Non-invasive screening of bladder cancer using digital microfluidics and FLIM technology combined with deep learning.

Non-invasive screening for bladder cancer is crucial for treatment and postoperative follow-up. This study combines digital microfluidics (DMF) technology with fluorescence lifetime imaging microscopy (FLIM) for urine analysis and introduces a novel non-invasive bladder cancer screening technique. Initially, the DMF was utilized to perform preliminary screening and enrichment of urine exfoliated cells from 54 participants, followed by cell staining and FLIM analysis to assess the viscosity of the intracellular microenvironment. Subsequently, a deep learning residual convolutional neural network was employed to automatically classify FLIM images, achieving a three-class prediction of high-risk (malignant), low-risk (benign), and minimal risk (normal) categories. The results demonstrated a high consistency with pathological diagnosis, with an accuracy of 91% and a precision of 93%. Notably, the method is sensitive for both high-grade and low-grade bladder cancer cases. This highly accurate non-invasive screening method presents a promising approach for bladder cancer screening with significant clinical application potential.

Enhancing the photodynamic effect of curcumin through modification with TiO2 nanoparticles and cationic polymers.

Curcumin (CUR), a natural compound extracted from turmeric, has shown potential as a photosensitizer in photodynamic therapy (PDT). The aim of this work was to enhance the efficacy of CUR by modifying it using titanium dioxide (TiO2) nanoparticles and a cationic polymer called Sofast to create a nanocomposite TiO2-CUR-Sofast (TCS). Compared to unmodified CUR, TCS exhibited a broadening toward longer wavelength in the absorption wavelength within the 400-550 nm range, leading to improved CUR absorption. Cellular uptake efficiency of TCS was also enhanced, and it demonstrated nearly 4.7-fold higher reactive oxygen species (ROS) generation than CUR. Furthermore, TCS displayed the ability to attach to the cell membrane and enter cells within a 30-min incubation period. Upon irradiation, TCS exhibited remarkable cytotoxicity, resulting in a significant reduction in the viability of various cancer cells. Autofluorescence lifetime imaging of intracellular reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavin adenine dinucleotide (FAD) enzymes indicated that cancer cells treated with TCS and irradiation undergo a metabolic pathway shift from oxidative phosphorylation to glycolysis. These findings highlight the potential of TCS as an effective PDT agent for cancer treatment.

RsPDR8, a member of ABCG subfamily, plays a positive role in regulating cadmium efflux and tolerance in radish (Raphanus sativus L.).

Radish (Raphanus sativus L.) is one of the most vital root vegetable crops worldwide. Cadmium (Cd), a non-essential and toxic heavy metal, can dramatically restrict radish taproot quality and safety. Although the Peiotrpic Drug Resistance (PDR) genes play crucial roles in heavy metal accumulation and transport in plants, the systematic identification and functional characterization of RsPDRs remain largely unexplored in radish. Herein, a total of 19 RsPDR genes were identified from the radish genome. A few RsPDRs, including RsPDR1, RsPDR8 and RsPDR12, showed significant differential expression under Cd and lead (Pb) stress in the 'NAU-YH' genotype. Interestingly, the plasma membrane-localized RsPDR8 exhibited significantly up-regulated expression and enhanced promoter activity under Cd exposure. Ectopic expression of RsPDR8 conferred Cd tolerance via reducing Cd accumulation in yeast cells. Moreover, the transient transformation of RsPDR8 revealed that it positively regulated Cd tolerance by promoting ROS scavenging and enhancing membrane permeability in radish. In addition, overexpression of RsPDR8 increased root elongation but deceased Cd accumulation compared with the WT plants in Arabidopsis, demonstrating that it could play a positive role in mediating Cd efflux and tolerance in plants. Together, these results would facilitate deciphering the molecular mechanism underlying RsPDR8-mediated Cd tolerance and detoxification in radish.

Early Detection of Cervical Cancer by Fluorescence Lifetime Imaging Microscopy Combined with Unsupervised Machine Learning.

Cervical cancer has high morbidity and mortality rates, affecting hundreds of thousands of women worldwide and requiring more accurate screening for early intervention and follow-up treatment. Cytology is the current dominant clinical screening approach, and though it has been used for decades, it has unsatisfactory sensitivity and specificity. In this work, fluorescence lifetime imaging microscopy (FLIM) was used for the imaging of exfoliated cervical cells in which an endogenous coenzyme involved in metabolism, namely, reduced nicotinamide adenine dinucleotide (phosphate) [NAD(P)H], was detected to evaluate the metabolic status of cells. FLIM images from 71 participants were analyzed by the unsupervised machine learning method to build a prediction model for cervical cancer risk. The FLIM method combined with unsupervised machine learning (FLIM-ML) had a sensitivity and specificity of 90.9% and 100%, respectively, significantly higher than those of the cytology approach. One cancer recurrence case was predicted as high-risk several months earlier using this method as compared to using current clinical methods, implying that FLIM-ML may be very helpful for follow-up cancer care. This study illustrates the clinical applicability of FLIM-ML as a detection method for cervical cancer screening and a convenient tool for follow-up cancer care.