bluuebunny/crossref_metadata_2025 huggingface dataset:
NMR-Based Metabolomics for a More Holistic and Sustainable Research in Food Quality Assessment: A Narrative Review
Authors: Ciampa Alessandra , Danesi Francesca , Picone Gianfranco
Published: December 2022

The ability of nuclear magnetic resonance spectroscopy (NMR) to extract chemical information from a complex mixture is invaluable and widely described in literature. Many applications of this technique in the foodomics field have highlighted how NMR could characterize food matrices, and it can be used all along its “life chain”: from farm to fork and from fork to the digestion process. The aim of this review is an attempt to show, firstly, the potential of NMR as a method based on green chemistry in sample preparation, and then in characterizing the nutritional qualities of agri-food products (with particular attention to their by-products) from a sustainable point of view. For instance, the NMR-based metabolomics approach has been used to enhance the nutritional properties of bio-products waste naturally rich in antioxidants and prebiotics. The reintroduction of these products in the food supply chain as functional foods or ingredients answers and satisfies the consumer demand for more food with high nutritional quality and more respect for the environment.
Impact of Dataset and Model Parameters on Machine Learning Performance for the Detection of GPS Spoofing Attacks on Unmanned Aerial Vehicles
Authors: Talaei Khoei Tala , Ismail Shereen , Shamaileh Khair Al , Devabhaktuni Vijay Kumar , Kaabouch Naima
Published: December 2022

GPS spoofing attacks are a severe threat to unmanned aerial vehicles. These attacks manipulate the true state of the unmanned aerial vehicles, potentially misleading the system without raising alarms. Several techniques, including machine learning, have been proposed to detect these attacks. Most of the studies applied machine learning models without identifying the best hyperparameters, using feature selection and importance techniques, and ensuring that the used dataset is unbiased and balanced. However, no current studies have discussed the impact of model parameters and dataset characteristics on the performance of machine learning models; therefore, this paper fills this gap by evaluating the impact of hyperparameters, regularization parameters, dataset size, correlated features, and imbalanced datasets on the performance of six most commonly known machine learning techniques. These models are Classification and Regression Decision Tree, Artificial Neural Network, Random Forest, Logistic Regression, Gaussian Naïve Bayes, and Support Vector Machine. Thirteen features extracted from legitimate and simulated GPS attack signals are used to perform this investigation. The evaluation was performed in terms of four metrics: accuracy, probability of misdetection, probability of false alarm, and probability of detection. The results indicate that hyperparameters, regularization parameters, correlated features, dataset size, and imbalanced datasets adversely affect a machine learning model’s performance. The results also show that the Classification and Regression Decision Tree classifier has an accuracy of 99.99%, a probability of detection of 99.98%, a probability of misdetection of 0.2%, and a probability of false alarm of 1.005%, after removing correlated features and using tuned parameters in a balanced dataset. Random Forest can achieve an accuracy of 99.94%, a probability of detection of 99.6%, a probability of misdetection of 0.4%, and a probability of false alarm of 1.01% in similar conditions.
Image Classification-Based Defect Detection of Railway Tracks Using Fiber Bragg Grating Ultrasonic Sensors
Authors: Dang Da-Zhi , Lai Chun-Cheung , Ni Yi-Qing , Zhao Qi , Su Boyang , Zhou Qi-Fan
Published: December 2022

Structural health monitoring (SHM) is vital to the maintenance of civil infrastructures. For rail transit systems, early defect detection of rail tracks can effectively prevent the occurrence of severe accidents like derailment. Non-destructive testing (NDT) has been implemented in railway online and offline monitoring systems using state-of-the-art sensing technologies. Data-driven methodologies, especially machine learning, have contributed significantly to modern NDT approaches. In this paper, an efficient and robust image classification model is proposed to achieve railway status identification using ultrasonic guided waves (UGWs). Experimental studies are conducted using a hybrid sensing system consisting of a lead–zirconate–titanate (PZT) actuator and fiber Bragg grating (FBG) sensors. Comparative studies have been firstly carried out to evaluate the performance of the UGW signals obtained by FBG sensors and high-resolution acoustic emission (AE) sensors. Three different rail web conditions are considered in this research, where the rail is: (1) intact without any defect; (2) damaged with an artificial crack; and (3) damaged with a bump on the surface made of blu-tack adhesives. The signals acquired by FBG sensors and AE sensors are compared in time and frequency domains. Then the research focuses on damage detection using a convolutional neural network (CNN) with the input of RGB spectrum images of the UGW signals acquired by FBG sensors, which are calculated using Short-time Fourier Transform (STFT). The proposed image classifier achieves high accuracy in predicting each railway condition. The visualization of the classifier indicates the high efficiency of the proposed paradigm, revealing the potential of the method to be applied to mass railway monitoring systems in the future.
Experimental Study on a Supercritical CO2 Centrifugal Compressor Used in a MWe Scale Power Cycle
Authors: Zhu Yuming , Liang Shiqiang , Guo Chaohong , Guo Yongxian , Li Zhigang , Gong Xinyu , Jiang Yuyan
Published: December 2022

The centrifugal compressor is the core component of supercritical CO2 power cycle, and its performance and operation stability are research hotspots. However, there are few experimental studies, especially for compressors used in Mwe-scale power cycles. In this paper, based on a 1 MWe supercritical CO2 power cycle, a single-stage centrifugal supercritical CO2 compressor is designed with speed of 40,000 RPM, a pressure ratio of 2.5 and a mass flow of 16.3 kg/s. In order to carry out the compressor test, a general experimental platform for MWe sCO2 compressors is built. In the test, the mass flow range is 13.5~18 kg/s and the maximum experimental pressure ratio is close to 2.0. The performance curve of the compressor of 31,000 ± 1000 RPM is obtained, and the historical curve of the experiment is given. Then, the experimental curve is compared with the design curve using a dimensionless method. The isentropic head coefficient of the experimental curve is lower than the design value, and the experimental curves shift towards the boundary of small flow coefficient. Finally, the influence of compressor inlet condensation on compressor performance and the change of operating boundary is preliminarily explained.
Assessing the Applicability of Machine Learning Models for Robotic Emotion Monitoring: A Survey
Authors: Khan Md Ayshik Rahman , Rostov Marat , Rahman Jessica Sharmin , Ahmed Khandaker Asif , Hossain Md Zakir
Published: December 2022

Emotion monitoring can play a vital role in investigating mental health disorders that contribute to 14% of global diseases. Currently, the mental healthcare system is struggling to cope with the increasing demand. Robot-assisted mental health monitoring tools can take the enormous strain off the system. The current study explored existing state-of-art machine learning (ML) models and signal data from different bio-sensors assessed the suitability of robotic devices for surveilling different physiological and physical traits related to human emotions and discussed their potential applicability for mental health monitoring. Among the selected 80 articles, we subdivided our findings in terms of two different emotional categories, namely—discrete and valence-arousal (VA). By examining two different types of signals (physical and physiological) from 10 different signal sources, we found that RGB images and CNN models outperformed all other data sources and models, respectively, in both categories. Out of the 27 investigated discrete imaging signals, 25 reached higher than 80% accuracy, while the highest accuracy was observed from facial imaging signals (99.90%). Besides imaging signals, brain signals showed better potentiality than other data sources in both emotional categories, with accuracies of 99.40% and 96.88%. For both discrete and valence-arousal categories, neural network-based models illustrated superior performances. The majority of the neural network models achieved accuracies of over 80%, ranging from 80.14% to 99.90% in discrete, 83.79% to 96.88% in arousal, and 83.79% to 99.40% in valence. We also found that the performances of fusion signals (a combination of two or more signals) surpassed that of the individual ones in most cases, showing the importance of combining different signals for future model development. Overall, the potential implications of the survey are discussed, considering both human computing and mental health monitoring. The current study will definitely serve as the base for research in the field of human emotion recognition, with a particular focus on developing different robotic tools for mental health monitoring.
Suggestions on the Development of Environmental Monitoring Technology of CO2 Geological Storage and Leakage under the Background of China’s “Double-Carbon” Strategy
Authors: Cui Yinan , Bai Jiajia , Liao Songlin , Cao Shengjiang , Liu Fangzhi
Published: December 2022

With the proposal of China’s national “double carbon” strategic goal, carbon capture, utilization and storage (CCUS) technology has attracted more and more attention. Due to the high cost, high energy consumption and high risk of CCUS technology, this technology is still in the initial stage of development in China. Among them, CO2 geological storage is one of the risks, and the environmental monitoring technology of CO2 storage leakage is particularly important in the large-scale popularization and application of CCUS technology in China. On the basis of extensive research on the related literature concerning CO2 storage and leakage, this paper begins with the types and mechanisms of CO2 storage, analyzes the ways and risks of CO2 storage and leakage and then summarizes the existing environmental monitoring technologies of CO2 geological storage and leakage. In the future, China can promote the progress of CO2 geological storage monitoring technology and help achieve the goal of “double carbon” by strengthening the research on CO2 storage mechanism and main control factors, perfecting the risk assessment method of CO2 storage, constructing the monitoring technology system of the CO2 storage life cycle, and standardizing the CO2 storage risk response system.
Short-Term Effects of Air Pollution on the Risk of Influenza in Jinan, China during 2020–2021: A Time-Series Analysis
Authors: Chen Fangfang , Liu Zhong , Huang Ting , Wang Baoyu , Sun Zhan , Gao Xibao , Wang Weiru
Published: December 2022

Background: Air pollution leads to many adverse diseases, especially respiratory diseases and cardiac symptoms. However, it has not been studied the association between air pollution and influenza cases in Jinan City, especially during the outbreak of COVID-19; Methods: The data were obtained from China’s Disease Information System, and influenza cases during 2020–2021 in Jinan City were collected from it. We used the generalized additive Poisson model to measure the association between air pollutants and the daily influenza cases after adjusting for possible influence variables; Results: There were 4767 influenza cases. PM2.5 and PM10 on lag 0, lag 3, and lag 4 were significantly associated with an increased risk of influenza; gaseous pollutants (NO2 and SO2) led to higher risk than particulate matter pollutants (PM2.5 and PM10). There were no significant differences for sex subgroup analyses. Except for O3, the incidence risk of males and females was highest on lag 3 and lag 4. For the study of different age groups, influenza cases aged over 59 years had a slightly larger relative risk when exposed to all air pollutants (except O3) than the younger group; Conclusions: The overall number of influenza cases decreased in 2020–2021. PM2.5, SO2, CO, and NO2 were significantly associated with the risk of influenza during 2020–2021. Countermeasures should be developed according to the characteristics of influenza risk to prevent and control it.
Correlation Structure and Co-Movement of Hunan Province’s Air Pollution: Evidence from the Multiscale Temporal Networks
Authors: Wang Fang , Zhang Zehui
Published: December 2022

In recent years, air pollution has become one of the main factors harming the livable environment for human beings. Governments have recognized the importance of controlling air pollution and reached a consensus that regional joint control of air pollution is an effective means of dealing with environmental degradation. In this work, we focus on the impact of fine particulate matter and nitrogen compounds on the air quality of 14 prefecture-level cities in China’s Hunan Province using the insights of complex networks, and further propose a joint treatment scheme for these two pollutants. Multiscale temporal networks are constructed based on the height cross-correlation coefficient, which allows us to assess the variable network structures concerning different time scales. We use four network properties to assess the network structures for the pollutants. Through the Jensen-Shannon divergence of the probability distribution of these network attributes, it is found that NO2 affects AQI more in a short time interval than in a longer time interval. The correlation of both NO2 and PM2.5 among the 14 cities in about 15 days can best reflect the air quality in Hunan Province. In addition, instead of NO2, PM2.5 has become the culprit of air pollution in Hunan Province. The co-movement of the pollutants among the 14 cities is significant. The co-movement of the PM2.5 pollutants can last 45 days, while that of NO2 pollutants will gradually decrease over time. Furthermore, by using spectral clustering based on the network node correlation, we classify the 14 cities into five regions and two regions for PM2.5 and NO2, respectively. It provides a feasible implementation guide for the environmental governance of regional cooperation.
In-Situ Photoelectron Spectroscopy Investigation of Sulfurization-Induced Sodiophilic Sites with Model Systems of α-sexithiophene and p-sexiphenyl
Authors: Liu Yuan , Lian Xu , Jiang Chonglai , Sun Zejun , Yang Jinlin , Ding Yishui , Chen Wei
Published: December 2022

Uncontrollable sodium dendrite growth results in poor cycling performance and severe safety issues, hindering practical applications of sodium metal batteries (SMBs). To stabilize sodium metal anodes (SMAs), various strategies have been developed including employing anode hosts and electrolyte additives to establish protective layers. Nevertheless, the understanding of interaction mechanisms between protective materials and SMAs is still limited, which is crucial for the rational design of protective materials. In this work, we investigated the interaction mechanism between sodium metal and sulfur-containing functional groups with comparative model systems of α-sexithiophene (6T) and p-sexiphenyl (6P) through in-situ photoelectron spectroscopy investigations and density functional theory (DFT) calculations. Our results show that sodium atoms tend to interact with sulfur atoms and their connected carbon atoms simultaneously as well as the aromatic carbon atoms of the end groups of 6T molecules, while no chemical interaction between Na and 6P molecules is observed. The observed sulfurization-induced sodiophilic sites can shed light on the rational design of sulfur-containing protective materials and the relevant interface engineering to stabilize SMAs.
An Information System Supporting Insurance Use Cases by Automated Anomaly Detection
Authors: Reis Thoralf , Kreibich Alexander , Bruchhaus Sebastian , Krause Thomas , Freund Florian , Bornschlegl Marco X. , Hemmje Matthias L.
Published: December 2022

The increasing availability of vast quantities of data from various sources significantly impacts the insurance industry, although this industry has always been data driven. It accelerates manual processes and enables new products or business models. On the other hand, it also burdens insurance analysts and other users that need to cope with this development parallel to other global changes. A novel information system (IS) for artificial intelligence (AI)-supported big data analysis, introduced within this paper, shall help to overcome user overload and to empower human data analysts in the insurance industry. The IS research’s focus lies neither in novel algorithms nor datasets but in concepts that combine AI and big data analysis for synergies, such as usability enhancements. For this purpose, this paper systematically designs and implements an AI2VIS4BigData reference model to help information systems conform to automatically detect anomalies and increase its users’ confidence and efficiency. Practical relevance is assured by an interview with an insurance analyst to verify the demand for the developed system and derive all requirements from two insurance industry user stories. A core contribution is the introduction of the IS. Another significant contribution is an extension of the AI2VIS4BigData service-based architecture and user interface (UI) concept on AI and machine learning (ML)-based user empowerment and data transformation. The implemented prototype was applied to synthetic data to enable the evaluation of the system. The quantitative and qualitative evaluations confirm the system’s usability and applicability to the insurance domain yet reveal the need for improvements toward bigger quantities of data and further evaluations with a more extensive user group.
Hand Gesture Signatures Acquisition and Processing by Means of a Novel Ultrasound System
Authors: Franceschini Stefano , Ambrosanio Michele , Pascazio Vito , Baselice Fabio
Published: December 2022

Hand gestures represent a natural way to express concepts and emotions which are peculiar to each culture. Several studies exploit biometric traits, such as fingerprint, iris or face for subject identification purposes. Within this paper, a novel ultrasound system for person identification that exploits hand gestures is presented. The system works as a sonar, measuring the ultrasonic pressure waves scattered by the subject’s hand, and analysing its Doppler information. Further, several transformations for obtaining time/frequency representations of the acquired signal are computed and a deep learning detector is implemented. The proposed system is cheap, reliable, contactless and can be easily integrated with other personal identification approaches allowing different security levels. The performances are evaluated via experimental tests carried out on a group of 25 volunteers. Results are encouraging, showing the promising potential of the system.
Subcritical Water Pretreatment for the Efficient Valorization of Sorghum Distillery Residue for the Biorefinery Platform
Authors: Singh Anusuiya , Chen Chiu-Wen , Patel Anil , Dong Cheng-Di , Singhania Reeta
Published: December 2022

The depletion of fossil fuels is resulting in an increased energy crisis, which is leading the paradigm shift towards alternative energy resources to overcome the issue. Lignocellulosic biomass or agricultural residue could be utilized to produce energy fuel (bioethanol) as it can resolve the issue of energy crisis and reduce environmental pollution that occurs due to waste generation from agriculture and food industries. A huge amount of sorghum distillery residue (SDR) is produced during the Kaoliang liquor production process, which may cause environmental problems. Therefore, the SDR generated can be utilized to produce bioethanol to meet current energy demands and resolve environmental problems. Using a central composite experimental design, the SDR was subjected to hydrothermal pretreatment. The conditions selected for hydrothermal pretreatment are 155 °C, 170 °C, and 185 °C for 10, 30, and 50 min, respectively. Based on the analysis, 150 °C for 30 min conditions for SDR hydrothermal pretreatment were selected as no dehydration product (Furfural and HMF) was detected in the liquid phase. Therefore, the pretreated slurry obtained using hydrothermal pretreatment at 150 °C for 30 min was subjected to enzymatic hydrolysis at 5% solid loading and 15 FPU/gds. The saccharification yield obtained at 72 h was 75.05 ± 0.5%, and 5.33 g/L glucose concentration. This non-conventional way of enzymatic hydrolysis eliminates the separation and detoxification process, favoring the concept of an economical and easy operational strategy in terms of biorefinery.
Development of Lanzyme as the Potential Enzyme Replacement Therapy Drug for Fabry Disease
Authors: Deng Mulan , Zhou Hongyu , Liang Zhicheng , Li Zhaoyang , Wang Yanping , Guo Wanyi , Zhao April Yuanyi , Li Fanghong , Mu Yunping , Zhao Allan Zijian
Published: December 2022

Fabry disease (FD) is a progressive multisystemic disease characterized by lysosomal enzyme deficiency. Enzyme replacement therapy (ERT) is one of the most significant advancements and breakthroughs in treating FD. However, limited resources and the high cost of ERT might prevent patients from receiving prompt and effective therapy, thereby resulting in severe complications. Future progress in ERT can uncover promising treatment options. In this study, we developed and validated a recombinant enzyme (Lanzyme) based on a CHO-S cell system to provide a new potential option for FD therapy. Our results indicated that Lanzyme was heavily glycosylated, and its highest activity was similar to a commercial enzyme (Fabrazyme®). Our pharmacokinetic assessment revealed that the half-life of Lanzyme was up to 11 min, which is nearly twice that of the commercial enzyme. In vivo experiments revealed that Lanzyme treatment sharply decreased the accumulation levels of Gb3 and lyso-Gb3 in various tissues of FD model mice, with superior or comparable therapeutic effects to Fabrazyme®. Based on these data, Lanzyme may represent a new and promising treatment approach for FD. Building this enzyme production system for ERT can offer additional choice, potentially with enhanced efficacy, for the benefit of patients with FD.
Diversity of AMPA Receptor Ligands: Chemotypes, Binding Modes, Mechanisms of Action, and Therapeutic Effects
Authors: Golubeva Elena A. , Lavrov Mstislav I. , Radchenko Eugene V. , Palyulin Vladimir A.
Published: December 2022

L-Glutamic acid is the main excitatory neurotransmitter in the central nervous system (CNS). Its associated receptors localized on neuronal and non-neuronal cells mediate rapid excitatory synaptic transmission in the CNS and regulate a wide range of processes in the brain, spinal cord, retina, and peripheral nervous system. In particular, the glutamate receptors selective to α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) also play an important role in numerous neurological disorders and attract close attention as targets for the creation of new classes of drugs for the treatment or substantial correction of a number of serious neurodegenerative and neuropsychiatric diseases. For this reason, the search for various types of AMPA receptor ligands and studies of their properties are attracting considerable attention both in academic institutions and in pharmaceutical companies around the world. This review focuses mainly on the advances in this area published since 2017. Particular attention is paid to the structural diversity of new chemotypes of agonists, competitive AMPA receptor antagonists, positive and negative allosteric modulators, transmembrane AMPA regulatory protein (TARP) dependent allosteric modulators, ion channel blockers as well as their binding sites. This review also presents the studies of the mechanisms of action of AMPA receptor ligands that mediate their therapeutic effects.
Cucurbitacins as Potent Chemo-Preventive Agents: Mechanistic Insight and Recent Trends
Authors: Tuli Hardeep Singh , Rath Prangya , Chauhan Abhishek , Ranjan Anuj , Ramniwas Seema , Sak Katrin , Aggarwal Diwakar , Kumar Manoj , Dhama Kuldeep , Lee E Hui Clarissa , Yap Kenneth Chun-Yong , Capinpin Sharah Mae , Kumar Alan Prem
Published: December 2022

Cucurbitacins constitute a group of cucumber-derived dietary lipids, highly oxidized tetracyclic triterpenoids, with potential medical uses. These compounds are known to interact with a variety of recognized cellular targets to impede the growth of cancer cells. Accumulating evidence has suggested that inhibition of tumor cell growth via induction of apoptosis, cell-cycle arrest, anti-metastasis and anti-angiogenesis are major promising chemo-preventive actions of cucurbitacins. Cucurbitacins may be a potential choice for investigations of synergism with other drugs to reverse cancer cells’ treatment resistance. The detailed molecular mechanisms underlying these effects include interactions between cucurbitacins and numerous cellular targets (Bcl-2/Bax, caspases, STAT3, cyclins, NF-κB, COX-2, MMP-9, VEGF/R, etc.) as well as control of a variety of intracellular signal transduction pathways. The current study is focused on the efforts undertaken to find possible molecular targets for cucurbitacins in suppressing diverse malignant processes. The review is distinctive since it presents all potential molecular targets of cucurbitacins in cancer on one common podium.
Tuning Liposome Stability in Biological Environments and Intracellular Drug Release Kinetics
Authors: Yang Keni , Tran Karolina , Salvati Anna
Published: December 2022

Ideal drug carriers should be stable in biological environments but eventually release their drug load once inside the targeted cells. These two aspects can be in contrast with each other, thus they need to be carefully tuned in order to achieve the desired properties for specific applications. Quantifying drug release profiles in biological environments or inside cells can be highly challenging, and standard methods to determine drug release kinetics in many cases cannot be applied to complex biological environments or cells. Within this context, the present work combined kinetic studies by flow cytometry with aging experiments in biological fluids and size-exclusion chromatography to determine drug release profiles in biological environments and inside cells. To this purpose, anionic and zwitterionic liposomes were used as model nanomedicines. By changing lipid composition, liposome stability in serum and intracellular release kinetics could be tuned and formulations with very different properties could be obtained. The methods presented can be used to characterize liposome release profiles in complex biological media, as well as inside cells. In this way, liposome composition can be tuned in order to achieve formulations with optimal balance between stability and release kinetics for specific applications.
Molecular Subtyping in Muscle-Invasive Bladder Cancer on Predicting Survival and Response of Treatment
Authors: Bejrananda Tanan , Saetang Jirakrit , Sangkhathat Surasak
Published: December 2022

Molecular classifications for urothelial bladder cancer appear to be promising in disease prognostication and prediction. This study investigated the novel molecular subtypes of muscle invasive bladder cancer (MIBC). Tumor samples and normal tissues of MIBC patients were submitted for transcriptome sequencing. Expression profiles were clustered using K-means clustering and principal component analysis. The molecular subtypes were also applied to The Cancer Genome Atlas (TCGA) dataset and analyzed for clinical outcome correlation. Three molecular subtypes of MIBC were discovered, clusters A, B, and C. The most differentially upregulated genes in cluster A were BDKRB1, EDNRA, AVPR1A, PDGFRB, and TNC, while the most upregulated genes in cluster C were collagen-related genes, PDGFRB, and PRKG1. For cluster B, COL6A3, COL1A2, COL6A2, tenascin C, and fibroblast growth factor 2 were statistically suppressed. When the centroids of clustering on PCA were applied to TCGA data, the clustering significantly predicted survival outcomes. Cluster B had the best overall survival (OS), and cluster C was associated with poor OS but exhibited the best response to perioperative chemotherapy. Among all groups, cluster B had a better pathologic response to neoadjuvant chemotherapy (40%). Based on the results of the present study, the novel clusters of subtype MIBC appear potentially suitable for integration into clinical practice.
Cardiovascular Complications of Viral Respiratory Infections and COVID-19
Authors: Franczuk Paweł , Tkaczyszyn Michał , Kulak Maria , Domenico Esabel , Ponikowski Piotr , Jankowska Ewa Anita
Published: December 2022

Viral respiratory infections (VRI) are the most prevalent type of infectious diseases and constitute one of the most common causes of contact with medical care. Regarding the pathophysiology of the cardiovascular system, VRI can not only exacerbate already existing chronic cardiovascular disease (such as coronary artery disease or heart failure) but also trigger new adverse events or complications (e.g., venous thromboembolism), the latter particularly in subjects with multimorbidity or disease-related immobilization. In the current paper, we provide a narrative review of diverse cardiovascular complications of VRI as well as summarize available data on the pathology of the circulatory system in the course of coronavirus disease 2019 (COVID-19).
Conducting Polymers as Versatile Tools for the Electrochemical Detection of Cancer Biomarkers
Authors: Kappen Jincymol , Skorupa Małgorzata , Krukiewicz Katarzyna
Published: December 2022

The detection of cancer biomarkers has recently become an established method for the early diagnosis of cancer. The sensitive analysis of specific biomarkers can also be clinically applied for the determination of response to treatment and monitoring of disease progression. Because of the ultra-low concentration of cancer biomarkers in body fluids, diagnostic tools need to be highly sensitive and specific. Conducting polymers (CPs) are particularly known to exhibit numerous features that enable them to serve as excellent materials for the immobilization of biomolecules and the facilitation of electron transfer. Their large surface area, porosity, and the presence of functional groups provide CPs with binding sites suitable for capturing biomarkers, in addition to their sensitive and easy detection. The aim of this review is to present a comprehensive summary of the available electrochemical biosensors based on CPs and their composites for the ultrasensitive detection of selected cancer biomarkers. We have categorized the study based on different types of targeted biomarkers such as DNAs, miRNAs, proteins, enzymes, neurotransmitters and whole cancer cells. The sensitivity of their detection is enhanced by the presence of CPs, providing a limit of detection as low as 0.5 fM (for miRNA) and 10 cells (for the detection of cancer cells). The methods of multiplex biomarker detection and cell capture are indicated as the most promising category, since they furnish more accurate and reliable results. Ultimately, we discuss the available CP-based electrochemical sensors and promising approaches for facilitating cancer diagnosis and treatment.
Review on the Selection of Aptamers and Application in Paper-Based Sensors
Authors: Wang Kaifei , Wang Minglu , Ma Teng , Li Wenyu , Zhang Hongyan
Published: December 2022

An aptamer is a synthetic oligonucleotide, referring to a single-stranded deoxyribonucleic acid or ribonucleic acid ligand produced by synthesis from outside the body using systematic evolution of ligands by exponential enrichment (SELEX) technology. Owing to their special screening process and adjustable tertiary structures, aptamers can bind to multiple targets (small molecules, proteins, and even whole cells) with high specificity and affinity. Moreover, due to their simple preparation and stable modification, they have been widely used to construct biosensors for target detection. The paper-based sensor is a product with a low price, short detection time, simple operation, and other superior characteristics, and is widely used as a rapid detection method. This review mainly focuses on the screening methods of aptamers, paper-based devices, and applicable sensing strategies. Furthermore, the design of the aptamer-based lateral flow assay (LFA), which underlies the most promising devices for commercialization, is emphasized. In addition, the development prospects and potential applications of paper-based biosensors using aptamers as recognition molecules are also discussed.
Nanotechnology-Enabled Biosensors: A Review of Fundamentals, Design Principles, Materials, and Applications
Authors: Ramesh Manickam , Janani Ravichandran , Deepa Chinnaiyan , Rajeshkumar Lakshminarasimhan
Published: December 2022

Biosensors are modern engineering tools that can be widely used for various technological applications. In the recent past, biosensors have been widely used in a broad application spectrum including industrial process control, the military, environmental monitoring, health care, microbiology, and food quality control. Biosensors are also used specifically for monitoring environmental pollution, detecting toxic elements’ presence, the presence of bio-hazardous viruses or bacteria in organic matter, and biomolecule detection in clinical diagnostics. Moreover, deep medical applications such as well-being monitoring, chronic disease treatment, and in vitro medical examination studies such as the screening of infectious diseases for early detection. The scope for expanding the use of biosensors is very high owing to their inherent advantages such as ease of use, scalability, and simple manufacturing process. Biosensor technology is more prevalent as a large-scale, low cost, and enhanced technology in the modern medical field. Integration of nanotechnology with biosensors has shown the development path for the novel sensing mechanisms and biosensors as they enhance the performance and sensing ability of the currently used biosensors. Nanoscale dimensional integration promotes the formulation of biosensors with simple and rapid detection of molecules along with the detection of single biomolecules where they can also be evaluated and analyzed critically. Nanomaterials are used for the manufacturing of nano-biosensors and the nanomaterials commonly used include nanoparticles, nanowires, carbon nanotubes (CNTs), nanorods, and quantum dots (QDs). Nanomaterials possess various advantages such as color tunability, high detection sensitivity, a large surface area, high carrier capacity, high stability, and high thermal and electrical conductivity. The current review focuses on nanotechnology-enabled biosensors, their fundamentals, and architectural design. The review also expands the view on the materials used for fabricating biosensors and the probable applications of nanotechnology-enabled biosensors.
Sensorimotor Rhythm-Based Brain–Computer Interfaces for Motor Tasks Used in Hand Upper Extremity Rehabilitation after Stroke: A Systematic Review
Authors: Fu Jianghong , Chen Shugeng , Jia Jie
Published: December 2022

Brain–computer interfaces (BCIs) are becoming more popular in the neurological rehabilitation field, and sensorimotor rhythm (SMR) is a type of brain oscillation rhythm that can be captured and analyzed in BCIs. Previous reviews have testified to the efficacy of the BCIs, but seldom have they discussed the motor task adopted in BCIs experiments in detail, as well as whether the feedback is suitable for them. We focused on the motor tasks adopted in SMR-based BCIs, as well as the corresponding feedback, and searched articles in PubMed, Embase, Cochrane library, Web of Science, and Scopus and found 442 articles. After a series of screenings, 15 randomized controlled studies were eligible for analysis. We found motor imagery (MI) or motor attempt (MA) are common experimental paradigms in EEG-based BCIs trials. Imagining/attempting to grasp and extend the fingers is the most common, and there were multi-joint movements, including wrist, elbow, and shoulder. There were various types of feedback in MI or MA tasks for hand grasping and extension. Proprioception was used more frequently in a variety of forms. Orthosis, robot, exoskeleton, and functional electrical stimulation can assist the paretic limb movement, and visual feedback can be used as primary feedback or combined forms. However, during the recovery process, there are many bottleneck problems for hand recovery, such as flaccid paralysis or opening the fingers. In practice, we should mainly focus on patients’ difficulties, and design one or more motor tasks for patients, with the assistance of the robot, FES, or other combined feedback, to help them to complete a grasp, finger extension, thumb opposition, or other motion. Future research should focus on neurophysiological changes and functional improvements and further elaboration on the changes in neurophysiology during the recovery of motor function.
Comparison of Methods for Emissivity Influence Suppression on Thermographic Data
Authors: Muzika Lukáš , Tesař Jiří , Švantner Michal , Skála Jiří , Honnerová Petra
Published: December 2022

Emissivity is a crucial parameter for a quantitative thermography measurement. It influences measured temperature using an infrared camera. Typically, the emissivity is handled by infrared camera software but often for more complex tasks—e.g., setting and controlling the emissivity of individual pixels—a custom-made solution must be created. This can be especially beneficial for active thermography measurement and dynamic building inspection by infrared thermography as many surfaces of interest with different emissivity occur in thermographic data. In literature, one technique for suppressing emissivity occurs most often—the technique used by infrared camera manufacturers. Nonetheless, two other techniques are marginally mentioned. The most complex technique is the one used by infrared camera manufacturers, which allows many parameters to be set, but it is difficult to incorporate it into own solution. In contrast, the second one can be adapted easily, and it uses the relationship between emissivity and the fourth power of temperatures. The third one is a scarcely used technique that occurs for some active thermography measurements, in which a thermographic sequence in counts is divided by a frame when temperature equilibrium is reached. The main goal of this article is to compare these individual techniques from the point of view of the accuracy and possibility of use. The experiment showed that all three methods can be successfully used for the suppression of emissivity influence.
Carbon Nanostructures as Therapeutic Cargoes: Recent Developments and Challenges
Authors: Singh Jagtar , Nayak Pallavi , Singh Gurdeep , Khandai Madhusmruti , Sarangi Rashmi Ranjan , Kar Mihir Kumar
Published: December 2022

Recent developments in nanotechnology and process chemistry have expanded the scope of nanostructures to the biomedical field. The ability of nanostructures to precisely deliver drugs to the target site not only reduces the amount of drug needed but also reduces systemic adverse effects. Carbon nanostructures gained traction in pharmaceutical technology in the last decade due to their high stability, ease of synthesis, tunable surface chemistry, and biocompatibility. Fullerene, nanotubes, nanodiamonds, nanodots, and nanoribbons are among the major carbon nanostructures that have been extensively studied for applications in tissue engineering, biosensing, bioimaging, theranostics, drug delivery, and gene therapy. Due to the fluorescent properties of functionalized nanostructures, they have been extensively studied for use as probes in cellular imaging. Moreover, these nanostructures are promising candidates for delivering drugs to the brain, bones, and deep-seated tumors. Still, research gaps need to be addressed regarding the toxicity of these materials in animals as well as humans. This review highlights the physicochemical properties of carbon nanostructures and their categories, methods of synthesis, various techniques for surface functionalization, major biomedical applications, mechanisms involving the cellular uptake of nanostructures, pharmacokinetic considerations, recent patents involving carbon-based nanostructures in the biomedical field, major challenges, and future perspectives.
Phytochemical Compounds and Anticancer Activity of Cladanthus mixtus Extracts from Northern Morocco
Authors: El Mihyaoui Amina , Charfi Saoulajan , Erbiai El Hadi , Pereira Mariana , Duarte Diana , Vale Nuno , Candela Castillo María Emilia , Badoc Alain , Lamarti Ahmed , Esteves da Silva Joaquim C. G. , Arnao Marino B.
Published: December 2022

Many of the chemotherapeutic drugs for the treatment of cancer are molecules identified and isolated from plants or their synthetic derivatives. This work aimed to identify the bioactive compounds using LC-MS and GC-MS and to evaluate the anticancer activity of the methanolic extracts of roots, stems, leaves, and flowers from Cladanthus mixtus. The anticancer activity was evaluated in vitro against two cancer cell lines: human breast carcinoma (MCF-7) and human prostate carcinoma (PC-3), using the MTT assay and microscopic observation. A human normal lung fibroblast (MRC-5) was included to determine the extract’s safety for non-tumoral cells. The chemical composition results by LC-MS analysis revealed the presence of 24 phenolic compounds. Furthermore, GC-MS analysis allowed the identification of many biomolecules belonging to terpenoids, esters, alcohols, alkanes, fatty acids, organic acids, benzenes, phenols, ketones, carbonyls, amines, sterols, and other groups. The findings suggest that the majority of C. mixtus extracts have antiproliferative activity against two cancer cell lines, MCF-7 and PC-3, and one non-tumoral cell line, MRC-5. The activity was dose-dependent, and the highest effect was obtained with leaf extract in the two cancer cell lines. Moreover, these extracts demonstrated an acceptable toxicological profile against normal cells. Overall, C. mixtus extracts revealed promising antitumor properties provided by their phytochemical composition.
How to Manage Patients with Lenalidomide-Refractory Multiple Myeloma
Authors: de Arriba de la Fuente Felipe , Montes Gaisán Carmen , de la Rubia Comos Javier
Published: December 2022

Although lenalidomide-based combinations, such as lenalidomide plus a proteasome inhibitor or an anti-CD38 monoclonal antibody, improve the overall response rate, progression-free survival, and overall survival of patients with relapsed/refractory multiple myeloma (RRMM), there is a tendency to use these regimens as a frontline treatment. This strategy has led to the development of refractoriness early in the disease course, usually after the patient’s first treatment. Since lenalidomide-free regimens have so far shown limited efficacy in lenalidomide-refractory patients, there is an unmet need for other treatment options. In this review, we discuss the therapeutic options available to treat the general population of lenalidomide-refractory patients (mono, double and triple refractory) and the subpopulation of patients with other high-risk features such as renal failure, extramedullary disease, and high-risk cytogenetics. Moreover, new promising individual therapies and the possible impact of immunotherapy in RRMM patients are debated.
Identification of Cancerous Skin Lesions Using Vibrational Optical Coherence Tomography (VOCT): Use of VOCT in Conjunction with Machine Learning to Diagnose Skin Cancer Remotely Using Telemedicine
Authors: Silver Frederick H. , Mesica Arielle , Gonzalez-Mercedes Michael , Deshmukh Tanmay
Published: December 2022

In this pilot study, we used vibrational optical tomography (VOCT), along with machine learning, to evaluate the specificity and sensitivity of using light and audible sound to differentiate between normal skin and skin cancers. The results reported indicate that the use of machine learning, and the height and location of the VOCT mechanovibrational peaks, have potential for being used to noninvasively differentiate between normal skin and different cancerous lesions. VOCT data, along with machine learning, is shown to predict the differences between normal skin and different skin cancers with a sensitivity and specificity at rates between 78 and 90%. The sensitivity and specificity will be improved using a larger database and by using other AI techniques. Ultimately, VOCT data, visual inspection, and dermoscopy, in conjunction with machine learning, will be useful in telemedicine to noninvasively identify potentially malignant skin cancers in remote areas of the country where dermatologists are not readily available.
Integrative Clinical and DNA Methylation Analyses in a Population-Based Cohort Identifies CDH17 and LRP2 as Risk Recurrence Factors in Stage II Colon Cancer
Authors: Tournier Benjamin , Aucagne Romain , Truntzer Caroline , Fournier Cyril , Ghiringhelli François , Chapusot Caroline , Martin Laurent , Bouvier Anne Marie , Manfredi Sylvain , Jooste Valérie , Callanan Mary B. , Lepage Côme
Published: December 2022

Stage II colon cancer (CC), although diagnosed early, accounts for 16% of CC deaths. Predictors of recurrence risk could mitigate this but are currently lacking. By using a DNA methylation-based clinical screening in real-world (n = 383) and in TCGA-derived cohorts of stage II CC (n = 134), we have devised a novel 40 CpG site-based classifier that can segregate stage II CC into four previously undescribed disease sub-classes that are characterised by distinct molecular features, including activation of MYC/E2F-dependant proliferation signatures. By multivariate analyses, hypermethylation of 2 CpG sites at genes CDH17 and LRP2, respectively, was found to independently confer either significantly increased (CDH17; p-value, 0.0203) or reduced (LRP2; p-value, 0.0047) risk of CC recurrence. Functional enrichment and immune cell infiltration analyses, on RNAseq data from the TCGA cohort, revealed cases with hypermethylation at CDH17 to be enriched for KRAS, epithelial-mesenchymal transition and inflammatory functions (via IL2/STAT5), associated with infiltration by ‘exhausted’ T cells. By contrast, LRP2 hypermethylated cases showed enrichment for mTORC1, DNA repair pathways and activated B cell signatures. These findings will be of value for improving personalised care paths and treatment in stage II CC patients.