Flavonoid as a Potent Antioxidant: Quantitative Structure-Activity Relationship Analysis, Mechanism Study, and Molecular Design by Synergizing Molecular Simulation and Machine Learning.

In this work, a quantitative structure-antioxidant activity relationship of flavonoids was performed using a machine learning (ML) method. To achieve lipid-soluble, highly antioxidant flavonoids, 398 molecular structures with various substitute groups were designed based on the flavonoid skeleton. The hydrogen dissociation energies (ΔG1, ΔG2, and ΔG3) related to multiple hydrogen atom transfer processes and the solubility parameter (δ) of flavonoids were calculated using molecular simulation. The group decomposition results and the calculated antioxidant parameters constituted the ML data set. The artificial neural network and random forest models were constructed to predict and analyze the contribution of the substitute groups and positions to the antioxidant activity. The results showed the hydroxyl group at positions B4', B5', and B6' and the branched alkyl group at position C3 in the flavonoid skeleton were the optimal choice for improving antioxidant activity and compatibility with apolar organic materials. Compared to the pyrogallol group-grafted flavonoid, the designed potent flavonoid decreased ΔG1 and δ by 2.2 and 15.1%, respectively, while ΔG2 and ΔG3 kept the favorable lower values. These findings suggest that an efficient flavonoid prefers multiple ortho-phenolic hydroxyl groups and suitable sites with hydrophobic groups. The combination of molecular simulation and the ML method may offer a new research approach for the molecular design of novel antioxidants.

Development and validation of a self-attention network-based algorithm to detect mediastinal lesions on computed tomography images.

Background: Diagnosis of mediastinal lesions on computed tomography (CT) images is challenging for radiologists, as numerous conditions can present as mass-like lesions at this site. This study aimed to develop a self-attention network-based algorithm to detect mediastinal lesions on CT images and to evaluate its efficacy in lesion detection. Methods: In this study, two separate large-scale open datasets [National Institutes of Health (NIH) DeepLesion and Medical Image Computing and Computer Assisted Intervention (MICCAI) 2022 Mediastinal Lesion Analysis (MELA) Challenge] were collected to develop a self-attention network-based algorithm for mediastinal lesion detection. We enrolled 921 abnormal CT images from the NIH DeepLesion dataset into the pretraining stage and 880 abnormal CT images from the MELA Challenge dataset into the model training and validation stages in a ratio of 8:2 at the patient level. The average precision (AP) and confidence score on lesion detection were evaluated in the validation set. Sensitivity to lesion detection was compared between the faster region-based convolutional neural network (R-CNN) model and the proposed model. Results: The proposed model achieved an 89.3% AP score in mediastinal lesion detection and could identify comparably large lesions with a high confidence score >0.8. Moreover, the proposed model achieved a performance boost of almost 2% in the competition performance metric (CPM) compared to the faster R-CNN model. In addition, the proposed model can ensure an outstanding sensitivity with a relatively low false-positive rate by setting appropriate threshold values. Conclusions: The proposed model showed excellent performance in detecting mediastinal lesions on CT. Thus, it can drastically reduce radiologists' workload, improve their performance, and speed up the reporting time in everyday clinical practice.

A Dataset on Population Activity Patterns in Typical Regions of North China.

This data article describes the "Typical Regional Activity Patterns" (TRAP) dataset, which is based on the Tackling Key Problems in Air Pollution Control Program. In order to explore the interaction between air pollution and physical activity, we collected activity patterns of 9,221 residents with different occupations and lifestyles for three consecutive days in typical regions (Jinan and Baoding) where air pollutant concentrations were higher than those in neighboring areas. The TRAP dataset consists of two aspects of information: demographic indicators (personal information, occupation, personal habits, and living situation) and physical activity pattern data (activity location and intensity); additionally, the exposure measures of physical activity patterns are included, which data users can match to various endpoints for their specific purpose. This dataset provides evidence for exploring the attributes of activity patterns of residents in northern China and for interdisciplinary researchers to develop strategies and measures for health education and health promotion.

In-situ self-crosslinking strategy for super-tough polylactic acid/ bio-based polyurethane blends.

As a bio-based degradable plastic, polylactic acid (PLA) is highly commercialized, but its inherent brittleness limits its widespread use. In-situ polymerization techniques are effective in improving the toughness of PLA. However, the enhancement of the toughening effect in polyurethanes (PUs) through in-situ self-crosslinking still requires improvement and heavily relies on petroleum-derived feedstocks in certain approaches. In this paper, 1,3-polypropanediol (PO3G) of bio-based origin rather than conventional polyols like polyethylene glycol (PEG) and poly propylene glycol (PPG) was used. PLA/PO3G-PU blends were prepared via an in-situ self-crosslinking strategy. With a notch impact and tensile strength of 55.95 kJ/m2 and 47.77 MPa (a retention rate of 68.9 % compared with pure PLA), respectively, PLA/PO3G-PU blends achieved a better balance between stiffness and toughness. This work provides a new option for PLA to achieve a stiffness-toughness balance and get rid of dependence on petrochemical resources.

Screening of Organic Small Molecule Excipients on Ternary Solid Dispersions Based on Miscibility and Hydrogen Bonding Analysis: Experiments and Molecular Simulation.

The preparation of solid dispersions by mixing insoluble drugs with polymers is the main way to improve the aqueous solubility of drugs. The introduction of organic small molecule excipients into binary solid dispersions is expected to further enhance drug solubility by regulating intermolecular hydrogen bonding within the system at the microscopic level. In this study, we used carbamazepine (CBZ) as the target drug and polyvinylpyrrolidone as the solid dispersion matrix and screened the third component from 13 organic small molecules with good miscibility in the solid dispersion based on the principle of similarity of solubility parameters. The hydrogen bonding parameters and dissociation Gibbs free energy of the 13 organic small molecule-CBZ dimer were calculated by quantum mechanical simulation, and the tryptophan (Try) was identified as the optimal third component of organic small molecule. The migration of CBZ in binary and ternary systems was also analyzed by molecular dynamics simulation. On this theoretical basis, the corresponding solid dispersions were prepared, characterized, and tested for solubility analysis, which verified that the drug solubility was stronger for the system with the addition of polar fractions and the Try was indeed the best third component of organic small molecule compound, which was consistent with the simulation predictions. This screening method may provide theoretical guidance for drug modification design and clinical studies.

Overcoming Long-Range Unidirectional Transport of Underwater Bubbles on Laser-Textured Single-Layer Superaerophobic Dual-Rail Arrays.

Understanding the behavior of gas bubbles in aqueous media has been a hot topic because of their vital roles in both scientific research and industrial applications. Wettability gradient force and Laplace pressure are two typical characteristics of bubble transport. However, most work about bubble transport is limited to a short distance. Therefore, we took inspiration from the structure of the Nepenthes pitcher and prepared superaerophobic dual-rail arrays (SDRA). Upon SDRA, with this structure of a uniform distribution of superaerophobic and superaerophilic zones, bubbles can be transported over long distances on the structure's surface. The underlying principle is that gas bubbles tend to spread out on the superaerophilic region until they make contact with the asymmetric superaerophobic barriers. An asymmetric spreading resistance force is generated, which is attributed to the different lengths of the three-phase contact line (TCL) between gas bubbles and superaerophobic barriers. In addition, diverse parameters are quantified to investigate the critical transport state between unidirection and bidirection. Under the function of SDRA, the structure surface can realize bubble collection. The transporter as well as the light-control-light shutter is also successfully deployed. The present study will inspire people to develop innovative strategies to effectively manipulate gas bubbles in practical applications.

Hydroxy silicone oil modified boron nitride for high thermal conductivity and low dielectric loss silicone rubber composites: experimental and molecular simulation studies.

Polymer-based composites are widely used in microelectronics and wireless communications, which require high thermal conductivity and low dielectric loss for effective heat dispersion and signal transmission. Different lengths of hydroxyl silicone oil chains modified boron nitride/silicone rubber composites were explored and prepared in this work. Experiments demonstrate that the long-chain modified BN improves the thermal conductivity and decreases the dielectric loss of composites. A molecular dynamics simulation was employed to study the mechanism and affecting variables. The calculated results indicated that the improvement of the thermal and dielectric properties is mainly related to the interfacial behavior, including interfacial compatibility, interfacial bond strength, and phonon matching. Based on the simulated interfacial behavior and thermal conductivity, the thermal and dielectric properties of different chain-length modified boron nitride/silicone rubber composites have been anticipated. The results show that the longer-chain modified boron nitride/silicone rubber composites have better thermal and dielectric properties. This research may give a theoretical foundation for the development of materials with designable performance for electronic devices.

Hydrogen Bonding Principle-Based Molecular Design of a Polymer Excipient and Impacts on Hydrophobic Drug Properties: Molecular Simulation and Experiment.

Although some commercial excipients for improving the solubility of highly crystalline drugs are widely used, they still cannot cover all types of hydrophobic drugs. In this regard, with phenytoin as the target drug, related molecular structures of polymer excipients were designed. The optimal repeating units of NiPAm and HEAm were screened out through quantum mechanical simulation and Monte Carlo simulation methods, and the copolymerization ratio was also determined. Using molecular dynamics simulation technology, it was confirmed that the dispersibility and intermolecular hydrogen bonds of phenytoin in the designed copolymer were better than those in the commercial PVP materials. At the same time, the designed copolymers and solid dispersions were also prepared during the experiment, and the improvement of their solubility was confirmed, which is in accordance with the simulation predictions. The new ideas and simulation technology may be used for drug modification and development.

Associations between Dietary Patterns and Physical Activity with Physical Fitness among Adolescents in Shandong Province, China: A Cross-Sectional Study.

BACKGROUND: The trend of physical fitness (PF) and physical activity (PA) among Chinese adolescents is not optimistic, and unhealthy dietary behaviors are common. PA and dietary patterns (DPs) have been linked to PF in adolescents, but the associations between DPs and PF with PF in Chinese adolescents are rarely discussed. METHODS: A total of 8796 adolescents aged 11-18 were enrolled from Shandong Province, China. The CNSPFS battery was applied to assess PF. PA levels and diet quality were determined using the Physical Activity Questionnaire for Adolescents and the modified Chinese Diet Quality Questionnaire, respectively. This study used factor analysis to identify DPs and linear regression models to investigate the association between PF and related factors. RESULTS: The average PF score of the participants was 75.67. Adolescents who were girls, lived in rural areas and were active in PA performed better on the PF test (p < 0.05). Boys whose fathers were university educated or above had a higher probability of achieving higher PF scores (OR 4.36, 95% CI 1.32-14.36); however, if their mothers were university educated or above, they had a lower probability of achieving higher PF scores (OR 0.22, 95% CI 0.063-0.76). Unhealthy dietary pattern was negatively correlated with cardiorespiratory fitness in boys (OR 0.56, 95% CI 0.31-0.98). The association between unhealthy dietary pattern and girls' BMI became significant after adjustment for PA (p < 0.05). CONCLUSIONS: Girls performed better in PF than boys. Highly educated fathers could contribute to improve the PF performance in boys. There were four DPs among adolescents in Shandong Province, and different DPs may have different effects on PF in boys and girls.

Multiple-Droplet Selective Manipulation Enabled by Laser-Textured Hydrophobic Magnetism-Responsive Slanted Micropillar Arrays with an Ultrafast Reconfiguration Rate.

Biomimetic structures based on the magnetic response have attracted ever-increasing attention in droplet manipulation. Till now, most methods for droplet manipulation by a magnetic response are only applicable to a single droplet. It is still a challenge to achieve on-demand and precise control of multiple droplets (≥2). In this paper, a strategy for on-demand manipulation of multiple droplets based on magnetism-responsive slanted micropillar arrays (MSMAs) is proposed. The Glaco-modified superhydrophobic surface is the basis of multiple-droplet manipulation. The droplet's motion mode (pinned, unidirectional, and bidirectional) can be readily fine-tuned by changing the volume of droplets and the speed of the magnetic field. The rapid movement of droplets (10-80 mm/s) in the horizontal direction is realized by the unidirectional waves of the micropillar array driven by a specific magnetic field. The bending angle of micropillars can be rapidly and reversibly adjusted from 0 to 90° under the action of a magnetic field. Meanwhile, the liquid-involved light, electric switch, and biomedical detection can be designed by manipulating the droplets on demand. The superiority of MSMAs in multiple-droplet programmable manipulation opens up an avenue for applications in microfluidic and biomedical engineering.

Characteristics of Artificial Intelligence Clinical Trials in the Field of Healthcare: A Cross-Sectional Study on ClinicalTrials.gov.

Artificial intelligence (AI) has driven innovative transformation in healthcare service patterns, despite a lack of understanding of its performance in clinical practice. We conducted a cross-sectional analysis of AI-related trials in healthcare based on ClinicalTrials.gov, intending to investigate the trial characteristics and AI's development status. Additionally, the Neo4j graph database and visualization technology were employed to construct an AI technology application graph, achieving a visual representation and analysis of research hotspots in healthcare AI. A total of 1725 eligible trials that were registered in ClinicalTrials.gov up to 31 March 2022 were included in this study. The number of trial registrations has dramatically grown each year since 2016. However, the AI-related trials had some design drawbacks and problems with poor-quality result reporting. The proportion of trials with prospective and randomized designs was insufficient, and most studies did not report results upon completion. Currently, most healthcare AI application studies are based on data-driven learning algorithms, covering various disease areas and healthcare scenarios. As few studies have publicly reported results on ClinicalTrials.gov, there is not enough evidence to support an assessment of AI's actual performance. The widespread implementation of AI technology in healthcare still faces many challenges and requires more high-quality prospective clinical validation.

Effect of caffeic acid esters on antioxidant activity and oxidative stability of sunflower oil: Molecular simulation and experiments.

Polyphenol, though used as antioxidants in food industry, suffers from poor solubility issues in vegetable oil. Usually, its solubility would be enhanced through esterification. This work investigated the antioxidant activity and oxidative stability of caffeic acid (CA) and its derivative modified esters by molecular simulation and experiments. Density functional theory (DFT) and molecular dynamic analysis revealed the antioxidant mechanism of CA esters attributing to the comprehensive effects. The lower hydrogen dissociation energy (ΔG) of CA esters with catechol moiety caused the transformation of antioxidant into quinone via the double hydrogen atom transfer reaction. Particularly, the second reduced hydrogen dissociation energy was the keypoint. The strong non-bond energy and hydrogen bond allowed CA esters and oil molecules to interact more efficiently. Hence, the ester moieties enhanced the antioxidant activity with 4.5-6.5 % ΔG reduction compared to CA. Rancimat and DSC assays validated the theoretical predictions. This result shows that the antioxidant activity of CA and its esters could be predicted by this molecular simulation way, which may aid in designing of new polyphenol antioxidant structure.

Polyurethanes Based on Polylactic Acid for 3D Printing and Shape-Memory Applications.

Polylactic acid (PLA) has received increased attention in the development of shape-memory polymers and biomedical materials owing to its excellent physical properties and good biocompatibility and biodegradability. However, the inherent brittleness and high shape-recovery temperature of this material limit its application in the human body. Herein, we fabricated a PLA-based thermoplastic polyurethane (PLA-TPU) prepared from modified PLA-diol, dicyclohexylmethane-4,4'-diisocyanate, and 1,4-butanediol to solve the limitations of pure PLA. The glass transition temperature (Tg) of the designed TPU can be tailored from 6 to 40.5 °C by adjusting the content of hard segments or molecular weight of soft segments. The shape of the designed TPU can be fixed at room temperature and recovered at temperatures above 37 °C. Moreover, the prepared PLA-TPUs exhibited recyclability, three-dimensional printing capability, non-cytotoxicity, blood compatibility, and biodegradability. The shape of PLA-TPU/nano-Fe3O4 composites can be recovered by exposure to near-infrared light. These results collectively indicate that PLA-TPUs and their composites may have potential applications as intelligent flexible medical scaffolds for surgical and medical implantation equipment.

Directional sweat transport of monolayered cotton-fabrics fabricated through femtosecond-laser induced hydrophilization for personal moisture and thermal management.

Personal moisture and thermal management fabrics that can facilitate sweat removal and regulating skin temperature are highly desired for improving human comfort and performance. Here, we demonstrate a hydrophobic/superhydrophilic Janus cotton-fabric through femtosecond-laser-induced hydrophilization. The engineering Janus cotton-fabric can unidirectionally transport human sweat spontaneously. More importantly, the Janus fabrics can maintain human body temperature 2-3 °C lower than the conventional cotton fabrics, implying the cooling effect in thermal environment. In addition, the Janus fabric has lower wet skin adhesion in comparison with a conventional hydrophilic cotton fabric. The water vapor transmission rate (WVTR) of a Janus fabric is comparable to the traditional hydrophilic cotton fabrics. Overall, the successful creation of the Janus fabrics provides new insights for the development of moisture-wicking/thermal-management fabrics for satisfying the growing demand of personal comfort.

Bio-Based, Recyclable and Self-Healing Polyurethane Composites with High Energy Dissipation and Shape Memory.

Rubber composites make an important contribution to eliminating vibration and noise owing to their unique viscoelasticity. However, it is important to find alternative bio-based products with high damping properties owing to the shortage of petrochemical resources and poor performance. The ability to self-heal is an additional characteristic that is highly desirable because it can further increase the service life and safety of such products. In this study, a bio-based polylactic acid thermoplastic polyurethane (PLA-TPU) and its composites (PLA-TPU/AO-80) are synthesized. The reversible sacrificial hydrogen bonds in the composites increase the peak value of the loss factor (tan δmax ) from 0.87 to 2.12 with a high energy dissipation efficiency of 99% at 50% strain. After being heated for 15 min, the healed sample recovers 81.98% of its comprehensive mechanical properties due to the reorganization of the hydrogen bonds. Its tensile strength remains at 93.4% after recycling five times. Moreover, its shape memory properties show a response temperature close to the human body temperature making it an ideal candidate for medical applications.

Mental Health Problems and Associated Factors among High School Students in Shandong Province of China: A Cross-Sectional Study.

Background: Although many studies have analyzed the mental health problems (MHP) of Chinese adolescents, the associations of Chinese high school students' MHP with personal and family circumstances, parents' educational level, physical activity, and school adaptation are rarely discussed. Methods: The participants were 9398 students who were randomly recruited from 30 high schools in Shandong, China. Self-reported questionnaires were used to collect data. Multivariate logistic regression models were used to investigate associations between MHP and related factors. Results: The positive rate of MHP among high school students was 27.0%. Female, intimate friends of the opposite sex, poor family economic status, father's educational level of primary school or below, low social competence, and high antisocial behavior were significantly associated with higher odds of having MHP (p < 0.05). Students' self-reported odds of having obsessive−compulsive disorder, interpersonal sensitivity, and depression were inversely related to their mother's educational level. Compared with students who did not exercise, students who engaged in more than 30 min of physical activity 5−6 times per week had 60% lower self-reported odds of depression. Conclusions: We recommend strengthening the prevention, early detection, and treatment of MHP in high school students, especially those whose parents have low educational attainment, inactivity, and high disruptive behaviors.

Machine-learning-assisted molecular design of phenylnaphthylamine-type antioxidants.

In this study, a total of 302 molecular structures of phenylnaphthylamine antioxidants based on N-phenyl-1-naphthylamine and N-phenyl-2-naphthylamine skeletons with various substituents were modeled by exhaustive methods. Antioxidant parameters, including the hydrogen dissociation energy, solubility parameter, and binding energy, were calculated through molecular simulations. Then, a group decomposition scheme was determined to decompose 302 antioxidants. The antioxidant parameters and decomposition results constituted machine-learning data sets. Using an artificial neural network model, a correlation coefficient between the predicted and true values above 0.88 and an average relative error within 6% were achieved. Random forest models were used to analyze the factors affecting antioxidant activity from chemical and physical perspectives; the results showed that amino and alkyl groups were conducive to improving antioxidant performance. Moreover, substituent positions 1, 7, and 10 of N-phenyl-1-naphthylamine and 3, 7, and 10 of N-phenyl-2-naphthylamine were found to be the optimal positions for modifications to improve antioxidant activity. Two potentially efficient phenylnaphthylamine antioxidant structures were proposed and their antioxidant parameters were also calculated; the hydrogen dissociation energy and solubility parameter decreased by more than 9% and 7%, respectively, whereas the binding energy increased by more than 16% compared with the benchmark of N-phenyl-1-naphthylamine. These results indicate that molecular simulation and machine learning could provide alternative tools for the molecular design of new antioxidants.

Effects of natural antioxidants on the oxidative stability of Eucommia ulmoides seed oil: Experimental and molecular simulation investigations.

Eucommia ulmoides seed oil with high health potential is prone to oxidative rancidity due to its rich unsaturated fatty acids. In this work, three natural antioxidants were selected for exploring the oxidation resistance of the oil compared with the common synthetic antioxidant BHT. Antioxidant activity and its dispersion and migration as well as oxygen barrier performance were predicted via the bond dissociation enthalpy (BDE), mean square displacement (MSD), binding energy (Ebinding) and permeability coefficient (S). The predicted comprehensive performance is as follows: myricetin > epicatechin > caffeic acid > BHT. Free radical scavenging assay and Rancimat assay confirmed the antioxidant activity and protective effect on oil. That is the protective effect of three natural antioxidants on Eucommia ulmoides seed oil is better than BHT and myricetin shows the optimal comprehensive performance. The induction period of myricetin/lipid system increased 164.5% compared with the control. The experimental results are in good consistent with the simulation predictions.

Magnetic-Actuated Robot Enables High-Performance Underwater Bubble Maneuvering on Laser-Textured Biomimetic Slippery Surfaces.

Controllable underwater gas bubble (UGB) transport on a surface is realized by geography-/stimuli-induced wettability gradient force (Fwet-grad). Unfortunately, the high-speed maneuvering of UGBs along free routes on planar surfaces remains challenging. Herein, a regime of magnetism-actuated robot (MAR) mounting on biomimetic laser-ablated lubricant-impregnated slippery surfaces (LA-LISS) is reported. Leveraging on LA-LISS, MAR-entrained UGBs can move along arbitrary directions through the loading of a tracing magnetic trigger. The underlying hydrodynamics is that MAR-entrained UGBs would be actuated slipping upon a giant magnetic-induced towing force (FM//). Once the magnetism stimuli is discharged, FM// vanishes immediately to immobilize the UGBs on LA-LISS. Thanks to the MAR's robust bubble affinity, a typical UGB (20 µL) on the optimized LA-LISS can be accelerated at 500 mm/s2 and gain an ultrafast velocity of over 205 mm/s that far exceeds previously reported figures. Moreover, fundamental physics renders MAR antibuoyancy, steering locomotive UGBs on the inclined LA-LISS. Significantly, an MAR propelling UGBs to configure desirable patterns, realize on-demand coalescence, remedy the cutoff switch, as well as facilitate a programmable light-control-light optical shutter is successfully deployed. Compared with previous smart surfaces, the current multifunctional regime is more competent for harnessing UGBs featuring an unparalleled transport velocity independent of the feeble Fwet-grad.

Deep Q-networks with web-based survey data for simulating lung cancer intervention prediction and assessment in the elderly: a quantitative study.

BACKGROUND: Lung cancer screening and intervention might be important to help detect lung cancer early and reduce the mortality, but little was known about lung cancer intervention strategy associated with intervention effect for preventing lung cancer. We employed Deep Q-Networks (DQN) to respond to this gap. The aim was to quantitatively predict lung cancer optimal intervention strategy and assess intervention effect in aged 65 years and older (the elderly). METHODS: We screened lung cancer high risk with web-based survey data and conducted simulative intervention. DQN models were developed to predict optimal intervention strategies to prevent lung cancer in elderly men and elderly women separately. We assessed the intervention effects to evaluate the optimal intervention strategy. RESULTS: Proposed DQN models quantitatively predicted and assessed lung cancer intervention. DQN models performed well in five stratified groups (elderly men, elderly women, men, women and the whole population). Stopping smoking and extending quitting smoking time were optimal intervention strategies in elderly men. Extending quitting time and reducing smoked cigarettes number were optimal intervention strategies in elderly women. In elderly men and women, the maximal reductions of lung cancer incidence were 31.81% and 24.62% separately. Lung cancer incidence trend was deduced from the year of 1984 to 2050, which predicted that the difference of lung cancer incidence between elderly men and women might be significantly decreased after thirty years quitting time. CONCLUSIONS: We quantitatively predicted optimal intervention strategy and assessed lung cancer intervention effect in the elderly through DQN models. Those might improve intervention effects and reasonably prevent lung cancer.