Microbial communities such as those residing in the human gut are highly diverse and complex, and many with important implications for health and diseases. The effects and functions of these microbial communities are determined not only by their species compositions and diversities but also by the dynamic intra- and inter-cellular states at the transcriptional level. Powerful and scalable technologies capable of acquiring single-microbe-resolution RNA sequencing information in order to achieve a comprehensive understanding of complex microbial communities together with their hosts are therefore utterly needed. Here we report the development and utilization of a droplet-based smRNA-seq (single-microbe RNA sequencing) method capable of identifying large species varieties in human samples, which we name smRandom-seq2. Together with a triple-module computational pipeline designed for the bacteria and bacteriophage sequencing data by smRandom-seq2 in four human gut samples, we established a single-cell level bacterial transcriptional landscape of human gut microbiome, which included 29,742 single microbes and 329 unique species. Distinct adaptive response states among species in Prevotella and Roseburia genera and intrinsic adaptive strategy heterogeneity in Phascolarctobacterium succinatutens were uncovered. Additionally, we identified hundreds of novel host-phage transcriptional activity associations in the human gut microbiome. Our results indicated that smRandom-seq2 is a high-throughput and high-resolution smRNA-seq technique that is highly adaptable to complex microbial communities in real-world situations and promises new perspectives in the understanding of human microbiomes.
Humans ; Gastrointestinal Microbiome/genetics* ; Bacteriophages/physiology* ; High-Throughput Nucleotide Sequencing ; Sequence Analysis, RNA/methods* ; Bacteria/virology*

Objective To propose strategies for developing clinical predictive models,aiming to assist researchers in conducting standardized clinical prediction model studies.Methods Literature review was conducted to summarize the operational steps and content for developing clinical predictive models.Then,a methodological framework was summarized and refined through expert consultation.Results The 11-step methodological framework for developing clinical predictive models was obtained by synthesizing the experience of 456 clinical predictive modeling studies and expert consultation,and the details were analyzed and elaborated.Conclusions This study presents methodological strategies and recommendations for the development of clinical predictive models,intended to serve as a guide for researchers.

Podocyte infolding glomerulopathy (PIG) is a pathologic type of podocyte glomerulopathy reported recently. The characteristic is that the ultrastructure related to podocytes, such as microspheres and microtubules, are folded into the glomerular basement membrane (GBM) under electron microscope. At present, there are few reports about this disease at home and abroad, and most of them are concentrated in Japan. The clinical characteristics and pathogenesis of PIG are still unclear. In this paper, we report a case of clinical manifestations of nephrotic syndrome, renal biopsy indicated PIG, after the treatment of glucocorticoid, hydroxychloroquine and tacrolimus, the patient's clinical symptoms were relieved and urinary protein decreased.

OBJECTIVE To explore the mechanism of Cinnamomi Cortex in the treatment of depression based on the network pharmacology method, and to verify the experimental results according to the results. METHODS The potential active components of Cinnamomi Cortex were screened by using the Chinese Medicine System Pharmacology Database Analysis Platform(TCMSP) and literatures related to the active components of Cinnamomi Cortex. The target of action of the active ingredients obtained by screening was predicted; the disease targets of depression were obtained from the DisGeNet database; the STRING database protein-protein interaction was used to construct PPI network; Gene Ontology(GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis for key targets through DAVID database. Based on the results of network pharmacology, the solid self-microemulsion of Cinnamomi Cortex oil was used as a reagent to establish a chronic unpredictable mild stress(CUMS) depression mice model, and the experimental verification was carried out by measuring the expression levels of neurotransmitters and inflammatory factors in mice. RESULTS A total of 22 active ingredients and 186 potential targets were obtained through screening. A total of 275 GO items were obtained by GO enrichment analysis, including 222 for biological processes, 18 for cellular composition, and 35 for molecular functions. A total of 96 signaling pathways were obtained from KEGG enrichment. The results of network pharmacology indicated that the antidepressant mechanism of Cinnamomi Cortex was related to neurotransmitter components and inflammatory response. The results of animal experiments indicated that Cinnamomi Cortex oil solid self-microemulsion could improve hippocampal damage caused by CUMS, and make neurons in the hippocampus neatly arranged and cell structure intact. At the same time, it could effectively increase the expression levels of 5-hydroxytryptamine(5-HT), NE and DA in the brain of CUMS mice(P<0.05), and reduce the expression levels of serum IL-6, IL-1β and TNF-α(P<0.05). The results of animal experiments were consistent with the results of network pharmacology. CONCLUSION The antidepressant activity of Cinnamomi Cortex has the characteristics of multi-component, multi-target and multi-pathway. Regulating the expression levels of neurotransmitters and inflammatory factors is an important way of its action, which provides a basis for the in-depth study of the antidepressant mechanism of Cinnamomi Cortex.

In order to conduct surface monitoring of the three-dimensional spine morphology of the human body in daily life, a spine morphology measuring method using "single camera, multi-view" to construct stereo vision is proposed. The images of the back of the human body with landmarks of spinous process are captured from multiple angles by moving a single camera, and based on the "Zhang Zhengyou calibration method" and the triangulation principle of binocular stereo vision, the spatial conversion matrices corresponding to each other between all images and the 3D coordinates of the landmarks are calculated. Then the spine evaluation angle used to evaluate the spine morphology is further calculated. The tests' results showed that the spine evaluation angle error of this method is within ±3°, and the correlation between the results and the X-ray film Cobb angles is 0.871. The visual detection algorithm used in this paper is non-radioactive, and because only one camera is used in the measurement process and there is no need to pre-set the camera's shooting pose, the operation is simple. The research results of this article can be used in a mobile phone-based intelligent detection system, which will be suitable for the group survey of scoliosis in communities, schools, families and other occasions, as well as for the long-term follow-up of confirmed patients. This will provide a reference for doctors to diagnose the condition, predict the development trend of the condition, and formulate treatment plans.
Algorithms ; Calibration ; Humans ; Imaging, Three-Dimensional ; Scoliosis/diagnostic imaging* ; Spine/diagnostic imaging* ; Technology