Multinomial machine learning identifies independent biomarkers by integrated metabolic analysis of acute coronary syndrome.

A multi-class classification model for acute coronary syndrome (ACS) remains to be constructed based on multi-fluid metabolomics. Major confounders may exert spurious effects on the relationship between metabolism and ACS. The study aims to identify an independent biomarker panel for the multiclassification of HC, UA, and AMI by integrating serum and urinary metabolomics. We performed a liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics study on 300 serum and urine samples from 44 patients with unstable angina (UA), 77 with acute myocardial infarction (AMI), and 29 healthy controls (HC). Multinomial machine learning approaches, including multinomial adaptive least absolute shrinkage and selection operator (LASSO) regression and random forest (RF), and assessment of the confounders were applied to integrate a multi-class classification biomarker panel for HC, UA and AMI. Different metabolic landscapes were portrayed during the transition from HC to UA and then to AMI. Glycerophospholipid metabolism and arginine biosynthesis were predominant during the progression from HC to UA and then to AMI. The multiclass metabolic diagnostic model (MDM) dependent on ACS, including 2-ketobutyric acid, LysoPC(18:2(9Z,12Z)), argininosuccinic acid, and cyclic GMP, demarcated HC, UA, and AMI, providing a C-index of 0.84 (HC vs. UA), 0.98 (HC vs. AMI), and 0.89 (UA vs. AMI). The diagnostic value of MDM largely derives from the contribution of 2-ketobutyric acid, and LysoPC(18:2(9Z,12Z)) in serum. Higher 2-ketobutyric acid and cyclic GMP levels were positively correlated with ACS risk and atherosclerosis plaque burden, while LysoPC(18:2(9Z,12Z)) and argininosuccinic acid showed the reverse relationship. An independent multiclass biomarker panel for HC, UA, and AMI was constructed using the multinomial machine learning methods based on serum and urinary metabolite signatures.

Enhanced Conductivity and Antibacterial Behavior of Cotton via the Electroless Deposition of Silver.

In this study, the surface-initiated atom transfer radical polymerization (SI-ATRP) technique and electroless deposition of silver (Ag) were used to prepare a novel multi-functional cotton (Cotton-Ag), possessing both conductive and antibacterial behaviors. It was found that the optimal electroless deposition time was 20 min for a weight gain of 40.4%. The physical and chemical properties of Cotton-Ag were investigated. It was found that Cotton-Ag was conductive and showed much lower electrical resistance, compared to the pristine cotton. The antibacterial properties of Cotton-Ag were also explored, and high antibacterial activity against both Escherichia coli and Staphylococcus aureus was observed.

Ultrasound assisted dispersive solid phase microextraction of inorganic arsenic from food and water samples using CdS nanoflowers combined with ICP-OES determination.

Direct determination of arsenic species in real samples is challenging due to their trace concentration and spectral interferences by coexisting ions. Herein, we proposed an ultrasound-assisted dispersive solid phase microextraction (DSPME) procedure for the analyses of the trace inorganic arsenic. The hydrothermally synthesized cadmium sulfide nanoparticles (CdS NPs) completely adsorbed both arsenic species within 20 s at the initial arsenic concentration of 100 µg L-1. The detection limit (3 S/m) of the proposed method was found to be 0.5 ± 0.2 and 0.8 ± 0.2 ng L-1 for As(III) and As(V), respectively. The accuracy of the method against the systematic and constant errors was confirmed by the analysis of the Standard Reference Material (SRM) (>95% recovery with <5% RSD). The Student's t-test values were found to be less than the critical Student's t value at a 95% confidence level. The method was successfully employed for the determination of arsenic in food samples.

Accelerated solar steam generation for efficient ions removal.

Solar-driven evaporation as a sustainable water purification technology exhibits great potential in solving the global water crisis. In this paper, a novel solar evaporator was successfully prepared with the reduced graphene oxide (rGO) and silver (Ag) nanowires as photothermal conversion media which were loaded into the sodium alginate (SA) hydrogel. It was found that the evaporation rate of the prepared evaporator reached 2.02 kg m-2 h-1 and the solar energy efficiency was 91% under one sun irradiation intensity. Furthermore, this novel solar evaporator exhibited expectant evaporation performance in the treatment of seawater and heavy metal sewage with excellent ion removal ability, with the removal efficiency of various ions higher than 99.9%. The ion-crosslinked SA hydrogel contained in the evaporator ensured the rejection of multivalentions based on the distribution of carboxylate anions, and the three-dimensional porous channel of the super-hydrophilic hydrogel provided the convenient path for the transport of water molecules and the escape of water vapor. The binary composition of rGO and silver nanowires enhanced the photothermal conversion and the thermal conductivity, which was beneficial to the stable supply of thermal energy for evaporation. The purpose of this paper is to provide reference for promoting the practical application of solar evaporation.