OBJECTIVE: To investigate the effects of the combined Yiqi Huoxue Jiedu formula (YHJF) on intestinal microbiota in elderly patients with pulmonary-derived sepsis and identify potential microbial targets. METHODS: A prospective randomized double-blind controlled trial was conducted. Elderly patients with pulmonary infection-induced sepsis admitted to the emergency department of Guangdong Provincial Hospital of Traditional Chinese Medicine (TCM), intensive care unit (ICU) of Fangcun Hospital, and ICU of Daxuecheng Hospital, from November 2020 to October 2021 were enrolled and randomized into two groups. Both groups received conventional Western medicine treatment. The observation group additionally received YHJF (composed of 15 g of Panax ginseng, 9 g of Panax notoginseng, and 3 g of Rheum palmatum, dissolved in 50 mL warm water) orally or via nasogastric tube twice daily for 7 days; while the control group received a placebo. Clinical data and fresh fecal samples were collected before treatment and on days 5-7 of treatment. Intestinal microbiota diversity and structure were analyzed via 16S rDNA sequencing and bioinformatics [α diversity, β diversity, and linear discriminant analysis effect size (LEfSe)]. RESULTS: Fifty-five patients were included (29 in the control group, 26 in the observation group). There were no significantly differences in gender, age, comorbidities, and baseline sequential organ failure assessment (SOFA), acute physiology and chronic health evaluation II (APACHE II), acute gastrointestinal injury (AGI) classification score, and gastrointestinal failure (GIF) score between the two groups. Compared to the control group, the observation group showed significantly lower serum procalcitonin, APACHE II score, and greater reduction in GIF score by day 7. Thirty fecal samples were collected pre-treatment (baseline group), 29 post-treatment from the control group, and 26 from the observation group. Gut microbiota α diversity analysis revealed that Simpson index in the observation group and control group were significantly decreased compared to the baseline group [0.75 (0.53, 0.91), 0.81 (0.32, 0.91) vs. 0.88 (0.87, 0.89), both P < 0.05], but there was no significantly difference between the observation group and the control group. There were no significantly differences in Chao1, Ace, and Shannon indices among three groups. β diversity analysis indicated that distinct microbiota structures among three groups (R² = 0.096, P = 0.026). Species difference analysis showed that, at the phylum level, Firmicutes (53.69%), Actinobacteria (16.23%), Proteobacteria (15.39%), and Bacteroidetes (9.57%) dominated, with no significant intergroup differences. At the genus level, 38 taxa showed significant differences. Compared to the control group, the observation group exhibited increased Erysipelatoclostridium (P = 0.014) and Faecalibacterium (P = 0.013), and decreased Bacteroides (P = 0.009), Bilophila (P = 0.005), Eggerthella (P = 0.002), and Collinsella (P = 0.043). LEfSe analysis highlighted Lactobacillus salivarius, Erysipelatoclostridium, Collinsella, Cloacibacillus, and Bacteroides as key discriminators. CONCLUSION YHJF combined with conventional therapy alters intestinal microbiota structure in patients with elderly pulmonary-derived sepsis, with Bacteroides, Erysipelatoclostridium, and Collinsella identified as potential microbial targets.
Humans ; Gastrointestinal Microbiome/drug effects* ; Drugs, Chinese Herbal/therapeutic use* ; Double-Blind Method ; Sepsis/drug therapy* ; Aged ; Prospective Studies ; RNA, Ribosomal, 16S/genetics* ; Male ; Female ; Panax notoginseng ; Rheum

Objective To investigate the value of whole lung CT radiomics combined with clinical and conventional CT features for differentiating non-tuberculous mycobacterial pulmonary disease(NTM-PD)and pulmonary tuberculosis(PTB).Methods Fifty-three NTM-PD(NTM-PD group)and 111 PTB(PTB group)patients diagnosed by mycobacteria culture were retrospectively collected.The patients were divided into training set(n=115,including 37 cases of NTM-PD and 78 cases of PTB)and test set(n=49,including 16 cases of NTM-PD and 33 cases of PTB)at the ratio of 7∶3.Patients'clinical and pulmonary CT manifestations of lesions were analyzed using univariate and multivariate logistic regression,and the independent impact factors for differentiating NTM-PD and PTB lesions were screened.The best radiomics features were extracted and screened based on whole lung CT.Based on independent impact factors,the best radiomics features and their combination,clinical-CT,radiomics and combined models were constructed with random forest,and the differential efficacy of each model was evaluated.Results Patients'age(OR=0.264),gender(OR=0.956),immunoglobulin G(OR=3.416),C reactive protein(OR=3.418)and bronchiectasis shown on CT(OR=0.285)were all independent impact factors for differentiating NTM-PD and PTB.Twelve best radiomics features were screened based on whole lung ROI.The AUC of combined model in training set and test set was 0.915 and 0.901,respectively,both higher than that of clinical model(AUC=0.832,0.801,Z=1.340,3.710,both P＜0.05)and radiomics model(AUC=0.877,0.821,Z=-2.520,-5.240,both P＜0.05).Conclusion Whole lung CT radiomics combined with clinical and conventional CT features could effectively distinguish NTM-PD and PTB.

Objective To investigate the value of whole lung CT radiomics combined with clinical and conventional CT features for differentiating non-tuberculous mycobacterial pulmonary disease(NTM-PD)and pulmonary tuberculosis(PTB).Methods Fifty-three NTM-PD(NTM-PD group)and 111 PTB(PTB group)patients diagnosed by mycobacteria culture were retrospectively collected.The patients were divided into training set(n=115,including 37 cases of NTM-PD and 78 cases of PTB)and test set(n=49,including 16 cases of NTM-PD and 33 cases of PTB)at the ratio of 7∶3.Patients'clinical and pulmonary CT manifestations of lesions were analyzed using univariate and multivariate logistic regression,and the independent impact factors for differentiating NTM-PD and PTB lesions were screened.The best radiomics features were extracted and screened based on whole lung CT.Based on independent impact factors,the best radiomics features and their combination,clinical-CT,radiomics and combined models were constructed with random forest,and the differential efficacy of each model was evaluated.Results Patients'age(OR=0.264),gender(OR=0.956),immunoglobulin G(OR=3.416),C reactive protein(OR=3.418)and bronchiectasis shown on CT(OR=0.285)were all independent impact factors for differentiating NTM-PD and PTB.Twelve best radiomics features were screened based on whole lung ROI.The AUC of combined model in training set and test set was 0.915 and 0.901,respectively,both higher than that of clinical model(AUC=0.832,0.801,Z=1.340,3.710,both P＜0.05)and radiomics model(AUC=0.877,0.821,Z=-2.520,-5.240,both P＜0.05).Conclusion Whole lung CT radiomics combined with clinical and conventional CT features could effectively distinguish NTM-PD and PTB.

Genomic instability remains an enabling feature of cancer and promotes malignant transformation. Alterations of DNA damage response (DDR) pathways allow genomic instability, generate neoantigens, upregulate the expression of programmed death ligand 1 (PD-L1) and interact with signaling such as cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING) signaling. Here, we review the basic knowledge of DDR pathways, mechanisms of genomic instability induced by DDR alterations, impacts of DDR alterations on immune system, and the potential applications of DDR alterations as biomarkers and therapeutic targets in cancer immunotherapy.