Discovery of urinary biosignatures for tuberculosis and nontuberculous mycobacteria classification using metabolomics and machine learning.

Nontuberculous mycobacteria (NTM) infection diagnosis remains a challenge due to its overlapping clinical symptoms with tuberculosis (TB), leading to inappropriate treatment. Herein, we employed noninvasive metabolic phenotyping coupled with comprehensive statistical modeling to discover potential biomarkers for the differential diagnosis of NTM infection versus TB. Urine samples from 19 NTM and 35 TB patients were collected, and untargeted metabolomics was performed using rapid liquid chromatography-mass spectrometry. The urine metabolome was analyzed using a combination of univariate and multivariate statistical approaches, incorporating machine learning. Univariate analysis revealed significant alterations in amino acids, especially tryptophan metabolism, in NTM infection compared to TB. Specifically, NTM infection was associated with upregulated levels of methionine but downregulated levels of glutarate, valine, 3-hydroxyanthranilate, and tryptophan. Five machine learning models were used to classify NTM and TB. Notably, the random forest model demonstrated excellent performance [area under the receiver operating characteristic (ROC) curve greater than 0.8] in distinguishing NTM from TB. Six potential biomarkers for NTM infection diagnosis, including methionine, valine, glutarate, 3-hydroxyanthranilate, corticosterone, and indole-3-carboxyaldehyde, were revealed from univariate ROC analysis and machine learning models. Altogether, our study suggested new noninvasive biomarkers and laid a foundation for applying machine learning to NTM differential diagnosis.

Time-course cross-species transcriptomics reveals conserved hepatotoxicity pathways induced by repeated administration of cyclosporine A.

Cyclosporine A (CsA) has shown efficacy against immunity-related diseases despite its toxicity in various organs, including the liver, emphasizing the need to elucidate its underlying hepatotoxicity mechanism. This study aimed to capture the alterations in genome-wide expression over time and the subsequent perturbations of corresponding pathways across species. Six data from humans, mice, and rats, including animal liver tissue, human liver microtissues, and two liver cell lines exposed to CsA toxic dose, were used. The microtissue exposed to CsA for 10 d was analyzed to obtain dynamically differentially expressed genes (DEGs). Single-time points data at 1, 3, 5, 7, and 28 d of different species were used to provide additional evidence. Using liver microtissue-based longitudinal design, DEGs that were consistently up- or down-regulated over time were captured, and the well-known mechanism involved in CsA toxicity was elucidated. Thirty DEGs that consistently changed in longitudinal data were also altered in 28-d rat in-house data with concordant expression. Some genes (e.g. TUBB2A, PLIN2, APOB) showed good concordance with identified DEGs in 1-d and 7-d mouse data. Pathway analysis revealed up-regulations of protein processing, asparagine N-linked glycosylation, and cargo concentration in the endoplasmic reticulum. Furthermore, the down-regulations of pathways related to biological oxidations and metabolite and lipid metabolism were elucidated. These pathways were also enriched in single-time-point data and conserved across species, implying their biological significance and generalizability. Overall, the human organoids-based longitudinal design coupled with cross-species validation provides temporal molecular change tracking, aiding mechanistic elucidation and biologically relevant biomarker discovery.

Enhancing Sensitivity in Gas Detection: Porous Structures in Organic Field-Effect Transistor-Based Sensors.

Gas detection is crucial for detecting environmentally harmful gases. Organic field-effect transistor (OFET)-based gas sensors have attracted attention due to their promising performance and potential for integration into flexible and wearable devices. This review examines the operating mechanisms of OFET-based gas sensors and explores methods for improving sensitivity, with a focus on porous structures. Researchers have achieved significant enhancements in sensor performance by controlling the thickness and free volume of the organic semiconductor layer. Additionally, innovative fabrication techniques like self-assembly and etching have been used to create porous structures, facilitating the diffusion of target gas molecules, and improving sensor response and recovery. These advancements in porous structure fabrication suggest a promising future for OFET-based gas sensors, offering increased sensitivity and selectivity across various applications.

Application of the Hollow-Fiber Infection Model to Personalized Precision Dosing of Isoniazid in a Clinical Setting.

BACKGROUND: The hollow-fiber infection model (HFIM) is a valuable tool for evaluating pharmacokinetics/pharmacodynamics relationships and determining the optimal antibiotic dose in monotherapy or combination therapy, but the application for personalized precision medicine in tuberculosis treatment remains limited. This study aimed to evaluate the efficacy of adjusted antibiotic doses for a tuberculosis patient using HFIM. METHODS: Model-based Bayesian forecasting was utilized to assess the proposed reduction of the isoniazid dose from 300 mg daily to 150 mg daily in a patient with an ultra-slow-acetylation phenotype. The efficacy of the adjusted 150-mg dose was evaluated in a time-to-kill assay performed using the bacterial isolate Mycobacterium tuberculosis (Mtb) H37Ra in a HFIM that mimicked the individual pharmacokinetic profile of the patient. RESULTS: The isoniazid concentration observed in the HFIM adequately reflected the target drug exposures simulated by the model. After 7 days of repeated dose administration, isoniazid killed 4 log10 Mtb CFU/mL in the treatment arm, while the control arm without isoniazid increased 1.6 log10 CFU/mL. CONCLUSION: Our results provide an example of the utility of the HFIM for predicting the efficacy of specific recommended doses of anti-tuberculosis drugs in real clinical setting.

Omics analysis unveils changes in the metabolome and lipidome of Caenorhabditis elegans upon polydopamine exposure.

Polydopamine (PDA) is an insoluble biopolymer with a dark brown-black color that forms through the autoxidation of dopamine. Because of its outstanding biocompatibility and durability, PDA holds enormous promise for various applications, both in the biomedical and non-medical domains. To ensure human safety, protect health, and minimize environmental impacts, the assessment of PDA toxicity is important. In this study, metabolomics and lipidomics assessed the impact of acute PDA exposure on Caenorhabditis elegans (C. elegans). The findings revealed a pronounced perturbation in the metabolome and lipidome of C. elegans at the L4 stage following 24 hours of exposure to 100 µg/mL PDA. The changes in lipid composition varied based on lipid classes. Increased lipid classes included lysophosphatidylethanolamine, triacylglycerides, and fatty acids, while decreased species involved in several sub-classes of glycerophospholipids and sphingolipids. Besides, we detected 37 significantly affected metabolites in the positive and 8 in the negative ion modes due to exposure to PDA in C. elegans. The metabolites most impacted by PDA exposure were associated with purine metabolism, biosynthesis of valine, leucine, and isoleucine; aminoacyl-tRNA biosynthesis; and cysteine and methionine metabolism, along with pantothenate and CoA biosynthesis; the citrate cycle (TCA cycle); and beta-alanine metabolism. In conclusion, PDA exposure may intricately influence the metabolome and lipidome of C. elegans. The combined application of metabolomics and lipidomics offers additional insights into the metabolic perturbations involved in PDA-induced biological effects and presents potential biomarkers for the assessment of PDA safety.

Green synthesis and characterization of α-Mn2O3 nanoparticles for antibacterial activity and efficient visible-light photocatalysis.

In this study, green synthesis, characterizations, photocatalytic performance, and antibacterial applications of α-Mn2O3 nanoparticles are reported. The synthesized nanoparticles were characterized by Fourier transform infrared spectroscopy (FT-IR), powder X-ray diffraction (XRD), transmission electron microscope (TEM), Scanning electron microscopy (SEM), energy dispersive X-ray analysis (EDX), Brunauer Emmett Teller (BET), Electrochemical Impedance Spectroscopy (EIS), Photoluminescence (PL), and Differential reflectance spectroscopy (DRS) analysis. The investigation verified that the α-Mn2O3 nanoparticles possessed a cubic structure, with a crystallite size of 23 nm. The SEM and TEM techniques were used to study the nanoscale morphology of α- Mn2O3 nanoparticles, which were found to be spherical with a size of 30 nm. Moreover, the surface area was obtained as 149.9 m2 g-1 utilizing BET analysis, and the band gap was determined to be 1.98 eV by DRS analysis. The photocatalysis performance of the α-Mn2O3 NPs was evaluated for degrading Eriochrome Black T (EBT) dye under visible light and degradation efficiency was 96% in 90 min. The photodegradation mechanism of EBT dye was clarified with the use of radical scavenger agents, and the degradation pathway was confirmed through Liquid Chromatography-Mass Spectrometry (LC-MS) analysis. Additionally, the produced nanoparticles could be extracted from the solution and continued to exhibit photocatalysis even after five repeated runs under the same optimal conditions. Also, the antibacterial activity of green synthesized α-Mn2O3 nanoparticles was investigated by using the broth microdilution method towards Enterococcus faecalis ATCC 29212 (Gram-positive), Staphylococcus aureus ATCC 29213 (Gram-positive), Salmonella typhimurium ATCC 14028 (Gram-negative), Klebsiella pneumoniae ATCC 7881 (Gram-negative), Escherichia coli ATCC 25922 (Gram-negative), Proteus mirabilis ATCC 7002 (Gram-negative), and Pseudomonas aeruginosa ATCC 27853 (Gram-negative) bacterial strains.

Metabolic phenotyping and global functional analysis facilitate metabolic signature discovery for tuberculosis treatment monitoring.

Tracking alterations in polar metabolite and lipid levels during anti-tuberculosis (TB) interventions is an emerging biomarker discovery and validation approach due to its sensitivity in capturing changes and reflecting on the host status. Here, we employed deep plasma metabolic phenotyping to explore the TB patient metabolome during three phases of treatment: at baseline, during intensive phase treatment, and upon treatment completion. Differential metabolites (DMs) in each period were determined, and the pathway-level biological alterations were explored by untargeted metabolomics-guided functional interpretations that bypassed identification. We identified 41 DMs and 39 pathways that changed during intensive phase completion. Notably, levels of certain amino acids including histidine, bile acids, and metabolites of purine metabolism were dramatically increased. The altered pathways included those involved in the metabolism of amino acids, glycerophospholipids, and purine. At the end of treatment, 44 DMs were discovered. The levels of glutamine, bile acids, and lysophosphatidylinositol significantly increased compared to baseline; the levels of carboxylates and hypotaurine declined. In addition, 37 pathways principally associated with the metabolism of amino acids, carbohydrates, and glycan altered at treatment completion. The potential of each DM for diagnosing TB was examined using a cohort consisting of TB patients, those with latent infections, and controls. Logistic regression revealed four biomarkers (taurine, methionine, glutamine, and acetyl-carnitine) that exhibited excellent performance in differential diagnosis. In conclusion, we identified metabolites that could serve as useful metabolic signatures for TB management and elucidated underlying biological processes affected by the crosstalk between host and TB pathogen during treatment.

Population pharmacokinetic model of rifampicin for personalized tuberculosis pharmacotherapy: Effects of SLCO1B1 polymorphisms on drug exposure.

BACKGROUND: Rifampicin (RIF) exhibits high pharmacokinetic (PK) variability among individuals; a low plasma concentration might result in unfavorable treatment outcomes and drug resistance. This study evaluated the contributions of non- and genetic factors to the interindividual variability of RIF exposure, then suggested initial doses for patients with different weight bands. METHODS: This multicenter prospective cohort study in Korea analyzed demographic and clinical data, the solute carrier organic anion transporter family member 1B1 (SLCO1B1) genotypes, and RIF concentrations. Population PK modeling and simulations were conducted using nonlinear mixed-effect modeling. RESULTS: In total, 879 tuberculosis (TB) patients were divided into a training dataset (510 patients) and a test dataset (359 patients). A one-compartment model with allometric scaling for effect of body size best described the RIF PKs. The apparent clearance (CL/F) was 16.6% higher among patients in the SLCO1B1 rs4149056 wild-type group than among patients in variant group, significantly decreasing RIF exposure in the wild-type group. The developed model showed better predictive performance compared with previously reported models. We also suggested that patients with body weights of <40 kg, 40-55 kg, 55-70 kg, and >70 kg patients receive RIF doses of 450, 600, 750, and 1050 mg/day, respectively. CONCLUSIONS: Total body weight and SLCO1B1 rs4149056 genotypes were the most significant covariates that affected RIF CL/F variability in Korean TB patients. We suggest initial doses of RIF based on World Health Organization weight-band classifications. The model may be implemented in treatment monitoring for TB patients.

Alterations of lipid-related genes during anti-tuberculosis treatment: insights into host immune responses and potential transcriptional biomarkers.

Background: The optimal diagnosis and treatment of tuberculosis (TB) are challenging due to underdiagnosis and inadequate treatment monitoring. Lipid-related genes are crucial components of the host immune response in TB. However, their dynamic expression and potential usefulness for monitoring response to anti-TB treatment are unclear. Methodology: In the present study, we used a targeted, knowledge-based approach to investigate the expression of lipid-related genes during anti-TB treatment and their potential use as biomarkers of treatment response. Results and discussion: The expression levels of 10 genes (ARPC5, ACSL4, PLD4, LIPA, CHMP2B, RAB5A, GABARAPL2, PLA2G4A, MBOAT2, and MBOAT1) were significantly altered during standard anti-TB treatment. We evaluated the potential usefulness of this 10-lipid-gene signature for TB diagnosis and treatment monitoring in various clinical scenarios across multiple populations. We also compared this signature with other transcriptomic signatures. The 10-lipid-gene signature could distinguish patients with TB from those with latent tuberculosis infection and non-TB controls (area under the receiver operating characteristic curve > 0.7 for most cases); it could also be useful for monitoring response to anti-TB treatment. Although the performance of the new signature was not better than that of previous signatures (i.e., RISK6, Sambarey10, Long10), our results suggest the usefulness of metabolism-centric biomarkers. Conclusions: Lipid-related genes play significant roles in TB pathophysiology and host immune responses. Furthermore, transcriptomic signatures related to the immune response and lipid-related gene may be useful for TB diagnosis and treatment monitoring.

Fiber-Type Transistor-Based Chemical and Physical Sensors Using Conjugated Polymers.

Fiber-type electronics is a crucial field for realizing wearable electronic devices with a wide range of sensing applications. In this paper, we begin by discussing the fabrication of fibers from conjugated polymers. We then explore the utilization of these fibers in the development of field-effect and electrochemical transistors. Finally, we investigate the diverse applications of these fiber-type transistors, encompassing chemical and physical sensors. Our paper aims to offer a comprehensive understanding of the use of conjugated polymers in fiber-type transistor-based sensors.

Natalizumab Treatment Induces Proinflammatory CD4 T Cells Preferentially in the Integrin ß7+ Compartment.

BACKGROUND AND OBJECTIVES: Natalizumab, a monoclonal humanized antibody targeting integrin α4, inhibits the transmigration of lymphocytes into the CNS by preventing the interaction of integrin α4ß1 with V-CAM expressed on brain vascular endothelial cells. Although natalizumab treatment reduces the clinical relapse rate in patients with relapsing-remitting MS, its discontinuation after reactivation of the JC virus is associated with a rebound of the disease in 20% of patients. The mechanisms of this rebound are not elucidated, but natalizumab increases the frequencies of circulating CD4 T cells expressing proinflammatory cytokines as well as the proportion of circulating Th17/Th1 cells (Th1-like Th17 cells). Gut-derived memory CD4 T cells are a population of growing interest in the pathogenesis of MS, but whether and how their properties are affected by natalizumab is not known. Here, we studied the phenotype and cytokine expression profile of circulating gut-derived memory CD4 T cells in patients with relapsing-remitting MS under natalizumab. METHODS: We identified gut-derived memory CD4 T cells by their expression of integrin ß7 and compared their properties and those of integrin ß7- memory CD4 T cells across healthy donors and patients with relapsing-remitting MS treated or not with natalizumab. We also compared the capacity of integrin ß7- and integrin ß7+ CD4 T-cell subsets to transmigrate in vitro across a model of blood-brain barrier. RESULTS: The proportions of proinflammatory Th17/Th1 cells as well as of IL-17A+IFNγ+ and IL-17A+GM-CSF+ cells were higher in memory CD4 T cells expressing integrin ß7 in patients receiving natalizumab compared with healthy donors and patients with relapsing-remitting MS not receiving natalizumab. By contrast, integrin ß7 negative memory CD4 T cells only presented a modest increased in their proportion of Th17/Th1 cells under natalizumab. We further observed that integrin ß7+ Th17/Th1 cells migrated as efficiently as integrin ß7- Th17/Th1 across a monolayer of brain microvascular endothelial cells. DISCUSSION: Our study shows that circulating integrin ß7+ memory CD4 T cells of patients with relapsing-remitting MS under natalizumab are enriched in proinflammatory cells supporting the hypothesis that integrin ß7+ memory CD4 T cells could play a pathogenic role in the disease rebound observed at natalizumab discontinuation.

Optimization of Gas-Sensing Properties in Poly(triarylamine) Field-Effect Transistors by Device and Interface Engineering.

In this study, we investigated the gas-sensing mechanism in bottom-gate organic field-effect transistors (OFETs) using poly(triarylamine) (PTAA). A comparison of different device architectures revealed that the top-contact structure exhibited superior gas-sensing performance in terms of field-effect mobility and sensitivity. The thickness of the active layer played a critical role in enhancing these parameters in the top-contact structure. Moreover, the distance and pathway for charge carriers to reach the active channel were found to significantly influence the gas response. Additionally, the surface treatment of the SiO2 dielectric with hydrophobic self-assembled mono-layers led to further improvement in the performance of the OFETs and gas sensors by effectively passivating the silanol groups. Under optimal conditions, our PTAA-based gas sensors achieved an exceptionally high response (>200%/ppm) towards NO2. These findings highlight the importance of device and interface engineering for optimizing gas-sensing properties in amorphous polymer semiconductors, offering valuable insights for the design of advanced gas sensors.

Pathway-level multi-omics analysis of the molecular mechanisms underlying the toxicity of long-term tacrolimus exposure.

Tacrolimus (TAC)-based treatment is associated with nephrotoxicity and hepatotoxicity; however, the underlying molecular mechanisms responsible for this toxicity have not been fully explored. This study elucidated the molecular processes underlying the toxic effects of TAC using an integrative omics approach. Rats were sacrificed after 4 weeks of daily oral TAC administration at a dose of 5 mg/kg. The liver and kidney underwent genome-wide gene expression profiling and untargeted metabolomics assays. Molecular alterations were identified using individual data profiling modalities and further characterized by pathway-level transcriptomics-metabolomics integration analysis. Metabolic disturbances were mainly related to an imbalance in oxidant-antioxidant status, as well as in lipid and amino acid metabolism in the liver and kidney. Gene expression profiles also indicated profound molecular alterations, including in genes associated with a dysregulated immune response, proinflammatory signals, and programmed cell death in the liver and kidney. Joint-pathway analysis indicated that the toxicity of TAC was associated with DNA synthesis disruption, oxidative stress, and cell membrane permeabilization, as well as lipid and glucose metabolism. In conclusion, our pathway-level integration of transcriptome and metabolome and conventional analyses of individual omics profiles, provided a more comprehensive picture of the molecular changes resulting from TAC toxicity. This study also serves as a valuable resource for subsequent investigations aiming to understand the mechanism underlying the molecular toxicology of TAC.

Multimodal plasma metabolomics and lipidomics in elucidating metabolic perturbations in tuberculosis patients with concurrent type 2 diabetes.

Type 2 diabetes mellitus (DM) poses a major burden for the treatment and control of tuberculosis (TB). Characterization of the underlying metabolic perturbations in DM patients with TB infection would yield insights into the pathophysiology of TB-DM, thus potentially leading to improvements in TB treatment. In this study, a multimodal metabolomics and lipidomics workflow was applied to investigate plasma metabolic profiles of patients with TB and TB-DM. Significantly different biological processes and biomarkers in TB-DM vs. TB were identified using a data-driven, knowledge-based framework. Changes in metabolic and signaling pathways related to carbohydrate and amino acid metabolism were mainly captured by amide HILIC column metabolomics analysis, while perturbations in lipid metabolism were identified by the C18 metabolomics and lipidomics analysis. Compared to TB, TB-DM exhibited elevated levels of bile acids and molecules related to carbohydrate metabolism, as well as the depletion of glutamine, retinol, lysophosphatidylcholine, and phosphatidylcholine. Moreover, arachidonic acid metabolism was determined as a potentially important factor in the interaction between TB and DM pathophysiology. In a correlation network of the significantly altered molecules, among the central nodes, chenodeoxycholic acid was robustly associated with TB and DM. Fatty acid (22:4) was a component of all significant modules. In conclusion, the integration of multimodal metabolomics and lipidomics provides a thorough picture of the metabolic changes associated with TB-DM. The results obtained from this comprehensive profiling of TB patients with DM advance the current understanding of DM comorbidity in TB infection and contribute to the development of more effective treatment.

Comprehensive lipid profiles investigation reveals host metabolic and immune alterations during anti-tuberculosis treatment: Implications for therapeutic monitoring.

In this study, we investigated the lipidome of tuberculosis patients during standard chemotherapy to discover biosignatures that could aid therapeutic monitoring. UPLC-QToF MS was used to analyze 82 baseline and treatment plasma samples of patients with pulmonary tuberculosis. Subsequently, a data-driven and knowledge-based workflow, including robust annotation, statistical analysis, and functional analysis, was applied to assess lipid profiles during treatment. Overall, the lipids species from 17 lipid subclasses were significantly altered by anti-tuberculosis chemotherapy. Cholesterol ester (CE), monoacylglycerols, and phosphatidylcholine (PC) were upregulated, whereas triacylglycerols, sphingomyelin, and ether-linked phosphatidylethanolamines (PE O-) were downregulated. Notably, PCs demonstrated a clear upward expression pattern during tuberculosis treatment. Several lipid species were identified as potential biomarkers for therapeutic monitoring, such as PC(42:6), PE(O-40:5), CE(24:6), and dihexosylceramide Hex2Cer(34:2;2 O). Functional and lipid gene enrichment analysis revealed alterations in pathways related to lipid metabolism and host immune responses. In conclusion, this study provides a foundation for the use of lipids as biomarkers for clinical management of tuberculosis.

Organo-photocatalytic C-H bond oxidation: an operationally simple and scalable method to prepare ketones with ambient air.

Oxidative C-H functionalization with O2 is a sustainable strategy to convert feedstock-like chemicals into valuable products. Nevertheless, eco-friendly O2-utilizing chemical processes, which are scalable yet operationally simple, are challenging to develop. Here, we report our efforts, via organo-photocatalysis, in devising such protocols for catalytic C-H bond oxidation of alcohols and alkylbenzenes to ketones using ambient air as the oxidant. The protocols employed tetrabutylammonium anthraquinone-2-sulfonate as the organic photocatalyst which is readily available from a scalable ion exchange of inexpensive salts and is easy to separate from neutral organic products. Cobalt(ii) acetylacetonate was found to be greatly instrumental to oxidation of alcohols and therefore was included as an additive in evaluating the alcohol scope. The protocols employed a nontoxic solvent, could accommodate a variety of functional groups, and were readily scaled to 500 mmol scale in a simple batch setting using round-bottom flasks and ambient air. A preliminary mechanistic study of C-H bond oxidation of alcohols supported the validity of one possible mechanistic pathway, nested in a more complex network of potential pathways, in which the anthraquinone form - the oxidized form - of the photocatalyst activates alcohols and the anthrahydroquinone form - the relevant reduced form of the photocatalyst - activates O2. A detailed mechanism, which reflected such a pathway and was consistent with previously accepted mechanisms, was proposed to account for formation of ketones from aerobic C-H bond oxidation of both alcohols and alkylbenzenes.

Systematic in situ hydration neutron reflectometry study on Nafion thin films.

Reported herein is a neutron reflectometry (NR) study on hydrated Nafion thin films (∼30 nm) on a silicon substrate with native oxide. The Nafion morphology is investigated systematically across the whole relative humidity range using both H2O and D2O vapours to enable a comparative study. By utilising this systematic approach two key results have been obtained. The first is that by leveraging the strong positive scattering signal from the D2O vapour, a complete and systematic water adsorption isotherm (Type II) for a Nafion thin film is produced. Utilising the slight negative scattering signal of the H2O enabled the quantification of the hydration dependent evolution of the formation of Nafion/water lamellae near the substrate surface. The number of lamellae layers increases continuously with hydration, and does not form abruptly. We also report the effects of swelling on the thin films across the relative humidity ranges. The work reported should prove useful in quantifying other hydration dependent properties of Nafion thin films such as conductivity and understanding Nafion/semiconductor based devices, as well as showcasing a NR methodology for other hydrophilic polymers.

Comprehensive lipid and lipid-related gene investigations of host immune responses to characterize metabolism-centric biomarkers for pulmonary tuberculosis.

Despite remarkable success in the prevention and treatment of tuberculosis (TB), it remains one of the most devastating infectious diseases worldwide. Management of TB requires an efficient and timely diagnostic strategy. In this study, we comprehensively characterized the plasma lipidome of TB patients, then selected candidate lipid and lipid-related gene biomarkers using a data-driven, knowledge-based framework. Among 93 lipids that were identified as potential biomarker candidates, ether-linked phosphatidylcholine (PC O-) and phosphatidylcholine (PC) were generally upregulated, while free fatty acids and triglycerides with longer fatty acyl chains were downregulated in the TB group. Lipid-related gene enrichment analysis revealed significantly altered metabolic pathways (e.g., ether lipid, linolenic acid, and cholesterol) and immune response signaling pathways. Based on these potential biomarkers, TB patients could be differentiated from controls in the internal validation (random forest model, area under the curve [AUC] 0.936, 95% confidence interval [CI] 0.865-0.992). PC(O-40:4), PC(O-42:5), PC(36:0), and PC(34:4) were robust biomarkers able to distinguish TB patients from individuals with latent infection and healthy controls, as shown in the external validation. Small changes in expression were identified for 162 significant lipid-related genes in the comparison of TB patients vs. controls; in the random forest model, their utilities were demonstrated by AUCs that ranged from 0.829 to 0.956 in three cohorts. In conclusion, this study introduced a potential framework that can be used to identify and validate metabolism-centric biomarkers.

Evaluating Automatic Segmentation for Swallowing-Related Organs for Head and Neck Cancer.

Purpose: To evaluate the accuracy of deep-learning-based auto-segmentation of the superior constrictor, middle constrictor, inferior constrictor, and larynx in comparison with a traditional multi-atlas-based method. Methods and Materials: One hundred and five computed tomography image datasets from 83 head and neck cancer patients were retrospectively collected and the superior constrictor, middle constrictor, inferior constrictor, and larynx were analyzed for deep-learning versus multi-atlas-based segmentation. Eighty-three computed tomography images (40 diagnostic computed tomography and 43 planning computed tomography) were used for training the convolutional neural network, and for atlas-based model training. The remaining 22 computed tomography datasets were used for validation of the atlas-based auto-segmentation versus deep-learning-based auto-segmentation contours, both of which were compared with the corresponding manual contours. Quantitative measures included Dice similarity coefficient, recall, precision, Hausdorff distance, 95th percentile of Hausdorff distance, and mean surface distance. Dosimetric differences between the auto-generated contours and manual contours were evaluated. Subjective evaluation was obtained from 3 clinical observers to blindly score the autosegmented structures based on the percentage of slices that require manual modification. Results: The deep-learning-based auto-segmentation versus atlas-based auto-segmentation results were compared for the superior constrictor, middle constrictor, inferior constrictor, and larynx. The mean Dice similarity coefficient values for the 4 structures were 0.67, 0.60, 0.65, and 0.84 for deep-learning-based auto-segmentation, whereas atlas-based auto-segmentation has Dice similarity coefficient results at 0.45, 0.36, 0.50, and 0.70, respectively. The mean 95th percentile of Hausdorff distance (cm) for the 4 structures were 0.41, 0.57, 0.59, and 0.54 for deep-learning-based auto-segmentation, but 0.78, 0.95, 0.96, and 1.23 for atlas-based auto-segmentation results, respectively. Similar mean dose differences were obtained from the 2 sets of autosegmented contours compared to manual contours. The dose-volume discrepancies and the average modification rates were higher with the atlas-based auto-segmentation contours. Conclusion: Swallowing-related structures are more accurately generated with DL-based versus atlas-based segmentation when compared with manual contours.

Genome-wide gene expression analysis reveals molecular insights into the drug-induced toxicity of nephrotoxic agents.

Drug-induced nephrotoxicity is frequently reported. However, the mechanisms underlying nephrotoxic medications and their overlapping molecular events, which might have therapeutic value, are unclear. We performed a genome-wide analysis of gene expression and a gene set enrichment analysis to identify common and unique pathways associated with the toxicity of colistin, ifosfamide, indomethacin, and puromycin. Rats were randomly allocated into the treatment or control group. The treatment group received a toxic dose once daily of each investigated drug for 1 week. Differentially expressed genes were found in the drug-treated kidney and liver compared to the control, except for colistin in the liver. Upregulated pathways were mainly related to cell death, cell cycle, protein synthesis, and immune response modulation in the kidney. Cell cycle was upregulated by all drugs. Downregulated pathways were associated with carbon metabolism, amino acid metabolism, and fatty acid metabolism. Indomethacin, colistin, and puromycin shared the most altered pathways in the kidney. Ifosfamide and indomethacin affected molecular processes greatly in the liver. Our findings provide insight into the mechanisms underlying the renal and hepatic adverse effects of the four drugs. Further investigation should explore the combinatory drug therapies that attenuate the toxic effects and maximize the effectiveness of nephrotoxic drugs.