Success in life is linked to executive functions, a collection of cognitive processes that support goal-directed behaviors. Executive functions is an umbrella term related to cognitive control, self-control, and more. Variations in executive functioning predict concurrent success in schooling, relationships, and behavior, as well as important life outcomes years later. Such findings may suggest that certain individuals are destined for good executive functioning and success. However, environmental influences on executive function and development have long been recognized. Recent research in this tradition demonstrates the power of social contextual influences on children\'s engagement of executive functions. Such findings suggest new interpretations of why individuals differ in executive functioning and associated life outcomes, including across cultures and socioeconomic statuses. These findings raise fundamental questions about how best to conceptualize, measure, and support executive functioning across diverse contexts. Future research addressing real-world dynamics and computational mechanisms will elucidate how executive functioning emerges in the world.

Leptospirosis is a global zoonotic disease caused by spirochete bacteria of the genus Leptospira. The disease exhibits a notable incidence in tropical and developing countries, and in Colombia, environmental, economic, social, and cultural conditions favor disease transmission, directly impacting both mortality and morbidity rates. Our objective was to establish the pooled lagged effect of runoff on leptospirosis cases in Colombia. For our study, we included the top 20 Colombian municipalities with the highest number of leptospirosis cases. Monthly cases of leptospirosis, confirmed by laboratory tests and spanning from 2007 to 2022, were obtained from the National Public Health Surveillance System. Additionally, we collected monthly runoff and atmospheric and oceanic data from remote sensors. Multidimensional poverty index values for each municipality were sourced from the Terridata repository. We employed causal inference and distributed lag nonlinear models to estimate the lagged effect of runoff on leptospirosis cases. Municipality-specific estimates were combined through meta-analysis to derive a single estimate for all municipalities under study. The pooled results for the 20 municipalities suggest a lagged effect for the 0 to 2, and 0-3 months of runoff on leptospirosis when the runoff is < 120 g/m2. No effect was identified for longer lagged periods (0-1, 0 to 4, 0 to 5, and 0-6 months) or higher runoff values. Incorporation of the multidimensional poverty index into the meta-analysis of runoff contributed to the models for the lagged periods of 0-3, and 0-4 months.

UNASSIGNED: IgA nephropathy (IgAN), a prevalent form of glomerulonephritis globally, exhibits complex pathogenesis. Cathepsins, cysteine proteases within lysosomes, are implicated in various physiological and pathological processes, including renal conditions. Prior observational studies have suggested a potential link between cathepsins and IgAN, yet the precise causal relationship remains unclear.
UNASSIGNED: We conducted a comprehensive bidirectional and multivariable Mendelian randomization (MR) study using publicly available genetic data to explore the causal association between cathepsins and IgAN systematically. Additionally, immunohistochemical (IHC) staining and enzyme-linked immunosorbent assay (ELISA) were employed to evaluate cathepsin expression levels in renal tissues and serum of IgAN patients. We investigated the underlying mechanisms via gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), and immune cell infiltration analysis. Molecular docking and virtual screening were also performed to identify potential drug candidates through drug repositioning.
UNASSIGNED: Univariate MR analyses demonstrated a significant link between increased cathepsin S (CTSS) levels and a heightened risk of IgAN. This was evidenced by an odds ratio (OR) of 1.041 (95% CI=1.009-1.073, P=0.012) as estimated using the inverse variance weighting (IVW) method. In multivariable MR analysis, even after adjusting for other cathepsins, elevated CTSS levels continued to show a strong correlation with an increased risk of IgAN (IVW P=0.020, OR=1.037, 95% CI=1.006-1.069). However, reverse MR analyses did not establish a causal relationship between IgAN and various cathepsins. IHC and ELISA findings revealed significant overexpression of CTSS in both renal tissues and serum of IgAN patients compared to controls, and this high expression was unique to IgAN compared with several other primary kidney diseases such as membranous nephropathy, minimal change disease and focal segmental glomerulosclerosis. Investigations into immune cell infiltration, GSEA, and GSVA highlighted the role of CTSS expression in the immune dysregulation observed in IgAN. Molecular docking and virtual screening pinpointed Camostat mesylate, c-Kit-IN-1, and Mocetinostat as the top drug candidates for targeting CTSS.
UNASSIGNED: Elevated CTSS levels are associated with an increased risk of IgAN, and this enzyme is notably overexpressed in IgAN patients\' serum and renal tissues. CTSS could potentially act as a diagnostic biomarker, providing new avenues for diagnosing and treating IgAN.

We introduce a simple diagnostic test for assessing the overall or partial goodness of fit of a linear causal model with errors being independent of the covariates. In particular, we consider situations where hidden confounding is potentially present. We develop a method and discuss its capability to distinguish between covariates that are confounded with the response by latent variables and those that are not. Thus, we provide a test and methodology for partial goodness of fit. The test is based on comparing a novel higher-order least squares principle with ordinary least squares. In spite of its simplicity, the proposed method is extremely general and is also proven to be valid for high-dimensional settings. Supplementary materials for this article are available online.

UNASSIGNED: Potential uncertainties and overtreatment exist in adjuvant chemotherapy for triple-negative breast cancer (TNBC) patients.
UNASSIGNED: This study aims to explore the performance of deep learning (DL) models in personalized chemotherapy selection and quantify the impact of baseline characteristics on treatment efficacy.
UNASSIGNED: Patients who received treatment recommended by models were compared to those who did not. Overall survival for treatment according to model recommendations was the primary outcome. To mitigate bias, inverse probability treatment weighting (IPTW) was employed. A mixed-effect multivariate linear regression was employed to visualize the influence of certain baseline features of patients on chemotherapy selection.
UNASSIGNED: A total of 10,070 female TNBC patients met the inclusion criteria. Treatment according to Self-Normalizing Balanced (SNB) individual treatment effect for survival data model recommendations was associated with a survival benefit (IPTW-adjusted hazard ratio: 0.53, 95% CI, 0.32-8.60; IPTW-adjusted risk difference: 12.90, 95% CI, 6.99-19.01; IPTW-adjusted the difference in restricted mean survival time: 5.54, 95% CI, 1.36-8.61), which surpassed other models and the National Comprehensive Cancer Network guidelines. No survival benefit for chemotherapy was seen for patients not recommended to receive this treatment. SNB predicted older patients with larger tumors and more positive lymph nodes are the optimal candidates for chemotherapy.
UNASSIGNED: These findings suggest that the SNB model may identify patients with TNBC who could benefit from chemotherapy. This novel analytical approach may provide debiased individual survival information and treatment recommendations. Further research is required to validate these models in clinical settings with more features and outcome measurements.

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BACKGROUND: Perceived discrimination in health care settings can have adverse consequences on mental health in minority groups. However, the association between perceived discrimination and mental health is prone to unmeasured confounding. The study aims to quantitatively evaluate the influence of unmeasured confounding in this association, using g-estimation.
METHODS: In a predominantly African American cohort, we applied g-estimation to estimate the association between perceived discrimination and mental health, adjusted and unadjusted for measured confounders. Mental health was measured using clinical diagnoses of anxiety, depression and bipolar disorder. Perceived discrimination was measured as the number of patient-reported discrimination events in health care settings. Measured confounders included demographic, socioeconomic, residential and health characteristics. The influence of confounding was denoted as α1 from g-estimation. We compared α1 for measured and unmeasured confounding.
RESULTS: Strong associations between perceived discrimination in health care settings and mental health outcomes were observed. For anxiety, the odds ratio (95% confidence interval) unadjusted and adjusted for measured confounders were 1.30 (1.21, 1.39) and 1.26 (1.17, 1.36), respectively. The α1 for measured confounding was -0.066. Unmeasured confounding with α1=0.200, which was over three times that of measured confounding, corresponds to an odds ratio of 1.12 (1.01, 1.24). Similar results were observed for other mental health outcomes.
CONCLUSIONS: Compared with measured confounding, unmeasured that was three times measured confounding was not enough to explain away the association between perceived discrimination and mental health, suggesting that this association is robust to unmeasured confounding. This study provides a novel framework to quantitatively evaluate unmeasured confounding.

[This corrects the article DOI: 10.3389/fgene.2024.1371607.].

Recent works have proposed regression models which are invariant across data collection environments [24, 20, 11, 16, 8]. These estimators often have a causal interpretation under conditions on the environments and type of invariance imposed. One recent example, the Causal Dantzig (CD), is consistent under hidden confounding and represents an alternative to classical instrumental variable estimators such as Two Stage Least Squares (TSLS). In this work we derive the CD as a generalized method of moments (GMM) estimator. The GMM representation leads to several practical results, including 1) creation of the Generalized Causal Dantzig (GCD) estimator which can be applied to problems with continuous environments where the CD cannot be fit 2) a Hybrid (GCD-TSLS combination) estimator which has properties superior to GCD or TSLS alone 3) straightforward asymptotic results for all methods using GMM theory. We compare the CD, GCD, TSLS, and Hybrid estimators in simulations and an application to a Flow Cytometry data set. The newly proposed GCD and Hybrid estimators have superior performance to existing methods in many settings.

UNASSIGNED: The immune system plays an important role in the development and treatment of thyroid cancer(THCA).However, the correlation between immune cells and THCA has not been systematically studied.
UNASSIGNED: This study used a two-sample Mendelian randomization (MR) study to determine the causal relationship between immune cell characteristics and THCA. Based on a large sample of publicly available genetic data, we explored the causal relationship between 731 immune cell characteristics and THCA risk. The 731 immunophenotypes were divided into 7 groups, including B cell panel(n=190),cDC panel(n=64),Maturation stages of T cell panel(n=79),Monocyte panel(n=43),Myeloid cell panel(n=64),TBNK panel(n=124),and Treg panel(n=167). The sensitivity of the results was analyzed, and heterogeneity and horizontal pleiotropy were excluded.
UNASSIGNED: After FDR correction, the effect of immunophenotype on THCA was not statistically significant. It is worth mentioning, however, that there are some unadjusted low P-values phenotypes. The odds ratio (OR) of CD62L on monocyte on THCA risk was estimated to be 0.953 (95% CI=0.930~0.976, P=1.005×10-4),and which was estimated to be 0.975(95% CI=0.961-0.989, P=7.984×10-4) for Resting Treg%CD4 on THCA risk. Furthermore, THCA was associated with a reduced risk of 5 immunophenotype:CD25 on CD39+ CD4 on Treg (OR=0.871, 95% CI=0.812~0.935, P=1.274×10-4), activated Treg AC (OR=0.884, 95% CI=0.820~0.953, P=0.001), activated & resting Treg % CD4 Treg (OR=0.872, 95%CI=0.811~0.937,P=2.109×10-4),CD28- CD25++ CD8br AC(OR=0.867,95% CI=0.809~0.930,P=6.09×10-5),CD28-CD127-CD25++CD8brAC(OR=0.875,95%CI=0.814~0.942,P=3.619×10-4).THCA was associated with an increased risk of Secreting Treg % CD4 Treg (OR=1.143, 95% CI=1.064~1.229, P=2.779×10-4) and CD19 on IgD+ CD24+ (OR=1.118, 95% CI=1.041~1.120, P=0.002).
UNASSIGNED: These findings suggest the causal associations between immune cells and THCA by genetic means. Our results may have the potential to provide guidance for future clinical research.