High-fidelity synthesis of microhole templates with low-surface-energy-enabled self-releasing photolithography.

Material patterning through templates has provided an efficient way to meet the critical requirement for surface function in various fields. Here, we develop a self-releasing photolithographic process to make large-area freestanding templates with precise patterns. The low surface energy of substrates by hydrophobic treatment with proper silane modification ensures the template self-releasing. This method eliminates the need of mechanical separation or any sacrificial layers. Major steps including UV exposure and baking are optimized to realize high-quality structures and the final release of templates. The negative photoresists of SU-8 and polyimide are chosen to confirm the feasibility of this process. Wafer-scale freestanding templates with uniform microhole arrays are obtained with high structural fidelity, smooth surfaces and excellent flexibility. The hole size ranges from several to several tens of micrometers with an extremely low variation (<1%). These advantages could promote the application of precisely structured templates for surface patterning in material and surface science.

Targeted anti-cancer therapy: Co-delivery of VEGF siRNA and Phenethyl isothiocyanate (PEITC) via cRGD-modified lipid nanoparticles for enhanced anti-angiogenic efficacy.

Anti-tumor angiogenesis therapy, targeting the suppression of blood vessel growth in tumors, presents a potent approach in the battle against cancer. Traditional therapies have primarily concentrated on single-target techniques, with a specific emphasis on targeting the vascular endothelial growth factor, but have not reached ideal therapeutic efficacy. In response to this issue, our study introduced a novel nanoparticle system known as CS-siRNA/PEITC&L-cRGD NPs. These chitosan-based nanoparticles have been recognized for their excellent biocompatibility and ability to deliver genes. To enhance their targeted delivery capability, they were combined with a cyclic RGD peptide (cRGD). Targeted co-delivery of gene and chemotherapeutic agents was achieved through the use of a negatively charged lipid shell and cRGD, which possesses high affinity for integrin αvß3 overexpressed in tumor cells and neovasculature. In this multifaceted approach, co-delivery of VEGF siRNA and phenethyl isothiocyanate (PEITC) was employed to target both tumor vascular endothelial cells and tumor cells simultaneously. The co-delivery of VEGF siRNA and PEITC could achieve precise silencing of VEGF, inhibit the accumulation of HIF-1α under hypoxic conditions, and induce apoptosis in tumor cells. In summary, we have successfully developed a nanoparticle delivery platform that utilizes a dual mechanism of action of anti-tumor angiogenesis and pro-tumor apoptosis, which provides a robust and potent strategy for the delivery of anti-cancer therapeutics.

EHR-BERT: A BERT-based model for effective anomaly detection in electronic health records.

OBJECTIVE: Physicians and clinicians rely on data contained in electronic health records (EHRs), as recorded by health information technology (HIT), to make informed decisions about their patients. The reliability of HIT systems in this regard is critical to patient safety. Consequently, better tools are needed to monitor the performance of HIT systems for potential hazards that could compromise the collected EHRs, which in turn could affect patient safety. In this paper, we propose a new framework for detecting anomalies in EHRs using sequence of clinical events. This new framework, EHR-Bidirectional Encoder Representations from Transformers (BERT), is motivated by the gaps in the existing deep-learning related methods, including high false negatives, sub-optimal accuracy, higher computational cost, and the risk of information loss. EHR-BERT is an innovative framework rooted in the BERT architecture, meticulously tailored to navigate the hurdles in the contemporary BERT method; thus, enhancing anomaly detection in EHRs for healthcare applications. METHODS: The EHR-BERT framework was designed using the Sequential Masked Token Prediction (SMTP) method. This approach treats EHRs as natural language sentences and iteratively masks input tokens during both training and prediction stages. This method facilitates the learning of EHR sequence patterns in both directions for each event and identifies anomalies based on deviations from the normal execution models trained on EHR sequences. RESULTS: Extensive experiments on large EHR datasets across various medical domains demonstrate that EHR-BERT markedly improves upon existing models. It significantly reduces the number of false positives and enhances the detection rate, thus bolstering the reliability of anomaly detection in electronic health records. This improvement is attributed to the model's ability to minimize information loss and maximize data utilization effectively. CONCLUSION: EHR-BERT showcases immense potential in decreasing medical errors related to anomalous clinical events, positioning itself as an indispensable asset for enhancing patient safety and the overall standard of healthcare services. The framework effectively overcomes the drawbacks of earlier models, making it a promising solution for healthcare professionals to ensure the reliability and quality of health data.

Enzyme Engineering Renders Chlorinase the Activity of Fluorinase.

Organofluorine compounds have attracted substantial attention owing to their wide application in agrochemistry. Fluorinase (FlA) is a unique enzyme in nature that can incorporate fluorine into an organic molecule. Chlorinase (SalL) has a similar mechanism as fluorinase and can use chloride but not fluoride as a substrate to generate 5'-chloro-deoxyadenosine (5'-ClDA) from S-adenosyl-l-methionine (SAM). Therefore, identifying the features that lead to this selectivity for halide ions is highly important. Here, we engineered SalL to gain the function of FlA. We found that residue Tyr70 plays a key role in this conversion through alanine scanning. Site-saturation mutagenesis experiments demonstrated that Y70A/C/S/T/G all exhibited obvious fluorinase activity. The G131S mutant of SalL, in which the previously thought crucial residue Ser158 for fluoride binding in FlA was introduced, did not exhibit fluorination activity. Compared with the Y70T single mutant, the double mutant Y70T/W129F increased 5'-fluoro-5-deoxyadenosine (5'-FDA) production by 76%. The quantum mechanics (QM)/molecular mechanics (MM) calculations suggested that the lower energy barriers and shorter nucleophilic distance from F- to SAM in the mutants than in the SalL wild-type may contribute to the activity. Therefore, our study not only renders SalL the activity of FlA but also sheds light on the enzyme selectivity between fluoride versus chloride.

Dimethylsiloxanes in dust from nine indoor microenvironments of Henan Province: Occurrence and human exposure assessment.

Dimethylsiloxanes (MSs) are widely used in daily life and industry, with indoors being the main release site. Detecting the levels of MSs in indoor dust is essential for assessing the risks of human exposure. In this study, the content of MSs (D3-D8 and L3-L16) was quantified in indoor dust samples from nine microenvironments of Henan Province. The detection frequency of the targets ranged from 5.00 % to 100 %. The sum concentration of dimethylsiloxanes (TSi) was in a range of 463-3.32 × 104 ng·g-1 (median: 1.92 × 103 ng·g-1). The sum concentration of linear dimethylsiloxanes (TLSi) from all microenvironments was higher than the sum concentration of cyclic dimethylsiloxanes (TCSi), which was consistent with previously reported results. D7 and D8 were the main cyclic dimethylsiloxane, which had similar sources based on Spearman correlation analysis (p < 0.001). Moreover, D8 was detected with high levels in indoor dust for the first time, which warrants further exploration. L8-L16 were the main linear dimethylsiloxanes, which may have been due to their widespread use in electronic equipment and office equipment. The Spearman analysis found that total organic carbon (TOC) in indoor dust had weak effect on MSs. Additionally, relatively high MS levels were recorded in high people-flow working microenvironments. Accordingly, the exposure doses of MSs via indoor dust intake were estimated for different age groups using the model of worst-case exposure and median concentration. Toddlers had the highest EDIs (95th percentile concentration, 90.7 ng·kg-1-bw·d-1) to MSs.

Detecting anomalous sequences in electronic health records using higher-order tensor networks.

Detecting anomalous sequences is an integral part of building and protecting modern large-scale health information technology (HIT) systems. These HIT systems generate a large volume of records of patients' state and significant events, which provide a valuable resource to help improve clinical decisions, patient care processes, and other issues. However, detecting anomalous sequences in electronic health records (EHR) remains a challenge in healthcare applications for several reasons, including imbalances in the data, complexity of relationships between events in the sequence, and the curse of dimensionality. Conventional anomaly detection methods use the finite sequence of events to discriminate sequences. They fail to incorporate salient event details under variable higher-order dependencies (e.g., duration between events) that can provide better discrimination of sequences in their models. To address this problem, we propose event sequence and subsequence anomaly detection algorithms that (1) use network-based representations of interactions in the data, (2) account for variable higher-order dependencies in the data, and (3) incorporate events duration for adequate discrimination of the data. The proposed approach identifies anomalies by monitoring the change in the graph after the test sequence is removed from the network. The change is quantified using graph distance metrics so that dramatic changes in the network can be attributed to the removed sequence. Furthermore, the proposed subsequence algorithm recommends plausible paths and salient information for the detected anomalous subsequences. Our results show that the proposed event sequence anomaly detection algorithm outperforms the baseline methods for both synthetic data and real-world EHR data.

Full Soft Capacitive Omnidirectional Tactile Sensor Based on Micro-Spines Electrode and Hemispheric Dielectric Structure.

Flourishing in recent years, intelligent electronics is desirably pursued in many fields including bio-symbiotic, human physiology regulatory, robot operation, and human-computer interaction. To support this appealing vision, human-like tactile perception is urgently necessary for dexterous object manipulation. In particular, the real-time force perception with strength and orientation simultaneously is critical for intelligent electronic skin. However, it is still very challenging to achieve directional tactile sensing that has eminent properties, and at the same time, has the feasibility for scale expansion. Here, a fully soft capacitive omnidirectional tactile (ODT) sensor was developed based on the structure of MWCNTs coated stripe electrode and Ecoflex hemisphere array dielectric. The theoretical analysis of this structure was conducted for omnidirectional force detection by finite element simulation. Combined with the micro-spine and the hemispheric hills dielectric structure, this sensing structure could achieve omnidirectional detection with high sensitivity (0.306 ± 0.001 kPa-1 under 10 kPa) and a wide response range (2.55 Pa to 160 kPa). Moreover, to overcome the inherent disunity in flexible sensor units due to nano-materials and polymer, machine learning approaches were introduced as a prospective technical routing to recognize various loading angles and finally performed more than 99% recognition accuracy. The practical validity of the design was demonstrated by the detection of human motion, physiological activities, and gripping of a cup, which was evident to have great potential for tactile e-skin for digital medical and soft robotics.

Flexible Wireless Passive LC Pressure Sensor with Design Methodology and Cost-Effective Preparation.

Continuous monitoring of physical motion, which can be successfully achieved via a wireless flexible wearable electronic device, is essential for people to ensure the appropriate level of exercise. Currently, most of the flexible LC pressure sensors have low sensitivity because of the high Young's modulus of the dielectric properties (such as PDMS) and the inflexible polymer films (as the substrate of the sensors), which don't have excellent stretchability to conform to arbitrarily curved and moving surfaces such as joints. In the LC sensing system, the metal rings, as the traditional readout device, are difficult to meet the needs of the portable readout device for the integrated and planar readout antenna. In order to improve the pressure sensitivity of the sensor, the Ecoflex microcolumn used as the dielectric of the capacitive pressure sensor was prepared by using a metal mold copying method. The Ecoflex elastomer substrates enhanced the levels of conformability, which offered improved capabilities to establish intimate contact with the curved and moving surfaces of the skin. The pressure was applied to the sensor by weights, and the resonance frequency curves of the sensor under different pressures were obtained by the readout device connected to the vector network analyzer. The experimental results show that resonant frequency decreases linearly with the increase of applied pressure in a range of 0-23,760 Pa with a high sensitivity of -2.2 MHz/KPa. We designed a coplanar waveguide-fed monopole antenna used to read the information of the LC sensor, which has the potential to be integrated with RF signal processing circuits as a portable readout device and a higher vertical readout distance (up to 4 cm) than the copper ring. The flexible LC pressure sensor can be attached to the skin conformally and is sensitive to limb bending and facial muscle movements. Therefore, it has the potential to be integrated as a body sensor network that can be used to monitor physical motion.