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Background In the development of Type 1 diabetes (T1D), there are critical states just before drastic changes, and identifying these pre-disease states may predict T1D or provide crucial early-warning signals. Unlike gene expression data, gut microbiome data can be collected noninvasively from stool samples. Gut microbiome sequencing data contain different levels of phylogenetic information that can be utilized to detect the tipping point in a reliable manner, thereby providing more accurate and effective early-warning signals. However, it is still difficult to detect the critical state of T1D based on gut microbiome data due to generally non-significant differences between healthy and critical states.Results To address this problem, we proposed a new method—microbiome network flow entropy (mNFE) based on a single sample from each individual—for detecting the critical state before seroconversion and abrupt transitions of T1D at various taxonomic levels. The numerical simulation validated the robustness of mNFE under different noise levels. Furthermore, based on real datasets, mNFE successfully identified the critical states and their dynamic network biomarkers (DNBs) at different taxonomic levels. In addition, we found some high-frequency species, which are closely related to the unique clinical characteristics of autoantibodies at the four levels, and identify some non-differential ‘dark species’ play important roles during the T1D progression.Conclusions mNFE can robustly and effectively detect the pre-disease states at various taxonomic levels and identify the corresponding DNBs with only a single sample for each individual. Therefore, our mNFE method provides a new approach for personalized pre-disease diagnosis and further for T1D preventative treatment by gut microbiome.

Circular RNA (circRNA) is a distinguishable circular formed long non-coding RNA (lncRNA), which has specific roles in transcriptional regulation, multiple biological processes. The identification of circRNA from other lncRNA is necessary for relevant research. In this study, we designed attention-based multi-instance learning (MIL) network architecture fed with a raw sequence, to learn the sparse features of RNA sequences and to accomplish the circRNAs identification task. The model outperformed the state-of-art models. Moreover, following the validation of the attention mechanism effectiveness by the handwritten digit dataset, the key sequence loci underlying circRNA’s recognition were obtained based on the corresponding attention score. Then, motif enrichment analysis identified some of the key motifs for circRNA formation. In conclusion, we designed deep learning network architecture suitable for learning gene sequences with sparse features and implemented it for the circRNA identification task, and the model has strong representation capability in the indication of some key loci.

Wearable health devices (WHDs) have become increasingly advantageous in long-term health monitoring and patient management. However, most people have not yet benefited from such innovative technologies, and the willingness to accept WHDs and their influencing factors are still unclear. Based on two behavioral theories: the theory of planned behavior (TPB) and the diffusion of innovation (DOI), this study aims to explore the influencing factors of willingness to use WHDs in community residents from the perspective of both internal and external factors. A convenience sample of 407 community residents were recruited from three randomly selected Community Health Service Centers (CHSCs) in Nanjing, China, and were investigated with a self-developed questionnaires. The mean score of willingness to use WHDs was 17.00 (range 5–25). In the dimensions of TPB, perceived behavioral control (β = 1.979, p < 0.001) was the strongest influencing factor. Subjective norms (β = 1.457, p < 0.001) and attitudes (β = 0.651, p = 0.016) were also positively associated with willingness. In innovation characteristics of DOI, compatibility (β = 0.889, p < 0.001) and observability (β = 0.576, p = 0.003) had positive association with the willingness to wear a WHD. This study supports the applicability of the two behavioral theories to interpret the willingness to use WHDs in Chinese community residents. Compared with the innovative features of WHDs, individual cognitive factors were more critical predictors of willingness to use.

BACKGROUND Radiomics can yield minable information from medical images, which can facilitate computer-aided diagnosis. However, the lack of repeatability and reproducibility of radiomic features (RFs) may hinder their generalizability in clinical applications. OBJECTIVES The aims of this study were to explore 3 main sources of variability in RFs, investigate their influencing magnitudes and patterns, and identify a subset of robust RFs for further studies. MATERIALS AND METHODS A chest phantom with nodules was scanned with different computed tomography (CT) scanners repeatedly with varying acquisition and reconstruction parameters (April-May 2019) to evaluate 3 sources of variability: test-retest, inter-CT, and intra-CT protocol variability. The robustness of the RFs was measured using the concordance correlation coefficient, dynamic range, and intraclass correlation coefficient (ICC). The influencing magnitudes and patterns were analyzed using the Friedman test and Spearman rank correlation coefficient. Stable and informative RFs were selected, and their redundancy was eliminated using hierarchical clustering. Clinical validation was also performed to verify the clinical effectiveness and potential enhancement of the generalizability of radiomics research. RESULTS A total of 1295 RFs that showed all 3 sources of variability were included. The reconstruction kernel and the iteration level showed the greatest (ICC, 0.35 ± 0.31) and the least (ICC, 0.63 ± 0.27) influence on magnitudes. The different sources of variability showed relatively consistent patterns of influence (false discovery rate

Background: To characterize clinicoradiologic and radiomic features for identifying opportunistic pulmonary infections (OPIs) misdiagnosed as lung cancers in patients with human immunodeficiency virus (HIV). Methods: Twenty-four HIV-infected patients who were misdiagnosed with lung cancers on CT images and had OPIs confirmed by pathological examination or integration of clinical and laboratory findings and 49 HIV-infected patients with lung cancers confirmed pathologically were included. Semiautomated segmentation of the lesion was implemented with an in-house software. The lesion boundary was adjusted manually by radiologists. A total of 99 nonenhanced-CT-based radiomic features were then extracted with PyRadiomics. The clinicoradiologic and radiomic features were compared between the OPI and cancer groups. Results: In the OPI group, 19 patients (79.2%) had tuberculosis (TB) infections, 2 (8.3%) had nontuberculosis mycobacterium (NTM) infections, 2 (8.3%) had cryptococcus infections and 1 (4.2%) had a mixed infection of TB and NTM. There were significant differences in age, proportion of smokers, smoking index, highly active antiretroviral therapy (HAART) duration, CD4 + counts and CD4 + /CD8 + ratio between the two groups (P=0.000, 0.012, 0.007, 0.002, 0.000, and 0.000, respectively). In peripheral-type lesions, the presence of pleural indentation was less common, and the presence of satellite lesions was more common in the OPI group (P=0.016 and 0.020, respectively). Four radiomic parameters of central-type lesions were significantly different, including large dependence high gray level emphasis (LDHGLE), skewness, inverse difference normalized (IDN) and kurtosis (P=0.008, 0.017, 0.017, and 0.017, respectively). However, neither CT features of central-type lesions nor radiomic parameters of peripheral-type lesions were significantly different between the two groups. Conclusions: Clinicoradiologic features together with radiomics may help identify OPIs mimicking lung cancers in HIV-infected patients.

Despite of scRNA-seq analytic algorithms developed, their performance for cell clustering cannot be quantified due to the unknown “true” clusters. Referencing the transcriptomic heterogeneity of cell clusters, a “true” mRNA number matrix of cell individuals was defined as ground truth. Based on the matrix and real data generation procedure, a simulation program (SSCRNA) for raw data was developed. Subsequently, the consistence between simulated data and real data was evaluated. Furthermore, the impact of sequencing depth, and algorithms for analyses on cluster accuracy was quantified. As a result, the simulation result is highly consistent with that of the real data. It is found that mis-classification rate can be attributed to multiple reasons on current scRNA platforms, and clustering accuracy is not only sensitive to sequencing depth increasement, but can also be reflected by the position of the cluster on TSNE plot. Among the clustering algorithms, Gaussian normalization method is more appropriate for current workflows. In the clustering algorithms, k-means&louvain clustering method performs better in dimension reduced data than full data, while k-means clustering method is stable under both situations. In conclusion, the scRNA simulation algorithm developed restores the real data generation process, discovered impact of parameters on mis-clustering, compared the normalization/clustering algorithms and provided novel insight into scRNA analyses.

Background Nowadays, computer technology is getting popular for clinical aided diagnosis, especially in the direction of medical images. It makes physician diagnosis of lung nodules more efficient by providing them with reliable and accurate segmentation. Methods A region growing based semi-automated pulmonary nodule segmentation algorithm (ReGANS) was developed with three improvements: an automatic threshold calculation method, a lesion area pre-projection method, and an optimized region growing method. The algorithm can quickly and accurately segment a whole lung nodule in a set of computed tomography (CT) images based on an initial manual point. Results The average time taken for ReGANS to segment 1 pulmonary nodule was 0.83s, and the probability rand index (PRI), global consistency error (GCE), and variation of information (VoI) from a comparison between the algorithm and the radiologist's 2 manual results were 0.93, 0.06, and 0.3 for the boundary range (BR), and 0.86, 0.06, 0.3 for the precise range (PR). The number of images covered by one pulmonary nodule in a CT image set was also evaluated to compare the segmentation algorithm with the radiologist's results, with an error rate of 15%. At the same time, the results were verified in multiple data sets to validate the robustness. Conclusions Compared with other algorithms, ReGANS can segment the lung nodule image region more quickly and more precisely. The experimental results show that ReGANS can assist medical imaging diagnosis and has good clinical application value. It also provides a faster and more convenient method for pre-data preparation of intelligent algorithms.

Background: The assessment of the severity of Corona Virus Disease 2019 (COVID-19) by clinical presentations cannot met the urgently clinical need so far. We aimed to establish a model of deep learning (DL)-based quantitative computed tomography (CT) and initial clinical features in prediction of the severity of COVID-19. Methods: One hundred and ninety-six hospitalized patients with COVID-19 were enrolled from Jan 20 to Feb 10, 2020 in our center, and were divided into the severe and non-severe group. The clinico-radiological data at admission were compared between the two groups. A least absolute shrinkage and selection operator (LASSO) logistic regression model for predicting the severity of COVID-19 was established, and the area under the receiver operating characteristic (AUC) values of the model, quantitative CT parameters significantly different in univariate analysis, and pneumonia severity index (PSI) were compared. Findings: In comparison with non-severe group (151 cases), severe patients (45 cases) had higher PSI (p

Multiple myeloma is the second largest malignant tumor of the blood system. Proteasome inhibitors (PIs) currently are effective drugs for some myeloma patients, but their prognosis varies. We extracted the transcriptome expression data and clinical information of myeloma patients from MMRF CoMMpass database, and used the Random Survival Forest Variable Hunting (RSF-VH) algorithm to select 6 highly prognosis-related genes and to develop a 6-genes scoring model, by which the risk score predicted were significantly associated with the progress-free survival (PFS, P

OBJECTIVE. The purpose of this study is to comprehensively investigate the role of multiple features seen on thin-section CT (TSCT) in the differential diagnosis of ground-glass nodules (GGNs) measuring 5-10 mm in diameter as invasive adenocarcinoma (IAC). MATERIALS AND METHODS. The TSCT features of 313 surgically diagnosed GGNs from 288 patients were retrospectively reviewed. A logistic regression model was applied, and the AUC values for the model and the size and attenuation of the lesions were compared using ROC curve analysis. RESULTS. A total of 247 lung adenocarcinomas in situ (AISs) and minimally invasive adenocarcinomas (MIAs) (hereafter referred to as the AIS-MIA group) and 66 invasive adenocarcinomas (IACs) were identified. Compared with the AIS-MIA group, the IAC groups were significantly larger in size and had higher attenuation values, a higher frequency of mixed GGNs (all p < 0.001), bubblelike appearance, spiculation, pleural indentation, different locations, and a lower frequency of clear tumor-lung interface (all p < 0.05). The logistic model included size and attenuation (both p < 0.001; odds ratio [OR], 1.872 and 1.009, respectively) as well as tumor-lung interface (p = 0.001; OR, 0.242), bubblelike appearance (p < 0.05; OR, 2.205), and type of nodule. The AUC value for the logistic model was 0.847 (sensitivity, 80.3%; specificity, 81.0%) and was significantly higher than that for size or attenuation (both p < 0.01). CONCLUSION. Radiologic features could help in the differential diagnosis of a GGN that was 5-10 mm in diameter as IAC versus AIS or MIA. GGNs larger than 8.12 mm and with attenuation greater than -449.52 HU were more likely to be IAC.

BACKGROUND: The assessment of the severity of coronavirus disease 2019 (COVID-19) by clinical presentation has not met the urgent clinical need so far. We aimed to establish a deep learning (DL) model based on quantitative computed tomography (CT) and initial clinical features to predict the severity of COVID-19. METHODS: One hundred ninety-six hospitalized patients with confirmed COVID-19 were enrolled from January 20 to February 10, 2020 in our centre, and were divided into severe and non-severe groups. The clinico-radiological data on admission were retrospectively collected and compared between the two groups. The optimal clinico-radiological features were determined based on least absolute shrinkage and selection operator (LASSO) logistic regression analysis, and a predictive nomogram model was established by five-fold cross-validation. Receiver operating characteristic (ROC) analyses were conducted, and the areas under the receiver operating characteristic curve (AUCs) of the nomogram model, quantitative CT parameters that were significant in univariate analysis, and pneumonia severity index (PSI) were compared. RESULTS: In comparison with the non-severe group (151 patients), the severe group (45 patients) had a higher PSI (P

Previous studies have suggested that cancer stem cells serve crucial functions in tumorigenesis, metastasis and therapy failure. Stem cell signaling transduction pathways are frequently dysregulated in cancer and associated with tumorigenesis, metastasis and the cell cycle, which are necessary for cancer proliferation. However, cancer stem cell-associated gene signatures have not been established for predicting patient outcomes in colorectal cancer. Using a gene-mining approach, the present study performed mRNA expression profiling in large colorectal cancer cohorts from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database, including a TCGA colorectal cancer cohort (n=383) and three independent validation series {"type":"entrez-geo","attrs":{"text":"GSE39582","term_id":"39582"}}GSE39582 (n=582), {"type":"entrez-geo","attrs":{"text":"GSE17536","term_id":"17536"}}GSE17536 (n=177) and {"type":"entrez-geo","attrs":{"text":"GSE17537","term_id":"17537"}}GSE17537 (n=55). The present study identified that an eight-gene signature in cancer stem cell signaling was associated with the overall survival and disease/recurrence-free survival of patients with colorectal. On the basis of this signature, patients in the TCGA training sets were divided into high-risk and low-risk subgroups with a significantly different overall survival rate (hazard ratio, 2.38; P=0.0005). The prognostic value of this signature was confirmed using three independent GEO colorectal cancer sets. Identifying this prognostic stem cell signaling signature may provide an efficient classification tool for clinical prognosis evaluation, and facilitate cancer stem cell-targeted therapy.