A Wetting and Capture Strategy Overcoming Electrostatic Repulsion for Electroreduction of Nitrate to Ammonia from Low-Concentration Sewage.

Ammonia production by electrocatalytic nitrate reduction reaction (NO3RR) in water streams is anticipated as a zero-carbon route. Limited by dilute nitrate in natural sewage and the electrostatic repulsion between NO3 - and cathode, NO3RR can hardly be achieved energy-efficiently. The hydrophilic Cu@CuCoO2 nano-island dispersed on support can enrich NO3 - and produce a sensitive current response, followed by electrosynthesis of ammonia through atomic hydrogen (*H) is reported. The accumulated NO3 - can be partially converted to NO2 - without external electric field input, confirming that the Cu@CuCoO2 nano-island can strongly bind NO3 - and then trigger the reduction via dynamic evolution between Cu-Co redox sites. Through the identification of intermediates and theoretical computation. it is found that the N-side hydrogenation of *NO is the optimal reaction step, and the formation of N─N dimer may be prevented. An NH3 product selectivity of 93.5%, a nitrate conversion of 96.1%, and an energy consumption of 0.079 kWh gNH3 -1 is obtained in 48.9 mg-N L-1 naturally nitrate-polluted streams, which outperforms many works using such dilute nitrate influent. Conclusively, the electrocatalytic system provides a platform to guarantee the self-sufficiency of dispersed ammonia production in agricultural regions.

Development of a test strip for rapid detection of Gymnodinium catenatum.

Harmful algal blooms (HABs) are major ecological and environmental problems in China's coastal waters and seriously threaten the stability of the marine ecosystem and human health. Gymnodinium catenatum is a toxic red tide dinoflagellate. It can produce paralytic shellfish toxins (PSP), which cause serious hazards to marine organisms, public health, and safety. In this paper, a test strip based on colloidal gold immunochromatography (GICG) was developed for the rapid detection of Gymnodinium catenatum. The experimental results showed that the test strip has good specificity and sensitivity. It not only detects the different components of Gymnodinium catenatum but also may detect algal toxins. The lowest density of Gymnodinium catenatum that can be detected by this test strip is approximately 120 cells/mL. Cross-reaction indicated that the test strip had a high specificity for Gymnodinium catenatum. This test strip provides a rapid method for in situ detection of Gymnodinium catenatum and a reference method for the monitoring of other harmful algae to serve as an early warning of upcoming red tides. It also provides a new way to prepare more detection methods for toxic algal toxins.

Bacterially mediated release and mobilization of As/Fe coupled to nitrate reduction in a sediment environment.

Metal-reducing bacteria play an important role in the release and mobilization of arsenic from sediments into groundwater. This study aimed to investigate the influence of nitrate on arsenic bio-release. Microcosm experiments consisting of high arsenic sediments and indigenous bacterium Bacillus sp. D2201 were conducted and the effects of nitrate on the mobilization of As/Fe determined. The results show arsenic release is triggered by iron reduction, which is regulated by nitrate. Increasing the nitrate concentration from 0 to 1 and 3 mM decreased Fe(III) reduction by 62.5% and 16.9% and decreased As(V) bio-release by 41.5% and 85.5%, respectively. Moreover, the results of step-wise Wenzel sequential extractions indicate nitrate addition prevents the transformation of poorly crystalline iron oxides to well crystalline iron oxides. Overall, nitrate appears to have a dual effect, inhibiting both iron reduction and arsenic release by incubation strain D2201. This study offers new insights regarding the biogeochemistry of arsenic in groundwater systems.

Fracture risk prediction in diabetes patients based on Lasso feature selection and Machine Learning.

Fracture risk among individuals with diabetes poses significant clinical challenges due to the multifaceted relationship between diabetes and bone health. Diabetes not only affects bone density but also alters bone quality and structure, thereby increases the susceptibility to fractures. Given the rising prevalence of diabetes worldwide and its associated complications, accurate prediction of fracture risk in diabetic individuals has emerged as a pressing clinical need. This study aims to investigate the factors influencing fracture risk among diabetic patients. We propose a framework that combines Lasso feature selection with eight classification algorithms. Initially, Lasso regression is employed to select 24 significant features. Subsequently, we utilize grid search and 5-fold cross-validation to train and tune the selected classification algorithms, including KNN, Naive Bayes, Decision Tree, Random Forest, AdaBoost, XGBoost, Multi-layer Perceptron (MLP), and Support Vector Machine (SVM). Among models trained using these important features, Random Forest exhibits the highest performance with a predictive accuracy of 93.87%. Comparative analysis across all features, important features, and remaining features demonstrate the crucial role of features selected by Lasso regression in predicting fracture risk among diabetic patients. Besides, by using a feature importance ranking algorithm, we find several features that hold significant reference values for predicting early bone fracture risk in diabetic individuals.

Establishment of colloidal gold immunochromatography strip for rapid detection of Karen mikimotoi and its application.

In China, harmful algal blooms (HABs) are one of the most prominent ecological disasters in the coastal areas. Among the harmful algae species that cause HABs, Karen mikimotoi is a kind of algae that appear frequently. It can secrete hemolytic toxins and fish toxins such as glycolipids and unsaturated fatty, posing a significant threat to marine life. In order to establish a fast and effective detection technology for Karen mikimotoi that can be promoted and applied on site, we have developed a test strip which is based on monoclonal antibody technology and the colloidal gold immune-chromatography assay (GICA). The experimental results show that this test strip can detect different growth stages including growth, and stable and recession period of Karen mikimotoi. The detection results can be displayed within 3-15 min. It had high sensitivity and specificity, with a detection limit of 754 cells/mL. A colorimetric card was made to further determine the concentration of algae detected. What is more, we had developed a method that can be used for on-site enrichment of algae cells using a syringe to detect lower concentrations of Karen mikimotoi, with a minimum detection concentration of 100 cells/mL. Also the test strip was used for on-site testing along the coast of China. This test strip not only serves as a warning for the outbreak of red tide, but also provides a new approach for the development of rapid detection technology for red tide algae.

Research advances in crosstalk between muscle and bone in osteosarcopenia (Review).

Osteosarcopenia is a burgeoning geriatric syndrome and a familiar disease among older individuals. It is characterized by reduced skeletal muscle mass and bone mineral density due to osteoporosis and sarcopenia. Its clinical manifestations include reduced physical performance and individuals becoming prone to falls during the aging process resulting in fractures and hospitalization, which seriously affects the quality of life of patients and increases the risk of death. Due to the aging social structure of the global population, the morbidity of osteosarcopenia is expected to continue to increase. Both muscle and bone belong to the motor system and originate from the mesoderm; therefore, sarcopenia and osteoporosis also share similar pathogenical factors, which influence and regulate each other. Studying the pathogenesis and treatment of osteosarcopenia is of great significance to improve the quality of life of patients. Therefore, the present study reviewed the research progress on sarcopenia and osteoporosis in osteosarcopenia from the standpoints of its definition, epidemiology, clinical manifestations and diagnosis, prevention and treatment.

Y chromosome polymorphisms may contribute to an increased risk of male-induced unexplained recurrent miscarriage.

The present study aims to explore the relationship between the Y chromosome polymorphisms (1qh+, inv(9), 9qh+, 16qh+, group D/G, Yqh- and Yqh+) and the risk of unexplained recurrent miscarriage (URM). A total of 507 couples with URM were recruited as case group and 465 healthy couples as control group. The Y chromosome polymorphisms of the male individuals were analysed with the G-banding technique, and the results of the chromosome G-banding analysis were determined using the International Naming Standards of Human Genetics (ISCN). Logistic regression analysis was used to analyse the risk factors for URM. The detection rate of Y chromosome polymorphisms in the case group (12.03%) was higher than that in the control group (2.15%). Y chromosome polymorphisms were detected at significantly higher rates in the case group than in the control group. Using the normal Y chromosomes in individuals of the case group as reference, the partners of their counterparts were more likely to experience miscarriage. The couples who were Y chromosome-polymorphism carriers had shorter gestational age, increased frequency of URM and longer average interval between pregnancies. The results of logistic regression analysis revealed that Y chromosome polymorphisms, shorter gestational age, a higher frequency of miscarriage and longer pregnancy interval were independent risk factors for URM. Y chromosome polymorphisms may be associated with the risk of URM and may play an important role in the development of URM.

Identification of candidate biomarkers that involved in the epigenetic transcriptional regulation for detection gastric cancer by iTRAQ based quantitative proteomic analysis.

BACKGROUND: The sensitivities and specificities of biomarkers for gastric cancer are insufficient for clinical detection, and new diagnostics are therefore urgently required. METHODS: A discovery set of gastric cancer tissues was labeled with iTRAQ reagents, separated using SCX chromatography, and identified using LC-ESI-MS/MS. A validation set of gastric cancer tissues was used to confirm the expression levels of potential markers. RESULTS: The present study detected metastasis-associated protein 2 (MTA2) and Histone deacetylases 1 (HDAC1) proteins that were overexpressed in gastric cancer tissues compared with that in adjacent gastric tissue. The sensitivity and specificity of MTA2 in detecting 76 cases gastric cancers were 57.9% (95% CI: 46.5%-69.3%) and 55.3% (95% CI: 43.8%-66.7%), respectively. The sensitivity and specificity of HDAC1 were 61.8% (95% CI: 50.7%-73%) and 63.2% (95% CI: 52.1%-74.3%), respectively. The co-expression of MTA2 and HDAC1 in gastric cancer achieved 65.3% sensitivity (95% CI: 51.5%-79.1%) and 65.2% specificity (95% CI: 50.9%-79.5%), which was strongly associated with lymph node metastasis and TNM staging. CONCLUSION: The present findings indicated a tight correlation between the MTA2 and HDAC1 expression level and lymph node metastasis and TNM staging in gastric cancers. Therefore, MTA2 and HDAC1 might be predictors of lymph node metastasis phenotype and possible target molecule for anticancer drug design in human gastric cancer.