Maternal victimization and neglected offspring: Child marriage, IPV and depression symptoms among Salar Muslim women.

BACKGROUND: Child neglect is prevalent in western rural China, yet there is limited research among ethnic minority communities. The Salar, a Turkic-Muslim ethnic minority residing primarily in western China, also face this specific problem. The group is deeply influenced by ethnicity, Islam and Chinese Confucianism, which in turn makes women vulnerable to child marriage and IPV. These victimizations, coupled with various life stressors, further complicate the challenges of providing adequate care for their children. OBJECTIVE: This study hypothesizes a relationship between child neglect and maternal child marriage, IPV victimization, and depression symptoms. PARTICIPANTS AND SETTING: 201 married Salar women from five villages in Xunhua Salar Autonomous County, China, were randomly selected to participate in the study. METHOD: A probability proportional to size (PPS) sampling approach was used to collect a random representative multi-stage cluster sample in 2022. Random effects Poisson regression models were used to test the hypotheses. RESULTS: The participants reported a 65.6 % rate of child neglect and a 30.8 % rate of IPV in the past year. 37.6 % experienced child marriage. Results revealed significant associations between child neglect and child marriage, IPV, and depression symptoms. A two-way interaction between IPV and depression symptoms was strongly positively associated with child neglect. CONCLUSIONS: This research indicates that Salar Muslim mothers who have experienced child marriage, adulthood victimization, and depression are at a higher risk of neglecting their children. The findings represent a valuable initial step toward researching and addressing the protection needs of women and children from Muslim ethnic minorities in China.

miRNAs mediate the impact of smoking on dental pulp stem cells via the p53 pathway.

Cigarette smoke changes the genomic and epigenomic imprint of cells. In this study, we investigated the biological consequences of extended cigarette smoke exposure on dental pulp stem cells (DPSCs) and the potential roles of miRNAs. DPSCs were treated with various doses of cigarette smoke condensate (CSC) for up to 6 weeks. Cell proliferation, survival, migration, and differentiation were evaluated. Cytokine and miRNA expression were profiled. The results showed that extended exposure to CSC significantly impaired the regenerative capacity of the DPSCs. Bioinformatic analysis showed that the cell cycle pathway, cancer pathways (small cell lung cancer, pancreatic, colorectal, and prostate cancer), and pathways for TNF, TGF-ß, p53, PI3K-Akt, mTOR, and ErbB signal transduction, were associated with altered miRNA profiles. In particular, 3 miRNAs has-miR-26a-5p, has-miR-26b-5p, and has-miR-29b-3p fine-tune the p53 and cell cycle signaling pathways to regulate DPSC cellular activities. The work indicated that miRNAs are promising targets to modulate stem cell regeneration and understanding miRNA-targeted genes and their associated pathways in smoking individuals have significant implications for disease control and prevention.

Spatial subsetting enables integrative modeling of oral squamous cell carcinoma multiplex imaging data.

Oral squamous cell carcinoma (OSCC), a prevalent and aggressive neoplasm, poses a significant challenge due to poor prognosis and limited prognostic biomarkers. Leveraging highly multiplexed imaging mass cytometry, we investigated the tumor immune microenvironment (TIME) in OSCC biopsies, characterizing immune cell distribution and signaling activity at the tumor-invasive front. Our spatial subsetting approach standardized cellular populations by tissue zone, improving feature reproducibility and revealing TIME patterns accompanying loss-of-differentiation. Employing a machine-learning pipeline combining reliable feature selection with multivariable modeling, we achieved accurate histological grade classification (AUC = 0.88). Three model features correlated with clinical outcomes in an independent cohort: granulocyte MAPKAPK2 signaling at the tumor front, stromal CD4+ memory T cell size, and the distance of fibroblasts from the tumor border. This study establishes a robust modeling framework for distilling complex imaging data, uncovering sentinel characteristics of the OSCC TIME to facilitate prognostic biomarkers discovery for recurrence risk stratification and immunomodulatory therapy development.

Cigarette smoking exposure disrupts the regenerative potential of dental pulp stem cells.

INTRODUCTION: Smoking is known to alter the regenerative and immunomodulatory properties of many types of mesenchymal stem cells (MSCs). This study investigates the impact of cigarette smoke exposure on the regenerative potential of dental pulp stem cells (DPSCs). METHODS: DPSCs were treated with various doses of cigarette smoke condensate (CSC) or nicotine. Cell proliferation and survival were evaluated by a water-soluble tetrazolium salt (WST-1) and a survival assay. DPSC migration, cytokine expression, mutagenesis, and the signaling pathway were also measured during CSC and nicotine treatment. RESULTS: Low concentrations of CSC and nicotine did not impair cell proliferation, but higher concentrations reduced cell proliferation. CSC and nicotine could impede DPSC survival and migration in a dose-dependent manner. In addition, the cytokine secretion expression profile was altered with CSC or nicotine treatments. In particular, secretion of IL-6, TNF-α, and IL-10 significantly increased, while TGF-ß1 levels showed different patterns after exposure to CSC or nicotine, as shown by ELISA and quantitative PCR. Nicotine treatment increased AKT (also known as protein kinase B) and extracellular signal-regulated kinase (ERK) phosphorylation. Finally, CSC induced higher levels of mutagenicity than nicotine, as shown by the Ames test. CONCLUSIONS: These findings suggest that cigarette smoke exposure alters the regenerative abilities of DPSCs in various ways. Future studies are warranted to further characterize the underlying molecular mechanisms of smoking-mediated damage to DPSCs, which will guide the personalized stem cell treatment plan for smoking patients.

Towards multiomic analysis of oral mucosal pathologies.

Oral mucosal pathologies comprise an array of diseases with worldwide prevalence and medical relevance. Affecting a confined space with crucial physiological and social functions, oral pathologies can be mutilating and drastically reduce quality of life. Despite their relevance, treatment for these diseases is often far from curative and remains vastly understudied. While multiple factors are involved in the pathogenesis of oral mucosal pathologies, the host's immune system plays a major role in the development, maintenance, and resolution of these diseases. Consequently, a precise understanding of immunological mechanisms implicated in oral mucosal pathologies is critical (1) to identify accurate, mechanistic biomarkers of clinical outcomes; (2) to develop targeted immunotherapeutic strategies; and (3) to individualize prevention and treatment approaches. Here, we review key elements of the immune system's role in oral mucosal pathologies that hold promise to overcome limitations in current diagnostic and therapeutic approaches. We emphasize recent and ongoing multiomic and single-cell approaches that enable an integrative view of these pathophysiological processes and thereby provide unifying and clinically relevant biological signatures.

Early prediction and longitudinal modeling of preeclampsia from multiomics.

Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear. We developed machine-learning models for early prediction of preeclampsia (first 16 weeks of pregnancy) and over gestation by analyzing six omics datasets from a longitudinal cohort of pregnant women. For early pregnancy, a prediction model using nine urine metabolites had the highest accuracy and was validated on an independent cohort (area under the receiver-operating characteristic curve [AUC] = 0.88, 95% confidence interval [CI] [0.76, 0.99] cross-validated; AUC = 0.83, 95% CI [0.62,1] validated). Univariate analysis demonstrated statistical significance of identified metabolites. An integrated multiomics model further improved accuracy (AUC = 0.94). Several biological pathways were identified including tryptophan, caffeine, and arachidonic acid metabolisms. Integration with immune cytometry data suggested novel associations between immune and proteomic dynamics. While further validation in a larger population is necessary, these encouraging results can serve as a basis for a simple, early diagnostic test for preeclampsia.

Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19.

The biological determinants underlying the range of coronavirus 2019 (COVID-19) clinical manifestations are not fully understood. Here, over 1,400 plasma proteins and 2,600 single-cell immune features comprising cell phenotype, endogenous signaling activity, and signaling responses to inflammatory ligands are cross-sectionally assessed in peripheral blood from 97 patients with mild, moderate, and severe COVID-19 and 40 uninfected patients. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identify and independently validate a multi-variate model classifying COVID-19 severity (multi-class area under the curve [AUC]training = 0.799, p = 4.2e-6; multi-class AUCvalidation = 0.773, p = 7.7e-6). Examination of informative model features reveals biological signatures of COVID-19 severity, including the dysregulation of JAK/STAT, MAPK/mTOR, and nuclear factor κB (NF-κB) immune signaling networks in addition to recapitulating known hallmarks of COVID-19. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for prevention and/or treatment of COVID-19 progression.

Understanding the tumor microenvironment in head and neck squamous cell carcinoma.

Head and neck squamous cell carcinoma (HNSCC) represents a heterogeneous group of tumors. While significant progress has been made using multimodal treatment, the 5-year survival remains at 50%. Developing effective therapies, such as immunotherapy, will likely lead to better treatment of primary and metastatic disease. However, not all HNSCC tumors respond to immune checkpoint blockade therapy. Understanding the complex cellular composition and interactions of the tumor microenvironment is likely to lead to new knowledge for effective therapies and treatment resistance. In this review, we discuss HNSCC characteristics, predictive biomarkers, factors influencing immunotherapy response, with a focus on the tumor microenvironment.

TALEs as double-edged swords in plant-pathogen interactions: Progress, challenges, and perspectives.

Xanthomonas species colonize many host plants and cause huge losses worldwide. Transcription activator-like effectors (TALEs) are secreted by Xanthomonas and translocated into host cells to manipulate the expression of target genes, especially by Xanthomonas oryzae pv. oryzae and Xanthomonas oryzae pv. oryzicola, which cause bacterial blight and bacterial leaf streak, respectively, in rice. In this review, we summarize the progress of studies on the interaction between Xanthomonas and hosts, covering both rice and other plants. TALEs are not only key factors that make plants susceptible but are also essential components of plant resistance. Characterization of TALEs and TALE-like proteins has improved our understanding of TALE evolution and promoted the development of gene editing tools. In addition, the interactions between TALEs and hosts have also provided strategies and possibilities for genetic engineering in crop improvement.

Pregnancy and severely reduced renal mass: A stress model showing renal hyperfiltration.

Pregnancy may increase signs of renovascular stress. We compared pregnant sham operated (S) and 5/6 nephrectomy (NX) rats to examine the effect of pregnancy on reduced nephron number. Blood pressure (BP), heart rate (HR), body weight (BW), food/water intake, serum creatinine (Cr), urinalyses were assessed weekly, and end pregnancy renal histology examined. NX showed decreased BW, elevated BP and Cr, and proteinuria. Histology revealed increased glomerular volume, increased tubular diameter and interstitial inflammation and fibrosis. This pilot shows that a pregnant 5/6th nephrectomy rat is a reliable model in which to evaluate renovascular stress with reduced nephrons.

Proteomic signatures predict preeclampsia in individual cohorts but not across cohorts - implications for clinical biomarker studies.

BACKGROUND: Early identification of pregnant women at risk for preeclampsia (PE) is important, as it will enable targeted interventions ahead of clinical manifestations. The quantitative analyses of plasma proteins feature prominently among molecular approaches used for risk prediction. However, derivation of protein signatures of sufficient predictive power has been challenging. The recent availability of platforms simultaneously assessing over 1000 plasma proteins offers broad examinations of the plasma proteome, which may enable the extraction of proteomic signatures with improved prognostic performance in prenatal care. OBJECTIVE: The primary aim of this study was to examine the generalizability of proteomic signatures predictive of PE in two cohorts of pregnant women whose plasma proteome was interrogated with the same highly multiplexed platform. Establishing generalizability, or lack thereof, is critical to devise strategies facilitating the development of clinically useful predictive tests. A second aim was to examine the generalizability of protein signatures predictive of gestational age (GA) in uncomplicated pregnancies in the same cohorts to contrast physiological and pathological pregnancy outcomes. STUDY DESIGN: Serial blood samples were collected during the first, second, and third trimesters in 18 women who developed PE and 18 women with uncomplicated pregnancies (Stanford cohort). The second cohort (Detroit), used for comparative analysis, consisted of 76 women with PE and 90 women with uncomplicated pregnancies. Multivariate analyses were applied to infer predictive and cohort-specific proteomic models, which were then tested in the alternate cohort. Gene ontology (GO) analysis was performed to identify biological processes that were over-represented among top-ranked proteins associated with PE. RESULTS: The model derived in the Stanford cohort was highly significant (p = 3.9E-15) and predictive (AUC = 0.96), but failed validation in the Detroit cohort (p = 9.7E-01, AUC = 0.50). Similarly, the model derived in the Detroit cohort was highly significant (p = 1.0E-21, AUC = 0.73), but failed validation in the Stanford cohort (p = 7.3E-02, AUC = 0.60). By contrast, proteomic models predicting GA were readily validated across the Stanford (p = 1.1E-454, R = 0.92) and Detroit cohorts (p = 1.1.E-92, R = 0.92) indicating that the proteomic assay performed well enough to infer a generalizable model across studied cohorts, which makes it less likely that technical aspects of the assay, including batch effects, accounted for observed differences. CONCLUSIONS: Results point to a broader issue relevant for proteomic and other omic discovery studies in patient cohorts suffering from a clinical syndrome, such as PE, driven by heterogeneous pathophysiologies. While novel technologies including highly multiplex proteomic arrays and adapted computational algorithms allow for novel discoveries for a particular study cohort, they may not readily generalize across cohorts. A likely reason is that the prevalence of pathophysiologic processes leading up to the "same" clinical syndrome can be distributed differently in different and smaller-sized cohorts. Signatures derived in individual cohorts may simply capture different facets of the spectrum of pathophysiologic processes driving a syndrome. Our findings have important implications for the design of omic studies of a syndrome like PE. They highlight the need for performing such studies in diverse and well-phenotyped patient populations that are large enough to characterize subsets of patients with shared pathophysiologies to then derive subset-specific signatures of sufficient predictive power.

A Peripheral Immune Signature of Labor Induction.

Approximately 1 in 4 pregnant women in the United States undergo labor induction. The onset and establishment of labor, particularly induced labor, is a complex and dynamic process influenced by multiple endocrine, inflammatory, and mechanical factors as well as obstetric and pharmacological interventions. The duration from labor induction to the onset of active labor remains unpredictable. Moreover, prolonged labor is associated with severe complications for the mother and her offspring, most importantly chorioamnionitis, uterine atony, and postpartum hemorrhage. While maternal immune system adaptations that are critical for the maintenance of a healthy pregnancy have been previously characterized, the role of the immune system during the establishment of labor is poorly understood. Understanding maternal immune adaptations during labor initiation can have important ramifications for predicting successful labor induction and labor complications in both induced and spontaneous types of labor. The aim of this study was to characterize labor-associated maternal immune system dynamics from labor induction to the start of active labor. Serial blood samples from fifteen participants were collected immediately prior to labor induction (baseline) and during the latent phase until the start of active labor. Using high-dimensional mass cytometry, a total of 1,059 single-cell immune features were extracted from each sample. A multivariate machine-learning method was employed to characterize the dynamic changes of the maternal immune system after labor induction until the establishment of active labor. A cross-validated linear sparse regression model (least absolute shrinkage and selection operator, LASSO) predicted the minutes since induction of labor with high accuracy (R = 0.86, p = 6.7e-15, RMSE = 277 min). Immune features most informative for the model included STAT5 signaling in central memory CD8+ T cells and pro-inflammatory STAT3 signaling responses across multiple adaptive and innate immune cell subsets. Our study reports a peripheral immune signature of labor induction, and provides important insights into biological mechanisms that may ultimately predict labor induction success as well as complications, thereby facilitating clinical decision-making to improve maternal and fetal well-being.

Single-Cell Analysis of the Neonatal Immune System Across the Gestational Age Continuum.

Although most causes of death and morbidity in premature infants are related to immune maladaptation, the premature immune system remains poorly understood. We provide a comprehensive single-cell depiction of the neonatal immune system at birth across the spectrum of viable gestational age (GA), ranging from 25 weeks to term. A mass cytometry immunoassay interrogated all major immune cell subsets, including signaling activity and responsiveness to stimulation. An elastic net model described the relationship between GA and immunome (R=0.85, p=8.75e-14), and unsupervised clustering highlighted previously unrecognized GA-dependent immune dynamics, including decreasing basal MAP-kinase/NFκB signaling in antigen presenting cells; increasing responsiveness of cytotoxic lymphocytes to interferon-α; and decreasing frequency of regulatory and invariant T cells, including NKT-like cells and CD8+CD161+ T cells. Knowledge gained from the analysis of the neonatal immune landscape across GA provides a mechanistic framework to understand the unique susceptibility of preterm infants to both hyper-inflammatory diseases and infections.

Integrated trajectories of the maternal metabolome, proteome, and immunome predict labor onset.

Estimating the time of delivery is of high clinical importance because pre- and postterm deviations are associated with complications for the mother and her offspring. However, current estimations are inaccurate. As pregnancy progresses toward labor, major transitions occur in fetomaternal immune, metabolic, and endocrine systems that culminate in birth. The comprehensive characterization of maternal biology that precedes labor is key to understanding these physiological transitions and identifying predictive biomarkers of delivery. Here, a longitudinal study was conducted in 63 women who went into labor spontaneously. More than 7000 plasma analytes and peripheral immune cell responses were analyzed using untargeted mass spectrometry, aptamer-based proteomic technology, and single-cell mass cytometry in serial blood samples collected during the last 100 days of pregnancy. The high-dimensional dataset was integrated into a multiomic model that predicted the time to spontaneous labor [R = 0.85, 95% confidence interval (CI) [0.79 to 0.89], P = 1.2 × 10-40, N = 53, training set; R = 0.81, 95% CI [0.61 to 0.91], P = 3.9 × 10-7, N = 10, independent test set]. Coordinated alterations in maternal metabolome, proteome, and immunome marked a molecular shift from pregnancy maintenance to prelabor biology 2 to 4 weeks before delivery. A surge in steroid hormone metabolites and interleukin-1 receptor type 4 that preceded labor coincided with a switch from immune activation to regulation of inflammatory responses. Our study lays the groundwork for developing blood-based methods for predicting the day of labor, anchored in mechanisms shared in preterm and term pregnancies.

Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19.

The biological determinants of the wide spectrum of COVID-19 clinical manifestations are not fully understood. Here, over 1400 plasma proteins and 2600 single-cell immune features comprising cell phenotype, basal signaling activity, and signaling responses to inflammatory ligands were assessed in peripheral blood from patients with mild, moderate, and severe COVID-19, at the time of diagnosis. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identified and independently validated a multivariate model classifying COVID-19 severity (multi-class AUCtraining = 0.799, p-value = 4.2e-6; multi-class AUCvalidation = 0.773, p-value = 7.7e-6). Features of this high-dimensional model recapitulated recent COVID-19 related observations of immune perturbations, and revealed novel biological signatures of severity, including the mobilization of elements of the renin-angiotensin system and primary hemostasis, as well as dysregulation of JAK/STAT, MAPK/mTOR, and NF-κB immune signaling networks. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for the prevention of COVID-19 progression.

Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions.

The dense network of interconnected cellular signalling responses that are quantifiable in peripheral immune cells provides a wealth of actionable immunological insights. Although high-throughput single-cell profiling techniques, including polychromatic flow and mass cytometry, have matured to a point that enables detailed immune profiling of patients in numerous clinical settings, the limited cohort size and high dimensionality of data increase the possibility of false-positive discoveries and model overfitting. We introduce a generalizable machine learning platform, the immunological Elastic-Net (iEN), which incorporates immunological knowledge directly into the predictive models. Importantly, the algorithm maintains the exploratory nature of the high-dimensional dataset, allowing for the inclusion of immune features with strong predictive capabilities even if not consistent with prior knowledge. In three independent studies our method demonstrates improved predictions for clinically relevant outcomes from mass cytometry data generated from whole blood, as well as a large simulated dataset. The iEN is available under an open-source licence.

VoPo leverages cellular heterogeneity for predictive modeling of single-cell data.

High-throughput single-cell analysis technologies produce an abundance of data that is critical for profiling the heterogeneity of cellular systems. We introduce VoPo (https://github.com/stanleyn/VoPo), a machine learning algorithm for predictive modeling and comprehensive visualization of the heterogeneity captured in large single-cell datasets. In three mass cytometry datasets, with the largest measuring hundreds of millions of cells over hundreds of samples, VoPo defines phenotypically and functionally homogeneous cell populations. VoPo further outperforms state-of-the-art machine learning algorithms in classification tasks, and identified immune-correlates of clinically-relevant parameters.

Multiomic immune clockworks of pregnancy.

Preterm birth is the leading cause of mortality in children under the age of five worldwide. Despite major efforts, we still lack the ability to accurately predict and effectively prevent preterm birth. While multiple factors contribute to preterm labor, dysregulations of immunological adaptations required for the maintenance of a healthy pregnancy is at its pathophysiological core. Consequently, a precise understanding of these chronologically paced immune adaptations and of the biological pacemakers that synchronize the pregnancy "immune clock" is a critical first step towards identifying deviations that are hallmarks of peterm birth. Here, we will review key elements of the fetal, placental, and maternal pacemakers that program the immune clock of pregnancy. We will then emphasize multiomic studies that enable a more integrated view of pregnancy-related immune adaptations. Such multiomic assessments can strengthen the biological plausibility of immunological findings and increase the power of biological signatures predictive of preterm birth.

Sorption and desorption of Sr onto a rough single fractured granite.

To investigate the migration behavior of Sr (strontium) in granite, a rough-walled single fracture of granite was prepared with the self-built rock splitting apparatus. Surface roughness angle and hydraulic aperture of the fracture were measured by 3D laser scanning and fluid flow experiment, respectively. The sorption and desorption breakthrough experiments of Sr in the single fracture were conducted at different flow rates and initial concentrates of Sr, and the important transport parameters were derived by fitting the BTCs and DPCs. The results suggested that the sorption of Sr onto the fracture surface was a linear and reversible ion-exchange process, unaffected by the variation of flow rate and initial concentrate of Sr.