Fuzhengjiedu formula exerts protective effect against LPS-induced acute lung injury via gut-lung axis.

BACKGROUND: Acute lung injury (ALI) is distinguished by rapid and severe respiratory distress and prolonged hypoxemia. A traditional Chinese medicine (TCM), known as the Fuzhengjiedu formula (FZJDF), has been shown to have anti-inflammatory benefits in both clinical and experimental studies. The precise underlying processes, nevertheless, are yet unclear. PURPOSE: This study sought to enlighten the protective mechanism of FZJDF in ALI through the standpoint of the gut-lung crosstalk. METHODS: The impact of FZJDF on lipopolysaccharide (LPS)-induced ALI murine model were investigated, and the lung injury score, serum interleukin-1ß (IL-1ß), and tumor necrosis factor-α (TNF-α) expression were measured to confirm its anti-inflammatory effects. Additionally, gut microbiota analysis and serum and fecal samples metabolomics were performed using metagenomic sequencing and high-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry, respectively. RESULTS: FZJDF significantly induced histopathological changes caused by LPS-induced ALI as well as downregulated the serum concentration of IL-1ß and TNF-α. Furthermore, FZJDF had an effect in gut microbiota disturbances, and linear discriminant effect size analysis identified signal transduction, cell motility, and amino acid metabolism as the potential mechanisms of action in the FZJDF-treated group. Several metabolites in the LPS and FZJDF groups were distinguished by untargeted metabolomic analysis. Correlations were observed between the relative abundance of microbiota and metabolic products. Comprehensive network analysis revealed connections among lung damage, gut microbes, and metabolites. The expression of glycine, serine, glutamate, cysteine, and methionine in the lung and colon tissues was dysregulated in LPS-induced ALI, and FZJDF reversed these trends. CONCLUSION: This study revealed that FZJDF considerably protected against LPS-induced ALI in mice by regulating amino acid metabolism via the gut-microbiota-lung axis and offered thorough and in-depth knowledge of the multi-system linkages of systemic illnesses.

Metaomics in Clinical Laboratory: Potential Driving Force for Innovative Disease Diagnosis.

Currently, more and more studies suggested that reductionism was lack of holistic and integrative view of biological processes, leading to limited understanding of complex systems like microbiota and the associated diseases. In fact, microbes are rarely present in individuals but normally live in complex multispecies communities. With the recent development of a variety of metaomics techniques, microbes could be dissected dynamically in both temporal and spatial scales. Therefore, in-depth understanding of human microbiome from different aspects such as genomes, transcriptomes, proteomes, and metabolomes could provide novel insights into their functional roles, which also holds the potential in making them diagnostic biomarkers in many human diseases, though there is still a huge gap to fill for the purpose. In this mini-review, we went through the frontlines of the metaomics techniques and explored their potential applications in clinical diagnoses of human diseases, e.g., infectious diseases, through which we concluded that novel diagnostic methods based on human microbiomes shall be achieved in the near future, while the limitations of these techniques such as standard procedures and computational challenges for rapid and accurate analysis of metaomics data in clinical settings were also examined.

Tai Chi for anxiety and depression symptoms in cancer, stroke, heart failure, and chronic obstructive pulmonary disease: A systematic review and meta-analysis.

BACKGROUND: Many middle-aged and older adults have more than one chronic health condition. It is therefore important to explore the effectiveness of interventions for multiple chronic conditions. Tai Chi is widely used in China and other countries, and many studies have examined the effect of Tai Chi on anxiety and depression. However, there are no systematic reviews of the effect of Tai Chi on anxiety and depression in various chronic conditions. This systematic review and meta-analysis aimed to evaluate the effects of Tai Chi on anxiety and depression symptoms in four chronic conditions: cancer, stroke, heart failure (HF), and chronic obstructive pulmonary disease (COPD). METHODS: We searched Chinese and English databases (Cochrane Library, PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP), Wanfang Database, and Sinomed) from inception to October 2020. Review Manager version 5.2 and Stata version 12.0 were used to perform a systematic review. The quality of the included studies was evaluated using the Cochrane risk of bias tool. The study was registered with the PROSPERO database (number CRD42020209594). RESULTS: Of the 596 studies identified, we included 25 randomized controlled trials involving 1819 participants. Combined analysis of the four diseases showed statistically significant differences between the Tai Chi and control groups for anxiety symptoms (SMD -0.99, 95%CI: -1.5, -0.47; P < 0.01) and depressive symptoms (SMD 0.70, 95%CI: -1.01, -0.39; P < 0.01). Subgroup analyses showed statistically significant differences between the Tai Chi and control groups for depressive symptoms in stroke (SMD -0.43, 95%CI: -0.67, -0.18; P < 0.01) and HF (SMD -0.57, 95%CI: -0.8, -0.33; P < 0.01). However, no statistically significant differences were found for depressive symptoms in COPD or cancer. There were statistically significant differences between the Tai Chi and control groups for anxiety symptoms in stroke (SMD -0.60, 95%CI: -0.88, -0.32; P < 0.01) and cancer (SMD -0.69, 95%CI: -1.22, -0.17; P < 0.01), but not in COPD or HF. Subgroup, sensitivity, meta regression, and publication bias analyses showed high heterogeneity correlated with a single study and study quality. Sensitivity analysis showed that most meta-analysis results had good stability, but those for anxiety symptoms in COPD were unstable; therefore, careful interpretation is required. CONCLUSION: Tai Chi has a positive effect on anxiety and depression, especially for patients with cancer, stroke, and HF. However, given the weak evidence, this approach is not a substitute for psychiatric treatment.

Recovery of a patient with severe COVID-19 by acupuncture and Chinese herbal medicine adjuvant to standard care.

There is currently no drug or therapy that can cure the coronavirus disease 2019 (COVID-19), which is highly contagious and can be life-threatening in severe cases. Therefore, seeking potential effective therapies is an urgent task. An older female at the Leishenshan Hospital in Wuhan, China, with a severe case of COVID-19 with significant shortness of breath and decrease in peripheral oxygen saturation (SpO2), was treated using manual acupuncture and Chinese herbal medicine granule formula Fuzheng Rescue Lung with Xuebijing Injection in addition to standard care. The patient's breath rate, SpO2, heart rate, ratio of neutrophil/lymphocyte (NLR), ratio of monocyte/lymphocyte (MLR), C-reactive protein (CRP), and chest computed tomography were monitored. Acupuncture significantly improved the patient's breathing function, increased SpO2, and decreased her heart rate. Chinese herbal medicine might make the effect of acupuncture more stable; the use of herbal medicine also seemed to accelerate the absorption of lung infection lesions when its dosage was increased. The combination of acupuncture and herbs decreased NLR from 14.14 to 5.83, MLR from 1.15 to 0.33 and CRP from 15.25 to 6.01 mg/L. These results indicate that acupuncture and Chinese herbal medicine, as adjuvants to standard care, might achieve better results in treating severe cases of COVID-19.

Biased Random Walk With Restart on Multilayer Heterogeneous Networks for MiRNA-Disease Association Prediction.

Numerous experiments have proved that microRNAs (miRNAs) could be used as diagnostic biomarkers for many complex diseases. Thus, it is conceivable that predicting the unobserved associations between miRNAs and diseases is extremely significant for the medical field. Here, based on heterogeneous networks built on the information of known miRNA-disease associations, miRNA function similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases, we developed a computing model of biased random walk with restart on multilayer heterogeneous networks for miRNA-disease association prediction (BRWRMHMDA) through enforcing degree-based biased random walk with restart (BRWR). Assessment results reflected that an AUC of 0.8310 was gained in local leave-one-out cross-validation (LOOCV), which proved the calculation algorithm's good performance. Besides, we carried out BRWRMHMDA to prioritize candidate miRNAs for esophageal neoplasms based on HMDD v2.0. We further prioritize candidate miRNAs for breast neoplasms based on HMDD v1.0. The local LOOCV results and performance analysis of the case study all showed that the proposed model has good and stable performance.

Computational Models for Self-Interacting Proteins Prediction.

Self-Interacting Proteins (SIPs), whose two or more copies can interact with each other, have significant roles in cellular functions and evolution of Protein Interaction Networks (PINs). Knowing whether a protein can act on itself is important to understand its functions. Previous studies on SIPs have focused on their structures and functions, while their whole properties are less emphasized. Not surprisingly, identifying SIPs is one of the most important works in biomedical research, which will help to understanding the function and mechanism of proteins. It is worth noting that high throughput methods can be used for SIPs prediction, but can be costly, time consuming and challenging. Therefore, it is urgent to design computational models for the identification of SIPs. In this review, the concept and function of SIPs were introduced in detail. We further introduced SIPs data and some excellent computational models that have been designed for SIPs prediction. Specially, the most existing approaches were developed based on machine learning through carrying out different extract feature methods. Finally, we discussed several difficult problems in developing computational models for SIPs prediction.

In Silico Prediction of Small Molecule-miRNA Associations Based on the HeteSim Algorithm.

Targeting microRNAs (miRNAs) with drug small molecules (SMs) is a new treatment method for many human complex diseases. Unsurprisingly, identification of potential miRNA-SM associations is helpful for pharmaceutical engineering and disease therapy in the field of medical research. In this paper, we developed a novel computational model of HeteSim-based inference for SM-miRNA Association prediction (HSSMMA) by implementing a path-based measurement method of HeteSim on a heterogeneous network combined with known miRNA-SM associations, integrated miRNA similarity, and integrated SM similarity. Through considering paths from an SM to a miRNA in the heterogeneous network, the model can capture the semantics information under each path and predict potential miRNA-SM associations based on all the considered paths. We performed global, miRNA-fixed local and SM-fixed local leave one out cross validation (LOOCV) as well as 5-fold cross validation based on the dataset of known miRNA-SM associations to evaluate the prediction performance of our approach. The results showed that HSSMMA gained the corresponding areas under the receiver operating characteristic (ROC) curve (AUCs) of 0.9913, 0.9902, 0.7989, and 0.9910 ± 0.0004 based on dataset 1 and AUCs of 0.7401, 0.8466, 0.6149, and 0.7451 ± 0.0054 based on dataset 2, respectively. In case studies, 2 of the top 10 and 13 of the top 50 predicted potential miRNA-SM associations were confirmed by published literature. We further implemented case studies to test whether HSSMMA was effective for new SMs without any known related miRNAs. The results from cross validation and case studies showed that HSSMMA could be a useful prediction tool for the identification of potential miRNA-SM associations.

Tuberculous peritonitis and pleurisy accompanied by pulmonary cryptococcosis: A case report.

Although the infectious diseases tuberculosis (TB) and cryptococcosis both cause formation of single or multiple nodules in immunodeficient hosts, cases of co-infection of these diseases are rarely seen. We report a patient who was co-infected with TB and cryptococcosis. A male patient with no clinical evidence of immunodeficiency presented with a 3-week history of abdominal distension accompanied by oedema of recurring lower extremities. The patient was diagnosed with tuberculous peritonitis and tuberculous pleurisy by an abdominal puncture biopsy. Several months after being treated for TB, the patient was diagnosed with Cryptococcus infection and received antifungal treatment. Computed tomographic and magnetic resonance imaging findings suggested that treatment was effective. This case illustrates the challenges encountered during assessment of neoplasms associated with TB and cryptococcosis. Differential diagnosis requires an abdominal puncture biopsy. Diagnosis of Cryptococcus infection also requires a positive cryptococcal culture and positive India ink staining analysis. Notably, our patient also showed no obvious symptoms of cryptococcosis after receiving anti-TB treatment. Accordingly, in this report, we discuss the possible pathogenic mechanisms that underlie the coincidence of both types of inflammatory lesions. We emphasize the need for a greater awareness of atypical presentations of TB accompanied by Cryptococcus infection.

MDAD: A Special Resource for Microbe-Drug Associations.

The human-associated microbiota is diverse and complex. It takes an essential role in human health and behavior and is closely related to the occurrence and development of disease. Although the diversity and distribution of microbial communities have been widely studied, little is known about the function and dynamics of microbes in the human body or the complex mechanisms of interaction between them and drugs, which are important for drug discovery and design. A high-quality comprehensive microbe and drug association database will be extremely beneficial to explore the relationship between them. In this article, we developed the Microbe-Drug Association Database (MDAD), a collection of clinically or experimentally supported associations between microbes and drugs, collecting 5,055 entries that include 1,388 drugs and 180 microbes from multiple drug databases and related publications. Moreover, we provided detailed annotations for each record, including the molecular form of drugs or hyperlinks from DrugBank, microbe target information from Uniprot and the original reference links. We hope MDAD will be a useful resource for deeper understanding of microbe and drug interactions and will also be beneficial to drug design, disease therapy and human health.