Fe2+-Coupled Organic-Substrate-Enhanced Nitrogen Removal in Two-Stage Anammox Biofilm Reactors

Anammox is a novel and energy-efficient biological nitrogen removal technology. Enhancing its performance in treating low-strength nitrogen wastewater is essential for expanding its practical applications. In response to challenges such as low nitrogen removal efficiency (NRE), poor operational stability, limited environmental resistance, and the interference of organic compounds commonly found in real wastewater, this study developed a two-stage upflow anammox biofilm reactor system (R1 and R2) enhanced by an Fe2+-coupled organic substrate strategy for deep nitrogen removal under low-nitrogen conditions. Results showed that sodium acetate at a chemical oxygen demand (COD) concentration of 40 mg/L provided the greatest enhancement to anammox activity, achieving an average total nitrogen removal efficiency (NRE) of 90.02%. However, the reactor performance was significantly inhibited under higher COD conditions (e.g., COD = 60 mg/L). Under an influent Fe2+ concentration of 10 mg/L, the reactors’ NRE increased and then decreased as the COD concentration rose from 0 to 100 mg/L, resulting in the highest efficiency being achieved at an average NRE of 94.11%, observed under 10 mg/L Fe2+ coupled with 60 mg/L of COD in the two-stage anammox system. Scanning electron microscopy revealed that the co-addition of Fe2+ and organic substrates led to the formation of granular protrusions and pores on the sludge surface, which favored the structural stability of the biomass. At a COD level of 40 mg/L, the contents of extracellular polymeric substances and heme c in anammox biofilm were significantly higher compared to the addition of 10 mg/L Fe2+ alone, whereas excessive COD inhibited both indicators. These findings suggest that moderate levels of Fe2+ coupled with organic matter can promote anammox activity for deep nitrogen removal, while excessive organics have inhibitory effects. This study provides theoretical support for enhancing nitrogen removal from low-strength wastewater using Fe2+ and organic-substrate-assisted anammox processes

Hollow ZIF-67 derived porous cobalt sulfide as an efficient bifunctional electrocatalyst for overall water splitting

A hollow ZIF-67 templating approach was used to fabricate a hollow cobalt sulfide superstructure with enhanced activity for overall water splitting.</jats:p

Predictive biomarkers for immune checkpoint inhibitors therapy in lung cancer

Immune checkpoint inhibitors (ICIs) have changed the treatment mode of lung cancer, extending the survival time of patients unprecedentedly. Once patients respond to ICIs, the median duration of response is usually longer than that achieved with cytotoxic or targeted drugs. Unfortunately, there is still a large proportion of lung cancer patients do not respond to ICI. Effective biomarkers are crucial for identifying lung cancer patients who can benefit from them. The first predictive biomarker is programmed death-ligand 1 (PD-L1), but its predictive value is limited to specific populations. With the development of single-cell sequencing and spatial imaging technologies, as well as the use of deep learning and artificial intelligence, the identification of predictive biomarkers has been greatly expanded. In this review, we will dissect the biomarkers used to predict ICIs efficacy in lung cancer from the tumor-immune microenvironment and host perspectives, and describe cutting-edge technologies to further identify biomarkers

Assessing the impact of different types of masks on COPD patients: a randomised controlled trial

Background Wearing masks imposes an additional respiratory burden on COPD patients. This study aimed to investigate the impact of various mask types on physiological parameters and subjective feelings in COPD patients. Methods This randomised, open-label, parallel-controlled trial randomly assigned 129 COPD patients from two Chinese hospitals to the N95 mask group, the surgical mask group and the no mask group, who were required to complete a 6-min rest (6MR) and a 6-min walking test (6MWT) while wearing their designated masks, and were assessed for blood pressure, oxygen saturation, pulse rate, Borg score, rating of perceived exertion (RPE) score, 6-min walk distance (6MWD) and subjective feeling score. Data were analysed using intention-to-treat analysis and per-protocol analysis. Results No significant differences were observed in blood pressure, oxygen saturation, pulse rate or the 6MWD among the three groups following a 6MR or 6MWT. Wearing N95 masks and surgical masks during the 6MWT significantly elevated perceived dyspnoea (p<0.001) and exertion scores (p<0.001) in COPD patients. The differences in the two scores between the highest and lowest groups were 2 and 4 points, respectively. Conclusion Wearing surgical masks or N95 masks for 6MR or 6MWT did not adversely affect physiological parameters in COPD patients. However, it significantly increased perceived dyspnoea and exertion

Fetal growth prediction: Establishing fetal growth prediction curves in the second trimester

BACKGROUND: Monitoring fetal weight during pregnancy has a guiding role in prenatal care. OBJECTIVE: To establish a personalized fetal growth curve for effectively monitoring fetal growth during pregnancy. METHODS: (1) This study retrospectively analyzed the birth weight database of 2,474 singleton newborns delivered normally at term. The personalized fetal growth curve model was formed by combining the estimating birth weight of newborns with the proportional weight formula. (2) Multiple linear stepwise regression method was used to estimate the birth weight of newborns. RESULTS: (1) Delivery gestational age, weight at first visit, maternal height, pre-pregnancy body mass index, fetal sex, parity had significant effects on birth weight. Based on these parameters, the formula for calculating term optimal weight was obtained (R2= 22.8%, P&lt; 0.001). (2) The personalized fetal growth curve was obtained according to the epidemiological factors input model of each pregnant woman. CONCLUSIONS: A model of personalized fetal growth curve can be established, and be used to evaluate fetal growth and development through estimated fetal weight monitoring.</jats:p

Establishment of a personalized fetal growth curve model

BACKGROUND: Fetal weight is one of the important indicators for judging whether fetal growth and development are normal. Fetal weight exceeding the normal range may lead to poor delivery outcomes. OBJECTIVE: We aimed to establish a personalized fetal growth curve in order to effectively monitor fetal growth during pregnancy. Fetal weight can be monitored while fetal growth and development are assessed. METHODS: This study retrospectively analyzed the birth weight and ultrasound database of 3,093 newborns delivered at normal term. The personalized fetal growth curve model was generated based on the birth weight formula established by Gardosi combined with the proportional weight equation. RESULTS: (1) The average birth weight of the single fetus at normal term was 3,457g. (2) According to the regression results of the proportion of fetal weight in full-term pregnancy and gestational week, the proportional weight equation is Weight% = 500.9 - 51.60GA + 1.727GA2- 0.01718GA3 (GA is gestational week), R2 is 98%, P&lt; 0.001. CONCLUSIONS: In this study, the normal birth weight of newborns and normal range of fetal weight can be estimated by using the personalized fetal growth curve model.</jats:p