Genetic Diversity in Mycobacterium tuberculosis Clinical Isolates and Resulting Outcomes of Tuberculosis Infection and Disease.

Tuberculosis claims more human lives than any other bacterial infectious disease and represents a clear and present danger to global health as new tools for vaccination, treatment, and interruption of transmission have been slow to emerge. Additionally, tuberculosis presents with notable clinical heterogeneity, which complicates diagnosis, treatment, and the establishment of nonrelapsing cure. How this heterogeneity is driven by the diversity ofclinical isolates of the causative agent, Mycobacterium tuberculosis, has recently garnered attention. Herein, we review advances in the understanding of how naturally occurring variation in clinical isolates affects transmissibility, pathogenesis, immune modulation, and drug resistance. We also summarize how specific changes in transcriptional responses can modulate infection or disease outcome, together with strain-specific effects on gene essentiality. Further understanding of how this diversity of M. tuberculosis isolates affects disease and treatment outcomes will enable the development of more effective therapeutic options and vaccines for this dreaded disease.

Unveiling the underestimated direct emissions of nitrous acid (HONO).

Gaseous nitrous acid (HONO) is a critical source of hydroxyl radicals (OH) in the troposphere. While both direct and secondary sources contribute to atmospheric HONO, direct emissions have traditionally been considered minor contributors. In this study, we developed δ15N and δ18O isotopic fingerprints to identify six direct HONO emission sources and conducted a 1-y case study on the isotopic composition of atmospheric HONO at rural and urban sites. Interestingly, we identified that livestock farming is a previously overlooked direct source of HONO and determined its HONO to ammonia (NH3) emission ratio. Additionally, our results revealed that spatial and temporal variations in atmospheric HONO isotopic composition can be partially attributed to direct emissions. Through a detailed HONO budget analysis incorporating agricultural sources, we found that direct HONO emissions accounted for 39~45% of HONO production in rural areas across different seasons. The findings were further confirmed by chemistry transport model simulations, highlighting the significance of direct HONO emissions and their impact on air quality in the North China Plain. These findings provide compelling evidence that direct HONO emissions play a more substantial role in contributing to atmospheric HONO than previously believed. Moreover, the δ15N and δ18O isotopic fingerprints developed in this study may serve as a valuable tool for further research on the atmospheric chemistry of reactive nitrogen gases.

Ten simple rules for managing laboratory information.

Information is the cornerstone of research, from experimental (meta)data and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging laboratory information management systems to transform this large information load into useful scientific findings.

Phase variation in Mycobacterium tuberculosis glpK produces transiently heritable drug tolerance.

The length and complexity of tuberculosis (TB) therapy, as well as the propensity of Mycobacterium tuberculosis to develop drug resistance, are major barriers to global TB control efforts. M. tuberculosis is known to have the ability to enter into a drug-tolerant state, which may explain many of these impediments to TB treatment. We have identified a mechanism of genetically encoded but rapidly reversible drug tolerance in M. tuberculosis caused by transient frameshift mutations in a homopolymeric tract (HT) of 7 cytosines (7C) in the glpK gene. Inactivating frameshift mutations associated with the 7C HT in glpK produce small colonies that exhibit heritable multidrug increases in minimal inhibitory concentrations and decreases in drug-dependent killing; however, reversion back to a fully drug-susceptible large-colony phenotype occurs rapidly through the introduction of additional insertions or deletions in the same glpK HT region. These reversible frameshift mutations in the 7C HT of M. tuberculosis glpK occur in clinical isolates, accumulate in M. tuberculosis-infected mice with further accumulation during drug treatment, and exhibit a reversible transcriptional profile including induction of dosR and sigH and repression of kstR regulons, similar to that observed in other in vitro models of M. tuberculosis tolerance. These results suggest that GlpK phase variation may contribute to drug tolerance, treatment failure, and relapse in human TB. Drugs effective against phase-variant M. tuberculosis may hasten TB treatment and improve cure rates.

Multi-Mode Ultrasonic Guided Waves Based Damage Detection in L-Bars with Asymmetric Cross-Section with Sum of Multiple Signals Method.

Bars are significant load-carrying components in engineering structures. In particular, L-bars are typical structural components commonly used in truss structures and have typical irregular asymmetric cross-sections. To ensure the safety of load-carrying bars, much research has been done for non-destructive testing (NDT). Ultrasonic guided waves have been widely applied in various NDT techniques for bars as a result of the long-range propagation, low attenuation, and high sensitivity to damages. Though good for inspection of ultrasonic guided waves in symmetric cross-section bar-like structures, the application in asymmetric ones lacks further research. Moreover, traditional damage detection in bars using ultrasonic guided waves usually depends on a single-mode at a lower frequency with lower sensitivity and accuracy. To make full use of all frequencies and modes, a multi-mode characteristic-based damage detection method is presented with the sum of multiple signals (SoM) strategy for L-bars with asymmetric cross-section. To control the desired mode in multi-mode ultrasonic guided waves, excitation optimization and weighted gathering are carried out by the analysis of the semi-analytical finite element (SAFE) method and the normal mode expansion (NME) method. An L-bar example with the asymmetric cross-section of 35 mm × 20 mm × 3 mm is used to specialize the proposed method, and some finite element (FE) models have been simulated to validate the mode control. In addition, one PZT is applied as a contrast in order to validate the multielement mode control. Then, more FE simulations experiments for damage detection have been performed to validate the damage detection method and verify the improvement in detection accuracy and damage sensitivity.

Bacterial Factors That Predict Relapse after Tuberculosis Therapy.

BACKGROUND: Approximately 5% of patients with drug-susceptible tuberculosis have a relapse after 6 months of first-line therapy, as do approximately 20% of patients after 4 months of short-course therapy. We postulated that by analyzing pretreatment isolates of Mycobacterium tuberculosis obtained from patients who subsequently had a relapse or were cured, we could determine any correlations between the minimum inhibitory concentration (MIC) of a drug below the standard resistance breakpoint and the relapse risk after treatment. METHODS: Using data from the Tuberculosis Trials Consortium Study 22 (development cohort), we assessed relapse and cure isolates to determine the MIC values of isoniazid and rifampin that were below the standard resistance breakpoint (0.1 µg per milliliter for isoniazid and 1.0 µg per milliliter for rifampin). We combined this analysis with clinical, radiologic, and laboratory data to generate predictive relapse models, which we validated by analyzing data from the DMID 01-009 study (validation cohort). RESULTS: In the development cohort, the mean (±SD) MIC of isoniazid below the breakpoint was 0.0334±0.0085 µg per milliliter in the relapse group and 0.0286±0.0092 µg per milliliter in the cure group, which represented a higher value in the relapse group by a factor of 1.17 (P=0.02). The corresponding MIC values of rifampin were 0.0695±0.0276 and 0.0453±0.0223 µg per milliliter, respectively, which represented a higher value in the relapse group by a factor of 1.53 (P<0.001). Higher MIC values remained associated with relapse in a multivariable analysis that included other significant between-group differences. In an analysis of receiver-operating-characteristic curves of relapse based on these MIC values, the area under the curve (AUC) was 0.779. In the development cohort, the AUC in a multivariable model that included MIC values was 0.875. In the validation cohort, the MIC values either alone or combined with other patient characteristics were also predictive of relapse, with AUC values of 0.964 and 0.929, respectively. The use of a model score for the MIC values of isoniazid and rifampin to achieve 75.0% sensitivity in cross-validation analysis predicted relapse with a specificity of 76.5% in the development cohort and a sensitivity of 70.0% and a specificity of 100% in the validation cohort. CONCLUSIONS: In pretreatment isolates of M. tuberculosis with decrements of MIC values of isoniazid or rifampin below standard resistance breakpoints, higher MIC values were associated with a greater risk of relapse than lower MIC values. (Funded by the National Institute of Allergy and Infectious Diseases.).

Rapidly Correcting Frameshift Mutations in the Mycobacterium tuberculosis orn Gene Produce Reversible Ethambutol Resistance and Small-Colony-Variant Morphology.

We have identified a previously unknown mechanism of reversible high-level ethambutol (EMB) resistance in Mycobacterium tuberculosis that is caused by a reversible frameshift mutation in the M. tuberculosisorn gene. A frameshift mutation in orn produces the small-colony-variant (SCV) phenotype, but this mutation does not change the MICs of any drug for wild-type M. tuberculosis However, the same orn mutation in a low-level EMB-resistant double embB-aftA mutant (MIC = 8 µg/ml) produces an SCV with an EMB MIC of 32 µg/ml. Reversible resistance is indistinguishable from a drug-persistent phenotype, because further culture of these orn-embB-aftA SCV mutants results in rapid reversion of the orn frameshifts, reestablishing the correct orn open reading frame, returning the culture to normal colony size, and reversing the EMB MIC back to that (8 µg/ml) of the parental strain. Transcriptomic analysis of orn-embB-aftA mutants compared to wild-type M. tuberculosis identified a 27-fold relative increase in the expression of embC, which is a cellular target for EMB. Expression of embC in orn-embB-aftA mutants was also increased 5-fold compared to that in the parental embB-aftA mutant, whereas large-colony orn frameshift revertants of the orn-embB-aftA mutant had levels of embC expression similar to that of the parental embB-aftA strain. Reversible frameshift mutants may contribute to a reversible form of microbiological drug resistance in human tuberculosis.

Incipient and Subclinical Tuberculosis: a Clinical Review of Early Stages and Progression of Infection.

Tuberculosis (TB) is the leading infectious cause of mortality worldwide, due in part to a limited understanding of its clinical pathogenic spectrum of infection and disease. Historically, scientific research, diagnostic testing, and drug treatment have focused on addressing one of two disease states: latent TB infection or active TB disease. Recent research has clearly demonstrated that human TB infection, from latent infection to active disease, exists within a continuous spectrum of metabolic bacterial activity and antagonistic immunological responses. This revised understanding leads us to propose two additional clinical states: incipient and subclinical TB. The recognition of incipient and subclinical TB, which helps divide latent and active TB along the clinical disease spectrum, provides opportunities for the development of diagnostic and therapeutic interventions to prevent progression to active TB disease and transmission of TB bacilli. In this report, we review the current understanding of the pathogenesis, immunology, clinical epidemiology, diagnosis, treatment, and prevention of both incipient and subclinical TB, two emerging clinical states of an ancient bacterium.

Copper(II) ions enhance the peroxidase-like activity and stability of keratin-capped gold nanoclusters for the colorimetric detection of glucose.

A method is described for the preparation of copper(II)-modified keratin-capped gold nanoclusters (AuNCs) with adjustable Au/Cu molar ratio through a two-step synthetic route. The introduction of Cu(II) is known to cause quenching of the fluorescence of such AuNCs. It is found, however, that the Cu(II) loaded AuNC (AuNC-Cu2+) display strongly enhanced peroxidase-like activity and improved chemical stability. This is assumed to be due to the synergistic effect of the gold and copper atoms and in contrast to the single components (pure AuNCs and copper ions). The kinetic parameters of the new peroxidase mimic show a higher Kcat value (12.1 × 10-4 s-1) and a lower Km value (53 µM) for H2O2 (compared to those of conventional AuNCs). The catalytic activity is stable and remains essentially unchanged after two months. The interactions of AuNCs with Cu(II) were characterized by fluorescence spectroscopy, UV-vis spectroscopy and X-ray photoelectron spectroscopy. Based on these findings, a glucose colorimetric assay at 452 nm was developed that has a detection range from 1.6 to 800 µM and a 0.26 µM detection limit. Graphical abstract Copper ion-modified keratin-capped gold nanoclusters (AuNC-Cu2+) exhibit enhanced peroxidase-like activity owing to the synergistic effect of the gold and copper atoms which is in contrast to pure AuNCs.

Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast.

Gene regulatory and metabolic network models have been used successfully in many organisms, but inherent differences between them make networks difficult to integrate. Probabilistic Regulation Of Metabolism (PROM) provides a partial solution, but it does not incorporate network inference and underperforms in eukaryotes. We present an Integrated Deduced And Metabolism (IDREAM) method that combines statistically inferred Environment and Gene Regulatory Influence Network (EGRIN) models with the PROM framework to create enhanced metabolic-regulatory network models. We used IDREAM to predict phenotypes and genetic interactions between transcription factors and genes encoding metabolic activities in the eukaryote, Saccharomyces cerevisiae. IDREAM models contain many fewer interactions than PROM and yet produce significantly more accurate growth predictions. IDREAM consistently outperformed PROM using any of three popular yeast metabolic models and across three experimental growth conditions. Importantly, IDREAM's enhanced accuracy makes it possible to identify subtle synthetic growth defects. With experimental validation, these novel genetic interactions involving the pyruvate dehydrogenase complex suggested a new role for fatty acid-responsive factor Oaf1 in regulating acetyl-CoA production in glucose grown cells.

Fucoidan Extracted from Undaria pinnatifida: Source for Nutraceuticals/Functional Foods.

The importance of fucoidan as a functional ingredient in food, health products, and pharmaceutics is well-recognized due to its beneficial biological effects. Fucoidan is usually extracted from brown seaweeds, including Undaria pinnatifida. Fucoidan exhibits beneficial bio-activity and has antioxidant, anticancer, and anticoagulant properties. This review focuses on the biological activity of U. pinnatifida-derived fucoidan and investigates its structure⁻activity or fraction⁻activity relationship. It also describes several fucoidan extracts, along with their claimed anticancer effects. It aims to provide information and thoughts for future research such as the development of fucoidan into functional foods or nutraceuticals.

Integrated Modeling of Gene Regulatory and Metabolic Networks in Mycobacterium tuberculosis.

Mycobacterium tuberculosis (MTB) is the causative bacterium of tuberculosis, a disease responsible for over a million deaths worldwide annually with a growing number of strains resistant to antibiotics. The development of better therapeutics would greatly benefit from improved understanding of the mechanisms associated with MTB responses to different genetic and environmental perturbations. Therefore, we expanded a genome-scale regulatory-metabolic model for MTB using the Probabilistic Regulation of Metabolism (PROM) framework. Our model, MTBPROM2.0, represents a substantial knowledge base update and extension of simulation capability. We incorporated a recent ChIP-seq based binding network of 2555 interactions linking to 104 transcription factors (TFs) (representing a 3.5-fold expansion of TF coverage). We integrated this expanded regulatory network with a refined genome-scale metabolic model that can correctly predict growth viability over 69 source metabolite conditions and predict metabolic gene essentiality more accurately than the original model. We used MTBPROM2.0 to simulate the metabolic consequences of knocking out and overexpressing each of the 104 TFs in the model. MTBPROM2.0 improves performance of knockout growth defect predictions compared to the original PROM MTB model, and it can successfully predict growth defects associated with TF overexpression. Moreover, condition-specific models of MTBPROM2.0 successfully predicted synergistic growth consequences of overexpressing the TF whiB4 in the presence of two standard anti-TB drugs. MTBPROM2.0 can screen in silico condition-specific transcription factor perturbations to generate putative targets of interest that can help prioritize future experiments for therapeutic development efforts.

Multi-study integration of brain cancer transcriptomes reveals organ-level molecular signatures.

We utilized abundant transcriptomic data for the primary classes of brain cancers to study the feasibility of separating all of these diseases simultaneously based on molecular data alone. These signatures were based on a new method reported herein--Identification of Structured Signatures and Classifiers (ISSAC)--that resulted in a brain cancer marker panel of 44 unique genes. Many of these genes have established relevance to the brain cancers examined herein, with others having known roles in cancer biology. Analyses on large-scale data from multiple sources must deal with significant challenges associated with heterogeneity between different published studies, for it was observed that the variation among individual studies often had a larger effect on the transcriptome than did phenotype differences, as is typical. For this reason, we restricted ourselves to studying only cases where we had at least two independent studies performed for each phenotype, and also reprocessed all the raw data from the studies using a unified pre-processing pipeline. We found that learning signatures across multiple datasets greatly enhanced reproducibility and accuracy in predictive performance on truly independent validation sets, even when keeping the size of the training set the same. This was most likely due to the meta-signature encompassing more of the heterogeneity across different sources and conditions, while amplifying signal from the repeated global characteristics of the phenotype. When molecular signatures of brain cancers were constructed from all currently available microarray data, 90% phenotype prediction accuracy, or the accuracy of identifying a particular brain cancer from the background of all phenotypes, was found. Looking forward, we discuss our approach in the context of the eventual development of organ-specific molecular signatures from peripheral fluids such as the blood.

In vivo screening for toxicity-modulating drug interactions identifies antagonism that protects against ototoxicity in zebrafish.

Introduction: Ototoxicity is a debilitating side effect of over 150 medications with diverse mechanisms of action, many of which could be taken concurrently to treat multiple conditions. Approaches for preclinical evaluation of drug-drug interactions that might impact ototoxicity would facilitate design of safer multi-drug regimens and mitigate unsafe polypharmacy by flagging combinations that potentially cause adverse interactions for monitoring. They may also identify protective agents that antagonize ototoxic injury. Methods: To address this need, we have developed a novel workflow that we call Parallelized Evaluation of Protection and Injury for Toxicity Assessment (PEPITA), which empowers high-throughput, semi-automated quantification of ototoxicity and otoprotection in zebrafish larvae via microscopy. We used PEPITA and confocal microscopy to characterize in vivo the consequences of drug-drug interactions on ototoxic drug uptake and cellular damage of zebrafish lateral line hair cells. Results and discussion: By applying PEPITA to measure ototoxic drug interaction outcomes, we discovered antagonistic interactions between macrolide and aminoglycoside antibiotics that confer protection against aminoglycoside-induced damage to lateral line hair cells in zebrafish larvae. Co-administration of either azithromycin or erythromycin in zebrafish protected against damage from a broad panel of aminoglycosides, at least in part via inhibiting drug uptake into hair cells via a mechanism independent from hair cell mechanotransduction. Conversely, combining macrolides with aminoglycosides in bacterial inhibition assays does not show antagonism of antimicrobial efficacy. The proof-of-concept otoprotective antagonism suggests that combinatorial interventions can potentially be developed to protect against other forms of toxicity without hindering on-target drug efficacy.

Sinusoidal high-intensity exercise does not elicit ventilatory limitation in chronic obstructive pulmonary disease.

During exercise at critical power (CP) in chronic obstructive pulmonary disease (COPD) patients, ventilation approaches its maximum. As a result of the slow ventilatory dynamics in COPD, ventilatory limitation during supramaximal exercise might be escaped using rapid sinusoidal forcing. Nine COPD patients [age, 60.2 ± 6.9 years; forced expiratory volume in the first second (FEV(1)), 42 ± 17% of predicted; and FEV(1)/FVC, 39 ± 12%] underwent an incremental cycle ergometer test and then four constant work rate cycle ergometer tests; tolerable duration (t(lim)) was recorded. Critical power was determined from constant work rate testing by linear regression of work rate versus 1/t(lim). Patients then completed fast (FS; 60 s period) and slow (SS; 360 s period) sinusoidally fluctuating exercise tests with mean work rate at CP and peak at 120% of peak incremental test work rate, and one additional test at CP; each for a 20 min target. The value of t(lim) did not differ between CP (19.8 ± 0.6 min) and FS (19.0 ± 2.5 min), but was shorter in SS (13.2 ± 4.2 min; P < 0.05). The sinusoidal ventilatory amplitude was minimal (37.4 ± 34.9 ml min(-1) W(-1)) during FS but much larger during SS (189.6 ± 120.4 ml min(-1) W(-1)). The total ventilatory response in SS reached 110 ± 8.0% of the incremental test peak, suggesting ventilatory limitation. Slow components in ventilation during constant work rate and FS exercises were detected in most subjects and contributed appreciably to the total response asymptote. The SS exercise was associated with higher mid-exercise lactate concentrations (5.2 ± 1.7, 7.6 ± 1.7 and 4.5 ± 1.3 mmol l(-1) in FS, SS and CP). Large-amplitude, rapid sinusoidal fluctuation in work rate yields little fluctuation in ventilation despite reaching 120% of the incremental test peak work rate. This high-intensity exercise strategy might be suitable for programmes of rehabilitative exercise training in COPD.

A review on guided-ultrasonic-wave-based structural health monitoring: From fundamental theory to machine learning techniques.

The development of structural health monitoring (SHM) techniques is of great importance to improve the structural efficiency and safety. With advantages of long propagation distances, high damage sensitivity, and economic feasibility, guided-ultrasonic-wave-based SHM is recognized as one of the most promising technologies for large-scale engineering structures. However, the propagation characteristics of guided ultrasonic waves in in-service engineering structures are highly complex, which results in difficulties in developing precise and efficient signal feature mining methods. The damage identification efficiency and reliability of existing guided ultrasonic wave methods cannot meet engineering requirements. With the development of machine learning (ML), numerous researchers have proposed improved ML methods that can be incorporated into guided ultrasonic wave diagnostic techniques for SHM of actual engineering structures. To highlight their contributions, this paper provides a state-of-the-art overview of the guided-wave-based SHM techniques enabled by ML methods. Accordingly, multiple stages required for ML-based guided ultrasonic wave techniques are discussed, including guided ultrasonic wave propagation modeling, guided ultrasonic wave data acquisition, wave signal pre-processing, guided wave data-based ML modeling, and physics-based ML modeling. By placing ML methods in the context of the guided-wave-based SHM for actual engineering structures, this paper also provides insights into future prospects and research strategies.

The Mycobacterium tuberculosis protein O-phosphorylation landscape.

Bacterial phosphosignalling has been synonymous with two-component systems and their histidine kinases, but many bacteria, including Mycobacterium tuberculosis (Mtb), also code for Ser/Thr protein kinases (STPKs). STPKs are the main phosphosignalling enzymes in eukaryotes but the full extent of phosphorylation on protein Ser/Thr and Tyr (O-phosphorylation) in bacteria is untested. Here we explored the global signalling capacity of the STPKs in Mtb using a panel of STPK loss-of-function and overexpression strains combined with mass spectrometry-based phosphoproteomics. A deep phosphoproteome with >14,000 unique phosphosites shows that O-phosphorylation in Mtb is a vastly underexplored protein modification that affects >80% of the proteome and extensively interfaces with the transcriptional machinery. Mtb O-phosphorylation gives rise to an expansive, distributed and cooperative network of a complexity that has not previously been seen in bacteria and that is on par with eukaryotic phosphosignalling networks. A resource of >3,700 high-confidence direct substrate-STPK interactions and their transcriptional effects provides signalling context for >80% of Mtb proteins and allows the prediction and assembly of signalling pathways for mycobacterial physiology.

In vivo screening for toxicity-modulating drug interactions identifies antagonism that protects against ototoxicity in zebrafish.

Ototoxicity is a debilitating side effect of over 150 medications with diverse mechanisms of action, many of which could be taken concurrently to treat multiple conditions. Approaches for preclinical evaluation of drug interactions that might impact ototoxicity would facilitate design of safer multi-drug regimens and mitigate unsafe polypharmacy by flagging combinations that potentially cause adverse interactions for monitoring. They may also identify protective agents that antagonize ototoxic injury. To address this need, we have developed a novel workflow that we call Parallelized Evaluation of Protection and Injury for Toxicity Assessment (PEPITA), which empowers high-throughput, semi-automated quantification of ototoxicity and otoprotection in zebrafish larvae. By applying PEPITA to characterize ototoxic drug interaction outcomes, we have discovered antagonistic interactions between macrolide and aminoglycoside antibiotics that confer protection against aminoglycoside-induced damage to lateral line hair cells in zebrafish larvae. Co-administration of either azithromycin or erythromycin in zebrafish protected against damage from a broad panel of aminoglycosides, at least in part via inhibiting drug uptake into hair cells via a mechanism independent from hair cell mechanotransduction. Conversely, combining macrolides with aminoglycosides in bacterial inhibition assays does not show antagonism of antimicrobial efficacy. The proof-of-concept otoprotective antagonism suggests that combinatorial interventions can potentially be developed to protect against other forms of toxicity without hindering on-target drug efficacy.

CsPbBr3-Based Nanostructures for Room-Temperature Sensing of Volatile Organic Compounds.

All-inorganic halide perovskites, as a dominant member of the perovskite family, have been proven to be excellent semiconductors due to the great successes for solar cells, light-emitting diodes, photodetectors, and nanocrystal photocatalysts. Despite the remarkable advances in those fields, there are few research studies focusing on gas and humidity-sensing performances, especially for pure CsPbBr3 and heterogeneous CsPbBr3@MoS2 composites. Here, we first report a valuable CsPbBr3 sensor prepared by electrospinning, and the excellent gas sensing performances are investigated. The CsPbBr3 sensor can quickly and effectively detect ethanolamine at room temperature. The response time is only 16 s, and the response to 100 ppm ethanolamine is as high as 29.87, besides the excellent repeatability and good stability. The theoretical detection limit is estimated to be 21 ppb. Furthermore, considering the irreplaceable role of heterostructures in regulating the electronic structure and supporting rich reaction boundaries, we also actively explored the EA sensitivity of inorganic CsPbBr3-based heterogeneous composites CsPbBr3@MoS2. At the same time, the roles of the critical capping agents OA and OAm are systematically investigated. This work demonstrates the great potential of all-inorganic halide perovskites in promising volatile organic compound detection.

Early alveolar macrophage response and IL-1R-dependent T cell priming determine transmissibility of Mycobacterium tuberculosis strains.

Mechanisms underlying variability in transmission of Mycobacterium tuberculosis strains remain undefined. By characterizing high and low transmission strains of M.tuberculosis in mice, we show here that high transmission M.tuberculosis strain induce rapid IL-1R-dependent alveolar macrophage migration from the alveolar space into the interstitium and that this action is key to subsequent temporal events of early dissemination of bacteria to the lymph nodes, Th1 priming, granulomatous response and bacterial control. In contrast, IL-1R-dependent alveolar macrophage migration and early dissemination of bacteria to lymph nodes is significantly impeded in infection with low transmission M.tuberculosis strain; these events promote the development of Th17 immunity, fostering neutrophilic inflammation and increased bacterial replication. Our results suggest that by inducing granulomas with the potential to develop into cavitary lesions that aids bacterial escape into the airways, high transmission M.tuberculosis strain is poised for greater transmissibility. These findings implicate bacterial heterogeneity as an important modifier of TB disease manifestations and transmission.