Tracing human life trajectory using gut microbial communities by context-aware deep learning.

The gut microbial communities are highly plastic throughout life, and the human gut microbial communities show spatial-temporal dynamic patterns at different life stages. However, the underlying association between gut microbial communities and time-related factors remains unclear. The lack of context-awareness, insufficient data, and the existence of batch effect are the three major issues, making the life trajection of the host based on gut microbial communities problematic. Here, we used a novel computational approach (microDELTA, microbial-based deep life trajectory) to track longitudinal human gut microbial communities' alterations, which employs transfer learning for context-aware mining of gut microbial community dynamics at different life stages. Using an infant cohort, we demonstrated that microDELTA outperformed Neural Network for accurately predicting the age of infant with different delivery mode, especially for newborn infants of vaginal delivery with the area under the receiver operating characteristic curve of microDELTA and Neural Network at 0.811 and 0.436, respectively. In this context, we have discovered the influence of delivery mode on infant gut microbial communities. Along the human lifespan, we also applied microDELTA to a Chinese traveler cohort, a Hadza hunter-gatherer cohort and an elderly cohort. Results revealed the association between long-term dietary shifts during travel and adult gut microbial communities, the seasonal cycling of gut microbial communities for the Hadza hunter-gatherers, and the distinctive microbial pattern of elderly gut microbial communities. In summary, microDELTA can largely solve the issues in tracing the life trajectory of the human microbial communities and generate accurate and flexible models for a broad spectrum of microbial-based longitudinal researches.

Artificial intelligence-enabled microbiome-based diagnosis models for a broad spectrum of cancer types.

Microbiome-based diagnosis of cancer is an increasingly important supplement for the genomics approach in cancer diagnosis, yet current models for microbiome-based diagnosis of cancer face difficulties in generality: not only diagnosis models could not be adapted from one cancer to another, but models built based on microbes from tissues could not be adapted for diagnosis based on microbes from blood. Therefore, a microbiome-based model suitable for a broad spectrum of cancer types is urgently needed. Here we have introduced DeepMicroCancer, a diagnosis model using artificial intelligence techniques for a broad spectrum of cancer types. Built based on the random forest models it has enabled superior performances on more than twenty types of cancers' tissue samples. And by using the transfer learning techniques, improved accuracies could be obtained, especially for cancer types with only a few samples, which could satisfy the requirement in clinical scenarios. Moreover, transfer learning techniques have enabled high diagnosis accuracy that could also be achieved for blood samples. These results indicated that certain sets of microbes could, if excavated using advanced artificial techniques, reveal the intricate differences among cancers and healthy individuals. Collectively, DeepMicroCancer has provided a new venue for accurate diagnosis of cancer based on tissue and blood materials, which could potentially be used in clinics.

Highly accurate diagnosis of pancreatic cancer by integrative modeling using gut microbiome and exposome data.

The noninvasive detection of pancreatic ductal adenocarcinoma (PDAC) remains an immense challenge. In this study, we proposed a robust, accurate, and noninvasive classifier, namely Multi-Omics Co-training Graph Convolutional Networks (MOCO-GCN). It achieved high accuracy (0.9 ± 0.06), F1 score (0.9± 0.07), and AUROC (0.89± 0.08), surpassing contemporary approaches. The performance of model was validated on an external cohort of German PDAC patients. Additionally, we discovered that the exposome may impact PDAC development through its complex interplay with gut microbiome by mediation analysis. For example, Fusobacterium hwasookii nucleatum, known for its ability to induce inflammatory responses, may serve as a mediator for the impact of rheumatoid arthritis on PDAC. Overall, our study sheds light on how exposome and microbiome in concert could contribute to PDAC development, and enable PDAC diagnosis with high fidelity and interpretability.

A third (booster) dose of the inactivated SARS-CoV-2 vaccine elicits immunogenicity and T follicular helper cell responses in people living with HIV.

Introduction: This study sought to explore the immunogenicity of a booster dose of an inactivated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine in people living with human immunodeficiency virus (HIV) and identify the factors affecting the magnitude of anti-SARS-CoV-2 antibody levels. Materials and methods: A total of 34 people living with HIV (PLWH) and 34 healthy donors (HD) were administered a booster dose of the same SARS-CoV-2 vaccine. Anti-SARS-CoV-2 antibody and immunoglobulin G (IgG) levels were measured using the SARS-CoV-2 S protein neutralizing antibody Enzyme-Linked Immunosorbent Assay (ELISA) and 2019-nCov IgG Chemiluminescent Immunoassay Microparticles, respectively. Spearman correlation analysis was used to measure the correlation between laboratory markers and neutralizing antibody and IgG levels. Peripheral blood mononuclear cells (PBMCs) were extracted from each subject using density gradient centrifugation and the numbers of memory T and T follicular helper (Tfh) cells were determined using flow cytometry. Results: PLWH had a marked reduction in CD4 and B cell levels that was accompanied by a lower CD4/CD8 T cell ratio. However, those who received a supplementary dose of inactivated SARS-CoV-2 vaccines exhibited antibody positivity rates that were analogous to levels previously observed. The booster vaccine led to a reduction in IgG and neutralizing antibody levels and the amplitude of this decline was substantially higher in the PLWH than HD group. Correlation analyses revealed a strong correlation between neutralizing antibody levels and the count and proportion of CD4 cells. Anti-SARS-CoV-2 IgG antibody levels followed a similar trend. The expression of memory T and Tfh cells was considerably lower in the PLWH than in the HD group. Discussion: PLWH had an attenuated immune response to a third (booster) administration of an inactivated SARS-CoV-2 vaccine, as shown by lower neutralizing antibody and IgG levels. This could be attributed to the reduced responsiveness of CD4 cells, particularly memory T and cTfh subsets. CD4 and cTfh cells may serve as pivotal markers of enduring and protective antibody levels. Vaccination dose recalibration may be critical for HIV-positive individuals, particularly those with a lower proportion of CD4 and Tfh cells.

Determination of solid products from the de-polymerization of poly(trimethylene terephthalate) in supercritical methanol.

A method based on high-resolution size-exclusion chromatography (SEC) was established to analyze the solid products from the depolymerization of poly(trimethylene terephthalate) (PTT) in supercritical methanol. In the qualitative analysis, four factors (chromatographic retention time, qualitative multi-wavelength ultraviolet spectra, linear internal-insert SEC and qualitative IR spectra) were considered. The main solid products from the process were dimethyl terephthalate (DMT), methyl-(2-hydroxypropyl) terephthalate (MHPT), bis(2-hydroxypropyl) terephthalate (BHPT), methyl-(2-hydroxyethyl) terephthalate (MHET), bis(2-hydroxyethyl) terephthalate (BHET), and hydroxyethyl-(2-hydroxypropyl) terephthalate (HEHPT). It is found that the method is of a high resolution among the solid products and has a fine repeatability. In addition, the solid products from the de-polymerization of poly(ethylene terephthalate) (PET) in similar process were also analyzed by this method. Furthermore, the effects of supercritical conditions on the distribution of the products were also discussed.