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.

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.

Correlation of drug exposure and bacterial susceptibility with treatment response for Mycobacterium avium complex lung disease: protocol for a prospective observational cohort study.

INTRODUCTION: The burden of Mycobacterium avium complex (MAC) lung disease is increasing globally and treatment outcome is in general poor. Therapeutic drug monitoring has the potential to improve treatment outcome by ensuring adequate drug exposure. However, very limited population-based studies exist for MAC lung disease. This study aims to describe the distribution of drug exposure for key antimycobacterial drugs at population level, and to analyse them in relationship to treatment outcome in patients with MAC lung disease. METHODS AND ANALYSIS: A prospective cohort aiming to include 100 adult patients diagnosed with and treated for MAC lung disease will be conducted in Shanghai Pulmonary Hospital, China. Blood samples will be collected after 1 month MAC treatment for measurement of macrolides, rifamycin, ethambutol, amikacin and/or fluoroquinolones, using a validated liquid-chromatography tandem mass spectrometry method. Respiratory samples will be collected at inclusion and once every 3 months for mycobacterial culture until treatment completion. Minimum inhibitory concentration (MIC) determination will be performed using a commercial broth microdilution plate. In addition to mycobacterial culture, disease severity and clinical improvement will be assessed from the perspective of lung function, radiological presentation and self-reported quality of life. Whole genome sequencing will be performed for any longitudinal isolates with significant change of MIC to explore the emergence of drug resistance-conferring mutations. The relationship between drug exposure and treatment outcome will be analysed and potential confounders will be considered for adjustment in multivariable models. Meanwhile, the associations between drug exposure in relation to MIC and markers of treatment response will be explored using Cox proportional hazards or binary logistic regression models, as appropriate. ETHICS AND DISSEMINATION: This study has been approved by the ethics committee of Shanghai Pulmonary Hospital (No. K22-149Z). Written and oral informed consent will be obtained from all participants. The study results will be submitted to a peer-reviewed journal. TRIAL REGISTERATION NUMBER: NCT05824988.

Risk factors for microbiological persistence after 6 months of treatment for Mycobacterium intracellulare and its impact on the drug-resistance profile.

Patients with Mycobacterium intracellulare pulmonary disease are more likely to experience poor treatment outcomes if they have been observed with microbiological persistence after 6 months of treatment. This study aims to identify the risk factors for microbiological persistence and describe the changes in the minimum inhibitory concentration (MIC) during antimycobacterial treatment. This retrospective case-control study enrolled patients diagnosed with M. intracellulare pulmonary disease between April 2017 and September 2021 at Shanghai Pulmonary Hospital. Patients with positive cultures after 6 months of treatment (positive group) were matched by age and sex in a 1:1 ratio to patients with negative conversion (negative group). Totally, 46 pairs of patients were analyzed. Risk factors for microbiological persistence at month 6 were smoking, previous tuberculosis treatment, chronic lung diseases, a positive baseline acid-fast bacilli smear, and adverse drug reactions; the risk was reduced by a regimen containing ethambutol, ≥3 effective drugs, and a higher pre-treatment absolute lymphocyte count. Regarding the drug-resistance profile, the negative group had a higher proportion of susceptibility to clarithromycin (100.0% vs 84.8%, P = 0.012). Most isolates were susceptible or intermediate to amikacin in both groups (93.5% and 84.8%, respectively). Nine patients (16.4%, 9/55) had a change in the drug-resistance profile, including four who changed from clarithromycin susceptible to clarithromycin resistant, and the other three reversed. Two pairs of isolates had a change in resistance to amikacin. In conclusion, risk factors for microbiological persistence were identified, and the change in MIC values during antimycobacterial treatment indicated the need for monitoring to enable timely adjustment of the regimen.IMPORTANCENontuberculous mycobacteria pulmonary disease (NTM-PD) has been recognized as an important public health issue because of its increasing incidence globally, low cure rate, and high recurrence rate. NTM-PD has innate resistance to many first-line anti-tuberculous drugs, which limits the treatment options. Mycobacterium intracellulare is reportedly the most important pathogenic NTM and accounts for the highest proportion of NTM-PD in China. A previous study suggested that poor microbiological response after 6 months of treatment is predictive of treatment failure. The present study investigated the risk factors associated with persistent positive sputum cultures by treatment month 6 in patients with M. intracellulare pulmonary disease and the variation in minimum inhibitory concentration patterns in clinical settings. This information might help to identify patients at higher risk of treatment failure and enable the timely provision of necessary interventions.

Comparative Proteomic Analysis of Exosomes Derived from Patients Infected with Non-Tuberculous Mycobacterium and Mycobacterium tuberculosis.

The non-tuberculous mycobacterium (NTM) is a very troublesome opportunistic pathogen, placing a heavy burden on public health. The pathogenesis of NTM pulmonary infection is not well-revealed yet, and its diagnosis is always challenging. This study aimed to use a comprehensive proteomics analysis of plasma exosomes to distinguish patients with rapidly growing NTM M. abscessus (MAB), slowly growing NTM M. intracellulare (MAC), and Mycobacterium tuberculosis (MTB). The identified protein components were quantified with label-free proteomics and determined with a bioinformatics analysis. The complement and coagulation were significantly enriched in patients with Mycobacterium infection, and a total of 24 proteins were observed with up-regulation, which included C1R, C1S, C2, MASP2, C4B, C8B, C9, etc. Of them, 18 proteins were significantly up-regulated in patients with MAB, while 6 and 10 were up-regulated in patients with MAC or MTB, respectively. Moreover, MAB infection was also related to the HIF-1 signaling pathway and phagosome processes, and MTB infection was associated with the p53 signaling pathway. This study provided a comprehensive description of the exosome proteome in the plasma of patients infected with MAB, MAC, and MTB and revealed potential diagnostic and differential diagnostic markers.

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.

Efficacy and safety of an innovative short-course regimen containing clofazimine for treatment of drug-susceptible tuberculosis: a clinical trial.

In preclinical studies, a new antituberculosis drug regimen markedly reduced the time required to achieve relapse-free cure. This study aimed to preliminarily evaluate the efficacy and safety of this four-month regimen, consisting of clofazimine, prothionamide, pyrazinamide and ethambutol, with a standard six-month regimen in patients with drug-susceptible tuberculosis. An open-label pilot randomized clinical trial was conducted among the patients with newly diagnosed bacteriologically-confirmed pulmonary tuberculosis. The primary efficacy end-point was sputum culture negative conversion. Totally, 93 patients were included in the modified intention-to-treat population. The rates of sputum culture conversion were 65.2% (30/46) and 87.2% (41/47) for short-course and standard regimen group, respectively. There was no difference on two-month culture conversion rates, time to culture conversion, nor early bactericidal activity (P > 0.05). However, patients on short-course regimen were observed with lower rates of radiological improvement or recovery and sustained treatment success, which was mainly attributed to higher percent of patients permanently changed assigned regimen (32.1% vs. 12.3%, P = 0.012). The main cause for it was drug-induced hepatitis (16/17). Although lowering the dose of prothionamide was approved, the alternative option of changing assigned regimen was chosen in this study. While in per-protocol population, sputum culture conversion rates were 87.0% (20/23) and 94.4% (34/36) for the respective groups. Overall, the short-course regimen appeared to have inferior efficacy and higher incidence of hepatitis but desired efficacy in per-protocol population. It provides the first proof-of-concept in humans of the capacity of the short-course approach to identify drug regimens that can shorten the treatment time for tuberculosis.

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.

A Multimodal Sensing CMOS Imager Based on Dual-Focus Imaging.

Advanced machine intelligence is empowered not only by the ever-increasing computational capability for information processing but also by sensors for collecting multimodal information from complex environments. However, simply assembling different sensors can result in bulky systems and complex data processing. Herein, it is shown that a complementary metal-oxide-semiconductor (CMOS) imager can be transformed into a compact multimodal sensing platform through dual-focus imaging. By combining lens-based and lensless imaging, visual information, chemicals, temperature, and humidity can be detected with the same chip and output as a single image. As a proof of concept, the sensor is equipped on a micro-vehicle, and multimodal environmental sensing and mapping is demonstrated. A multimodal endoscope is also developed, and simultaneous imaging and chemical profiling along a porcine digestive tract is achieved. The multimodal CMOS imager is compact, versatile, and extensible and can be widely applied in microrobots, in vivo medical apparatuses, and other microdevices.

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.

Visual-Olfactory Synergistic Perception Based on Dual-Focus Imaging and a Bionic Learning Architecture.

The synergistic interaction of vision and olfaction is critical for both natural and artificial intelligence systems to recognize and adapt to complex environments. However, current bioinspired systems with visual and olfactory sensations are mostly assembled with separate and heterogeneous sensors, inevitably leading to bulky systems and incompatible datasets. Here, we demonstrate on-chip integration of visual and olfactory sensations through a dual-focus imaging approach. By combining lens-based visual imaging and lensless colorimetric imaging, a target object and its odor fingerprint can be captured with a single complementary metal-oxide-semiconductor imager, and the obtained multimodal images are analyzed with a bionic learning architecture for information fusion and perception. To demonstrate the capability of this system, we adapted it to food detection and achieved 100% accuracy in identifying meat freshness and category with a 10 s sampling time. In addition to the highly integrated sensor design, our approach exhibits superior accuracy and efficiency in object recognition, providing a promising approach for robotic sensing and perception.

Drug resistance profile of Mycobacterium kansasii clinical isolates before and after 2-month empirical antimycobacterial treatment.

OBJECTIVES: Mycobacterium kansasii pulmonary disease is frequently misdiagnosed and treated as tuberculosis, especially in countries with high tuberculosis burden. This study aimed to investigate the drug resistance profile of M.kansasii in patients with M.kansasii pulmonary disease in Shanghai and to determine the variations in drug resistance after 2 months of antimycobacterial treatment. METHODS: All patients with a diagnosis of M.kansasii pulmonary disease from 2017 to 2019 in Shanghai were retrospectively analysed. Whole-genome sequencing was performed, and the minimum inhibitory concentration (MIC) to antimycobacterial drugs was measured using the broth microdilution method. RESULTS: In total, 191 patients had a diagnosis of M.kansasii pulmonary disease. Of them, 24.1% (46/191) had persistent positive culture after 2 months of antimycobacterial treatment. Whole-genome sequencing revealed that the 46 paired isolates had a difference of <17 single nucleotide polymorphisms, thus excluding the possibility of exogenous reinfection. More than 90% of the baseline isolates were sensitive to rifampin, clarithromycin, moxifloxacin, or amikacin, whereas a high resistance to ethambutol (118/191, 61.8%) and 4 µg/mL of isoniazid (32/191, 16.8%) were observed. Two isolates presented high resistance to rifamycin (i.e. a rifampin MIC of >8 µg/mL and a rifabutin MIC of 8 µg/mL) both containing the rpoB mutation (S454L). The increase of MIC to rifampin, ethambutol, and/or isoniazid was identified in 50.0% (23/46) of the patients. DISCUSSION: A high prevalence of innate resistance to ethambutol and isoniazid was observed among circulating M.kansasii clinical strains in Shanghai. The increase in drug resistance under empirical antimycobacterial treatment highlighted the urgency of definitive species identification before initiating treatment.

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.

Whole transcriptome analysis reveals non-coding RNA's competing endogenous gene pairs as novel form of motifs in serous ovarian cancer.

The non-coding RNA (ncRNA) regulation appears to be associated to the diagnosis and targeted therapy of complex diseases. Motifs of non-coding RNAs and genes in the competing endogenous RNA (ceRNA) network would probably contribute to the accurate prediction of serous ovarian carcinoma (SOC). We conducted a microarray study profiling the whole transcriptomes of eight human SOCs and eight controls and constructed a ceRNA network including mRNAs, long ncRNAs, and circular RNAs (circRNAs). Novel form of motifs (mRNA-ncRNA-mRNA) were identified from the ceRNA network and defined as non-coding RNA's competing endogenous gene pairs (ceGPs), using a proposed method denoised individualized pair analysis of gene expression (deiPAGE). 18 cricRNA's ceGPs (cceGPs) were identified from multiple cohorts and were fused as an indicator (SOC index) for SOC discrimination, which carried a high predictive capacity in independent cohorts. SOC index was negatively correlated with the CD8+/CD4+ ratio in tumour-infiltration, reflecting the migration and growth of tumour cells in ovarian cancer progression. Moreover, most of the RNAs in SOC index were experimentally validated involved in ovarian cancer development. Our results elucidate the discriminative capability of SOC index and suggest that the novel competing endogenous motifs play important roles in expression regulation and could be potential target for investigating ovarian cancer mechanism or its therapy.

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.

Online Accurate Detection of Breath Acetone Using Metal Oxide Semiconductor Gas Sensor and Diffusive Gas Separation.

Breath acetone (BrAce) level is an indicator of lipid oxidation rate, which is crucial for evaluating the status of ketoacidosis, ketogenic diet, and fat burning during exercise. Despite its usefulness, detecting BrAce accurately is challenging because exhaled breath contains an enormous variety of compounds. Although many sensors and devices have been developed for BrAce measurement, most of them were tested with only synthetic or spiked breath samples, and few can detect low concentration BrAce in an online manner, which is critical for extending application areas and the wide acceptance of the technology. Here, we show that online detection of BrAce can be achieved using a metal oxide semiconductor acetone sensor. The high accuracy measurement of low concentration BrAce was enabled by separating major interference gases utilizing their large diffusion coefficients, and the accuracy is further improved by the correction of humidity effect. We anticipate that the approach can push BrAce measurement closer to being useful for various applications.

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.

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.

A Robust and Generalizable Immune-Related Signature for Sepsis Diagnostics.

High-throughput sequencing can detect tens of thousands of genes in parallel, providing opportunities for improving the diagnostic accuracy of multiple diseases including sepsis, which is an aggressive inflammatory response to infection that can cause organ failure and death. Early screening of sepsis is essential in clinic, but no effective diagnostic biomarkers are available yet. Here, we present a novel method, Recurrent Logistic Regression, to identify diagnostic biomarkers for sepsis from the blood transcriptome data. A panel including five immune-related genes, LRRN3, IL2RB, FCER1A, TLR5, and S100A12, are determined as diagnostic biomarkers (LIFTS) for sepsis. LIFTS discriminates patients with sepsis from normal controls in high accuracy (AUROC = 0.9959 on average; IC = [0.9722-1.0]) on nine validation cohorts across three independent platforms, which outperforms existing markers. Our analysis determined an accurate prediction model and reproducible transcriptome biomarkers that can lay a foundation for clinical diagnostic tests and biological mechanistic studies.

Integrated Approaches for COVID-19 Case Finding and Their Impact on Timeliness for Disease Containment - Changning District, Shanghai Municipality, China, January-July, 2020.

What is already known on this topic? The demand for containing the virus and protecting the economy is high on the agenda of policymakers during the coronavirus disease 2019 (COVID-19) pandemic. Modelling studies indicated that highly effective contact tracing and case isolation were enough to contain the spread of COVID-19 at the early stages, but this has not been validated in real world contexts. What is added by this report? Integrated case finding approaches, including outpatient monitoring, exposed people quarantining, and contact tracing, effectively contained the spread of COVID-19 in a densely populated district in Shanghai Municipality, China. Active case-finding involving quarantine of exposed persons and contact tracing could reduce the time from symptom onset to COVID-19 diagnosis, thus reducing the risk of local transmission. What are the implications for public health practice? Active case-finding should be prioritized as an effective approach to minimize the risk of local transmission in future pandemics. Integrated COVID-19 case finding approaches applied in Shanghai may inform public health policy in other regions where strict lockdown is not applicable.