Prednisolone Nanoprecipitation with Dean Instability Microfluidics Mixer

Dean flow and Dean instability play an important role in inertial microfluidics, with a wide application in mixing and sorting. However, most studies are limited to Dean flow in the microscale. This work first reports the application of Dean instability on organic nanoparticles synthesis at De up to 198. The channel geometry (the tortuous channel) is optimized by simulation, in which the mixing efficiency is considered. With the optimized design, prednisolone nanoparticles are synthesized, and the size of the most abundant prednisolone nanoparticles is down to 100 nm with an increase in the Re and De and smallest size down to 46 nm. This work serves as an ice-breaker to the real application of Dean instability by demonstrating its ability in mixing and nanomaterials like nanoparticle synthesis.

Magnetic Particle Imaging: From Tracer Design to Biomedical Applications in Vasculature Abnormality

Magnetic particle imaging (MPI) is an emerging non-invasive tomographic technique based on the response of magnetic nanoparticles (MNPs) to oscillating drive fields at the center of a static magnetic gradient. In contrast to magnetic resonance imaging (MRI), which is driven by uniform magnetic fields and projects the anatomic information of the subjects, MPI directly tracks and quantifies MNPs in vivo without background signals. Moreover, it does not require radioactive tracers and has no limitations on imaging depth. This article first introduces the basic principles of MPI and important features of MNPs for imaging sensitivity, spatial resolution, and targeted biodistribution. The latest research aiming to optimize the performance of MPI tracers is reviewed based on their material composition, physical properties, and surface modifications. While the unique advantages of MPI have led to a series of promising biomedical applications, recent development of MPI in investigating vascular abnormalities in cardiovascular and cerebrovascular systems, and cancer are also discussed. Finally, recent progress and challenges in the clinical translation of MPI are discussed to provide possible directions for future research and development.

Using a deep learning prior for accelerating hyperpolarized 13 C MRSI on synthetic cancer datasets

PURPOSE

We aimed to incorporate a deep learning prior with k-space data fidelity for accelerating hyperpolarized carbon-13 MRSI, demonstrated on synthetic cancer datasets.

METHODS

A two-site exchange model, derived from the Bloch equation of MR signal evolution, was firstly used in simulating training and testing data, that is, synthetic phantom datasets. Five singular maps generated from each simulated dataset were used to train a deep learning prior, which was then employed with the fidelity term to reconstruct the undersampled MRI k-space data. The proposed method was assessed on synthetic human brain tumor images (N = 33), prostate cancer images (N = 72), and mouse tumor images (N = 58) for three undersampling factors and 2.5% additive Gaussian noise. Furthermore, varied levels of Gaussian noise with SDs of 2.5%, 5%, and 10% were added on synthetic prostate cancer data, and corresponding reconstruction results were evaluated.

RESULTS

For quantitative evaluation, peak SNRs were approximately 32 dB, and the accuracy was generally improved for 5 to 8 dB compared with those from compressed sensing with L1-norm regularization or total variation regularization. Reasonable normalized RMS error were obtained. Our method also worked robustly against noise, even on a data with noise SD of 10%.

CONCLUSION

The proposed singular value decomposition + iterative deep learning model could be considered as a general framework that extended the application of deep learning MRI reconstruction to metabolic imaging. The morphology of tumors and metabolic images could be measured robustly in six times acceleration using our method.

Light-Responsive Materials in Droplet Manipulation for Biochemical Applications

Miniaturized droplets, characterized by well-controlled microenvironments and capability for parallel processing, have significantly advanced the studies on enzymatic evolution, molecular diagnostics, and single-cell analysis. However, manipulation of small-sized droplets, including moving, merging, and trapping of the targeted droplets for complex biochemical assays and subsequent analysis, is not trivial and remains technically demanding. Among various techniques, light-driven methods stand out as a promising candidate for droplet manipulation in a facile and flexible manner, given the features of contactless interaction, high spatiotemporal resolution, and biocompatibility. This review therefore compiles an in-depth discussion of the governing mechanisms underpinning light-driven droplet manipulation. Besides, light-responsive materials, representing the core of light-matter interaction and the key character converting light into different forms of energy, are particularly assessed in this review. Recent advancements in light-responsive materials and the most notable applications are comprehensively archived and evaluated. Continuous innovations and rational engineering of light-responsive materials are expected to propel the development of light-driven droplet manipulation, equip droplets with enhanced functionality, and broaden the applications of droplets for biochemical studies and routine biochemical investigations.

Ultraviolet Circularly Polarized Luminescence in Chiral Perovskite Nanoplatelet-Molecular Hybrids: Direct Binding Versus Efficient Triplet Energy Transfer

The development of ultraviolet circularly polarized light (UVCPL) sources has the potential to benefit plenty of practical applications but remains a challenge due to limitations in available material systems and a limited understanding of the excited state chirality transfer. Herein, by constructing hybrid structures of the chiral perovskite CsPbBr3 nanoplatelets and organic molecules, excited state chirality transfer is achieved, either via direct binding or triplet energy transfer, leading to efficient UVCPL emission. The underlying photophysical mechanisms of these two scenarios are clarified by comprehensive optical studies. Intriguingly, UVCPL realized via the triple energy transfer, followed by the triplet-triplet annihilation upconversion processes, demonstrates a 50-fold enhanced dissymmetry factor glum . Furthermore, stereoselective photopolymerization of diacetylene monomer is demonstrated by using such efficient UVCPL. This study provides both novel insights and a practical approach for realizing UVCPL, which can also be extended to other material systems and spectral regions, such as visible and near-infrared.

Impact of healthy lifestyles on risk of hypertension in the Chinese population: finding from SHUN-CVD study

INTRODUCTION

Lifestyle factors are known to play a role in the development of hypertension. We aimed to study the relationship between lifestyle and hypertension in a Chinese population.

METHODS

This study involved 3,329 participants (1,463 men and 1,866 women) aged 18-96 years in the Shenzhen-Hong Kong United Network on Cardiovascular Disease. A healthy lifestyle score was derived from 5 factors: no smoking, no alcohol consumption, active physical activity, normal body mass index, and a healthy diet. Multiple logistic regression was used to investigate the relationship between lifestyle score and hypertension. The influence of each lifestyle component on hypertension was also assessed.

RESULTS

In the overall population, 950 (28.5%) participants had hypertension. The risk of hypertension decreased with increasing healthy lifestyle scores. Compared with participants with the lowest score (score: 0), the multivariable odds ratios (ORs) and corresponding 95% confidence intervals for participants with scores 3, 4, and 5 were 0.65 (0.41-1.01), 0.62 (0.40-0.97), and 0.37 (0.22-0.61), respectively (P for trend <0.001). After adjusting for age, sex, and diabetes, the score was associated with hypertension risk (P for trend = 0.005). Compared with a lifestyle score of 0, the adjusted OR for hypertension for participants with a score of 5 was 0.46 (0.26-0.80).

CONCLUSIONS

The risk of hypertension is inversely related to the healthy lifestyle score. This reinforces the need to address lifestyle to reduce the risk of hypertension.

Thermosensitive Scattering Hydrogels Based on Triblock Poly-Ethers: A Novel Approach to Solar Radiation Regulation

Energy conservation in buildings is paramount, especially considering that glass accounts for 50% of energy consumption. The solar heat gain coefficient (SHGC) of glass is a critical energy-saving index for transparent structures. However, the fixed SHGC of ordinary glass makes it difficult to provide both summer shading and winter heating. In this study, we synthesized a hydrogel with a thermosensitive scattering (TS) property using triblock polyether and acrylamide. This hydrogel can realize the transition of clearness and atomization based on the temperature. When sealed within a glass cavity, it exhibits a high SHGC of 0.682 in its transparent state and a low SHGC of less than 0.31 when atomized. The lower critical solution temperature (LCST) of the TS glass can be adjusted from 0 to 70 °C to suit different regions. The photothermal properties of the material remained stable after 200 hot and cold cycles and 200 h of ultraviolet irradiation. This glass can prevent solar radiation from entering the room in summer, thereby reducing air conditioning usage and power consumption. In winter, it allows solar heat radiation to enter the room, minimizing the need for artificial heating. Its adaptable temperature design makes it an excellent solution for designers to create energy-efficient building exteriors.

Light-Driven Spatiotemporal Pickering Emulsion Droplet Manipulation Enabled by Plasmonic Hybrid Microgels

The past decades have witnessed the development of various stimuli-responsive materials with tailored functionalities, enabling droplet manipulation through external force fields. Among different strategies, light exhibits excellent flexibility for contactless control of droplets, particularly in three-dimensional space. Here, we present a facile synthesis of plasmonic hybrid microgels based on the electrostatic heterocoagulation between cationic microgels and anionic Au nanoparticles. The hybrid microgels are effective stabilizers of oil-in-water Pickering emulsions. In addition, the laser irradiation on Au nanoparticles creats a "cascade effect" to thermally responsive microgels, which triggers a change in microgel wettability, resulting in microgel desorption and emulsion destabilization. More importantly, the localized heating generated by a focused laser induces the generation of a vapor bubble inside oil droplets, leading to the formation of a novel air-in-oil-in-water (A/O/W) emulsion. These A/O/W droplets are able to mimic natural microswimmers in an aqueous environment by tracking the motion of a laser spot, thus achieving on-demand droplet merging and chemical communication between isolated droplets. Such proposed systems are expected to extend the applications of microgel-stabilized Pickering emulsions for substance transport, programmed release and controlled catalytic reactions.

Flow study of Dean's instability in high aspect ratio microchannels

Dean's flow and Dean's instability have always been important concepts in the inertial microfluidics. Curved channels are widely used for applications like mixing and sorting but are limited to Dean's flow only. This work first reports the Dean's instability flow in high aspect ratio channels on the deka-microns level for [Formula: see text]. A new channel geometry (the tortuous channel), which creates a rolled-up velocity profile, is presented and studied experimentally and numerically along with other three typical channel geometries at Dean's flow condition and Dean's instability condition. The tortuous channel generates a higher De environment at the same Re compared to the other channels and allows easier Dean's instability creation. We further demonstrate the use of multiple vortexes for mixing. The mixing efficiency is considered among different channel patterns and the tortuous channel outperforms the others. This work offers more understanding of the creation of Dean's instability at high aspect ratio channels and the effect of channel geometry on it. Ultimately, it demonstrates the potential for applications like mixing and cell sorting.

MetaRF: attention-based random forest for reaction yield prediction with a few trails

Artificial intelligence has deeply revolutionized the field of medicinal chemistry with many impressive applications, but the success of these applications requires a massive amount of training samples with high-quality annotations, which seriously limits the wide usage of data-driven methods. In this paper, we focus on the reaction yield prediction problem, which assists chemists in selecting high-yield reactions in a new chemical space only with a few experimental trials. To attack this challenge, we first put forth MetaRF, an attention-based random forest model specially designed for the few-shot yield prediction, where the attention weight of a random forest is automatically optimized by the meta-learning framework and can be quickly adapted to predict the performance of new reagents while given a few additional samples. To improve the few-shot learning performance, we further introduce a dimension-reduction based sampling method to determine valuable samples to be experimentally tested and then learned. Our methodology is evaluated on three different datasets and acquires satisfactory performance on few-shot prediction. In high-throughput experimentation (HTE) datasets, the average yield of our methodology's top 10 high-yield reactions is relatively close to the results of ideal yield selection.

A Silicon-Based PDMS-PEG Copolymer Microfluidic Chip for Real-Time Polymerase Chain Reaction Diagnosis

Polydimethylsiloxane (PDMS) has been widely used to make lab-on-a-chip devices, such as reactors and sensors, for biological research. Real-time nucleic acid testing is one of the main applications of PDMS microfluidic chips due to their high biocompatibility and transparency. However, the inherent hydrophobicity and excessive gas permeability of PDMS hinder its applications in many fields. This study developed a silicon-based polydimethylsiloxane-polyethylene-glycol (PDMS-PEG) copolymer microfluidic chip, the PDMS-PEG copolymer silicon chip (PPc-Si chip), for biomolecular diagnosis. By adjusting the modifier formula for PDMS, the hydrophilic switch occurred within 15 s after contact with water, resulting in only a 0.8% reduction in transmittance after modification. In addition, we evaluated the transmittance at a wide range of wavelengths from 200 nm to 1000 nm to provide a reference for its optical property study and application in optical-related devices. The improved hydrophilicity was achieved by introducing a large number of hydroxyl groups, which also resulted in excellent bonding strength of PPc-Si chips. The bonding condition was easy to achieve and time-saving. Real-time PCR tests were successfully conducted with higher efficiency and lower non-specific absorption. This chip has a high potential for a wide range of applications in point-of-care tests (POCT) and rapid disease diagnosis.

Digital solution for detection of undiagnosed diabetes using machine learning-based retinal image analysis

INTRODUCTION

Undiagnosed diabetes is a global health issue. Previous studies have estimated that about 24.1%-75.1% of all diabetes cases are undiagnosed, leading to more diabetic complications and inducing huge healthcare costs. Many current methods for diabetes diagnosis rely on metabolic indices and are subject to considerable variability. In contrast, a digital approach based on retinal image represents a stable marker of overall glycemic status.

RESEARCH DESIGN AND METHODS

Our study involves 2221 subjects for developing a classification model, with 945 subjects with diabetes and 1276 controls. The training data included 70% and the testing data 30% of the subjects. All subjects had their retinal images taken using a non-mydriatic fundus camera. Two separate data sets were used for external validation. The Hong Kong testing data contain 734 controls without diabetes and 660 subjects with diabetes, and the UK testing data have 1682 subjects with diabetes.

RESULTS

The 10-fold cross-validation using the support vector machine approach has a sensitivity of 92% and a specificity of 96.2%. The separate testing data from Hong Kong provided a sensitivity of 99.5% and a specificity of 91.1%. For the UK testing data, the sensitivity is 98.0%. The accuracy of the Caucasian retinal images is comparable with that of the Asian data. It implies that the digital method can be applied globally. Those with diabetes complications in both Hong Kong and UK data have a higher probability of risk of diabetes compared with diabetes subjects without complications.

CONCLUSIONS

A digital machine learning-based method to estimate the risk of diabetes based on retinal images has been developed and validated using both Asian and Caucasian data. Retinal image analysis is a fast, convenient, and non-invasive technique for community health applications. In addition, it is an ideal solution for undiagnosed diabetes prescreening.

A Nonlinear Maximum Correntropy Information Filter for High-Dimensional Neural Decoding

Neural signal decoding is a critical technology in brain machine interface (BMI) to interpret movement intention from multi-neural activity collected from paralyzed patients. As a commonly-used decoding algorithm, the Kalman filter is often applied to derive the movement states from high-dimensional neural firing observation. However, its performance is limited and less effective for noisy nonlinear neural systems with high-dimensional measurements. In this paper, we propose a nonlinear maximum correntropy information filter, aiming at better state estimation in the filtering process for a noisy high-dimensional measurement system. We reconstruct the measurement model between the high-dimensional measurements and low-dimensional states using the neural network, and derive the state estimation using the correntropy criterion to cope with the non-Gaussian noise and eliminate large initial uncertainty. Moreover, analyses of convergence and robustness are given. The effectiveness of the proposed algorithm is evaluated by applying it on multiple segments of neural spiking data from two rats to interpret the movement states when the subjects perform a two-lever discrimination task. Our results demonstrate better and more robust state estimation performance when compared with other filters.

High-performance wireless powering for peripheral nerve neuromodulation systems

Neuromodulation of peripheral nerves with bioelectronic devices is a promising approach for treating a wide range of disorders. Wireless powering could enable long-term operation of these devices, but achieving high performance for miniaturized and deeply placed devices remains a technological challenge. We report the miniaturized integration of a wireless powering system in soft neuromodulation device (15 mm length, 2.7 mm diameter) and demonstrate high performance (about 10%) during in vivo wireless stimulation of the vagus nerve in a porcine animal model. The increased performance is enabled by the generation of a focused and circularly polarized field that enhances efficiency and provides immunity to polarization misalignment. These performance characteristics establish the clinical potential of wireless powering for emerging therapies based on neuromodulation.