Casual effects of gut microbiota on risk of infections: a two-sample Mendelian randomization study.

Background: The correlation between gut microbiota and infections has garnered significant attention in previous studies; nevertheless, our understanding of the causal relationships and mechanisms between specific microbial species and infections remains limited. Methods: This study aimed to employ Mendelian randomization (MR) using single-nucleotide polymorphisms (SNPs) and genome-wide association study (GWAS) data of European ancestry to explore the genetic-level relationships between distinct types of gut microbiota and susceptibility to infections. Our analysis encompassed three prevalent infections: intestinal infections, pneumonia, and urinary tract infections, while concurrently examining various types of gut microbiota. Results: We identified 18 protective gut microbiotas alongside 13 associated with increased infection risk. Particularly noteworthy are certain microbial communities capable of producing butyrate, such as the Ruminococcaceae and Lachnospiraceae families, which exhibited both favorable and unfavorable effects. Additionally, we observed a few certain communities linked to infection susceptibility, including ErysipelotrichaceaeUCG003 (OR = 0.13, 95% CI: 0.054-0.33, p = 1.24E-05), Collinsella (OR = 3.25, 95% CI: 2.00-5.27, p = 1.87E-06), and NB1n (OR = 1.24, 95% CI: 1.09-1.40, p = 1.12E-03). Conclusion: This study reveals complex relationships between gut microbiota and various infections. Our findings could potentially offer new avenues for exploring prevention and treatment strategies for infectious diseases.

Single Copper Atom Photocatalyst Powers an Integrated Catalytic Cascade for Drug-Resistant Bacteria Elimination.

To address the issue posed by drug-resistant bacteria and inspired by natural antimicrobial enzymes, we report the atomically doped copper on guanine-derived nanosheets (G-Cu) that possess the integrated catalytic cascade property of glucose oxidase and peroxidase, yielding free radicals to eliminate drug-resistant bacteria upon light irradiation. Density functional theory calculations demonstrate that copper could notably promote oxygen activation and H2O2 splitting on the G-Cu complexes. Further all-atom simulation and experimental data indicate that the lysis of bacteria is mainly induced by cell membrane damage and the elevation of intracellular reactive oxygen species. Lastly, the G-Cu complexes efficiently eliminate the staphylococci in the infected wounds and accelerate their closure in a murine model, with negligible side effects on the normal tissues. Therefore, our G-Cu complexes may provide an efficient nonantibiotic alternative to the current treatments for bacterial infections.

Computer-aided diagnosis of cervical dysplasia using colposcopic images.

Background: computer-aided diagnosis of medical images is becoming more significant in intelligent medicine. Colposcopy-guided biopsy with pathological diagnosis is the gold standard in diagnosing CIN and invasive cervical cancer. However, it struggles with its low sensitivity in differentiating cancer/HSIL from LSIL/normal, particularly in areas with a lack of skilled colposcopists and access to adequate medical resources. Methods: the model used the auto-segmented colposcopic images to extract color and texture features using the T-test method. It then augmented minority data using the SMOTE method to balance the skewed class distribution. Finally, it used an RBF-SVM to generate a preliminary output. The results, integrating the TCT, HPV tests, and age, were combined into a naïve Bayes classifier for cervical lesion diagnosis. Results: the multimodal machine learning model achieved physician-level performance (sensitivity: 51.2%, specificity: 86.9%, accuracy: 81.8%), and it could be interpreted by feature extraction and visualization. With the aid of the model, colposcopists improved the sensitivity from 53.7% to 70.7% with an acceptable specificity of 81.1% and accuracy of 79.6%. Conclusion: using a computer-aided diagnosis system, physicians could identify cancer/HSIL with greater sensitivity, which guided biopsy to take timely treatment.

An 8-gene DNA methylation signature predicts the recurrence risk of cervical cancer.

OBJECTIVE: This study examined the predictive utility of DNA methylation for cervical cancer recurrence. METHODS: DNA methylation and RNA expression data for patients with cervical cancer were downloaded from The Cancer Genome Atlas. Differentially methylated genes (DMGs) and differentially expressed genes were screened and extracted via correlation analysis. A support vector machine (SVM)-based recurrence prediction model was established using the selected DMGs. Cox regression analysis and receiver operating characteristic curve analysis were used for self-evaluation. The Gene Expression Omnibus (GEO) database was applied for external validation. Functional enrichment was determined using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. RESULTS: An eight-gene DNA methylation signature identified patients with a high risk of recurrence (area under the curve = 0.833). The SVM score was an independent risk factor for recurrence (hazard ratio [HR] = 0.418; 95% confidence interval [CI] = 0.26-0.67). The independent GEO database analysis further supported the result. CONCLUSION: An eight-gene DNA methylation signature predictive of cervical cancer recurrence was identified in this study, and this signature may help identify patients at high risk of recurrence and improve clinical treatment.

Learning from imbalanced fetal outcomes of systemic lupus erythematosus in artificial neural networks.

OBJECTIVE: To explore an effective algorithm based on artificial neural network to pick correctly the minority of pregnant women with SLE suffering fetal loss outcomes from the majority with live birth and train a well behaved model as a clinical decision assistant. METHODS: We integrated the thoughts of comparative and focused study into the artificial neural network and presented an effective algorithm aiming at imbalanced learning in small dataset. RESULTS: We collected 469 non-trivial pregnant patients with SLE, where 420 had live-birth outcomes and the other 49 patients ended in fetal loss. A well trained imbalanced-learning model had a high sensitivity of 19/21 ([Formula: see text]) for the identification of patients with fetal loss outcomes. DISCUSSION: The misprediction of the two patients was explainable. Algorithm improvements in artificial neural network framework enhanced the identification in imbalanced learning problems and the external validation increased the reliability of algorithm. CONCLUSION: The well-trained model was fully qualified to assist healthcare providers to make timely and accurate decisions.

Prediction of fetal loss in Chinese pregnant patients with systemic lupus erythematosus: a retrospective cohort study.

OBJECTIVE: To develop a predictive model for fetal loss in women with systemic lupus erythematosus (SLE). DESIGN: A retrospective cohort study. SETTING: Data were collected in a tertiary medical centre, located in Shanghai, China, from September 2011 to May 2017. PARTICIPANTS: 338 pregnancies with SLE were analysed retrospectively. Cases of multiple pregnancy and those in which artificial abortion was performed for personal reasons were excluded. PRIMARY OUTCOME MEASURES: Fetal loss was the primary outcome. A stepwise regression to identify the predictors related to the fetal loss and coefficient B of each variable was used to develop a predictive model and make a corresponding risk classification. The Hosmer-Lemeshow test, Omnibus test and area under the receiver-operating characteristic curve (AUC) were used to assess the goodness-of-fit and discrimination of the predictive model. A 10-fold cross validation was used to assess the model for overfitting. RESULTS: Unplanned pregnancies (OR 2.84, 95% CI 1.12 to 7.22), C3 hypocomplementemia (OR 5.46, 95% CI 2.30 to 12.97) and 24 hour-urinary protein level (0.3≤protein<1.0 g/24 hours: OR 2.10, 95% CI 0.63 to 6.95; protein≥1.0 g/24 hours: OR 5.89, 95% CI 2.30 to 15.06) were selected by the stepwise regression. The Hosmer-Lemeshow test resulted in p=0.325; the Omnibus test resulted in p<0.001 and the AUC was 0.829 (95% CI 0.744 to 0.91) in the regression model. The corresponding risk score classification was divided into low risk (0-3) and high risk groups (>3), with a sensitivity of 60.5%, a specificity of 93.3%, positive likelihood ratio of 9.03 and negative likelihood ratio of 0.42. CONCLUSIONS: A predictive model for fetal loss in women with SLE was developed using the timing of conception, C3 complement and 24 hour-urinary protein level. This model may help clinicians in identifying women with high risk pregnancies, thereby carrying out monitoring or/and interventions for improving fetal outcomes.

Management and outcomes of pregnancy with or without lupus nephritis: a systematic review and meta-analysis.

BACKGROUND: Although it is well established that systemic lupus erythematosus (SLE) negatively affects pregnancy outcomes, there is insufficient evidence on the effect of lupus nephritis (LN) on antenatal management and pregnancy outcomes. We performed a systematic review and meta-analysis to determine the association of LN with management and pregnancy outcomes in SLE patients. METHODS: Embase, Medline, Cochrane, and ClinicalTrials.gov were carefully searched for relevant English and Chinese language studies. A total of 2,987 articles were reviewed. Data were extracted that compared management and pregnancy outcomes in SLE pregnant women with LN vs without LN. Risk of bias was assessed by a modified version of the Newcastle-Ottawa Scale and the STROBE checklist. Combined odds ratios (OR) and 95% confidence intervals (CI) were obtained and sensitivity analysis was performed using RevMan 5.3 software. RESULTS: Sixteen studies, including 1,760 pregnant patients with SLE, were included. Gestational hypertension (OR=5.65, 95% CI=2.94-10.84), preeclampsia (OR=2.84, 95% CI=1.87-4.30), SLE flare (OR=2.66, 95% CI=1.51-4.70), renal flare (OR=15.18, 95% CI=5.89-39.14), proteinuria (OR=8.86, 95% CI=4.75-16.52), and hypocomplementemia (OR=2.86, 95% CI=1.68-4.87) were significantly affected in pregnant women with LN. Anti-Sjögren's syndrome-related antigen A/Ro autoantibodies were negatively associated with pregnant women with LN (OR=0.57, 95% CI=0.33-0.98). Pregnant women with LN presented a significant decrease in live births (OR=0.62, 95% CI=0.49-0.80) and a significant increase in preterm births (OR=1.92, 95% CI=1.49-2.49) and fetal growth restriction (OR=1.43, 95% CI=1.08-1.91). Regarding antenatal management, steroids (OR=2.48, 95% CI=1.59-3.87) and immunosuppressant treatment (OR=6.77, 95% CI=3.30-13.89) were more frequently used in women with LN. CONCLUSION: This review identified a significant association between the aforementioned outcomes and SLE pregnant patients with LN. In patients with SLE, LN increased the risks for adverse pregnancy outcomes and the use of medication. Therefore, special treatment and close monitoring should be allocated to pregnant women with LN.

Pregnancy outcomes among Chinese women with and without systemic lupus erythematosus: a retrospective cohort study.

OBJECTIVE: To completely and quantifiably determine the effect of systemic lupus erythematosus (SLE) on pregnancy outcomes in a Chinese cohort. DESIGN: A retrospective cohort study. SETTING: Data were collected at a tertiary medical centre located in Shanghai, China, from September 2011 to May 2017. PARTICIPANTS: We assigned 338 pregnant women with SLE to the study cohort and 1014 randomly selected pregnant women without SLE (three for every woman with SLE) to a comparison cohort. The relevant medical records of all pregnant women were retrospectively reviewed. Cases of multiple pregnancy and cases in which an artificial abortion was performed for personal reasons were excluded. PRIMARY AND SECONDARY OUTCOME MEASURES: Maternal and fetal outcomes were primary outcomes, and management of antenatal care was the secondary outcome. RESULTS: The risks of pregnancy-induced hypertension (OR 2.68, 95% CI 1.75 to 4.09), pre-eclampsia (OR 3.13, 95% CI 1.95 to 5.03) and premature rupture of membranes (OR 2.53, 95% CI 1.46 to 4.40) were significantly different between women with and without SLE. Gestational diabetes was negatively associated with SLE in pregnant women (OR 0.49, 95% CI 0.28 to 0.85). Pregnant women with SLE displayed significantly higher rates of fetal loss (OR 10.23, 95% CI 5.08 to 20.59), including spontaneous abortion (OR 4.42, 95% CI 1.52 to 12.80), therapeutic abortion (OR 16.57, 95% CI 5.80 to 47.35) and stillbirth (OR 13.25, 95% CI 1.49 to 118.11), and a higher risk of preterm birth (OR 3.15, 95% CI 2.21 to 4.50), intrauterine growth restriction (OR 2.20, 95% CI 1.35 to 3.58), a child who was small for the gestational age (OR 1.86, 95% CI 1.11 to 3.13), a caesarean section (OR 4.73, 95% CI 3.30 to 6.80) or a neonatal intensive care unit admission (OR 3.48, 95% CI 2.21 to 5.48) than women in the non-SLE population after adjusting for confounding factors. CONCLUSIONS: In this study, SLE significantly increased the risk of adverse pregnancy outcomes. Therefore, a preconception assessment and close antenatal monitoring by both rheumatologists and obstetricians should be performed in pregnant women with SLE.