Magnetic resonance imaging features for differentiating tuberculous from pyogenic spondylitis: a meta-analysis.

OBJECTIVE: To perform a meta-analysis comparing the MRI features of tuberculous and pyogenic spondylitis, using histopathological results and/or blood culture as the standard reference. MATERIALS AND METHODS: PubMed, Embase, Web of Science, and Cochrane Library were searched for English-language studies on the MRI features of tuberculous and pyogenic spondylitis published between January 2010 and February 2023. Risk for bias and concerns regarding applicability were assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Pooled MRI features' proportions were calculated using a bivariate random-effects model. RESULTS: Thirty-two studies met the inclusion criteria: 21 for tuberculous spondylitis, three for pyogenic spondylitis, and eight for both. Of the nine informative MRI features comparing tuberculous spondylitis to pyogenic spondylitis, involvement of ≥ 2 vertebral bodies (92% vs. 88%, P = .004), epidural extension (77% vs. 25%, P < .001), paravertebral collection (91% vs. 84%, P < .001), subligamentous spread (93% vs. 24%, P < .001), thin and regular abscess wall (94% vs. 18%, P < .001), vertebral collapse (68% vs. 24%, P < .001), and kyphosis (39% vs. 3%, P < .01) were more suggestive of tuberculous spondylitis, while disc signal change (82% vs. 95%, P < .001) and disc height loss (22% vs. 59%, P < .001) were more suggestive of pyogenic spondylitis. CONCLUSION: Involvement of ≥ 2 vertebral vertebral bodies, soft tissue attribution, thin and regular abscess wall, vertebral collapse, and kyphosis were MRI features more common in tuberculous spondylitis, while disc signal change and height loss were more common in pyogenic spondylitis.

Construction of a Self-Assembled Polyelectrolyte/Graphene Oxide Multilayer Film and Its Interaction with Metal Ions.

In this study, a composite multilayer film onto gold was constructed from two charged building blocks, i.e., negatively charged graphene oxide (GO) and a branched polycation (polyethylenimine, PEI) via layer-by-layer (LbL) self-assembly technology, and this process was monitored in situ with quartz crystal microbalance (QCM) under different experimental conditions. This included the differences in frequency (Δf) as well as the changes in dissipation to yield information on the absorbed mass and viscoelastic properties of the formed PEI/GO multilayer films. The experimental conditions were optimized to obtain a high amount of the adsorbed mass of the self-assembled multilayer film. The surface morphology of the PEI/GO multilayer film onto gold was studied with atomic force microscopy (AFM). It was found that the positively charged PEI chains were combined with the oppositely charged GO to form an assembled film on the QCM sensor surface, in a wrapped and curled fashion. Raman and UV-vis spectra also showed that the intensities of the GO-characteristic signals are almost linearly related to the layer number. To explore the films for their use in divalent ion detection, the frequency response of the PEI/GO multilayer-modified QCM sensor to the exposure of aqueous solutions solution of Cu2+, Ca2+, Zn2+, and Sn2+ was further studied using QCM. Based on the Sauerbrey equation and the weight of different ions, the number of metal ions adsorbed per unit area on the surface of QCM sensors was calculated. For metal ion concentrations of 40 ppm, the adsorption capacities per unit area of Cu2+, Zn2+, Sn2+, and Ca2+ were found to be 1.7, 3.2, 0.7, and 4.9 nmol/cm2, respectively. Thus, in terms of the number of adsorbed ions per unit area, the QCM sensor modified by PEI/GO multilayer film shows the largest adsorption capacity of Ca2+. This can be rationalized by the relative hydration energies.

iRNAD: a computational tool for identifying D modification sites in RNA sequence.

MOTIVATION: Dihydrouridine (D) is a common RNA post-transcriptional modification found in eukaryotes, bacteria and a few archaea. The modification can promote the conformational flexibility of individual nucleotide bases. And its levels are increased in cancerous tissues. Therefore, it is necessary to detect D in RNA for further understanding its functional roles. Since wet-experimental techniques for the aim are time-consuming and laborious, it is urgent to develop computational models to identify D modification sites in RNA. RESULTS: We constructed a predictor, called iRNAD, for identifying D modification sites in RNA sequence. In this predictor, the RNA samples derived from five species were encoded by nucleotide chemical property and nucleotide density. Support vector machine was utilized to perform the classification. The final model could produce the overall accuracy of 96.18% with the area under the receiver operating characteristic curve of 0.9839 in jackknife cross-validation test. Furthermore, we performed a series of validations from several aspects and demonstrated the robustness and reliability of the proposed model. AVAILABILITY AND IMPLEMENTATION: A user-friendly web-server called iRNAD can be freely accessible at http://lin-group.cn/server/iRNAD, which will provide convenience and guide to users for further studying D modification.

iPSW(2L)-PseKNC: A two-layer predictor for identifying promoters and their strength by hybrid features via pseudo K-tuple nucleotide composition.

The promoter is a regulatory DNA region about 81-1000 base pairs long, usually located near the transcription start site (TSS) along upstream of a given gene. By combining a certain protein called transcription factor, the promoter provides the starting point for regulated gene transcription, and hence plays a vitally important role in gene transcriptional regulation. With explosive growth of DNA sequences in the post-genomic age, it has become an urgent challenge to develop computational method for effectively identifying promoters because the information thus obtained is very useful for both basic research and drug development. Although some prediction methods were developed in this regard, most of them were limited at merely identifying whether a query DNA sequence being of a promoter or not. However, based on their strength-distinct levels for transcriptional activation and expression, promoter should be divided into two categories: strong and weak types. Here a new two-layer predictor, called "iPSW(2L)-PseKNC", was developed by fusing the physicochemical properties of nucleotides and their nucleotide density into PseKNC (pseudo K-tuple nucleotide composition). Its 1st-layer serves to predict whether a query DNA sequence sample is of promoter or not, while its 2nd-layer is able to predict the strength of promoters. It has been observed through rigorous cross-validations that the 1st-layer sub-predictor is remarkably superior to the existing state-of-the-art predictors in identifying the promoters and non-promoters, and that the 2nd-layer sub-predictor can do what is beyond the reach of the existing predictors. Moreover, the web-server for iPSW(2L)-PseKNC has been established at http://www.jci-bioinfo.cn/iPSW(2L)-PseKNC, by which the majority of experimental scientists can easily get the results they need.

iKcr-PseEns: Identify lysine crotonylation sites in histone proteins with pseudo components and ensemble classifier.

Lysine crotonylation (Kcr) is an evolution-conserved histone posttranslational modification (PTM), occurring in both human somatic and mouse male germ cell genomes. It is important for male germ cell differentiation. Information of Kcr sites in proteins is very useful for both basic research and drug development. But it is time-consuming and expensive to determine them by experiments alone. Here, we report a novel predictor called iKcr-PseEns that is established by incorporating five tiers of amino acid pairwise couplings into the general pseudo amino acid composition. It has been observed via rigorous cross-validations that the new predictor's sensitivity (Sn), specificity (Sp), accuracy (Acc), and stability (MCC) are 90.53%, 95.27%, 94.49%, and 0.826, respectively. For the convenience of most experimental scientists, a user-friendly web-server for iKcr-PseEns has been established at http://www.jci-bioinfo.cn/iKcr-PseEns, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved.

iPTM-mLys: identifying multiple lysine PTM sites and their different types.

MOTIVATION: Post-translational modification, abbreviated as PTM, refers to the change of the amino acid side chains of a protein after its biosynthesis. Owing to its significance for in-depth understanding various biological processes and developing effective drugs, prediction of PTM sites in proteins have currently become a hot topic in bioinformatics. Although many computational methods were established to identify various single-label PTM types and their occurrence sites in proteins, no method has ever been developed for multi-label PTM types. As one of the most frequently observed PTMs, the K-PTM, namely, the modification occurring at lysine (K), can be usually accommodated with many different types, such as 'acetylation', 'crotonylation', 'methylation' and 'succinylation'. Now we are facing an interesting challenge: given an uncharacterized protein sequence containing many K residues, which ones can accommodate two or more types of PTM, which ones only one, and which ones none? RESULTS: To address this problem, a multi-label predictor called IPTM-MLYS: has been developed. It represents the first multi-label PTM predictor ever established. The novel predictor is featured by incorporating the sequence-coupled effects into the general PseAAC, and by fusing an array of basic random forest classifiers into an ensemble system. Rigorous cross-validations via a set of multi-label metrics indicate that the first multi-label PTM predictor is very promising and encouraging. AVAILABILITY AND IMPLEMENTATION: For the convenience of most experimental scientists, a user-friendly web-server for iPTM-mLys has been established at http://www.jci-bioinfo.cn/iPTM-mLys, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved. CONTACT: wqiu@gordonlifescience.org, xxiao@gordonlifescience.org, kcchou@gordonlifescience.orgSupplementary information: Supplementary data are available at Bioinformatics online.

pRNAm-PC: Predicting N(6)-methyladenosine sites in RNA sequences via physical-chemical properties.

Just like PTM or PTLM (post-translational modification) in proteins, PTCM (post-transcriptional modification) in RNA plays very important roles in biological processes. Occurring at adenine (A) with the genetic code motif (GAC), N(6)-methyldenosine (m(6)A) is one of the most common and abundant PTCMs in RNA found in viruses and most eukaryotes. Given an uncharacterized RNA sequence containing many GAC motifs, which of them can be methylated, and which cannot? It is important for both basic research and drug development to address this problem. Particularly with the avalanche of RNA sequences generated in the postgenomic age, it is highly demanded to develop computational methods for timely identifying the N(6)-methyldenosine sites in RNA. Here we propose a new predictor called pRNAm-PC, in which RNA sequence samples are expressed by a novel mode of pseudo dinucleotide composition (PseDNC) whose components were derived from a physical-chemical matrix via a series of auto-covariance and cross covariance transformations. It was observed via a rigorous jackknife test that, in comparison with the existing predictor for the same purpose, pRNAm-PC achieved remarkably higher success rates in both overall accuracy and stability, indicating that the new predictor will become a useful high-throughput tool for identifying methylation sites in RNA, and that the novel approach can also be used to study many other RNA-related problems and conduct genome analysis. A user-friendly Web server for pRNAm-PC has been established at http://www.jci-bioinfo.cn/pRNAm-PC, by which users can easily get their desired results without needing to go through the mathematical details.

iCataly-PseAAC: Identification of Enzymes Catalytic Sites Using Sequence Evolution Information with Grey Model GM (2,1).

Enzymes play pivotal roles in most of the biological reaction. The catalytic residues of an enzyme are defined as the amino acids which are directly involved in chemical catalysis; the knowledge of these residues is important for understanding enzyme function. Given an enzyme, which residues are the catalytic sites, and which residues are not? This is the first important problem for in-depth understanding the catalytic mechanism and drug development. With the explosive of protein sequences generated during the post-genomic era, it is highly desirable for both basic research and drug design to develop fast and reliable method for identifying the catalytic sites of enzymes according to their sequences. To address this problem, we proposed a new predictor, called iCataly-PseAAC. In the prediction system, the peptide sample was formulated with sequence evolution information via grey system model GM(2,1). It was observed by the rigorous jackknife test and independent dataset test that iCataly-PseAAC was superior to exist predictions though its only use sequence information. As a user-friendly web server, iCataly-PseAAC is freely accessible at http://www.jci-bioinfo.cn/iCataly-PseAAC. A step-by-step guide has been provided on how to use the web server to get the desired results for the convenience of most experimental scientists.

iDNA-Methyl: identifying DNA methylation sites via pseudo trinucleotide composition.

Predominantly occurring on cytosine, DNA methylation is a process by which cells can modify their DNAs to change the expression of gene products. It plays very important roles in life development but also in forming nearly all types of cancer. Therefore, knowledge of DNA methylation sites is significant for both basic research and drug development. Given an uncharacterized DNA sequence containing many cytosine residues, which one can be methylated and which one cannot? With the avalanche of DNA sequences generated during the postgenomic age, it is highly desired to develop computational methods for accurately identifying the methylation sites in DNA. Using the trinucleotide composition, pseudo amino acid components, and a dataset-optimizing technique, we have developed a new predictor called "iDNA-Methyl" that has achieved remarkably higher success rates in identifying the DNA methylation sites than the existing predictors. A user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/iDNA-Methyl, where users can easily get their desired results. We anticipate that the web-server predictor will become a very useful high-throughput tool for basic research and drug development and that the novel approach and technique can also be used to investigate many other DNA-related problems and genome analysis.

iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid components.

Meiosis and recombination are the two opposite aspects that coexist in a DNA system. As a driving force for evolution by generating natural genetic variations, meiotic recombination plays a very important role in the formation of eggs and sperm. Interestingly, the recombination does not occur randomly across a genome, but with higher probability in some genomic regions called "hotspots", while with lower probability in so-called "coldspots". With the ever-increasing amount of genome sequence data in the postgenomic era, computational methods for effectively identifying the hotspots and coldspots have become urgent as they can timely provide us with useful insights into the mechanism of meiotic recombination and the process of genome evolution as well. To meet the need, we developed a new predictor called "iRSpot-TNCPseAAC", in which a DNA sample was formulated by combining its trinucleotide composition (TNC) and the pseudo amino acid components (PseAAC) of the protein translated from the DNA sample according to its genetic codes. The former was used to incorporate its local or short-rage sequence order information; while the latter, its global and long-range one. Compared with the best existing predictor in this area, iRSpot-TNCPseAAC achieved higher rates in accuracy, Mathew's correlation coefficient, and sensitivity, indicating that the new predictor may become a useful tool for identifying the recombination hotspots and coldspots, or, at least, become a complementary tool to the existing methods. It has not escaped our notice that the aforementioned novel approach to incorporate the DNA sequence order information into a discrete model may also be used for many other genome analysis problems. The web-server for iRSpot-TNCPseAAC is available at http://www.jci-bioinfo.cn/iRSpot-TNCPseAAC. Furthermore, for the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the current web server to obtain their desired result without the need to follow the complicated mathematical equations.

Emergency treatment on facial laceration of dog bite wounds with immediate primary closure: a prospective randomized trial study.

BACKGROUND: To investigate the emergency treatment on facial laceration of dog bite wounds and identify whether immediate primary closure is feasible. METHODS: Six hundred cases with facial laceration attacked by dog were divided into two groups randomly and evenly. After thorough debridement, the facial lacerations of group A were left open, while the lacerations of group B were undertaken immediate primary closure. Antibiotics use was administrated only after wound infected, not prophylactically given. The infection rate, infection time and healing time were analyzed. RESULTS: The infection rate of group A and B was 8.3% and 6.3% respectively (P>0.05); the infection time was 26.3 ± 11.6h and 24.9 ± 13.8h respectively (P>0.05), the healing time was 9.12 ± 1.30 d and 6.57 ± 0.49 d respectively (P<0.05) in taintless cases, 14.24 ± 2.63 d and 10.65 ± 1.69 d respectively (P<0.05) in infected cases.Compared with group A, there was no evident tendency in increasing infection rate (8.3% in group A and 6.3% in group B respectively) and infection period (26.3 ± 11.6h in group A and 24.9 ± 13.8h in group B respectively) in group B. Meanwhile, in group B, the wound healing time was shorter than group A statistically in both taintless cases (9.12 ± 1.30 d in group A and 6.57 ± 0.49 d in group B respectively) and infected cases (14.24 ± 2.63 d in group A and 10.65 ± 1.69 d in group B respectively). CONCLUSION: The facial laceration of dog bite wounds should be primary closed immediately after formal and thoroughly debridement. And the primary closure would shorten the healing time of the dog bite wounds without increasing the rate and period of infection. There is no potentiality of increasing infection incidence and infection speed, compared immediate primary closure with the wounds left open. On the contrary, primary closure the wounds can promote its primary healing. Prophylactic antibiotics administration was not recommended. and the important facial organ or tissue injuries should be secondary reconditioned.

Intelligent assistant diagnosis for pediatric inguinal hernia based on a multilayer and unbalanced classification model.

As one of the most common diseases in pediatric surgery, an inguinal hernia is usually diagnosed by medical experts based on clinical data collected from magnetic resonance imaging (MRI), computed tomography (CT), or B-ultrasound. The parameters of blood routine examination, such as white blood cell count and platelet count, are often used as diagnostic indicators of intestinal necrosis. Based on the medical numerical data on blood routine examination parameters and liver and kidney function parameters, this paper used machine learning algorithm to assist the diagnosis of intestinal necrosis in children with inguinal hernia before operation. In the work, we used clinical data consisting of 3,807 children with inguinal hernia symptoms and 170 children with intestinal necrosis and perforation caused by the disease. Three different models were constructed according to the blood routine examination and liver and kidney function. Some missing values were replaced by using the RIN-3M (median, mean, or mode region random interpolation) method according to the actual necessity, and the ensemble learning based on the voting principle was used to deal with the imbalanced datasets. The model trained after feature selection yielded satisfactory results with an accuracy of 86.43%, sensitivity of 84.34%, specificity of 96.89%, and AUC value of 0.91. Therefore, the proposed methods may be a potential idea for auxiliary diagnosis of inguinal hernia in children.

Integrating unsupervised language model with multi-view multiple sequence alignments for high-accuracy inter-chain contact prediction.

Accurate identification of inter-chain contacts in the protein complex is critical to determine the corresponding 3D structures and understand the biological functions. We proposed a new deep learning method, ICCPred, to deduce the inter-chain contacts from the amino acid sequences of the protein complex. This pipeline was built on the designed deep residual network architecture, integrating the pre-trained language model with three multiple sequence alignments (MSAs) from different biological views. Experimental results on 709 non-redundant benchmarking protein complexes showed that the proposed ICCPred significantly increased inter-chain contact prediction accuracy compared to the state-of-the-art approaches. Detailed data analyses showed that the significant advantage of ICCPred lies in the utilization of pre-trained transformer language models which can effectively extract the complementary co-evolution diversity from three MSAs. Meanwhile, the designed deep residual network enhances the correlation between the co-evolution diversity and the patterns of inter-chain contacts. These results demonstrated a new avenue for high-accuracy deep-learning inter-chain contact prediction that is applicable to large-scale protein-protein interaction annotations from sequence alone.

Identifying TME signatures for cervical cancer prognosis based on GEO and TCGA databases.

The mortality rate from cervical cancer (CESC), a malignant tumor that affects women, has increased significantly globally in recent years. The discovery of biomarkers points to a direction for the diagnosis of cervical cancer with the advancement of bioinformatics technology. The goal of this study was to look for potential biomarkers for the diagnosis and prognosis of CESC using the GEO and TCGA databases. Because of the high dimension and small sample size of the omic data, or the use of biomarkers generated from a single omic data, the diagnosis of cervical cancer may be inaccurate and unreliable. The purpose of this study was to search the GEO and TCGA databases for potential biomarkers for the diagnosis and prognosis of CESC. We begin by downloading CESC (GSE30760) DNA methylation data from GEO, then perform differential analysis on the downloaded methylation data and screen out the differential genes. Then, using estimation algorithms, we score immune cells and stromal cells in the tumor microenvironment and perform survival analysis on the gene expression profile data and the most recent clinical data of CESC from TCGA. Then, using the 'limma' package and Venn plot in R language to perform differential analysis of genes and screen out overlapping genes, these overlapping genes were then subjected to GO and KEGG functional enrichment analysis. The differential genes screened by the GEO methylation data and the differential genes screened by the TCGA gene expression data were intersected to screen out the common differential genes. A protein-protein interaction (PPI) network of gene expression data was then created in order to discover important genes. The PPI network's key genes were crossed with previously identified common differential genes to further validate them. The Kaplan-Meier curve was then used to determine the prognostic importance of the key genes. Survival analysis has shown that CD3E and CD80 are important for the identification of cervical cancer and can be considered as potential biomarkers for cervical cancer.

Stacking-ac4C: an ensemble model using mixed features for identifying n4-acetylcytidine in mRNA.

N4-acetylcytidine (ac4C) is a modification of cytidine at the nitrogen-4 position, playing a significant role in the translation process of mRNA. However, the precise mechanism and details of how ac4C modifies translated mRNA remain unclear. Since identifying ac4C sites using conventional experimental methods is both labor-intensive and time-consuming, there is an urgent need for a method that can promptly recognize ac4C sites. In this paper, we propose a comprehensive ensemble learning model, the Stacking-based heterogeneous integrated ac4C model, engineered explicitly to identify ac4C sites. This innovative model integrates three distinct feature extraction methodologies: Kmer, electron-ion interaction pseudo-potential values (PseEIIP), and pseudo-K-tuple nucleotide composition (PseKNC). The model also incorporates the robust Cluster Centroids algorithm to enhance its performance in dealing with imbalanced data and alleviate underfitting issues. Our independent testing experiments indicate that our proposed model improves the Mcc by 15.61% and the ROC by 5.97% compared to existing models. To test our model's adaptability, we also utilized a balanced dataset assembled by the authors of iRNA-ac4C. Our model showed an increase in Sn of 4.1%, an increase in Acc of nearly 1%, and ROC improvement of 0.35% on this balanced dataset. The code for our model is freely accessible at https://github.com/louliliang/ST-ac4C.git, allowing users to quickly build their model without dealing with complicated mathematical equations.

Integrative approach for classifying male tumors based on DNA methylation 450K data.

Malignancies such as bladder urothelial carcinoma, colon adenocarcinoma, liver hepatocellular carcinoma, lung adenocarcinoma and prostate adenocarcinoma significantly impact men's well-being. Accurate cancer classification is vital in determining treatment strategies and improving patient prognosis. This study introduced an innovative method that utilizes gene selection from high-dimensional datasets to enhance the performance of the male tumor classification algorithm. The method assesses the reliability of DNA methylation data to distinguish the five most prevalent types of male cancers from normal tissues by employing DNA methylation 450K data obtained from The Cancer Genome Atlas (TCGA) database. First, the chi-square test is used for dimensionality reduction and second, L1 penalized logistic regression is used for feature selection. Furthermore, the stacking ensemble learning technique was employed to integrate seven common multiclassification models. Experimental results demonstrated that the ensemble learning model utilizing multiple classification models outperformed any base classification model. The proposed ensemble model achieved an astonishing overall accuracy (ACC) of 99.2% in independent testing data. Moreover, it may present novel ideas and pathways for the early detection and treatment of future diseases.

DTI-BERT: Identifying Drug-Target Interactions in Cellular Networking Based on BERT and Deep Learning Method.

Drug-target interactions (DTIs) are regarded as an essential part of genomic drug discovery, and computational prediction of DTIs can accelerate to find the lead drug for the target, which can make up for the lack of time-consuming and expensive wet-lab techniques. Currently, many computational methods predict DTIs based on sequential composition or physicochemical properties of drug and target, but further efforts are needed to improve them. In this article, we proposed a new sequence-based method for accurately identifying DTIs. For target protein, we explore using pre-trained Bidirectional Encoder Representations from Transformers (BERT) to extract sequence features, which can provide unique and valuable pattern information. For drug molecules, Discrete Wavelet Transform (DWT) is employed to generate information from drug molecular fingerprints. Then we concatenate the feature vectors of the DTIs, and input them into a feature extraction module consisting of a batch-norm layer, rectified linear activation layer and linear layer, called BRL block and a Convolutional Neural Networks module to extract DTIs features further. Subsequently, a BRL block is used as the prediction engine. After optimizing the model based on contrastive loss and cross-entropy loss, it gave prediction accuracies of the target families of G Protein-coupled receptors, ion channels, enzymes, and nuclear receptors up to 90.1, 94.7, 94.9, and 89%, which indicated that the proposed method can outperform the existing predictors. To make it as convenient as possible for researchers, the web server for the new predictor is freely accessible at: https://bioinfo.jcu.edu.cn/dtibert or http://121.36.221.79/dtibert/. The proposed method may also be a potential option for other DITs.

Identifying Pupylation Proteins and Sites by Incorporating Multiple Methods.

Pupylation is an important posttranslational modification in proteins and plays a key role in the cell function of microorganisms; an accurate prediction of pupylation proteins and specified sites is of great significance for the study of basic biological processes and development of related drugs since it would greatly save experimental costs and improve work efficiency. In this work, we first constructed a model for identifying pupylation proteins. To improve the pupylation protein prediction model, the KNN scoring matrix model based on functional domain GO annotation and the Word Embedding model were used to extract the features and Random Under-sampling (RUS) and Synthetic Minority Over-sampling Technique (SMOTE) were applied to balance the dataset. Finally, the balanced data sets were input into Extreme Gradient Boosting (XGBoost). The performance of 10-fold cross-validation shows that accuracy (ACC), Matthew's correlation coefficient (MCC), and area under the ROC curve (AUC) are 95.23%, 0.8100, and 0.9864, respectively. For the pupylation site prediction model, six feature extraction codes (i.e., TPC, AAI, One-hot, PseAAC, CKSAAP, and Word Embedding) served to extract protein sequence features, and the chi-square test was employed for feature selection. Rigorous 10-fold cross-validations indicated that the accuracies are very high and outperformed its existing counterparts. Finally, for the convenience of researchers, PUP-PS-Fuse has been established at https://bioinfo.jcu.edu.cn/PUP-PS-Fuse and http://121.36.221.79/PUP-PS-Fuse/as a backup.

The Prognosis of Skip Metastasis in Papillary Thyroid Microcarcinoma Is Better Than That of Continuous Metastasis.

CONTEXT: A few papillary thyroid microcarcinomas (PTMCs) may have skip metastasis (SLNM), but the risk factors remain controversial and the prognosis is unclear. OBJECTIVES: To investigate the incidence, lymph node metastasis (LNM) patterns, risk factors, and prognosis of SLNM in PTMCs. METHODS: We reviewed the medical records of PTMC patients who underwent thyroid surgery in our institution. Analyses of risk factors were performed for SLNM. Recurrence-free survival (RFS) of SLNM, central lymph node metastasis (CLNM), and continuous metastasis (CLNM and lateral lymph node metastasis [CLNM + LLNM]) were compared after propensity score matching (PSM). RESULTS: SLNM was detected in 1.7% (50/3923) and frequently involved level III (66.7%). Compared with CLNM + LLNM, SLNM had more LNM at a single level (P < 0.01) and less LNM at 2 levels (P < 0.05). A tumor size of 0.5 to 1 cm (odds ratio [OR], 2.26; 95% CI, 1.27-4.00) and location in the upper pole (OR, 3.30; 95% CI, 2.02-5.40) were independent risk factors for SLNM. A total of 910 (23.2%) PTMCs with LNM were included in the prognostic analysis. At a median follow-up of 60 months, the RFS of SLNM did not differ from that of CLNM (P = 0.10) but was significantly higher than that of CLNM + LLNM (P < 0.01) after using PSM. CONCLUSIONS: When the tumor size is 0.5 to 1 cm or its location is in the upper pole, we must remain vigilant to SLNM in PTMC. Because its prognosis is comparable to that of only CLNM and better than that of CLNM + LLNM, less intensive treatment should be considered.

Long-term outcomes and prognostic factors in papillary thyroid microcarcinoma patients with distant metastases.

PURPOSES: Distant metastasis from papillary thyroid microcarcinoma (PTMC) is extremely rare and the long-term outcomes and independent prognostic factors remain unclear. The present study aimed to investigate clinicopathological characteristics and evaluate the long-term outcomes and prognostic factors of PTMC patients with distant metastases (DM) who underwent surgery and radioactive iodine (131I) treatment. METHODS: We retrospectively reviewed the medical records of 13,441 patients with thyroid cancer (including 1697 cases with PTMC) who underwent 131I treatment at our institution between January 2008 and December 2019. PTMC patients with distant metastases with sufficient clinical follow-up data were enrolled in this cohort study. The overall survival (OS) and progression-free survival (PFS) were analyzed by the Kaplan-Meier method and the prognostic factors were assessed by Cox proportional hazards. RESULTS: Thirty-three PTMC patients with DM were enrolled in this study. The median follow-up was 75 months (range: 5-151 months). The 5-year and 10-year OS rates were 96.97 and 81.41%, respectively, and the 5-year and 10-year PFS rates were 90.46 and 69.68%, respectively. Multivariate analysis showed that male sex (P = 0.005), radioactive iodine refractory PTMC (P = 0.033), and symptomatic DM (P = 0.022) were significantly associated with worse 10-year PFS in PTMC patients with DM. No independent predictor related to poor 10-year OS was found in the present study. CONCLUSIONS: The prognosis of PTMC patients becomes worse after the development of DM. Male sex, radioactive iodine refractory PTMC, and symptomatic DM were identified as independent factors associated with PFS.