A Compact Differential Dynamic Microscopy-based Device (cDDM): An Approach Tool for Early Detection of Hypercoagulable State in Transfusion-Dependent-ß-Thalassemia Patients.

ß-Thalassemia especially transfusion-dependent thalassemia (TDT) associates with a hypercoagulable state, which is the main cause of thromboembolic events (TEE). Plasma viscosity and rheological parameters could be essential markers for determining hypercoagulable state in ß-thalassemia patients. The traditional methods for measuring viscosity are often limited by large sample volumes and are impractical for routine clinical monitoring. The compact differential dynamic microscopy-based device (cDDM), an optical microscopy for quantitative rheological assessment, was developed and applied for prognosis of the hypercoagulable state in ß-TDT with and without splenectomy. The device was performed plasma viscosity measurement using low plasma volume (8 µL) and revealed a value as modulus of complex viscosity |η(ω)| in 7 min. We also parallelly demonstrated the correlation of the viscosity and related-coagulable parameters: complete blood count, prothrombin time (PT), activated partial thromboplastin time (APTT), protein C (PC), protein S (PS), CD62P and CD63 expression, and platelet aggregation test. The thalassemia plasma exhibited a higher value of |η(ω)| than healthy plasma, which can represent a different viscoelastic property among the groups. Even all related-coagulable parameters indicated hypercoagulable state in both nonsplenectomies and splenectomies ß-TDT patients when compared to control, only high platelet numbers significantly correlated to high plasma viscosity in the splenectomy group. However, the other coagulable parameters have shown a trend of positive relationship with high plasma viscosity in all ß-1thalassemia TDT patients. The relative results suggested that our device would be an approach tool for early detection of hypercoagulable state in transfusion-dependent-ß-thalassemia patients, which can help to prevent TEE and the critical consequent-complications.

The in vitro assessment of rheological properties and dentin remineralization of saliva substitutes containing propolis and aloe vera extracts.

Saliva substitutes with enhanced dentin remineralization properties were expected to help manage caries progression in patients with xerostomia. This in vitro study examined the rheological properties and remineralization action of experimental saliva substitutes containing propolis extract and aloe vera extract on demineralized dentin. Four experimental saliva substitutes were formulated with varying concentrations of propolis extract (P) and aloe vera extract (A) were prepared. A commercial saliva substitute (Biotene Oral Rinse) was used as a commercial comparison. The rheological properties and viscosity of these materials were measured using a strain-controlled rheometer (n = 3). The remineralizing actions of saliva substitutes on demineralized dentin after 2 weeks were determined using ATR-FTIR and SEM-EDX (n = 8). The results were expressed as a percentage increase in the mineral-to-matrix ratio. Biotene demonstrated a significantly higher viscosity (13.5 mPa·s) than experimental saliva substitutes (p<0.05). The addition of extracts increased the viscosity of the saliva substitutes from 4.7 mPa·s to 5.2 mPa·s. All formulations showed minimal shear thinning behavior, which was the viscoelastic properties of natural saliva. The formulation containing 5 wt% of propolis exhibited the highest increase in the median mineral-to-matrix ratio (25.48%). The SEM-EDX analysis revealed substantial mineral precipitation in demineralized dentin, especially in formulations with 5 wt% or 2.5 wt% of propolis. The effect of the aloe vera extract was minimal. The addition of propolis and aloe vera extracts increased the viscosity of saliva substitutes. the addition of propolis for 2.5 or 5 wt% to saliva substitutes increased mineral apatite precipitation and tubule occlusion. To conclude, the saliva substitute containing propolis extract demonstrated superior remineralizing actions compared with those containing only aloe vera extract.

Dual-Responsive Carbon Quantum Dots for the Simultaneous Detection of Cytosine and 5-Methylcytosine Interpreted by a Machine Learning-Assisted Smartphone.

DNA methylation is an epigenetic alteration that results in 5-methylcytosine (5-mC) through the addition of a methyl group to the fifth carbon of a cytosine (C) residue. The methylation level, the ratio of 5-mC to C, in urine might be related to the whole-body epigenetic status and the occurrence of common cancers. To date, never before have any nanomaterials been developed to simultaneously determine C and 5-mC in urine samples. Herein, a dual-responsive fluorescent sensor for the urinary detection of C and 5-mC has been developed. This assay relied on changes in the optical properties of nitrogen-doped carbon quantum dots (CQDs) prepared by microwave-assisted pyrolysis. In the presence of C, the blue-shifted fluorescence intensity of the CQDs increased. However, fluorescence quenching was observed upon the addition of 5-mC. This was primarily due to photoinduced electron transfer as confirmed by the density functional theory calculation. In urine samples, our sensitive fluorescent sensor had detection limits for C and 5-mC of 43.4 and 74.4 µM, respectively, and achieved satisfactory recoveries ranging from 103.5 to 115.8%. The simultaneous detection of C and 5-mC leads to effective methylation level detection, achieving recoveries in the range of 104.6-109.5%. Besides, a machine learning-enabled smartphone was also developed, which can be effectively applied to the determination of methylation levels (0-100%). These results demonstrate a simple but very effective approach for detecting the methylation level in urine, which could have significant implications for predicting the clinical prognosis.

BiTNet: Hybrid deep convolutional model for ultrasound image analysis of human biliary tract and its applications.

Certain life-threatening abnormalities, such as cholangiocarcinoma, in the human biliary tract are curable if detected at an early stage, and ultrasonography has been proven to be an effective tool for identifying them. However, the diagnosis often requires a second opinion from experienced radiologists, who are usually overwhelmed by many cases. Therefore, we propose a deep convolutional neural network model, named biliary tract network (BiTNet), developed to solve problems in the current screening system and to avoid overconfidence issues of traditional deep convolutional neural networks. Additionally, we present an ultrasound image dataset for the human biliary tract and demonstrate two artificial intelligence (AI) applications: auto-prescreening and assisting tools. The proposed model is the first AI model to automatically screen and diagnose upper-abdominal abnormalities from ultrasound images in real-world healthcare scenarios. Our experiments suggest that prediction probability has an impact on both applications, and our modifications to EfficientNet solve the overconfidence problem, thereby improving the performance of both applications and of healthcare professionals. The proposed BiTNet can reduce the workload of radiologists by 35% while keeping the false negatives to as low as 1 out of every 455 images. Our experiments involving 11 healthcare professionals with four different levels of experience reveal that BiTNet improves the diagnostic performance of participants of all levels. The mean accuracy and precision of the participants with BiTNet as an assisting tool (0.74 and 0.61, respectively) are statistically higher than those of participants without the assisting tool (0.50 and 0.46, respectively (p<0.001)). These experimental results demonstrate the high potential of BiTNet for use in clinical settings.

Rapid detection of cancer DNA in human blood using cysteamine-capped AuNPs and a machine learning-enabled smartphone.

DNA methylation occurs when a methyl group is added to a cytosine (C) residue's fifth carbon atom, forming 5-methylcytosine (5-mC). Cancer genomes have a distinct methylation landscape (Methylscape), which could be used as a universal cancer biomarker. This study developed a simple, low-cost, and straightforward Methylscape sensing platform using cysteamine-decorated gold nanoparticles (Cyst/AuNPs), in which the sensing principle is based on methylation-dependent DNA solvation. Normal and cancer DNAs have distinct methylation profiles; thus, they can be distinguished by observing the dispersion of Cyst/AuNPs adsorbed on these DNA aggregates in MgCl2 solution. After optimising the MgCl2, Cyst/AuNPs, DNA concentration, and incubation time, the optimised conditions were used for leukemia screening, by comparing the relative absorbance (ΔA 650/525). Following the DNA extraction from actual blood samples, this sensor demonstrated effective leukemia screening in 15 minutes with high sensitivity, achieving 95.3% accuracy based on the measurement by an optical spectrophotometer. To further develop for practical realisation, a smartphone assisted by machine learning was used to screen cancer patients, achieving 90.0% accuracy in leukemia screening. This sensing platform can be applied not only for leukemia screening but also for other cancers associated with epigenetic modification.