Gut microbiota in preterm infants with late-onset sepsis and pneumonia: a pilot case-control study.

BACKGROUND: Late-onset sepsis (LOS) and pneumonia are common infectious diseases, with high morbidity and mortality in neonates. This study aimed to investigate the differences in the gut microbiota among preterm infants with LOS, or pneumonia, and full-term infants. Furthermore, this study aimed to determine whether there is a correlation between intestinal pathogenic colonization and LOS. METHODS: In a single-center case‒control study, 16 S rRNA gene sequencing technology was used to compare gut microbiota characteristics and differences among the LOS group, pneumonia group, and control group. RESULTS: Our study revealed that the gut microbiota in the control group was more diverse than that in the LOS group and pneumonia group (P < 0.05). No significant differences in diversity were detected between the LOS and pneumonia groups (P > 0.05). Compared with the control group, the abundances of Akkermansia, Escherichia/Shigella, and Enterococcus increased, while the abundances of Bacteroides and Stenotrophomonas decreased in the LOS and pneumonia groups. The pathogenic bacteria in infants with LOS were consistent with the distribution of the main bacteria in the intestinal microbiota. An increase in Escherichia/Shigella abundance may predict a high risk of LOS occurrence, with an area under the curve (AUC) of 0.773. CONCLUSION: Changes in the gut microbiota composition were associated with an increased risk of LOS and pneumonia. The dominant bacteria in the gut microbiota of the LOS group were found to be associated with the causative pathogen of LOS. Moreover, preterm infants exhibiting an elevated abundance of Escherichia/Shigella may be considered potential candidates for predicting the onset of LOS.

Association of metals with early postnatal gut microbiota among infants admitted to the neonatal intensive care unit.

The gut microbiota is closely related to infant health. However, the impact of environmental factors on the gut microbiota has not been widely investigated, particularly in vulnerable populations such as infants admitted to the neonatal intensive care unit (NICU). This study investigated the association between exposure to 12 metals and the composition of the gut microbiota in infants admitted to the NICU. Metal concentrations were determined in serum samples obtained from 107 infants admitted to the NICU at Hunan Children's hospital, China. Gut microbiota data were derived from 16S rRNA sequencing using stool samples. Generalized linear regression (GLR) models and Bayesian kernel machine regression (BKMR) analyses were used to estimate the associations between metals and both alpha-diversity indices and bacterial taxa. The GLR models showed that tin correlated negatively with the Shannon index (ß = -0.55, 95% conficence interval [CI]: -0.79, -0.30, PFDR< 0.001) and positively with the Simpson index (ß = 0.26, 95% CI: 0.13, 0.39, PFDR< 0.001). The BKMR analysis yielded similar results, showing that tin had the largest posterior inclusion probability for both the Shannon (0.986) and the Simpson (0.796) indices. Tin, cadmium, mercury, lead, and thallium were associated with changes in one or more taxa at the genus level. The BKMR analysis also revealed a negative correlation between metal mixtures and Clostridium_sensu_stricto, and tin contibuted mostly to the negative correlation. Early postnatal exposure to metals were associated with differences in the microbiome among infants admitted to the NICU. However, as the study was cross-sectional, these relationships must be confirmed in further studies.

Progress of Influenza Viruses and Inhibitors.

Influenza is an acute respiratory disease caused by influenza viruses. It has the characteristics of fast transmission and strong infectivity, and it does great harm to human health and survival. It is estimated that the seasonal influenza epidemics every year will cause about one billion cases of infections and hundreds of thousands of deaths worldwide, while influenza A virus is the leading cause of infection and death. Currently, the main drugs used in clinics to treat influenza viruses are neuraminidase inhibitors, and these drugs have shown excellent efficacy in treating influenza viruses. However, various mutant strains have developed resistance to these effective drugs in clinics (such as the subtype mutant strains of H274Y in H1N1 or H5N1 and E119V in H3N2 have developed resistance to Oseltamivir). Influenza viruses mutate frequently, and new viral strains are constantly discovered, and the pandemics will break out at any time. Therefore, it is urgent to develop efficient and broad-spectrum drugs to prevent and treat the influenza pandemic caused by the emerging new subtypes. This review focuses on describing the pandemic history, the structure, function and prevention methods of influenza viruses and the progress of the development of anti-influenza drugs, to provide the reference for prevention and treatment of influenza viruses and development of influenza virus inhibitors.

Prevalence of post-traumatic stress disorder among residents of Shanghai standardized training programs during the COVID-19 outbreak: a cross-sectional study.

Background: The COVID-19 pandemic may have increased the prevalence of psychiatric disorders, such as anxiety, depressive disorders, and post-traumatic stress disorder (PTSD), among healthcare workers. Purpose: This study aims to investigate the prevalence of PTSD and its risk factors among residents in the standardized residency training programs (SRTPs) in Shanghai during the COVID-19 outbreak. Participants and methods: An online cross-sectional survey was conducted between December 17, 2021, and January 7, 2022, among SRPT residents from 15 hospitals in Shanghai, China. Questionnaires comprising general information, medical-related traumatic event experiences, the PTSD Checklist (PCL-5), and the perceived social support scale (PSSS) were distributed to the participants using the online Questionnaire Star electronic system. Results: We included 835 valid responses for the analysis. In total, 654 residents (78.3%) had experienced at least one traumatic event, and 278 residents (33.3%) were found to have PTSD symptoms. The age 26-30 years old, female sex, and increased resident working hours were identified as the risk factors for PTSD (p < 0.05), and perceived social support had a significant negative association with PTSD (p < 0.05). Conclusion: During the COVID-19 pandemic, there was a high prevalence of PTSD among SRTPs residents in Shanghai. The age 26-30 years old, female sex, and increased resident working hours were identified as risk factors for PTSD, while perceived social support was identified as a protective factor against PTSD. The present findings can be applied in STRPs management and provide useful information for designing special interventions and protocols for SRTPs residents.

Multimodal data fusion based on IGERNNC algorithm for detecting pathogenic brain regions and genes in Alzheimer's disease.

At present, the study on the pathogenesis of Alzheimer's disease (AD) by multimodal data fusion analysis has been attracted wide attention. It often has the problems of small sample size and high dimension with the multimodal medical data. In view of the characteristics of multimodal medical data, the existing genetic evolution random neural network cluster (GERNNC) model combine genetic evolution algorithm and neural network for the classification of AD patients and the extraction of pathogenic factors. However, the model does not take into account the non-linear relationship between brain regions and genes and the problem that the genetic evolution algorithm can fall into local optimal solutions, which leads to the overall performance of the model is not satisfactory. In order to solve the above two problems, this paper made some improvements on the construction of fusion features and genetic evolution algorithm in GERNNC model, and proposed an improved genetic evolution random neural network cluster (IGERNNC) model. The IGERNNC model uses mutual information correlation analysis method to combine resting-state functional magnetic resonance imaging data with single nucleotide polymorphism data for the construction of fusion features. Based on the traditional genetic evolution algorithm, elite retention strategy and large variation genetic algorithm are added to avoid the model falling into the local optimal solution. Through multiple independent experimental comparisons, the IGERNNC model can more effectively identify AD patients and extract relevant pathogenic factors, which is expected to become an effective tool in the field of AD research.

Changes and significance of gut microbiota in children with focal epilepsy before and after treatment.

Objective: To better understand the alterations in gut microbiota and metabolic pathways in children with focal epilepsy, and to further investigate the changes in the related gut microbiota and metabolic pathways in these children before and after treatment. Methods: Ten patients with newly diagnosed focal epilepsy in Hunan Children's Hospital from April, 2020 to October, 2020 were recruited into the case group. The case group was further divided into a pre-treatment subgroup and a post-treatment subgroup. Additionally, 14 healthy children of the same age were recruited into a control group. The microbial communities were analyzed using 16s rDNA sequencing data. Metastas and LEfSe were used to identify different bacteria between and within groups. The Kyoto Encyclopedia of Genes and Genomes database was used to KEGG enrichment analysis. Results: There were significant differences in α diversity among the pre-treatment, post-treatment, and control groups. Besides, the differences in gut microbiota composition in 3 groups were identified by principal co-ordinates analysis (PCoA), which showed a similar composition of the pre-treatment and post-treatment subgroups. At the phyla level, the relative abundance of Actinobacteria in the pre-treatment subgroup was significantly higher than that in the control group, which decreased significantly after 3 months of treatment and showed no significant difference between the control group. In terms of the genus level, Escherichia/Shigella, Streptococcus, Collinsella, and Megamonas were enriched in the pre-treatment subgroup, while Faecalibacterium and Anaerostipes were enriched in the control group. The relative abundance of Escherichia/Shigella, Streptococcus, Collinsella, and Megamonas was reduced significantly after a three-month treatment. Despite some genera remaining significantly different between the post-treatment subgroup and control group, the number of significantly different genera decreased from 9 to 4 through treatment. Notably, we found that the carbohydrate metabolism, especially succinate, was related to focal epilepsy. Conclusion: Children with focal epilepsy compared with healthy controls were associated with the statistically significant differences in the gut microbiota and carbohydrate metabolism. The differences were reduced and the carbohydrate metabolism improved after effective treatment. Our research may provide new directions for understanding the role of gut microbiota in the pathogenesis of focal epilepsy and better alternative treatments.

Psychometric Properties of the Chinese Version of the Primary Care Post-Traumatic Stress Disorder Screen-5 for Medical Staff Exposed to the COVID-19 Pandemic.

PURPOSE: The COVID-19 pandemic may increase the development of psychiatric disorders, such as posttraumatic stress disorder (PTSD) among medical staff. A brief validated screening tool is essential for the early diagnosis of PTSD. The purpose of the present study was to evaluate the validation of a Chinese version of the Primary Care-PTSD-5 (C-PC-PTSD-5) and determine an appropriate cutoff score with optimal sensitivity and specificity for medical staff in China during the COVID-19 pandemic. PARTICIPANTS AND METHODS: An online cross-sectional survey was conducted on medical staff (n = 1104) from 17 medical institutions in Shanghai. Questionnaires comprising general information, medical-related traumatic event experiences, the PTSD Checklist (PCL-5), and C-PC-PTSD-5 were distributed to participants using the online Questionnaire Star electronic system. Internal consistency, convergent validity, and test-retest reliability were calculated. Receiver operating characteristic (ROC) analysis was performed to determine diagnostic accuracy and the optimal cutoff score of the C-PC-PTSD-5 for medical staff. RESULTS: We included 1062 valid questionnaires for the analysis. Data of 838 traumatic experiences were analyzed. Internal consistency of the C-PC-PTSD-5 was satisfied (Cronbach's α = 0.756). The total score of the C-PC-PTSD-5 showed good test-retest reliability (r = 0.746). We found a strong correlation between the C-PC-PTSD-5 score and PCL-5 total score (r = 0.669, p < 0.001), which indicated good convergent validity. The ROC analysis showed an area under the curve of 0.81 ± 0.016. A cutoff score of 2 provided optimal sensitivity and specificity for the C-PC-PTSD-5 (sensitivity = 0.632, specificity = 0.871, Youden index = 0.503, and overall efficiency = 0.768). CONCLUSION: Our results indicated that the C-PC-PTSD-5 can be employed as a brief and efficient screening instrument for medical staff exposed to the COVID-19 pandemic. A score of 2 was identified as the optimal threshold for probable clinical PTSD symptoms.