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BACKGROUND: Gastric cancer is one of the global health concerns. A series of studies on the stomach have confirmed the role of the microbiome in shaping gastrointestinal diseases. Delineation of microbiome signatures to distinguish chronic gastritis from gastric cancer will provide a non-invasive preventative and treatment strategy. In this study, we performed whole metagenome shotgun sequencing of fecal samples to enhance the detection of rare bacterial species and increase genome sequence coverage. Additionally, we employed multiple bioinformatics approaches to investigate the potential targets of the microbiome as an indicator of differentiating gastric cancer from chronic gastritis. RESULTS: A total of 65 patients were enrolled, comprising 33 individuals with chronic gastritis and 32 with gastric cancer. Within each group, the chronic gastritis group was sub-grouped into intestinal metaplasia (n = 15) and non-intestinal metaplasia (n = 18); the gastric cancer group, early stage (stages 1 and 2, n = 13) and late stage (stages 3 and 4, n = 19) cancer. No significant differences in alpha and beta diversities were detected among the patient groups. However, in a two-group univariate comparison, higher Fusobacteria abundance was identified in phylum; Fusobacteria presented higher abundance in gastric cancer (LDA scored 4.27, q = 0.041 in LEfSe). Age and sex-adjusted MaAsLin and Random Forest variable of importance (VIMP) analysis in species provided meaningful features; Bacteria_caccae was the most contributing species toward gastric cancer and late-stage cancer (beta:2.43, se:0.891, p:0.008, VIMP score:2.543). In contrast, Bifidobacterium_longum significantly contributed to chronic gastritis (beta:-1.8, se:0.699, p:0.009, VIMP score:1.988). Age, sex, and BMI-adjusted MasAsLin on metabolic pathway analysis showed that GLCMANNANAUT-PWY degradation was higher in gastric cancer and one of the contributing species was Fusobacterium_varium. CONCLUSION: Microbiomes belonging to the pathogenic phylum Fusobacteria and species Bacteroides_caccae and Streptococcus_anginosus can be significant targets for monitoring the progression of gastric cancer. Whereas Bifidobacterium_longum and Lachnospiraceae_bacterium_5_1_63FAA might be protection biomarkers against gastric cancer.
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Bactérias , Fezes , Gastrite , Metagenoma , Neoplasias Gástricas , Humanos , Neoplasias Gástricas/microbiologia , Masculino , Feminino , Pessoa de Meia-Idade , Gastrite/microbiologia , Fezes/microbiologia , Bactérias/genética , Bactérias/classificação , Bactérias/isolamento & purificação , Idoso , Microbioma Gastrointestinal/genética , AdultoRESUMO
Since the 1970s, artificial intelligence (AI) has played an increasingly pivotal role in the medical field, enhancing the efficiency of disease diagnosis and treatment. Amidst an aging population and the proliferation of chronic disease, the prevalence of complex surgeries for high-risk multimorbid patients and hard-to-heal wounds has escalated. Healthcare professionals face the challenge of delivering safe and effective care to all patients concurrently. Inadequate management of skin wounds exacerbates the risk of infection and complications, which can obstruct the healing process and diminish patients' quality of life. AI shows substantial promise in revolutionizing wound care and management, thus enhancing the treatment of hospitalized patients and enabling healthcare workers to allocate their time more effectively. This review details the advancements in applying AI for skin wound assessment and the prediction of healing timelines. It emphasizes the use of diverse algorithms to automate and streamline the measurement, classification, and identification of chronic wound healing stages, and to predict wound healing times. Moreover, the review addresses existing limitations and explores future directions.
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Video 1Endoscopic full-thickness resection using double endoscope-assisted snare traction facilitates precise resection of a large exophytic gastric subepithelial lesion.
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The discovery of SARS-CoV-2 RNA in the periodontal tissues of patients who tested positive for COVID-19, 24 days post the initial symptom onset, indicates the oral cavity could serve as a viral reservoir. This research aims to investigate the antiviral capabilities of Ovatodiolide, introducing a novel periodontal ligament organoid model for the study of SARS-CoV-2. We have successfully established a reliable and expandable organoid culture from the human periodontal ligament, showcasing characteristics typical of epithelial stem cells. This organoid model enables us to delve into the lesser-known aspects of dental epithelial stem cell biology and their interactions with viruses and oral tissues. We conducted a series of in vitro and ex vivo studies to examine the inhibitory impacts of Ova on SARS-CoV-2. Our findings indicate that Ovatodiolide molecules can bind effectively to the NRP1 active domain. Our study identifies potential interaction sites for Ovatodiolide (OVA) within the b1 domain of the NRP1 receptor. We generated point mutations at this site, resulting in three variants: Y25A, T44A, and a double mutation Y25A/T44A. While these mutations did not alter the binding activity of the spike protein, they did impact the concentration of OVA required for inhibition. The inhibitory concentrations for these variants are 15 µM for Y25A, 15.2 µM for T44A, and 25 µM for the double mutant Y25A/T44A. In addition, in vitro inhibition experiments demonstrate that the EC50 of Ova against the main protease (Mpro) of the SARS-CoV-2 virus is 7.316 µM. Our in vitro studies and the use of the periodontal ligament organoid model highlight Ovatodiolide's potential as a small molecule therapeutic agent that impedes the virus's ability to bind to the Neuropilin-1 receptor on host cells. The research uncovers various pathways and biochemical strategies through which Ovatodiolide may function as an effective antiviral small molecule drug.