The relationship between systemic inflammation and tumor-associated bacteria is largely unknown in colorectal cancer (CRC). The primary aim of this study was to investigate the prognostic effects of the systemic inflammation response index (SIRI) on the survival outcomes of CRC patients who experienced surgical therapy, and the second aim was to reveal the potential association between SIRI levels and tumor-associated bacteria in CRC. We recruited a cohort of 298 CRC patients who experienced surgical resection in Wuhan Union Hospital. These patients were assigned to the low and high groups based on the cut-off value of SIRI. We utilized 1:1 propensity score matching (PSM) to reduce the potential confounding factors between the low SIRI group (N = 83) and the high SIRI group (N = 83). The total DNA of 166 paraffin-embedded tumor tissues and 24 frozen tumor tissues was extracted and amplified, and 16 S rRNA sequencing was employed to uncover the composition of microbiota between low and high SIRI groups. Survival analysis uncovered that the high SIRI cohort exhibited significantly shorter overall and disease-free survival time than low SIRI companions after PSM. The ROC analyses showed that the prediction abilities of SIRI were much higher than other serum inflammatory biomarkers for survival outcomes. The microbial richness and diversity in the low SIRI group were remarkably higher than those in the high SIRI group. At the phylum level, we found that Proteobacteria, Synergistetes, WPS-2, Thermil, Fusobacteria were enriched in the high SIRI group. Cupriavidus, Thermus, Ochrobactrum, Cupriavidus, Acidovorax were enriched in the high SIRI group at the genus level. 16 S rRNA based on frozen samples also obtained similar results. SIRI is a promising and novel prognostic biomarker among CRC sufferers who underwent surgical removal. There existed significant differences in the diversity and compositions of tumor-associated bacteria between the low and high SIRI groups.

Portal vein tumor thrombosis (PVTT) frequently leads to malignant ascites (MA) in individuals with hepatocellular carcinoma (HCC), remaining a bottleneck in the treatment. This study aimed to explore the differences in microbes in paired groups and provide novel insights into PVTT and MA-related treatments. Formalin-fixed paraffin embedding ascite samples were collected from MA secondary to HCC and benign ascites (BA) secondary to liver cirrhosis (LC). Ascitic microbiota profiles were determined in the HCC and LC groups by 16S rRNA sequencing. Prognostic risk factors were screened using survival analysis. The correlation between the significantly different microbial signatures in the groups with PVTT (WVT) and non-PVTT (NVT) and clinical characteristics was explored. The expression of different immune cells was determined by labeling four markers in the MA tissue chips using multiplex immunohistochemistry. A total of 240 patients (196 with HCC with MA and 44 with LC with BA) were included in this study. Microbial profiles differed between the HCC and LC groups. PVTT and Barcelona Clinic Liver Cancer stage were shown to be prognostic risk factors. Significant differences in the alpha and beta diversities were observed between the WVT and NVT groups. Gammaproteobacteria and Acinetobacter were the most abundant in the HCC MA. Differences in microbial signatures between the WVT and NVT groups were correlated with the level of C-reactive protein and apolipoprotein A1. This study revealed the microbial differences in the tumor microenvironment of MA secondary to HCC and BA secondary to LC. IMPORTANCE: First, we explored the alteration of the ascites ecosystem through the microbiota in patients with hepatocellular carcinoma (HCC) and liver cirrhosis. Second, this is the first clinical study to investigate the differences between patients with HCC with and without portal vein tumor thrombosis via 16S rRNA sequencing. These results revealed a decreased microbial diversity and metabolic dysregulation in individuals with HCC and portal vein tumor thrombosis. Gammaproteobacteria and Acinetobacter were the most abundant in the HCC malignant ascitic fluid. Our study provides a new perspective on treating malignant ascites secondary to HCC.

Nutritional and inflammatory status was associated with prognosis in various types of malignant cancer, including colorectal cancer (CRC). This clinical research was performed to estimate the prognostic role of immune-nutritional indexes CRC in patients and to set up a survival nomogram based on the significant immune-nutritional indexes. 1024 CRC patients underwent surgical resection from Wuhan Union Hospital were enrolled and divided into the test cohort ( ) and validation cohort ( ). A total of 19 immune-nutritional indexes were included into our analysis. The Cox regression analysis was utilized to identify the informative immune-nutritional indexes which were closely associated with overall survival (OS) and disease-free survival (DFS). Survival nomograms were created in the test set and further verified in the validation set. Td-ROC was curved to estimate the predictive performance of survival nomograms for CRC patients. Body mass index (BMI), chemotherapy, TNM stage, T stage, lactate dehydrogenase (LDH)/prealbumin (PA), monocytes (MON)/albumin (ALB), and prognostic nutritional index (PNI) were seven potent prognostic biomarkers of CRC patients. We created an OS-nomogram based on the seven risk indexes, and the predictive accuracy expressed with area under curve (AUC) was 0.826 for 1-year, 0.809 for 3-year, and 0.80 for 5-year OS rates in the test set and 0.795 for 1-year, 0.749 for 3-year, and 0.647 for 5-year OS rates in the validation set. TNM stage, T stage, LDH/ALB, and MON/ALB were risk factors for unfavorable DFS in CRC patients. We further built a DFS-nomogram based on the four risk factors, and the predictive performance presented with AUC was 0.806 for 1-year, 0.763 for 3-year, and 0.82 for 5-year DFS rates in the test set, and 0.704 for 1-year, 0.692 for 3-year, and 0.692 for 5-year DFS rates in the validation set. Our survival nomogram based on immune-nutritional indexes is a useful and potential prognostic tool in CRC patients.

Background: This study aimed to identify a novel inflammatory index and construct a nomogram for predicting in‐hospital mortality due to severe fever with thrombocytopenia syndrome (SFTS).Methods: This cohort included 610 patients with SFTS hospitalized in Wuhan Union Hospital between March 2017 and November 2022. The ratio of C‐reactive protein (CRP) to the prognostic nutritional index (PNI) was calculated and used to reflect patients' inflammatory status. Propensity score matching (PSM) was utilized to balance confounding factors between the low‐ and high‐CRP/PNI groups. SFTS individuals from Jinyinhu Hospital were used as the validation cohort.Results: Patients with SFTS and high CRP/PNI were significantly correlated with a higher percentage of severe and critical SFTS types and higher in‐hospital mortality rates than those with low CRP/PNI. CRP/PNI was the potent risk indicator for in‐hospital mortality in individuals with SFTS. The CRP/PNI nomogram showed a good predictive value for in‐hospital mortality in patients with SFTS. After PSM, the predictive performance of CRP/PNI for 28‐day mortality was excellent. Finally, the CRP/PNI could still assess patients with SFTS at different risks based on SFTS data from another medical center.Conclusion: The CPR/PNI ratio exhibited a strong positive correlation with the SFTS disease type and could predict in‐hospital mortality in the early stages of SFTS. The CPR/PNI ratio could substantially help clinicians facilitate the early identification of patients with high‐risk SFTS and the timely initiation of intensive therapy.

Ulcerative colitis (UC) is a lifelong autoimmune disease associated with a high prevalence of mental disorders. An abnormal gut–brain axis plays a key role in UC. Low-intensity pulsed ultrasound (LIPUS) has been reported to alleviate neuroinflammation. This study aimed to evaluate the therapeutic effects of LIPUS in an experimental colitis model. A mouse model of colitis induced by dextran sulfate sodium (DSS) was established to evaluate the therapeutic effects of LIPUS irradiation. Intestinal inflammation and the mucosal barrier were detected using reverse transcriptase polymerase chain reaction and immunofluorescence staining. The key proteins of the NLRP3 inflammatory pathway in different groups were measured by western blotting. 16S rRNA sequencing and ultra-performance liquid-chromatography tandem mass spectrometry were applied for the detection of bacteria, metabolites, and neurotransmitters. LIPUS irradiation significantly improved the mucosal barrier in DSS-induced colitis mice and relieved intestinal inflammation and neuroinflammation by downregulating the NLRP3 inflammatory pathway. 16S rRNA sequencing showed that LIPUS irradiation significantly upregulated the abundance of Bacteroides and glutamate metabolism. Metabolic analysis revealed that the most significant metabolites between the DSS + LIPUS and DSS groups were mostly involved in alanine, aspartate, and glutamate metabolism. The detection of neurotransmitters revealed that the levels of gamma-amino butyric acid (GABA) were significantly upregulated in DSS-induced colitis mice after LIPUS irradiation, and correlation analysis revealed a positive correlation between GABA and Bacteroides. LIPUS irradiation not only alleviated gut inflammation and neuroinflammation by regulating the NLRP3 inflammatory pathway in DSS-induced colitis mice but also increased GABA levels by upregulating Bacteroides, indicating that LIPUS might be a promising therapeutic technology for UC treatment.

As one of several common blurs, motion blur images are quite common in life. There are many ways to remove motion blur, but each has some problems, such as checkerboard artifacts, poor restoration of image texture details, and high algorithm costs. This paper proposes an improved blind motion deblurring method with Generative Adversarial Network, which achieved good results. The method in this paper uses a combination of up-sampling and convolution to replace the deconvolution of the traditional generative adversarial network model, effectively removing the common checkerboard artifacts in image processing, and the problems of poor texture detail restoration. The results show that the method in the article has achieved the expected purpose, it has achieved obvious deblurring effects from both objective and subjective evaluation indicators.