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BACKGROUND: Copper (Cu), an essential trace mineral regulating multiple actions of inflammation and oxidative stress, has been implicated in risk for preterm birth (PTB). OBJECTIVES: This study aimed to determine the association of maternal Cu concentration during pregnancy with PTB risk and gestational duration in a large multicohort study including diverse populations. METHODS: Maternal plasma or serum samples of 10,449 singleton live births were obtained from 18 geographically diverse study cohorts. Maternal Cu concentrations were determined using inductively coupled plasma mass spectrometry. The associations of maternal Cu with PTB and gestational duration were analyzed using logistic and linear regressions for each cohort. The estimates were then combined using meta-analysis. Associations between maternal Cu and acute-phase reactants (APRs) and infection status were analyzed in 1239 samples from the Malawi cohort. RESULTS: The maternal prenatal Cu concentration in our study samples followed normal distribution with mean of 1.92 μg/mL and standard deviation of 0.43 μg/mL, and Cu concentrations increased with gestational age up to 20 wk. The random-effect meta-analysis across 18 cohorts revealed that 1 μg/mL increase in maternal Cu concentration was associated with higher risk of PTB with odds ratio of 1.30 (95% confidence interval [CI]: 1.08, 1.57) and shorter gestational duration of 1.64 d (95% CI: 0.56, 2.73). In the Malawi cohort, higher maternal Cu concentration, concentrations of multiple APRs, and infections (malaria and HIV) were correlated and associated with greater risk of PTB and shorter gestational duration. CONCLUSIONS: Our study supports robust negative association between maternal Cu and gestational duration and positive association with risk for PTB. Cu concentration was strongly correlated with APRs and infection status suggesting its potential role in inflammation, a pathway implicated in the mechanisms of PTB. Therefore, maternal Cu could be used as potential marker of integrated inflammatory pathways during pregnancy and risk for PTB.

Abstract Objective Breast cancer has become the most prevalent malignant tumor in women, and the occurrence of distant metastasis signifies a poor prognosis. Utilizing predictive models to forecast distant metastasis in breast cancer presents a novel approach. This study aims to utilize readily available clinical data and advanced machine learning algorithms to establish an accurate clinical prediction model. The overall objective is to provide effective decision support for clinicians. Methods Data from 239 patients from two centers were analyzed, focusing on clinical blood biomarkers (tumor markers, liver and kidney function, lipid profile, cardiovascular markers). Spearman correlation and the least absolute shrinkage and selection operator regression were employed for feature dimension reduction. A predictive model was built using LightGBM and validated in training, testing, and external validation cohorts. Feature importance correlation analysis was conducted on the clinical model and the comprehensive model, followed by univariate and multivariate regression analysis of these features. Results Through internal and external validation, we constructed a LightGBM model to predict de novo bone metastasis in newly diagnosed breast cancer patients. The area under the receiver operating characteristic curve values of this model in the training, internal validation test, and external validation test1 cohorts were 0.945, 0.892, and 0.908, respectively. Our validation results indicate that the model exhibits high sensitivity, specificity, and accuracy, making it the most accurate model for predicting bone metastasis in breast cancer patients. Carcinoembryonic Antigen, creatine kinase, albumin-globulin ratio, Apolipoprotein B, and Cancer Antigen 153 (CA153) play crucial roles in the model’s predictions. Lipoprotein a, CA153, gamma-glutamyl transferase, α-Hydroxybutyrate dehydrogenase, alkaline phosphatase, and creatine kinase are positively correlated with breast cancer bone metastasis, while white blood cell ratio and total cholesterol are negatively correlated. Conclusion This study successfully utilized clinical blood biomarkers to construct an artificial intelligence model for predicting distant metastasis in breast cancer, demonstrating high accuracy. This suggests potential clinical utility in predicting and identifying distant metastasis in breast cancer. These findings underscore the potential prospect of developing economically efficient and readily accessible predictive tools in clinical oncology.

This study aimed to determine the prognostic value and microenvironmental crosstalk of exosome-related signatures in human epidermal growth factor receptor 2 positive breast cancer (HER2+ BC). Transcriptome sequencing and clinicopathological data were downloaded from the Cancer Genome Atlas. The 10X single cell sequencing dataset was downloaded from the National Center for Biotechnology Information Gene Expression Omnibus. Exosomes-Related Genes were extracted from the ExoCarta and Gene Set Enrichment Analysis databases. FGF9, SF3B4, and EPCAM were found and deemed the most accurate predictive signatures. Patients with HER2+ BC were subtyped into three groupings by exosome prognostic gene (EPGs). The expression of SF3B4 was positively linked with the infiltration of macrophages, neutrophils, and CD4+ T cells. The expression characteristics of EPGs were associated with the biological process of “response to xenobiotic stimuli.” Interactions were relatively high between malignant epithelial cells and fibroblasts, endothelial cells, monocytes, and macrophages. Malignant epithelial cells interact more with fibroblasts and endothelial cells. The migration inhibitory factor pathway was the primary outgoing signaling pattern, while the C-C motif chemokine ligand pathway was the primary incoming signaling pattern for communication between malignant epithelial cells and macrophages. This study described the role of exosome signatures in the prognosis and microenvironment of HER2+ BC and provided a basis for future research.

Abstract Breast cancer metastasis significantly impacts women's health globally. This study aimed to construct predictive models using clinical blood markers and ultrasound data to predict distant metastasis in breast cancer patients, ensuring clinical applicability, cost-effectiveness, relative non-invasiveness, and accessibility of these models. Analysis was conducted on data from 416 patients across two centers, focusing on clinical blood markers (tumor markers, liver and kidney function indicators, blood lipid markers, cardiovascular biomarkers) and maximum lesion diameter from ultrasound. Feature reduction was performed using Spearman correlation and LASSO regression. Two models were built using LightGBM: a clinical model (using clinical blood markers) and a combined model (incorporating clinical blood markers and ultrasound features), validated in training, internal test, and external validation (test1) cohorts. Feature importance analysis was conducted for both models, followed by univariate and multivariate regression analyses of these features. The AUC values of the clinical model in the training, internal test, and external validation (test1) cohorts were 0.950, 0.795, and 0.883, respectively. The combined model showed AUC values of 0.955, 0.835, and 0.918 in the training, internal test, and external validation (test1) cohorts, respectively. Clinical utility curve analysis indicated the combined model's superior net benefit in identifying breast cancer with distant metastasis across all cohorts. This suggests the combined model's superior discriminatory ability and strong generalization performance. Creatine kinase isoenzyme (CK-MB), CEA, CA153, albumin, creatine kinase, and maximum lesion diameter from ultrasound played significant roles in model prediction. CA153, CK-MB, lipoprotein (a), and maximum lesion diameter from ultrasound positively correlated with breast cancer distant metastasis, while indirect bilirubin and magnesium ions showed negative correlations. This study successfully utilized clinical blood markers and ultrasound data to develop AI models for predicting distant metastasis in breast cancer. The combined model, incorporating clinical blood markers and ultrasound features, exhibited higher accuracy, suggesting its potential clinical utility in predicting and identifying breast cancer distant metastasis. These findings highlight the potential prospects of developing cost-effective and accessible predictive tools in clinical oncology.

Abstract Background mcr-1-positive Escherichia coli has emerged as a significant threat to human health, veterinary health, and food safety in recent years. After the prohibition of colistin as a feed additive in animal husbandry in China, a noticeable reduction in both colistin resistance and the prevalence of mcr-1 was observed in E. coli from animals and humans. Objectives To assess the prevalence of the colistin resistance gene mcr-1 and characterize its genetic context in E. coli strains derived from fecal and meat samples from food-producing animals in China. Methods A total of 1,353 fecal samples and 836 food samples were collected between 2019 and 2020 in China. E. coli isolates were identified using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and their susceptibility to colistin were determined using the broth microdilution method. The colistin-resistant E. coli isolates were screened for the presence of mcr by PCR analysis and sequencing. The minimal inhibitory concentrations (MICs) of 15 antimicrobial agents against the mcr-1-positive strains were further tested using the agar dilution method, conjugation assays were performed, and whole genome sequencing was performed using Illumina HiSeq. Results In total, 1,403 E. coli strains were isolated. Thirteen isolates from chicken meat (n = 7), chickens (n = 3), and pigs (n = 3) were resistant to colistin with MIC values of 4 to 16 mg/L, and carried mcr-1. All mcr-1-positive strains, except for isolate AH20PE105, contained multiple resistance genes and exhibited multidrug-resistant phenotypes. They belonged to 10 sequence types (STs), including a novel ST (ST14521). mcr-1 was located on IncI2 (n = 9), IncX4 (n = 2), and IncHI2 (n = 2) plasmids, which were highly similar to other mcr-1-carrying plasmids sharing the same incompatibility type. Seven mcr-1-carrying plasmids could be successfully conjugally transferred to E. coli C600. Conclusions While the low prevalence of mcr-1 (0.93%) identified in this study may not immediately seem alarming, the very emergence of this gene merits attention given its implications for colistin resistance and public health. Hence, ongoing surveillance of mcr-1 in E. coli remains crucial.

Abstract Candidalysin, a cytolytic peptide toxin secreted by the human fungal pathogen Candida albicans, is critical for fungal pathogenesis. Yet, its intracellular targets have not been extensively mapped. Here, we performed a high-throughput enhanced yeast two-hybrid (HT-eY2H) screen to map the interactome of all eight Ece1 peptides with their direct human protein targets and identified a list of potential interacting proteins, some of which were shared between the peptides. CCNH, a regulatory subunit of the CDK-activating kinase (CAK) complex involved in DNA damage repair, was identified as one of the host targets of candidalysin. Mechanistic studies revealed that candidalysin triggers a significantly increased double-strand DNA breaks (DSBs), as evidenced by the formation of γ-H2AX foci and colocalization of CCNH and γ-H2AX. Importantly, candidalysin binds directly to CCNH to activate CAK to inhibit DNA damage repair pathway. Loss of CCNH alleviates DSBs formation under candidalysin treatment. Depletion of candidalysin-encoding gene fails to induce DSBs and stimulates CCNH upregulation in a murine model of oropharyngeal candidiasis. Collectively, our study reveals that a secreted fungal toxin acts to hijack the canonical DNA damage repair pathway by targeting CCNH and to promote fungal infection.

Inadequate diet and frequent symptomatic infections are considered major causes of growth stunting in low-income countries, but interventions targeting these risk factors have achieved limited success. Asymptomatic infections can restrict growth, but little is known about their role in global stunting prevalence. We investigated factors related to length-for-age Z-score (LAZ) at 24 months by constructing an interconnected network of various infections, biomarkers of inflammation (as assessed by alpha-1-acid glycoprotein [AGP]), and growth (insulin-like growth factor 1 [IGF-1] and collagen X biomarker [CXM]) at 18 months, as well as other children, maternal, and household level factors. Among 604 children, there was a continuous decline in mean LAZ and increased mean length deficit from birth to 24 months. At 18 months of age, the percentage of asymptomatic children who carried each pathogen was: 84.5% enterovirus, 15.5% parechovirus, 7.7% norovirus, 4.6% rhinovirus, 0.6% rotavirus, 69.6% Campylobacter, 53.8% Giardia lamblia, 11.9% malaria parasites, 10.2% Shigella, and 2.7% Cryptosporidium. The mean plasma IGF-1 concentration was 12.5 ng/ml and 68% of the children had systemic inflammation (plasma AGP concentration >1 g/L). Shigella infection was associated with lower LAZ at 24 months through both direct and indirect pathways, whereas enterovirus, norovirus, Campylobacter, Cryptosporidium, and malaria infections were associated with lower LAZ at 24 months indirectly, predominantly through increased systemic inflammation and reduced plasma IGF-1 and CXM concentration at 18 months.

The present study adopted the structural equation modeling approach to examine Chinese university students' metacognition, critical thinking skills, and academic writing. In particular, this research explored whether awareness in metacognition can foster critical thinking and, thus, lead to enhancement in academic writing. The measure for exploring metacognitive writing strategies covered metacognitive knowledge and regulation in academic writing. The measure for understanding learners' critical thinking encompassed the following five skills: inference, recognition of assumptions, deduction, interpretations, and evaluation of arguments. The academic writing assessment was based on an internal test. The participants consisted of 644 third-year students from a Chinese university. Three models tested: (1) the role of metacognition in academic writing; (2) the role of metacognition in critical thinking; and (3) correlations between metacognition, critical thinking skills, and academic writing. The results indicated significant relationships between the three variables, and the implications based on these findings were discussed.

In this paper, an exploratory study was conducted to investigate a potential measurement bias in happiness studies using 0-10 Likert scales. A total of 121 college students from a public university in California participated in the study by completing a hypothetical subjective well-being survey. The survey consisted of 23 scenarios with varying levels of happiness or unhappiness, and the students were asked to rate their level of happiness/unhappiness on the 11-point Likert Scales. The results showed that there was an inconsistent understanding of the scale when the expected value was between 7 and 8 on the 0 to 10 Likert scale, which leads to a higher variance and lower observation values. Based on these findings, it is suggested that an alternative scale length or a -5 to 5 scale should be considered to improve data quality.

ObjectiveThis study aims to develop an artificial intelligence model utilizing clinical blood markers, ultrasound data, and breast biopsy pathological information to predict the distant metastasis in breast cancer patients.MethodsData from two medical centers were utilized, Clinical blood markers, ultrasound data, and breast biopsy pathological information were separately extracted and selected. Feature dimensionality reduction was performed using Spearman correlation and LASSO regression. Predictive models were constructed using LR and LightGBM machine learning algorithms and validated on internal and external validation sets. Feature correlation analysis was conducted for both models.ResultsThe LR model achieved AUC values of 0.892, 0.816, and 0.817 for the training, internal validation, and external validation cohorts, respectively. The LightGBM model achieved AUC values of 0.971, 0.861, and 0.890 for the same cohorts, respectively. Clinical decision curve analysis showed a superior net benefit of the LightGBM model over the LR model in predicting distant metastasis in breast cancer. Key features identified included creatine kinase isoenzyme (CK-MB) and alpha-hydroxybutyrate dehydrogenase.ConclusionThis study developed an artificial intelligence model using clinical blood markers, ultrasound data, and pathological information to identify distant metastasis in breast cancer patients. The LightGBM model demonstrated superior predictive accuracy and clinical applicability, suggesting it as a promising tool for early diagnosis of distant metastasis in breast cancer.

Analyzing polysorbate 20 (PS20) composition and the impact of each component on stability and safety is crucial due to formulation variations and individual tolerance. The similar structures and polarities of PS20 components make accurate separation, identification, and quantification challenging. In this work, a high-resolution quantitative method was developed using single-dimensional high-performance liquid chromatography (HPLC) with charged aerosol detection (CAD) to separate 18 key components with multiple esters. The separated components were characterized by ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) with an identical gradient as the HPLC-CAD analysis. The polysorbate compound database and library were expanded over 7-time compared to the commercial database. The method investigated differences in PS20 samples from various origins and grades for different dosage forms to evaluate the composition-process relationship. UHPLC-Q-TOF-MS identified 1329 to 1511 compounds in 4 batches of PS20 from different sources. The method observed the impact of 4 degradation conditions on peak components, identifying stable components and their tendencies to change. HPLC-CAD and UHPLC-Q-TOF-MS results provided insights into fingerprint differences, distinguishing quasi products.

Forest succession affects aboveground vegetation and belowground microbial community composition, in which litter degradation plays an important role in nutrient cycling. However, limited information is available on how microbial communities change during litter degradation in forests undergoing succession due to disease. In this study, the bacterial communities and functions in litter degradation during the forest succession from pure Pinus forest (PPF), mixed Pinus and Liquidambar forest (MPF and MLF), and pure Liquidambar forest (PLF) were investigated. The results showed that the bacterial community richness and diversity in both needles or leaves and branch litters increased progressively with forest succession. Proteobacteria, Actinobacteria, Bacteroidetes, and Acidobacteria were the most abundant bacterial phyla in the litter degradation during forest succession. The abundance of Bacteroidetes in branches increased significantly, while the abundance of genus Paraburkholderia decreased during forest succession. The different forests formed distinct bacterial community structures during litter decomposition. Functionally, chemoheterotrophy was the most abundant functional guild, followed by nitrogen fixation, intracellular parasitism, and urealysis. The abundance of nitrogen fixation decreased significantly during forest succession. Similarly, the different forests formed distinct bacterial functional structures in the needle or leaf during the succession. However, only two functional structures were formed in the branch. These results suggest that the bacterial community and its functions undergo significant changes during forest succession, particularly from the pure-pine to mixed-pine forest. These results provide a clear understanding of the changes in bacterial communities and functions during litter degradation in forests undergoing disease-induced succession.

Abstract Background The Large-scale Assessment of the Key health-promoting Activities of two New mass drug administration regimens with Azithromycin (LAKANA) trial in Mali aims to evaluate the efficacy and safety of azithromycin (AZI) mass drug administration (MDA) to 1–11-month-old infants as well as the impact of the intervention on antimicrobial resistance (AMR) and mechanisms of action of azithromycin. To improve the transparency and quality of this clinical trial, we prepared this statistical analysis plan (SAP). Methods/design LAKANA is a cluster randomized trial that aims to address the mortality and health impacts of biannual and quarterly AZI MDA. AZI is given to 1–11-month-old infants in a high-mortality setting where a seasonal malaria chemoprevention (SMC) program is in place. The participating villages are randomly assigned to placebo (control), two-dose AZI (biannual azithromycin-MDA), and four-dose AZI (quarterly azithromycin-MDA) in a 3:4:2 ratio. The primary outcome of the study is mortality among the intention-to-treat population of 1–11-month-old infants. We will evaluate relative risk reduction between the study arms using a mixed-effects Poisson model with random intercepts for villages, using log link function with person-years as an offset variable. We will model outcomes related to secondary objectives of the study using generalized linear models with considerations on clustering. Conclusion The SAP written prior to data collection completion will help avoid reporting bias and data-driven analysis for the primary and secondary aims of the trial. If there are deviations from the analysis methods described here, they will be described and justified in the publications of the trial results. Trial registration ClinicalTrials.gov ID NCT04424511 . Registered on 11 June 2020.

Lipid-based nutrient supplements (LNS) have been found to improve child growth and reduce child mortality. However, the mechanistic pathways for these improvements warrant exploration. One potential pathway is linked to improvement in intestinal health. Our study aimed to test a hypothesis that small-quantity LNS (SQ-LNS) could reduce the levels of intestinal inflammation, repair and permeability of children. As intestinal health markers we measured fecal calprotectin, regenerating 1B protein (REG1B) and alpha-1-antitrypsin concentrations at 18 months of age (after 12 months of supplementation) and 1 year later (12 months after cessation of supplementation). In this analysis, we included data of 735 children who participated in a randomised dietary supplementation trial in rural Malawi; 243 children who received 20 g/day SQ-LNS from 6 to 18 months of age were in the SQ-LNS group, while the others who received no dietary supplementation during this period were in the control group. At 18 months of age, the mean concentrations of calprotectin, REG1B and alpha-1-antitrypsin were 241, 105 µg/g and 7.1 mg/dl, respectively, in the SQ-LNS group, and 224, 105 µg/g and 7.4 mg/dl, respectively, in the control group, and did not differ between the SQ-LNS and control groups. We conclude that SQ-LNS provision did not have an impact on children's intestinal health in rural Malawi.

BackgroundPregnant women in Malawi are at risk of selenium deficiency, which can have adverse effects on pregnancy outcomes. Interventions for improving selenium status are needed.ObjectivesTo assess the effect of provision of small-quantity lipid-based nutrient supplements (SQ-LNSs) to Malawian women during pregnancy on their plasma selenium concentrations at 36 wk of gestation.MethodsPregnant women (≤20 wk of gestation) were randomly assigned to receive daily either: 1) iron and folic acid (IFA); 2) multiple micronutrients (MMN; 130 µg selenium per capsule); or 3) SQ-LNS (130 µg selenium/20 g). Plasma selenium concentrations were measured by inductively coupled plasma mass spectrometry at baseline and after ≥16 wk of intervention (at 36 wk of gestation) and compared by intervention group.ResultsAt 36 wk of gestation, median (quartile 1, quartile 3) plasma selenium concentrations (micromoles per liter) were 0.96 (0.73, 1.23), 0.94 (0.78, 1.18), and 1.01 (0.85, 1.28) in the IFA, MMN, and SQ-LNS groups, respectively. Geometric mean (GM) plasma selenium concentration was 5.4% (95% CI: 1.8%, 9.0%) higher in the SQ-LNS group than in the MMN group and tended to be higher than in the IFA group (+4.2%; 95% CI: 1.0%, 7.8%). The prevalence of adjusted plasma selenium concentrations

BackgroundEnvironmental enteric dysfunction (EED) is common in low- and middle-income countries and associated with childhood undernutrition. The composition of gut microbiota has been implicated in the pathogenesis of EED. Our aim was to assess the associations between gut microbiota and EED biomarkers in rural Malawian children. We hypothesized that there would be an inverse association between microbiota maturity and diversity and fecal concentrations of EED biomarkers.MethodsWe used data from fecal samples collected at 6, 18 and 30 months from 611 children who were followed up during a nutrition intervention trial. The primary time point for analysis was 18 months. Microbiota data were obtained through 16S rRNA sequencing and variables included microbiota maturity and diversity, phylogenetic dissimilarity and relative abundances of individual taxa. EED biomarkers included calprotectin (marker of inflammation), alpha-1 antitrypsin (intestinal permeability) and REG1B (intestinal damage).ResultsThere was an inverse association between microbiota maturity and diversity and fecal concentrations of all 3 EED biomarkers at 18 months (p≤0.001). The results were similar at 30 months, while at 6 months inverse associations were found only with calprotectin and alpha-1 antitrypsin concentrations. At 18 months, EED biomarkers were not associated with phylogenetic dissimilarity, but at 6 and 30 months several associations were observed. Individual taxa predicting EED biomarker concentrations at 18 months included several Bifidobacterium and Enterobacteriaceae taxa as well as potentially displaced oral taxa.ConclusionsOur findings support the hypothesis of an inverse association between microbiota maturity and diversity and EED in rural Malawian children.

Abstract Background Extracellular vesicles (EVs) from human umbilical cord mesenchymal stem cells (hUMSCs) are widely considered to be the best mediators for cell-free therapy. An understanding of their composition, especially RNA, is particularly important for the safe and precise application of EVs. Up to date, the knowledge of their RNA components is limited to NGS sequencing and cannot provide a comprehensive transcriptomic landscape, especially the long and full-length transcripts. Our study first focused on the transcriptomic profile of hUMSC-EVs based on nanopore sequencing. Methods In this study, different EV subtypes (exosomes and microvesicles) derived from hUMSCs were isolated and identified by density gradient centrifugation. Subsequently, the realistic long transcriptomic profile in different subtypes of hUMSC-EVs was systematically compared by nanopore sequencing and bioinformatic analysis. Results Abundant transcript variants were identified in EVs by nanopore sequencing, 69.34% of which transcripts were fragmented. A series of full-length and long transcripts was also observed and showed a significantly higher proportion of intact or near-complete transcripts in exosomes than that in microvesicles derived from hUMSCs. Although the composition of RNA biotypes transported by different EV subtypes was similar, the distribution of transcripts and genes revealed the inter-heterogeneity and intra-stability between exosomes and microvesicles. Further, 85 different expressed transcripts (56 genes) and 7 fusion genes were identified. Pathway enrichment analysis showed that upregulated-expressed genes in microvesicles were mainly enriched in multiple neurodegenerative diseases, while upregulated-expressed genes in exosomes were mainly enriched in neutrophil extracellular trap formation, suggesting different functional tendencies of EV subtypes. Conclusions This study provides a novel understanding of different types of hUMSC-EVs, which not only suggests different transcriptome sorting mechanisms between exosomes and microvesicles, but also shows that different EV subtypes from the same source have different physiological functions, suggesting distinct clinical application prospects.

Abstract Background The complex interaction between malaria and undernutrition leads to increased mortality and morbidity rate among young children in malaria-endemic regions. Results from previous interventions suggest that improving nutritional status of young children may reduce the burden of malaria. This study tested a hypothesis that provision of lipid-based nutrient supplements (LNS) or corn-soy blend (CSB) supplementation to 6–18-month-old children in Malawi would reduce the prevalence of asymptomatic malaria among them. Methods A total of 840 6-month-old children were enrolled in a randomized trial. The participants received 12-month supplementation with three different daily dietary supplementations: CSB, soy-LNS, or milk-LNS, and one control group without supplementation. The prevalence rate of asymptomatic Plasmodium falciparum was determined by real-time PCR from the participant’s dried blood spots (DBS) collected at the baseline and every 3 months. The global null hypothesis was tested using modified Poisson regression to estimate the prevalence ratio (PR) between the control group and three intervention groups at all ages combined. All the models were adjusted for malaria at baseline, season of DBS sample collection, site of enrolment, and household asset Z-score. Results All children combined, the prevalence of P. falciparum was 14.1% at enrollment, 8.7% at 9 months, 11.2% at 12 months, 13.0% at 15 months and 22.4% at 18 months of age. Among all samples that were taken after enrolment, the prevalence was 12.1% in control group, 12.2% in milk-LNS, 14.0% in soy-LNS, and 17.2% in CSB group. Compared to children in the control group the prevalence ratio of positive malaria tests was 1.19 (95% CI 0.81–1.74; P = 0.372) in the milk-LNS group, 1.32 (95% CI 0.88–1.96; P = 0.177) in the soy-LNS group and 1.72 (95% CI 1.19–2.49; P = 0.004) in the CSB group. Conclusion The study findings do not support a hypothesis that LNS or CSB supplementation would reduce the prevalence of asymptomatic malaria among Malawian children. In contrast, there was a signal of a possible increase in malaria prevalence among children supplemented with CSB.

BackgroundSmall-quantity lipid-based nutrient supplements (SQ-LNSs) have been shown to reduce the prevalence of child anemia and iron deficiency, but effects on other micronutrients are less well known. Identifying subgroups who benefit most from SQ-LNSs could support improved program design.ObjectivesWe aimed to identify study-level and individual-level modifiers of the effect of SQ-LNSs on child hemoglobin (Hb), anemia, and inflammation-adjusted micronutrient status outcomes.MethodsWe conducted a 2-stage meta-analysis of individual participant data from 13 randomized controlled trials of SQ-LNSs provided to children 6-24 mo of age (n = 15,946). We generated study-specific and subgroup estimates of SQ-LNSs compared with control, and pooled the estimates using fixed-effects models. We used random-effects meta-regression to examine potential study-level effect modifiers.ResultsSQ-LNS provision decreased the prevalence of anemia (Hb < 110 g/L) by 16% (relative reduction), iron deficiency (plasma ferritin < 12 µg/L) by 56%, and iron deficiency anemia (IDA; Hb < 110 g/L and plasma ferritin 12 mo and provided 9 (as opposed to

ObjectivesInadequate iodine intake during pregnancy increases the risk of neonatal morbidity and mortality. We aimed to evaluate whether prenatal supplements containing iodine affect urinary iodine concentrations (UIC) of pregnant women in Malawi.DesignA randomised controlled trial. Pregnant women (n 1391) were assigned to consume 60 mg/d Fe and 400 µg/d folic acid (IFA) or 18 vitamins and minerals including 250 µg/d iodine (MMN) or 20 g/d small-quantity lipid-based nutrient supplements (SQ-LNS) with similar nutrient contents as MMN group, plus macronutrients (LNS) until childbirth. In a sub-study (n 317), we evaluated group geometric mean urinary iodine concentration (UIC) (µg/L) at 36 weeks of gestation controlling for baseline UIC and compared median (baseline) and geometric mean (36 weeks) UIC with WHO cut-offs: UIC < 150, 150-249, 250-499 and ≥500 reflecting insufficient, adequate, above requirements and excessive iodine intakes, respectively.SettingMangochi District, Malawi.ParticipantsWomen ≤20 weeks pregnant.ResultsGroups had comparable background characteristics. At baseline, overall median (Q1, Q3) UIC (319 (167, 559)) suggested iodine intakes above requirements. At 36 weeks, the geometric mean (95 % CI) UIC of the IFA (197 (171, 226)), MMN (212 (185, 243)) and LNS (220 (192, 253)) groups did not differ (P = 0·53) and reflected adequate intakes.ConclusionsIn this setting, provision of supplements containing iodine at the recommended dose to pregnant women with relatively high iodine intakes at baseline, presumably from iodised salt, has no impact on the women's UIC. Regular monitoring of the iodine status of pregnant women in such settings is advisable. Clinicaltrials.gov identifier: NCT01239693.

BackgroundVitamin A (VA) deficiency is prevalent in preschool-aged children in sub-Saharan Africa.ObjectivesWe assessed the effect of small-quantity lipid-based nutrient supplements (SQ-LNS) given to women during pregnancy and lactation and their children from 6 to 18 mo of age on women's plasma and milk retinol concentrations in Malawi, and children's plasma retinol concentration in Malawi and Ghana.MethodsPregnant women (≤20 wk of gestation) were randomized to receive daily: 1) iron and folic acid (IFA) during pregnancy only; 2) multiple micronutrients (MMN; 800 μg retinol equivalent (RE)/capsule), or 3) SQ-LNS (800 μg RE/20g) during pregnancy and the first 6 mo postpartum. Children of mothers in the SQ-LNS group received SQ-LNS (400 μg RE/20 g) from 6 to 18 mo of age; children of mothers in the IFA and MMN groups received no supplement. Plasma retinol was measured in mothers at ≤20 and 36 wk of gestation and 6 mo postpartum, and in children at 6 and 18 mo of age. Milk retinol was measured at 6 mo postpartum. VA status indicators were compared by group.ResultsAmong Malawian mothers, geometric mean (95% CI) plasma retinol concentrations at 36 wk of gestation and 6 mo postpartum were 0.97 μmol/L (0.94, 1.01 μmol/L) and 1.35 μmol/L (1.31, 1.39 μmol/L), respectively; geometric mean (95% CI) milk retinol concentration at 6 mo postpartum was 1.04 μmol/L (0.97, 1.13 μmol/L); results did not differ by intervention group. Geometric mean (95% CI) plasma retinol concentrations for Malawian children at 6 and 18 mo of age were 0.78 μmol/L (0.75, 0.81 μmol/L) and 0.81 μmol/L (0.78, 0.85 μmol/L), respectively, and for Ghanaian children they were 0.85 μmol/L (0.82, 0.88 μmol/L) and 0.88 μmol/L (0.85, 0.91 μmol/L), respectively; results did not differ by intervention group in either setting.ConclusionsSQ-LNS had no effect on VA status of mothers or children, possibly because of low responsiveness of the VA status indicators.