mlDEEPre: Multi-Functional Enzyme Function Prediction With Hierarchical Multi-Label Deep Learning

As a great challenge in bioinformatics, enzyme function prediction is a significant step toward designing novel enzymes and diagnosing enzyme-related diseases. Existing studies mainly focus on the mono-functional enzyme function prediction. However, the number of multi-functional enzymes is growing rapidly, which requires novel computational methods to be developed. In this paper, following our previous work, DEEPre, which uses deep learning to annotate mono-functional enzyme's function, we propose a novel method, mlDEEPre, which is designed specifically for predicting the functionalities of multi-functional enzymes. By adopting a novel loss function, associated with the relationship between different labels, and a self-adapted label assigning threshold, mlDEEPre can accurately and efficiently perform multi-functional enzyme prediction. Extensive experiments also show that mlDEEPre can outperform the other methods in predicting whether an enzyme is a mono-functional or a multi-functional enzyme (mono-functional vs. multi-functional), as well as the main class prediction across different criteria. Furthermore, due to the flexibility of mlDEEPre and DEEPre, mlDEEPre can be incorporated into DEEPre seamlessly, which enables the updated DEEPre to handle both mono-functional and multi-functional predictions without human intervention

Biaxial strain modulated electronic structures of layered two-dimensional MoSiGeN4 Rashba systems

The two-dimensional (2D) MA2Z4 family has received extensive attention in manipulating its electronic structure and achieving intriguing physical properties. However, engineering the electronic properties remains a challenge. Herein, based on first-principles calculations, we systematically investigate the effect of biaxial strains on the electronic structures of 2D Rashba MoSiGeN4 (MSGN), and further explore how the interlayer interactions affect the Rashba spin splitting in such strained layered MSGNs. After applying biaxial strains, the band gap decreases monotonically with increasing tensile strains but increases when the compressive strains are applied. An indirect-direct-indirect band gap transition is induced by applying a moderate compressive strain (< 5%) in the MSGNs. Due to the symmetry breaking and moderate spin-orbit coupling (SOC), the monolayer MSGN possess an isolated Rashba spin splitting (R) near the Fermi level, which could be effectively regulated to the Lifshitz transition (L) by biaxial strain. For instance, a L-R-L transformation of Fermi surface is presented in monolayer and a more complex and changeable L-R-L-R evolution is observed in bilayer and trilayer MSGNs as the biaxial strain vary from -8% to 12%, which actually depend on the appearance, variation, and vanish of the Mexican hat band in the absence of SOC under different strains. The contribution of Mo-dz2 orbital hybridized with N-pz orbital in the highest valence band plays a dominant role on the band evolution under biaxial strains, where the R-L evolution corresponds to the decreased Mo-dz2 orbital contribution. Our study highlights the biaxial strain controllable Rashba spin splitting, in particular the introduction and even the evolution of Lifshitz transition near Fermi surface, which makes the strained MSGNs as promising candidates for future applications in spintronic devices.Comment: 21 pages, 7 figures, supplementary informatio

Blocking interaction between SHP2 and PD‐1 denotes a novel opportunity for developing PD‐1 inhibitors

Small molecular PD‐1 inhibitors are lacking in current immuno‐oncology clinic. PD‐1/PD‐L1 antibody inhibitors currently approved for clinical usage block interaction between PD‐L1 and PD‐1 to enhance cytotoxicity of CD8+ cytotoxic T lymphocyte (CTL). Whether other steps along the PD‐1 signaling pathway can be targeted remains to be determined. Here, we report that methylene blue (MB), an FDA‐approved chemical for treating methemoglobinemia, potently inhibits PD‐1 signaling. MB enhances the cytotoxicity, activation, cell proliferation, and cytokine‐secreting activity of CTL inhibited by PD‐1. Mechanistically, MB blocks interaction between Y248‐phosphorylated immunoreceptor tyrosine‐based switch motif (ITSM) of human PD‐1 and SHP2. MB enables activated CTL to shrink PD‐L1 expressing tumor allografts and autochthonous lung cancers in a transgenic mouse model. MB also effectively counteracts the PD‐1 signaling on human T cells isolated from peripheral blood of healthy donors. Thus, we identify an FDA‐approved chemical capable of potently inhibiting the function of PD‐1. Equally important, our work sheds light on a novel strategy to develop inhibitors targeting PD‐1 signaling axis

Primary hepatic malignant triton tumor mimicking hepatocellular carcinoma by demonstrating arterial-phase hypervascularity and subsequent washout on dynamic contrast-enhanced imaging: a case report and literature review

BackgroundMalignant Triton tumor (MTT) is a relatively rare subtype of malignant peripheral nerve sheath tumor (MPNST) characterized by rhabdomyosarcoma differentiation. There are no distinct features of MTT, and it is easy to misdiagnose preoperatively.Case presentationHere, we describe a rare case of primary hepatic MTT in a 56-year-old male who presented with nonspecific abdominal pain for 1 day. Magnetic resonance imaging and abdominal computed tomography revealed an extremely large mass located in the right liver with intratumoral hemorrhage, arterial-phase hypervascularity and subsequent washout on dynamic contrast-enhanced imaging and the possibility of intrahepatic metastasis. Tumor marker levels revealed only an elevated level of alpha-fetoprotein (AFP: 5304.0 ng/mL). Then, he received transcatheter arterial chemoembolization combined with lenvatinib and pembrolizumab, and he was diagnosed with hepatocellular carcinoma. After 3 months of neoadjuvant therapy, we resected the hepatic cancer and adherent diaphragmatic pleura. MTT was confirmed by postoperative pathology and immunohistochemistry.ConclusionDespite the preoperative diagnosis of hepatocellular carcinoma with a rising serum AFP level, typical CT and MRI findings, histopathology assessment showing MPNST with rhabdomyosarcoma differentiation confirms the diagnosis of primary hepatic MTT

Transcriptome Profile Analysis Reveals an Estrogen Induced LncRNA Associated with Lipid Metabolism and Carcass Traits in Chickens (Gallus Gallus)

Background/Aims: Accumulating evidences have demonstrated that long noncoding RNAs (lncRNA) play important roles in hepatic lipid metabolism in mammals. However, no systematic screening of the potential lncRNAs in the livers of laying hens has been performed, and few studies have been reported concerning the effects of the lncRNAs on lipid metabolism in the livers of chickens during egg-laying period. The purpose of this study was to compare the difference in lncRNA expression in the livers of pre-laying and peak-laying hens at the age of 20 and 30 weeks old by transcriptome sequencing and to investigate the interaction networks among lncRNAs, mRNAs and miRNAs. Moreover, the regulatory mechanism and biological function of lncLTR, a significantly differentially expressed lncRNA in the liver between pre- and peak-laying hens, was explored in vitro and in vivo. Methods: Bioinformatics analyses were conducted to identify the differentially expressed (DE) lncRNAs between the two groups of hens. The target genes of the DE lncRNA were predicated for further functional enrichment. An integrated analysis was performed among the DE lncRNA datasets, DE mRNAs and DE miRNA datasets obtained from the same samples to predict the interaction relationship. In addition, in vivo and in vitro trials were carried out to determine the expression regulation of lncLTR, and polymorphism association analysis was conducted to detect the biological role of ncLTR. Results: A total of 124 DE lncRNAs with a P-value ≤ 0.05 were identified. Among them, 44 lncRNAs including 30 known and 14 novel lncRNAs were significant differentially expressed (SDE) with FDR ≤ 0.05. Thirty-two lncRNAs were upregulated and 12 were downregulated in peak-laying group compared with pre-laying group. The functional enrichment results revealed that target genes of some lncRNAs are involved in the lipid metabolism process. Integrated analysis suggested that some of the genes involved in lipid metabolism might be regulated by both the lncRNA and the miRNA. In addition, an upregulated lncRNA, designated lncLTR, was demonstrated to be induced by estrogen via ERβ signaling. The c242. G&#x3e;A SNP in lncLTR was significantly associated with chicken carcass weight, evisceration weight, semi-evisceration weight, head weight, double-wing weight, claw weight traits, and blood biochemical index, especially for the blood triglyceride content. Conclusion: A series of lncRNAs associated with lipid metabolism in the livers of chickens were identified by transcriptome sequencing and functional analysis, providing a valuable data resource for further studies on chicken hepatic metabolism activities. LncLTR was regulated by estrogen via ERβ signaling and associated with chicken carcass trait and blood triglyceride content

Quantitative EEG parameters can improve the predictive value of the non-traumatic neurological ICU patient prognosis through the machine learning method

BackgroundBetter outcome prediction could assist in reliable classification of the illnesses in neurological intensive care unit (ICU) severity to support clinical decision-making. We developed a multifactorial model including quantitative electroencephalography (QEEG) parameters for outcome prediction of patients in neurological ICU.MethodsWe retrospectively analyzed neurological ICU patients from November 2018 to November 2021. We used 3-month mortality as the outcome. Prediction models were created using a linear discriminant analysis (LDA) based on QEEG parameters, APACHEII score, and clinically relevant features. Additionally, we compared our best models with APACHEII score and Glasgow Coma Scale (GCS). The DeLong test was carried out to compare the ROC curves in different models.ResultsA total of 110 patients were included and divided into a training set (n=80) and a validation set (n = 30). The best performing model had an AUC of 0.85 in the training set and an AUC of 0.82 in the validation set, which were better than that of GCS (training set 0.64, validation set 0.61). Models in which we selected only the 4 best QEEG parameters had an AUC of 0.77 in the training set and an AUC of 0.71 in the validation set, which were similar to that of APACHEII (training set 0.75, validation set 0.73). The models also identified the relative importance of each feature.ConclusionMultifactorial machine learning models using QEEG parameters, clinical data, and APACHEII score have a better potential to predict 3-month mortality in non-traumatic patients in neurological ICU

DeteX: A highly accurate software for detecting SNV and InDel in single and paired NGS data in cancer research

Background: Genetic testing is becoming more and more accepted in the auxiliary diagnosis and treatment of tumors. Due to the different performance of the existing bioinformatics software and the different analysis results, the needs of clinical diagnosis and treatment cannot be met. To this end, we combined Bayesian classification model (BC) and fisher exact test (FET), and develop an efficient software DeteX to detect SNV and InDel mutations. It can detect the somatic mutations in tumor-normal paired samples as well as mutations in a single sample.Methods: Combination of Bayesian classification model (BC) and fisher exact test (FET).Results: We detected SNVs and InDels in 11 TCGA glioma samples, 28 clinically targeted capture samples and 2 NCCL-EQA standard samples with DeteX, VarDict, Mutect, VarScan and GatkSNV. The results show that, among the three groups of samples, DeteX has higher sensitivity and precision whether it detects SNVs or InDels than other callers and the F1 value of DeteX is the highest. Especially in the detection of substitution and complex mutations, only DeteX can accurately detect these two kinds of mutations. In terms of single-sample mutation detection, DeteX is much more sensitive than the HaplotypeCaller program in Gatk. In addition, although DeteX has higher mutation detection capabilities, its running time is only .609 of VarDict, which is .704 and .343 longer than VarScan and MuTect, respectively.Conclusion: In this study, we developed DeteX to detect SNV and InDel mutations in single and paired samples. DeteX has high sensitivity and precision especially in the detection of substitution and complex mutations. In summary, DeteX from NGS data is a good SNV and InDel caller

A decade of complex fractionated electrograms catheter-based ablation for atrial fibrillation: Literature analysis, meta-analysis and systematic review

AbstractBackgroundIt has been a decade since the complex fractionated atrial electrograms (CFAEs) were first established following the publication of Nademanee's standards. However, the status and focus of CFAE research are unclear, as is the efficacy of additional CFAE ablation in atrial fibrillation (AF). This literature review and meta-analysis were designed to determine the status of CFAE research and the efficacy and complications of CFAE ablation alone, pulmonary vein isolation (PVI) alone and PVI plus CFAE ablation in AF.MethodsWith the assistance from reference librarians and investigators trained in systematic review, we conducted a literature search of MEDLINE (via PubMed), Embase, the Cochrane Library, ScienceDirect, Wiley Blackwell and Web of Knowledge, using “complex fractionated atrial electrograms” for MeSH and keyword search.ResultsThe literature on CFAEs increased from 2007, mainly focusing on mapping studies, with mechanism studies increasing significantly from 2012. Fifteen trials with 1525 patients were qualified for our meta-analysis. Success rates were as follows. Overall (P < 0.001): CFAE ablation alone, 23.5–26.2%; PVI, 64.7%; PVI plus CFAE ablation, 67.0%. Single ablation: PVI, 60.4%; PVI plus CFAEs, 68.8% (OR 1.53, 95% CI 1.07–2.20, P = 0.02). Re-ablation: PVI, 69.0%; PVI plus CFAEs, 77.2% (OR 1.54, 95% CI 1.06–2.24, P = 0.02). Paroxysmal AF: PVI, 76.7%; PVI plus CFAEs, 79.1% (OR 1.20, 95% CI 0.79–1.81, P = 0.39). Persistent or permanent AF: PVI, 47.9%; PVI plus CFAEs, 58.7% (OR = 1.59, 95% CI 1.13–2.24, P = 0.008). Complication rates: PVI, 2.6%; PVI plus CFAEs, 3.4% (OR 1.22, 95% CI 0.58–2.57, P = 0.61).ConclusionsIn the literature, CFAE mapping studies preceded mechanism studies. CFAE ablation alone is insufficient for the treatment of AF. Additional CFAE ablation after adequate PVI or PVI plus linear ablation improves the outcome of single ablation and re-ablation without increasing complications, especially in persistent or permanent AF. There are insufficient data to support a similar improvement in paroxysmal AF or inducible AF after PVI for paroxysmal AF