Search

Abstract Background Pulmonary tuberculosis (PTB) is a prevalent chronic disease associated with a significant economic burden on patients. Using machine learning to predict hospitalization costs can allocate medical resources effectively and optimize the cost structure rationally, so as to control the hospitalization costs of patients better. Methods This research analyzed data (2020–2022) from a Kashgar pulmonary hospital’s information system, involving 9570 eligible PTB patients. SPSS 26.0 was used for multiple regression analysis, while Python 3.7 was used for random forest regression (RFR) and MLP. The training set included data from 2020 and 2021, while the test set included data from 2022. The models predicted seven various costs related to PTB patients, including diagnostic cost, medical service cost, material cost, treatment cost, drug cost, other cost, and total hospitalization cost. The model’s predictive performance was evaluated using R-square (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) metrics. Results Among the 9570 PTB patients included in the study, the median and quartile of total hospitalization cost were 13,150.45 (9891.34, 19,648.48) yuan. Nine factors, including age, marital status, admission condition, length of hospital stay, initial treatment, presence of other diseases, transfer, drug resistance, and admission department, significantly influenced hospitalization costs for PTB patients. Overall, MLP demonstrated superior performance in most cost predictions, outperforming RFR and multiple regression; The performance of RFR is between MLP and multiple regression; The predictive performance of multiple regression is the lowest, but it shows the best results for Other costs. Conclusion The MLP can effectively leverage patient information and accurately predict various hospitalization costs, achieving a rationalized structure of hospitalization costs by adjusting higher-cost inpatient items and balancing different cost categories. The insights of this predictive model also hold relevance for research in other medical conditions.

Introduction: Hepatic cystic echinococcosis (HCE) is a widely seen parasitic infection. Biological activity is crucial for treatment planning. This work aims to explore the potential applications of a deep learning radiomics (DLR) model, based on CT images, in predicting the biological activity grading of hepatic cystic echinococcosis.Methods: A retrospective analysis of 160 patients with hepatic echinococcosis was performed (127 and 33 in training and validation sets). Volume of interests (VOIs) were drawn, and radiomics features and deep neural network features were extracted. Feature selection was performed on the training set, and radiomics score (Rad Score) and deep learning score (Deep Score) were calculated. Seven diagnostics models (based on logistic regression algorithm) for the biological activity grading were constructed using the selected radiomics features and two deep model features respectively. All models were evaluated using the receiver operating characteristic curve, and the area under the curve (AUC) was calculated. A nomogram was constructed using the combined model, and its calibration, discriminatory ability, and clinical utility were assessed.Results: 12, 6 and 10 optimal radiomics features, deep learning features were selected from two deep learning network (DLN) features, respectively. For biological activity grading of hepatic cystic echinococcosis, the combined model demonstrated strong diagnostic performance, with an AUC value of 0.888 (95% CI: 0.837–0.936) in the training set and 0.876 (0.761–0.964) in the validation set. The clinical decision analysis curve indicated promising results, while the calibration curve revealed that the nomogram’s prediction result was highly compatible with the actual result.Conclusion: The DLR model can be used for predicting the biological activity grading of hepatic echinococcosis.

Summary: Background: Green space is an important part of the human living environment, with many epidemiological studies estimating its impact on human health. However, no study has quantitatively assessed the credibility of the existing evidence, impeding their translations into policy decisions and hindering researchers from identifying new research gaps. This overview aims to evaluate and rank such evidence credibility. Methods: Following the PRISMA guideline, we systematically searched PubMed, Web of Science, and Embase databases for systematic reviews with meta-analyses concerning green spaces and health outcomes published up to January 15, 2024. We categorized the credibility of meta-analytical evidence from interventional studies into four levels (i.e., high, moderate, low, and very low) using the Grading of Recommendation, Assessment, Development and Evaluations framework, based on five domains including risk of bias, inconsistency, indirectness, imprecision, and publication bias. Further, we recalculated all the meta-analyses from observational studies and classified evidence into five levels (i.e., convincing, highly suggestive, suggestive, weak, and non-significant) by considering stringent thresholds for P-values, sample size, robustness, heterogeneity, and testing for biases. Findings: In total, 154 meta-analysed associations (interventional = 44, observational = 110) between green spaces and health outcomes were graded. Among meta-analyses from interventional studies, zero, four (wellbeing, systolic blood pressure, negative affect, and positive affect), 20, and 20 associations between green spaces and health outcomes were graded as high, moderate, low, and very low credibility evidence, respectively. Among meta-analyses from observational studies, one (cardiovascular disease mortality), four (prevalence/incidence of diabetes mellitus, preterm birth, and small for gestational age infant, and all-cause mortality), 12, 22, and 71 associations were categorized as convincing, highly suggestive, suggestive, weak, and non-significant evidence, respectively. Interpretation: The current evidence largely confirms beneficial associations between green spaces and human health. However, only a small subset of these associations can be deemed to have a high or convincing credibility. Hence, future better designed primary studies and meta-analyses are still needed to provide higher quality evidence for informing health promotion strategies. Funding: The National Natural Science Foundation of China of China; the Guangzhou Science and Technology Program; the Guangdong Medical Science and Technology Research Fund; the Research Grant Council of the Hong Kong SAR; and Sino-German mobility program.

Background: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide. The development of biomarkers for early detection and monitoring of HCC has not shown significant progress. Meanwhile, the second adenomatous polyposis-related gene, MUTYH, which encodes a DNA glycosylase, has been observed in its contribution to oxidative DNA damage repair. Abnormal expression of MUTYH can reduce cell survival rate. Therefore, this study investigated the usefulness of MUTYH in diagnosing and prognosis HCC. Materials and methods: Using The Cancer Genome Atlas (TCGA) data, we analyzed the prognostic value of MUTYH in HCC. We used logistic regression, Wilcoxon signed-rank test, and Kruskal–Wallis test to examine MUTYH expression concerning clinical-pathologic characteristics. Univariate and multivariate Cox regression methods and Kaplan-Meier analysis were applied to determine the related prognostic factors of HCC. The enrichment analysis (GSEA) was used to determine the critical pathways associated with MUTYH. The single-sample gene set enrichment analysis (ssGSEA) was conducted to examine the correlation between MUTYH expression and cancer immune infiltration. Results: The higher expression of MUTYH in HCC patients was associated with a poorer overall survival rate and a shorter disease-specific survival rate. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that all differentially expressed genes (DEGs) between the high and low expression levels of MUTYH significantly enriched in the trace ligand-receptor interaction, cell cycle, oocyte meiosis, gap junction, and DNA replication. Group analysis revealed the signals of their open access. The neuron system, M phase, DNA repair, Rho GTPase effector, and cell cycle checkpoints were significantly enriched. ssGSEA showed a positive correlation between MUTYH expression and the infiltration levels of Th2 cells, NK cells, and T helper cells. Moreover, a negative correlation was found between MUTYH expression and the infiltration levels of dendritic cells (DCs) and cytotoxic cells. Conclusions: MUTYH expression levels were positively correlated with immune checkpoint gene expression levels in HCC tissues. The expression level of MUTYH was related to the prognosis of HCC and the immune infiltration of HCC.

Abstract Background Crohn's disease (CD) is a chronic non-specific inflammatory bowel disease. Current CD therapeutics cannot fundamentally change the natural course of CD. Therefore, it is of great significance to find new treatment strategies for CD. Preclinical and clinical studies have shown that mesenchymal stromal cells (MSCs) are a promising therapeutic approach. However, the mechanism by which MSCs alleviate CD and how MSCs affect gut microbes are still unclear and need further elucidation. Methods We used 2,4,6-trinitrobenzenesulfonic acid (TNBS) to induce experimental colitis in mice and analysed the microbiota in faecal samples from the control group, the TNBS group and the TNBS + MSC group with faecal 16S rDNA sequencing. Subsequent analyses of alpha and beta diversity were all performed based on the rarified data. PICRUStII analysis was performed on the 16S rRNA gene sequences to infer the gut microbiome functions. Results MSC Treatment improved TNBS-induced colitis by increasing survival rates and relieving symptoms. A distinct bacterial signature was found in the TNBS group that differed from the TNBS + MSC group and controls. MSCs prevented gut microbiota dysbiosis, including increasing α-diversity and the amount of Bacteroidetes Firmicutes and Tenericutes at the phylum level and decreasing the amount of Proteobacteria at the phylum level. MSCs alleviated the increased activities of sulphur and riboflavin metabolism. Meanwhile some metabolic pathways such as biosynthesis of amino acids lysine biosynthesis sphingolipid metabolism and secondary bile acid biosynthesis were decreased in the TNBS group compared with the control group and the TNBS + MSC group Conclusions Overall, our findings preliminarily confirmed that colitis in mice is closely related to microbial and metabolic dysbiosis. MSC treatment could modulate the dysregulated metabolism pathways in mice with colitis, restoring the abnormal microbiota function to that of the normal control group. This study provides insight into specific intestinal microbiota and metabolism pathways linked with MSC treatment, suggesting a new approach to the treatment of CD.

Background: Patients who are coinfected with human immunodeficiency virus 1 (HIV) and Mycobacterium tuberculosis (TB) benefit from timely diagnosis and treatment. In the present study frequencies of CD3+, CD4+, and CD8+ T cells among peripheral blood mononuclear cells (PBMCs) of patients in the Kashi region of China infected with HIV, TB, and both HIV and TB (HIV-TB) were investigated to provide a basis for rapid identification of coinfected patients. Methods: A total of 62 patients with HIV, TB, or HIV-TB who were first hospitalized at our institution were included in the study, as were 30 controls. PBMCs were isolated, and the frequencies of CD3+, CD4+, and CD8+ T cells were determined via flow cytometry. Results: The frequency of CD4+ T cells and the CD4/CD8 ratio were significantly lower in the HIV-TB group than in the other three groups. In fever patients the frequency of CD4+ T cells and the CD4/CD8 ratio were significantly lower in the HIV-TB group than in the HIV group and the TB group. In patients who exhibited rapid weight loss there were no significant differences in the frequency of CD4+ T cells or the CD4/CD8 ratio between the groups. The results of treatment were compared in the HIV, TB, and HIV-TB groups after 7 days, and there were obvious improvements in the frequency of CD4+ T cells and the CD4/CD8 ratio. Conclusion: Clinical symptoms and the degree of immune injury can heighten suspicion for HIV-TB coinfection.

Background: Hepatocellular carcinoma (HCC) has become the world’s primary cause of cancer death. Obesity, hyperglycemia, and dyslipidemia are all illnesses that are part of the metabolic syndrome. In recent years, this risk factor has become increasingly recognized as a contributing factor to HCC. Around the world, non-alcoholic fatty liver disease (NAFLD) is on the rise, especially in western countries. In the past, the exact pathogenesis of NAFLD that progressed to metabolic risk factors (MFRs)-associated HCC has not been fully understood.Methods: Two groups of the GEO dataset (including normal/NAFLD and HCC with MFRs) were used to analyze differential expression. Differentially expressed genes of HCC were verified by overlapping in TCGA. In addition, functional enrichment analysis, modular analysis, Receiver Operating Characteristic (ROC) analysis, LASSO analysis, and Genes with key survival characteristics were analyzed.Results: We identified six hub genes (FABP5, SCD, CCL20, AGPAT9(GPAT3), PLIN1, and IL1RN) that may be closely related to NAFLD and HCC with MFRs. We constructed survival and prognosis gene markers based on FABP5, CCL20, AGPAT9(GPAT3), PLIN1, and IL1RN.This gene signature has shown good diagnostic accuracy in both NAFLD and HCC and in predicting HCC overall survival rates.Conclusion: As a result of the findings of this study, there is some guiding significance for the diagnosis and treatment of liver disease associated with NAFLD progression.

Active pulmonary tuberculosis (ATB), which is more infectious and has a higher mortality rate compared with non-active pulmonary tuberculosis (non-ATB), needs to be diagnosed accurately and timely to prevent the tuberculosis from spreading and causing deaths. However, traditional differential diagnosis methods of active pulmonary tuberculosis involve bacteriological testing, sputum culturing and radiological images reading, which is time consuming and labour intensive. Therefore, an artificial intelligence model for ATB differential diagnosis would offer great assistance in clinical practice. In this study, computer tomography (CT) scans images and corresponding clinical information of 1160 ATB patients and 1131 patients with non-ATB were collected and divided into training, validation, and testing sets. A 3-dimension (3D) Nested UNet model was utilized to delineate lung field regions in the CT images, and three different pre-trained deep learning models including 3D VGG-16, 3D EfficientNet and 3D ResNet-50 were used for classification and differential diagnosis task. We also collected an external testing set with 100 ATB cases and 100 Non-ATB cases for further validation of the model. In the internal and external testing set, the 3D ResNet-50 model outperformed other models, reaching an AUC of 0.961 and 0.946, respectively. The 3D ResNet-50 model reached even higher levels of diagnostic accuracy than experienced radiologists, while the CT images reading and diagnosing speed was 10 times faster than human experts. The model was also capable of visualizing clinician interpretable lung lesion regions important for differential diagnosis, making it a powerful tool assisting ATB diagnosis. In conclusion, we developed an auxiliary tool to differentiate active and non-active pulmonary tuberculosis, which would have broad prospects in the bedside.

Background: Accurate localization and classification of intracerebral hemorrhage (ICH) lesions are of great significance for the treatment and prognosis of patients with ICH. The purpose of this study is to develop a symmetric prior knowledge based deep learning model to segment ICH lesions in computed tomography (CT).Methods: A novel symmetric Transformer network (Sym-TransNet) is designed to segment ICH lesions in CT images. A cohort of 1,157 patients diagnosed with ICH is established to train (n = 857), validate (n = 100), and test (n = 200) the Sym-TransNet. A healthy cohort of 200 subjects is added, establishing a test set with balanced positive and negative cases (n = 400), to further evaluate the accuracy, sensitivity, and specificity of the diagnosis of ICH. The segmentation results are obtained after data pre-processing and Sym-TransNet. The DICE coefficient is used to evaluate the similarity between the segmentation results and the segmentation gold standard. Furthermore, some recent deep learning methods are reproduced to compare with Sym-TransNet, and statistical analysis is performed to prove the statistical significance of the proposed method. Ablation experiments are conducted to prove that each component in Sym-TransNet could effectively improve the DICE coefficient of ICH lesions.Results: For the segmentation of ICH lesions, the DICE coefficient of Sym-TransNet is 0.716 ± 0.031 in the test set which contains 200 CT images of ICH. The DICE coefficients of five subtypes of ICH, including intraparenchymal hemorrhage (IPH), intraventricular hemorrhage (IVH), extradural hemorrhage (EDH), subdural hemorrhage (SDH), and subarachnoid hemorrhage (SAH), are 0.784 ± 0.039, 0.680 ± 0.049, 0.359 ± 0.186, 0.534 ± 0.455, and 0.337 ± 0.044, respectively. Statistical results show that the proposed Sym-TransNet can significantly improve the DICE coefficient of ICH lesions in most cases. In addition, the accuracy, sensitivity, and specificity of Sym-TransNet in the diagnosis of ICH in 400 CT images are 91.25%, 98.50%, and 84.00%, respectively.Conclusion: Compared with recent mainstream deep learning methods, the proposed Sym-TransNet can segment and identify different types of lesions from CT images of ICH patients more effectively. Moreover, the Sym-TransNet can diagnose ICH more stably and efficiently, which has clinical application prospects.

Osteosarcoma (OS) is a kind of malignant tumor originating from mesenchymal tissue Bone mesenchymal stem cells-derived extracellular vesicles (BMSCs-EVs) can play important roles in OS. This study investigated the mechanism of BMSCs-EVs on OS. BMSC surface antigens and adipogenic and osteogenic differentiation were detected by flow cytometry, and oil red O and alizarin red staining. EVs were isolated from BMSCs by differential centrifugation and identified by transmission electron microscopy, nanoparticle tracking analysis, and Western blot (WB). miR-206 and neurensin-2 (NRSN2) levels in human osteoblast hFOB 1.19 or OS cells (143B, MG-63, Saos2, HOS) were detected by RT-qPCR. Human OS cells with lower miR-206 levels were selected and treated with BMSCs-EVs or pSUPER-NRSN2. The uptake of EVs by 143B cells, cell proliferation, apoptosis, invasion, and migration were detected by immunofluorescence, 5-ethynyl-2’-deoxyuridine (EdU) and colony formation assays, flow cytometry, scratch test, and transwell assays. The binding sites between miR-206 and NRSN2 were predicted by Starbase database and verified by dual-luciferase assay. The OS xenograft model was established and treated by BMSCs-EVs. Tumor growth rate and volume, cell proliferation, and p-ERK1/2, ERK1/2, and Bcl-xL levels were detected by vernier caliper, immunohistochemistry, and WB. BMSCs-EVs were successfully extracted. miR-206 was diminished and NRSN2 was promoted in OS cells. BMSCs-EVs inhibited proliferation, migration, and invasion, and promoted apoptosis of OS cells. BMSCs-EVs carried miR-206 into OS cells. Inhibition of miR-206 in EVs partially reversed the inhibitory effect of EVs on malignant behaviors of OS cells. miR-206 targeted NRSN2. Overexpression of NRSN2 reversed the inhibitory effect of EVs on OS cells. NRSN2 activated the ERK1/2-Bcl-xL pathway. BMSC-EVs inhibited OS growth in vivo. In summary, BMSC-EVs targeted NRSN2 and inhibited the ERK1/2-Bcl-xL pathway by carrying miR-206 into OS cells, thus inhibiting OS progression.

ObjectiveBased on the respiratory disease big data platform in southern Xinjiang, we established a model that predicted and diagnosed chronic obstructive pulmonary disease, bronchiectasis, pulmonary embolism and pulmonary tuberculosis, and provided assistance for primary physicians.MethodsThe method combined convolutional neural network (CNN) and long-short-term memory network (LSTM) for prediction and diagnosis of respiratory diseases. We collected the medical records of inpatients in the respiratory department, including: chief complaint, history of present illness, and chest computed tomography. Pre-processing of clinical records with “jieba” word segmentation module, and the Bidirectional Encoder Representation from Transformers (BERT) model was used to perform word vectorization on the text. The partial and total information of the fused feature set was encoded by convolutional layers, while LSTM layers decoded the encoded information.ResultsThe precisions of traditional machine-learning, deep-learning methods and our proposed method were 0.6, 0.81, 0.89, and F1 scores were 0.6, 0.81, 0.88, respectively.ConclusionCompared with traditional machine learning and deep-learning methods that our proposed method had a significantly higher performance, and provided precise identification of respiratory disease.

As a major infectious disease, tuberculosis (TB) still poses a threat to people’s health in China. As a triage test for TB, reading chest radiography with traditional approach ends up with high inter-radiologist and intra-radiologist variability, moderate specificity and a waste of time and medical resources. Thus, this study established a deep convolutional neural network (DCNN) based artificial intelligence (AI) algorithm, aiming at diagnosing TB on posteroanterior chest X-ray photographs in an effective and accurate way. Altogether, 5,000 patients with TB and 4,628 patients without TB were included in the study, totaling to 9,628 chest X-ray photographs analyzed. Splitting the radiographs into a training set (80.4%) and a testing set (19.6%), three different DCNN algorithms, including ResNet, VGG, and AlexNet, were trained to classify the chest radiographs as images of pulmonary TB or without TB. Both the diagnostic accuracy and the area under the receiver operating characteristic curve were used to evaluate the performance of the three AI diagnosis models. Reaching an accuracy of 96.73% and marking the precise TB regions on the radiographs, ResNet algorithm-based AI outperformed the rest models and showed excellent diagnostic ability in different clinical subgroups in the stratification analysis. In summary, the ResNet algorithm-based AI diagnosis system provided accurate TB diagnosis, which could have broad prospects in clinical application for TB diagnosis, especially in poor regions with high TB incidence.

Genetic factors are important factors in chronic obstructive pulmonary disease (COPD) onset. Plenty of risk and new causative genes for COPD have been identified in patients of the Chinese Han population. In contrast, we know considerably little concerning the genetics in the Kashi COPD population (Uyghur). This study aims at clarifying the genetic maps regarding COPD susceptibility in Kashi (China). Whole-exome sequencing (WES) was used to analyze three Uyghur families with COPD in Kashi (eight patients and one healthy control). Sanger sequencing was also used to verify the WES results in 541 unrelated Uyghur COPD patients and 534 Uyghur healthy controls. WES showed 72 single nucleotide variants (SNVs), two deletions, and small insertions (InDels), 26 copy number variants (CNVs), and 34 structural variants (SVs), including g.71230620T > A (rs12449210T > A, NC_000,016.10) in the HYDIN axonemal central pair apparatus protein (HYDIN) gene and g.61190482A > G (rs777591A > G, NC_000002.12) in the ubiquitin-specific protease 34 (USP34) gene. After Sanger sequencing, we found that rs777591“AA” under different genetic models except for the dominant model (adjusted OR = 0.8559, 95%CI 0.6568–1.115, p > .05), could significantly reduce COPD risk, but rs12449210T > A was not related to COPD. In stratified analysis of smoking status, rs777591“AA” reduced COPD risk significantly among the nonsmoker group. Protein and mRNA expression of USP34 in cigarette smoke extract-treated BEAS-2b cells increased significantly compared with those in the control group. Our findings associate the USP34 rs777591“AA” genotype as a protector factor in COPD.

ObjectiveSeveral β-carboline alkaloids (βCBs), such as harmine, harmaline, harmane, and nor-harmane, are effective for Alzheimer’s disease mouse models. They can be found in some plants, common foodstuffs, and blank plasma of various mammals. However, whether these compounds in mammals are exogenous or endogenous remain unclear.MethodsThe exposure levels of βCBs and of neurotransmitters in plasma and tissues of pup rats, aging rats, mice of different physiological states, and healthy volunteers were detected by using UPLC-MS/MS. Plasma and tissue samples from 110 newborn rats up to 29 days old at 11 sampling points were collected and were analyzed to determine the concentration variation of βCBs in the developmental phase of newborn rats. The plasma of rats aged 2 to 18 months was used to detect the variation trend of βCBs and with some neurotransmitters. The plasma samples of normal C57BL/6 mice, APP/PS1 double transgenic mice, and scopolamine-induced memory impairment mice were collected and were analyzed to compare the difference of βCBs in different physiological states. The exposure levels of βCBs such as harmine, harmaline, and harmane in plasma of 550 healthy volunteers were also detected and analyzed on the basis of gender, race, and age.ResultsResults showed that harmine was the main compound found in rats, mice, and human, which can be detected in a newborn rat plasma (0.16 ± 0.03 ng/ml) and brain (0.33 ± 0.14 ng/g) without any exogenous consumption. The concentration of harmine in rat plasma showed a decreasing trend similar to the exposure levels of neurotransmitters such as 5-hydroxytryptamine, acetylcholine chloride, glutamic acid, tyrosine, and phenylalanine during the growth period of 18 months. The harmine exposure in rats and human indicates high dependence on the physiological and pathological status such as aging, gender, and race.ConclusionThe dynamic changes of harmine exposure in different animals and human, in vivo, at developmental and physiological states indicate that harmine is a naturally and widely distributed endogenous substance in different mammals and human. In addition to exogenous ingestion, spontaneous synthesis might be another important source of harmine in mammals, which should be verified by further experiment.

A set of integral floating vibration isolation methods was developed in this study to remedy the lack of effective measures for over-track buildings. The effect of vibration isolation pads was investigated experimentally; the resulting field measurements were used to determine the time-frequency dynamic responses of vehicle-induced vibration and isolation effects. The characteristics of the vibration source appear to significantly affect the frequency domain distribution of vibration inside the building. When no vibration isolation measures are taken, the internal vibration of the cultural center exceeds the limit. BSW vibration isolation pads R480, R550, and R800 are effective in the frequency range of 16–80 Hz. The vibration isolation level in the Z direction is between 5.6 and 7.3 dB. After floating vibration isolation treatment, the maximum Z vibration level of the cultural center is 56.4 dB in daytime hours and 52.9 dB at night, which satisfies the relevant standard. The proposed method is shown to effectively hinder the structural vibration caused by subways on surrounding buildings.

Abstract Background Gallbladder cancer (GBC) is the most malignant cancer occurring in the biliary tract cancer featured with undesirable prognosis, in which most patients die within a year of cholecystectomy. Long noncoding RNAs (lncRNAs) function as critical regulators of multiple stages of cancers. Herein, the mechanism of lncRNA metastasis associated lung adenocarcinoma transcript 1 (MALAT1) in GBC is investigated. Methods Microarray-based analysis initially provided data suggesting that the expression of MALAT1 was up-regulated while that of the ABI family member 3 binding protein (ABI3BP) was down-regulated in GBC tissues and cell lines. Kaplan-Meier method was then adopted to analyze the relationship between the MALAT1 expression and overall survival and disease-free survival of patients with GBC. A set of in vitro and in vivo experiments were conducted by transducing ABI3BP-vector or sh-MALAT1 into GBC cells. Results The results confirmed that the cancer prevention effects triggered by restored ABI3BP and depleted MALAT1 as evidenced by suppressed cell growth and enhanced cell senescence. MALAT1 was observed to down-regulate ABI3BP expression through recruitment of the enhancer of zeste homolog 2 (EZH2) to the ABI3BP promoter region while the silencing of MALAT1 or suppression of H3K27 methylation was observed to promote the expression of ABI3BP. Furthermore, GBC patients with high expression of MALAT1 indicated poor prognosis. Conclusion The current study clarifies that MALAT1 silencing and ABI3BP elevation impede the GBC development through the H3K27 methylation suppression induced by EZH2, highlighting a promising competitive paradigm for therapeutic approaches of GBC.

The mechanisms of adaptive resistance of Escherichia coli to aminoglycosides remain unclear. Our RNA-Seq study found that expression of yhjX was markedly upregulated during initial exposure to subinhibitory concentrations of gentamicin. The expression of yhjX was then downregulated dramatically during a second exposure to gentamicin compared to the first exposure. YhjX encodes a putative transporter of the major facilitator superfamily, which is known to be the sole target of the YpdA/YpdB two-component system, the expression of which is highly and specifically induced by pyruvate. To investigate the effect of yhjX on the adaptive resistance of E. coli, in the present study, we constructed yhjX deletion and complemented strains of E. coli ATCC25922. Changes in extracellular pyruvate levels of wide-type and yhjX mutant were measured to determine whether YhjX functions as a pyruvate transporter. The results showed that yhjX deletion improved the growth of E. coli in medium containing subinhibitory concentrations of gentamicin. The yhjX deletion mutant did not exhibit adaptive resistance to subinhibitory concentrations of gentamicin. YhjX might not function as a pyruvate efflux pump in E. coli but was associated with the decrease following a sharp increase in the extracellular pyruvate level. Our findings indicate that yhjX regulates the growth of E. coli in the presence of a subinhibitory concentration of gentamicin and mediates the adaptive resistance to gentamicin.

Background: Accurate localization and classification of intracerebral hemorrhage (ICH) lesions are of great significance for the treatment and prognosis of patients with ICH. The purpose of this study is to develop a symmetric prior knowledge based deep learning model to segment ICH lesions in computed tomography (CT). Methods: A novel symmetric Transformer network (Sym-TransNet) is designed to segment ICH lesions in CT images. A cohort of 1,157 patients diagnosed with ICH is established to train (n = 857), validate (n = 100), and test (n = 200) the Sym-TransNet. A healthy cohort of 200 subjects is added, establishing a test set with balanced positive and negative cases (n = 400), to further evaluate the accuracy, sensitivity, and specificity of the diagnosis of ICH. The segmentation results are obtained after data pre-processing and Sym-TransNet. The DICE coefficient is used to evaluate the similarity between the segmentation results and the segmentation gold standard. Furthermore, some recent deep learning methods are reproduced to compare with Sym-TransNet, and statistical analysis is performed to prove the statistical significance of the proposed method. Ablation experiments are conducted to prove that each component in Sym-TransNet could effectively improve the DICE coefficient of ICH lesions. Results: For the segmentation of ICH lesions, the DICE coefficient of Sym-TransNet is 0.716 ± 0.031 in the test set which contains 200 CT images of ICH. The DICE coefficients of five subtypes of ICH, including intraparenchymal hemorrhage (IPH), intraventricular hemorrhage (IVH), extradural hemorrhage (EDH), subdural hemorrhage (SDH), and subarachnoid hemorrhage (SAH), are 0.784 ± 0.039, 0.680 ± 0.049, 0.359 ± 0.186, 0.534 ± 0.455, and 0.337 ± 0.044, respectively. Statistical results show that the proposed Sym-TransNet can significantly improve the DICE coefficient of ICH lesions in most cases. In addition, the accuracy, sensitivity, and specificity of Sym-TransNet in the diagnosis of ICH in 400 CT images are 91.25%, 98.50%, and 84.00%, respectively. Conclusion: Compared with recent mainstream deep learning methods, the proposed Sym-TransNet can segment and identify different types of lesions from CT images of ICH patients more effectively. Moreover, the Sym-TransNet can diagnose ICH more stably and efficiently, which has clinical application prospects. [ABSTRACT FROM AUTHOR]