Range Of Fields Research Articles

Study on Oxymatrine-Based Research from 2001 to 2022: A Bibliometric Analysis.

Oxymatrine is a quinolizidine alkaloid mainly derived from Kushen; it possesses various therapeutic effects, such as organ- and tissue-protective, anticancer, and antiviral effects. The research directions for oxymatrine remain broad. In order to explore the overall status of oxymatrine-based research, we carried out a bibliometric analysis to summarize the oxymatrine-based, English-written studies published in the past 22 years. In total, 267 studies were included, most of which were original. The number of annual studies slowly increased with some fluctuations. Other than China, 11 different countries conducted studies on oxymatrine; the variety in the country of origin of these publications is presented as a recently increasing trend. Many affiliates and researchers have participated in oxymatrine-based research. Various treatment mechanisms involving different oxymatrine pathways have led to research in a wide range of fields, being published in numerous journals. Two particularly popular research fields related to oxymatrine involved anticancer and anti-inflammation. From this research, we concluded that with increasing and continuous in-depth studies, more therapeutic effects and mechanisms will be elucidated, and oxymatrine may present as a viable option for the treatment of additional diseases.

Adaptive Method for Exploring Deep Learning Techniques for Subtyping and Prediction of Liver Disease

The term “Liver disease” refers to a broad category of disorders affecting the liver. There are a variety of common liver ailments, such as hepatitis, cirrhosis, and liver cancer. Accurate and early diagnosis is an emergent demand for the prediction and diagnosis of liver disease. Conventional diagnostic techniques, such as radiological, CT scan, and liver function tests, are often time-consuming and prone to inaccuracies in several cases. An application of machine learning (ML) and deep learning (DL) techniques is an efficient approach to diagnosing diseases in a wide range of medical fields. This type of machine-related learning can handle various tasks, such as image recognition, analysis, and classification, because it helps train large datasets and learns to identify patterns that might not be perceived by humans. This paper is presented here with an evaluation of the performance of various DL models on the estimation and subtyping of liver ailment and prognosis. In this manuscript, we propose a novel approach, termed CNN+LSTM, which is an integration of convolutional neural network (CNN) and long short-term memory (LSTM) networks. The results of the study prove that ML and DL can be used to improve the diagnosis and prognosis of liver disease. The CNN+LSTM model achieves a better accuracy of 98.73% compared to other models such as CNN, Recurrent Neural Network (RNN), and LSTM. The incorporation of the proposed CNN+LSTM model has better results in terms of accuracy (98.73%), precision (99%), recall (98%), F1 score (98%), and AUC (Area Under the Curve)-ROC (Receiver Operating Characteristic) (99%), respectively. The use of the CNN+LSTM model shows robustness in predicting the liver ailment with an accurate diagnosis and prognosis.

Generation of multiple vector beams through cascaded beam displacers and a segmented digital hologram

Complex vector light modes, characterized by a non-uniform transverse polarization distribution, have pervaded a wide range of research fields. In this study, we propose an approach that enables the simultaneous generation of multiple vector beams based on a spatially segmented digital hologram and two or more cascaded beam displacers. More precisely, an input beam is separated into multiple parallel copies spatially separated, which are then sent to the center of each segmented hologram, enabling independent modulation of each beam. The modulated beams are then judiciously recombined with a beam displacer to generate multiple vector modes in a simultaneous way. We demonstrated our technique with two arbitrary vector modes but the technique can be easily extended to more by inserting additional beam displacers. To assess the quality of the generated vector modes, we employed Stokes polarimetry to reconstruct their transverse polarization distribution and to measure their degree of non-separability. We envision that this technique will find significant applications in various fields, including optical communications, optical sensing, and optical tweezers to mention a few.

Cryogenic calibration for Hall probes

A superconducting undulator (SCU) prototype with a magnet length of 4m is being developed for the SHINE (Shanghai High Repetition Rate XFEL and Extreme Light Facility) project. The period length of the undulator is 16mm, the period number is 250, and the pole gap is 5mm. A point measurement system with three Hall probes will be used to measure the magnetic fields in the gap. The Hall probes fixed on a sledge will experience temperatures of about 4.2K to 50K when they move through the undulator gap. A temperature-dependent calibration system for these hall probes is necessary. The paper introduces the configuration of the cryogenic calibration system, and the result for the sensitivity and nonlinearity of one Hall probe from 2.8K to 300K over a magnetic field range of ±1.9T.

Assessment of health professional students’ perceptions and knowledge about stroke rehabilitation teams and the role of speech and language therapists in Bangladesh

Aims: Effective collaboration and interdisciplinary methods are crucial in the field of stroke treatment. The engagement of speech and language therapists (SLTs) with stroke rehabilitation teams is not familiar in the context of Bangladesh. The aims were to investigate health professional students’ perceptions and experiences of the stroke rehabilitation team and the role of the speech and language therapist (SLT). Methods: We tailored a structured questionnaire to suit the stroke rehabilitation landscape in Bangladesh where a diverse group of 150 students enrolled in various disciplines including physiotherapy (PT), occupational therapy (OT), speech and language therapy (SLT), psychology, medicine (MBBS), and nursing participated in the survey. Results: Our study, which covered a broad range of fields, found a positive tendency toward collaborative team models including Multidisciplinary, Transdisciplinary, and Interdisciplinary. The Kruskal–Wallis test’s statistical analysis (p greater than 0.05) revealed a thorough comprehension of the nuances of various team models. Students, however, showed ambiguity in understanding the particular roles and specialties of certain experts. There were several interesting results, such as the preference for the Multidisciplinary Team (MDT) model by 93.30% of occupational therapy students against the Transdisciplinary Team (TDT) model by 79.30% of SLT students. Additionally, the Interdisciplinary Team (IDT) model received support from 10.7% of participants. Conclusion: Bangladesh reported findings of this study highlight the significance of bridging the gap between academic and clinical training, cleaning up common misconceptions, and encouraging cooperative inter-professional teamwork. This includes promoting a thorough knowledge of the various responsibilities played in the field of stroke rehabilitation, particularly those of speech and language therapists.

Technical note: development of a simulation framework, enabling the investigation of locally tuned single energy proton radiography

Range uncertainties remain a limitation for the confined dose distribution that proton therapy can offer. The uncertainty stems from the ambiguity when translating CT Hounsfield Units (HU) into proton stopping powers. Proton Radiography (PR) can be used to verify the proton range. Specifically, PR can be used as a quality-control tool for CBCT-based synthetic CTs. An essential part of the work illustrating the potential of PR has been conducted using multi-layer ionization chamber (MLIC) detectors and mono-energetic PR. Due to the dimensions of commercially available MLICs, clinical adoption is cumbersome. Here, we present a simulation framework exploring locally-tuned single energy (LTSE) proton radiography and corresponding potential compact PR detector designs. Based on a planning CT data set, the presented framework models the water equivalent thickness. Subsequently, it analyses the proton energies required to pass through the geometry within a defined ROI. In the final step, an LTSE PR is simulated using the MCsquare Monte Carlo code. In an anatomical head phantom, we illustrate that LTSE PR allows for a significantly shorter longitudinal dimension of MLICs. We compared PR simulations for two exemplary 30 × 30 mm2 proton fields passing the phantom at a 90° angle at an anterior and a posterior location in an iso-centric setup. The longitudinal distance over which all spots per field range out is significantly reduced for LTSE PR compared to mono-energetic PR. In addition, we illustrate the difference in shape of integral depth dose (IDD) when using constrained PR energies. Finally, we demonstrate the accordance of simulated and experimentally acquired IDDs for an LTSE PR acquisition. As the next steps, the framework will be used to investigate the sensitivity of LTSE PR to various sources of errors. Furthermore, we will use the framework to systematically explore the dimensions of an optimized MLIC design for daily clinical use.

Neurite Growth and Electrical Activity in PC-12 Cells: Effects of H3 Receptor-Inspired Electromagnetic Fields and Inherent Schumann Frequencies

Cells are continually exposed to a range of electromagnetic fields (EMFs), including those from the Schumann resonance to radio waves. The effects of EMFs on cells are diverse and vary based on the specific EMF type. Recent research suggests potential therapeutic applications of EMFs for various diseases. In this study, we explored the impact of a physiologically patterned EMF, inspired by the H3 receptor associated with wakefulness, on PC-12 cells in vitro. Our hypothesis posited that the application of this EMF to differentiated PC-12 cells could enhance firing patterns at specific frequencies. Cell electrophysiology was assessed using a novel device, allowing the computation of spectral power density (SPD) scores for frequencies between 1 Hz and 128 Hz. T-tests comparing SPD at certain frequencies (e.g., 29 Hz, 30 Hz, and 79 Hz) between the H3-EMF and control groups showed a significantly higher SPD in the H3 group (p < 0.050). Moreover, at 7.8 Hz and 71 Hz, a significant correlation was observed between predicted and percentages of cells with neurites (R = 0.542). Key findings indicate the efficacy of the new electrophysiology measure for assessing PC-12 cell activity, a significant increase in cellular activity with the H3-receptor-inspired EMF at specific frequencies, and the influence of 7.8 Hz and 71 Hz frequencies on neurite growth. The overall findings support the idea that the electrical frequency profiles of developing cell systems can serve as an indicator of their progression and eventual cellular outcomes.

Cellular Alterations Due to Direct and Indirect Interaction of Nanomaterials with Nucleic Acids.

Deoxyribonucleic acid (DNA) represents the main reservoir of genetic information in the cells, which is why it is protected in the nucleus. Entry into the nucleus is, in general, difficult, as the nuclear membrane is a selective barrier to molecules longer than 40 kDa. However, in some cases, the size of certain nanoparticles (NPs) allows their internalization into the nucleus, thus causing a direct effect on the DNA structure. NPs can also induce indirect effects on DNA through reactive oxygen species (ROS) generation. In this context, nanomaterials are emerging as a disruptive tool for the development of novel therapies in a broad range of biomedical fields; although their effect on cell viability is commonly studied, further interactions with DNA or indirect alterations triggered by the internalization of these materials are not always clarified, since the small size of these materials makes them perfectly suitable for interaction with subcellular structures, such as the nucleus. In this context, and using as a reference the predicted interactions presented in a computational model, we describe and discuss the observed direct and indirect effects of the implicated nanomaterials on DNA.

Mode transformation of dynamic spin wave well modes in the magnetic stripes

In this work, we present an experimental and micromagnetic simulation study of the mode transformation of dynamic spin wave well modes in the magnetic stripes. With a highly precise lock-in ferromagnetic resonance measurement system, the spin wave modes, including the quantized backward volume magnetostatic spin waves and the spin wave well modes, can both be clearly detected in the transversely magnetized stripes. The mode transformations of dynamic spin wave well modes are detected in a field range that the moments near the edge of the stripe are not fully magnetized. Further micromagnetic simulation and analyzation show that the boundary condition change in the potential well might be the main reason for the mode transformation of spin wave well modes. These results might be helpful for the further understanding of the spin wave dynamics in the finite ferromagnets.

Snap-through instability of a high arch of magneto elastomers with a giant magnetic actuation

In our previous work [Chen et al., Int. J. Solids Struct. 246–247, 111607 (2022)], we designed a flat arch structure for hard magnetic soft material. It used snap-through instability for remote actuation and large deformation in a small magnetic field range (25–170 Oe). The ratio of the magnetic-driven deflection to the arch length was about 0.05. To improve this ratio, we designed a high arch of magneto elastomers, which expanded the range of ratio from 0.05 to 0.5, increasing it by an order of magnitude. However, raising the arch height extended the driving range while unintentionally increasing the critical magnetic field for snap-through. To overcome this challenge, we reduced the bending stiffness of the arch-shaped structure, effectively lowering the critical magnetic field. Our goal is to optimize the structure of hard magnetic soft materials to achieve a broader driving range under quasi-static applied magnetic field conditions. This is accomplished by adjusting the arch height and structural stiffness to balance different performance requirements. Our research could have a significant impact on applications and devices that utilize magnetic materials.

Angular Position Sensor Based on Anisotropic Magnetoresistive and Anomalous Nernst Effect

Magnetic position sensors have extensive applications in various industrial sectors and consumer products. However, measuring angles in the full range of 0–360° in a wide field range using a single magnetic sensor remains a challenge. Here, we propose a magnetic position sensor based on a single Wheatstone bridge structure made from a single ferromagnetic layer. By measuring the anisotropic magnetoresistance (AMR) signals from the bridge and two sets of anomalous Nernst effect (ANE) signals from the transverse ports on two perpendicular Wheatstone bridge arms concurrently, we show that it is possible to achieve 0–360° angle detection using a single bridge sensor. The combined use of AMR and ANE signals allows a mean angle error in the range of 0.51–1.05° within a field range of 100 Oe–10,000 Oe to be achieved.

Combining Wave and Particle Effects in the Simulation of X-ray Phase Contrast—A Review

X-ray phase-contrast imaging (XPCI) is a family of imaging techniques that makes contrast visible due to phase shifts in the sample. Phase-sensitive techniques can potentially be several orders of magnitude more sensitive than attenuation-based techniques, finding applications in a wide range of fields, from biomedicine to materials science. The accurate simulation of XPCI allows for the planning of imaging experiments, potentially reducing the need for costly synchrotron beam access to find suitable imaging parameters. It can also provide training data for recently proposed machine learning-based phase retrieval algorithms. The simulation of XPCI has classically been carried out using wave optics or ray optics approaches. However, these approaches have not been capable of simulating all the artifacts present in experimental images. The increased interest in dark-field imaging has also prompted the inclusion of scattering in XPCI simulation codes. Scattering is classically simulated using Monte Carlo particle transport codes. The combination of the two perspectives has proven not to be straightforward, and several methods have been proposed. We review the available literature on the simulation of XPCI with attention given to particular methods, including the scattering component, and discuss the possible future directions for the simulation of both wave and particle effects in XPCI.

Quantitative measurements of reactive oxygen species partitioning in electron transfer flavoenzyme magnetic field sensing.

Biological magnetic field sensing that gives rise to physiological responses is of considerable importance in quantum biology. The radical pair mechanism (RPM) is a fundamental quantum process that can explain some of the observed biological magnetic effects. In magnetically sensitive radical pair (RP) reactions, coherent spin dynamics between singlet and triplet pairs are modulated by weak magnetic fields. The resulting singlet and triplet reaction products lead to distinct biological signaling channels and cellular outcomes. A prevalent RP in biology is between flavin semiquinone and superoxide (O2 •-) in the biological activation of molecular oxygen. This RP can result in a partitioning of reactive oxygen species (ROS) products to form either O2 •- or hydrogen peroxide (H2O2). Here, we examine magnetic sensing of recombinant human electron transfer flavoenzyme (ETF) reoxidation by selectively measuring O2 •- and H2O2 product distributions. ROS partitioning was observed between two static magnetic fields at 20nT and 50μT, with a 13% decrease in H2O2 singlet products and a 10% increase in O2 •- triplet products relative to 50µT. RPM product yields were calculated for a realistic flavin/superoxide RP across the range of static magnetic fields, in agreement with experimental results. For a triplet born RP, the RPM also predicts about three times more O2 •- than H2O2, with experimental results exhibiting about four time more O2 •- produced by ETF. The method presented here illustrates the potential of a novel magnetic flavoprotein biological sensor that is directly linked to mitochondria bioenergetics and can be used as a target to study cell physiology.

Anti-Arthritic, Anti-Inflammatory, Thrombolytic, Membrane Stabilizing, Antifungal and Cytotoxic Activity of Polyscias scutellaria Leaf Extract: An In-vitro Analysis

The objective of this investigations was to evaluate in Vitro anti-arthritic, anti-inflammatory, thrombolytic, membrane stabilizing, antifungal and cytotoxic activities from the methanolic leaf extract of Polyscias scutellaria. Primary evaluation of methanolic extract of Polyscias scutellaria leaf (MEPSL) was performed via phytochemical screening. Presence of Alkaloids, flavonoids, saponins, glycosides, carbohydrates and reducing sugars were observed by phytochemical screening, among other secondary compounds. Using in vitro methods of protein denaturation, the anti-arthritic and anti-inflammatory properties of MEPSL was investigated. The results showed that the extracts significantly slowed down arthritis and inflammation with the percent inhibition of 94.59% and 86.33% at the conc. of 1000 µg/mL compared to the standard diclofenac sodium (98.19%) and acetyle salicylic acid (98.56%) at same concentration respectively. The thrombolytic activity of the extracts was additionally examined by clotlysis method, and the results showed that the ability to break up blood clots increased with the amount of extract used with the value of 97.32% which is very significant compared to the standard streptokinase which showed clotlysis of 91.304%. MEPSL was also showed action that stabilized membranes by using heat induced hemolysis method, which could be helpful in treating conditions like bleeding and swelling with the percent of protection value of 58.87% when compared to standard diclofenac sodium with the percent of protection value 73.63%. Antifungal action was also seen, which shows that it could be used to treat diseases caused by fungi with the zone of inhibition 7-36 mm varying on the type of fungi. Lastly, in vitro method was used to investigate the extracts' damaging effect on Artemia salina by using shrimp lethality assay. The results showed significant cytotoxicity with the LC50 value of 1.057 µg/mL compared to the standard vincristine sulphate (LC50 value of 0.608 µg/mL). To sum up, it is clear that the phytochemical found in this plant can be used for wide range of drug discovery field due to its potent pharmacological actions.

Functional Classification of Intraocular Lenses Based on Defocus Curves: A Scoping Review and Cluster Analysis.

To explore a potential functional classification of intraocular lenses (IOLs) based on monocular visual acuity defocus curves (VADCs) as a primary end-point. A systematic literature search was conducted using PubMed. Two independent reviewers screened the literature for inclusion and data extraction. Inclusion criteria were full-text primary clinical studies of IOLs, published in English from 2010 onward, involving patients undergoing cataract or refractive lens exchange. A cluster analysis was conducted to explore similarities in the range of field (RoF) and increase of visual acuity from intermediate to near (ΔVA). A total of 107 studies were ultimately included from the 436 identified in the systematic search, with an additional 5 studies added through the snowballing technique search. The cluster analysis was conducted using 69 reports that included monocular VADCs. Two main categories were identified based on the achieved RoF for 0.2 and 0.3 logMAR: full (FRoF) and partial (PRoF) RoF IOLs. Three subcategories were identified for FRoF depending on ΔVA: continuous (FRoF-C), smooth (FRoF-Sm), and steep (FRoF-St). On the other hand, PRoF IOLs shared the characteristic of monotonous decrease in visual acuity and were subclassified into two subcategories depending on the achieved RoF: narrowed (PRoF-N) and extended (PRoF-Ex). An additional subcategory was added to PRoF, enhanced (PRoF-En), for 7 reports alternating between PRoF-N and PRoF-Ex depending on the use of 0.2 or 0.3 logMAR as a cut-off for calculating the RoF. IOLs can be functionally classified into six types depending on the RoF and shape of the monocular VADC. [J Refract Surg. 2024;40(2):e108-e116.].

Suppression of electromagnetic crosstalk by differential excitation for SAW generation

Surface acoustic waves (SAWs) hold a vast potential in various fields such as spintronics, quantum acoustics, and electron-quantum optics, but an electromagnetic wave emanating from SAW generation circuits has often been a major hurdle. Here, we investigate a differential excitation method of interdigital transducers to generate SAWs while reducing the electromagnetic wave. The results show that electromagnetic waves are suppressed by more than 90 % in all directions. This suppression overcomes the operating limits and improves the scalability of SAW systems. Our results promise to facilitate the development of SAW-based applications in a wide range of research fields.

Machine learning based classification of vector field configurations

Magnetic materials at the nanoscale are important for science and technology. A key aspect for their research and advancement is the understanding of the emerging magnetization vector field configurations within samples and devices. A systematic parameter space exploration—varying for example material parameters, temperature, or sample geometry—leads to the creation of many thousands of field configurations that need to be sighted and classified. This task is usually carried out manually, for example by looking at a visual representation of the field configurations. We report that it is possible to automate this process using an unsupervised machine learning algorithm, greatly reducing the human effort. We use a combination of convolutional auto-encoder and density-based spatial clustering of applications with noise (DBSCAN) algorithm. To evaluate the method, we create the magnetic phase diagram of a FeGe disc as a function of changing external magnetic field using computer simulation to generate the configurations. We find that the classification algorithm is accurate, fast, requires little human intervention, and compares well against the published results in the literature on the same material geometry and range of external fields. Our study shows that machine learning can be a powerful tool in the research of magnetic materials by automating the classification of magnetization field configurations.

Investigation on a high-power V-band transit-time oscillator under a low guiding magnetic field with a focusing cathode

Low guiding magnetic fields are favored for high-power microwave sources in practical applications for the sake of compactness and miniaturization, especially for permanent magnet packaging to replace the bulky and heavy solenoid system. A novel focusing cathode is proposed in a V-band transit-time oscillator to reduce the requirement for guiding magnetic fields. Particle-in-cell simulation results indicate that in the diode region, the radial electric field and electron beam current are reduced with the focusing cathode, further leading to improved transmission of the electron beam under a low guiding magnetic field. With an input of diode voltage of 393 kV, a beam current of 5.25 kA, and a low guiding magnetic field of 0.6 T, the transit-time oscillator outputs a microwave with an average power of 566 MW and a frequency of 58.6 GHz, resulting in a conversion efficiency of 27.3%. The proposed V-band transit-time oscillator with a focusing cathode can also stably work under a large range of input voltage and magnetic field, desired for further research with permanent magnet packaging.

DNA Framework-Templated Fabrication of Ultrathin Electroactive Gold Nanosheets.

Generally, two-dimensional gold nanomaterials have unique properties and functions that offer exciting application prospects. However, the crystal phases of these materials tend to be limited to the thermodynamically stable crystal structure. Herein, we report a DNA framework-templated approach for the ambient aqueous synthesis of freestanding and microscale amorphous gold nanosheets with ultrathin sub-nanometer thickness. We observe that extended single-stranded DNA on DNA nanosheets can induce site-specific metallization and enable precise modification of the metalized nanostructures at predefined positions. More importantly, the as-prepared gold nanosheets can serve as an electrocatalyst for glucose oxidase-catalyzed aerobic oxidation, exhibiting enhanced electrocatalytic activity (~3-fold) relative to discrete gold nanoclusters owing to a larger electrochemical active area and wider band gap. The proposed DNA framework-templated metallization strategy is expected to be applicable in a broad range of fields, from catalysis to new energy materials.

DNA Framework‐Templated Fabrication of Ultrathin Electroactive Gold Nanosheets

AbstractGenerally, two‐dimensional gold nanomaterials have unique properties and functions that offer exciting application prospects. However, the crystal phases of these materials tend to be limited to the thermodynamically stable crystal structure. Herein, we report a DNA framework‐templated approach for the ambient aqueous synthesis of freestanding and microscale amorphous gold nanosheets with ultrathin sub‐nanometer thickness. We observe that extended single‐stranded DNA on DNA nanosheets can induce site‐specific metallization and enable precise modification of the metalized nanostructures at predefined positions. More importantly, the as‐prepared gold nanosheets can serve as an electrocatalyst for glucose oxidase‐catalyzed aerobic oxidation, exhibiting enhanced electrocatalytic activity (~3‐fold) relative to discrete gold nanoclusters owing to a larger electrochemical active area and wider band gap. The proposed DNA framework‐templated metallization strategy is expected to be applicable in a broad range of fields, from catalysis to new energy materials.