Objective:To verify the specific differentiated subsets of monocytes in sepsis, and to screen and construct the differential gene set of monocytes used for early diagnosis of sepsis.Methods:Patients with sepsis admitted to Guangdong Provincial People's Hospital from June 2020 to March 2021 were enrolled, and peripheral blood mononuclear cells (PBMC) were extracted. Single-cell sequencing technology and pseudo-time analysis were used to verify the differential subsets of monocytes. Bioinformatics methods were used to analyze the expression of genes in differential subsets of monocytes and screen out differential genes for the preliminary construction of a candidate differential gene set. The digital polymerase chain reaction (PCR) technology was used to verify the candidate differential genes in PBMC of sepsis patients and sepsis human myeloid leukemia mononuclear cells (THP-1) models, and the Venn diagram was used to construct the final differential gene set of monocytes. Gene Expression Omnibus (GEO) database was used to validate the differential gene set of monocytes.Results:① The results of cell annotation and pseudo-time analysis showed that the differentiation of NEAT1 +CD163 + monocyte occurred in the early stage of sepsis was significantly different from other subsets, which validated that NEAT1 +CD163 + monocyte was the characteristic subset in the pathological process of sepsis. ② Twenty-two differential genes related to sepsis were screened out from the gene expression of NEAT1 +CD163 + monocyte. After further verification by digital PCR, basic leucine zipper ATF-like transcription factor (BATF), JUNB proto-oncogene, carcinoembryonic antigen-related cell adhesion molecule 4 (CEACAM4), chromosome 9 open reading frame 95 (C9orf95), G protein subunit alpha 15 (GNA15), complement C3a receptor 1 (C3AR1), transforming growth factor beta 1 (TGFB1) and mitochondrial carrier homolog 1 (MTCH1) were screened out to construct the final differential gene set of monocytes. ③ The external validation results showed that C9orf95 gene had no data in GSE154918 and GSE133822 from GEO, it was excluded during validation. In GSE154918, the expressions of BATF, JUNB, CEACAM4, GNA15, C3AR1, TGFB1, and MTCH1 in the sepsis group were significantly higher than those in the healthy control group (log 2expression level: BATF was 12.78±0.08 vs. 11.39±0.35, JUNB was 16.88±0.07 vs. 16.04±0.03, CEACAM4 was 14.73±0.08 vs. 13.77±0.05, GNA15 was 13.16±0.06 vs. 12.30±0.04, C3AR1 was 14.62±0.13 vs. 12.87±0.05, TGFB1 was 16.95±0.05 vs. 16.57±0.36, MTCH1 was 14.80±0.02 vs. 14.61±0.15, all P < 0.05). In GSE133822, the expressions of BATF, CEACAM4, GNA15, and C3AR1 in the sepsis group were significantly higher than those in the health control group (log 2expression level: BATF was 8.66±0.16 vs. 7.92±0.14, CEACAM4 was 9.20±0.16 vs. 8.36±0.20, GNA15 was 10.66±0.18 vs. 10.13±0.16, C3AR1 was 11.49±0.27 vs. 10.48±0.16, all P < 0.05), while the expressions of JUNB, TGFB1, and MTCH1 were not statistically different between two groups. The results of gene set variation analysis (GSVA) showed that the enrichment scores of monocytes differential gene set of sepsis group were significantly higher than those of the healthy control group in both GSE154918 (0.38±0.04 vs. -0.44±0.02) and GSE133822 (0.56±0.02 vs. 0.20±0.05, both P < 0.01). Receiver operator characteristic curve (ROC curve) analysis showed that the differential gene set of monocytes had a reliable diagnostic value for early sepsis with the area under ROC curve (AUC) of 0.993 [95% confidence interval (95% CI) was 0.980-1.000] in GSE154918 and 0.944 (95% CI was 0.873-1.000) in GSE133822. Conclusion:A differential gene set of monocytes (BATF, JUNB, CEACAM4, GNA15, C3AR1, TGFB1, and MTCH1) screened out by single-cell sequencing and digital PCR technology has a reliable diagnostic value for the early sepsis, and may provide a new idea for the early diagnosis of sepsis.

Sepsis is a life-threatening organ dysfunction caused by dysregulated host response to infection. Immunosuppression is an important factor of secondary infection in the late state of sepsis, including multi-drugs resistant bacteria, which ultimately leads to the death of patients. The aim of this article was to help clinical staffs better manage patients with sepsis, improve long-term survival rate of the patients, and reduce their re-hospitalization rate by reviewing the relationship between sepsis-induced immunosuppression and multi-drugs resistant bacteria through three aspects: the mechanism of sepsis-induced immunosuppression, the mechanism of antibiotic resistance and the relationship between sepsis-induced immunosuppression and secondary infections.

Objective:To compare the characteristics and outcomes of culture-positive sepsis (CPS) with culture-negative sepsis (CNS) patients in order to understand the impact of CNS on prognosis and explore the possible risk factors for mortality.Methods:A retrospective cohort study was conducted. Patients with sepsis were identified from the Medical Information Mart for Intensive Care database-Ⅳ v0.4 (MIMIC-Ⅳ v0.4). Patients were divided into CPS and CNS groups according to the culture results within 24 hours before and after the diagnosis of sepsis. General information, baseline characteristics, and medical operation data between CNS and CPS groups were compared. Logistic regression analysis was used to calculate the relationship between CNS and in-hospital mortality under three regression models. Chi-square analysis and mediation analysis were used to analyze the effect of initial antibiotic and prior antibiotic use within 90 days on the in-hospital mortality of CNS. Results:A total of 8 587 patients with sepsis were enrolled in the final analysis, including 5 483 patients in the CPS group and 3 104 patients in the CNS group. Compared with the CPS group, the patients in the CNS group were younger [years old: 68 (56, 79) vs. 70 (58, 81)], had higher sequential organ failure assessment (SOFA) score and higher proportion of using mechanical ventilation, renal replacement therapy and vasopressin within 24 hours after intensive care unit (ICU) admission [SOFA score: 3 (2, 5) vs. 3 (2, 4), mechanical ventilation: 48.61% (1 509/3 104) vs. 39.25% (2 152/5 483), renal replacement therapy: 13.69% (425/3 104) vs. 9.68% (531/5 483), vasopressin: 15.79% (490/3 104) vs. 13.44% (737/5 483)], longer length of ICU stay [days: 5 (3, 10) vs. 3 (2, 6)] and higher in-hospital mortality [25.00% (776/3 104) vs. 18.53% (1 016/5 483)], with significant differences (all P < 0.01). However, there was no significant difference in gender, ICU type, simplified acute physiology score Ⅱ (SAPS Ⅱ), and Charlson comorbidity index (CCI) score between the two groups. After adjustment for multiple confounding factors, CNS was still a risk factor for in-hospital mortality [odds ratio ( OR) = 1.441, 95% confidence interval (95% CI) was 1.273-1.630, P < 0.001]. The results of Chi-square analysis and mediation analysis showed that the initial antibiotic had no significant effect on the higher in-hospital mortality of CNS, while the prior use of antibiotics within 90 days was related to higher in-hospital mortality of CNS ( OR = 1.683, 95% CI was 1.328-2.134, P < 0.05). The mediating effect of CNS in prior antibiotic use within 90 days and in-hospital death was significant ( Z = 5.302, P < 0.001), accounting for 7.58%. Conclusions:Compared with CPS, CNS was more severe and had a worse prognosis. Prior use of antibiotics within 90 days may be related to the higher in-hospital mortality of CNS patients, but it could not fully explain the high mortality of CNS.

Objective: To examine whether presepsin level can serve as a distinguishing marker between G- bacteria and G+ bacteria, fungal infection in sepsis patients. Methods: A prospective observation study was conducted on the consecutive patients with positive bacterial cultures in intensive care unit (ICU) from June 2017 to November 2018. The patients were divided into the G- group, G+ group and fungal group. Blood samples were collected upon admission to measure the levels of presepsin and procalcitonin (PCT). Results: (1) Of the 156 patients met the inclusion criteria. 96 (62% G- rods, 25 (16%) G+ microbes, and 35 (22%) fungi were detected. (2) Presepsin concentrations were significantly higher in the G- group compared with the G+ and fungal groups (P = 0.000). (3) Presepsin level has a higher accuracy in differentiating G- sepsis from Gram+ and fungal sepsis than PCT level [area under the curve (AUC): 0.809 vs 0.712]. The AUC value of a combination of presepsin and PCT level was significantly larger than that of presepsin level alone in differentiating G- sepsis from Gram+ and fungal sepsis (AUC: 0.866 vs 0.809). Conclusions In contrast to PCT, presepsin is a good discriminative biomarker in different infections.