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When piglets are exposed to pathogens for a long period, the immune system organs, among them the spleen, play a major role in combating the stress caused by those pathogens. In the present study, the effect on splenocyte function was investigated in a model of weaned piglets in which stress was induced by multiple low doses of lipopolysaccharide (LPS).

Main Problem: Anhedonia is a critical diagnostic symptom of major depressive disorder (MDD), being associated with poor prognosis. Understanding the neural mechanisms underlying anhedonia is of great significance for individuals with MDD, and it encourages the search for objective indicators that can reliably identify anhedonia. Methods: A predictive model used connectome‐based predictive modeling (CPM) for anhedonia symptoms was developed by utilizing pre‐treatment functional connectivity (FC) data from 59 patients with MDD. Node‐based FC analysis was employed to compare differences in FC patterns between melancholic and non‐melancholic MDD patients. The support vector machines (SVM) method was then applied for classifying these two subtypes of MDD patients. Results: CPM could successfully predict anhedonia symptoms in MDD patients (positive network: r = 0.4719, p < 0.0020, mean squared error = 23.5125, 5000 iterations). Compared to non‐melancholic MDD patients, melancholic MDD patients showed decreased FC between the left cingulate gyrus and the right parahippocampus gyrus (p_bonferroni = 0.0303). This distinct FC pattern effectively discriminated between melancholic and non‐melancholic MDD patients, achieving a sensitivity of 93.54%, specificity of 67.86%, and an overall accuracy of 81.36% using the SVM method. Conclusions: This study successfully established a network model for predicting anhedonia symptoms in MDD based on FC, as well as a classification model to differentiate between melancholic and non‐melancholic MDD patients. These findings provide guidance for clinical treatment. [ABSTRACT FROM AUTHOR]

BACKGROUND: Air pollution has been associated with gestational hypertension (GH) and preeclampsia, but susceptible windows of exposure and potential vulnerability by comorbidities, such as prenatal depression, remain unclear. METHODS: We ascertained GH and preeclampsia cases in a prospective pregnancy cohort in Los Angeles, CA. Daily levels of ambient particulate matters (with a diameter of ≤10 μm [PM10] or ≤2.5 μm [PM2.5]), nitrogen dioxide, and ozone were averaged for each week from 12 weeks preconception to 20 gestational weeks. We used distributed lag models to identify susceptible exposure windows, adjusting for potential confounders. Analyses were additionally stratified by probable prenatal depression to explore population vulnerability. RESULTS: Among 619 participants, 60 developed preeclampsia and 42 developed GH. We identified a susceptible window for exposure to PM2.5 from 1 week preconception to 11 weeks postconception: higher exposure (5 µg/m3) within this window was associated with an average of 8% (95% CI, 1%–15%) higher risk of GH. Among participants with probable prenatal depression (n=179; 32%), overlapping sensitive windows were observed for all pollutants from 8 weeks before to 10 weeks postconception with increased risk of GH (PM2.5, 16% [95% CI, 3%–31%]; PM10, 39% [95% CI, 13%–72%]; nitrogen dioxide, 65% [95% CI, 17%–134%]; and ozone, 45% [95% CI, 9%–93%]), while the associations were close to null among those without prenatal depression. Air pollutants were not associated with preeclampsia in any analyses. CONCLUSIONS: We identified periconception through early pregnancy as a susceptible window of air pollution exposure with an increased risk of GH. Prenatal depression increases vulnerability to air pollution exposure and GH. [ABSTRACT FROM AUTHOR]

Introduction Secondhand smoke (SHS) exposure during pregnancy is linked to adverse birth outcomes, such as low birth weight and preterm birth. While questionnaires are commonly used to assess SHS exposure, their ability to capture true exposure can vary, making it difficult for researchers to harmonize SHS measures. This study aimed to compare self-reported SHS exposure with measurements of airborne SHS in personal samples of pregnant women. Methods SHS was measured on 48-hour integrated personal PM2.5 Teflon filters collected from 204 pregnant women, and self-reported SHS exposure measures were obtained via questionnaires. Descriptive statistics were calculated for airborne SHS measures, and analysis of variance tests assessed group differences in airborne SHS concentrations by self-reported SHS exposure. Results Participants were 81% Hispanic, with a mean (standard deviation [SD]) age of 28.2 (6.0) years. Geometric mean (SD) personal airborne SHS concentrations were 0.14 (9.41) µg/m3. Participants reporting lower education have significantly higher airborne SHS exposure (p  = .015). Mean airborne SHS concentrations were greater in those reporting longer duration with windows open in the home. There was no association between airborne SHS and self-reported SHS exposure; however, asking about the number of smokers nearby in the 48-hour monitoring period was most correlated with measured airborne SHS (Two + smokers: 0.30 µg/m3 vs. One: 0.12 µg/m3 and Zero: 0.15 µg/m3; p  = .230). Conclusions Self-reported SHS exposure was not associated with measured airborne SHS in personal PM2.5 samples. This suggests exposure misclassification using SHS questionnaires and the need for harmonized and validated questions to characterize this exposure in health studies. Implications This study adds to the growing body of evidence that measurement error is a major concern in pregnancy research, particularly in studies that rely on self-report questionnaires to measure SHS exposure. The study introduces an alternative method of SHS exposure assessment using objective optical measurements, which can help improve the accuracy of exposure assessment. The findings emphasize the importance of using harmonized and validated SHS questionnaires in pregnancy health research to avoid biased effect estimates. This study can inform future research, practice, and policy development to reduce SHS exposure and its adverse health effects. [ABSTRACT FROM AUTHOR]

As the long‐term consequences of coronavirus disease 2019 (COVID‐19) have not been defined, it is necessary to explore persistent symptoms, long‐term respiratory impairment, and impact on quality of life over time in COVID‐19 survivors. In this prospective cohort study, convalescent individuals diagnosed with COVID‐19 were followed‐up 2 and 3 years after discharge from hospital. Participants completed an in‐person interview to assess persistent symptoms and underwent blood tests, pulmonary function tests, chest high‐resolution computed tomography, and the 6‐min walking test. There were 762 patients at the 2‐year follow‐up and 613 patients at the 3‐year follow‐up. The mean age was 60 years and 415 (54.5%) were men. At 3 years, 39.80% of the participants had at least one symptom; most frequently, fatigue, difficulty sleeping, joint pain, shortness of breath, muscle aches, and cough. The participants experienced different degrees of pulmonary function impairment, with decreased carbon monoxide diffusion capacity being the main feature; results remained relatively stable over the 2–3 years. Multiple logistic regression analysis demonstrated that female sex and smoking were independently associated with impaired diffusion capacity. A subgroup analysis based on disease severity was performed, indicating that there was no difference in other parameters of lung function except forced vital capacity at 3‐year follow‐up. Persistent radiographic abnormalities, most commonly fibrotic‐like changes, were observed at both timepoints. At 3 years, patients had a significantly improved Mental Component Score compared with that at 2 years, with a lower percentage with anxiety. Our study indicated that symptoms and pulmonary abnormalities persisted in COVID‐19 survivors at 3 years. Further studies are warranted to explore the long‐term effects of COVID‐19 and develop appropriate rehabilitation strategies. [ABSTRACT FROM AUTHOR]