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Current staging methods do not accurately predict the risk of disease recurrence and benefit of adjuvant chemotherapy for patients who have had surgery for stage II colon cancer. We postulated that expression patterns of multiple microRNAs (miRNAs) could, if combined into a single model, improve postoperative risk stratification and prediction of chemotherapy benefit for these patients. Using miRNA microarrays, we analysed 40 paired stage II colon cancer tumours and adjacent normal mucosa tissues, and identified 35 miRNAs that were differentially expressed between tumours and normal tissue. Using paraffin-embedded specimens from a further 138 patients with stage II colon cancer, we confirmed differential expression of these miRNAs using qRT-PCR. We then built a six-miRNA-based classifier using the LASSO Cox regression model, based on the association between the expression of every miRNA and the duration of individual patients' disease-free survival. We validated the prognostic and predictive accuracy of this classifier in both the internal testing group of 138 patients, and an external independent group of 460 patients. Using the LASSO model, we built a classifier based on the six miRNAs: miR-21-5p, miR-20a-5p, miR-103a-3p, miR-106b-5p, miR-143-5p, and miR-215. Using this tool, we were able to classify patients between those at high risk of disease progression (high-risk group), and those at low risk of disease progression (low-risk group). Disease-free survival was significantly different between these groups in every set of patients. In the initial training group of patients, 5-year disease-free survival was 89% (95% CI 77·3–94·4) for the low-risk group, and 60% (46·3–71·0) for the high-risk group (hazard ratio [HR] 4·24, 95% CI 2·13–8·47; p<0·0001). In the internal testing set of patients, 5-year disease-free survival was 85% (95% CI 74·3–91·8) for the low-risk group, and 57% (42·8–68·5) for the high-risk group (HR 3·63, 1·86–7·01; p<0·0001), and in the independent validation set of patients, was 85% (79·6–89·0) for the low-risk group and 54% (46·4–61·1) for the high-risk group (HR 3·70, 2·56–5·35; p<0·0001). The six-miRNA-based classifier was an independent prognostic factor for, and had better prognostic value than, clinicopathological risk factors and mismatch repair status. In an ad-hoc analysis, the patients in the high-risk group were found to have a favourable response to adjuvant chemotherapy (HR 1·69, 1·17–2·45; p=0·0054). We developed two nomograms for clinical use that integrated the six-miRNA-based classifier and four clinicopathological risk factors to predict which patients might benefit from adjuvant chemotherapy after surgery for stage II colon cancer. Our six-miRNA-based classifier is a reliable prognostic and predictive tool for disease recurrence in patients with stage II colon cancer, and might be able to predict which patients benefit from adjuvant chemotherapy. It might facilitate patient counselling and individualise management of patients with this disease. Natural Science Foundation of China.

Discharging large amounts of domestic and industrial wastewater drastically increases the reactive nitrogen content in aquatic ecosystems, which causes severe ecological stress and biodiversity loss. This paper reviews three common types of denitrification processes, including physical, chemical, and biological processes, and mainly focuses on the membrane technology for nitrogen recovery. The applicable conditions and effects of various treatment methods, as well as the advantages, disadvantages, and influencing factors of membrane technologies, are summarized. Finally, it is proposed that developing effective combinations of different treatment methods and researching new processes with high efficiency, economy, and energy savings, such as microbial fuel cells and anaerobic osmotic membrane bioreactors, are the research and development directions of wastewater treatment processes.

Although base editors are useful tools for precise genome editing, current base editors can only convert either adenines or cytosines. We developed a dual adenine and cytosine base editor (A&C-BEmax) by fusing both deaminases with a Cas9 nickase to achieve C-to-T and A-to-G conversions at the same target site. Compared to single base editors, A&C-BEmax’s activity on adenines is slightly reduced, whereas activity on cytosines is higher and RNA off-target activity is substantially decreased. Base editors that modify both adenine and cytosine broaden the potential applications of base editing.

The child mortality rate is a leading factor in the well-being and development of a nation. It measures the quality of life for a given population. This study aimed to determine the effects of under-five child mortality in Ethiopia. The authors used a cross-sectional study design via the 2019 Ethiopian Demographic and Health Survey. For our study, we used 3837 births recorded by mothers in seven regions of Ethiopia. In this study, the author employed the Bayesian and classical logistic regression models. The study found that the household size, number of under-five children, Sex of child, twin, births in the last five years, and breastfeeding status are significant predictors of child mortality in Ethiopia. Consequently, governmental, non-governmental, and other concerned bodies should focus on targeted healthcare interventions for mothers and children by updating their health intervention policies. In addition, improved health services are needed for better health care for children and mothers. Education should be given to mothers during pregnancy and after birth. This helps improve health for mothers and children, along with addressing other risk factors.

Background Joint modeling is widely used in medical research to properly analyze longitudinal biomarkers and survival outcomes simultaneously and to guide appropriate interventions in public health. However, such models become increasingly complex and computationally intensive when accounting for multiple features of these outcomes. The need for computationally efficient methods in joint modeling of competing risks survival outcomes and longitudinal biomarkers is particularly critical in clinical and epidemiological settings, where prompt decision-making is essential. Moreover, there is very little literature on joint modeling of competing risks survival and skewed longitudinal data using Integrated Nested Laplace Approximations (INLA), despite its growing popularity in Bayesian inference. This paper presents a computationally efficient inference approach for modeling competing risks survival and skewed longitudinal data using INLA. Methods We propose cause-specific competing risks joint models with a semi-parametric mixed-effects longitudinal submodel and second-order random walk baseline hazards. The proposed models are reformulated as latent Gaussian models to enable efficient Bayesian inference using INLA. The INLA approach and its R packages are also presented. Various smoothing spline functions, distributions, and association structures were evaluated for both approaches. The INLAjoint and R2WinBUGS R packages were employed for the INLA and Markov-Chain Monte-Carlo (MCMC) approaches, respectively, to approximate the posterior marginals of the proposed joint models. Model comparisons and performance evaluations were performed using the deviance information criterion, relative bias, coverage probability, and root mean squared error. Results We evaluated the computational efficiency and estimation performance of the INLA and MCMC approaches using real-world chronic kidney disease (CKD) follow-up data and an extensive confirmatory simulation study. We also conducted several model comparisons by considering different specifications related to smoothing spline approximations, non-Gaussian (skewed) distributions, and association structures to identify the best-fitting models for the CKD data and ensure robust statistical inference. Conclusion The application and simulation results revealed that both approaches provide accurate statistical estimation and inference. However, INLA significantly reduces the computational burden of the proposed joint models.

Antenatal care (ANC) contacts, along with enhanced health facilities for delivery, are essential components of maternal and child healthcare, as these significantly contribute to both mothers and their newborn child's health. Antennal care contacts primarily help women maintain normal pregnancies by detecting pre-existing conditions and preventing complications that may arise during childbirth. This study intended to determine possible factors that affect both ANC contact and place of delivery among women in Ethiopia. The 2019 Ethiopian Mini Demographic and Health Survey data were used for this study. A total weighted sample of 3,926 women nested within 68 zones was used. The bivariate multilevel logistic regression model was utilized to assess the association between antenatal care contact and place of delivery and determinant factors among reproductive-aged women in Ethiopia. In this study, 57% and 47.5% of women had no ANC contacts and home delivery respectively. Similarly, about 36.73% of women delivered at home and didn't utilize the recommended ANC contacts. Only 32.99% of women have both health facility delivery and at least four ANC contacts during their pregnancy. Women who reside in rural areas were 0.612 and 0.352 times less likely to have ANC and health facility delivery compared to women who reside in urban areas. Whereas, the estimated odds of women with higher education levels were 3.803 and 8.406 times the estimated odds of women with no education. A high proportion of women are still delivering their new child at home and still don't have at least four ANC contacts during their pregnancy. Women's age, women education level, marital status, wealth status, sex of household head, place of residence, and region were significant predictors of antenatal care visits and place of delivery simultaneously in Ethiopia. Although the country tried to maximize these services, it still requires expansion of health facilities media campaigns, and women's literacy to reduce maternal and newborn child mortality in Ethiopia.

Background Children worldwide can live lives free from various illnesses and disabilities due to vaccination. For instance, vaccination has eliminated smallpox, a deformative and frequently fatal illness that claimed an estimated 300 million lives in the twentieth century. However, due to a lack of access to immunization and other health services, 14.3 million infants in 2022 still did not receive their first dose of the Diphtheria-Tetanus-Pertussis (DTP) vaccine, and an additional 6.2 million received only a portion of the scheduled dose. This study aimed to assess prevalence and determinant factors of immunization among under-five children in Somalia using Somalia Health and Demographic Survey (SHDS) Data. Methods The study design was cross-sectional, utilizing the SHDS 2020 data. A total of 3916 under-five children who fulfilled the inclusion criteria were included in this study. Count regression models were employed to explore factors associated with the number of vaccinations received per child. Results In this study, 9.14% of children did not receive any vaccination during their childhood. Different candidate count regression models were compared. Using AIC and BIC, the Negative-binomial (NB) regression model was found to be the best fit. From this model, we found that women ages 20–24 (IRR = 1.192, 95% CI: 1.083, 1.313) and 25–29 (IRR = 1.180, 95% CI: 1.068, 1.305) had a higher number of vaccinations per child compared to women in the 15–19 age group. Women who attended primary education (IRR = 1.090, 95% CI: 1.034, 1.150) and secondary education (IRR = 1.157, 95% CI: 1.058, 1.266) had a higher number of vaccinations per child compared to uneducated women) also correlated with increased vaccination Parity (IRR = 1.090, 95% CI: 1.031–1.153), and wealth quantile (IRR = 1.110, 95% CI: 1.012, 1.217) positively influenced vaccination attendance. Regional disparities were also found to be significant, with Togdheer, Sool, Sanaag, Bari, Nugaal, Bay, Bakool, Mudug, Hiiraan and Galgaduud significantly different from Awdal region. In Negative-Binomial, age, region, residence, educational level, wealth quantile, child size at birth, parity and birth order emerged as key predictors, revealing complex determinants of vaccination utilization in Somalia. Conclusions A large proportion of children did not complete the full vaccination schedule. Socio-demographic factors, such as age, region, residence, educational level, wealth quantile, child size at birth, parity, and birth order, had a significant impact on the number of children vaccinated in Somalia. These findings underscore the importance of targeted interventions for addressing these factors. Implementing initiatives based on these conclusions has the potential to enhance vaccination coverage and child health outcomes.

Abstract Context Several small studies have suggested that the gut microbiome might influence osteoporosis, but there is little evidence from human metabolomics studies to explain this association. Objective This study examined the association of gut microbiome dysbiosis with osteoporosis and explored the potential pathways through which this association occurs using fecal and serum metabolomics. Methods We analyzed the composition of the gut microbiota by 16S rRNA profiling and bone mineral density using dual-energy X-ray absorptiometry in 1776 community-based adults. Targeted metabolomics in feces (15 categories) and serum (12 categories) were further analyzed in 971 participants using ultra-high-performance liquid chromatography coupled to tandem mass spectrometry. Results This study showed that osteoporosis was related to the beta diversity, taxonomy, and functional composition of the gut microbiota. The relative abundance of Actinobacillus, Blautia, Oscillospira, Bacteroides, and Phascolarctobacterium was positively associated with osteoporosis. However, Veillonellaceae other, Collinsella, and Ruminococcaceae other were inversely associated with the presence of osteoporosis. The association between microbiota biomarkers and osteoporosis was related to levels of peptidases and transcription machinery in microbial function. Fecal and serum metabolomics analyses suggested that tyrosine and tryptophan metabolism and valine, leucine, and isoleucine degradation were significantly linked to the identified microbiota biomarkers and to osteoporosis, respectively. Conclusion This large population-based study provided robust evidence connecting gut dysbiosis, fecal metabolomics, and serum metabolomics with osteoporosis. Our results suggest that gut dysbiosis and amino acid metabolism could be targets for intervention in osteoporosis.

Components of the cell division machinery typically function at varying cell cycle stages and intracellular locations. To dissect cellular mechanisms during the rapid division process, small-molecule probes act as complementary approaches to genetic manipulations, with advantages of temporal and in some cases spatial control and applicability to multiple model systems. This Review focuses on recent advances in chemical probes and applications to address select questions in cell division. We discuss uses of both enzyme inhibitors and chemical inducers of dimerization, as well as emerging techniques to promote future investigations. Overall, these concepts may open new research directions for applying chemical probes to advance cell biology. This Review summarizes recent progress in small-molecule probes used to address questions in cell division and appraises some emerging techniques that can be adapted to cell division studies.

A few studies have suggested that the consumption of fruit and vegetables (FV) may benefit bone health, but limited data are available in Asian subjects. We examined the association between FV intake and bone mineral density (BMD) and osteoporosis in Chinese adults. This population-based cross-sectional study involved 2083 women and 1006 men aged 40-75 years in Guangzhou, China. Habitual dietary data was collected from a 79-item food frequency questionnaire by face-to-face interviews. The BMD was measured for the whole body (WB), lumbar spine (LS), total hip (TH) and femur neck (FN) with dual-energy X-ray absorptiometry. After adjustment for potential covariates, we observed dose-dependent associations between total FV intake and BMD and osteoporosis risk. The mean BMD was higher in tertile 3 vs. tertile 1 by 1.33% (TH) and 1.31% (FN) for FV, and 1.10% (WB), 1.57% (TH), and 2.05% (FN) for fruit (all P-trends < 0.05). Significant beneficial associations with BMD at some sites were also found in most fruit categories but not in total vegetables or their subgroups. The odds ratios (95% confidence interval) of osteoporosis (T-score ≤ -2.5) in tertile 3 (vs. 1) were 0.73 (0.58-0.92), 0.37 (0.22-0.60), and 0.71 (0.52-0.97) for FV; 0.82 (0.66-1.03), 0.48 (0.30-0.77) and 0.89 (0.61-1.12) for fruit; and 0.80 (0.64-1.01), 0.57 (0.35-0.92) and 0.76 (0.55-1.05) for vegetables at the LS, TH, and FN, respectively. The favorable association between FV intake and the occurrence of osteoporosis was evident only in subjects with lower BMI (<24.0 kg/m2, P-trends < 0.05). Greater intake of FV was independently associated with a higher BMD and a lower presence of osteoporosis in middle-aged and elderly Chinese subjects with lower BMI. Fruit tended to have more contribution to the favorable association than vegetables.