Search Results (170)

Background/Objectives: The application of predictive and explainable artificial intelligence to bioinformatics data such as single nucleotide polymorphism (SNP) information is attracting rising attention in the diagnosis of various diseases. However, there are few reviews available on the recent progress of genetic artificial intelligence for the early diagnosis of gastrointestinal disease (GID). The purpose of this study is to complete a systematic review on the recent progress of genetic artificial intelligence in GID. Methods: The source of data was ten original studies from PubMed. The ten original studies were eligible according to the following criteria: (participants) the dependent variable of GID or associated disease; (interventions/comparisons) artificial intelligence; (outcomes) accuracy, the area under the curve (AUC), and/or variable importance; a publication year of 2010 or later; and the publication language of English. Results: The performance outcomes reported varied within 79–100 for accuracy (%) and 63–98 for the AUC (%). Random forest was the best approach (AUC 98%) for the classification of inflammatory bowel disease with 13 single nucleotide polymorphisms (SNPs). Similarly, random forest was the best method (R-square 99%) for the regression of the gut microbiome SNP saturation number. The following SNPs were discovered to be major variables for the prediction of GID or associated disease: rs2295778, rs13337626, rs2296188, rs2114039 (esophageal adenocarcinoma); rs28785174, rs60532570, rs13056955, rs7660164 (Crohn’s disease early intestinal resection); rs4945943 (Crohn’s disease); rs316115020, rs316420452 (calcium metabolism); rs738409_G, rs2642438_A, rs58542926_T, rs72613567_TA (steatotic liver disease); rs148710154, rs75146099 (esophageal squamous cell carcinoma). The following demographic and health-related variables were found to be important predictors of GID or associated disease besides SNPs: age, body mass index, disease behavior, immune cell type, intestinal microbiome, MARCKS protein, smoking, and SNP density/number. No deep learning study was found even though deep learning was used as a search term together with machine learning. Conclusions: Genetic artificial intelligence is effective and non-invasive as a decision support system for GID. Full article

Pine wilt disease (PWD) is a serious forest disease caused by pine wood nematode (PWN). To examine the relationship between coniferous endophytes and PWD resistance, this study investigated endophytic bacterial and fungal communities in five conifer species: two Japanese black pine populations (Pinus thunbergii from Qingdao University, PQ, and Fushan Forest Park, PF), Chinese arborvitae (Platycladus orientalis, PO), cedar (Cedrus deodara, CD), and Masson pine (Pinus massoniana, PM). Results showed a strong correlation between endophytic microbial diversity and PWD resistance. PO with high PWD resistance hosted the most unique bacterial species, while PM with low PWD resistance had the fewest unique bacteria and significantly lower ACE and Shannon indices. At the bacterial genus level, dominant genera in resistant conifers often showed high nematocidal activity, whereas those in susceptible plants boosted nematode reproduction. PQ featured the unique dominant genus Pantoea, and PO’s unique Acinetobacter and the shared genus Bacillus (with CD) both displayed high toxicity to PWNs. In contrast, PF’s Pseudomonas and PM’s Stenotrophomonas significantly promoted nematode reproduction. Fungal community analysis revealed that the unique endophytic fungi in PQ are more abundant than those in PF, and the Shannon index of its endophytic fungi is comparable to that of CD and significantly higher than that of PF. PF’s dominant fungal genus Pestalotiopsis might facilitate nematode invasion, and its fungal Shannon index is significantly lower than PQ’s. Eight bacterial strains were isolated from these five conifer plants, with six highly nematocidal strains originating from PQ, CD, and PO. This study offers evidence that endophytic microbial communities critically influence PWD resistance, offering a microbial basis for developing resistant conifer cultivars through microbiome engineering. Full article

Background/Objectives: Early diagnosis of periodontitis remains challenging using traditional clinical methods. This systematic review and meta-analysis evaluated the diagnostic accuracy of artificial intelligence (AI) models trained on non-invasive or minimally invasive biomarkers—including saliva, gingival crevicular fluid (GCF), and immunologic profiles—for diagnosing and classifying periodontitis in human subjects. Methods: A comprehensive search of PubMed/MEDLINE, Scopus, Web of Science, EMBASE, and Cochrane CENTRAL was conducted from database inception to June 2025. Eligible studies used AI or machine learning models with patient-derived biomarker data and reported diagnostic performance metrics. Results: Seven studies were included, employing various AI models such as random forest, artificial neural networks, and gradient boosting. Biomarkers were derived from saliva (n = 4), saliva-derived biomarkers from oral rinse (n = 1), immunologic profiles (n = 1), and tissue-based gene expression (n = 1). Reported area under the receiver operating characteristic (ROC) curve (AUC) ranged from 0.83 to 0.96. Meta-analysis of studies with comparable outcomes showed a pooled sensitivity of 0.89 (95% CI: 0.84–0.93), a specificity of 0.87 (95% CI: 0.80–0.92), and a summary AUC of 0.92. Subgroup analysis revealed that models using salivary biomarkers achieved a higher pooled AUC (0.94) than those using GCF or immunologic markers (AUC: 0.89). Sensitivity analyses excluding studies with unclear bias did not significantly alter pooled estimates, affirming robustness. The overall certainty of evidence was rated as moderate to high. Conclusions: AI-based diagnostic models utilizing salivary, microbiome, or immunologic biomarkers demonstrated quantitatively high accuracy; however, the overall certainty of evidence was rated as moderate to high due to limitations in study design and validation. Full article

Urban expansion fragments once-contiguous forest patches, generating pronounced edge gradients that modulate soil physicochemical properties and biodiversity. We quantified how fragmentation reshaped the soil microbiome continuum and its implications for soil carbon storage in a temperate urban mixed deciduous forest. A total of 18 plots were considered in this study, with six plots for each fragment type. Intact interior forest (F), internal forest path fragment (IF), and external forest path fragment (EF) soils were sampled at 0–15, 15–30, and 30–45 cm depths and profiled through phospholipid-derived fatty acid (PLFA) chemotyping and amino sugar proxies for living microbiome and microbial-derived necromass assessment, respectively. Carbon fractionation was performed through the chemical oxidation method. Diversity indices (Shannon–Wiener, Pielou evenness, Margalef richness, and Simpson dominance) were calculated based on the determined fatty acids derived from the phospholipid fraction. The microbial biomass ranged from 85.1 to 214.6 nmol g⁻¹ dry soil, with the surface layers of F exhibiting the highest values (p < 0.01). Shannon diversity declined systematically from F > IF > EF. The microbial necromass varied from 11.3 to 23.2 g⋅kg⁻¹. Fragmentation intensified the stratification of carbon pools, with organic carbon decreasing by approximately 14% from F to EF. Our results show that EFs possess a declining microbiome continuum that weakens their carbon sequestration capacity in urban forests. Full article

Ziziphus lotus (L.) Lam. (Rhamnaceae), a key shrub species native to North Africa, is commonly found in arid and semi-arid regions. Renowned for its resilience under harsh conditions, it forms vegetation clusters that influence the surrounding environment. These clusters create microhabitats that promote biodiversity, reduce soil erosion, and improve soil fertility. However, in agricultural fields, Z. lotus is often regarded as an undesirable species. This study investigated the bacterial diversity and community composition along spatial gradients around Z. lotus patches in barley-planted and non-planted fields. Using 16S rRNA gene sequencing, 84 soil samples were analyzed from distances of 0, 3, and 6 m from Z. lotus patches. MiSeq sequencing generated 143,424 reads, representing 505 bacterial ASVs across 22 phyla. Alpha-diversity was highest at intermediate distances (3 m), while beta-diversity analyses revealed significant differences in community composition across distances (p = 0.035). Pseudomonadota dominated close to the shrub (44% at 0 m) but decreased at greater distances, whereas Bacillota and Actinobacteriota displayed distinct spatial patterns. A core microbiome comprising 44 ASVs (8.7%) was shared across all distances, with the greatest number of unique ASVs identified at 3 m. Random forest analysis highlighted Skermanella and Rubrobacter as key discriminatory taxa. These findings emphasize the spatial structuring of bacterial communities around Z. lotus patches, demonstrating the shrub’s substantial influence on bacterial dynamics in arid ecosystems. Full article

Plant microbiomes are vital for the growth and health of their host. Tree-associated microbiomes are shaped by multiple factors, of which the host is one of the key determinants. Whether different host genotypes affect the structure and diversity of the tissue-associated microbiome and how specific taxa enriched in different tree tissues are not yet well illustrated. Chinese fir (Cunninghamia lanceolata) is an important tree species for both economy and ecosystem in the subtropical regions of Asia. In this study, we investigated the tissue-specific fungal community structure and diversity of nine different Chinese fir genotypes (39 years) grown in the same field. With non-metric multidimensional scaling (NMDS) analysis, we revealed the divergence of the fungal community from rhizosphere soil (RS), fine roots (FRs), and thick roots (TRs). Through analysis with α-diversity metrics (Chao1, Shannon, Pielou, ACE, Good‘s coverage, PD-tree, Simpson, Sob), we confirmed the significant difference of the fungal community in RS, FR, and TR samples. Yet, the overall fungal community difference was not observed among nine genotypes for the same tissues (RS, FR, TR). The most abundant fungal genera were Russula in RS, Scytinostroma in FR, and Subulicystidium in TR. Functional prediction with FUNGuild analysis suggested that ectomycorrhizal fungi were commonly enriched in rhizosphere soil, while saprotroph–parasite and potentially pathogenic fungi were more abundant in root samples. Specifically, genotype N104 holds less ectomycorrhizal and pathogenic fungi in all tissues (RS, FR, TR) compared to other genotypes. Additionally, significant correlations of several endophytic fungal taxa (Scytinostroma, Neonothopanus, Lachnum) with the growth traits (tree height, diameter, stand volume) were observed. This addresses that the interaction between tree roots and the fungal community is a reflection of tree growth, supporting the “trade-off” hypothesis between growth and defense in forest trees. In summary, we revealed tissue-specific, as well as host genotype-specific and genotype-common characters of the structure and functions of their fungal communities. Full article

The priming effect (PE), a microbially mediated process, critically regulates the balance between carbon sequestration and mineralization. This study used soils from different soil depths (0–20 cm, 20–40 cm, and 40–60 cm) under Picea schrenkiana forest in the Tianshan Mountains as the research object. An indoor incubation experiment was conducted by adding three concentrations (1% SOC, 2% SOC, and 3% SOC) of ¹³C-labelled glucose. We applied ¹³C isotope probe-phospholipid fatty acid (PLFA-SIP) technology to investigate the influence of readily labile organic carbon inputs on soil priming effect (PE), microbial community shifts at various depths, and the mechanisms underlying soil PE. The results indicated that the addition of ¹³C-labeled glucose accelerated the mineralization of soil organic carbon (SOC); CO₂ emissions were highest in the 0–20 cm soil layer and decreased trend with increasing soil depth, with significant differences observed across different soil layers (p < 0.05). Soil depth had a positive direct effect on the cumulative priming effect (CPE); however, it showed negative indirect effects through physico-chemical properties and microbial biomass. The CPE of the 0–20 cm soil layer was significantly positively correlated with ¹³C-Gram-positive bacteria, ¹³C-Gram-negative bacteria, and ¹³C-actinomycetes. The CPE of the 20–40 cm and 40–60 cm soil layers exhibited a significant positive correlation with cumulative mineralization (CM) and microbial biomass carbon (MBC). Glucose addition had the largest and most significant positive effect on the CPE. Glucose addition positively affected PLFAs and particularly microbial biomass. This study provides valuable insights into the dynamics of soil carbon pools at varying depths following glucose application, advancing the understanding of forest soil carbon sequestration. Full article

Forest musk deer (Moschus berezovskii Flerov), a critically endangered ruminant species, faces extinction risks, with captive populations further threatened by prevalent digestive and immune disorders. This study utilized comparative metagenomic sequencing to assess intestinal microbiota structure and functional profiles between wild populations in Chongqing and Hunan and captive individuals. Wild populations exhibited a Pseudomonadota-dominated gut microbiota (significantly more abundant than in captive counterparts), enriched with lignin-degrading genera Novosphingobium and Acinetobacter. In contrast, the captive group demonstrated increased abundances of Bacillota/Bacteroidota, alongside abnormal proliferation of Escherichia and Clostridium. Both alpha and beta diversity analyses confirmed significant compositional divergences among the three groups, with wild populations maintaining higher diversity than captive populations. Notably, while substantial disparities in microbial abundance existed between wild populations (attributed to habitat vegetation differences), core microbial diversity and carbohydrate metabolic functions exhibited convergence. Functional analyses marked divergences in metabolic pathways: Captive microbiota showed enrichment in translation and glycan metabolism pathways, whereas wild populations displayed pronounced enrichment in immune regulation and environmental sensing pathways. These findings establish a theoretical foundation for optimizing wild population conservation strategies and developing science-based captive management protocols. Full article

Background/Objectives: This study aimed to examine the effects of a Fucoidan-rich extract from Saccharina latissima (SLE-F) on differential gut microbiota composition, intestinal inflammation status, and microbial functional gene expression in participants infected with Dengue or Oropouche virus at the Hermanos Ameijeiras Hospital in Havana, Cuba. Methods: Fecal samples were collected at baseline, day 28, and day 90 from 90 healthy adults, some of whom contracted the virus during the study period. Functional gene analysis was conducted using two approaches—the Kruskal–Wallis H test and linear discriminant analysis effect size—applied to ortholog-level data normalized by read count and gene copy number. Results: Infected participants exhibited significantly lower Lachnospiraceae-to-Enterobacteriaceae (LE) ratios, indicating increased intestinal inflammation. High-dose SLE-F treatment led to a significant reduction in the LE ratio (p = 0.006), suggesting a strong anti-inflammatory effect. Microbiome analysis revealed a shift from dysbiosis to a more balanced composition by the end of the study, characterized by increased abundances of Akkermansia muciniphila, Bifidobacterium adolescentis, and B. longum, along with decreased pro-inflammatory taxa such as Fusobacterium. Conclusions: Genetic analysis provided distinct yet complementary insights into the microbiome’s functional responses to infection and therapeutic modulation by Fucoidan. These findings highlight the therapeutic potential of high-dose Fucoidan in reducing gut inflammation and promoting microbiome recovery following viral infections. Full article

Symbiotic bacteria play a pivotal role in the biology and ecology of herbivorous insects, affecting host growth and adaptation. However, the effects of host identity on the symbiotic microbiota of gall-inducing insects remain less explored. In this study, we utilized high-throughput sequencing to investigate the effects of different oak hosts on the structure and diversity of the symbiotic microbial community in the asexual larvae of the gall-inducing wasp Trichagalma acutissimae. Host plant species significantly altered the alpha and beta diversity of symbiotic microbiota of T. acutissimae. At the phylum level, Proteobacteria was the predominant microflora in both groups, with significantly higher abundance in larvae parasitizing Quercus acutissima than in those parasitizing Q. variabilis. Pseudomonas, which has been identified as responsible for tannin decomposition, was the most dominant genus in T. acutissimae larvae infesting both hosts. LEfSe analysis revealed substantial differences in the symbiotic microbial communities between the two hosts while also highlighting some commonalities. Functional prediction analysis indicated no significant difference in the functional roles of symbiotic bacteria between larvae infesting the two hosts. These findings suggest that the symbiotic microbiome of T. acutissimae larvae is influenced by host plant species, yet different microbial compositions may perform similar functions, implying the potential role of symbiotic microbiota in the adaptation to high-tannin oak leaves. This research enhances our understanding of the symbiotic relationship between forest pests and their associated microbes. Full article

Background: Functional endoscopic sinus surgery (FESS) is the treatment of choice for medically refractory CRS. However, the success rate of FESS is dependent on both baseline medical and demographic characteristics. Consequently, we performed an analysis of systemic/nasal cytokines and the sinus microbiome to assess their impact on the SNOT-22 response after functional endoscopic sinus surgery (FESS). Methods: A prospective observational study was performed on 44 patients with chronic rhinosinusitis undergoing FESS between December 2021 and September 2022. Diseased sinus tissue from 25 patients was subjected to whole-exome sequencing (WES) for taxonomical profiling of the sinus bacterial composition. Additional data collection included demographics, comorbidities, baseline sinonasal outcome test scores, post-operative sinonasal outcome test scores (at 3–4 months), and nasal/systemic cytokines. Results: Our analysis demonstrated that CRSwNP patients in the surgical responder cohort had statistically significantly higher median [P25, P75] levels of intra-nasal IL-5, indicating type 2 sinonasal disease (63 pg/μL [28, 118] versus 17 pg/μL [16.6, 18], p = 0.04). At the genus level, the relative abundance of Staphylococcus was significantly higher in the surgical non-responder cohort compared to the responder group. An ROC curve was highly accurate at distinguishing responders versus non-responders to FESS based on a microbiota-based random forest model (AUC = 0.92). Conclusions: Intra-nasal IL-5 levels and the bacterial composition of the sinus microbiome may be important predictors of symptomatic response after sinus surgery. Full article

Climate-change-induced drought threatens forest restoration by limiting seedling establishment. To address this, we developed synthetic bacterial communities (SynComs) tailored to support drought tolerance in two Mediterranean tree species, Quercus pubescens and Sorbus domestica. Bacteria were isolated from forest soil exposed to long-term drought, sampling across soil depths and root-associated compartments. We selected strains with key plant-beneficial traits, including exopolysaccharide (EPS) production, hormone synthesis (auxin, ABA), siderophore release, and osmotic tolerance. SynComs were assembled based on functional complementarity and ecological origin. Biofilm assays showed that even weak individual producers could enhance community-level performance. After initial screening on Arabidopsis thaliana, the most and least effective SynComs were tested on Q. pubescens and S. domestica seedlings. Compared to controls, the best-performing SynComs reduced the proportion of drought-symptomatic seedlings by 47% in Q. pubescens and 71% in S. domestica, outperforming single-strain inoculants. Notably, EPS-rich SynCom B aligned with the conservative root traits of Q. pubescens, while hormone-rich SynCom F matched the acquisitive strategy of S. domestica. Predictive modeling identified bacterial identity and symptom timing as key predictors of drought resilience. Our results highlight the value of matching microbial traits with plant strategies and drought context for climate-smart forest restoration. Full article

Aggressive canine behavior poses a significant threat to public health. Understanding aggressive behavior is crucial for canine socialization and human–dog interactions. This study conducted an exploratory analysis of working dogs to investigate changes in gut microbiota and neurotransmitters associated with aggressive behavior. Notably, it represents the first research to systematically differentiate canine aggression into offensive and defensive subtypes for investigation. In this study, 56 working dogs from three regions of China, comprising different breeds (11 Spaniels, 13 German Shepherds, and 32 Belgian Malinois), aged 4.89 ± 1.54 years, and of both sexes (38 males and 18 females), were assessed and grouped for aggressive behavior using a C-BARQ-based questionnaire. Then, 16S rRNA sequencing and ELISA were employed to compare differences in gut microbiota and serotonin concentrations between aggressive (n = 35) and non-aggressive (n = 21) groups, as well as between offensive (n = 26) and defensive (n = 9) aggression subgroups. β-diversity analysis confirmed no significant correlation between aggressive behavior and gut microbiota composition (p > 0.05), suggesting a limited role of gut microbiota in modulating host behavior. Comparative analysis of gut microbiota composition revealed no significant differences in phylum-level abundance among different aggression types (p > 0.05). Notably, the non-aggressive group exhibited significantly higher relative abundances of Escherichia-Shigella, Erysipelotrichaceae_UCG-003, and Clostridium_sensu_stricto_1 compared to the aggressive group (p < 0.05). Random forest analysis identified Lactobacillus as a biomarker for canine aggressive behavior and Turicibacter as a discriminatory factor between offensive and defensive aggression. The results demonstrated a strong correlation between aggression and 5-HT neurotransmission. Serum serotonin levels were significantly lower in both the defensive (39.92 ± 2.58 ng/mL) and offensive (50.07 ± 3.90 ng/mL) aggression groups compared to the non-aggressive group (59.49 ± 2.76 ng/mL), with the lowest levels found in defensively aggressive dogs. The defensive group showed significantly lower serotonin levels than the offensive group (p < 0.001). The results demonstrate that different behavioral phenotypes in aggressive dogs lead to distinct gut microbiome compositions. This suggests that microbiome analysis may facilitate early diagnosis and preventive intervention before aggressive behavior manifests. As such, 5-HT shows potential as a monitoring tool for diagnosing canine aggression, with significant practical applications in canine behavior management. Full article

The aim of the present study was to evaluate the effect of grazing the low deciduous forest (LDF) vegetation on the diversity of the rumen microbiome in growing lambs and its relationship with volatile fatty acid (VFA) profiles. After a 35-day indoor acclimatization (stabilization period), the lambs were assigned to two groups: housed (CG, n = 4) and grazing (EG, n = 4). The grazing lambs had a 14-day habituation period in the LDF (4 h/day) and a further 30 grazing days when fodder intake was observed. Ruminal samples were collected at the end of the stabilization, on day 14 post-stabilization (14DPS), and on day 44 post-stabilization (44DPS). The ruminal butyrate concentration showed a progressive decrease of approximately 23% over the time (p = 0.0130). The qualitative composition (p = 0.001) and relative proportions of bacteria (p = 0.004) in EG-44DPS exhibited a greater diversity, with 107 total genera and 19 unique, significant abundances in 13 genera with a higher presence of Bacteroidales_RF16_group, Lachnospiraceae_ND3007_group, and WCHB1-41. Moreover, significant functional profiles are associated with key metabolic pathways in bacteria and are interconnected by the need to generate energy and biosynthetic precursors and to manage available nitrogen and carbon. Finally, eight bacterial genera were identified as biomarkers correlated with the increase in VFA in EG-44DPS. Full article

Evaluating forest functional indicators is essential for effective forest management planning. In this study, we conducted a metagenome-based comparative analysis of the rhizosphere soils from a natural Quercus mongolica stand and an artificial Larix kaempferi stand on Mt. Gariwang, Pyeongchang, Republic of Korea. Both stands originally belonged to the same natural forest and represent the current dominant tree species. Bacterial communities were affected more by climate than by tree species. A bacterial co-occurrence network analysis showed more complex interactions in the Q. mongolica than in L. kaempferi. Conversely, fungal communities were affected more by tree species than by climate. Specifically, the Q. mongolica stand had a high ratio of saprotrophic fungi, whereas the L. kaempferi stand exhibited a higher ratio of fungi associated with nutrient decomposition and harbored a high proportion of fungi that form ectomycorrhizae with long-distance exploration types. These fungal traits serve as indicators of soil ecological changes in Q. mongolica and L. kaempferi stands. Our comparative analysis of the microbial communities of Q. mongolica and L. kaempferi, representative plantation species in the Republic of Korea, revealed seasonal and host species–specific bacterial and fungal associations, and fungal communities were less variable than bacterial communities and more accurately reflected host tree traits. Full article