Unitig-centered pan-genome machine learning approach for predicting antibiotic resistance and discovering novel resistance genes in bacterial strains.

In current genomic research, the widely used methods for predicting antimicrobial resistance (AMR) often rely on prior knowledge of known AMR genes or reference genomes. However, these methods have limitations, potentially resulting in imprecise predictions owing to incomplete coverage of AMR mechanisms and genetic variations. To overcome these limitations, we propose a pan-genome-based machine learning approach to advance our understanding of AMR gene repertoires and uncover possible feature sets for precise AMR classification. By building compacted de Brujin graphs (cDBGs) from thousands of genomes and collecting the presence/absence patterns of unique sequences (unitigs) for Pseudomonas aeruginosa, we determined that using machine learning models on unitig-centered pan-genomes showed significant promise for accurately predicting the antibiotic resistance or susceptibility of microbial strains. Applying a feature-selection-based machine learning algorithm led to satisfactory predictive performance for the training dataset (with an area under the receiver operating characteristic curve (AUC) of > 0.929) and an independent validation dataset (AUC, approximately 0.77). Furthermore, the selected unitigs revealed previously unidentified resistance genes, allowing for the expansion of the resistance gene repertoire to those that have not previously been described in the literature on antibiotic resistance. These results demonstrate that our proposed unitig-based pan-genome feature set was effective in constructing machine learning predictors that could accurately identify AMR pathogens. Gene sets extracted using this approach may offer valuable insights into expanding known AMR genes and forming new hypotheses to uncover the underlying mechanisms of bacterial AMR.

Acacetin alleviates autoimmune myocarditis by regulating CD4+ T cell mitochondrial respiration.

BACKGROUND: Myocarditis refers to an autoimmune inflammatory response of the myocardium with characterization of self-reactive CD4+ T cell activation, which lacks effective treatment and has a poor prognosis. Acacetin is a natural flavonoid product that has been reported to have anti-inflammatory effects. However, acacetin has not been investigated in myocarditis. METHODS: Oral acacetin treatment was administered in an experimental autoimmune myocarditis model established with myosin heavy chain-alpha peptide. Echocardiography, pathological staining, and RT-qPCR were used to detect cardiac function, myocardial injury, and inflammation levels. Flow cytometry was utilized to detect the effect of acacetin on CD4+ T cell function. RNA-seq, molecular docking, and microscale thermophoresis (MST) were employed to investigate potential mechanisms. Seahorse analysis, mitoSOX, JC-1, and mitotracker were utilized to detect the effect of acacetin on mitochondrial function. RESULTS: Acacetin attenuated cardiac injury and fibrosis as well as heart dysfunction, and reduced cardiac inflammatory cytokines and ratio of effector CD4+ T and Th17 cells. Acacetin inhibited CD4+ T cell activation, proliferation, and Th17 cell differentiation. Mechanistically, the effects of acacetin were related to reducing mitochondrial complex II activity thereby inhibiting mitochondrial respiration and mitochondrial reactive oxygen species in CD4+ T cells. CONCLUSION: Acacetin may be a valuable therapeutic drug in treating CD4+ T cell-mediated myocarditis.

Mental toughness in adolescents: bridging family relationships and depression across personality traits.

BACKGROUND: Adolescence is a pivotal stage vulnerable to mental health issues like anxiety and depression. While family relationships, mental toughness, and personality traits are known to impact adolescent mental health, their interactive and moderating roles are not fully understood. AIM: This study aims to investigate the mediating role of mental toughness in the relationship between family relationships and depression among high school students, and to examine the varying impacts of personality traits on this mediation. METHOD: A cross-sectional study was conducted on a sample of 734 adolescents. Participants completed measures assessing family relationships, mental toughness, personality traits, and mental health outcomes (depression). Latent Profile Analysis, Multiple Regression Analysis, and Structural Equation Modeling, to investigate these relationships. RESULTS: The study found that mental toughness significantly mediates the relationship between family relationships and depression. Notably, this mediating effect varied between personality type; it was more pronounced in the moderate-reserved type compared to the proactive-engaged type. LPA identified two distinct personality types of students based on their personality traits, with differential patterns of family relationships, mental toughness, and depression. Multiple regression analysis indicated that character and adaptability, components of mental toughness, were significant negative predictors of depression. CONCLUSION: The study contributes to understanding the dynamics of adolescent mental health, particularly in the context of Chinese high school students. It underscores the importance of considering family dynamics, personality traits, and mental toughness in developing effective mental health interventions for adolescents.

Cognitive control in honesty and dishonesty under different conflict scenarios: insights from reaction time.

This study investigated the role of cognitive control in moral decision-making, focusing on conflicts between financial temptations and the integrity of honesty. We employed a perceptual task by asking participants to identify which side of the diagonal contained more red dots within a square to provoke both honest and dishonest behaviors, tracking their reaction times (RTs). Participants encountered situations with no conflict, ambiguous conflict, and clear conflict. Their behaviors in the clear conflict condition categorized them as either "honest" or "dishonest." Our findings suggested that, in ambiguous conflict situations, honest individuals had significantly longer RTs and fewer self-interest responses than their dishonest counterparts, suggesting a greater need for cognitive control to resolve conflicts and a lesser tendency toward self-interest. Moreover, a negative correlation was found between participants' number of self-interest responses and RTs in ambiguous conflict situations (r = -0.27 in study 1 and r = -0.66 in study 2), and a positive correlation with cheating numbers in clear conflict situations (r = 0.36 in study 1 and r = 0.82 in study 2). This suggests less cognitive control was required for self-interest and cheating responses, bolstering the "Will" hypothesis. We also found that a person's self-interest tendency could predict their dishonest behavior. These insights extend our understanding of the role of cognitive control plays in honesty and dishonesty, with potential applications in education, policy-making, and business ethics.

Alexithymia and peer victimisation: interconnected pathways to adolescent non-suicidal self-injury.

BACKGROUND: The prevalence of non-suicidal self-injury (NSSI) among adolescents underscores the importance of understanding the complex factors that drive this behaviour. Framed within broader constructs of emotional regulation theories, alexithymia and peer victimisation are thought to interact to influence NSSI behaviours. AIM: This research addresses whether alexithymia and peer victimisation serve as risk factors for NSSI and, if so, how these factors interact with each other. METHOD: This quantitative study analysed data from 605 adolescents, using a range of validated self-report measures including the Toronto Alexithymia Scale. Statistical analyses including one-way analysis of variance, multiple regression and structural equation modelling were employed to scrutinise the relationships among the variables. RESULTS: Alexithymia and peer victimisation significantly predicted NSSI behaviours. Specifically, the 'difficulty in identifying feelings' subscale of alexithymia emerged as a noteworthy predictor of NSSI (P < 0.001). Peer victimisation mediated the relationship between alexithymia and NSSI, explaining approximately 24.50% of alexithymia's total effect on NSSI. In addition, age was a significant predictor of NSSI, but gender and education years were not (P > 0.05). These relationships were found to be invariant across genders. CONCLUSIONS: This study enriches our understanding of the interplay between alexithymia, peer victimisation and NSSI, particularly within the Chinese context. Its findings have significant implications for a rethinking of alexithymia's theoretical construct and interventions targeting emotional literacy and peer dynamics among adolescents. Future research could benefit from a longitudinal design to establish causality.

Prognosticating Fetal Growth Restriction and Small for Gestational Age by Medical History.

This study aimed to develop and externally validate a prognostic prediction model for screening fetal growth restriction (FGR)/small for gestational age (SGA) using medical history. From a nationwide health insurance database (n=1,697,452), we retrospectively selected visits of 12-to-55-year-old females to healthcare providers. This study used machine learning (including deep learning) and 54 medical-history predictors. The best model was a deep-insight visible neural network (DI-VNN). It had area under the curve of receiver operating characteristics (AUROC) 0.742 (95% CI 0.734 to 0.750) and a sensitivity of 49.09% (95% CI 47.60% to 50.58% at with 95% specificity). Our model used medical history for screening FGR/SGA with moderate accuracy by DI-VNN. In future work, we will compare this model with those from systematically-reviewed, previous studies and evaluate if this model's usage impacts patient outcomes.

Unraveling the Complex Interactions of Psychological Factors Contributing to Cyber Reactive Aggression Among College Students: Network and Mediation Analyses.

Cyber reactive aggression (CRA) among college students is a prevalent and harmful phenomenon. Psychological characteristics, such as trait anger (TA), hostile attribution bias (HAB), and revenge motivation (RM), are known to contribute to reactive aggression. However, the interactions between these factors in the context of cyberspace and their contribution to CRA among college students have not been extensively studied. This cross-sectional study aimed to identify the associations among psychological characteristics, demographic factors, and CRA among Chinese college students through Mixed Graphical Model (MGM) network and mediation effect analyses. A total of 926 participants completed questionnaires assessing TA, HAB, RM, and CRA. The study found both direct and indirect relationships between TA and CRA, with HAB and RM serving as mediating factors. Comparisons indicated that HAB had a more significant impact on the three indirect effects than RM. Furthermore, gender was found to be associated with TA and CRA, while the left-behind experience strongly influenced HAB but had no association with other variables. This study highlights the importance of considering psychological characteristics and demographic factors in understanding CRA among college students, suggesting that effective psychological interventions, such as anger management, and promoting positive attribution training, may help reduce CRA among college students and inform the development of targeted interventions to reduce cyber aggression.

Low- and high-level information analyses of transcriptome connecting endometrial-decidua-placental origin of preeclampsia subtypes: A preliminary study.

BACKGROUND: Existing proposed pathogenesis for preeclampsia (PE) was only applied for early onset subtype and did not consider pre-pregnancy and competing risks. We aimed to decipher PE subtypes by identifying related transcriptome that represents endometrial maturation and histologic chorioamnionitis. METHODS: We utilized eight arrays of mRNA expression for discovery (n=289), and other eight arrays for validation (n=352). Differentially expressed genes (DEGs) were overlapped between those of: (1) healthy samples from endometrium, decidua, and placenta, and placenta samples under histologic chorioamnionitis; and (2) placenta samples for each of the subtypes. They were all possible combinations based on four axes: (1) pregnancy-induced hypertension; (2) placental dysfunction-related diseases (e.g., fetal growth restriction [FGR]); (3) onset; and (4) severity. RESULTS: The DEGs of endometrium at late-secretory phase, but none of decidua, significantly overlapped with those of any subtypes with: (1) early onset (p-values ≤0.008); (2) severe hypertension and proteinuria (p-values ≤0.042); or (3) chronic hypertension and/or severe PE with FGR (p-values ≤0.042). Although sharing the same subtypes whose DEGs with which significantly overlap, the gene regulation was mostly counter-expressed in placenta under chorioamnionitis (n=13/18, 72.22%; odds ratio [OR] upper bounds ≤0.21) but co-expressed in late-secretory endometrium (n=3/9, 66.67%; OR lower bounds ≥1.17). Neither the placental DEGs at first-nor second-trimester under normotensive pregnancy significantly overlapped with those under late-onset, severe PE without FGR. CONCLUSIONS: We identified the transcriptome of endometrial maturation in placental dysfunction that distinguished early- and late-onset PE, and indicated chorioamnionitis as a PE competing risk. This study implied a feasibility to develop and validate the pathogenesis models that include pre-pregnancy and competing risks to decide if it is needed to collect prospective data for PE starting from pre-pregnancy including chorioamnionitis information.

Plasma metabolites of aromatic amino acids associate with clinical severity and gut microbiota of Parkinson's disease.

Gut microbial proteolytic metabolism has been reportedly altered in Parkinson's disease (PD). However, the circulating aromatic amino acids (AAA) described in PD are inconsistent. Here we aimed to investigate plasma AAA profiles in a large cohort of PD patients, and examine their correlations with clinical severity and gut microbiota changes. We enrolled 500 participants including 250 PD patients and 250 neurologically normal controls. Plasma metabolites were measured using liquid chromatography mass spectrometry. Faecal samples were newly collected from 154 PD patients for microbiota shotgun metagenomic sequencing combined with data derived from 96 PD patients reported before. Data were collected regarding diet, medications, and motor and non-motor symptoms of PD. Compared to controls, PD patients had higher plasma AAA levels, including phenylacetylglutamine (PAGln), p-cresol sulfate (Pcs), p-cresol glucuronide (Pcg), and indoxyl sulfate (IS). Multivariable linear regression analyses, with adjustment for age, sex, and medications, revealed that the plasma levels of PAGln (coefficient 4.49, 95% CI 0.40-8.58, P = 0.032) and Pcg (coefficient 1.79, 95% CI 0.07-3.52, P = 0.042) positively correlated with motor symptom severity but not cognitive function. After correcting for abovementioned potential confounders, these AAA metabolites were also associated with the occurrence of constipation in PD patients (all P < 0.05). Furthermore, plasma levels of AAA metabolites were correlated with the abundance of specific gut microbiota species, including Bacteroides sp. CF01-10NS, Bacteroides vulgatus, and Clostridium sp. AF50-3. In conclusion, elevated plasma AAA metabolite levels correlated with disease characteristics in PD, suggesting that upregulated proteolytic metabolism may contribute to the pathophysiology of PD.

Characterization and Comparative Analysis of the Complete Plastomes of Five Epidendrum (Epidendreae, Orchidaceae) Species.

Epidendrum, one of the three largest genera of Orchidaceae, exhibits significant horticultural and ornamental value and serves as an important research model in conservation, ecology, and evolutionary biology. Given the ambiguous identification of germplasm and complex evolutionary relationships within the genus, the complete plastome of this genus (including five species) were firstly sequenced and assembled to explore their characterizations. The plastomes exhibited a typical quadripartite structure. The lengths of the plastomes ranged from 147,902 bp to 150,986 bp, with a GC content of 37.16% to 37.33%. Gene annotation revealed the presence of 78-82 protein-coding genes, 38 tRNAs, and 8 rRNAs. A total of 25-38 long repeats and 130-149 SSRs were detected. Analysis of relative synonymous codon usage (RSCU) indicated that leucine (Leu) was the most and cysteine (Cys) was the least. The consistent and robust phylogenetic relationships of Epidendrum and its closely related taxa were established using a total of 43 plastid genomes from the tribe Epidendreae. The genus Epidendrum was supported as a monophyletic group and as a sister to Cattleya. Meanwhile, four mutational hotspots (trnCGCA-petN, trnDGUC-trnYGUA, trnSGCU-trnGUCC, and rpl32-trnLUAG) were identified for further phylogenetic studies. Our analysis demonstrates the promising utility of plastomes in inferring the phylogenetic relationships of Epidendrum.

Yeast cell detection using fuzzy automatic contrast enhancement (FACE) and you only look once (YOLO).

In contemporary biomedical research, the accurate automatic detection of cells within intricate microscopic imagery stands as a cornerstone for scientific advancement. Leveraging state-of-the-art deep learning techniques, this study introduces a novel amalgamation of Fuzzy Automatic Contrast Enhancement (FACE) and the You Only Look Once (YOLO) framework to address this critical challenge of automatic cell detection. Yeast cells, representing a vital component of the fungi family, hold profound significance in elucidating the intricacies of eukaryotic cells and human biology. The proposed methodology introduces a paradigm shift in cell detection by optimizing image contrast through optimal fuzzy clustering within the FACE approach. This advancement mitigates the shortcomings of conventional contrast enhancement techniques, minimizing artifacts and suboptimal outcomes. Further enhancing contrast, a universal contrast enhancement variable is ingeniously introduced, enriching image clarity with automatic precision. Experimental validation encompasses a diverse range of yeast cell images subjected to rigorous quantitative assessment via Root-Mean-Square Contrast and Root-Mean-Square Deviation (RMSD). Comparative analyses against conventional enhancement methods showcase the superior performance of the FACE-enhanced images. Notably, the integration of the innovative You Only Look Once (YOLOv5) facilitates automatic cell detection within a finely partitioned grid system. This leads to the development of two models-one operating on pristine raw images, the other harnessing the enriched landscape of FACE-enhanced imagery. Strikingly, the FACE enhancement achieves exceptional accuracy in automatic yeast cell detection by YOLOv5 across both raw and enhanced images. Comprehensive performance evaluations encompassing tenfold accuracy assessments and confidence scoring substantiate the robustness of the FACE-YOLO model. Notably, the integration of FACE-enhanced images serves as a catalyst, significantly elevating the performance of YOLOv5 detection. Complementing these efforts, OpenCV lends computational acumen to delineate precise yeast cell contours and coordinates, augmenting the precision of cell detection.

[Relationship between acupoint sensitization and changes in dorsal root ganglion neuron excitability in myocardial ischemia mice].

OBJECTIVE: To investigate the relationship between the sensitization state of acupoints on the surface of the myocardial ischemia (MI) model mice and the changes in the electrophysiological properties of the dorsal root ganglion (DRG) neurons in the corresponding spinal cord segment, and its underlying mechanism. METHODS: Sixty-eight male C57BL/6J mice were randomly divided into control and model groups (34 mice in each group). The model group received an intraperitoneal injection of 160 mg/kg isoproterenol (ISO) to establish the MI model, and the control group received an injection of the same dose of normal saline as the model group. After modeling for about 6 days, MI proportion was measured by HE staining to verify the pathological changes in the heart tissue. Evans blue (EB) dye was injected into the tail vein of mice to reflect the size, location, distribution, and number of exudates on the body surface. Then, whole-cell membrane currents, intrinsic excitability and membrane properties of different types of DRG neurons were evaluated by electrophysiological experiment in vitro. RESULTS: Compared with the control group, the heart size was larger, with pathological outcomes showing enlarged myocardial hypertrophy, destroyed structure of cardiomyocytes, with mononuclear cell infiltration among the cardiomyocytes in the model group. Compared with the control group, the number of EB exudation points was significantly increased (P<0.01), which were mainly concentrated in the epidermis near the T1-T5 segment of the spinal cord, "Feishu" (BL13), "Jueyinshu" (BL14) and "Xinshu" (BL15) in the model group. Compared with the control group, the rheobase and action potential amplitude (APA) of DRG medium-sized neurons were obviously decreased (P<0.01, P<0.05), while the whole-cell membrane currents, the spike numbers, the average instantaneous frequency, and the average discharge frequency were markedly increased (P<0.01). There were no significant alterations in the membrane properties and intrinsic excitability induced by depolarized currents of small-sized neurons between groups. Compared with the control group, the whole-cell membrane currents, spike numbers, and the average instantaneous frequency were significantly increased in the model group(P<0.05, P<0.01) while rheobase was significantly decreased (P<0.05) in DRG medium-sized neurons labeled with biotin and CGRP. CONCLUSION: After the mice were modeled by ISO, the DRG medium-size neurons in the T1-T5 segment of the spinal cord may mediate the sensitization of acupoints on the body surface through their different neuronal membrane properties and intrinsic excitabilities.

Long-Read Genome Sequencing of Abscondita cerata (Coleoptera: Lampyridae), the Endemic Firefly of Taiwan.

Abscondita cerata is the most abundant and widely distributed endemic firefly species in Taiwan and is considered a key environmental and ecological indicator organism. In this study, we report the first long-read genome sequencing of Abs. cerata sequenced by Nanopore technology. The draft genome size, 967 Mb, was measured through a hybrid approach that consisted of assembling using 11.25-Gb Nanopore long reads and polishing using 9.47-Gb BGI PE100 short reads. The drafted genome was assembled into 4,855 contigs, with the N50 reaching 325.269 kb length. The assembled genome was predicted to possess 55,206 protein-coding genes, of which 20,862 (37.78%) were functionally annotated with public databases. 47.11% of the genome sequences consisted of repeat elements; among them DNA transposons accounted for the largest proportion (26.79%). A BUSCO (Benchmarking Universal Single Copy Orthologs) evaluation demonstrated that the genome and gene completeness were 84.8% and 79%, respectively. The phylogeny constructed using 1,792 single copy genes was consistent with previous studies. The comparative transcriptome between adult male head and lantern tissues revealed (1) the vision of Abs. cerata is primarily UV-sensitive to environmental twilight, which determines when it begins its nocturnal activity, (2) the major expressed OR56d receptor may be correlated to suitable humidity sensing, and (3) Luc1-type luciferase is responsible for Abs. cerata's luminescent spectrum.

Using bacterial pan-genome-based feature selection approach to improve the prediction of minimum inhibitory concentration (MIC).

Background: Predicting the resistance profiles of antimicrobial resistance (AMR) pathogens is becoming more and more important in treating infectious diseases. Various attempts have been made to build machine learning models to classify resistant or susceptible pathogens based on either known antimicrobial resistance genes or the entire gene set. However, the phenotypic annotations are translated from minimum inhibitory concentration (MIC), which is the lowest concentration of antibiotic drugs in inhibiting certain pathogenic strains. Since the MIC breakpoints that classify a strain to be resistant or susceptible to specific antibiotic drug may be revised by governing institutes, we refrained from translating these MIC values into the categories "susceptible" or "resistant" but instead attempted to predict the MIC values using machine learning approaches. Results: By applying a machine learning feature selection approach on a Salmonella enterica pan-genome, in which the protein sequences were clustered to identify highly similar gene families, we showed that the selected features (genes) performed better than known AMR genes, and that models built on the selected genes achieved very accurate MIC prediction. Functional analysis revealed that about half of the selected genes were annotated as hypothetical proteins (i.e., with unknown functional roles), and that only a small portion of known AMR genes were among the selected genes, indicating that applying feature selection on the entire gene set has the potential of uncovering novel genes that may be associated with and may contribute to pathogenic antimicrobial resistances. Conclusion: The application of the pan-genome-based machine learning approach was indeed capable of predicting MIC values with very high accuracy. The feature selection process may also identify novel AMR genes for inferring bacterial antimicrobial resistance phenotypes.

Enhanced encapsulation of lutein using soy protein isolate nanoparticles prepared by pulsed electric field and pH shifting treatment.

In this study, soy protein isolate (SPI) was modified by a pulsed electric field (PEF) combined with pH shifting treatment (10 kV/cm, pH 11) to prepare SPI nanoparticles (PSPI11) for efficient loading of lutein. The results showed that when the mass ratio of SPI to lutein was 25:1, the encapsulation efficiency of lutein in PSPI11 increased from 54% to 77%, and the loading capacity increased by 41% compared to the original SPI. The formed SPI-lutein composite nanoparticles (PSPI11-LUTNPs) had smaller, more homogeneous sizes and larger negative charges than SPI7-LUTNPs. The combined treatment favored the unfolding of the SPI structure and could expose its interior hydrophobic groups to bind with lutein. Nanocomplexation with SPIs significantly improved the solubility and stability of lutein, with PSPI11 showing the greatest improvement. As a result, PEF combined with pH shifting pretreatment is an effective method for developing SPI nanoparticles loaded and protected with lutein.

Human-guided deep learning with ante-hoc explainability by convolutional network from non-image data for pregnancy prognostication.

BACKGROUND AND OBJECTIVE: Deep learning is applied in medicine mostly due to its state-of-the-art performance for diagnostic imaging. Supervisory authorities also require the model to be explainable, but most explain the model after development (post hoc) instead of incorporating explanation into the design (ante hoc). This study aimed to demonstrate a human-guided deep learning with ante-hoc explainability by convolutional network from non-image data to develop, validate, and deploy a prognostic prediction model for PROM and an estimator of time of delivery using a nationwide health insurance database. METHODS: To guide modeling, we constructed and verified association diagrams respectively from literatures and electronic health records. Non-image data were transformed into meaningful images utilizing predictor-to-predictor similarities, harnessing the power of convolutional neural network mostly used for diagnostic imaging. The network architecture was also inferred from the similarities. RESULTS: This resulted the best model for prelabor rupture of membranes (n=883, 376) with the area under curves 0.73 (95% CI 0.72 to 0.75) and 0.70 (95% CI 0.69 to 0.71) respectively by internal and external validations, and outperformed previous models found by systematic review. It was explainable by knowledge-based diagrams and model representation. CONCLUSIONS: This allows prognostication with actionable insights for preventive medicine.

[Research on mechanisms of acupoint sensitization in gastric ulcer mice from perspective of dorsal root ganglion neuronal excitability].

OBJECTIVE: To investigate the relationship between acupoint sensitization on the body surface and neuronal intrinsic excitability of the medium- and small-size dorsal root ganglion (DRG) neurons from the perspective of ion channel kinetics in mice with gastric ulcer. METHODS: Male C57BL/6J mice were randomly divided into control (n=32) and model groups (n=34). The gastric ulcer model was established by injection of 60% glacial acetic acid (0.2 mL/100 g) into the gastric wall muscle layer and submucosa near the pylorus in the minor curvature of the stomach. In contrast, the same dose of normal saline was injected in the same way in the control group. Six days after modeling, Evans blue (EB) solution was injected into the mouse's tail vein for observing the number and distribution of the exudation blue spots on the body surface. Histopathological changes of the gastric tissue were observed by H.E. staining. Then, whole-cell membrane currents and intrinsic excitability of medium- and small-size neurons in the spinal T9-T11 DRGs were measured by in vitro electrophysiology combining with biocytin-ABC method. RESULTS: In the control group, EB exudation blue spots were not obvious, while in the model group, the blue spots on the body surface were densely distributed in the area of spinal T9-T11 segments, the epigastric region, and the skin around "Zhongwan" (CV12) and "Huaroumen" (ST24) regions, and near the surgical incision region. Compared with the control group, the model group had a high level of eosinophilic infiltrates in the submucosa of gastric tissues, severe gastric fossa structure damage, gastric fundus gland dilation and other pathological manifestations. The number of exudation blue spots was proportional to the degree of inflammatory reaction in the stomach. In comparison with the control group, the spike discharges of type II of medium-size DRG neurons in T9-T11 segments were decreased, and the current of whole-cell membrane was increased, basic intensity was decreased (P<0.05), discharge frequency and discharge number were increased (P<0.01,P<0.000 1); while the discharges of type I small-size DRG neurons were decreased, those of type II neurons increased, the whole-cell membrane current was decreased, and discharge frequency and discharge number were decreased (P<0.01, P<0.000 1). CONCLUSION: Both the medium- and small-size DRG neurons from the spinal T9-T11 segments involve in gastric ulcer-induced acupoint sensitization via their different spike discharge activities. And intrinsic excitability of these DRG neurons can not only dynamically encode the plasticity of acupoint sensitization, but also can help us understand the neural mechanism of acupoint sensitization induced by visceral injury.

Bioinformatic Analysis Reveals both Oversampled and Underexplored Biosynthetic Diversity in Nonribosomal Peptides.

The traditional natural product discovery approach has accessed only a fraction of the chemical diversity in nature. The use of bioinformatic tools to interpret the instructions encoded in microbial biosynthetic genes has the potential to circumvent the existing methodological bottlenecks and greatly expand the scope of discovery. Structural prediction algorithms for nonribosomal peptides (NRPs), the largest family of microbial natural products, lie at the heart of this new approach. To understand the scope and limitation of the existing prediction algorithms, we evaluated their performances on NRP synthetase biosynthetic gene clusters. Our systematic analysis shows that the NRP biosynthetic landscape is uneven. Phenylglycine and its derivatives as a group of NRP building blocks (BBs), for example, have been oversampled, reflecting an extensive historical interest in the glycopeptide antibiotics family. In contrast, the benzoyl BB, including 2,3-dihydroxybenzoate (DHB), has been the most underexplored, hinting at the possibility of a reservoir of as yet unknown DHB containing NRPs with functional roles other than a siderophore. Our results also suggest that there is still vast unexplored biosynthetic diversity in nature, and the analysis presented herein shall help guide and strategize future natural product discovery campaigns. We also discuss possible ways bioinformaticians and biochemists could work together to improve the existing prediction algorithms.