PubMed

Environ Toxicol Pharmacol. 2022 Nov 29:104031. doi: 10.1016/j.etap.2022.104031. Online ahead of print.NO ABSTRACTPMID:36460283 | DOI:10.1016/j.etap.2022.104031

Toxicology. 2022 Nov 29:153386. doi: 10.1016/j.tox.2022.153386. Online ahead of print.NO ABSTRACTPMID:36460222 | DOI:10.1016/j.tox.2022.153386

Toxicol Appl Pharmacol. 2022 Nov 29:116327. doi: 10.1016/j.taap.2022.116327. Online ahead of print.NO ABSTRACTPMID:36460058 | DOI:10.1016/j.taap.2022.116327

Water Res. 2022 Nov 20;229:119397. doi: 10.1016/j.watres.2022.119397. Online ahead of print.NO ABSTRACTPMID:36459892 | DOI:10.1016/j.watres.2022.119397

Mar Pollut Bull. 2022 Nov 29;186:114385. doi: 10.1016/j.marpolbul.2022.114385. Online ahead of print.NO ABSTRACTPMID:36459772 | DOI:10.1016/j.marpolbul.2022.114385

J Pharm Biomed Anal. 2022 Nov 21;224:115171. doi: 10.1016/j.jpba.2022.115171. Online ahead of print.NO ABSTRACTPMID:36459765 | DOI:10.1016/j.jpba.2022.115171

Atherosclerosis. 2022 Nov 24;364:20-28. doi: 10.1016/j.atherosclerosis.2022.11.019. Online ahead of print.NO ABSTRACTPMID:36459728 | DOI:10.1016/j.atherosclerosis.2022.11.019

Redox Biol. 2022 Nov 26;58:102556. doi: 10.1016/j.redox.2022.102556. Online ahead of print.NO ABSTRACTPMID:36459717 | DOI:10.1016/j.redox.2022.102556

PLoS One. 2022 Dec 2;17(12):e0278660. doi: 10.1371/journal.pone.0278660. eCollection 2022.NO ABSTRACTPMID:36459516 | DOI:10.1371/journal.pone.0278660

Metabolomics. 2022 Dec 2;18(12):101. doi: 10.1007/s11306-022-01954-z.NO ABSTRACTPMID:36459297 | DOI:10.1007/s11306-022-01954-z

Clin Exp Med. 2022 Dec 2. doi: 10.1007/s10238-022-00959-1. Online ahead of print.NO ABSTRACTPMID:36459278 | DOI:10.1007/s10238-022-00959-1

Clin Exp Med. 2022 Dec 2. doi: 10.1007/s10238-022-00958-2. Online ahead of print.NO ABSTRACTPMID:36459277 | DOI:10.1007/s10238-022-00958-2

J Bone Miner Res. 2022 Dec 2. doi: 10.1002/jbmr.4754. Online ahead of print.NO ABSTRACTPMID:36458982 | DOI:10.1002/jbmr.4754

Chem Commun (Camb). 2022 Dec 2. doi: 10.1039/d2cc05053f. Online ahead of print.NO ABSTRACTPMID:36458684 | DOI:10.1039/d2cc05053f

Front Microbiol. 2022 Nov 15;13:1046894. doi: 10.3389/fmicb.2022.1046894. eCollection 2022.ABSTRACTDepression is a heterogeneous mental disorder that has been linked to disturbances in the gut microbiome. As an essential part of the gut microbiome, gut virome may play critical roles in disease progression and development. However, the relationship between the effect of gut virome on neurotransmitter metabolism and depression is unknown. We evaluated the alterations of gut virome and neurotransmitters in chronic restraint stress (CRS)-induced mouse model of depression based on viral metagenomics and LC-MS/MS metabolomics analyses. The results reveal that the gut virome profile of CRS group differed significantly from CON group. Microviridae was the most abundant differential viral family in both groups, followed by Podoviridae, while Siphoviridae was only enriched in CRS group of the top 100 differential viruses. The differential viruses that predicted to Enterobacteriaceae phage, Gammaproteobacteria phage and Campylobacteraceae phage were enriched in CRS group. Furthermore, 12 differential neurotransmitters primarily involved in the tryptophan metabolism pathway were altered in depressive-like mice. Besides, tryptamine and 5-methoxytryptamine hydrochloride were strongly associated with differential viruses belonging to Podoviridae and Microviridae. Our findings provide new insight into understanding the potential role of the gut virome and metabolites in depression.PMID:36458183 | PMC:PMC9706091 | DOI:10.3389/fmicb.2022.1046894

Front Psychiatry. 2022 Nov 15;13:1002828. doi: 10.3389/fpsyt.2022.1002828. eCollection 2022.ABSTRACTMajor depressive disorder (MDD) is one of the most common psychiatric disorders that accompany psychophysiological and mood changes. However, the pathophysiology-based disease mechanism of MDD is not yet fully understood, and diagnosis is also conducted through interviews with clinicians and patients. Diagnosis and treatment of MDD are limited due to the absence of biomarkers underlying the pathophysiological mechanisms of MDD. Although various attempts have been made to discover metabolite biomarkers for the diagnosis and treatment response of MDD, problems with sample size and consistency of results have limited clinical application. In addition, it was reported that future biomarker studies must consider exposure to antidepressants, which is the main cause of heterogeneity in depression subgroups. Therefore, the purpose of this study is to discover and validate biomarkers for the diagnosis of depression in consideration of exposure to drug treatment including antidepressants that contribute to the heterogeneity of the MDD subgroup. In the biomarker discovery and validation set, the disease group consisted of a mixture of patients exposed and unexposed to drug treatment including antidepressants for the treatment of MDD. The serum metabolites that differed between the MDD patients and the control group were profiled using mass spectrometry. The validation set including the remission group was used to verify the effectiveness as a biomarker for the diagnosis of depression and determination of remission status. The presence of different metabolites between the two groups was confirmed through serum metabolite profiling between the MDD patient group and the control group. Finally, Acetylcarnitine was selected as a biomarker. In validation, acetylcarnitine was significantly decreased in MDD and was distinguished from remission status. This study confirmed that the discovered acetylcarnitine has potential as a biomarker for diagnosing depression and determining remission status, regardless of exposure to drug treatment including antidepressants.PMID:36458116 | PMC:PMC9707625 | DOI:10.3389/fpsyt.2022.1002828

Front Artif Intell. 2022 Nov 15;5:1050439. doi: 10.3389/frai.2022.1050439. eCollection 2022.ABSTRACTRapid development of biotechnology has led to the generation of vast amounts of multi-omics data, necessitating the advancement of bioinformatics and artificial intelligence to enable computational modeling to diagnose and predict clinical outcome. Both conventional machine learning and new deep learning algorithms screen existing data unbiasedly to uncover patterns and create models that can be valuable in informing clinical decisions. We summarized published literature on the use of AI models trained on omics datasets, with and without clinical data, to diagnose, risk-stratify, and predict survivability of patients with non-malignant liver diseases. A total of 20 different models were tested in selected studies. Generally, the addition of omics data to regular clinical parameters or individual biomarkers improved the AI model performance. For instance, using NAFLD fibrosis score to distinguish F0-F2 from F3-F4 fibrotic stages, the area under the curve (AUC) was 0.87. When integrating metabolomic data by a GMLVQ model, the AUC drastically improved to 0.99. The use of RF on multi-omics and clinical data in another study to predict progression of NAFLD to NASH resulted in an AUC of 0.84, compared to 0.82 when using clinical data only. A comparison of RF, SVM and kNN models on genomics data to classify immune tolerant phase in chronic hepatitis B resulted in AUC of 0.8793-0.8838 compared to 0.6759-0.7276 when using various serum biomarkers. Overall, the integration of omics was shown to improve prediction performance compared to models built only on clinical parameters, indicating a potential use for personalized medicine in clinical setting.PMID:36458100 | PMC:PMC9705954 | DOI:10.3389/frai.2022.1050439

Front Mol Biosci. 2022 Nov 15;9:1059094. doi: 10.3389/fmolb.2022.1059094. eCollection 2022.ABSTRACTHost-microbiome interactions are known to have substantial effects on human health, but the diversity of the human microbiome makes it difficult to definitively attribute specific microbiome features to a host phenotype. One approach to overcoming this challenge is to use animal models of host-microbiome interaction, but it must be determined that relevant aspects of host-microbiome interactions are reflected in the animal model. One such experimental validation is an experiment by Ridura et al. In that experiment, transplanting a microbiome from a human into a mouse also conferred the human donor's obesity phenotype. We have aggregated a collection of previously published host-microbiome mouse-model experiments and combined it with thousands of sequenced and annotated bacterial genomes and metametabolomic pathways. Three computational models were generated, each model reflecting an aspect of host-microbiome interactions: 1) Predict the change in microbiome community structure in response to host diet using a community interaction network, 2) Predict metagenomic data from microbiome community structure, and 3) Predict host obesogenesis from modeled microbiome metagenomic data. These computationally validated models were combined into an integrated model of host-microbiome-diet interactions and used to replicate the Ridura experiment in silico. The results of the computational models indicate that network-based models are significantly more predictive than similar but non-network-based models. Network-based models also provide additional insight into the molecular mechanisms of host-microbiome interaction by highlighting metabolites and metabolic pathways proposed to be associated with microbiome-based obesogenesis. While the models generated in this study are likely too specific to the animal models and experimental conditions used to train our models to be of general utility in a broader understanding of obesogenesis, the approach detailed here is expected to be a powerful tool of investigating multiple types of host-microbiome interactions.PMID:36458093 | PMC:PMC9705962 | DOI:10.3389/fmolb.2022.1059094

Front Cardiovasc Med. 2022 Nov 15;9:1023282. doi: 10.3389/fcvm.2022.1023282. eCollection 2022.ABSTRACTBACKGROUND: Chronic thromboembolic pulmonary hypertension (CTEPH) patients may present with atherosclerotic lesions in their pulmonary arteries, but their clinical characteristics remain unclear. The metabolic pathways associated with the atherosclerotic lesions may explain their occurrence and have implications for interventions, but they have not been investigated.METHODS: We collected pulmonary endarterectomy (PEA) samples of CTEPH patients from December 2016 to August 2021. Following a detailed pathological examination of the PEA specimen, the patients were divided into those with and without lesions, and age- and sex matching were performed subsequently using propensity score matching (n = 25 each). Metabolomic profiling was used to investigate the metabolites of the proximal lesions in the PEA specimens.RESULTS: In our study population, 27.2% of all PEA specimens were found to contain atherosclerotic lesions. CTEPH patients with atherosclerotic lesions were more likely to have a history of symptomatic embolism and had a longer timespan between embolism and surgery, whereas the classic risk factors of systemic and coronary circulation could not distinguish CTEPH patients with or without atherosclerotic lesions. Metabolomic profiling revealed that the formation of atherosclerotic lesions in CTEPH was closely related to altered glycine, serine, and threonine metabolic axes, possibly involved in cellular senescence, energy metabolism, and a proinflammatory microenvironment.CONCLUSION: The occurrence of atherosclerotic lesions in the pulmonary arteries of CTEPH was associated with symptomatic thromboembolic history and prolonged disease duration. The results revealed a new link between atherosclerotic lesions and aberrant amino acid metabolism in the context of CTEPH for the first time. This study has characterized the clinical and metabolic profiles of this distinct group of CTEPH patients, providing new insights into disease pathogenesis and potential interventions.PMID:36457807 | PMC:PMC9705335 | DOI:10.3389/fcvm.2022.1023282

J Parasit Dis. 2022 Dec;46(4):1133-1146. doi: 10.1007/s12639-022-01513-2. Epub 2022 Jul 4.ABSTRACTCryptosporidium is a pathogenic protozoan parasite infecting the gastrointestinal epithelium of human and animal hosts. In farm animals, cryptosporidiosis causes significant economic losses including deaths in newborn animals, retarded growth, increased labor involved and high cost of drugs. The detection of Cryptosporidium oocysts in fecal samples is traditionally dependent on examination of stained slides by light microscope or by advanced microscopical tools such as: electron microscopy and phase contrast microscopy. Immunological diagnosis using either antibody or antigen detection could offer high sensitivity and specificity. Examples for these tests are Enzyme Linked Immunosorbent Assay (ELISA), Immunochromatographic tests, Immunochromatographic lateral flow (ICLF), Immunofluorescence assays (IFA) and Flow cytometry coupled with cell sorting. Molecular methods could differentiate species and genotypes of Cryptosporidium and help in studying the epidemiological features of this parasite with rapid, simple and sensitive procedures. Nanotechnology-based platforms could improve the sensitivity and specificity of other detection methods like: ELISA, ICLF, IFA and polymerase chain reaction. As the available prophylactic and therapeutic drugs or natural products treatments are insufficient and no approved vaccines are available, the best approach to control this parasite is by following firm hygienic measures. Many vaccine attempts were performed using hyperimmune colostrum, live or attenuated vaccines, recombinant and Deoxyribonucleic acid vaccines. Also, Clustered Regularly Interspaced Short Palindromic Repeats/Cas9 technology could help in Cryptosporidium genome editing to improve drug and vaccine discovery. Another approach that could be useful for assigning drug targets is metabolomics. Probiotics were also used successfully in the treatment of acute diarrhea and they proved a limiting effect on cryptosporidiosis in animal models. In addition, nanotherapy-based approaches could provide a good strategy for improving the potency of any type of drugs against Cryptosporidium and give good anti-cryptosporidial effects. In conclusion, accurate diagnosis using advanced techniques is the key to the control and prevention of cryptosporidiosis.PMID:36457776 | PMC:PMC9606155 | DOI:10.1007/s12639-022-01513-2