Effect of high-fat diet and growth stage on the diversity and composition of intestinal microbiota in healthy bovine livestock.

BACKGROUND: This study aimed to investigate the composition of bacteria in the bovine rectum and their functions during growth, in relation to different diets. Fecal samples were collected from 6-, 12-, 18- and 24-month cattle fed high-fat diet, and healthy female parents fed regular diet. Total DNA was amplified (V3-V4 of 16S rRNA) and submitted to barcode-DNA pyrosequencing. Intestinal microbiota profiles and functions were then analyzed. RESULTS: A total of 114 512 operational taxonomic units were detected from the 1 802 243 sequences obtained. In 6-month-old and female parent groups, the top three abundant phyla were Bacteroidetes (37.6%, 32.2%), Firmicutes (34.4%, 48.2%) and Proteobacteria (9.1%, 6.3%); in the 12-, 18- and 24-month groups, they were Proteobacteria (45.5%, 47.1%, 38.8%), Firmicutes (27.4%, 22.2%, 20.1%) and Bacteroidetes (14.9%, 19.4%, 17.7%), respectively. Paludibacter and Desulfopila in abundance showed negative (P < 0.001) and positive (P < 0.05) correlation, respectively, to cattle weight gain through metagenomic functional prediction of methane, cysteine and methionine metabolism. Meanwhile, cofactor/vitamin and amino acid metabolic processes were significantly higher in bacteria from the regular diet group than high-fat diet groups, with markedly lower cellular processes and signaling, and reduced glycan biosynthesis and metabolism (P < 0.01). CONCLUSIONS: The 6-month cattle and female parents shared similar intestinal bacteria; the community structure of fecal microbiota was significantly affected by high-fat diet in older cattle. © 2017 Society of Chemical Industry.

Proteomic analysis to elucidate degeneration of Clostridium beijerinckii NCIMB 8052 and role of Ca(2+) in strain recovery from degeneration.

Degeneration of solventogenic Clostridium strains is one of the major barriers in bio-butanol production. A degenerated Clostridium beijerinckii NCIMB 8052 strain (DG-8052) was obtained without any genetic manipulation. Supplementation of CaCO3 to fermentation medium could partially recover metabolism of DG-8052 by more than 50 % increase of cell growth and solvent production. This study investigated the protein expression profile of DG-8052 and its response to CaCO3 treatment. Compared with WT-8052, the lower expressed proteins were responsible for disruption of RNA secondary structures and DNA repair, sporulation, signal transduction, transcription regulation, and membrane transport in DG-8052. Interestingly, accompanied with the decreased glucose utilization and lower solvent production, there was a decreased level of sigma-54 modulation protein which may indicate that the level of sigma-54 activity may be associated with the observed strain degeneration. For the addition of CaCO3, proteomic and biochemical study results revealed that besides buffer capacity, Ca(2+) could stabilize heat shock proteins, increase DNA synthesis and replication, and enhance expression of solventogenic enzymes in DG-8052, which has a similar contribution in WT-8052.

Application value of a computer-aided diagnosis and management system for the detection of lung nodules.

Background: Computer-aided diagnosis (CAD) systems can help reduce radiologists' workload. This study assessed the value of a CAD system for the detection of lung nodules on chest computed tomography (CT) images. Methods: The study retrospectively analyzed the CT images of patients who underwent routine health checkups between August 2019 and November 2019 at 3 hospitals in China. All images were first assessed by 2 radiologists manually in a blinded manner, which was followed by assessment with the CAD system. The location and classification of the lung nodules were determined. The final diagnosis was made by a panel of experts, including 2 associate chief radiologists and 1 chief radiologist at the radiology department. The sensitivity for nodule detection and false-positive nodules per case were calculated. Results: A total of 1,002 CT images were included in the study, and the process was completed for 999 images. The sensitivity of the CAD system and manual detection was 90.19% and 49.88% (P<0.001), respectively. Similar sensitivity was observed between manual detection and the CAD system in lung nodules >15 mm (P=0.08). The false-positive nodules per case for the CAD system were 0.30±0.84 and those for manual detection were 0.24±0.68 (P=0.12). The sensitivity of the CAD system was higher than that of the radiologists, but the increase in the false-positive rate was only slight. Conclusions: In addition to reducing the workload for medical professionals, a CAD system developed using a deep-learning model was highly effective and accurate in detecting lung nodules and did not demonstrate a meaningfully higher the false-positive rate.

A stratified analysis of a deep learning algorithm in the diagnosis of diabetic retinopathy in a real-world study.

BACKGROUND: The aim of our research was to prospectively explore the clinical value of a deep learning algorithm (DLA) to detect referable diabetic retinopathy (DR) in different subgroups stratified by types of diabetes, blood pressure, sex, BMI, age, glycosylated hemoglobin (HbA1c), diabetes duration, urine albumin-to-creatinine ratio (UACR), and estimated glomerular filtration rate (eGFR) at a real-world diabetes center in China. METHODS: A total of 1147 diabetic patients from Shanghai General Hospital were recruited from October 2018 to August 2019. Retinal fundus images were graded by the DLA, and the detection of referable DR (moderate nonproliferative DR or worse) was compared with a reference standard generated by one certified retinal specialist with more than 12 years of experience. The performance of DLA across different subgroups stratified by types of diabetes, blood pressure, sex, BMI, age, HbA1c, diabetes duration, UACR, and eGFR was evaluated. RESULTS: For all 1674 gradable images, the area under the receiver operating curve, sensitivity, and specificity of the DLA for referable DR were 0.942 (95% CI, 0.920-0.964), 85.1% (95% CI, 83.4%-86.8%), and 95.6% (95% CI, 94.6%-96.6%), respectively. The DLA showed consistent performance across most subgroups, while it showed superior performance in the subgroups of patients with type 1 diabetes, UACR ≥ 30 mg/g, and eGFR < 90 mL/min/1.73m2 . CONCLUSIONS: This study showed that the DLA was a reliable alternative method for the detection of referable DR and performed superior in patients with type 1 diabetes and diabetic nephropathy who were prone to DR.

Artificial intelligence-enabled screening for diabetic retinopathy: a real-world, multicenter and prospective study.

INTRODUCTION: Early screening for diabetic retinopathy (DR) with an efficient and scalable method is highly needed to reduce blindness, due to the growing epidemic of diabetes. The aim of the study was to validate an artificial intelligence-enabled DR screening and to investigate the prevalence of DR in adult patients with diabetes in China. RESEARCH DESIGN AND METHODS: The study was prospectively conducted at 155 diabetes centers in China. A non-mydriatic, macula-centered fundus photograph per eye was collected and graded through a deep learning (DL)-based, five-stage DR classification. Images from a randomly selected one-third of participants were used for the DL algorithm validation. RESULTS: In total, 47 269 patients (mean (SD) age, 54.29 (11.60) years) were enrolled. 15 805 randomly selected participants were reviewed by a panel of specialists for DL algorithm validation. The DR grading algorithms had a 83.3% (95% CI: 81.9% to 84.6%) sensitivity and a 92.5% (95% CI: 92.1% to 92.9%) specificity to detect referable DR. The five-stage DR classification performance (concordance: 83.0%) is comparable to the interobserver variability of specialists (concordance: 84.3%). The estimated prevalence in patients with diabetes detected by DL algorithm for any DR, referable DR and vision-threatening DR were 28.8% (95% CI: 28.4% to 29.3%), 24.4% (95% CI: 24.0% to 24.8%) and 10.8% (95% CI: 10.5% to 11.1%), respectively. The prevalence was higher in female, elderly, longer diabetes duration and higher glycated hemoglobin groups. CONCLUSION: This study performed, a nationwide, multicenter, DL-based DR screening and the results indicated the importance and feasibility of DR screening in clinical practice with this system deployed at diabetes centers. TRIAL REGISTRATION NUMBER: NCT04240652.

Probiotic Bacillus amyloliquefaciens C-1 Improves Growth Performance, Stimulates GH/IGF-1, and Regulates the Gut Microbiota of Growth-Retarded Beef Calves.

Growth retardation of calves is defined as a symptom of impaired growth and development, probably due to growth hormone disorder as well as natural and environmental factors in livestock. The growth-promoting effects of probiotics were determined in 50 growth-retarded growth calves. They were supplied with Bacillus amyloliquefaciens C-1 (Ba, 4 × 1010CFU/d, n = 16), B. subtilis (Bs, 4 × 1010CFU/d, n = 18), and negative control (NC, n = 16) for 30 days. Pre- and post-intervention, the growth performance (weight gain rate, feed intake and feed conversion rate) was analyzed, the serum GH, IGH-1 and immunoglobulin levels were assayed, and the fecal microbiota was detected. Calves in Ba and Bs groups demonstrated increased body weight gain, feed intake and GH/IGF-1 levels, as well as a more efficient feed conversion rate, compared with NC group (P < 0.05). Additionally, the abundances of bacteria contributing to the production of energy and SCFAs (short chain fatty acids), including Proteobacteria, Rhodospirillaceae, Campylobacterales, and Butyricimonas were increased compared with NC group (P < 0.05, FDR < 0.1); and the suspected pathogens, which included Anaeroplasma and Acholeplasma were decreased (P < 0.05, FDR < 0.1) in both the Bs and Ba groups. Akkermansia, which is involved in the intestinal mucosal immune response, was increased in Bs group after intervention (P < 0.05, FDR < 0.1), but exhibited no obvious difference in Ba group. The increased bacterial genera in Ba group were Sphaerochaeta and Treponema (P < 0.05, FDR < 0.1). These results indicate that the probiotics B. amyloliquefaciens and B. subtilis exhibited similar therapeutic potential in terms of growth performance by regulating hormones, and improving the intestinal and rumen development in growth-retarded animals.

Sigma Factor Regulated Cellular Response in a Non-solvent Producing Clostridium beijerinckii Degenerated Strain: A Comparative Transcriptome Analysis.

Clostridium beijerinckii DG-8052, derived from NCIMB 8052, cannot produce solvent or form spores, a phenomenon known as degeneration. To explore the mechanisms of degeneration at the gene level, transcriptomic profiles of the wild-type 8052 and DG-8052 strains were compared. Expression of 5168 genes comprising 98.6% of the genome was assessed. Interestingly, 548 and 702 genes were significantly up-regulated in the acidogenesis and solventogenesis phases of DG-8052, respectively, and mainly responsible for the phosphotransferase system, sugar metabolic pathways, and chemotaxis; meanwhile, 699 and 797 genes were significantly down-regulated, respectively, and mainly responsible for sporulation, oxidoreduction, and solventogenesis. The functions of some altered genes, including 286 and 333 at the acidogenesis and solventogenesis phases, respectively, remain unknown. Dysregulation of the fermentation machinery was accompanied by lower transcription levels of glycolysis rate-limiting enzymes (pfk and pyk), and higher transcription of cell chemotaxis genes (cheA, cheB, cheR, cheW, and cheY), controlled mainly by σ54 at acidogenesis. Meanwhile, abnormal spore formation was associated with repressed spo0A, sigE, sigF, sigG, and sigK which are positively regulated by σ70, and correspondingly inhibited expression of CoA-transferase at the solventogenesis phase. These findings indicated that morphological and physiological changes in the degenerated Clostridium strain may be related to altered expression of sigma factors, providing valuable targets for strain development of Clostridium species.

Transcriptional analysis of degenerate strain Clostridium beijerinckii DG-8052 reveals a pleiotropic response to CaCO3-associated recovery of solvent production.

Degenerate Clostridium beijerinckii strain (DG-8052) can be partially recovered by supplementing CaCO3 to fermentation media. Genome resequencing of DG-8052 showed no general regulator mutated. This study focused on transcriptional analysis of DG-8052 and its response to CaCO3 treatment via microarray. The expressions of 5168 genes capturing 98.6% of C. beijerinckii NCIMB 8052 genome were examed. The results revealed that with addition of CaCO3 565 and 916 genes were significantly up-regulated, and 704 and 1044 genes significantly down-regulated at acidogenic and solventogenic phase of DG-8052, respectively. These genes are primarily responsible for glycolysis to solvent/acid production (poR, pfo), solventogensis (buk, ctf, aldh, adh, bcd) and sporulation (spo0A, sigE, sigma-70, bofA), cell motility and division (ftsA, ftsK, ftsY, ftsH, ftsE, mreB, mreC, mreD, rodA), and molecular chaperones (grpE, dnaK, dnaJ, hsp20, hsp90), etc. The functions of some altered genes in DG-8052, totalling 5.7% at acidogenisis and 8.0% at sovlentogenisis, remain unknown. The response of the degenerate strain to CaCO3 was suggested significantly pleiotropic. This study reveals the multitude of regulatory function that CaCO3 has in clostridia and provides detailed insights into degeneration mechanisms at gene regulation level. It also enables us to develop effective strategies to prevent strain degeneration in future.