Testing latent classes in gut microbiome data using generalized Poisson regression models.

Human microbiome research has gained increasing importance due to its critical roles in comprehending human health and disease. Within the realm of microbiome research, the data generated often involves operational taxonomic unit counts, which can frequently present challenges such as over-dispersion and zero-inflation. To address dispersion-related concerns, the generalized Poisson model offers a flexible solution, effectively handling data characterized by over-dispersion, equi-dispersion, and under-dispersion. Furthermore, the realm of zero-inflated generalized Poisson models provides a strategic avenue to simultaneously tackle both over-dispersion and zero-inflation. The phenomenon of zero-inflation frequently stems from the heterogeneous nature of study populations. It emerges when specific microbial taxa fail to thrive in the microbial community of certain subjects, consequently resulting in a consistent count of zeros for these individuals. This subset of subjects represents a latent class, where their zeros originate from the genuine absence of the microbial taxa. In this paper, we introduce a novel testing methodology designed to uncover such latent classes within generalized Poisson regression models. We establish a closed-form test statistic and deduce its asymptotic distribution based on estimating equations. To assess its efficacy, we conduct an extensive array of simulation studies, and further apply the test to detect latent classes in human gut microbiome data from the Bogalusa Heart Study.

Testing latent class of subjects with structural zeros in negative binomial models with applications to gut microbiome data.

Human microbiome research has become a hot-spot in health and medical research in the past decade due to the rapid development of modern high-throughput. Typical data in a microbiome study consisting of the operational taxonomic unit counts may have over-dispersion and/or structural zero issues. In such cases, negative binomial models can be applied to address the over-dispersion issue, while zero-inflated negative binomial models can be applied to address both issues. In practice, it is essential to know if there is zero-inflation in the data before applying negative binomial or zero-inflated negative binomial models because zero-inflated negative binomial models may be unnecessarily complex and difficult to interpret, or may even suffer from convergence issues if there is no zero-inflation in the data. On the other hand, negative binomial models may yield invalid inferences if the data does exhibit excessive zeros. In this paper, we develop a new test for detecting zero-inflation resulting from a latent class of subjects with structural zeros in a negative binomial regression model by directly comparing the amount of observed zeros with what would be expected under the negative binomial regression model. A closed form of the test statistic as well as its asymptotic properties are derived based on estimating equations. Intensive simulation studies are conducted to investigate the performance of the new test and compare it with the classical Wald, likelihood ratio, and score tests. The tests are also applied to human gut microbiome data to test latent class in microbial genera.

Up-regulation of the HSP72 by Foxa1 in MCF-7 human breast cancer cell line.

Forkhead box protein A1 (Foxa1) is an evolutionarily conserved winged helix transcription factor. In this study, the effect of Foxa1 on the expression of HSP72 was examined by RT-PCR and Western blot in Foxa1 overexpression or deficient cells. The results showed overexpression of Foxa1 promoted the expression of HSP72, while Foxa1 depletion, induced by antisense oligonucleotides, decreased the expression of HSP72 in MCF-7 cells under normal and heat stress condition. Electrophoretic mobility shift assay and chromatin immunoprecipitation revealed that Foxa1 bound to HSP72 promoter, and heat stress promoted its DNA binding activity. Luciferase reporter showed that Foxa1 also increased the transcription activity of HSP72 promoter. These results indicate an important role for Foxa1 as a novel regulator of expression of HSP72.

Comparative study of porcine anti-human lymphocyte immunoglobulin and rabbit anti-human thymocyte immunoglobulin as a first-line treatment of acquired severe aplastic anemia.

Porcine anti-human lymphocyte immunoglobulin (pALG) and rabbit anti-human thymocyte immunoglobulin (rATG) are the only two ATGs for severe aplastic anemia (SAA) treatment in China. 148 treatment-naïve SAA patients who received ATG combined with cyclosporine A (CsA) therapy were analysed retrospectively. The patients were divided into a pALG group (n = 114) and a rATG group (n = 34). After three months, the pALG and rATG groups had an overall response (OR) of 65.8% and 44.1%, respectively (P = 0.023); after six months, the OR reached 74.6% and 64.7%, respectively (P = 0.361). The pALG group had markedly better time-related efficacy than the rATG group (P = 0.03). The overall survival (OS) and event-free survival (EFS) between groups had no significant difference (P > 0.1). The pALG and rATG groups did not significantly differ in terms of recurrence (8.8% vs. 5.9%, P = 0.734) or PNH clonal transformation (5.3% vs. 2.9%, P = 1.000), whereas a significant difference was found in the incidence of MDS/AML transformation (2.6% vs. 11.8%, P = 0.049). We found that pALG achieved a better time-related efficacy than rATG for the treatment of SAA; nonetheless, no significant difference in the OS or EFS of pALG compared with rATG.

[Detection of miR-122a and miR-224 expression in hepatocellular carcinoma by real-time fluorescence quantitative RT-PCR].

OBJECTIVE: To detect the expressions of miR-122 and miR-224 in hepatocellular carcinoma (HCC) by real-time fluorescence quantitative RT-PCR and investigate the significance of miRNAs in early diagnosis of HCC. METHOD: 2(-Delta Delta CT) method was used for quantitative analysis of the expression pattern of miR-122 and miR-224 in 35 HCC and adjacent normal tissues. All the quantitative results were confirmed by Northern blotting. RESULTS: Compared with adjacent normal tissues, the HCC tissues showed significant miR-122 down-regulation (P<0.01) and miR-224 over-expression (P<0.001). CONCLUSIONS: HCC has obvious alteration in the expression patterns of miR-122 and miR-224, and real-time fluorescence quantitative RT-PCR provide a new means for early, efficient, and accurate diagnosis of HCC.

Effect of Siraitia grosvenorii polysaccharide on glucose and lipid of diabetic rabbits induced by feeding high fat/high sucrose chow.

The Siraitia grosvenorii polysaccharide (SGP) from the Siraitia grosvenorii (Swingle) was isolated and purified. The therapeutic effects of SGP on diabetic rabbits induced by feeding high fat/high sucrose chow were studied. After administration of SGP for 4 weeks, the fasting blood glucose (FBG), plasma insulin levels (INS), plasma total cholesterol (TC), triglyceride (TG), and HDL-C were assayed. The results showed that administration of SGP can significantly decrease plasma total cholesterol, triglyceride, and glucose levels; and increase HDL-C levels after 4 weeks of treatment. The antihyperglycaemic effect of SGP at dose of 100 mg.kg(-1) bw was the most significant in three dosage groups. Furthermore, SGP could restore the blood lipid levels of diabetic rabbits (P<.05). These data indicate that SGP not only ameliorates the lipid disorder, but also lowers plasma glucose levels. So SGP have obvious glucose-lowering effect on hyperglycaemic rabbits induced by feeding high fat/high sucrose chow, its mechanism may be related to amelioration of lipid metabolism and restoring the blood lipid levels of hyperglycaemic rabbits.