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Many machine learning applications in bioinformatics currently rely on matching gene identities when analyzing input gene signatures and fail to take advantage of preexisting knowledge about gene functions. To further enable comparative analysis of OMICS datasets, including target deconvolution and mechanism of action studies, we develop an approach that represents gene signatures projected onto their biological functions, instead of their identities, similar to how the word2vec technique works in natural language processing. We develop the Functional Representation of Gene Signatures (FRoGS) approach by training a deep learning model and demonstrate that its application to the Broad Institutes L1000 datasets results in more effective compound-target predictions than models based on gene identities alone. By integrating additional pharmacological activity data sources, FRoGS significantly increases the number of high-quality compound-target predictions relative to existing approaches, many of which are supported by in silico and/or experimental evidence. These results underscore the general utility of FRoGS in machine learning-based bioinformatics applications. Prediction networks pre-equipped with the knowledge of gene functions may help uncover new relationships among gene signatures acquired by large-scale OMICs studies on compounds, cell types, disease models, and patient cohorts.

Abstract Aims This study compared the prevalences of metabolic syndrome and of cardiac or kidney comorbidities among patients with hepatocellular carcinoma (HCC) associated with metabolic dysfunction-related fatty liver disease (MAFLD), chronic infection with hepatitis B or C virus (HBV or HCV), or the combination of MAFLD and chronic HBV infection. Methods Medical records were retrospectively analyzed for patients with HCC who underwent hepatectomy between March 2013 and March 2023. Patients with HCC of different etiologies were compared in terms of their clinicodemographic characteristics and laboratory data before surgery. Results Of the 2422 patients, 1,822 (75.2%) were chronically infected with HBV without MAFLD and HCV, 415 (17.2%) had concurrent MAFLD and chronic HBV infection but no HCV infection, 121 (5.0%) had MAFLD without hepatitis virus infection, and 64 (2.6%) were chronically infected with HCV in the presence or absence of MAFLD and HBV infection. Compared to patients chronically infected with HBV without MAFLD and HCV, those with MAFLD but no hepatitis virus infection showed significantly lower prevalence of cirrhosis, ascites, portal hypertension, alpha-fetoprotein concentration ≥ 400 ng/mL, tumor size > 5 cm, multinodular tumors and microvascular invasion. Conversely, they showed significantly higher prevalence of metabolic syndrome, hypertension, type 2 diabetes, abdominal obesity, history of cardiovascular disease, T-wave alterations, hypertriglyceridemia and hyperuricemia, as well as higher risk of arteriosclerotic cardiovascular disease. Compared to patients with MAFLD but no hepatitis virus infection, those with concurrent MAFLD and chronic infection with HBV showed significantly higher prevalence of cirrhosis, ascites and portal hypertension, but significantly lower prevalence of hypertension and history of cardiovascular disease. Compared to patients with other etiologies, those chronically infected with HCV in the presence or absence of MAFLD and HBV infection, showed significantly higher prevalence of cirrhosis, portal hypertension, ascites, and esophagogastric varices. Conclusion Patients with HCC associated with MAFLD tend to have a background of less severe liver disease than those with HCC of other etiologies, but they may be more likely to suffer metabolic syndrome or comorbidities affecting the heart or kidneys.

Abstract 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.

ObjectiveThis retrospective cohort study aimed to investigate the composition and diversity of lung microbiota in patients with severe pneumonia and explore its association with short-term prognosis.MethodsA total of 301 patients diagnosed with severe pneumonia underwent bronchoalveolar lavage fluid metagenomic next-generation sequencing (mNGS) testing from February 2022 to January 2024. After applying exclusion criteria, 236 patients were included in the study. Baseline demographic and clinical characteristics were compared between survival and non-survival groups. Microbial composition and diversity were analyzed using alpha and beta diversity metrics. Additionally, LEfSe analysis and machine learning methods were employed to identify key pathogenic microorganism associated with short-term mortality. Microbial interaction modes were assessed through network co-occurrence analysis.ResultsThe overall 28-day mortality rate was 37.7% in severe pneumonia. Non-survival patients had a higher prevalence of hypertension and exhibited higher APACHE II and SOFA scores, higher procalcitonin (PCT), and shorter hospitalization duration. Microbial α and β diversity analysis showed no significant differences between the two groups. However, distinct species diversity patterns were observed, with the non-survival group showing a higher abundance of Acinetobacter baumannii, Klebsiella pneumoniae, and Enterococcus faecium, while the survival group had a higher prevalence of Corynebacterium striatum and Enterobacter. LEfSe analysis identified 29 distinct terms, with 10 potential markers in the non-survival group, including Pseudomonas sp. and Enterococcus durans. Machine learning models selected 16 key pathogenic bacteria, such as Klebsiella pneumoniae, significantly contributing to predicting short-term mortality. Network co-occurrence analysis revealed greater complexity in the non-survival group compared to the survival group, with differences in central genera.ConclusionOur study highlights the potential significance of lung microbiota composition in predicting short-term prognosis in severe pneumonia patients. Differences in microbial diversity and composition, along with distinct microbial interaction modes, may contribute to variations in short-term outcomes. Further research is warranted to elucidate the clinical implications and underlying mechanisms of these findings.

Copper (Cu) is a trace element, essential for fish growth. In the current study, in addition to growth performance, we first explored the effects of Cu on collagen synthesis and myofiber growth and development in juvenile grass carp (Ctenopharyngodon idella). A total of 1080 fish (11.16 ± 0.01 g) were randomly divided into 6 treatments (3 replicates per treatment) to receive five doses of organic Cu, which were Cu citrate (CuCit) at 0.99 (basal diet), 2.19, 4.06, 6.15, and 8.07 mg/kg, and one dose of inorganic Cu (CuSO4·5H2O at 3.15 mg/kg), for 9 weeks. The results showed appropriate Cu level (4.06 mg/kg) enhanced growth performance, improved nutritional Cu status, and downregulated Cu-transporting ATPase 1 mRNA levels in the hepatopancreas, intestine, and muscle of juvenile grass carp. Meanwhile, collagen content in fish muscle was increased after Cu intake, which was probably due to the following pathways: (1) activating CTGF/TGF-β1/Smads signaling pathway to regulate collagen transcription; (2) upregulating of La ribonucleoprotein domain family 6 (LARP6) mRNA levels to regulate translation initiation; (3) increasing proline hydroxylase, lysine hydroxylase, and lysine oxidase activities to regulate posttranslational modifications. In addition, optimal Cu group increased myofiber diameters and the frequency of myofibers with diameter >50 μm, which might be associated with upregulation of cyclin B, cyclin D, cyclin E, proliferating cell nuclear antigen, myogenic determining factor (MyoD), myogenic factor 5, myogenin (MyoG), myogenic regulatory factor 4 and myosin heavy chain (MyHC) and downregulation of myostatin mRNA levels, increasing protein levels of MyoD, MyoG and MyHC in fish muscle. Finally, based on percentage weight gain (PWG), serum ceruloplasmin (Cp) activity and collagen content in fish muscle, Cu requirements were determined as 4.74, 4.37 and 4.62 mg/kg diet (CuCit as Cu source) of juvenile grass carp, respectively. Based on PWG and Cp activity, compared to CuSO4·5H2O, the efficacy of CuCit were 131.80% and 115.38%, respectively. Our findings provide new insights into Cu supplementation to promote muscle growth in fish, and help improve the overall productivity of aquaculture.

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. Keywords: Peutz-Jeghers syndrome, Metabolomics, LC-MS, Mucosa-associated microbiota, Bacteria, Author(s): Sui Wang[sup.2], Guan-Jun Kou[sup.1], Xiao-Han Zhao[sup.1], Gang Huang[sup.1], Jue-Xin Wang[sup.1], Lin Tian[sup.1], Xiu-Li Zuo[sup.1], Yan-Qing Li[sup.1], Jia-Yong Wang[sup.3] and Yan-Bo Yu[sup.1] Introduction Peutz-Jeghers syndrome (PJS) is a rare genetic [...]

Nucleotide-binding and oligomerization domain (NOD)-like receptors (NLRs), classified as pattern recognition receptors (PRRs), play crucial roles in innate immune responses. However, research on this topic in crustaceans remains limited. In the present study, 27 NLR family members were identified and characterized based on the redclaw crayfish genome. Gene structure, conserved domain, and motif analyses revealed that these NLR genes were classified into four subfamilies with three major domains, and phylogenetic and structural analyses further confirmed their evolutionary conservation. qRT-PCR analysis showed that the relative expression levels of several NLR genes changed significantly under the DIV1 challenge. More importantly, in vivo silencing of NAWD1 gene in Cherax quadricarinatus promoted DIV1 replication . These findings, together with those of previous studies, suggest that NLR genes in crustaceans may play a key role in immune responses to viral infections.

Background and aim: Spontaneous rupture of hepatocellular carcinoma (HCC) is a life-threatening complication, and patients who experience it are formally assigned to stage T4 in the TNM system, while many clinicians informally assign them to stage C in the more widely used Barcelona Clinic Liver Cancer (BCLC) system. The present study explored whether these re-staging practices are appropriate for HCC patients who suffer tumor rupture. Methods: We retrospectively reviewed the records of 1952 HCC patients who underwent hepatic resection at our hospital between January 2017 and June 2021. We compared recurrence-free and overall survival between 143 patients who had BCLC stage A or B disease at the time of spontaneous rupture and 449 patients who had BCLC stage C disease without rupture. Results: Overall survival rate was significantly higher among the 143 patients (1, 3, 5-year survival rate was 80.3%, 60.4%, 51.4%) with rupture than among the 449 (1, 3, 5-year survival rate was 69.5%, 41.5%, 32.4%) with BCLC stage C disease (hazard ratio 1.65, 95% confidence interval 1.29 to 2.12). The two groups had similar recurrence-free survival (hazard ratio 1.19, 95% confidence interval 0.92 to 1.53), but most patients with rupture were able to receive interventional and potentially curative treatments after recurrence, whereas most patients in BCLC stage C received interventional or supportive care. Similar results were obtained after propensity score matching. Conclusion: HCC patients who experience spontaneous rupture tumor while in BCLC stage A or B have better prognosis than patients in BCLC stage C without rupture. Our results suggest that HCC patients who suffer rupture in BCLC stage A or B should not be assigned to BCLC stage C.

Objectives: In this study, a new method of standardising hand and finger position in a single file (SSFile) relative to the webcam was proposed to reduce parallax errors and improve accuracy and consistency of measurements of finger range of motion (ROM) performed over teleconsultation. Methods: Seven prefabricated rings (diameters: 0–6 cm) were used to promote joint angle fixation and extension during a simulated teleconsultation. Each finger joint ROM was measured three times by two raters using three methods. With SSFile method and screenshots taken during teleconsultation, finger ROM was measured by using a goniometer and open-source ImageJ software. Reliability of the new method was compared to goniometric measurements taken during in-person consultations. Results: When compared to in-person goniometry and ImageJ software, the intra- and inter-rater correlation (ICC) and Pearson's correlation coefficient of the new method was excellent (inter-rater ICC = 0.983–0.997, intra-rater ICC = 0.927–0.999, r = 0.990–0.993). The Bland-Altman plot showed 93 % of the values between in-person and on-screen goniometry methods lied within ±5° of clinically acceptable error. Conclusion: Results of this study suggest that the new method of standardising hand and finger position in a single file (SSFile) is reliable and consistent for teleconsultation use. Level of evidence: Diagnostic Studies – Investigating a Diagnostic Test (Level II).