Quantitative detection of citrate for early stage diagnosing of prostate cancer:Discriminating normal to cancer in prostate tissues

Prostate cancer(PC)biomarker-citrate detection is clinically important to diagnose PC in early stages.Methylquinolinium iodide(Q)conjugated indole-phenylboronic acid(IB)was designed as a red-emissive QIB probe for the detection of citrate through Lewis acid-base reaction and intramolecular charge transfer(ICT)sensing mechanisms.Boronic acid acts as Lewis acid as well as citrate(Lewis base)recognition unit.The probe reacted with citrate,showing enhanced red emissions.Since the probe has excellent water solubility and great biocompatibility,practical application in biological systems is possible.Citrate was monitored precisely in the mitochondria organelle(in vitro)of living cells with a positive charge on QIB.Also,endogenous(in situ)citrate was detected quantitatively to discriminate non-cancerous and PC mice,observed strong and lower(negligible)emission intensity on non-cancerous and cancerous prostate tissues,respectively.Because,the concentration of citrate is higher in healthy prostate compared with PC prostate.Furthermore,the analysis of sliced prostate tissues can give PC-related information for clinical diagnosis to prevent and treat PC in the initial stages.Therefore,we believe that the present probe is a promising biochemical reagent in diagnosing PC.

Cognitive Computing-Based CDSS in Medical Practice

Importance.The last decade has witnessed the advances of cognitive computing technologies that learn at scale and reason with purpose in medicine studies.From the diagnosis of diseases till the generation of treatment plans,cognitive computing encompasses both data-driven and knowledge-driven machine intelligence to assist health care roles in clinical decision-making.This review provides a comprehensive perspective from both research and industrial efforts on cognitive computing-based CDSS over the last decade.Highlights.(1)A holistic review of both research papers and industrial practice about cognitive computing-based CDSS is conducted to identify the necessity and the characteristics as well as the general framework of constructing the system.(2)Several of the typical applications of cognitive computing-based CDSS as well as the existing systems in real medical practice are introduced in detail under the general framework.(3)The limitations of the current cognitive computing-based CDSS is discussed that sheds light on the future work in this direction.Conclusion.Different from medical content providers,cognitive computing-based CDSS provides probabilistic clinical decision support by automatically learning and inferencing from medical big data.The characteristics of managing multimodal data and computerizing medical knowledge distinguish cognitive computing-based CDSS from other categories.Given the current status of primary health care like high diagnostic error rate and shortage of medical resources,it is time to introduce cognitive computing-based CDSS to the medical community which is supposed to be more open-minded and embrace the convenience and low cost but high efficiency brought by cognitive computing-based CDSS.