Traditional Chinese medicine combined with western medicine for the treatment of secondary pulmonary tuberculosis: A PRISMA-compliant meta-analysis

The mechanism of Gejie Zhilao Pill in treating tuberculosis based on network pharmacology and molecular docking verification

Introduction

Gejie Zhilao Pill (GJZLP), a traditional Chinese medicine formula is known for its unique therapeutic effects in treating pulmonary tuberculosis. The aim of this study is to further investigate its underlying mechanisms by utilizing network pharmacology and molecular docking techniques.

Methods

Using TCMSP database the components, potential targets of GJZLP were identified. Animal-derived components were supplemented through the TCMID and BATMAN-TCM databases. Tuberculosis-related targets were collected from the TTD, OMIM, and GeneCards databases. The intersection target was imported into the String database to build the PPI network. The Metascape platform was employed to carry out Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Heatmaps were generated through an online platform (https://www.bioinformatics.com.cn). Molecular docking was conducted between the core targets and core compounds to explore their binding strengths and patterns at the molecular level.

Results

61 active ingredients and 118 therapeutic targets were identified. Quercetin, Luteolin, epigallocatechin gallate, and beta-sitosterol showed relatively high degrees in the network. IL6, TNF, JUN, TP53, IL1B, STAT3, AKT1, RELA, IFNG, and MAPK3 are important core targets. GO and KEGG revealed that the effects of GJZLP on tuberculosis mainly involve reactions to bacterial molecules, lipopolysaccharides, and cytokine stimulation. Key signaling pathways include TNF, IL-17, Toll-like receptor and C-type lectin receptor signaling. Molecular docking analysis demonstrated a robust binding affinity between the core compounds and the core proteins. Stigmasterol exhibited the lowest binding energy with AKT1, indicating the most stable binding interaction.

Discussion

This study has delved into the efficacious components and molecular mechanisms of GJZLP in treating tuberculosis, thereby highlighting its potential as a promising therapeutic candidate for the treatment of tuberculosis.

Diagnosis of pulmonary tuberculosis with 3D neural network based on multi-scale attention mechanism

This paper presents a novel multi-scale attention residual network (MAResNet) for diagnosing patients with pulmonary tuberculosis (PTB) by computed tomography (CT) images. First, a three-dimensional (3D) network structure is applied in MAResNet based on the continuity and correlation of nodal features on different slices of CT images. Secondly, MAResNet incorporates the residual module and Convolutional Block Attention Module (CBAM) to reuse the shallow features of CT images and focus on key features to enhance the feature distinguishability of images. In addition, multi-scale inputs can increase the global receptive field of the network, extract the location information of PTB, and capture the local details of nodules. The expression ability of both high-level and low-level semantic information in the network can also be enhanced. The proposed MAResNet shows excellent results, with overall 94% accuracy in PTB classification. MAResNet based on 3D CT images can assist doctors make more accurate diagnosis of PTB and alleviate the burden of manual screening. In the experiment, a called Grad-CAM was employed to enhance the class activation mapping (CAM) technique for analyzing the model's output, which can identify lesions in important parts of the lungs and make transparent decisions.

Immunoregulatory and Anti-cancer Activities of Combination Treatment of Novel Four-Herb Formula and Doxorubicin in 4T1-Breast Cancer Bearing Mice

OBJECTIVE

To investigate the in vivo immunomodulatory and anti-tumor mechanisms of the combined treatment of novel Four-Herb formula (4HF) and doxorubicin in triple-negative breast cancer (TNBC).

METHODS

Murine-derived triple-negative mammary carcinoma cell line, 4T1 cells, was cultured and inoculated into mouse mammary glands. Sixty-six mice were randomly assigned into 6 groups (n=11 in ench): naïve, control, LD 4HF (low dose 4HF), HD 4HF (high dose 4HF), LD 4HF + D (low dose and doxorubicin), and D (doxorubicin). Apart from the naïve group, each mouse received subcutaneous inoculation with 5 × 105 4T1 cells resuspended in 100 µL of normal saline in the mammary fat pads. Starting from the day of tumor cell inoculation, tumors were grown for 6 days. The LD and HD groups received daily oral gavage of 658 and 2,630 mg/kg 4HF, respectively. The LD 4HF+D group received daily oral gavage of 658 mg/kg 4HF and weekly intraperitoneal injection of doxorubicin (5 mg/kg). The D group received weekly intraperitoneal injections of doxorubicin (5 mg/kg). The treatment naïve mice received daily oral gavage of 0.2 mL double distilled water and 0.1 mL normal saline via intraperitoneal injection once a week. The control group received daily oral gavage of 0.2 mL double-distilled water. The treatment period was 30 days. At the end of treatment, mice organs were harvested to analyze immunological activities via immunophenotyping, gene and multiplex analysis, histological staining, and gut microbiota analysis.

RESULTS

Mice treated with the combination of 4HF and doxorubicin resulted in significantly reduced tumor and spleen burdens (P<0.05), altered the hypoxia and overall immune lymphocyte landscape, and manipulated gut microbiota to favor the anti-tumor immunological activities. Moreover, immunosuppressive genes, cytokines, and chemokines such as C-C motif chemokine 2 and interleukin-10 of tumors were significantly downregulated (P<0.05). 4HF-doxorubicin combination treatment demonstrated synergetic activities and was most effective in activating the anti-tumor immune response (P<0.05).

CONCLUSION

The above results provide evidence for evaluating the immune regulating mechanisms of 4HF in breast cancer and support its clinical significance in its potential as an adjunctive therapeutic agent or immune supplement.

Tuberculosis in older adults: case studies from four countries with rapidly ageing populations in the western pacific region

BACKGROUND

The Western Pacific Region has one of the fastest-growing populations of older adults (≥ 65 years) globally, among whom tuberculosis (TB) poses a particular concern. This study reports country case studies from China, Japan, the Republic of Korea, and Singapore reflecting on their experiences in managing TB among older adults.

FINDINGS

Across all four countries, TB case notification and incidence rates were highest among older adults, but clinical and public health guidance focused on this population was limited. Individual country reports illustrated a range of practices and challenges. Passive case finding remains the norm, with limited active case finding (ACF) programs implemented in China, Japan, and the Republic of Korea. Different approaches have been trialled to assist older adults in securing an early diagnosis, as well as adhering to their TB treatment. All countries emphasised the need for person-centred approaches that include the creative application of new technology and tailored incentive programs, as well as reconceptualisation of how we provide treatment support. The use of traditional medicines was found to be culturally entrenched among older adults, with a need for careful consideration of their complementary use. TB infection testing and the provision of TB preventive treatment (TPT) were underutilised with highly variable practice.

CONCLUSION

Older adults require specific consideration in TB response policies, given the burgeoning aging population and their high TB risk. Policymakers, TB programs and funders must invest in and develop locally contextualised practice guidelines to inform evidence-based TB prevention and care practices for older adults.

Secondary Pulmonary Tuberculosis Identification Via pseudo-Zernike Moment and Deep Stacked Sparse Autoencoder

Secondary pulmonary tuberculosis (SPT) is one of the top ten causes of death from a single infectious agent. To recognize SPT more accurately, this paper proposes a novel artificial intelligence model, which uses Pseudo Zernike moment (PZM) as the feature extractor and deep stacked sparse autoencoder (DSSAE) as the classifier. In addition, 18-way data augmentation is employed to avoid overfitting. This model is abbreviated as PZM-DSSAE. The ten runs of 10-fold cross-validation show this model achieves a sensitivity of 93.33% ± 1.47%, a specificity of 93.13% ± 0.95%, a precision of 93.15% ± 0.89%, an accuracy of 93.23% ± 0.81%, and an F1 score of 93.23% ± 0.83%. The area-under-curve reaches 0.9739. This PZM-DSSAE is superior to 5 state-of-the-art approaches.