Dietary protein restriction diminishes sucrose reward and reduces sucrose-evoked mesolimbic dopamine signaling.

Objective: A growing literature suggests manipulating dietary protein status decreases sweet consumption in rodents and in humans. Underlying neurocircuit mechanisms have not yet been determined, but previous work points towards hedonic rather than homeostatic pathways. Here we hypothesized that a history of protein restriction reduces sucrose seeking by altering mesolimbic dopamine signaling. Methods: We tested this hypothesis using established behavioral tests of palatability and motivation, including the 'palatability contrast' and conditioned place preference (CPP) tests. We used modern optical sensors for measuring real-time nucleus accumbens (NAc) dopamine dynamics during sucrose consumption, via fiber photometry, in male C57/Bl6J mice maintained on low-protein high-carbohydrate (LPHC) or control (CON) diet for ∼5 weeks. Results: A history of protein restriction decreased the consumption of a sucrose 'dessert' in sated mice by ∼50% compared to controls [T-test, p< 0.05]. The dopamine release in NAc during sucrose consumption was reduced, also by ∼50%, in LPHC-fed mice compared to CON [T-test, p< 0.01]. Furthermore, LPHC-feeding blocked the sucrose-conditioned place preference we observed in CON-fed mice [paired T-test, p< 0.05], indicating reduced motivation. This was accompanied by a 33% decrease in neuronal activation of the NAc core, as measured by c-Fos immunolabeling from brains collected directly after the CPP test. Conclusions: Despite ongoing efforts to promote healthier dietary habits, adherence to recommendations aimed at reducing the intake of added sugars and processed sweets remains challenging. This study highlights chronic dietary protein restriction as a nutritional intervention that suppresses the motivation for sucrose intake, via blunted sucrose-evoke dopamine release in NAc.

A pH ultra-sensitive hydrated iridium oxyhydroxide films electrochemical sensor for label-free detection of Vibrio parahaemolyticus.

Vibrio parahaemolyticus (V. parahaemolyticus) is a major foodborne pathogen, which can cause serious foodborne illnesses like diarrhoea. Rapid on-site detection of foodborne pathogens is an ideal way to respond to foodborne illnesses. Herein, we provide an electrochemical sensor for rapid on-site detection. This sensor utilized a pH-sensitive metal-oxide material for the concurrent isothermal amplification and label-free detection of nucleic acids. Based on a pH-sensitive hydrated iridium oxide oxyhydroxide film (HIROF), the electrode transforms the hydrogen ion compound generated during nucleic acid amplification into potential, so as to achieve a real-time detection. The results can be transmitted to a smartphone via Bluetooth. Moreover, HIROF was applied in nucleic acid device detection, with a super-Nernst sensitivity of 77.6 mV/pH in the pH range of 6.0-8.5, and the sensitivity showed the best results so far. Detection of V. parahaemolyticus by this novel method showed a detection limit of 1.0 × 103 CFU/mL, while the time consumption was only 30 min, outperforming real-time fluorescence loop-mediated isothermal amplification (LAMP). Therefore, the characteristics of compact, portable, and fast make the sensor more widely used in on-site detection.

Unlocking opioid neuropeptide dynamics with genetically encoded biosensors.

Neuropeptides are ubiquitous in the nervous system. Research into neuropeptides has been limited by a lack of experimental tools that allow for the precise dissection of their complex and diverse dynamics in a circuit-specific manner. Opioid peptides modulate pain, reward and aversion and as such have high clinical relevance. To illuminate the spatiotemporal dynamics of endogenous opioid signaling in the brain, we developed a class of genetically encoded fluorescence sensors based on kappa, delta and mu opioid receptors: κLight, δLight and µLight, respectively. We characterized the pharmacological profiles of these sensors in mammalian cells and in dissociated neurons. We used κLight to identify electrical stimulation parameters that trigger endogenous opioid release and the spatiotemporal scale of dynorphin volume transmission in brain slices. Using in vivo fiber photometry in mice, we demonstrated the utility of these sensors in detecting optogenetically driven opioid release and observed differential opioid release dynamics in response to fearful and rewarding conditions.

Constructing Built-in-Electric Field for Boosting Electrocatalytic Water Splitting.

Electrocatalytic water splitting shows great potential for producing clean and green hydrogen, but it is hindered by slow reaction kinetics. Advanced electrocatalysts are needed to lower the energy barriers. The establishment of built-in electric fields (BIEF) in heterointerfaces has been found to be beneficial for speeding up electron transfer, increasing electrical conductivity, adjusting the local reaction environment, and optimizing the chemisorption energy with intermediates. Engineering and modifying the BIEF in heterojunctions offer significant opportunities to enhance the electronic properties of catalysts, thus improving the reaction kinetics. This comprehensive review focuses on the latest advances in BIEF engineering in heterojunction catalysts for efficient water electrolysis. It highlights the fundamentals, engineering, modification, characterization, and application of BIEF in electrocatalytic water splitting. The review also discusses the challenges and future prospects of BIEF engineering. Overall, this review provides a thorough examination of BIEF engineering for the next generation of water electrolysis devices.

Trends in the burden and determinants of HIV in the Asia-Pacific region (1990-2019): An age-period-cohort analysis of the 2019 Global Burden of Disease Study.

Although the burden of the human immunodeficiency virus (HIV) in the Asia-Pacific region is increasingly severe, comprehensive evidence of the burden of HIV is scarce. We aimed to report the burden of HIV in people aged 15-79 years from 1990 to 2019 using data from the Global Burden of Disease Study (GBD) 2019. We analyzed rates of age-standardized disability-adjusted life years (ASDR), age-standardized mortality (ASMR), and age-standardized incidence (ASIR) in our age-period-cohort analysis by sociodemographic index (SDI). According to HIV reports in 2019 from 29 countries in the Asia-Pacific region, the low SDI group in Papua New Guinea had the highest ASDR, ASMR, and ASIR. From 1990 to 2019, the ASDR, ASIR, and ASMR of persons with acquired immune deficiency syndrome (AIDS) increased in 21 (72%) of the 29 countries in the Asia-Pacific region. During the same period, the disability-adjusted life years (DALYs) of AIDS patients in the low SDI group in the region grew the fastest, particularly in Nepal. The incidence of HIV among individuals aged 20-30 years in the low-middle SDI group was higher than that of those in the other age groups. In 2019, unsafe sex was the main cause of HIV-related ASDR in the region's 29 countries, followed by drug use. The severity of the burden of HIV/AIDS in the Asia-Pacific region is increasing, especially among low SDI groups. Specific public health policies should be formulated based on the socioeconomic development level of each country to alleviate the burden of HIV/AIDS.

[Toxicity of bisphenol S on human normal ovarian epithelial cells mediated by ERß-MAPK signaling pathway].

OBJECTIVE: To investigate the effects of long-term(7 days and 14 days) bisphenol S(BPS) exposure on the ERß-MAPK signaling pathway, hormone secretion phenotype and cell cycle in human normal ovarian epithelial cells IOSE 80 at actual human exposure level. METHODS: Physiologically based pharmacokinetic model combined with BPS levels in the serum of women along the Yangtze River in China was used to determine the dosing concentrations of BPS, and vehicle control and 17 ß-estradiol(E_2) control were used. Complete medium with corresponding concentrations(0, 6.79×10~(-6), 6.79×10~(-4), 6.79×10~(-2), 6.79 µmol/L BPS and 10 nmol/L E_2) was replaced every 2 days. mRNA expressions of estrogen receptor(ERß and GPR30), key genes in MAPK signaling pathway(P38/JNK/ERK signaling pathway) and gonadotropin-releasing hormone-related genes(GnRH-I, GnRH-II and GnRH-R) were measured by qPCR. The ERß-MAPK signaling pathway inhibitors were employed to detect the effect of long-term exposure to BPS on the cell cycle by flow cytometry. Dose-response relationship analysis was performed to calculate the benchmark does lower confidence limits. RESULTS: Compared to the vehicle control, after 7 days exposure to BPS, the ratio of G_2/M phase was significantly increased(P<0.05), and the mRNA expressions of GnRH-I, GnRH-II and GnRH-R were significantly decreased(P<0.05); after 14 days exposure to BPS, the mRNA expressions of ESR2, MAPK3, and MAPK9 were significantly increased(P<0.05), and the mRNA expressions of GnRH-II and GnRH-R were significantly decreased(P<0.05). The GnRH-II mRNA expression level of BPS treatment for 7 days; the G_0/G_1 phase ratio, MAPK3 and MAPK8 mRNA expression level of BPS exposure for 14 days; and the GnRH-I mRNA expression level after BPS treatment for 7 days and 14 days showed a good dose-response relationship but with poor fit. CONCLUSION: Long-term low-dose exposure to BPS may cause cell cycle arrest by activating the ERß-MAPK signaling pathway, and may lead to changes in the hormone secretion of IOSE 80 cells.

Macroscopic tunneling probe of Moiré spin textures in twisted CrI3.

Various noncollinear spin textures and magnetic phases have been predicted in twisted two-dimensional CrI3 due to competing ferromagnetic (FM) and antiferromagnetic (AFM) interlayer exchange from moiré stacking-with potential spintronic applications even when the underlying material possesses a negligible Dzyaloshinskii-Moriya or dipole-dipole interaction. Recent measurements have shown evidence of coexisting FM and AFM layer order in small-twist-angle CrI3 bilayers and double bilayers. Yet, the nature of the magnetic textures remains unresolved and possibilities for their manipulation and electrical readout are unexplored. Here, we use tunneling magnetoresistance to investigate the collective spin states of twisted double-bilayer CrI3 under both out-of-plane and in-plane magnetic fields together with detailed micromagnetic simulations of domain dynamics based on magnetic circular dichroism. Our results capture hysteretic and anisotropic field evolutions of the magnetic states and we further uncover two distinct non-volatile spin textures (out-of-plane and in-plane domains) at ≈1° twist angle, with a different global tunneling resistance that can be switched by magnetic field.

Applications of functional neurotransmitter release imaging with genetically encoded sensors in psychiatric research.

Psychiatric research encompasses diverse methodologies to understand the complex interplay between neurochemistry and behavior in mental health disorders. Despite significant advancements in pharmacological interventions, there remains a critical gap in understanding the precise functional changes underlying psychiatric conditions and the mechanisms of action of therapeutic agents. Genetically encoded sensors have emerged as powerful tools to address these challenges by enabling real-time monitoring of neurochemical dynamics in specific neuronal populations. This prospective explores the utility of neurotransmitter binding genetically encoded sensors in uncovering the nature of neuronal dysregulation underpinning mental illness, assessing the impact of pharmaceutical interventions, and facilitating the discovery of novel treatments.

Associations of Urinary Heavy Metal Mixtures with High Remnant Cholesterol among US Adults: Evidence from the National Health and Nutrition Examination Survey (1998-2018).

The main objective of our study is to explore the associations between combined exposure to urinary heavy metals and high remnant cholesterol (HRC), a known cardiovascular risk factor. Utilizing data from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018, we conducted a cross-sectional analysis of 5690 participants, assessing urinary concentrations of ten heavy metals. Ten heavy metals in urine were measured by inductively coupled plasma mass spectrometry (ICP-MS). Fasting residual cholesterol ≥0.8 mmol/L was defined as HRC (using blood samples). Statistical analyses included weighted multivariable logistic regression, weighted quantile sum (WQS) regression, quantile g-computation (qgcomp), and Bayesian kernel machine regression (BKMR) to evaluate the associations of heavy metal exposure with HRC. Stratified analyses based on individual characteristics were also conducted. Multivariable logistic regression found that the four metals (OR Q4 vs. Q1: 1.33, 95% CI: 1.01-1.75 for barium (Ba); OR Q4 vs. Q1: 1.50, 95% CI: 1.16-1.94 for cadmium (Cd); OR Q4 vs. Q1: 1.52, 95% CI: 1.15-2.01 for mercury (Hg); OR Q4 vs. Q1: 1.35, 95% CI: 1.06-1.73 for lead (Pb)) were positively correlated with the elevated risk of HRC after adjusting for covariates. In addition, all three mixed models, including WQS (OR: 1.25; 95% CI: 1.07-1.46), qgcomp (OR: 1.17; 95% CI: 1.03-1.34), and BKMR, consistently showed a significant positive correlation between co-exposure to heavy metal mixtures and HRC, with Ba and Cd being the main contributors within the mixture. These associations were more pronounced in younger adults (20 to 59 years), males, and those with a higher body mass index status (≥25 kg/m2). Our findings reveal a significant relationship between exposure to the mixture of heavy metals and HRC among US adults, with Ba and Cd being the major contributors to the mixture's overall effect. Public health efforts aimed at reducing heavy metal exposure can help prevent HRC and, in turn, cardiovascular disease.

Silica aggravates pulmonary fibrosis through disrupting lung microbiota and amino acid metabolites.

Silicosis, recognized as a severe global public health issue, is an irreversible pulmonary fibrosis caused by the long-term inhalation of silica particles. Given the intricate pathogenesis of silicosis, there is no effective intervention measure, which poses a severe threat to public health. Our previous study reported that dysbiosis of lung microbiota is associated with the development of pulmonary fibrosis, potentially involving the lipopolysaccharides/toll-like receptor 4 pathway. Similarly, the process of pulmonary fibrosis is accompanied by alterations in metabolic pathways. This study employed a combined approach of 16S rDNA sequencing and metabolomic analysis to investigate further the role of lung microbiota in silicosis delving deeper into the potential pathogenesis of silicosis. Silica exposure can lead to dysbiosis of the lung microbiota and the occurrence of pulmonary fibrosis, which was alleviated by a combination antibiotic intervention. Additionally, significant metabolic disturbances were found in silicosis, involving 85 differential metabolites among the three groups, which are mainly focused on amino acid metabolic pathways. The changed lung metabolites showed a substantial correlation with lung microbiota. The relative abundance of Pseudomonas negatively correlated with L-Aspartic acid, L-Glutamic acid, and L-Threonine levels. These results indicate that dysbiosis in pulmonary microbiota exacerbates silica-induced fibrosis through impacts on amino acid metabolism, providing new insights into the potential mechanisms and interventions of silicosis.

Attentional adversarial training for few-shot medical image segmentation without annotations.

Medical image segmentation is a critical application that plays a significant role in clinical research. Despite the fact that many deep neural networks have achieved quite high accuracy in the field of medical image segmentation, there is still a scarcity of annotated labels, making it difficult to train a robust and generalized model. Few-shot learning has the potential to predict new classes that are unseen in training with a few annotations. In this study, a novel few-shot semantic segmentation framework named prototype-based generative adversarial network (PG-Net) is proposed for medical image segmentation without annotations. The proposed PG-Net consists of two subnetworks: the prototype-based segmentation network (P-Net) and the guided evaluation network (G-Net). On one hand, the P-Net as a generator focuses on extracting multi-scale features and local spatial information in order to produce refined predictions with discriminative context between foreground and background. On the other hand, the G-Net as a discriminator, which employs an attention mechanism, further distills the relation knowledge between support and query, and contributes to P-Net producing segmentation masks of query with more similar distributions as support. Hence, the PG-Net can enhance segmentation quality by an adversarial training strategy. Compared to the state-of-the-art (SOTA) few-shot segmentation methods, comparative experiments demonstrate that the proposed PG-Net provides noticeably more robust and prominent generalization ability on different medical image modality datasets, including an abdominal Computed Tomography (CT) dataset and an abdominal Magnetic Resonance Imaging (MRI) dataset.

Exploring adverse events of Vilazodone: evidence from the FAERS database.

OBJECTIVE: This study aims to conduct an exhaustive evaluation of Vilazodone's safety in clinical application and to unearth the potential adverse event (AE) risks associated with its utilization based on FDA Adverse Event Reporting System (FAERS) database. METHODS: This research employed data spanning from the first quarter of 2011 to the third quarter of 2023 from the FAERS database. Various signal detection methodologies, including the Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Empirical Bayesian Geometric Mean (EBGM), were utilized to ascertain the correlation between Vilazodone and specific AEs. RESULTS: The study compiled a total of 17,439,268 reports of drug AEs, out of which 5,375 were related to Vilazodone. Through signal mining, 125 Preferred Terms (PTs) encompassing 27 System Organ Classes (SOCs) were identified. The findings indicated a higher prevalence among females and patients within the 45 to 65 age bracket. The principal categories of AEs included Psychiatric disorders, Nervous system disorders, and Gastrointestinal disorders, with prevalent incidents of Diarrhoea, Nausea, and Insomnia. Moreover, the study identified robust signals of novel potential AEs, notably in areas such as sleep disturbances (Sleep paralysis, Hypnagogic hallucination, Rapid eye movements sleep abnormal, Sleep terror, Terminal insomnia, Tachyphrenia), sexual dysfunctions (Female orgasmic disorder, Orgasm abnormal, Disturbance in sexual arousal, Spontaneous penile erection, Anorgasmia, Sexual dysfunction, Ejaculation delayed), and other symptoms and injuries (Electric shock sensation, Violence-related symptom, Gun shot wound). CONCLUSION: Although Vilazodone presents a positive prospect in the management of MDD, the discovery of AEs linked to its use, particularly the newly identified potential risks such as sleep and sexual dysfunctions, necessitates heightened vigilance among clinicians.

A LAMP-based hydrogen ion selective electrochemical sensor for highly sensitive detection of Mycoplasma pneumoniae.

A concise and rapid detection method for Mycoplasma pneumoniae is urgently required due to its severe impact on human health. To meet such a need, this study proposed and constructed an innovative point-of-care testing (POCT) platform that consists of a hydrogen ion-selective loop-mediated isothermal amplification (H+-LAMP) sensor and an electrochemical detection device. The H+-LAMP sensor successfully integrated the working and reference electrodes and converted the H+ generated during the LAMP process into an electrochemical signal. High sensitivity and stability for pathogen detection were also achieved by treating the working electrode with an electrodeposited polyaniline solid contact layer and by using an ion-selective membrane. As a result, the sensor shows a sensitivity of 68.26 mV per pH, a response time of less than 2 s, and a potential drift of less than 5 mV within one hour, which well meets the urgent need. The results also demonstrated that the detection limit for Mycoplasma pneumoniae was lowered to 1 copy per µL, the nucleic acid extraction and detection process could be completed in 30 minutes, and the impact of interfering ions on the sensor was negligible. Validation with 20 clinical samples yielded satisfactory results. More importantly, the storage lifespan of such an electrochemical sensor is over seven days, which is a great advantage for on-site pathogen detection. Therefore, the hydrogen ion-selective sensor constructed in this investigation is particularly suitable as a core component for instant pathogen detection platforms.

Analysis of Methylesterase Gene Family in Fragaria vesca Unveils Novel Insights into the Role of FvMES2 in Methyl Salicylate-Mediated Resistance against Strawberry Gray Mold.

Methylesterases (MESs) hydrolyze carboxylic ester and are important for plant metabolism and defense. However, the understanding of MES' role in strawberries against pathogens remains limited. This study identified 15 FvMESs with a conserved catalytic triad from the Fragaria vesca genome. Spatiotemporal expression data demonstrated the upregulated expression of FvMESs in roots and developing fruits, suggesting growth involvement. The FvMES promoter regions harbored numerous stress-related cis-acting elements and transcription factors associated with plant defense mechanisms. Moreover, FvMES2 exhibited a significant response to Botrytis cinerea stress and showed a remarkable correlation with the salicylic acid (SA) signaling pathway. Molecular docking showed an efficient binding potential between FvMES2 and methyl salicylate (MeSA). The role of FvMES2 in MeSA demethylation to produce SA was further confirmed through in vitro and in vivo assays. After MeSA was applied, the transient overexpression of FvMES2 in strawberries enhanced their resistance to B. cinerea compared to wild-type plants.

Stacking learning based on micro-CT radiomics for outcome prediction in the early-stage of silica-induced pulmonary fibrosis model.

Silicosis is a progressive pulmonary fibrosis disease caused by long-term inhalation of silica. The early diagnosis and timely implementation of intervention measures are crucial in preventing silicosis deterioration further. However, the lack of screening and diagnostic measures for early-stage silicosis remains a significant challenge. In this study, silicosis models of varying severity were established through a single exposure to silica with different doses (2.5mg/mice or 5mg/mice) and durations (4 weeks or 12 weeks). The diagnostic performance of computed tomography (CT) quantitative analysis was assessed using lung density biomarkers and the lung density distribution histogram, with a particular focus on non-aerated lung volume. Subsequently, we developed and evaluated a stacking learning model for early diagnosis of silicosis after extracting and selecting features from CT images. The CT quantitative analysis reveals that while the lung densitometric biomarkers and lung density distribution histogram, as traditional indicators, effectively differentiate severe fibrosis models, they are unable to distinguish early-stage silicosis. Furthermore, these findings remained consistent even when employing non-aerated areas, which is a more sensitive indicator. By establishing a radiomics stacking learning model based on non-aerated areas, we can achieve remarkable diagnostic performance to distinguish early-stage silicosis, which can provide a valuable tool for clinical assistant diagnosis. This study reveals the potential of using non-aerated lung areas as a region of interest in stacking learning for early diagnosis of silicosis, providing new insights into early detection of this disease.

Preoperative prediction of lymph node metastasis in colorectal cancer using 18F-FDG PET/CT peritumoral radiomics analysis.

BACKGROUND: Radiomics has been used in the diagnosis of tumor lymph node metastasis (LNM). However, to date, most studies have been based on intratumoral radiomics. Few studies have focused on the use of 18F-fluorodeoxyglucose positron emission computed tomography (18F-FDG PET/CT) peritumoral radiomics for the diagnosis of LNM in colorectal cancer (CRC). PURPOSE: Determining the value of radiomics features extracted from 18F-FDG PET/CT images of the peritumoral region in predicting LNM in patients with CRC. METHODS: The clinical data and preoperative 18F-FDG PET/CT images of 244 CRC patients were retrospectively analyzed. Intratumoral and peritumoral radiomics features were screened using the mutual information method, and least absolute shrinkage and selection operator regression. Based on the selected radiomics features, a radiomics score (Rad-score) was calculated, and independent risk factors obtained from univariate and multivariate logistic regression analyses were used to construct clinical and combined (Radiomics + Clinical) models. The performance of these models was evaluated using the DeLong test, while their clinical utility was assessed by decision curve analysis. Finally, a nomogram was constructed to visualize the predictive model. RESULTS: The most optimal set of features retained by the feature filtering process were all peritumoral radiomic features. Carcinoembryonic antigen levels, PET/CT-reported lymph node status and Rad-score were found to be independent risk factors for LNM. All three LNM risk assessment models exhibited good predictive performance, with the combined model showing the best classification results, with areas under the curve of 0.85 and 0.76 in the training and validation groups, respectively. The DeLong test revealed that the performance of the combined model was superior to that of the clinical and radiomics models in both the training and validation groups, although this difference was only statistically significant in the training group. DCA indicated that the combined model displayed better clinical utility. CONCLUSIONS: 18F-FDG PET/CT peritumoral radiomics is uniquely suited to predict the presence of LNM in patients with CRC. In particular, the predictive efficacy of LNM for precision therapy and individualized patient management can be improved by using a combination of clinical risk factors.

Prefrontal cortical dynorphin peptidergic transmission constrains threat-driven behavioral and network states.

Prefrontal cortical (PFC) circuits provide top-down control of threat reactivity. This includes ventromedial PFC (vmPFC) circuitry, which plays a role in suppressing fear-related behavioral states. Dynorphin (Dyn) has been implicated in mediating negative affect and maladaptive behaviors induced by severe threats and is expressed in limbic circuits, including the vmPFC. However, there is a critical knowledge gap in our understanding of how vmPFC Dyn-expressing neurons and Dyn transmission detect threats and regulate expression of defensive behaviors. Here, we demonstrate that Dyn cells are broadly activated by threats and release Dyn locally in the vmPFC to limit passive defensive behaviors. We further demonstrate that vmPFC Dyn-mediated signaling promotes a switch of vmPFC networks to a fear-related state. In conclusion, we reveal a previously unknown role of vmPFC Dyn neurons and Dyn neuropeptidergic transmission in suppressing defensive behaviors in response to threats via state-driven changes in vmPFC networks.

Copper-Catalyzed 1,2-Sulfonyletherification of 1,3-Dienes.

A selective three-component 1,2-sulfonyl etherification of aryl 1,3-dienes enabled by copper catalysis to afford biologically interesting alkenyl 1,2-sulfone ether derivatives through C-S and C-O bond formation is described. The protocol proceeds with the sulfonyl chloride and alcohols under simple, mild, and base-free conditions, providing a straightforward route to sulfonylated allyl ether compounds with broad functional group tolerance and excellent chemo- and regioselectivity. Mechanistic studies indicate that the selective alkene difunctionalization includes a key copper-mediated single-electron transfer process.

Altered mucosal bacteria and metabolomics in patients with Peutz-Jeghers syndrome.

BACKGROUND: Peutz-Jeghers syndrome (PJS) is a rare genetic disorder characterized by the development of pigmented spots, gastrointestinal polyps and increased susceptibility to cancers. Currently, most studies have investigated intestinal microbiota through fecal microbiota, and there are few reports about mucosa-associated microbiota. It remains valuable to search for the key intestinal microbiota or abnormal metabolic pathways linked to PJS. AIM: This study aimed to assess the structure and composition of mucosa-associated microbiota in patients with PJS and to explore the potential influence of intestinal microbiota disorders and metabolite changes on PJS. METHODS: The bacterial composition was analyzed in 13 PJS patients and 12 controls using 16S rRNA gene sequencing (Illumina MiSeq) for bacteria. Differential analyses of the intestinal microbiota were performed from the phylum to species level. Liquid chromatography-tandem mass spectrometry (LC‒MS) was used to detect the differentially abundant metabolites of PJS patients and controls to identify different metabolites and metabolic biomarkers of small intestinal mucosa samples. RESULTS: High-throughput sequencing confirmed the special characteristics and biodiversity of the mucosa microflora in patients with PJS. They had lower bacterial biodiversity than controls. The abundance of intestinal mucosal microflora was significantly lower than that of fecal microflora. In addition, lipid metabolism, amino acid metabolism, carbohydrate metabolism, nucleotide metabolism and other pathways were significantly different from those of controls, which were associated with the development of the enteric nervous system, intestinal inflammation and development of tumors. CONCLUSION: This is the first report on the mucosa-associated microbiota and metabolite profile of subjects with PJS, which may be meaningful to provide a structural basis for further research on intestinal microecology in PJS.

Understanding and exploring the diversity of soil microorganisms in tea (Camellia sinensis) gardens: toward sustainable tea production.

Leaves of Camellia sinensis plants are used to produce tea, one of the most consumed beverages worldwide, containing a wide variety of bioactive compounds that help to promote human health. Tea cultivation is economically important, and its sustainable production can have significant consequences in providing agricultural opportunities and lowering extreme poverty. Soil parameters are well known to affect the quality of the resultant leaves and consequently, the understanding of the diversity and functions of soil microorganisms in tea gardens will provide insight to harnessing soil microbial communities to improve tea yield and quality. Current analyses indicate that tea garden soils possess a rich composition of diverse microorganisms (bacteria and fungi) of which the bacterial Proteobacteria, Actinobacteria, Acidobacteria, Firmicutes and Chloroflexi and fungal Ascomycota, Basidiomycota, Glomeromycota are the prominent groups. When optimized, these microbes' function in keeping garden soil ecosystems balanced by acting on nutrient cycling processes, biofertilizers, biocontrol of pests and pathogens, and bioremediation of persistent organic chemicals. Here, we summarize research on the activities of (tea garden) soil microorganisms as biofertilizers, biological control agents and as bioremediators to improve soil health and consequently, tea yield and quality, focusing mainly on bacterial and fungal members. Recent advances in molecular techniques that characterize the diverse microorganisms in tea gardens are examined. In terms of viruses there is a paucity of information regarding any beneficial functions of soil viruses in tea gardens, although in some instances insect pathogenic viruses have been used to control tea pests. The potential of soil microorganisms is reported here, as well as recent techniques used to study microbial diversity and their genetic manipulation, aimed at improving the yield and quality of tea plants for sustainable production.