemotherapy in melanoma patients. Therefore, strategies that reduce the risk of adverse events in patients taking checkpoint inhibitors should be developed.