Ursolic acid loaded tri-block copolymer nanoparticles based on triphenylphosphine for mitochondria-targeted cancer therapy.

A novel biodegradable amphiphilic triblock copolymer, polyphosphate, polyethylene glycol, and polylactic acid (PAEEP-PEG-PLLA), was synthesized by twice ring-opening polymerization and triphenylphosphine (TPP) was grafted onto the block copolymer to synthesize a carrier material TPP-PAEEP-PEG-PLLA, which was identified by1H-nuclear magnetic resonance (1H-NMR) spectroscopy. The TPP-PAEEP-PEG-PLLA nanoparticles encapsulated with ursolic acid (UA) were prepared by the emulsion-solvent evaporation method and characterized by dynamic light scattering. The mitochondrial targeting ability of fluorescently labeled nanoparticles was evaluated by laser confocal microscopy. The average particle size and surface charge of the UA -loaded nanoparticle solution were 180.07 ± 1.67 nm and +15.57 ± 1.33 mV, respectively. The biocompatibility of nanoparticles was briefly evaluated by erythrocyte hemolysis assay.In vitrocell proliferation assay and scratch migration assay were performed to compare the difference in anti-tumor effect between UA and UA nanoparticles. The results showed that TPP-modified triblock copolymers had good mitochondrial targeting and improved the low bioavailability of UA, and UA nanoparticles exhibited more pronounced anti-tumor capabilities. In summary, the results suggested that our UA nanoparticles were a promising drug-targeted delivery system for the treatment of tumors.

Analysis of the Relation Between Balance Control Subsystems: A Structural Equation Modeling Approach.

Balance plays a crucial role in human life and social activities. Maintaining balance is a relatively complex process that requires the participation of various balance control subsystems (BCSes). However, previous studies have primarily focused on evaluating an individual's overall balance ability or the ability of each BCS in isolation, without considering how they influence (or interact with) each other. The first study used clinical scales to evaluate the functions of the four BCSes, namely Reactive Postural Control (RPC), Anticipatory Postural Adjustment (APA), Dynamic Gait (DG), and Sensory Orientation (SO), and psychological factors such as fear of falling (FOF). A hierarchical structural equation modeling (SEM) was used to investigate the relationship between the BCSes and their association with FOF. The second study involved using posturography to measure and extract parameters from the center of pressure (COP) signal. SEM with sparsity constraint was used to analyze the relationship between vision, proprioception, and vestibular sense on balance based on the extracted COP parameters. The first study revealed that the RPC, APA, DG and SO indirectly influenced each other through their overall balance ability, and their association with FOF was not the same. APA has the strongest association with FOF, while RPC has the least association with FOF. The second study revealed that sensory inputs, such as vision, proprioception, and vestibular sensing, directly affected each other, but their associations were not identical. Among them, proprioception plays the most important role in the three sensory subsystems. This study provides the first numerical evidence that the BCSes are not independent of each other and exist in direct or indirect interplay. This approach has important implications for the diagnosis and management of balance-related disorders in clinical settings and improving our understanding of the underlying mechanisms of balance control.

Automated Prediction of Infant Cognitive Development Risk by Video: A Pilot Study.

OBJECTIVE: Cognition is an essential human function, and its development in infancy is crucial. Traditionally, pediatricians used clinical observation or medical imaging to assess infants' current cognitive development (CD) status. The object of pediatricians' greater concern is however their future outcomes, because high-risk infants can be identified early in life for intervention. However, this opportunity has not yet been realized. Fortunately, some recent studies have shown that the general movement (GM) performance of infants around 3-4 months after birth might reflect their future CD status, which gives us an opportunity to achieve this goal by cameras and artificial intelligence. METHODS: First, infants' GM videos were recorded by cameras, from which a series of features reflecting their bilateral movement symmetry (BMS) were extracted. Then, after at least eight months of natural growth, the infants' CD status was evaluated by the Bayley Infant Development Scale, and they were divided into high-risk and low-risk groups. Finally, the BMS features extracted from the early recorded GM videos were fed into the classifiers, using late infant CD risk assessment as the prediction target. RESULTS: The area under the curve, recall and precision values reached 0.830, 0.832, and 0.823 for two-group classification, respectively. CONCLUSION: This pilot study demonstrates that it is possible to automatically predict the CD of infants around the age of one year based on their GMs recorded early in life. SIGNIFICANCE: This study not only helps clinicians better understand infant CD mechanisms, but also provides an economical, portable and non-invasive way to screen infants at high-risk early to facilitate their recovery.

Assessment of Multivariate Information Transmission in Space-Time-Frequency Domain: A Case Study for EEG Signals.

OBJECTIVE: Multivariate signal (MS) analysis, especially the assessment of its information transmission (for example, from the perspective of network science), is the key to our understanding of various phenomena in biology, physics and economics. Although there is a large amount of literature demonstrating that MS can be decomposed into space-time-frequency domain information, there seems to be no research confirming that multivariate information transmission (MIT) in these three domains can be quantified. Therefore, in this study, we attempted to combine dynamic mode decomposition (DMD) and parallel communication model (PCM) together to realize it. METHODS: We first regarded MS as a large-scale system and then used DMD to decompose it into specific subsystems with their own intrinsic oscillatory frequencies. At the same time, the transition probability matrix (TPM) of information transmission within and between MS at two consecutive moments in each subsystem can also be calculated. Then, communication parameters (CPs) derived from each TPM were calculated in order to quantify the MIT in the space-time-frequency domain. In this study, multidimensional electroencephalogram (EEG) signals were used to illustrate our method. RESULTS: Compared with traditional EEG brain networks, this method shows greater potential in EEG analysis to distinguish between patients and healthy controls. CONCLUSION: This study demonstrates the feasibility of measuring MIT in the space-time-frequency domain simultaneously. SIGNIFICANCE: This study shows that MIT analysis in the space-time-frequency domain is not only completely different from the MS decomposition in these three domains, but also can reveal many new phenomena behind MS that have not yet been discovered.

Analysis of ECG Signals by Dynamic Mode Decomposition.

OBJECTIVE: Based on cybernetics, a large system can be divided into subsystems, and the stability of each can determine the overall properties of the system. However, this stability analysis perspective has not yet been employed in electrocardiogram (ECG) signals. This is the first study to attempt to evaluate whether the stability of decomposed ECG subsystems can be analyzed in order to effectively investigate the overall performance of ECG signals, and aid in disease diagnosis. METHODS: We used seven different cardiac pathologies (myocardial infarction, cardiomyopathy, bundle branch block, dysrhythmia, hypertrophy, myocarditis, and valvular heart disease) to illustrate our method. Dynamic mode decomposition (DMD) was first used to decompose ECG signals into dynamic modes (DMs) which can be regarded as ECG subsystems. Then, the features related to the DMs stabilities were extracted, and nine common classifiers were implemented for classification of these pathologies. RESULTS: Most features were significant for differentiating the above-mentioned groups (p value<0.05 after Bonferroni correction). In addition, our method outperformed all existing methods for cardiac pathology classification. CONCLUSION: We have provided a new spatial and temporal decomposition method, namely DMD, to study ECG signals. SIGNIFICANCE: Our method can reveal new cardiac mechanisms, which can contribute to the comprehensive understanding of its underlying mechanisms and disease diagnosis, and thus, can be widely used for ECG signal analysis in the future.

Gait Analysis by Causal Decomposition.

Recent studies have investigated bilateral gaits based on the causality analysis of kinetic (or kinematic) signals recorded using both feet. However, these approaches have not considered the influence of their simultaneous causation, which might lead to inaccurate causality inference. Furthermore, the causal interaction of these signals has not been investigated within their frequency domain. Therefore, in this study we attempted to employ a causal-decomposition approach to analyze bilateral gait. The vertical ground reaction force (VGRF) signals of Parkinson's disease (PD) patients and healthy control (HC) individuals were taken as an example to illustrate this method. To achieve this, we used ensemble empirical mode decomposition to decompose the left and right VGRF signals into intrinsic mode functions (IMFs) from the high to low frequency bands. The causal interaction strength (CIS) between each pair of IMFs was then assessed through the use of their instantaneous phase dependency. The results show that the CISes between pairwise IMFs decomposed in the high frequency band of VGRF signals can not only markedly distinguish PD patients from HC individuals, but also found a significant correlation with disease progression, while other pairwise IMFs were not able to produce this. In sum, we found for the first time that the frequency specific causality of bilateral gait may reflect the health status and disease progression of individuals. This finding may help to understand the underlying mechanisms of walking and walking-related diseases, and offer broad applications in the fields of medicine and engineering.

1HNMR-based metabolomic profile of rats with experimental acute pancreatitis.

BACKGROUND: Acute pancreatitis (AP) is a common inflammatory disease of the pancreas accompanied by serious metabolic disturbances. Nevertheless, the specific metabolic process of this disease is still unclear. Characterization of the metabolome may help identify biomarkers for AP. To identify potential biomarkers, this study therefore investigated the 1H-nuclear magnetic resonance (NMR)-based metabolomic profile of AP. METHODS: Fourteen male adult Sprague-Dawley rats were randomized into two groups: the AP group, in which AP was induced by retrograde ductal infusion of 3.5% sodium taurocholate; and the sham operation group (SO), in which rats were infused with 0.9% saline. Blood samples were obtained 12 hours later and a 600 MHz superconducting NMR spectrometer was used to detect plasma metabolites. Principal components analysis (PCA) and partial least squares-discriminant analysis after orthogonal signal correction (OSC-PLS-DA) were used to analyze both longitudinal Eddy-delay (LED) and Carr-Purcell-Meiboom-Gill (CPMG) spectra. RESULTS: Differences in plasma metabolites between the two groups were detected by PCA and PLS-DA of 1HNMR spectra. Compared with the SO group, plasma levels of lactate (δ 1.3, 1.34, 4.1), valine (δ 0.98, 1.02), succinic acid (δ 2.38), 3-hydroxybutyric acid (3-HB, δ 1.18), high density lipoprotein (HDL, δ 0.8), and unsaturated fatty acid (UFA, δ 2.78, 5.3) were elevated in the AP group, while levels of glycerol (δ 3.58, 3.66), choline (δ 3.22), trimethylamine oxide (TMAO, δ 3.26), glucose (δ 3-4), glycine (δ 3.54), very low density lipoprotein (VLDL, δ 1.34) and phosphatidylcholine (Ptd, δ 2.78) were decreased. CONCLUSIONS: AP has a characteristic metabolic profile. Lactate, valine, succinic acid, 3-HB, HDL, UFA, glycerol, choline, TMAO, glucose, glycine, VLDL, and Ptd may be potential biomarkers of early stage AP.

[Pharmacological study on free anthraquinones compounds in rhubarb in rats with experimental acute pancreatitis].

OBJECTIVE: To verify the pharmacological hypothesis of prescriptions by studying the targeted distribution of major components in stewed rhubarb in the rat model with acute pancreatitis (AP). METHOD: Normal SD rats (control group, n = 5) and the AP model induced with intraperitoneal cerulein (model group, n = 5) were taken as the experimental objects. Rats of the two groups were orally administered with stewed rhubarb granules (20 g x kg(-1)). Their heart, liver, spleen, lung, kidney and pancreas were collected two hours after the administration. Such constituents as emodin, chrysophanol, physcion, rhein and aloe-emodin and their concentrations in each tissue homogenate were detected by high performance liquid chromatography-mass-mass. RESULT: Aloe-emodin and physcion in stewed rhubarb whose concentrations in liver and kidney of normal rats were higher than that in pancreatic tissues, while the distribution spectrums and concentrations of the remaining components in pancreatic tissues had no significant difference with that of other organs. The concentrations of emodin, aloe-emodin, rhein and chrysophanol in stewed rhubarb in pancreatic tissues of the AP model group were higher than that in other tissues and organs, while their concentrations in pancreatic, renal and splenic tissues were notably higher than that in the normal group. CONCLUSION: In the conditions of AP, effective components in stewed rhubarb show a targeted distribution feature in pancreas, which provides experimental basis for the pharmacological hypothesis of prescriptions.

[Effects of micron Chinese herbal Liu-He-Dan ointment on inflammation and oxidative stress in rats with acute pancreatitis].

OBJECTIVE: To evaluate the effects of micron Chinese herbal Liu-He-Dan ointment (MLHD) on inflammation and oxidative stress in rats with acute pancreatitis (AP). METHODS: Eighteen SD rats were randomized into three groups: normal group (n = 6), AP group (n = 6) and MLHD group (n = 6). The rat model of AP was induced by intra-peritoneal injection of 150 g/L L-arginine with dosage of 2.0 mg/g twice within 2 hours. As control, the normal rats were injected the same amount of saline. The MLHD was applied to the abdomen area of the AP rats in MLHD group for 72 h. The serum and pancreas of the rats were obtained. The level of amylase, TNF-alpha, IL-6, IL-10 in serum and SOD, MDA in the pancreas were detected and quantified. RESULTS: Compared with AP group, the serum levels of amylase, TNF-alpha, IL-6, IL-10 were significantly decreased in MLHD group (P < 0.05). And the SOD in pancreas increased significantly (P < 0.05), but a decreasing trend of MDA level in MLHD group (P > 0.05) was observed. The serum level of the pathological score of pancreas in treatment group were similar to that in the model group (P > 0.05). CONCLUSION: Micron Liu-He-Dan ointment may have therapeutic and protective effects on AP by antioxidation and alleviating inflammatory response.