Diagnostic Prediction of portal vein thrombosis in chronic cirrhosis patients using data-driven precision medicine model.

BACKGROUND: Portal vein thrombosis (PVT) is a significant issue in cirrhotic patients, necessitating early detection. This study aims to develop a data-driven predictive model for PVT diagnosis in chronic hepatitis liver cirrhosis patients. METHODS: We employed data from a total of 816 chronic cirrhosis patients with PVT, divided into the Lanzhou cohort (n = 468) for training and the Jilin cohort (n = 348) for validation. This dataset encompassed a wide range of variables, including general characteristics, blood parameters, ultrasonography findings and cirrhosis grading. To build our predictive model, we employed a sophisticated stacking approach, which included Support Vector Machine (SVM), Naïve Bayes and Quadratic Discriminant Analysis (QDA). RESULTS: In the Lanzhou cohort, SVM and Naïve Bayes classifiers effectively classified PVT cases from non-PVT cases, among the top features of which seven were shared: Portal Velocity (PV), Prothrombin Time (PT), Portal Vein Diameter (PVD), Prothrombin Time Activity (PTA), Activated Partial Thromboplastin Time (APTT), age and Child-Pugh score (CPS). The QDA model, trained based on the seven shared features on the Lanzhou cohort and validated on the Jilin cohort, demonstrated significant differentiation between PVT and non-PVT cases (AUROC = 0.73 and AUROC = 0.86, respectively). Subsequently, comparative analysis showed that our QDA model outperformed several other machine learning methods. CONCLUSION: Our study presents a comprehensive data-driven model for PVT diagnosis in cirrhotic patients, enhancing clinical decision-making. The SVM-Naïve Bayes-QDA model offers a precise approach to managing PVT in this population.

Improving bulk RNA-seq classification by transferring gene signature from single cells in acute myeloid leukemia.

The advances in single-cell RNA sequencing (scRNA-seq) technologies enable the characterization of transcriptomic profiles at the cellular level and demonstrate great promise in bulk sample analysis thereby offering opportunities to transfer gene signature from scRNA-seq to bulk data. However, the gene expression signatures identified from single cells are typically inapplicable to bulk RNA-seq data due to the profiling differences of distinct sequencing technologies. Here, we propose single-cell pair-wise gene expression (scPAGE), a novel method to develop single-cell gene pair signatures (scGPSs) that were beneficial to bulk RNA-seq classification to transfer knowledge across platforms. PAGE was adopted to tackle the challenge of profiling differences. We applied the method to acute myeloid leukemia (AML) and identified the scGPS from mouse scRNA-seq that allowed discriminating between AML and control cells. The scGPS was validated in bulk RNA-seq datasets and demonstrated better performance (average area under the curve [AUC] = 0.96) than the conventional gene expression strategies (average AUC$\le$ 0.88) suggesting its potential in disclosing the molecular mechanism of AML. The scGPS also outperformed its bulk counterpart, which highlighted the benefit of gene signature transfer. Furthermore, we confirmed the utility of scPAGE in sepsis as an example of other disease scenarios. scPAGE leveraged the advantages of single-cell profiles to enhance the analysis of bulk samples revealing great potential of transferring knowledge from single-cell to bulk transcriptome studies.

Deciphering associations between gut microbiota and clinical factors using microbial modules.

MOTIVATION: Human gut microbiota plays a vital role in maintaining body health. The dysbiosis of gut microbiota is associated with a variety of diseases. It is critical to uncover the associations between gut microbiota and disease states as well as other intrinsic or environmental factors. However, inferring alterations of individual microbial taxa based on relative abundance data likely leads to false associations and conflicting discoveries in different studies. Moreover, the effects of underlying factors and microbe-microbe interactions could lead to the alteration of larger sets of taxa. It might be more robust to investigate gut microbiota using groups of related taxa instead of the composition of individual taxa. RESULTS: We proposed a novel method to identify underlying microbial modules, i.e. groups of taxa with similar abundance patterns affected by a common latent factor, from longitudinal gut microbiota and applied it to inflammatory bowel disease (IBD). The identified modules demonstrated closer intragroup relationships, indicating potential microbe-microbe interactions and influences of underlying factors. Associations between the modules and several clinical factors were investigated, especially disease states. The IBD-associated modules performed better in stratifying the subjects compared with the relative abundance of individual taxa. The modules were further validated in external cohorts, demonstrating the efficacy of the proposed method in identifying general and robust microbial modules. The study reveals the benefit of considering the ecological effects in gut microbiota analysis and the great promise of linking clinical factors with underlying microbial modules. AVAILABILITY AND IMPLEMENTATION: https://github.com/rwang-z/microbial_module.git.

GPGPS: a robust prognostic gene pair signature of glioma ensembling IDH mutation and 1p/19q co-deletion.

MOTIVATION: Many studies have shown that IDH mutation and 1p/19q co-deletion can serve as prognostic signatures of glioma. Although these genetic variations affect the expression of one or more genes, the prognostic value of gene expression related to IDH and 1p/19q status is still unclear. RESULTS: We constructed an ensemble gene pair signature for the risk evaluation and survival prediction of glioma based on the prior knowledge of the IDH and 1p/19q status. First, we separately built two gene pair signatures IDH-GPS and 1p/19q-GPS and elucidated that they were useful transcriptome markers projecting from corresponding genome variations. Then, the gene pairs in these two models were assembled to develop an integrated model named Glioma Prognostic Gene Pair Signature (GPGPS), which demonstrated high area under the curves (AUCs) to predict 1-, 3- and 5-year overall survival (0.92, 0.88 and 0.80) of glioma. GPGPS was superior to the single GPSs and other existing prognostic signatures (avg AUC = 0.70, concordance index = 0.74). In conclusion, the ensemble prognostic signature with 10 gene pairs could serve as an independent predictor for risk stratification and survival prediction in glioma. This study shed light on transferring knowledge from genetic alterations to expression changes to facilitate prognostic studies. AVAILABILITY AND IMPLEMENTATION: Codes are available at https://github.com/Kimxbzheng/GPGPS.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

bvnGPS: a generalizable diagnostic model for acute bacterial and viral infection using integrative host transcriptomics and pretrained neural networks.

MOTIVATION: The confusion of acute inflammation infected by virus and bacteria or noninfectious inflammation will lead to missing the best therapy occasion resulting in poor prognoses. The diagnostic model based on host gene expression has been widely used to diagnose acute infections, but the clinical usage was hindered by the capability across different samples and cohorts due to the small sample size for signature training and discovery. RESULTS: Here, we construct a large-scale dataset integrating multiple host transcriptomic data and analyze it using a sophisticated strategy which removes batch effect and extracts the common information from different cohorts based on the relative expression alteration of gene pairs. We assemble 2680 samples across 16 cohorts and separately build gene pair signature (GPS) for bacterial, viral, and noninfected patients. The three GPSs are further assembled into an antibiotic decision model (bacterial-viral-noninfected GPS, bvnGPS) using multiclass neural networks, which is able to determine whether a patient is bacterial infected, viral infected, or noninfected. bvnGPS can distinguish bacterial infection with area under the receiver operating characteristic curve (AUC) of 0.953 (95% confidence interval, 0.948-0.958) and viral infection with AUC of 0.956 (0.951-0.961) in the test set (N = 760). In the validation set (N = 147), bvnGPS also shows strong performance by attaining an AUC of 0.988 (0.978-0.998) on bacterial-versus-other and an AUC of 0.994 (0.984-1.000) on viral-versus-other. bvnGPS has the potential to be used in clinical practice and the proposed procedure provides insight into data integration, feature selection and multiclass classification for host transcriptomics data. AVAILABILITY AND IMPLEMENTATION: The codes implementing bvnGPS are available at https://github.com/Ritchiegit/bvnGPS. The construction of iPAGE algorithm and the training of neural network was conducted on Python 3.7 with Scikit-learn 0.24.1 and PyTorch 1.7. The visualization of the results was implemented on R 4.2, Python 3.7, and Matplotlib 3.3.4.

meGPS: a multi-omics signature for hepatocellular carcinoma detection integrating methylome and transcriptome data.

MOTIVATION: Hepatocellular carcinoma (HCC) is a primary malignancy with a poor prognosis. Recently, multi-omics molecular-level measurement enables HCC diagnosis and prognosis prediction, which is crucial for early intervention of personalized therapy to diminish mortality. Here, we introduce a novel strategy utilizing DNA methylation and RNA expression data to achieve a multi-omics gene pair signature (GPS) for HCC discrimination. RESULTS: The immune genes with negative correlations between expression and promoter methylation are enriched in the highly connected cancer-related pathway network, which are considered as the candidates for HCC detection. After that, we separately construct a methylation GPS (mGPS) and an expression GPS (eGPS), and then assemble them as a meGPS with five gene pairs, in which the significant methylation and expression changes occur between HCC tumor and non-tumor groups. Reliable performance has been validated by independent tissue (age, gender and etiology) and blood datasets. This study proposes a procedure for multi-omics GPS identification and develops a novel HCC signature using both methylome and transcriptome data, suggesting potential molecular targets for the detection and therapy of HCC. AVAILABILITY AND IMPLEMENTATION: Models are available at https://github.com/bioinformaticStudy/meGPS.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Drug exposure and susceptibility of second-line drugs correlate with treatment response in patients with multidrug-resistant tuberculosis: a multicentre prospective cohort study in China.

BACKGROUND: Understanding the impact of drug exposure and susceptibility on treatment response of multidrug-resistant tuberculosis (MDR-TB) will help to optimise treatment. This study aimed to investigate the association between drug exposure, susceptibility and response to MDR-TB treatment. METHODS: Drug exposure and susceptibility for second-line drugs were measured for patients with MDR-TB. Multivariate analysis was applied to investigate the impact of drug exposure and susceptibility on sputum culture conversion and treatment outcome. Probability of target attainment was evaluated. Random Forest and CART (Classification and Regression Tree) analysis was used to identify key predictors and their clinical targets among patients on World Health Organization-recommended regimens. RESULTS: Drug exposure and corresponding susceptibility were available for 197 patients with MDR-TB. The probability of target attainment was highly variable, ranging from 0% for ethambutol to 97% for linezolid, while patients with fluoroquinolones above targets had a higher probability of 2-month culture conversion (56.3% versus 28.6%; adjusted OR 2.91, 95% CI 1.42-5.94) and favourable outcome (88.8% versus 68.8%; adjusted OR 2.89, 95% CI 1.16-7.17). Higher exposure values of fluoroquinolones, linezolid and pyrazinamide were associated with earlier sputum culture conversion. CART analysis selected moxifloxacin area under the drug concentration-time curve/minimum inhibitory concentration (AUC0-24h/MIC) of 231 and linezolid AUC0-24h/MIC of 287 as best predictors for 6-month culture conversion in patients receiving identical Group A-based regimens. These associations were confirmed in multivariate analysis. CONCLUSIONS: Our findings indicate that target attainment of TB drugs is associated with response to treatment. The CART-derived thresholds may serve as targets for early dose adjustment in a future randomised controlled study to improve MDR-TB treatment outcome.

Drug Exposure and Minimum Inhibitory Concentration Predict Pulmonary Tuberculosis Treatment Response.

BACKGROUND: Prospective studies correlating pharmacokinetic/pharmacodynamic (PK/PD) indices to clinical responses are urgently needed. This study aimed to find clinically relevant PK/PD thresholds that can be used for treatment optimization. METHODS: Pharmacokinetic sampling and minimum inhibitory concentration (MIC) measurements were performed for patients with culture-confirmed tuberculosis (TB). Classification and regression tree (CART) analysis was applied to obtain PK and/or PD thresholds for first-line drugs predictive of 2-week/month culture conversion, treatment outcome determined at 6-8 months, acute kidney injury (AKI), and drug-induced liver injury (DILI). Least absolute shrinkage and selection operator (LASSO) logistic regression was used for model development and validation. RESULTS: Finally, 168 and 52 patients with TB were included in development and validation cohorts for analysis, respectively. Area under the concentration-time curve (AUC)/MIC below CART-derived thresholds for pyrazinamide of 8.42, pyrazinamide of 2.79, or rifampicin of 435.45 were the predominant predictors of 2-week culture conversion, 2-month culture conversion, or treatment success, respectively. Isoniazid AUC >21.78 mg · h/L or rifampicin AUC >82.01 mg · h/L were predictive of DILI or AKI during TB treatment. The predictive performance of trained LASSO models in the validation cohort was evaluated by receiver operating characteristic curves and ranged from 0.625 to 0.978. CONCLUSIONS: PK/PD indices and drug exposure of TB drugs were associated with clinical outcome and adverse events. The effect of CART-derived thresholds for individualized dosing on treatment outcome should be studied in a randomized controlled trial.

Long non-coding RNA pairs to assist in diagnosing sepsis.

BACKGROUND: Sepsis is the major cause of death in Intensive Care Unit (ICU) globally. Molecular detection enables rapid diagnosis that allows early intervention to minimize the death rate. Recent studies showed that long non-coding RNAs (lncRNAs) regulate proinflammatory genes and are related to the dysfunction of organs in sepsis. Identifying lncRNA signature with absolute abundance is challenging because of the technical variation and the systematic experimental bias. RESULTS: Cohorts (n = 768) containing whole blood lncRNA profiling of sepsis patients in the Gene Expression Omnibus (GEO) database were included. We proposed a novel diagnostic strategy that made use of the relative expressions of lncRNA pairs, which are reversed between sepsis patients and normal controls (eg. lncRNAi > lncRNAj in sepsis patients and lncRNAi < lncRNAj in normal controls), to identify 14 lncRNA pairs as a sepsis diagnostic signature. The signature was then applied to independent cohorts (n = 644) to evaluate its predictive performance across different ages and normalization methods. Comparing to common machine learning models and existing signatures, SepSigLnc consistently attains better performance on the validation cohorts from the same age group (AUC = 0.990 & 0.995 in two cohorts) and across different groups (AUC = 0.878 on average), as well as cohorts processed by an alternative normalization method (AUC = 0.953 on average). Functional analysis demonstrates that the lncRNA pairs in SepsigLnc are functionally similar and tend to implicate in the same biological processes including cell fate commitment and cellular response to steroid hormone stimulus. CONCLUSION: Our study identified 14 lncRNA pairs as signature that can facilitate the diagnosis of septic patients at an intervenable point when clinical manifestations are not dramatic. Also, the computational procedure can be generalized to a standard procedure for discovering diagnostic molecule signatures.

Drug exposure of first-line anti-tuberculosis drugs in China: A prospective pharmacological cohort study.

AIM: Exploring the need for optimization of drug exposure to improve tuberculosis (TB) treatment outcome is of great importance. We aimed to describe drug exposure at steady state as well as the population pharmacokinetics (PK) of rifampicin (RIF), isoniazid (INH) and pyrazinamide (PZA) in Chinese TB patients. METHODS: A prospective multicentre PK study of RIF, INH and PZA was conducted in China between January 2015 and December 2017. Six blood samples were collected from each subject for drug concentration measurement. Nonlinear mixed effect analyses were used to develop population PK models. RESULTS: In total, 217 patients were included. Positive correlations between body weight, clearance and volume of distribution were identified for RIF and PZA, whereas body weight only influenced clearance for INH. In addition, males had higher RIF clearance and thus lower RIF exposure than women. Acetylator status was significantly associated with INH clearance as INH exposure in intermediate and fast acetylators was significantly lower than in slow acetylators, especially in low-weight bands. Simulations also showed significantly lower drug exposures in low-weight bands for all three drugs. Patients weighing <38 kg were respectively exposed to 30.4%, 45.9% and 18.0% lower area under the concentration-time curve of RIF, INH and PZA than those weighing ≥70 kg. Higher doses by addition of one fixed-dose combination tablet or 150 mg INH were simulated and found to be effective in improving INH drug exposures, especially in low-weight bands. CONCLUSION: PK variability of first-line anti-TB drugs is common in Chinese TB patients. The developed population PK models can be used to optimize drug exposures in Chinese patients. Moreover, standard dosing needs to be adjusted to increase target attainment.

Association between alcohol consumption in midlife and cognitive function in old age: Findings from the China health and Nutrition Survey.

BACKGROUND AND AIMS: Alcohol consumption has been reported to impair the physical and mental health of the elderly. This study aimed to explore the association between alcohol consumption patterns in midlife and cognition in the elderly among the Chinese population. METHODS AND RESULTS: Study subjects were individuals aged ≥45 years in the shared database of the China Health and Nutrition Survey in 1997, who were followed up in 2006. A questionnaire was used to collect information about alcohol consumption (frequency, amount and type). Alcohol consumption (grams/week) was classified into none, light (≤84), light-to-moderate (84.01-168), moderate-to-heavy (168.01-336) and heavy (≥336.01) categories in men, and none, light (<42) and moderate (≥42) categories in women. Cognitive function was measured in 2006 using a subset of items from the modified Telephone Interview for Cognitive Status. The lowest quintile was used as the cut-off point for cognitive impairment. A multivariate logistic regression model was applied. The study involved 1926 participants with a mean age of 56.91 years, and men accounted for 51.66% of the total participants. Drinking behaviours and cognitive scores had significant sexual difference (P < 0.001). Cognitive impairment was identified in 135 men and 237 women. Compared with light drinking, heavy drinking and non-drinking were associated with cognitive impairment in men [adjusted odds ratio (aOR) and 95% CI were 2.19 (1.59-3.00), 1.54 (1.21-1.96), respectively; P < 0.001]. Compared with light drinkers, female non-drinkers and moderate drinkers were associated with cognitive impairment [aOR and 95% CI were 1.54 (1.16-2.03) and 1.75 (1.08-2.85), respectively; P < 0.001]. CONCLUSIONS: Scientific evidence on the adverse effects of heavy drinking on elderly cognition and the possibly protective effects of light drinking could influence policy decisions on alcohol consumption in China.

Longitudinal profiling of gut microbiome among tuberculosis patients under anti-tuberculosis treatment in China: protocol of a prospective cohort study.

BACKGROUND: Anti-tuberculosis therapy requires at least six-month treatment with continuous administration of combined antibiotics, including isoniazid, rifampicin, pyrazinamide, and ethambutol. The long-term exposure to antibiotics could cause consequent changes in gut microbiota, which may alter the gastrointestinal function and drug absorption in patients, thereby affect the outcome of treatment. The study aims to characterize the longitudinal changes of gut microbiota among tuberculosis (TB) patients under standardized first-line treatment and provide an understanding of the association between alterations in gut microbiota composition and unfavorable clinical outcomes. METHODS: The study is a multicenter, observational prospective cohort study. Three study sites are purposively selected in the western (Sichuan Province) and eastern (Jiangsu Province and Shanghai) parts of China. Three-hundred patients with bacteriologically confirmed pulmonary TB are enrolled. All eligible patients should be investigated using structured questionnaires before treatment initiation; and be followed up during the treatment at Day-14, Month-2, Month-5, the end of treatment and the sixth month after ending therapy. Stool samples are to be collected at each visit, consisting of six stool samples from each patient. Additionally, 60 healthy volunteers from Sichuan province and Shanghai city will be recruited as healthy controls to form the baseline of patient gut microbiota in the Chinese population. The dynamic changes of gut microbiota in terms of alpha diversity, beta diversity, taxonomic composition are to be illustrated individually from the time at diagnosis until the sixth month after therapy is completed. Furthermore, the diversity and component of gut microbiota will be compared between the groups with and without unfavorable treatment outcome in terms of adverse effect and treatment failure. DISCUSSION: Studies on the clinical manifestations, adverse reactions, and gut microbiota alterations will provide scientifically-sound evidence on the impact of gut microbiota alterations on TB treatment outcomes. The study is not only useful for guiding personalized TB treatment but also sheds light on the effects of continuous antibiotics administration on gut microbiota. TRIAL REGISTRATION: Chinese Clinical Trial Registry, trial ID: ChiCTR1900023369, May 24, 2019. Retrospectively registered.

Research on the Mechanism of Aggregation-Induced Emission through Supramolecular Metal-Organic Frameworks with Mechanoluminescent Properties and Application in Press-Jet Printing.

This study investigates the mechanism of AIE in the solid state through supramolecular metal-organic frameworks and mechanoluminescent materials for the first time. Herein, four novel differently substituted Schiff base building blocks, SB1-SB4, exhibit typical AIE properties with various fluorescence emissions from yellow to green. SB1-SB4 are linked through C-H···O hydrogen bonding interactions to construct supramolecular metal-organic frameworks (SMOFs): namely, SMOFSB1-SMOFSB4. Particularly, among these SMOFs, SMOFSB3 is observed to have micropores in the 3D supramolecular structure and exhibits mechanoluminescent properties (grinding). An emission turn-on mechanism occurs with destruction of micropores by grinding and blockage of intramolecular rotations of the methyl and acetonitrile in the micropores, resulting in emission turn-on in SMOFSB3. Single-crystal X-ray structures, powder X-ray diffraction, emission spectra at room temperature, temperature-dependent emission spectra, DFT calculations, and a charge separation hypothesis well demonstrate the emission turn-on mechanism, which is consistent with the mechanism of AIE. More importantly, the molecules demonstrated potential application for press-jet printing.

Multicenter Study of the Emergence and Genetic Characteristics of Pyrazinamide-Resistant Tuberculosis in China.

The aim of this study was to investigate the epidemiology of pyrazinamide (PZA) resistance and the associated risk factors as well as to evaluate the pncA gene loci as a marker for PZA resistance in China. A population-based multicenter study of pulmonary tuberculosis (TB) cases was carried out from 2011 to 2013 in four Chinese districts/counties with different geographic and socioeconomic features. Testing for multidrug-resistant tuberculosis (MDR-TB) and susceptibility to PZA was done by the proportion method on Lowenstein-Jensen medium and Bactec MGIT 960, respectively. Mutations in the pncA gene were identified by sequencing. Among 878 culture-positive cases, 147 (16.7%) were resistant to PZA, with a significantly higher proportion among MDR isolates than among the first-line drug-susceptible isolates (30.2% versus 7.7%; P < 0.001). In total, 136 isolates had a nonsynonymous pncA mutation, with a comparable diagnostic performance between Beijing family and non-Beijing family as well as between MDR-TB and first-line drug-susceptible TB. Furthermore, the mutations in isolates with high-level PZA resistance (MIC > 500 mg/liter) were observed mainly in three regions of the pncA gene (codons 51 to 76, codons 130 to 142, and codons 163 to 180). Patients with prior treatment history had a significantly higher risk for PZA monoresistance (odds ratio [OR], 2.86; 95% confidence interval [CI], 1.363 to 6.015) and MDR PZA resistance (OR, 6.47; 95% CI, 3.186 to 13.15), while the additional factors associated with MDR PZA resistance were the patient's age (OR, 1.02; 95% CI, 1.003 to 1.042), lung cavity (OR, 2.64; 95% CI, 1.296 to 5.391). These findings suggest that it is a priority to identify PZA resistance in MDR-TB and that a rapid molecular diagnostic test based on pncA mutations in the Chinese settings where MDR-TB prevalence is high should be developed.

Determination of MIC Breakpoints for Second-Line Drugs Associated with Clinical Outcomes in Multidrug-Resistant Tuberculosis Treatment in China.

Our study aims to identify the clinical breakpoints (CBPs) of second-line drugs (SLDs) above which standard therapy fails in order to improve multidrug-resistant tuberculosis (MDR-TB) treatment. MICs of SLDs were determined for M. tuberculosis isolates cultured from 207 MDR-TB patients in a prospective cohort study in China between January 2010 and December 2012. Classification and regression tree (CART) analysis was used to identify the CBPs predictive of treatment outcome. Of the 207 MDR-TB isolates included in the present study, the proportion of isolates above the critical concentration recommended by WHO ranged from 5.3% in pyrazinamide to 62.8% in amikacin. By selecting pyrazinamide as the primary node (CBP, 18.75 mg/liter), 72.1% of sputum culture conversions at month four could be predicted. As for treatment outcome, pyrazinamide (CBP, 37.5 mg/liter) was selected as the primary node to predict 89% of the treatment success, followed by ofloxacin (CBP, 3 mg/liter), improving the predictive capacity of the primary node by 10.6%. Adjusted by identified confounders, the CART-derived pyrazinamide CBP remained the strongest predictor in the model of treatment outcome. Our findings indicate that the critical breakpoints of some second-line drugs and PZA need to be reconsidered in order to better indicate MDR-TB treatment outcome.

Electron-deficient Fe3O4@AC-NH2@Cu-MOF nanoparticles for enhanced degradation of electron-rich benzene derivatives via synergistic adsorption and catalytic oxidation.

Benzene derivatives in wastewater have negative impacts on ecosystems and human health, making their removal prior to discharge imperative. In this study, Fe3O4@AC-NH2@Cu-opa (AC-NH2 = aminoclay, Cu-opa = [Cu(opa)(bipy)0.5(H2O)]n (H2opa = 3-(4-oxypyridinium-1-yl) phthalic acid)) nanoparticles (NPs) were synthesized as adsorbent and catalyst for phenolic compound removal from wastewater. Fe3O4@AC-NH2@Cu-opa NPs demonstrated outstanding performance in the adsorption of phenol, exhibiting a remarkable adsorption capacity of up to 166.39 mg g-1 according to the Langmuir model. The composite also exhibited higher Fenton activity toward the degradation of electron-rich organic phenolic pollutants, with a rate approximately 3.4 times higher than that of Fe3O4 alone. The high catalytic activity of the composite was attributed to the large surface area and abundant active sites of the 2D charge-separated Cu-MOF. Meanwhile, the superparamagnetism of the Fe3O4 core enabled magnetic recollection and reuse without any significant loss of activity. Therefore, use of Fe3O4@AC-NH2@Cu-opa/H2O2 shows potential in an efficient method for the removal of phenolic compounds from wastewater.

Intra-sample reversed pairs based on differentially ranked genes reveal biosignature for ovarian cancer.

Ovarian cancer, a major gynecological malignancy, often remains undetected until advanced stages, necessitating more effective early screening methods. Existing biomarker based on differential genes often suffers from variations in clinical practice. To overcome the limitations of absolute gene expression values including batch effects and biological heterogeneity, we introduced a pairwise biosignature leveraging intra-sample differentially ranked genes (DRGs) and machine learning for ovarian cancer detection across diverse cohorts. We analyzed ten cohorts comprising 872 samples with 796 ovarian cancer and 76 normal. Our method, DRGpair, involves three stages: intra-sample ranking differential analysis, reversed gene pair analysis, and iterative LASSO regression. We identified four DRG pairs, demonstrating superior diagnostic performance compared to current state-of-the-art biomarkers and differentially expressed genes in seven independent cohorts. This rank-based approach not only reduced computational complexity but also enhanced the specificity and effectiveness of biomarkers, revealing DRGs as promising candidates for ovarian cancer detection and offering a scalable model adaptable to varying cohort characteristics.

Deep learning model to discriminate diverse infection types based on pairwise analysis of host gene expression.

Accurate detection of pathogens, particularly distinguishing between Gram-positive and Gram-negative bacteria, could improve disease treatment. Host gene expression can capture the immune system's response to infections caused by various pathogens. Here, we present a deep neural network model, bvnGPS2, which incorporates the attention mechanism based on a large-scale integrated host transcriptome dataset to precisely identify Gram-positive and Gram-negative bacterial infections as well as viral infections. We performed analysis of 4,949 blood samples across 40 cohorts from 10 countries using our previously designed omics data integration method, iPAGE, to select discriminant gene pairs and train the bvnGPS2. The performance of the model was evaluated on six independent cohorts comprising 374 samples. Overall, our deep neural network model shows robust capability to accurately identify specific infections, paving the way for precise medicine strategies in infection treatment and potentially also for identifying subtypes of other diseases.

Synthesis and biological evaluation of phosphonate analogues of nevirapine.

A series of nevirapine-based analogues containing the phosphonate functionality were prepared and evaluated in vitro against HIV RT. The effect of the phosphonate was evaluated against the wild type and Y181C HIV replication. An in vivo PK study was performed on a select analogue.