Multi-feature concatenation and multi-classifier stacking: An interpretable and generalizable machine learning method for MDD discrimination with rsfMRI.

Major depressive disorder (MDD) is a serious and heterogeneous psychiatric disorder that needs accurate diagnosis. Resting-state functional MRI (rsfMRI), which captures multiple perspectives on brain structure, function, and connectivity, is increasingly applied in the diagnosis and pathological research of MDD. Different machine learning algorithms are then developed to exploit the rich information in rsfMRI and discriminate MDD patients from normal controls. Despite recent advances reported, the MDD discrimination accuracy has room for further improvement. The generalizability and interpretability of the discrimination method are not sufficiently addressed either. Here, we propose a machine learning method (MFMC) for MDD discrimination by concatenating multiple features and stacking multiple classifiers. MFMC is tested on the REST-meta-MDD data set that contains 2428 subjects collected from 25 different sites. MFMC yields 96.9% MDD discrimination accuracy, demonstrating a significant improvement over existing methods. In addition, the generalizability of MFMC is validated by the good performance when the training and testing subjects are from independent sites. The use of XGBoost as the meta classifier allows us to probe the decision process of MFMC. We identify 13 feature values related to 9 brain regions including the posterior cingulate gyrus, superior frontal gyrus orbital part, and angular gyrus, which contribute most to the classification and also demonstrate significant differences at the group level. The use of these 13 feature values alone can reach 87% of MFMC's full performance when taking all feature values. These features may serve as clinically useful diagnostic and prognostic biomarkers for MDD in the future.

Selective Oxidation of Vitamin D3 Enhanced by Long-Range Effects of a Substrate Channel Mutation in Cytochrome P450BM3 (CYP102A1).

Vitamin D deficiency affects nearly half the population, with many requiring or opting for supplements with vitamin D3(VD3), the precursor of vitamin D (1α,25-dihydroxyVD3). 25-HydroxyVD3, the circulating form of vitamin D, is a more effective supplement than VD3 but its synthesis is complex. We report here the engineering of cytochrome P450BM3(CYP102A1) for the selective oxidation of VD3 to 25-hydroxyVD3. Long-range effects of the substrate-channel mutation Glu435Ile promoted binding of the VD3 side chain close to the heme, enhancing VD3 oxidation activity that reached 6.62 g of 25-hydroxyVD3 isolated from a 1-litre scale reaction (69.1% yield; space-time-yield 331 mg/L/h).

Inflammation-Responsive Mesoporous Silica Nanoparticles with Synergistic Anti-inflammatory and Joint Protection Effects for Rheumatoid Arthritis Treatment.

PURPOSE: Joint destruction is a major burden and an unsolved problem in rheumatoid arthritis (RA) patients. We designed an intra-articular mesoporous silica nanosystem (MSN-TP@PDA-GlcN) with anti-inflammatory and joint protection effects. The nanosystem was synthesized by encapsulating triptolide (TP) in mesoporous silica nanoparticles and coating it with pH-sensitive polydopamine (PDA) and glucosamine (GlcN) grafting on the PDA. The nano-drug delivery system with anti-inflammatory and joint protection effects should have good potency against RA. METHODS: A template method was used to synthesize mesoporous silica (MSN). MSN-TP@PDA-GlcN was synthesized via MSN loading with TP, coating with PDA and grafting of GlcN on PDA. The drug release behavior was tested. A cellular inflammatory model and a rat RA model were used to evaluate the effects on RA. In vivo imaging and microdialysis (MD) system were used to analyze the sustained release and pharmacokinetics in RA rats. RESULTS: TMSN-TP@PDA-GlcN was stable, had good biocompatibility, and exhibited sustained release of drugs in acidic environments. It had excellent anti-inflammatory effects in vitro and in vivo. It also effectively repaired joint destruction in vivo without causing any tissue toxicity. In vivo imaging and pharmacokinetics experiments showed that the nanosystem prolonged the residence time, lowered the Cmax value and enhanced the relative bioavailability of TP. CONCLUSIONS: These results demonstrated that MSN-TP@PDA-GlcN sustained the release of drugs in inflammatory joints and produced effective anti-inflammatory and joint protection effects on RA. This study provides a new strategy for the treatment of RA.

The association between ultra-processed food intake and age-related hearing loss: a cross-sectional study.

OBJECTIVES: This study aimed to explore the association between ultra-processed foods and age-related hearing loss. METHODS: Cross-sectional analyses based on data from a nationally representative sample of 1075 adults aged over 50 in the US was performed. The odds ratios (ORs) and 95% confidence intervals (CIs) for hearing loss according to ultra-processed foods intake quartiles were calculated using a multiple adjusted logistic regression model. Restricted cubic spline model was used to flexibly model potential nonlinear relations between ultra-processed foods intake and possibility of hearing loss. We also explored statistical interactions and conducted subgroup analyses where they were found to be significant. RESULTS: Ultra-processed foods intake was significantly correlated with high-frequency hearing loss. After controlling for all covariables, individuals in the fourth quartile of Ultra-processed foods consumption had a 2.8 times higher chance of developing high-frequency hearing loss than individuals in the first quartile of Ultra-processed foods consumption. We also found that the association was more significant in non-Hispanic whites. CONCLUSIONS: This study discovered an association between Ultra-processed foods intake and the incidence of high-frequency hearing loss, which was more significant in non-Hispanic whites.

Development and validation of a model for predicting the early occurrence of RF in ICU-admitted AECOPD patients: a retrospective analysis based on the MIMIC-IV database.

BACKGROUND: This study aims to construct a model predicting the probability of RF in AECOPD patients upon hospital admission. METHODS: This study retrospectively extracted data from MIMIC-IV database, ultimately including 3776 AECOPD patients. The patients were randomly divided into a training set (n = 2643) and a validation set (n = 1133) in a 7:3 ratio. First, LASSO regression analysis was used to optimize variable selection by running a tenfold k-cyclic coordinate descent. Subsequently, a multifactorial Cox regression analysis was employed to establish a predictive model. Thirdly, the model was validated using ROC curves, Harrell's C-index, calibration plots, DCA, and K-M curve. RESULT: Eight predictive indicators were selected, including blood urea nitrogen, prothrombin time, white blood cell count, heart rate, the presence of comorbid interstitial lung disease, heart failure, and the use of antibiotics and bronchodilators. The model constructed with these 8 predictors demonstrated good predictive capabilities, with ROC curve areas under the curve (AUC) of 0.858 (0.836-0.881), 0.773 (0.746-0.799), 0.736 (0.701-0.771) within 3, 7, and 14 days in the training set, respectively and the C-index was 0.743 (0.723-0.763). Additionally, calibration plots indicated strong consistency between predicted and observed values. DCA analysis demonstrated favorable clinical utility. The K-M curve indicated the model's good reliability, revealed a significantly higher RF occurrence probability in the high-risk group than that in the low-risk group (P < 0.0001). CONCLUSION: The nomogram can provide valuable guidance for clinical practitioners to early predict the probability of RF occurrence in AECOPD patients, take relevant measures, prevent RF, and improve patient outcomes.

A target-triggered strand displacement-assisted target recycling based on carbon dots-based fluorescent probe and MSNs@PDA nanoparticles for miRNA amplified detection and fluorescence imaging.

A target-triggered strand displacement-assisted target recycling based on carbon dots-based fluorescent probe and mesoporous silica nanoparticles@polydopamine (MSNs@PDA) was established to detect miRNA. The surface of MSNs rich in mesopores was coated with a layer of PDA, which can adsorb and quench the fluorescence of single-stranded Fuel DNA with fluorescent carbon dots (CDs) modified at the end through fluorescence resonance energy transfer (FRET). After adding double-stranded DNA-gold nanoparticles (dsDNA-AuNPs) and target let-7a, it will trigger two toehold-mediated strand displacement reactions (TSDR), leading to the recovery of fluorescence and the recycling of target let-7a (excitation wavelength: 380 nm; emission wavelength: 458 nm). The recovery value of fluorescence is proportional to the logarithm of the target microRNA let-7a concentration, thus realizing the sensitivity amplification detection of disease markers. The MSNs@PDA@Fuel DNA-CDs/dsDNA-AuNPs nanoplatform based on the strategy of "on-off-on" and TSDR cyclic amplification may hold great potential as an effective and safe nanoprobe for accurate fluorescence imaging of diseases related to miRNA with low abundances.

Investigating alcohol consumption in China via wastewater-based epidemiology.

Alcohol abuse and addiction is a public health issue of global concern. Wastewater-based epidemiology (WBE) is a forceful and effective complementary tool for investigating chemical consumption. This study examined alcohol consumption in major cities of China via WBE and compared WBE estimates with other data sources. A simple and valid ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method was developed for the determination of two alcohol metabolites, ethyl glucuronide (EtG) and ethyl sulfate (EtS) in wastewater. The optimized method was applied to 62 sewage samples collected from wastewater treatment plants (WWTPs) in 31 provincial capital cities across China in the fourth quarter of 2020. The methodology established in this study was validated with the lower limit of quantification (LLOQ) up to 0.1 µg/L, good linearity in the range of 0.1-50 µg/L, intra-day and inter-day precision less than 5.58% and 5.55%, respectively, and the recoveries of the extracts were higher than 97.14%. The consumption range of alcohol estimated via WBE was 6.09 ± 4.56 ethanol/person/day (EPD) in the capital cities of China. Alcohol consumption varies significantly between cities in China, with WBE estimating lower alcohol consumption than WHO and lower than foreign countries. Investing in alcohol consumption based on WBE has great potential to accurately and efficiently estimate alcohol consumption.

A high-quality assembled genome of a representative peach landrace, 'Feichenghongli', and analysis of distinct late florescence and narrow leaf traits.

BACKGROUND: Peach (Prunus persica L. Batsch) is one of the most popular fruits worldwide. Although the reference genome of 'Lovell' peach has been released, the diversity of genome-level variations cannot be explored with one genome. To detect these variations, it is necessary to assemble more genomes. RESULTS: We sequenced and de novo assembled the genome of 'Feichenghongli' (FCHL), a representative landrace with strict self-pollination, which maintained the homozygosity of the genome as much as possible. The chromosome-level genome of FCHL was 239.06 Mb in size with a contig N50 of 26.93 Mb and only 4 gaps at the scaffold level. The alignment of the FCHL genome with the reference 'Lovell' genome enabled the identification of 432535 SNPs, 101244 insertions and deletions, and 7299 structural variants. Gene family analysis showed that the expanded genes in FCHL were enriched in sesquiterpenoids and triterpenoid biosynthesis. RNA-seq analyses were carried out to investigate the two distinct traits of late florescence and narrow leaves. Two key genes, PpDAM4 and PpAGL31, were identified candidates for the control of flower bud dormancy, and an F-box gene, PpFBX92, was identified as a good candidate gene in the regulation of leaf size. CONCLUSIONS: The assembled high-quality genome could deepen our understanding of variations among diverse genomes and provide valuable information for identifying functional genes and improving the molecular breeding process.

Bioinformatics and system biology approach to identify the influences of SARS-CoV-2 infections to idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease patients.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), better known as COVID-19, has become a current threat to humanity. The second wave of the SARS-CoV-2 virus has hit many countries, and the confirmed COVID-19 cases are quickly spreading. Therefore, the epidemic is still passing the terrible stage. Having idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) are the risk factors of the COVID-19, but the molecular mechanisms that underlie IPF, COPD, and CVOID-19 are not well understood. Therefore, we implemented transcriptomic analysis to detect common pathways and molecular biomarkers in IPF, COPD, and COVID-19 that help understand the linkage of SARS-CoV-2 to the IPF and COPD patients. Here, three RNA-seq datasets (GSE147507, GSE52463, and GSE57148) from Gene Expression Omnibus (GEO) is employed to detect mutual differentially expressed genes (DEGs) for IPF, and COPD patients with the COVID-19 infection for finding shared pathways and candidate drugs. A total of 65 common DEGs among these three datasets were identified. Various combinatorial statistical methods and bioinformatics tools were used to build the protein-protein interaction (PPI) and then identified Hub genes and essential modules from this PPI network. Moreover, we performed functional analysis under ontologies terms and pathway analysis and found that IPF and COPD have some shared links to the progression of COVID-19 infection. Transcription factors-genes interaction, protein-drug interactions, and DEGs-miRNAs coregulatory network with common DEGs also identified on the datasets. We think that the candidate drugs obtained by this study might be helpful for effective therapeutic in COVID-19.

PreDTIs: prediction of drug-target interactions based on multiple feature information using gradient boosting framework with data balancing and feature selection techniques.

Discovering drug-target (protein) interactions (DTIs) is of great significance for researching and developing novel drugs, having a tremendous advantage to pharmaceutical industries and patients. However, the prediction of DTIs using wet-lab experimental methods is generally expensive and time-consuming. Therefore, different machine learning-based methods have been developed for this purpose, but there are still substantial unknown interactions needed to discover. Furthermore, data imbalance and feature dimensionality problems are a critical challenge in drug-target datasets, which can decrease the classifier performances that have not been significantly addressed yet. This paper proposed a novel drug-target interaction prediction method called PreDTIs. First, the feature vectors of the protein sequence are extracted by the pseudo-position-specific scoring matrix (PsePSSM), dipeptide composition (DC) and pseudo amino acid composition (PseAAC); and the drug is encoded with MACCS substructure fingerings. Besides, we propose a FastUS algorithm to handle the class imbalance problem and also develop a MoIFS algorithm to remove the irrelevant and redundant features for getting the best optimal features. Finally, balanced and optimal features are provided to the LightGBM Classifier to identify DTIs, and the 5-fold CV validation test method was applied to evaluate the prediction ability of the proposed method. Prediction results indicate that the proposed model PreDTIs is significantly superior to other existing methods in predicting DTIs, and our model could be used to discover new drugs for unknown disorders or infections, such as for the coronavirus disease 2019 using existing drugs compounds and severe acute respiratory syndrome coronavirus 2 protein sequences.

The Critical Role of Equilibrative Nucleoside Transporter-2 in Modulating Cerebral Damage and Vascular Dysfunction in Mice with Brain Ischemia-Reperfusion.

BACKGROUND: Cerebral vascular protection is critical for stroke treatment. Adenosine modulates vascular flow and exhibits neuroprotective effects, in which brain extracellular concentration of adenosine is dramatically increased during ischemic events and ischemia-reperfusion. Since the equilibrative nucleoside transporter-2 (Ent2) is important in regulating brain adenosine homeostasis, the present study aimed to investigate the role of Ent2 in mice with cerebral ischemia-reperfusion. METHODS: Cerebral ischemia-reperfusion injury was examined in mice with transient middle cerebral artery occlusion (tMCAO) for 90 minutes, followed by 24-hour reperfusion. Infarct volume, brain edema, neuroinflammation, microvascular structure, regional cerebral blood flow (rCBF), cerebral metabolic rate of oxygen (CMRO2), and the production of reactive oxygen species (ROS) were examined following the reperfusion. RESULTS: Ent2 deletion reduced the infarct volume, brain edema, and neuroinflammation in mice with cerebral ischemia-reperfusion. tMCAO-induced disruption of brain microvessels was ameliorated in Ent2-/- mice, with a reduced expression of matrix metalloproteinases-9 and aquaporin-4 proteins. Following the reperfusion, the rCBF of the wild-type (WT) mice was quickly restored to the baseline, whereas, in Ent2-/- mice, rCBF was slowly recovered initially, but was then higher than that in the WT mice at the later phase of reperfusion. The improved CMRO2 and reduced ROS level support the beneficial effects caused by the changes in the rCBF of Ent2-/- mice. Further studies showed that the protective effects of Ent2 deletion in mice with tMCAO involve adenosine receptor A2AR. CONCLUSIONS: Ent2 plays a critical role in modulating cerebral collateral circulation and ameliorating pathological events of brain ischemia and reperfusion injury.

The association between exposure to volatile organic compounds and serum lipids in the US adult population.

BACKGROUND AND AIM: Epidemiological evidence on the relationship between exposure to volatile organic compounds (VOCs), both single and mixed, and serum lipid levels is limited, and their relationship remains unclear. Our study aimed to investigate the associations of exposure to VOCs with serum lipid levels in the US adult population. METHODS AND RESULTS: The study examined the association of 16 VOC levels (2-methylhippuric acid, 3- and 4-methylhippuric acid, N-acetyl-S-(2-carbamoylethyl)-L-cysteine, N-acetyl-S-(N-methylcarbamoyl)-L-cysteine, 2-aminothiazoline-4-carboxylic acid, N-acetyl-S-(benzyl)-L-cysteine, N-acetyl-S-(n-propyl)-L-cysteine, N-acetyl-S-(2-carboxyethyl)-L-cysteine, N-acetyl-S-(2-cyanoethyl)-L-cysteine, N-acetyl-S-(3,4-dihydroxybutyl)-L-cysteine, N-acetyl-S-(2-hydroxypropyl)-L-cysteine. N-Acetyl-S-(3-hydroxypropyl)-L-cysteine, mandelic acid, N-acetyl-S-(4-hydroxy-2-butenyl)-L-cysteine, phenylglyoxylic acid and N-acetyl-S-(3-hydroxypropyl-1-methyl)-L-cysteine) with total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL) and high-density lipoprotein cholesterol (HDL) using data from the National Health and Nutrition Examination Survey (NHANES) between 2011 and 2015, and a total of 1410 adults were enrolled. The association was evaluated by Bayesian kernel machine regression (BKMR), multiple linear regression and weighted quantile sum (WQS) regression. In BKMR analysis, exposure to VOCs is positively correlated with levels of TC, TG, and LDL-C. However, statistical significance was observed only for the impact on TG. Our linear regression analysis and WQS regression generally support the BKMR results. Several VOCs were positively associated with serum lipid profiles (e.g., the ln-transformed level of mandelic acid (MA) displayed an increase in estimated changes of 7.01 (95% CIs: 2.78, 11.24) mg/dL for TC level), even after the effective number of tests for multiple testing (P < 0.05). CONCLUSIONS: Exposure to VOCs was associated with serum lipids, and more studies are needed to confirm these findings.

MiR-200c regulates invasion, proliferation and EMT of anaplastic thyroid cancer cells by targeting parathyroid hormone like hormone.

This study aimed to explore the specific effect of miR-200c in anaplastic thyroid cancer (ATC). Hth74 and ARO cell lines were used. Proliferation, invasion, and colony formation activities of Hth74 and ARO cell lines affected by miR-200c were studied. Expression of epithelial-to-mesenchymal transition (EMT) markers (E-cadherin, N-cadherin, Slug, and Snail) in the Hth74 and ARO cell lines were validated by western blot and qRT-PCR. In addition, the regulation of the parathyroid hormone-like hormone (PTHLH) by miR-200c was assessed. Overexpression of miR-200c inhibited the invasion, proliferation, and colony formation of the ATC cell lines, whereas its downregulation achieved the opposite results. PTHLH was found to be regulated negatively by miR-200c through a miR-200c binding site within the 3'-UTR of PTHLH. miR-200c repressed the proliferation, invasion, and EMT process of cells in ATC cell lines by targeting PTHLH post-transcriptionally, which indicates that miR-200c may be a potential target for the treatment of ATC.

Development and validation of a clinical prediction model to estimate the risk of critical patients with COVID-19.

The outbreak of coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality. This study was aimed to develop and validate a prediction model based on clinical features to estimate the risk of patients with COVID-19 at admission progressing to critical patients. Patients admitted to the hospital between January 16, 2020, and March 10, 2020, were retrospectively enrolled, and they were observed for at least 14 days after admission to determine whether they developed into severe pneumonia. According to the clinical symptoms, all patients were divided into four groups: mild, normal, severe, and critical. A total of 390 patients with COVID-19 pneumonia were identified, including 212 severe patients and 178 nonsevere patients. The least absolute shrinkage and selection operator (LASSO) regression reduced the variables in the model to 6, which are age, number of comorbidities, computed tomography severity score, lymphocyte count, aspartate aminotransferase, and albumin. The area under curve of the model in the training set is 0.898, and the specificity and sensitivity were 89.7% and 75.5%. The prediction model, nomogram might be useful to access the onset of severe and critical illness among COVID-19 patients at admission, which is instructive for clinical diagnosis.

TRIM66 hastens the malignant progression of non-small cell lung cancer via modulating MMP9-mediated TGF-ß/SMAD pathway.

OBJECTIVE: To investigate regulatory function and underlying mechanism of TRIM66 in non-small cell lung cancer (NSCLC). METHODS: TRIM66 and MMP9 expression in NSCLC cells and tissues was assayed via qRT-PCR and western blot. CCK-8, colony formation, Transwell and flow cytometry assays were conducted to measure cell functional alternations in NSCLC. Western blot was employed to measure expression as well as phosphorylation levels of epithelial-mesenchymal transition-(EMT) and TGF-ß/SMAD pathways-related proteins. Co-immunoprecipitation (Co-IP) assay was done to probe interaction between TRIM66 and MMP9. Xenograft in vivo experiment and tumor metastasis model in nude mice were utilized to investigate effects of TRIM66 on tumor growth of NSCLC. RESULTS: TRIM66 and MMP9 were conspicuously highly expressed in NSCLC cells and tissues. High TRIM66 level was markedly correlated with metastasis. Silencing TRIM66 prominently repressed the proliferation, migration and invasion of transfected cells, while inducing cell apoptosis. Whereas forced expression of TRIM66 exerted the opposite effect. The aberrant expression of TRIM66 modulated EMT pathway. TRIM66 also regulated MMP9 expression, and the interaction between them was validated by Co-IP assay. Overexpression of MMP9 could activate TGF-ß/SMAD pathway. Rescue experiments manifested that si-MMP9 or SB431542 could partially reverse phenotypes induced by TRIM66. In vivo experiments revealed that silencing TRIM66 could hamper NSCLC tumor growth and metastasis. CONCLUSION: TRIM66 and MMP9 were up-regulated in NSCLC. TRIM66 facilitated the malignant progression of NSCLC through modulating MMP9-mediated TGF-ß/SMAD pathway.

Development and validation of a prognostic nomogram for early stage non-small cell lung cancer: a study based on the SEER database and a Chinese cohort.

OBJECTIVE: This study aimed to construct a nomogram to effectively predict the overall survival (OS) of patients with early-stage non-small-cell lung cancer (NSCLC). METHODS: For the training and internal validation cohorts, a total of 26,941 patients with stage I and II NSCLC were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. A nomogram was constructed based on the risk factors affecting prognosis using a Cox proportional hazards regression model. And 505 patients were recruited from Jiaxing First Hospital for external validation. The discrimination and calibration of the nomogram were evaluated by C-index and calibration curves. RESULTS: A Nomogram was created after identifying independent prognostic factors using univariate and multifactorial factor analysis. The C-index of this nomogram was 0.726 (95% CI, 0.718-0.735) and 0.721 (95% CI, 0.709-0.734) in the training cohort and the internal validation cohort, respectively, and 0.758 (95% CI, 0.691-0.825) in the external validation cohort, which indicates that the model has good discrimination. Calibration curves for 1-, 3-, and 5-year OS probabilities showed good agreement between predicted and actual survival. In addition, DCA analysis showed that the net benefit of the new model was significantly higher than that of the TNM staging system. CONCLUSION: We developed and validated a survival prediction model for patients with non-small cell lung cancer in the early stages. This new nomogram is superior to the traditional TNM staging system and can guide clinicians to make the best clinical decisions.

RNF144A suppresses ovarian cancer stem cell properties and tumor progression through regulation of LIN28B degradation via the ubiquitin-proteasome pathway.

OBJECTIVE: Cancer stem cells (CSCs) are the main driving force of tumorigenesis, metastasis, recurrence, and drug resistance in epithelial ovarian cancer (EOC). The current study aimed to explore the regulatory effects of ring finger protein 144A (RNF144A), an E3 ubiquitin ligase, in the maintenance of CSC properties and tumor development in EOC. METHODS: The expressions of RNF144A in EOC tissue samples and cells were examined. The knockdown or overexpression of a target gene was achieved by transfecting EOC cells with short hairpin RNA or adenoviral vectors. A mouse xenograft model was constructed by inoculating nude mice with EOC cells. Co-immunoprecipitation was used to determine the interaction between RNF144A and LIN28B. RESULTS: Downregulated RNF144A expression was observed in ovarian tumor tissues and EOC cells. Low RNF144A expression was positively associated with poor survival of EOC patients. RNF144A knockdown significantly enhanced sphere formation and upregulated stem cell markers in EOC cells, while RNF144A overexpression prevented EOC cells from acquiring stem cell properties. Also, the upregulation of RNF144A inhibited ovarian tumor growth and aggressiveness in cell culture and mouse xenografts. Further analysis revealed that RNF144A induced LIN28B degradation through ubiquitination in EOC cells. LIN28B upregulation restored the expressions of stem cell pluripotency-associated transcription factors in EOC cells overexpressing RNF144A. CONCLUSION: Taken together, our findings highlight the therapeutic potential of restoring RNF144A expression and thereby suppressing LIN28B-associated oncogenic signaling for EOC treatment. • Ring finger protein 144A (RNF144A) is downregulated in epithelial ovarian cancer (EOC) tissues and cell lines. • The overexpression of RNF144A prevents EOC cells from acquiring stem cell properties and inhibits ovarian tumor growth. • RNF144A induces LIN28B degradation through ubiquitination in EOC cells. • LIN28B upregulation restores the expressions of stem cell pluripotency-associated transcription factors in EOC cells overexpressing RNF144A.

The application research of AI image recognition and processing technology in the early diagnosis of the COVID-19.

BACKGROUND: This study intends to establish a combined prediction model that integrates the clinical symptoms,the lung lesion volume, and the radiomics features of patients with COVID-19, resulting in a new model to predict the severity of COVID-19. METHODS: The clinical data of 386 patients with COVID-19 at several hospitals, as well as images of certain patients during their hospitalization, were collected retrospectively to create a database of patients with COVID-19 pneumonia. The contour of lungs and lesion locations may be retrieved from CT scans using a CT-image-based quantitative discrimination and trend analysis method for COVID-19 and the Mask R-CNN deep neural network model to create 3D data of lung lesions. The quantitative COVID-19 factors were then determined, on which the diagnosis of the development of the patients' symptoms could be established. Then, using an artificial neural network, a prediction model of the severity of COVID-19 was constructed by combining characteristic imaging features on CT slices with clinical factors. ANN neural network was used for training, and tenfold cross-validation was used to verify the prediction model. The diagnostic performance of this model is verified by the receiver operating characteristic (ROC) curve. RESULTS: CT radiomics features extraction and analysis based on a deep neural network can detect COVID-19 patients with an 86% sensitivity and an 85% specificity. According to the ROC curve, the constructed severity prediction model indicates that the AUC of patients with severe COVID-19 is 0.761, with sensitivity and specificity of 79.1% and 73.1%, respectively. CONCLUSIONS: The combined prediction model for severe COVID-19 pneumonia, which is based on deep learning and integrates clinical aspects, pulmonary lesion volume, and radiomics features of patients, has a remarkable differential ability for predicting the course of disease in COVID-19 patients. This may assist in the early prevention of severe COVID-19 symptoms.

Changes of retinal ganglion cell complex after vitrectomy in rhegmatogenous retinal detachment patients and its correlation with inflammatory blood biomarkers.

PURPOSE: To compare retinal ganglion cell complex (GCC) parameters between rhegmatogenous retinal detachment (RRD) eyes and normal contralateral eyes after vitrectomy and to evaluate their correlation with inflammatory blood markers. METHODS: We investigated 25 eyes that underwent vitrectomy due to RRD. Venous blood samples were collected from all participants before 08:00 a.m. on the second day of admission after a 12-hour fast for blood counts. The differences of retinal structure between RRD and contralateral eyes were compared 1 week postoperatively. RESULTS: Focal loss volume (FLV) (2.009 ± 1.286)% was significantly increased compared with the contralateral eyes (p <  0.001). Monocyte-to-high-density lipoprotein was significantly positively correlated with GCC thickness parameters, and negatively correlated with FLV and global loss volume (GLV). Postoperative best-corrected visual acuity was negatively correlated with GLV (p = 0.039, R2 = 0.172). CONCLUSION: Retinal ganglion cells (RGCs) loss might present early postoperatively in RRD eyes, and was associated with systemic inflammation. RGCs loss might affect postoperative vision.

Xanthatin Alleviates LPS-Induced Inflammatory Response in RAW264.7 Macrophages by Inhibiting NF-κB, MAPK and STATs Activation.

Xanthatin (XT) is a sesquiterpene lactone isolated from the Chinese herb Xanthium, which belongs to the Asteraceae family. In this study, we developed an inflammation model via stimulating macrophage cell line (RAW 264.7 cells) with lipopolysaccharide (LPS), which was applied to assess the anti-inflammatory effect and probable mechanisms of xanthatin. When compared with the only LPS-induced group, cells that were pretreated with xanthatin were found to decrease the amount of nitric oxide (NO), reactive oxygen species (ROS) and associated pro-inflammatory factors (TNF-α, IL-1ß and IL-6), and downregulate the mRNA expression of iNOS, COX-2, TNF-α, IL-1ß, and IL-6. Interestingly, phosphorylated levels of related proteins (STAT3, ERK1/2, SAPK/JNK, IκBα, p65) were notably increased only with the LPS-activated cells, while the expression of these could be reverted by pre-treatment with xanthatin in a dose-dependent way. Meanwhile, xanthatin was also found to block NF-κB p65 from translocating into the nucleus and activating inflammatory gene transcription. Collectively, these results demonstrated that xanthatin suppresses the inflammatory effects through downregulating the nuclear factor kappa-B (NF-κB), mitogen-activated protein kinase (MAPK) and signal transducer and activator of transcription (STATs) signaling pathways. Taken together, xanthatin possesses the potential to act as a good anti-inflammatory medication candidate.