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Book
Idiopathic Pulmonary Fibrosis: An Update on Pathogenesis
Idiopathic pulmonary fibrosis (IPF) is a progressive, lethal fibrotic lung disease that occurs primarily in middle-aged and elderly adults. It is a major cause of morbidity and mortality. With an increase in life expectancy, the economic burden of IPF is expected to continuously rise in the near future. Although the exact pathophysiological mechanisms underlying IPF remain not known. Significant progress has been made in our understanding of the pathogenesis of this devastating disease in last decade. The current paradigm assumes that IPF results from sustained or repetitive lung epithelial injury and subsequent activation of fibroblasts and myofibroblast differentiation. Persistent myofibroblast phenotype contributes to excessive deposition of the extracellular matrix (ECM) and aberrant lung repair, leading to tissue scar formation, distortion of the alveolar structure, and irreversible loss of lung function. Treatments of patients with IPF by pirfenidone and nintedanib have shown significant reduction of lung function decline and slowing of disease progression in patients with IPF. However, these drugs do not cure the disease. In this review, we discuss recent advances on the pathogenesis of IPF and highlight the development of novel therapeutic strategies against the disease.
Journal Article
Distribution rules of 8-mer spectra and characterization of evolution state in animal genome sequences
Background
Studying the composition rules and evolution mechanisms of genome sequences are core issues in the post-genomic era, and k-mer spectrum analysis of genome sequences is an effective means to solve this problem.
Result
We divided total 8-mers of genome sequences into 16 kinds of XY-type due to XY dinucleotides number in 8-mers. Previous works explored that the independent unimodal distributions observed only in three CG-type 8-mer spectra, while non-CG type 8-mer spectra have not the universal phenomenon from prokaryotes to eukaryotes. On this basis, we analyzed the distribution variation of non-CG type 8-mer spectra across 889 animal genome sequences. Following the evolutionary order of animals from primitive to more complex, we found that the spectrum distributions gradually transition from unimodal to tri-modal. The relative distance from the average frequency of each non-CG type 8-mers to the center frequency is different within a species and among different species. For the 8-mers contain CG dinucleotides, we further divided these into 16 subsets, where each 8-mer contains both CG and XY dinucleotides, called XY1_CG1 subsets. We found that the separability values of XY1_CG1 spectra are closely related to the evolution and specificity of animals. Considering the constraint of Chargaff’s second parity rule, we finally obtained 10 separability values as the feature set to characterize the evolution state of genome sequences. In order to verify the rationality of the feature set, we used 14 common classification algorithms to perform binary classification tests. The results showed that the accuracy (
Acc
) ranged between 98.70% and 83.88% among birds, other vertebrates and mammals.
Conclusion
We proposed a credible feature set to characterizes the evolution state of genomes and obtained satisfied results by the feature set on large scale classification of animals.
Journal Article
Experimental investigation on dynamic strength and energy dissipation characteristics of gas outburst‐prone coal
We report laboratory experiments to investigate the dynamic failure characteristics of outburst‐prone coal using a split Hopkinson pressure bar (SHPB). For comparison, two groups of experiments are completed on contrasting coals—the first outburst‐prone and the second outburst‐resistant. The dynamic mechanical properties, failure processes, and energy dissipation of both outburst‐prone and outburst‐resistant coals are comparatively analyzed according to the obtained dynamic compressive and tensile stress‐strain curves. Results show that the dynamic stress‐strain response of both outburst‐prone and outburst‐resistant coal specimens comprises stages of compression, linear elastic deformation, then microfracture evolution, followed by unstable fracture propagation culminating in rapid unloading. The mechanical properties of both outburst‐prone and outburst‐resistant coal specimens exhibit similar features: The uniaxial compressive strength and indirect tensile strength increase linearly with the applied strain rate, and the peak strain increases nonlinearly with the strain rate, whereas the elastic modulus does not exhibit any clear strain rate dependency. Differences in the dynamic failure characteristics between outburst‐prone and outburst‐resistant coals also exist. The hardening effect of strain rate on outburst‐prone coal is more apparent than on outburst‐resistant coal, which is reflected in the dynamic increase factor at the same strain rate. However, the dynamic strength of outburst‐prone coals is still lower than that of outburst‐resistant coals due to its low quasi‐static strength. The dissipated energy of outburst‐prone coal is smaller than that of outburst‐resistant coal. Therefore, the outburst‐prone coal, characterized by low strength, high deformability, and small energy dissipation when dynamically loaded to failure, is more favorably disposed to the triggering and propagation of gas outbursts. We report laboratory experiments to investigate the dynamic failure characteristics of outburst‐prone coal using a split Hopkinson pressure bar (SHPB). The dynamic mechanical properties, dynamic increase factor, failure processes, and energy dissipation of both outburst‐prone and outburst‐resistant coals are obtained and comparatively analyzed.
Journal Article
Machine learning-based prediction of in-hospital mortality for critically ill patients with sepsis-associated acute kidney injury
This study aims to develop and validate a prediction model in-hospital mortality in critically ill patients with sepsis-associated acute kidney injury (SA-AKI) based on machine learning algorithms.
Patients who met the criteria for inclusion were identified in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and divided according to the validation (
= 2440) and development (
= 9756, 80%) queues. Ensemble stepwise feature selection method was used to screen for effective features. The prediction models of short-term mortality were developed by seven machine learning algorithms. Ten-fold cross-validation was used to verify the performance of the algorithm in the development queue. The area under the receiver operating characteristic curve (ROC-AUC) was used to evaluate the differentiation accuracy and performance of the prediction model in the validation queue. The best-performing model was interpreted by Shapley additive explanations (SHAP).
A total of 12,196 patients were enrolled in this study. Eleven variables were finally chosen to develop the prediction model. The AUC of the random forest (RF) model was the highest value both in the Ten-fold cross-validation and evaluation (AUC: 0.798, 95% CI: 0.774-0.821). According to the SHAP plots, old age, low Glasgow Coma Scale (GCS) score, high AKI stage, reduced urine output, high Simplified Acute Physiology Score (SAPS II), high respiratory rate, low temperature, low absolute lymphocyte count, high creatinine level, dysnatremia, and low body mass index (BMI) increased the risk of poor prognosis.
The RF model developed in this study is a good predictor of in-hospital mortality for patients with SA-AKI in the intensive care unit (ICU), which may have potential applications in mortality prediction.
Journal Article
Integrated transcriptomic and single-cell RNA-seq analysis identifies CLCNKB, KLK1 and PLEKHA4 as key gene of AKI-to-CKD progression
Studies have demonstrated a significant connection between acute kidney injury (AKI) and chronic kidney disease (CKD). The purpose of this study was to identify biomarkers linked to the advancement of AKI and CKD, aiming to offer new targets and insights for treating and intervening in these conditions.
Initially, candidate genes were identified by overlapping the results from differential expression analyses of AKI and CKD. Biomarkers were subsequently identified using machine learning algorithms, receiver operating characteristic curve analysis, expression analysis and experimental verification. Functional enrichment, drug prediction analyses and immune cells infiltration were conducted to investigate the functional mechanisms of the identified biomarkers. Furthermore, single-cell analyses were performed to examine the trends of biomarker expression across different cell types.
CLCNKB, KLK1 and PLEKHA4 were identified as biomarkers by the screening. Subsequently, enrichment analysis showed that CLCNKB was notably enriched in oxidative phosphorylation and the degradation of valine, leucine, and isoleucine in both AKI and CKD datasets. CLCNKB, KLK1 and PLEKHA4 were found to be significantly associated with multiple immune cell types. The regulatory network indicated that PLEKHA4 might play a more important role in the progression of AKI and CKD. Furthermore, it was discovered that CLCNKB, KLK1, and PLEKHA4 are commonly targeted by tetrachlorodibenzodioxin. Finally, in the single-cell data analysis, Type A intercalated cell and Collecting duct-principal cell were identified as the key cells. It was observed that the expression trends of these biomarkers were different under different differentiation states of the key cell subpopulations.
CLCNKB, KLK1 and PLEKHA4 were identified as biomarkers related to the development of AKI and CKD in this study, and new ideas were provided for the research on the potential mechanisms of the progression of AKI and CKD.
Journal Article
The correlation between the pathological characteristics of pulmonary invasive mucinous adenocarcinoma and radiomic features and abnormal expression of the FoxM1 and Sox9 genes
We aimed to analyze the correlation between radiomic features of pulmonary invasive mucinous adenocarcinoma (PIMA), abnormal expression of the FoxM1 and Sox9 genes, and pathological characteristics of the tumor.
From June 2021 to October 2024, we selected 150 patients with PIMA and 150 patients without PIMA from Ningbo No.2 Hospital. CT radiomic parameters and the serum mRNA levels of FoxM1 and Sox9 were compared between the groups and correlated with pathological features.
Patients with PIMA exhibited higher Kurtosis and Entropy in CT radiomic features and increased mRNA levels of FoxM1 and Sox9 compared to patients without PIMA (P < 0.05). These parameters were negatively correlated with differentiation degree and positively correlated with TNM staging (P < 0.05). Kurtosis, Entropy, plasma FoxM1 mRNA, and Sox9 mRNA are all influencing factors of PIMA (P < 0.05). The combined AUC for differential diagnosis of PIMA was the highest for Kurtosis, Entropy, plasma FoxM1, and Sox9 mRNA levels.
CT radiomic parameters and the plasma mRNA levels of FoxM1 and Sox9 among patients with PIMA were closely associated with tumor differentiation and TNM staging, offering invaluable references for the differential diagnosis of PIMA.
Journal Article
Advancements in the Intelligent Detection of Driver Fatigue and Distraction: A Comprehensive Review
Detecting the factors affecting drivers’ safe driving and taking early warning measures can effectively reduce the probability of automobile safety accidents and improve vehicle driving safety. Considering the two factors of driver fatigue and distraction state, their influences on driver behavior are elaborated from both experimental data and an accident library analysis. Starting from three modes and six types, intelligent detection methods for driver fatigue and distraction detection from the past five years are reviewed in detail. Considering its wide range of applications, the research on machine vision detection based on facial features in the past five years is analyzed, and the methods are carefully classified and compared according to their innovation points. Further, three safety warning and response schemes are proposed in light of the development of autonomous driving and intelligent cockpit technology. Finally, the paper summarizes the current state of research in the field, presents five conclusions, and discusses future trends.
Journal Article
Exploring objective feature sets in constructing the evolution relationship of animal genome sequences
Background
Exploring evolution regularities of genome sequences and constructing more objective species evolution relationships at the genomic level are high-profile topics. Based on the evolution mechanism of genome sequences proposed in our previous research, we found that only the 8-mers containing CG or TA dinucleotides correlate directly with the evolution of genome sequences, and the relative frequency rather than the actual frequency of these 8-mers is more suitable to characterize the evolution of genome sequences.
Result
Therefore, two types of feature sets were obtained, they are the relative frequency sets of CG1 + CG2 8-mers and TA1 + TA2 8-mers. The evolution relationships of mammals and reptiles were constructed by the relative frequency set of CG1 + CG2 8-mers, and two types of evolution relationships of insects were constructed by the relative frequency sets of CG1 + CG2 8-mers and TA1 + TA2 8-mers respectively. Through comparison and analysis, we found that evolution relationships are consistent with the known conclusions. According to the evolution mechanism, we considered that the evolution relationship constructed by CG1 + CG2 8-mers reflects the evolution state of genome sequences in current time, and the evolution relationship constructed by TA1 + TA2 8-mers reflects the evolution state in the early stage.
Conclusion
Our study provides objective feature sets in constructing evolution relationships at the genomic level.
Journal Article
Racial and Ethnic Disparities in Tuberculosis Incidence, Arkansas, USA, 2010–2021
We conducted an epidemiologic assessment of disease distribution by race/ethnicity to identify subpopulation-specific drivers of tuberculosis (TB). We used detailed racial/ethnic categorizations for the 932 TB cases diagnosed in Arkansas, USA, during 2010-2021. After adjusting for age and sex, racial/ethnic disparities persisted; the Native Hawaiian/Pacific Islander (NHPI) group had the highest risk for TB (risk ratio 173.6, 95% CI 140.6-214.2) compared with the non-Hispanic White group, followed by Asian, Hispanic, and non-Hispanic Black. Notable racial/ethnic disparities existed across all age groups; NHPI persons 0-14 years of age were at a particularly increased risk for TB (risk ratio 888, 95% CI 403-1,962). The risks for sputum smear-positive pulmonary TB and extrapulmonary TB were both significantly higher for racial/ethnic minority groups. Our findings suggest that TB control in Arkansas can benefit from a targeted focus on subpopulations at increased risk for TB.
Journal Article