Multi-factor combined biomarker screening strategy to rapidly diagnose Alzheimer's disease and evaluate drug effect based on a rat model

Alzheimer's disease(AD)represents the main form of dementia;however,valid diagnosis and treatment measures are lacking.The discovery of valuable biomarkers through omics technologies can help solve this problem.For this reason,metabolomic analysis using ultra-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry(UPLC-Q-TOF-MS)was carried out on plasma,hippocampus,and cortex samples of an AD rat model.Based on the metabolomic data,we report a multi-factor combined biomarker screening strategy to rapidly and accurately identify potential biomarkers.Compared with the usual procedure,our strategy can identify fewer biomarkers with higher diagnostic specificity and sensitivity.In addition to diagnosis,the potential biomarkers identified using our strategy were also beneficial for drug evaluation.Multi-factor combined biomarker screening strategy was used to identify differential metabolites from a rat model of amyloid beta peptide 1e40(Aβ_(1-40))plus ibotenic acid-induced AD(compared with the controls)for the first time;lysophosphatidylcholine(LysoPC)and intermediates of sphingolipid metabolism were screened as potential biomarkers.Subsequently,the effects of donepezil and pine nut were successfully reflected by regulating the levels of the abovementioned biomarkers and metabolic profile distribution in partial least squaresdiscriminant analysis(PLS-DA).This novel biomarker screening strategy can be used to analyze other metabolomic data to simultaneously enable disease diagnosis and drug evaluation.

Equidistant combination wavelength screening and step-by-step phase-out method for the near-infrared spectroscopic analysis of serum urea nitrogen

We applied near-infrared(NIR)spectroscopy with chemometrics for the rapid and reagent-fee analysis of serum urea nitrogen(SUN).The modeling is based on the average effect of multiple sample partitions to achieve parameter selection with stability.A multiparameter optimization platform with Norris derivative filter-partial least squares(Norris-PLS)was developed to select the most suitable mode(d=2,s=33,g=15).Using equidistant combination PLS(EC-PLS)with four parameters(initial wavelength I,number of wavelengths N,number of wavelength gaps G and latent variables LV),we performed wavelength screening after eliminating high-absorption wavebands.The optimal EC-PLS parameters were I=1228 nm,N=26,G=16 and LV=12.The root-mean square error(SEP),correlation coefficient(R_(p))for prediction and ratio of performance-to-deviation(RPD)for validation were 1.03 mmol L^(-1),0.992 and 7.6,respectively.We proposed the wavelength step-by-step phase-out PLS(WSP-PLS)to remove redun-dant wavelengths in the top 100 EC-PLS models with improved prediction performance.The combination of 19 wavelengths was identifed as the optimal model for SUN.The SEP,Rp and RPD in validation were 1.01 mmol L^(-1),0.992 and 7.7,respectively.The prediction effect and wavelength complexity were better than those of EC-PIS.Our results showed that NIR spectroscopy combined with the EC-PLS and WSP-PLS methods enabled the high-precision analysis ofSUN.WSP-PLS is a secondary optimization method that can further optimize any wavelength moc odel obtained through other continuous or discrete strategies to establish a simple and better model.