Prevalence of Cobalt in the Environment and Its Role in Biological Processes

Trends in the prevalence of osteoporosis and effects of heavy metal exposure using interpretable machine learning

There is limited evidence that heavy metals exposure contributes to osteoporosis. Multi-parameter scoring machine learning (ML) techniques were developed using National Health and Nutrition Examination Survey data to predict osteoporosis based on heavy metal exposure levels. For generating an optimal predictive model for osteoporosis, 12 ML models were used. Identification was carried out using the model that performed the best. For interpretation of models, Shapley additive explanation (SHAP) methods and partial dependence plots (PDP) were integrated into a pipeline and incorporated into the ML pipeline. By regressing osteoporosis on survey cycles, logistic regression was used to evaluate linear trends in osteoporosis over time. For the purpose of training and validating predictive models, 5745 eligible participants were randomly selected into training and testing set. It was evident from the results that the gradient boosting decision tree model performed the best among the predictive models, attributing to an accuracy rate of 89.40 % in the testing set. Based on the model results, the area under the curve and F1 score were 0.88 and 0.39, respectively. As a result of the SHAP analysis, urinary Co, urinary Tu, blood Cd, and urinary Hg levels were identified as the most influential factors influencing osteoporosis. Urinary Co (0.20-6.10 μg/mg creatinine), urinary Tu (0.06-1.93 μg/mg creatinine), blood Cd (0.07-0.50 μg/L), and urinary Hg (0.06-0.75 μg/mg creatinine) levels displayed a distinctive upward trend with risk of osteoporosis as values increased. Our analysis revealed that urinary Co, urinary Tu, blood Cd, and urinary Hg played a significant role in predictability.

Metals in Honey from Bees as a Proxy of Environmental Contamination in the United States

This is the first large bio-surveillance study examining the contents and geographic variation of metals of public health concern-arsenic (As), lead (Pb), cadmium (Cd), nickel (Ni), chromium (Cr), and cobalt (Co)-in honey samples collected across the United States. Metal concentrations were measured using ICP-MS, and the spatial distribution pattern of these contaminants was evaluated using statistical and GIS tools. The mean (highest) values (in μg/kg) were 3.8 (170) for As, 8.0 (451) for Pb, and 0.75 (8.1) for Cd. These values, as well as the mean (highest) concentrations of 29.5 (516) for Ni, 14.3 (166) for Co, and 19.6 (11) for Cr, were markedly lower than global averages reported in other countries. The study identified distinct geographic patterns of honey contamination; particularly high As levels were found in northwestern states, while high Co was measured in the southeast. Health risk calculations based on the hazard quotient (HQ) and hazard index (HI) were below 1 for a daily tablespoon (21g) of honey consumption, indicating no adverse health concerns for children and adults, and all samples fell below the 1.0x10-6 threshold for carcinogenic risk. The variation in metal concentrations found in samples from different states may reflect the influence of air, water, or soil pollution, as well as differential accumulation across plant species, and the distinct geographic clustering of As and Co warrants further investigation to determine the sources of these metals and to assess public health risks, particularly for As, a well-known carcinogen. In sum, this initial study provides baseline values of metal concentrations in honey that can be useful for monitoring future pollution trends, identifying target areas where reductions of emissions or remediation efforts are most critical, and facilitating discovery research in environmental exposure and health sciences.

Terminal deoxynucleotidyl transferase-mediated CRISPR sensing platform for simple and point-of-care detection of cobalt pollution

The excessive use of cobalt in various chemical industries and arbitrary discharge of industrial wastewater have led to increased cobalt pollution in soil and water resources, increasing the risk of human exposure to high concentrations of cobalt and necessitating an urgent need for on-site monitoring platform for cobalt pollution. In this study, the terminal deoxynucleotidyl transferase (TdT)-CRISPR platform has been developed. In this platform, cobalt as a cofactor of TdT, can significantly improve the tailing efficiency of TdT-mediated extension. Therefore, when cobalt is present, the detection probe can be extended with poly(T) tails through the TdT-mediated extension, which can be subsequently served as the DNA activator for Cas12a, leading to the cleavage of fluorescence reporter molecules and triggering turn-on fluorescence signals. Consequently, this dual amplification sensing strategy of TdT-CRISPR platform demonstrated exceptional sensitivity (0.83 nM) and high specificity for cobalt over other ions. Furthermore, the method was successfully employed for the detection of cobalt in tap water and river samples. CRISPR-lateral flow assays (CRISPR-LFAs) were evaluated in this study for the simple and point-of-care detection of cobalt pollution. The assays are capable of detecting cobalt concentrations as low as 50 nM, which is significantly lower than the environmental standards of 16.9 μM, through strip analysis with the naked eye. These results commonly suggest that the TdT-CRISPR platform holds significant promise for monitoring cobalt pollution, providing a robust and sensitive solution for on-site detection and contributing to the mitigation of cobalt contamination risks in environmental matrices.

Metals on the Menu-Analyzing the Presence, Importance, and Consequences

Metals are integral components of the natural environment, and their presence in the food supply is inevitable and complex. While essential metals such as sodium, potassium, magnesium, calcium, iron, zinc, and copper are crucial for various physiological functions and must be consumed through the diet, others, like lead, mercury, and cadmium, are toxic even at low concentrations and pose serious health risks. This study comprehensively analyzes the presence, importance, and consequences of metals in the food chain. We explore the pathways through which metals enter the food supply, their distribution across different food types, and the associated health implications. By examining current regulatory standards for maximum allowable levels of various metals, we highlight the importance of ensuring food safety and protecting public health. Furthermore, this research underscores the need for continuous monitoring and management of metal content in food, especially as global agricultural and food production practices evolve. Our findings aim to inform dietary recommendations, food fortification strategies, and regulatory policies, ultimately contributing to safer and more nutritionally balanced diets.

Micronutrient Status and Breast Cancer: A Narrative Review

Breast cancer is one of the most common cancers worldwide, at the same time being one of the most prevalent causes of women's death. Many factors such as alcohol, weight fluctuations, or hormonal replacement therapy can potentially contribute to breast cancer development and progression. Another important factor in breast cancer onset includes micronutrient status. In this narrative review, we analyzed 23 micronutrients and their possible influence on breast cancer onset and progression. Further, the aim of this study was to investigate the impact of micronutrient status on the prevention of breast cancer and its possible influence on various therapeutic pathways. We researched meta-analyses, systemic and narrative reviews, retrospective studies, as well as original studies on human and animal models. The results of these studies indicate a possible correlation between the different levels of micronutrients and a decreased risk of breast cancer as well as a better survival rate. However, further studies are necessary to establish adequate doses of supplementation of the chosen micronutrients and the exact mechanisms of micronutrient impact on breast cancer therapy.

Trace Element Concentration in the Blood and Aqueous Humor of Subjects with Eye Cataract

Cataract, characterized by the opacification of the lens, is the leading cause of reversible blindness and visual impairment globally. The study aims to investigate the role of trace elements such as Cd, Co, Cr, Cu, Fe, Hg, Mn, Ni, Pb, Se, and Zn in the development and severity of cataract. Elements were quantified by inductively coupled plasma mass spectrometry in blood and aqueous humor of 32 cataract cases and 27 controls living in the Latium region, Italy. The association between element concentration in blood and aqueous humor and cataract severity, gender, and age of subjects were also assessed. Results showed Cr levels significantly elevated in both blood and aqueous humor of cataract cases, with concentrations that increased with cataract severity. In addition, blood Pb levels were significantly higher in older cases and positively correlated with the age of cataract cases, while blood Co and Cu levels negatively correlated with cataract severity, suggesting changes in the levels of these elements. In conclusion, this study provides evidence of the involvement of specific elements in cataract development and severity, and the findings highlighted important avenues for future research. Understanding the biological mechanism underlying element-induced cataract may contribute to preventing cataractogenesis and providing targeted interventions.

Nanomaterials Based on 2,7,12,17-Tetra-tert-butyl-5,10,15,20-tetraaza-21H,23H-porphine Exhibiting Bifunctional Sensitivity for Monitoring Chloramphenicol and Co2

Monitoring antibiotic retention in human body fluids after treatment and controlling heavy metal content in water are important requirements for a healthy society. Therefore, the approach proposed in this study is based on developing new optical sensors using porphyrin or its bifunctional hybrid materials made with AuNPs to accomplish the accurate detection of chloramphenicol and cobalt. To produce the new optical chloramphenicol sensors, 2,7,12,17-tetra-tert-butyl-5,10,15,20-tetraaza-21H,23H-porphine (TBAP) was used, both alone in an acid medium and as a hybrid material with AuNPs in a water-DMSO acidified environment. The same hybrid material in the unchanged water-DMSO medium was the sensing material used for Co2+ monitoring. The best results of the hybrid materials were explained by the synergistic effects between the TBAP azaporphyrin and AuNPs. Chloramphenicol was accurately detected in the range of concentrations between 3.58 × 10-6 M and 3.37 × 10-5 M, and the same hybrid material quantified Co2+ in the concentration range of 8.92 × 10-5 M-1.77 × 10-4 M. In addition, we proved that AuNPs can be used for the detection of azaporphyrin (from 2.66 × 10-5 M to 3.29 × 10-4 M), making them a useful tool to monitor porphyrin retention after cancer imaging procedures or in porphyria disease. In conclusion, we harnessed the multifunctionality of this azaporphyrin and of its newly obtained AuNP plasmonic hybrids to detect chloramphenicol and Co2+ quickly, simply, and with high precision.

Maternal cobalt concentration and risk of spontaneous preterm birth: the role of fasting blood glucose and lipid profiles

Introduction

Spontaneous preterm birth (SPB) is a significant cause of neonatal mortality, yet its etiology remains unclear. Cobalt, an essential trace element, might be a risk factor for SPB. This study aims to investigate the relationship between maternal serum cobalt concentration and SPB, and to clarify the role of blood lipids and fasting blood glucose (FBG) in this relationship.

Methods

We conducted a nested case-control study within the Beijing Birth Cohort Study. Serum samples were obtained from 222 pregnant women with SPB and 224 controls during the first (7-13 weeks of pregnancy) and third trimesters (32-42 weeks of pregnancy). Serum cobalt concentration was determined using inductively coupled plasma mass spectrometry (ICP-MS). Fasting blood glucose and lipids levels were detected using a fully automated biochemical immunoassay instrument. Logistic regression models and linear regression models were established to explore the association between serum cobalt concentration and the risk of SPB in pregnant women, and to test the mediating effect of fasting blood glucose (FBG) and lipids.

Results

We found that the serum cobalt concentration in mothers with SPB and controls was similar in the first trimester, with values of 0.79 (0.58-1.10) ng/mL and 0.75 (0.51-1.07) ng/mL, respectively. However, in the third trimester, the cobalt concentration increased to 0.88 (0.59-1.14) ng/mL and 0.84 (0.52-1.19) ng/mL, respectively. In the logistic regression model, when considering the third trimester of pregnancy, after adjusting for ethnicity, pre-pregnancy body mass index (BMI), maternal age, education, income, and parity, it was observed that the medium level of cobalt concentration (0.63-1.07 ng/ml) had a negative correlation with the risk of SPB. The odds ratio (OR) was 0.56, with a 95% confidence interval of 0.34-0.90 ng/mL and a p-value of 0.02. This suggests that cobalt in this concentration range played a protective role against SPB. Additionally, it was found that FBG in the third trimester of pregnancy had a partial intermediary role, accounting for 9.12% of the association. However, no relationship between cobalt and SPB risk was found in the first trimester.

Conclusion

During the third trimester, intermediate levels of maternal cobalt appear to offer protection against SPB, with FBG playing a partial mediating role. To further clarify the optimal cobalt concentrations during pregnancy for different populations, a multi-center study with a larger sample size is necessary. Additionally, exploring the specific mechanism of FBG's mediating role could provide valuable insights for improving the prevention of SPB.