bluuebunny/crossref_metadata_2025 huggingface dataset:
Antioxidant Activity and Oxidative Stress-Oriented Apoptosis Pathway in Saccharides Supplemented Cryopreserved Sperm of Pacific Abalone, Haliotis discus hannai
Authors: Hossen Shaharior , Sukhan Zahid Parvez , Cho Yusin , Lee Won Kyo , Kho Kang Hee
Published: June 2022

The Pacific abalone Haliotis discus hannai is a highly commercialized seafood in Southeast Asia. The aim of the present study was to determine the antioxidant activity and oxidative stress-oriented apoptosis pathway in saccharides supplemented cryopreserved sperm of Pacific abalone. Cryopreserved sperm showed impaired antioxidant defenses due to the reduced mRNA abundance of antioxidant genes (CAT, Cu/Zn-SOD, Mn-SOD, GPx, GR, and BCL-2), apoptosis inhibitor (HSP70, and HSP90) gene, and enzymatic antioxidant activity compared to fresh sperm. Such impaired antioxidant defenses caused an increase in the mRNA expression of apoptosis genes (Bax, and Caspase-3), finally leading to apoptosis. The impaired antioxidant defense also increased O2•− production and lipid peroxidation (MDA) levels, which further accelerated apoptosis. Considering all the experimental findings, an apoptosis pathway of cryopreserved sperm has been adopted for the first time. Specifically, sperm cryopreserved using 3% sucrose combined with 8% dimethyl sulfoxide (DMSO) showed improved mRNA stability, enzymatic activity, and DNA integrity with reduced O2•− production and MDA levels compared to sperm cryopreserved with the other types of examined cryoprotectants (8% ethylene glycol + 1% glucose, 6% propylene glycol + 2% glucose, 2% glycerol + 3% glucose, and 2% methanol + 4% trehalose). The present study suggests that 3% sucrose combined with 8% DMSO is suitable to cryopreserve the sperm of this valuable species for molecular conservation.
A New Intensity Adjustment Technique of Emission Spectral Analysis When Measured at the Upper Limit of the Dynamic Range of Charge-Coupled Devices
Authors: Popova Anna N. , Sukhomlinov Vladimir S. , Mustafaev Aleksandr S.
Published: June 2022

The article presents a method of mathematical correction to be applied to the results of measuring the intensity of spectral lines using charge-coupled devices (CCDs) in the presence of the blooming effect. This technique is particularly applicable in atomic emission spectroscopy. It enables expansion of the dynamic range of spark emission spectrometers and significantly minimizes the result distortions of the measurements taken in the area of high element concentrations. The authors devised a mathematical model and proposed an algorithm to adjust the measured intensity of analytical lines at the photo detector upper limit, in addition to an algorithm for processing data from the spectra recording system. The proposed mathematical algorithm was integrated into the software for the SPAS-02 and SPAS-05 spark spectrometers produced in Russia, and tested in determining the chemical composition of steels. The findings show that the actual dispersion of the analytical line intensity distribution may exceed the measured dispersion by a factor of 1.5, and their intensities may differ by a factor of 2. This algorithm may be implemented in atomic emission spectrometer software and makes it possible to adjust the calibration curves for a range of high alloying element concentrations when the analytical line intensity is at the upper limit of the CCD dynamic range.
Use of Filters to Smooth Out Signals Collected through Mobile Devices in the Static and Dynamic Balance Assessment: A Systematic Review
Authors: Sá Karina , Souza Givago , Callegari Bianca , Belgamo Anderson , Cabral André , Gorla José , Silva Anselmo Costa e
Published: June 2022

Background: When performing motion analysis using sensors, the signal often comes with noise and it is necessary to use filters to exclude unwanted frequencies. For this reason, the objective of this work was to carry out a systematic review on the filters used in data recorded from smartphone applications for static and dynamic balance assessment. Methods: A systematic literature review was performed on the PubMed, ScienceDirect, Scopus, Technology Research and Web of Science databases, using the search strategy: smartphone, “mobile technology”, evaluation, “postural stability”, and balance. Results: 427 articles were found (PubMed = 107; ScienceDirect = 67; Scopus = 106; Web of Science = 95; Technology research database = 52). After applying the inclusion criteria and removing duplicates, nine studies were eligible for this review. In these studies, the fourth-order Butterworth low-pass filter was the most applied (N = 6) and the cutoff frequency of 4 Hz (N = 2) was the most frequent. Conclusions: In general, few studies have adequately described the filter used in signal processing. This step, when hidden, negatively affects the reproducibility of studies. Understanding and describing the signal processing is important not only for the correct description of the results but also for the reproducibility of the studies.
The Vision-Based Data Reader in IoT System for Smart Factory
Authors: Hsu Tse-Chuan , Tsai Yao-Hong , Chang Dong-Meau
Published: June 2022

The proposed research is based on a real plastic injection factory for cutting board production. Most existing approaches for smart manufacturing tried to build the total solution of IoT by moving forward to the standard of industry 4.0. Under the cost considerations, this will not be acceptable to most factories, so we proposed the vision based technology to solve their immediate problem. Real-time machine condition monitoring is important for making great products and measuring line productivity or factory productivity. The study focused on a vision-based data reader (VDR) in edge computing for smart factories. A simple camera embedded in Field Programmable Gate Array (FPGA) was attached to monitor the screen on the control panel of the machines. Each end device was preprogrammed to capture images and process data on its own. The preprocessing step was then performed to have the normalized illumination of the captured image. A saliency map was generated to detect the required region for recognition. Finally, digit recognition was performed and the recognized digits were sent to the IoT system. The most significant contribution of the proposed VDR system used the compact deep learning model for training and testing purposes to fit the requirement of cost consideration and real-time monitoring in edge computing. To build the compact model, different convolution filters were tested to fit the performance requirement. Experimentations on a real plastic cutting board factory showed the improvement in manufacturing products by the proposed system and achieved a high digit recognition accuracy of 97.56%. In addition, the prototype system had low power and low latency advantages.
Enhanced Eco-Friendly Concrete Nano-Change with Eggshell Powder
Authors: Shcherban’ Evgenii M. , Stel’makh Sergey A. , Beskopylny Alexey N. , Mailyan Levon R. , Meskhi Besarion , Varavka Valery , Beskopylny Nikita , El’shaeva Diana
Published: June 2022

One of the unifying factors for all countries is the large consumption of chicken, and other, eggs in food and other types of economic activity. After using various types of eggs for their intended purpose, a large amount of waste accumulates in the form of eggshells. Currently, this problem exists and needs a non-trivial, original solution. The aim of the work was to fill the scientific gap in the direction of studying the microstructure formation of improved nano-modified environmentally-friendly concrete based on eggshell powder and obtaining a concrete composition for the manufacture of an industrial sample of such a material. An environmentally-friendly concrete was obtained, the characteristics of which were improved relative to standard concrete by modifying it with eggshell powder, for which the optimal dosage was determined. The most effective was the replacement of part of the cement with eggshell powder in the amount of 10%. The maximum increase in strength characteristics ranged from 8% to 11%. The modulus of elasticity increased by 4% compared to the control samples without eggshell powder. The maximum reduction in deformations under axial compression and tension in comparison with the control values ranged from 5% to 10%. The study of the composite’s microstructure nano-modified with eggshell powder, and an analysis of the changes occurring in this microstructure due to nano-modification, confirmed the improvement in characteristics and the optimal dosage of eggshell powder.
Dynamic Fire Risk Classification Prediction of Stadiums: Multi-Dimensional Machine Learning Analysis Based on Intelligent Perception
Authors: Lu Ying , Fan Xiaopeng , Zhao Zhipan , Jiang Xuepeng
Published: June 2022

Stadium fires can easily cause massive casualties and property damage. The early risk prediction of stadiums will be able to reduce the incidence of fires by making corresponding fire safety management and decision making in an early and targeted manner. In the field of building fires, some studies apply data mining techniques and machine learning algorithms to the collected risk hazard data for fire risk prediction. However, most of these studies use all attributes in the dataset, which may degrade the performance of predictive models due to data redundancy. Furthermore, machine learning algorithms are numerous and applied to fewer stadium fires, and it is crucial to explore models suitable for predicting stadium fire risk. The purpose of this study was to identify salient features to build a model for predicting stadium fire risk predictions. In this study, we designed an index attribute threshold interval to classify and quantify different fire risk data. We then used Gradient Boosting-Recursive Feature Elimination (GB-RFE) and Pearson correlation analysis to perform efficient feature selection on risk feature attributes to find the most informative salient feature subsets. Two cross-validation strategies were employed to address the dataset imbalance problem. Using the smart stadium fire risk data set provided by the Wuhan Emergency Rescue Detachment, the optimal prediction model was obtained based on the identified significant features and six machine learning methods of 12 combination forms, and full features were input as an experimental comparison study. Five performance evaluation metrics were used to evaluate and compare the combined models. Results show that the best performing model had an F1 score of 81.9% and an accuracy of 93.2%. Meanwhile, by introducing a precision-recall curve to explain the actual classification performance of each model, AdaBoost achieves the highest Auprc score (0.78), followed by SVM (0.77), which reveals more stable performance under such imbalanced data.
Spiral Strip
Authors: Skok Gregor
Published: June 2022

A visual graphic called Spiral Strip is presented consisting of color-coded segments arranged in a spiral. The width and length of each individual segment can be different, which can be used to convey additional information (besides the color coding). Although the primary motivation for developing the new graphic was related to climatic data, it can also be used with other types of data. Several examples based on climatic, population-size, and historical data are used to show the graphic’s visual appearance and main properties. The examples also demonstrate how the meaning of the graphic can be easy to grasp and intuitively understood by a casual observer. An easy-to-use Python code package for drawing the Spiral Strips was published in a public repository.
Principle and Implementation of Stokes Vector Polarization Imaging Technology
Authors: Wang Yong , Su Yuqing , Sun Xiangyu , Hao Xiaorui , Liu Yanping , Zhao Xiaolong , Li Hongsheng , Zhang Xiushuo , Xu Jing , Tian Jingjing , Kong Xiaofei , Wang Zhiwei , Yang Jie
Published: June 2022

Compared with traditional imaging methods, polarization imaging has its unique advantages in many directions and has great development prospects. It is one of the hot spots of research and development at home and abroad. Based on the polarization imaging principle of Stokes vector, the realization methods of non-simultaneous polarization imaging and simultaneous polarization imaging are introduced, respectively according to the different polarization modulation methods of Stokes vector acquisition. Non-simultaneous polarization imaging is mainly introduced in two ways: rotary polarization imaging, electrically controlled polarization imaging, and the simultaneous polarization imaging is mainly introduced in three ways: divided amplitude polarization imaging, divided aperture polarization imaging, and divided focal plane polarization imaging. In this paper, the principle and realization of polarization imaging based on Stokes vector are introduced to boost the application of polarization imaging and promote the research and development of polarization imaging technology.
Influence of Air Pollution Factors on Corrosion of Metal Equipment in Transmission and Transformation Power Stations
Authors: Chen Xiufang , Zhang Zhenyu , Zhang Hanping , Yan Hanbing , Liu Fengchun , Tu Shan
Published: June 2022

In this paper, the atmospheric environmental corrosion grade of substations in 11 prefecture level cities in Shanxi Province is evaluated through a metal hanging sheet experiment. Combined with the main environmental factors, such as temperature, relative humidity, and the concentration of main pollution factors, such as SO2, Cl−, dust, etc., the influence of various factors on the corrosion of transmission and transformation equipment is analyzed, and the corresponding anti-corrosion measures are put forward.
A Novel Missing Data Imputation Approach for Time Series Air Quality Data Based on Logistic Regression
Authors: Chen Mei , Zhu Hongyu , Chen Yongxu , Wang Youshuai
Published: June 2022

Missing values in air quality datasets bring trouble to exploration and decision making about the environment. Few imputation methods aim at time series air quality data so that they fail to handle the timeliness of the data. Moreover, most imputation methods prefer low-missing-rate datasets to relatively high-missing-rate datasets. This paper proposes a novel missing data imputation method, called FTLRI, for time series air quality data based on the traditional logistic regression and a presented “first Five & last Three” model, which can explain relationships between disparate attributes and extract data that are extremely relevant, both in terms of time and attributes, to the missing data, respectively. To investigate the performance of FTLRI, it is benchmarked with five classical baselines and a new dynamic imputation method using a neural network with average hourly concentration data of pollutants from three disparate stations in Lanzhou in 2019 under different missing rates. The results show that FTLRI has a significant advantage over the compared imputation approaches, both in the particular short-term and long-term time series air quality data. Furthermore, FTLRI has good performance on datasets with a relatively high missing rate, since it only selects the data extremely related to the missing values instead of relying on all the other data like other methods.
Engineering CRISPR/Cas13 System against RNA Viruses: From Diagnostics to Therapeutics
Authors: Xue Yi , Chen Zhenzhen , Zhang Wenxian , Zhang Jingjing
Published: June 2022

Over the past decades, RNA viruses have been threatened people’s health and led to global health emergencies. Significant progress has been made in diagnostic methods and antiviral therapeutics for combating RNA viruses. ELISA and RT-qPCR are reliable methods to detect RNA viruses, but they suffer from time-consuming procedures and limited sensitivities. Vaccines are effective to prevent virus infection and drugs are useful for antiviral treatment, while both need a relatively long research and development cycle. In recent years, CRISPR-based gene editing and modifying tools have been expanded rapidly. In particular, the CRISPR-Cas13 system stands out from the CRISPR-Cas family due to its accurate RNA-targeting ability, which makes it a promising tool for RNA virus diagnosis and therapy. Here, we review the current applications of the CRISPR-Cas13 system against RNA viruses, from diagnostics to therapeutics, and use some medically important RNA viruses such as SARS-CoV-2, dengue virus, and HIV-1 as examples to demonstrate the great potential of the CRISPR-Cas13 system.
Stem Cell Growth and Differentiation in Organ Culture: New Insights for Uterine Fibroid Treatment
Authors: Salas Ana , Beltrán-Flores Silvia , Évora Carmen , Reyes Ricardo , Montes de Oca Francisco , Delgado Araceli , Almeida Teresa A.
Published: June 2022

Organ culture allows for the understanding of normal and tumor cell biology, and tissues generally remain viable for 5–7 days. Strikingly, we determined that myometrial and MED12 mutant leiomyoma cells repopulated cell-depleted tissue slices after 20 days of culture. Using immunofluorescence and quantitative PCR of stem cell and undifferentiated cell markers, we observed clusters of CD49b+ cells in tumor slices. CD49b+ cells, however, were sparsely detected in the myometrial slices. Almost all LM cells strongly expressed Ki67, while only a few myometrial cells were stained for this proliferation marker. The CD73 marker was expressed only in tumor cells, whereas the mesenchymal stem cell receptor KIT was detected only in normal cells. HMGA2 and CD24 showed broader expression patterns and higher signal intensity in leiomyoma than in myometrial cells. In this study, we propose that activating CD49b+ stem cells in myometrium leads to asymmetrical division, giving rise to transit-amplifying KIT+ cells that differentiate to smooth muscle cells. On the contrary, activated leiomyoma CD49b+ cells symmetrically divide to form clusters of stem cells that divide and differentiate to smooth muscle cells without losing proliferation ability. In conclusion, normal and mutant stem cells can proliferate and differentiate in long-term organ culture, constituting a helpful platform for novel therapeutic discovery.
SIPGCN: A Novel Deep Learning Model for Predicting Self-Interacting Proteins from Sequence Information Using Graph Convolutional Networks
Authors: Wang Ying , Wang Lin-Lin , Wong Leon , Li Yang , Wang Lei , You Zhu-Hong
Published: June 2022

Protein is the basic organic substance that constitutes the cell and is the material condition for the life activity and the guarantee of the biological function activity. Elucidating the interactions and functions of proteins is a central task in exploring the mysteries of life. As an important protein interaction, self-interacting protein (SIP) has a critical role. The fast growth of high-throughput experimental techniques among biomolecules has led to a massive influx of available SIP data. How to conduct scientific research using the massive amount of SIP data has become a new challenge that is being faced in related research fields such as biology and medicine. In this work, we design an SIP prediction method SIPGCN using a deep learning graph convolutional network (GCN) based on protein sequences. First, protein sequences are characterized using a position-specific scoring matrix, which is able to describe the biological evolutionary message, then their hidden features are extracted by the deep learning method GCN, and, finally, the random forest is utilized to predict whether there are interrelationships between proteins. In the cross-validation experiment, SIPGCN achieved 93.65% accuracy and 99.64% specificity in the human data set. SIPGCN achieved 90.69% and 99.08% of these two indicators in the yeast data set, respectively. Compared with other feature models and previous methods, SIPGCN showed excellent results. These outcomes suggest that SIPGCN may be a suitable instrument for predicting SIP and may be a reliable candidate for future wet experiments.
Unique Deep Radiomic Signature Shows NMN Treatment Reverses Morphology of Oocytes from Aged Mice
Authors: Habibalahi Abbas , Campbell Jared M. , Bertoldo Michael J. , Mahbub Saabah B. , Goss Dale M. , Ledger William L. , Gilchrist Robert B. , Wu Lindsay E. , Goldys Ewa M.
Published: June 2022

The purpose of this study is to develop a deep radiomic signature based on an artificial intelligence (AI) model. This radiomic signature identifies oocyte morphological changes corresponding to reproductive aging in bright field images captured by optical light microscopy. Oocytes were collected from three mice groups: young (4- to 5-week-old) C57BL/6J female mice, aged (12-month-old) mice, and aged mice treated with the NAD+ precursor nicotinamide mononucleotide (NMN), a treatment recently shown to rejuvenate aspects of fertility in aged mice. We applied deep learning, swarm intelligence, and discriminative analysis to images of mouse oocytes taken by bright field microscopy to identify a highly informative deep radiomic signature (DRS) of oocyte morphology. Predictive DRS accuracy was determined by evaluating sensitivity, specificity, and cross-validation, and was visualized using scatter plots of the data associated with three groups: Young, old and Old + NMN. DRS could successfully distinguish morphological changes in oocytes associated with maternal age with 92% accuracy (AUC~1), reflecting this decline in oocyte quality. We then employed the DRS to evaluate the impact of the treatment of reproductively aged mice with NMN. The DRS signature classified 60% of oocytes from NMN-treated aged mice as having a ‘young’ morphology. In conclusion, the DRS signature developed in this study was successfully able to detect aging-related oocyte morphological changes. The significance of our approach is that DRS applied to bright field oocyte images will allow us to distinguish and select oocytes originally affected by reproductive aging and whose quality has been successfully restored by the NMN therapy.
Magnetoliposomes Containing Multicore Nanoparticles and a New Antitumor Thienopyridine Compound with Potential Application in Chemo/Thermotherapy
Authors: Lopes Fábio A. C. , Fernandes André V. F. , Rodrigues Juliana M. , Queiroz Maria-João R. P. , Almeida Bernardo G. , Pires Ana , Pereira André M. , Araújo João P. , Castanheira Elisabete M. S. , Rodrigues Ana Rita O. , Coutinho Paulo J. G.
Published: June 2022

Multicore magnetic nanoparticles of manganese ferrite were prepared using carboxymethyl dextran as an agglutinating compound or by an innovative method using melamine as a cross-coupling agent. The nanoparticles prepared using melamine exhibited a flower-shape structure, a saturation magnetization of 6.16 emu/g and good capabilities for magnetic hyperthermia, with a specific absorption rate (SAR) of 0.14 W/g. Magnetoliposome-like structures containing the multicore nanoparticles were prepared, and their bilayer structure was confirmed by FRET (Förster Resonance Energy Transfer) assays. The nanosystems exhibited sizes in the range of 250–400 nm and a low polydispersity index. A new antitumor thienopyridine derivative, 7-[4-(pyridin-2-yl)-1H-1,2,3-triazol-1-yl]thieno[3,2-b]pyridine, active against HeLa (cervical carcinoma), MCF-7 (breast adenocarcinoma), NCI-H460 (non-small-cell lung carcinoma) and HepG2 (hepatocellular carcinoma) cell lines, was loaded in these nanocarriers, obtaining a high encapsulation efficiency of 98 ± 2.6%. The results indicate that the new magnetoliposomes can be suitable for dual cancer therapy (combined magnetic hyperthermia and chemotherapy).
NMDA and AMPA Receptors at Synapses: Novel Targets for Tau and α-Synuclein Proteinopathies
Authors: Italia Maria , Ferrari Elena , Diluca Monica , Gardoni Fabrizio
Published: June 2022

A prominent feature of neurodegenerative diseases is synaptic dysfunction and spine loss as early signs of neurodegeneration. In this context, accumulation of misfolded proteins has been identified as one of the most common causes driving synaptic toxicity at excitatory glutamatergic synapses. In particular, a great effort has been placed on dissecting the interplay between the toxic deposition of misfolded proteins and synaptic defects, looking for a possible causal relationship between them. Several studies have demonstrated that misfolded proteins could directly exert negative effects on synaptic compartments, altering either the function or the composition of pre- and post-synaptic receptors. In this review, we focused on the physiopathological role of tau and α-synuclein at the level of postsynaptic glutamate receptors. Tau is a microtubule-associated protein mainly expressed by central nervous system neurons where it exerts several physiological functions. In some cases, it undergoes aberrant post-translational modifications, including hyperphosphorylation, leading to loss of function and toxic aggregate formation. Similarly, aggregated species of the presynaptic protein α-synuclein play a key role in synucleinopathies, a group of neurological conditions that includes Parkinson’s disease. Here, we discussed how tau and α-synuclein target the postsynaptic compartment of excitatory synapses and, specifically, AMPA- and NMDA-type glutamate receptors. Notably, recent studies have reported their direct functional interactions with these receptors, which in turn could contribute to the impaired glutamatergic transmission observed in many neurodegenerative diseases.
Multi-Channel Vision Transformer for Epileptic Seizure Prediction
Authors: Hussein Ramy , Lee Soojin , Ward Rabab
Published: June 2022

Epilepsy is a neurological disorder that causes recurrent seizures and sometimes loss of awareness. Around 30% of epileptic patients continue to have seizures despite taking anti-seizure medication. The ability to predict the future occurrence of seizures would enable the patients to take precautions against probable injuries and administer timely treatment to abort or control impending seizures. In this study, we introduce a Transformer-based approach called Multi-channel Vision Transformer (MViT) for automated and simultaneous learning of the spatio-temporal-spectral features in multi-channel EEG data. Continuous wavelet transform, a simple yet efficient pre-processing approach, is first used for turning the time-series EEG signals into image-like time-frequency representations named Scalograms. Each scalogram is split into a sequence of fixed-size non-overlapping patches, which are then fed as inputs to the MViT for EEG classification. Extensive experiments on three benchmark EEG datasets demonstrate the superiority of the proposed MViT algorithm over the state-of-the-art seizure prediction methods, achieving an average prediction sensitivity of 99.80% for surface EEG and 90.28–91.15% for invasive EEG data.
Anti-Tumorigenic Effect of a Novel Derivative of 2-Hydroxyoleic Acid and the Endocannabinoid Anandamide on Neuroblastoma Cells
Authors: Golan Hana , Mechoulam Raphael , Smoum Reem , Cohen-Zada Efrat , Pri-Chen Sara , Wiener Sapir , Grinberg Igor , Bar-Lev Dekel D. , Haj Christeeneh G. , Fisher Tamar , Toren Amos
Published: June 2022

Modulation of the endogenous cannabinoid system has been suggested as a potential anticancer strategy. In the search for novel and less toxic therapeutic options, structural modifications of the endocannabinoid anandamide and the synthetic derivative of oleic acid, Minerval (HU-600), were done to obtain 2-hydroxy oleic acid ethanolamide (HU-585), which is an HU-600 derivative with the anandamide side chain. We showed that treatment of SK-N-SH neuroblastoma cells with HU-585 induced a better anti-tumorigenic effect in comparison to HU-600 as evidenced by 3-[4,5-dimethylthiazole-2-yl]-2,5-diphenyltetrazolium bromide assay, colony-forming assay, and migration assay. Moreover, HU-585 demonstrated pro-apoptotic properties shown by increased levels of activated caspase-3 following treatment and a better senescence induction effect in comparison to HU-600, as demonstrated by increased activity of lysosomal β-galactosidase. Finally, we observed that combined treatment of HU-585 with the senolytic drugs ABT-263 in vitro, and ABT-737 in vivo resulted in enhanced anti-proliferative effects and reduced neuroblastoma xenograft growth in comparison to treatment with HU-585 alone. Based on these results, we suggest that HU-585 is a pro-apoptotic and senescence-inducing compound, better than HU-600. Hence, it may be a beneficial option for the treatment of resistant neuroblastoma especially when combined with senolytic drugs that enhance its anti-tumorigenic effects.
Diagnostic Value of AngioPLUS Microvascular Imaging in Thyroid Nodule Diagnosis Using Quantitative and Qualitative Vascularity Grading
Authors: Chambara Nonhlanhla , Liu Shirley Yuk Wah , Lo Xina , Ying Michael
Published: June 2022

This study investigated the diagnostic value of the Angio Planewave Ultrasensitive (AngioPLUS) Doppler ultrasound in improving the efficacy of grey scale ultrasound in thyroid nodule diagnosis. The EU TIRADS was used for the grey scale ultrasound assessment of 94 thyroid nodules. conventional Doppler and AngioPLUS Doppler ultrasound images were evaluated using qualitative vascularity grading, where predominant central vascularity indicated malignancy-suspicion, and quantitative regional vascularity assessment, where predominant peripheral vascularity using a ratio vascularity index (RVI) of > 1 indicated benign disease. Diagnostic performance outcomes of sole and combination approaches were calculated based on final pathologic results. Using sole EU TIRADS and AngioPLUS + power Doppler imaging (APDI) based on qualitative vascularity and RVI, the results were a sensitivity of 83.3% vs. 83.3 vs. 66.7% and a specificity of 50% vs. 81.3% vs. 73.4, respectively. EU TIRADS combined with APDI significantly improved the specificity using both qualitative vascularity and RVI assessment approaches (84.4% and 81%, respectively, p < 0.05); and slightly reduced the sensitivity (76.7% and 58.1%). For cytologically-equivocal thyroid nodules, the combination approach using qualitative vascularity assessment outperformed the EU TIRADS (sensitivity: both were 88.9%; specificity: 77.4% vs. 38.7%, p < 0.05; and AUROC: 0.83 vs. 0.62, p < 0.05). APDI combined with EU TIRADS is diagnostically efficient in stratifying thyroid nodules, particularly cytologically-equivocal nodules.
A Dual-Band High-Sensitivity THz Metamaterial Sensor Based on Split Metal Stacking Ring
Authors: Lu Xuejing , Ge Hongyi , Jiang Yuying , Zhang Yuan
Published: June 2022

Terahertz (THz)-detection technology has been proven to be an effective and rapid non-destructive detection approach in biomedicine, quality control, and safety inspection, among other applications. However, the sensitivity of such a detection method is limited due to the insufficient power of the terahertz source and the low content, or ambiguous characteristics, of the analytes to be measured. Metamaterial (MM) is an artificial structure in which periodic sub-wavelength units are arranged in a regular manner, resulting in extraordinary characteristics beyond those possessed by natural materials. It is an effective method to improve the ability of terahertz spectroscopy detection by utilizing the metamaterial as a sensor. In this paper, a dual-band, high-sensitivity THz MM sensor based on the split metal stacking ring resonator (SMSRR) is proposed. The appliance exhibited two resonances at 0.97 and 2.88 THz in the range of 0.1 to 3 THz, realizing multi-point matching between the resonance frequency and the characteristic frequency of the analytes, which was able to improve the reliability and detection sensitivity of the system. The proposed sensor has good sensing performance at both resonant frequencies and can achieve highest sensitivities of 304 GHz/RIU and 912 GHz/RIU with an appropriate thickness of the analyte. Meanwhile, the advantage of multi-point matching of the proposed sensor has been validated by distinguishing four edible oils based on their different refractive indices and demonstrating that the characteristics obtained in different resonant frequency bands are consistent. This work serves as a foundation for future research on band extension and multi-point feature matching in terahertz detection, potentially paving the way for the development of high-sensitivity THz MM sensors.
Altered Regional Activity and Network Homogeneity within the Fronto-Limbic Network at Rest in Medicine-Free Obsessive–Compulsive Disorder
Authors: Chen Yunhui , Ou Yangpan , Lv Dan , Yu Zengyan , Shang Tinghuizi , Ma Jidong , Zhan Chuang , Ding Zhenning , Yang Xu , Xiao Jian , Yang Ru , Sun Zhenghai , Zhang Guangfeng , Wang Xiaoping , Guo Wenbin , Li Ping
Published: June 2022

Functional abnormalities in brain areas within the fronto-limbic network have been widely reported in obsessive–compulsive disorder (OCD). However, region- and network-level brain activities of the fronto-limbic network at rest have not been simultaneously investigated in OCD. In this study, 40 medicine-free and non-comorbidity patients with OCD and 38 age-, education-, and gender-matched healthy controls (HCs) underwent a resting-state functional magnetic-resonance-imaging scan. Fractional amplitude of low-frequency fluctuations (fALFF), network homogeneity (NH), and support vector machine were used to analyze the data. Patients with OCD showed increased fALFF in the right orbital frontal cortex (OFC), increased NH in the left OFC, and decreased NH in the right putamen. Decreased NH of the right putamen was negatively correlated with the Y-BOCS total and compulsive behavior scores. Furthermore, a combination of NH in the left OFC and right putamen could be applied to differentiate OCD from HCs with optimum specificity and sensitivity. The current findings emphasize the crucial role of the fronto-limbic network in the etiology of OCD.
Prediction Model of Hemorrhage Transformation in Patient with Acute Ischemic Stroke Based on Multiparametric MRI Radiomics and Machine Learning
Authors: Meng Yucong , Wang Haoran , Wu Chuanfu , Liu Xiaoyu , Qu Linhao , Shi Yonghong
Published: June 2022

Intravenous thrombolysis is the most commonly used drug therapy for patients with acute ischemic stroke, which is often accompanied by complications of intracerebral hemorrhage transformation (HT). This study proposed to build a reliable model for pretreatment prediction of HT. Specifically, 5400 radiomics features were extracted from 20 regions of interest (ROIs) of multiparametric MRI images of 71 patients. Furthermore, a minimal set of all-relevant features were selected by LASSO from all ROIs and used to build a radiomics model through the random forest (RF). To explore the significance of normal ROIs, we built a model only based on abnormal ROIs. In addition, a model combining clinical factors and radiomics features was further built. Finally, the models were tested on an independent validation cohort. The radiomics model with 14 All-ROIs features achieved pretreatment prediction of HT (AUC = 0.871, accuracy = 0.848), which significantly outperformed the model with only 14 Abnormal-ROIs features (AUC = 0.831, accuracy = 0.818). Besides, combining clinical factors with radiomics features further benefited the prediction performance (AUC = 0.911, accuracy = 0.894). So, we think that the combined model can greatly assist doctors in diagnosis. Furthermore, we find that even if there were no lesions in the normal ROIs, they also provide characteristic information for the prediction of HT.
Improving Attention through Individualized fNIRS Neurofeedback Training: A Pilot Study
Authors: Gu Yue , Yang Liu , Chen He , Liu Wenzheng , Liang Zhenhu
Published: June 2022

Attention is a particularly important indicator in life, as inattention can lead to many negative consequences. As a non-invasive intervention, real-time neurofeedback training can effectively enhance individuals’ attention adjustment abilities. However, previous studies have neglected to consider differences among individuals. In this study, an individualized neurofeedback training (INT) method based on functional near-infrared spectroscopy (fNIRS) was proposed for attention improvement and compared with non-individualized neurofeedback training (NINT). The neurofeedback channels and thresholds were determined individually for each subject. Then, participants conducted four runs of neurofeedback training. Two attention tests (i.e., AX version of continuous performance task (AX-CPT) and attention network test (ANT)) were used to assess the performance of the neurofeedback training. The length of time that the two groups of participants continuously kept their oxygenated hemoglobin concentration above a threshold showed an increasing trend, and the improvement rate of the INT group was higher than that of the NINT group. The reaction times for both groups showed a downward trend, but the INT group declined more significantly. In the fNIRS data, it was observed that the activation degree of the INT group in the middle and dorsolateral prefrontal areas was higher than that of the NINT group. It is preliminarily proved that the proposed INT method can effectively improve the attention level, and its overall performance is better than that of the NINT method.
Response Modification Factors for Multi-Span Reinforced Concrete Bridges in Pakistan
Authors: Butt Muhammad Jamal , Waseem Muhammad , Sikandar Muhammad Ali , Zamin Bakht , Ahmad Mahmood , Muayad Sabri Sabri Mohanad
Published: June 2022

In Pakistan, updated codes covering seismic provisions for reinforced concrete bridges do not exist. The majority of the bridge design uses different versions of AASTHO-LRFD provisions. Response modification factors recommended for usage in these codes are primarily for bridges derived from conditions and bridges in the United States of America. This research focuses on the seismic assessment of three real multi-spans simply supported reinforced concrete bridges in Pakistan having multiple bents. This typology of bridges is very common in Pakistan. Non-linear static pushover analysis is performed to derive seismic capacity curves for these bridges, which were used to compute response modification factors. The study results show that response modification factors vary between 4.50 and 5.0 for the bridges in the longitudinal and transverse directions. The results of this work may serve as input in developing the seismic design code of bridges in Pakistan.
Implications for Immunotherapy of Breast Cancer by Understanding the Microenvironment of a Solid Tumor
Authors: Franzén Alexander S. , Raftery Martin J. , Pecher Gabriele
Published: June 2022

Breast cancer is poorly immunogenic due to immunosuppressive mechanisms produced in part by the tumor microenvironment (TME). The TME is a peritumoral area containing significant quantities of (1) cancer-associated fibroblasts (CAF), (2) tumor-infiltrating lymphocytes (TIL) and (3) tumor-associated macrophages (TAM). This combination protects the tumor from effective immune responses. How these protective cell types are generated and how the changes in the developing tumor relate to these subsets is only partially understood. Immunotherapies targeting solid tumors have proven ineffective largely due to this protective TME barrier. Therefore, a better understanding of the interplay between the tumor, the tumor microenvironment and immune cells would both advance immunotherapeutic research and lead to more effective immunotherapies. This review will summarize the current understanding of the microenvironment of breast cancer giving implications for future immunotherapeutic strategies.
Prospective Comparison of the Performance of MRI Versus CT in the Detection and Evaluation of Peritoneal Surface Malignancies
Authors: Chia Claramae Shulyn , Wong Louis Choon Kit , Hennedige Tiffany Priyanthi , Ong Whee Sze , Zhu Hong-Yuan , Tan Grace Hwei Ching , Kwek Jin Wei , Seo Chin Jin , Wong Jolene Si Min , Ong Chin-Ann Johnny , Thng Choon Hua , Soo Khee Chee , Teo Melissa Ching Ching
Published: June 2022

Background: The performance of MRI versus CT in the detection and evaluation of peritoneal surface malignancies (PSM) remains unclear in the current literature. Our study is the first prospective study in an Asian center comparing the two imaging modalities, validated against intra-operative findings. Methods: A total of 36 patients with PSM eligible for CRS-HIPEC underwent both MRI and CT scans up to 6 weeks before the operation. The scans were assessed for the presence and distribution of PSM and scored using the peritoneal cancer index (PCI), which were compared against PCI determined at surgery. Results: Both MRI and CT were 100% sensitive and specific in detecting the overall presence of PSM. Across all peritoneal regions, the sensitivity and specificity for PSM detection was 49.1% and 93.0% for MRI, compared to 47.8% and 95.1% for CT (p = 0.76). MRI was more sensitive than CT for small bowel disease, although the difference did not reach statistical significance. Comparing PCI on imaging with intra-operative PCI, the mean difference was found to be −3.4 ± 5.4 (p < 0.01) for MRI, and −3.9 ± 4.1 (p < 0.01) for CT. The correlation between imaging and intra-operative PCI was poor, with a concordance coefficient of 0.76 and 0.79 for MRI and CT, respectively. Within individual peritoneal regions, there was also poor agreement between imaging and intra-operative PCI for both modalities, other than in regions 1 and 3. Conclusion: MRI and CT are comparable in the detection and evaluation of PSM. While sensitive in the overall detection of PSM, they are likely to underestimate the true disease burden.
Imaging of Pediatric Testicular and Para-Testicular Tumors: A Pictural Review
Authors: Hermann Anne-Laure , L’Herminé-Coulomb Aurore , Irtan Sabine , Audry Georges , Cardoen Liesbeth , Brisse Hervé J. , Vande Perre Saskia , Pointe Hubert Ducou Le
Published: June 2022

Pre- and post-pubertal testicular tumors are two distinct entities in terms of epidemiology, diagnosis and treatment. Most pre-pubertal tumors are benign; the most frequent are teratomas, and the most common malignant tumors are yolk-sac tumors. Post-pubertal tumors are similar to those found in adults and are more likely to be malignant. Imaging plays a pivotal role in the diagnosis, staging and follow-up. The appearance on ultrasonography (US) is especially helpful to differentiate benign lesions that could be candidates for testis-sparing surgery from malignant ones that require radical orchidectomy. Some specific imaging patterns are described for benign lesions: epidermoid cysts, mature cystic teratomas and Leydig-cell tumors. Benign tumors tend to be well-circumscribed, with decreased Doppler flow on US, but malignancy should be suspected when US shows an inhomogeneous, not-well-described lesion with internal blood flow. Imaging features should always be interpreted in combination with clinical and biological data including serum levels of tumor markers and even intra-operative frozen sections in case of conservative surgery to raise any concerns of malignity. This review provides an overview of imaging features of the most frequent testicular and para-testicular tumor types in children and the value of imaging in disease staging and monitoring children with testicular tumors or risk factors for testicular tumors.
Incidence, Mortality, and Trends of Prostate Cancer in Mexico from 2000 to 2019: Results from the Global Burden of Disease Study 2019
Authors: Beltran-Ontiveros Saul A. , Fernandez-Galindo Martha A. , Moreno-Ortiz Jose M. , Contreras-Gutierrez Jose A. , Madueña-Molina Jesus , Arambula-Meraz Eliakym , Leal-Leon Emir , Becerril-Camacho Delia M. , Picos-Cardenas Veronica J. , Angulo-Rojo Carla , Velazquez Diana Z. , Jimenez-Trejo Francisco , Gallardo-Vera Francisco , Diaz Daniel
Published: June 2022

In 2019, the Global Burden of Disease (GBD) estimated that prostate cancer (PC) was the 16th most common cause of death globally in males. In Mexico, PC epidemiology has been studied by a number of metrics and over various periods, although without including the most up-to-date estimates. Herein, we describe and compare the burdens and trends of PC in Mexico and its 32 states from 2000 to 2019. For this study, we extracted online available data from the GBD 2019 to estimate the crude and age-standardized rates (ASR per 100,000 people) of the incidence and mortality of PC. In Mexico, PC caused 27.1 thousand (95% uncertainty intervals, 20.6–36.0 thousand) incident cases and 9.2 thousand (7.7–12.7 thousand) deaths in males of all ages in 2019. Among the states, Sinaloa had the greatest ASR of incidence, and Guerrero had the highest mortality. The burden of PC showed an increasing trend, although the magnitude of change differed between metrics and locations. We found both an increasing national trend and subnational variation in the burden of PC. Our results confirm the need for updated and timely estimates to design effective diagnostic and treatment campaigns in locations where the burden of PC is the highest.
Catalyst-Based Biomolecular Logic Gates
Authors: Winston Dennis S. , Boehr David D.
Published: June 2022

Regulatory processes in biology can be re-conceptualized in terms of logic gates, analogous to those in computer science. Frequently, biological systems need to respond to multiple, sometimes conflicting, inputs to provide the correct output. The language of logic gates can then be used to model complex signal transduction and metabolic processes. Advances in synthetic biology in turn can be used to construct new logic gates, which find a variety of biotechnology applications including in the production of high value chemicals, biosensing, and drug delivery. In this review, we focus on advances in the construction of logic gates that take advantage of biological catalysts, including both protein-based and nucleic acid-based enzymes. These catalyst-based biomolecular logic gates can read a variety of molecular inputs and provide chemical, optical, and electrical outputs, allowing them to interface with other types of biomolecular logic gates or even extend to inorganic systems. Continued advances in molecular modeling and engineering will facilitate the construction of new logic gates, further expanding the utility of biomolecular computing.
Rice Straw as Green Waste in a HTiO2@AC/SiO2 Nanocomposite Synthesized as an Adsorbent and Photocatalytic Material for Chlorpyrifos Removal from Aqueous Solution
Authors: Shahawy Abeer El , Al-Mhyawi Saedah R. , Mubarak Mahmoud F. , Mousa Abdullah E. , Ragab Ahmed H.
Published: June 2022

A nano-HTiO2@activated carbon-amorphous silica nanocomposite catalyst (HTiO2@AC/SiO2) is utilized to photo breakdown catalytically and adsorb chlorpyrifos insecticide. SEM, TEM, and X-ray diffraction were used to examine HTiO2@AC/SiO2, synthesized through sol–gel synthesis. With an average size of 7–9 nm, the crystallized form of HTiO2 is the most common form found. At varied pH, catalyst doses, agitation speed, initial pesticide concentrations, contact periods, and temperatures, HTiO2@AC/SiO2 was examined for efficiency under visible light and in darkness. Because of the pseudo-second-order kinetics observed for chlorpyrifos, chemisorption is believed to dominate the adsorption process, as indicated by an estimated activation energy of 182.769 kJ/mol, which indicates that chemisorption dominates the adsorption process in this study. The maximal adsorption capacity of chlorpyrifos is 462.6 mg g−1, according to the Langmuir isotherms, which infer this value. When exposed to visible light, the adsorption capacity of HTiO2@AC/SiO2 increased somewhat as the temperature rose (283 k 323 k 373 k), indicating an exothermic change in Gibbs free energy during the process (−1.8 kJ/mol), enthalpy change (−6.02 kJ/mol), and entropy change (0.014 J/mol K), respectively, at 298.15 K. Negative (ΔS) describes a process with decreased unpredictability and suggests spontaneous adsorption. HTiO2@AC/SiO2 may be a promising material.