Sensitive sensing platform based on Co, Mo doped electrospun nanofibers for simultaneous electrochemical detection of dopamine and uric acid.

Abnormal levels of dopamine (DA) and uric acid (UA) in the human body are valuable indicators for monitoring human health, as they are associated with certain diseases. Therefore, it is crucial to develop sensitive and simultaneous analytical techniques for DA and UA in diagnosing the related diseases. Herein, the Co- and Mo- doped carbon nanofibers (Co, Mo@CNFs) electrochemical biosensor was developed successfully for the sensitive and accurate simultaneous detection of DA and UA. A straightforward electrospinning technique followed by a carbonization process was employed for the synthesis of Co, Mo@CNFs, and the encapsulation of Co and Mo within CNFs served to not only prevent nanoparticle agglomeration, thus providing more active sites, but also to facilitate rapid electron transfer. By incorporating Co and Mo into CNFs, the electrocatalytic activity of the modified electrode was greatly improved due to the beneficial conductivity and synergistic effects of transition metals. This enhancement effectively addressed issues such as the overlapping anodic peaks that occur when DA and UA are oxidized concurrently. Due to the mentioned synergistic contributions, the modified Co, Mo@CNFs electrode (Co, Mo@CNFs/GCE) achieved remarkable sensitivity for the simultaneous detection of DA and UA, while also exhibiting strong anti-interference ability. The detection limits for DA and UA were 2.35 nmol L-1 and 0.16 µmol L-1, respectively. We applied the developed Co, Mo@CNFs/GCE electrochemical biosensor to detect DA and UA in 50-fold diluted serum and urine samples. The results affirm the biosensor's reliability and precision. Moreover, the developed Co, Mo@CNFs/GCE biosensor demonstrated excellent performance in simultaneously detecting DA and UA, providing an efficient and dependable detection approach for clinical diagnosis and bioanalysis.

X-CNV: genome-wide prediction of the pathogenicity of copy number variations.

BACKGROUND: Gene copy number variations (CNVs) contribute to genetic diversity and disease prevalence across populations. Substantial efforts have been made to decipher the relationship between CNVs and pathogenesis but with limited success. RESULTS: We have developed a novel computational framework X-CNV ( www.unimd.org/XCNV ), to predict the pathogenicity of CNVs by integrating more than 30 informative features such as allele frequency (AF), CNV length, CNV type, and some deleterious scores. Notably, over 14 million CNVs across various ethnic groups, covering nearly 93% of the human genome, were unified to calculate the AF. X-CNV, which yielded area under curve (AUC) values of 0.96 and 0.94 in training and validation sets, was demonstrated to outperform other available tools in terms of CNV pathogenicity prediction. A meta-voting prediction (MVP) score was developed to quantitively measure the pathogenic effect, which is based on the probabilistic value generated from the XGBoost algorithm. The proposed MVP score demonstrated a high discriminative power in determining pathogenetic CNVs for inherited traits/diseases in different ethnic groups. CONCLUSIONS: The ability of the X-CNV framework to quantitatively prioritize functional, deleterious, and disease-causing CNV on a genome-wide basis outperformed current CNV-annotation tools and will have broad utility in population genetics, disease-association studies, and diagnostic screening.