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For the multistage progression of prostate cancer (PCa) and resistance to immunotherapy, tumour‐associated macrophage is an essential contributor. Although immunotherapy is an important and promising treatment modality for cancer, most patients with PCa are not responsive towards it. In addition to exploring new therapeutic targets, it is imperative to identify highly immunotherapy‐sensitive individuals. This research aimed to establish a signature risk model, which derived from the macrophage, to assess immunotherapeutic responses and predict prognosis. Data from the UCSC‐XENA, GEO and TISCH databases were extracted for analysis. Based on both single‐cell datasets and bulk transcriptome profiles, a macrophage‐related score (MRS) consisting of the 10‐gene panel was constructed using the gene set variation analysis. MRS was highly correlated with hypoxia, angiogenesis, and epithelial‐mesenchymal transition, suggesting its potential as a risk indicator. Moreover, poor immunotherapy responses and worse prognostic performance were observed in the high‐MRS group of various immunotherapy cohorts. Additionally, APOE, one of the constituent genes of the MRS, affected the polarisation of macrophages. In particular, the reduced level of M2 macrophage and tumour progression suppression were observed in PCa xenografts which implanted in Apolipoprotein E‐knockout mice. The constructed MRS has the potential as a robust prognostic prediction tool, and can aid in the treatment selection of PCa, especially immunotherapy options. [ABSTRACT FROM AUTHOR]

Abstract For the multistage progression of prostate cancer (PCa) and resistance to immunotherapy, tumour‐associated macrophage is an essential contributor. Although immunotherapy is an important and promising treatment modality for cancer, most patients with PCa are not responsive towards it. In addition to exploring new therapeutic targets, it is imperative to identify highly immunotherapy‐sensitive individuals. This research aimed to establish a signature risk model, which derived from the macrophage, to assess immunotherapeutic responses and predict prognosis. Data from the UCSC‐XENA, GEO and TISCH databases were extracted for analysis. Based on both single‐cell datasets and bulk transcriptome profiles, a macrophage‐related score (MRS) consisting of the 10‐gene panel was constructed using the gene set variation analysis. MRS was highly correlated with hypoxia, angiogenesis, and epithelial‐mesenchymal transition, suggesting its potential as a risk indicator. Moreover, poor immunotherapy responses and worse prognostic performance were observed in the high‐MRS group of various immunotherapy cohorts. Additionally, APOE, one of the constituent genes of the MRS, affected the polarisation of macrophages. In particular, the reduced level of M2 macrophage and tumour progression suppression were observed in PCa xenografts which implanted in Apolipoprotein E‐knockout mice. The constructed MRS has the potential as a robust prognostic prediction tool, and can aid in the treatment selection of PCa, especially immunotherapy options.

Abstract The Coronavirus Disease 2019 (COVID‐19) epidemic has constituted a Public Health Emergency of International Concern. Chest computed tomography (CT) can help early reveal abnormalities indicative of lung disease. Thus, accurate and automatic localisation of lung lesions is particularly important to assist physicians in rapid diagnosis of COVID‐19 patients. The authors propose a classifier‐augmented generative adversarial network framework for weakly supervised COVID‐19 lung lesion localisation. It consists of an abnormality map generator, discriminator and classifier. The generator aims to produce the abnormality feature map M to locate lesion regions and then constructs images of the pseudo‐healthy subjects by adding M to the input patient images. Besides constraining the generated images of healthy subjects with real distribution by the discriminator, a pre‐trained classifier is introduced to enhance the generated images of healthy subjects to possess similar feature representations with real healthy people in terms of high‐level semantic features. Moreover, an attention gate is employed in the generator to reduce the noise effect in the irrelevant regions of M. Experimental results on the COVID‐19 CT dataset show that the method is effective in capturing more lesion areas and generating less noise in unrelated areas, and it has significant advantages in terms of quantitative and qualitative results over existing methods.

Abstract In China, several problems were common in the telemedicine systems, such as the poor network stability and difficult interconnection. A new telemedicine system jointly driven by multinetwork integration and remote control has been designed to address these problems. A multilink aggregation algorithm and an overlay network for telemedicine system (ONTMS) were developed to improve network stability, and a non‐intervention remote control method was designed for Internet of Things (IoT) devices/systems. The authors monitored the network parameters, and distributed the questionnaire to participants, for evaluating the telemedicine system and services. Under a detection bandwidth of 8 Mbps, the aggregation parameters of Unicom 4G, Telecom 4G, and China Mobile 4G were optimal, with an uplink bandwidth, delay, and packet loss ratio (PLR) of 7.93 Mbps, 58.80 ms, and 0.06%, respectively. These parameters were significantly superior to those of China Mobile 4G, the best single network (p < 0.001). Through the ONTMS, the mean round‐trip transporting delay from Beijing to Sanya was 76 ms, and the PLR was 0 at vast majority of time. A total of 1988 participants, including 1920 patients and 68 doctors, completed the questionnaires. More than 97% of participants felt that the audio and video transmission and remote control were fluent and convenient. 96% of patients rated the telemedicine services with scores of 4 or 5. This system has shown robust network property and excellent interaction ability, and satisfied the needs of patients and doctors.

The Coronavirus Disease 2019 (COVID‐19) epidemic has constituted a Public Health Emergency of International Concern. Chest computed tomography (CT) can help early reveal abnormalities indicative of lung disease. Thus, accurate and automatic localisation of lung lesions is particularly important to assist physicians in rapid diagnosis of COVID‐19 patients. The authors propose a classifier‐augmented generative adversarial network framework for weakly supervised COVID‐19 lung lesion localisation. It consists of an abnormality map generator, discriminator and classifier. The generator aims to produce the abnormality feature map M to locate lesion regions and then constructs images of the pseudo‐healthy subjects by adding M to the input patient images. Besides constraining the generated images of healthy subjects with real distribution by the discriminator, a pre‐trained classifier is introduced to enhance the generated images of healthy subjects to possess similar feature representations with real healthy people in terms of high‐level semantic features. Moreover, an attention gate is employed in the generator to reduce the noise effect in the irrelevant regions of M. Experimental results on the COVID‐19 CT dataset show that the method is effective in capturing more lesion areas and generating less noise in unrelated areas, and it has significant advantages in terms of quantitative and qualitative results over existing methods. [ABSTRACT FROM AUTHOR]

Abstract Cuproptosis is a novel cell death pathway, and the regulatory mechanism in pancreatic cancer (PC) is unclear. The authors aimed to figure out whether cuproptosis‐related lncRNAs (CRLs) could predict prognosis in PC and the underlying mechanism. First, the prognostic model based on seven CRLs screened by the least absolute shrinkage and selection operator Cox analysis was constructed. Following this, the risk score was calculated for pancreatic cancer patients and divided patients into high and low‐risk groups. In our prognostic model, PC patients with higher risk scores had poorer outcomes. Based on several prognostic features, a predictive nomogram was established. Furthermore, the functional enrichment analysis of differentially expressed genes between risk groups was performed, indicating that endocrine and metabolic pathways were potential regulatory pathways between risk groups. TP53, KRAS, CDKN2A, and SMAD4 were dominant mutated genes in the high‐risk group and tumour mutational burden was positively correlated with the risk score. Finally, the tumour immune landscape indicated patients in the high‐risk group were more immunosuppressive than that in the low‐risk group, with lower infiltration of CD8+ T cells and higher M2 macrophages. Above all, CRLs can be applied to predict PC prognosis, which is closely correlated with the tumour metabolism and immune microenvironment.

Cuproptosis is a novel cell death pathway, and the regulatory mechanism in pancreatic cancer (PC) is unclear. The authors aimed to figure out whether cuproptosis‐related lncRNAs (CRLs) could predict prognosis in PC and the underlying mechanism. First, the prognostic model based on seven CRLs screened by the least absolute shrinkage and selection operator Cox analysis was constructed. Following this, the risk score was calculated for pancreatic cancer patients and divided patients into high and low‐risk groups. In our prognostic model, PC patients with higher risk scores had poorer outcomes. Based on several prognostic features, a predictive nomogram was established. Furthermore, the functional enrichment analysis of differentially expressed genes between risk groups was performed, indicating that endocrine and metabolic pathways were potential regulatory pathways between risk groups. TP53, KRAS, CDKN2A, and SMAD4 were dominant mutated genes in the high‐risk group and tumour mutational burden was positively correlated with the risk score. Finally, the tumour immune landscape indicated patients in the high‐risk group were more immunosuppressive than that in the low‐risk group, with lower infiltration of CD8+ T cells and higher M2 macrophages. Above all, CRLs can be applied to predict PC prognosis, which is closely correlated with the tumour metabolism and immune microenvironment. [ABSTRACT FROM AUTHOR]

Objectives: The objectives of this study were to identify the coping strategies used by cancer and non-cancer patients with palliative needs, and to verify if there were differences in the coping strategies adopted between sociodemographic groups. Methods: This is a cross-sectional study carried out from September to November 2019, at Maputo Central Hospital, in the units of Medicine, Surgery, Orthopedics, Gynecology and Obstetrics. Eligible patients (n =94) were included in the study and answered a self-completion scale adapted from the Coping Strategies Inventory by Folkman and Lazarus together with a sociodemographic questionnaire. Results: Our study demonstrates that the most used coping strategies were Social Support, followed by Planful Problem Solving, Escape-Avoidance, and Positive Reappraisal strategies. In addition, significant differences were observed between religious beliefs, with Christians resorting more to coping strategies related to Social Support, Accepting Responsibility and Escape-Avoidance than Evangelicals, and between different levels of education, with greater resort to Social Support, Accepting Responsibility, Planful Problem Solving, and Positive Reappraisal in patients with high education. Conclusions: The results indicate that most of the respondents in this study used more adaptive coping strategies, such as Social Support and Positive Reappraisal, and less avoidant strategies, such as Distancing and Confrontation. There is a need to reinforce positive strategies from health professionals to increase satisfaction, autonomy, and promote patient's quality of life. [ABSTRACT FROM AUTHOR]

Abstract With the speeding up of social activities, rapid changes in lifestyles, and an increase in the pressure in professional fields, people are suffering from several types of sleep‐related disorders. It is a very tedious task for clinicians to monitor the entire sleep durations of the subjects and analyse the sleep staging in traditional and manual laboratory environmental methods. For the purpose of accurate diagnosis of different sleep disorders, we have considered the automated analysis of sleep epochs, which were collected from the subjects during sleep time. The complete process of an automated approach of sleep stages’ classification is majorly executed through four steps: pre‐processing the raw signals, feature extraction, feature selection, and classification. In this study, we have extracted 12 statistical properties from input signals. The proposed models are tested in three different combinations of features sets. In the first experiment, the feature set contained all the 12 features. The second and third experiments were conducted with the nine and five best features. The patient records come from the ISRUC‐Sleep database. The highest classification accuracy was achieved for sleep staging through combinations with the five feature set. From the categories of the subjects, the reported accuracy results were found to exceed above 90%. As per the outcome from the proposed system the random forest classification techniques achieved best accuracy incomparable to that of the other two classifiers.

Abstract Improving the accuracy of transformer dissolved gas analysis is always an important demand for power companies. However, the requirement for large numbers of fault samples becomes an obstacle to this demand. This article creatively uses a large number of health data, which is much easier to obtain by power companies, to improve diagnosis accuracy. Comprehensive investigations from the view of both data set and methodology to deal with this problem are presented. A data set consists of 9595 health samples and 993 fault samples is used for analysis. The characteristics of the data set and the influence of the health data on diagnostic accuracy are discussed. The performance of many state‐of‐art algorithms that handle the imbalanced problem is evaluated. Meanwhile, an efficient fault diagnosis algorithm named self‐paced ensemble (SPE) is presented. In SPE, classification hardness is proposed to include the data characteristic in the classification. This method can guarantee the diversity of the data set and keep high performance. According to the experiment results, the superior of SPE is confirmed and also proves that involving more health samples can improve transformer diagnosis when fault data are limited.

Computer vision which is commonly termed as CV is described as the study that helps the computer to recognize and grab the content of digital images like photographs and videos using suitable techniques and recent technologies. In the field of the healthcare sector, the latest technological advancements have been adopted to provide better services to mankind. These are not only saving time of the doctors or Medical practitioners but also saving the lives of the patients. Artificial Intelligence (AI) technology has also been implemented in the healthcare system to help the medical practitioners to recognize and diagnose the patient's problem more precisely and assist the doctor in the simplest way to analyze the patient's condition from which they are suffering from. It also helps the doctors to give the right treatment in the right way depending on the current status of the patients [ABSTRACT FROM AUTHOR]

Duchenne muscular dystrophy (DMD) is an X-linked genetic disease affecting 1 in 5000 young males worldwide annually. Patients experience muscle weakness and loss of ambulation at an early age, with ∼75% reduced life expectancy. Recently developed genetic editing strategies aim to convert severe DMD phenotypes to a milder disease course. Among these, the antisense oligonucleotide (AO)-mediated exon skipping and the adeno-associated viral-delivered clustered regularly interspaced short palindromic repeat (CRISPR) associated protein 9 (adeno-associated viral (AAV)-delivered CRISPR/Cas9) gene editing have shown promising results in restoring dystrophin protein expression and functionality in skeletal and heart muscle in both animals and human cells in vivo and in vitro. However, therapeutic benefits currently remain unclear. The aim of this review is to compare the potential therapeutic benefits, efficacy, safety, and clinical progress of AO-mediated exon skipping and CRISPR/Cas9 gene-editing strategies. Both techniques have demonstrated therapeutic benefit and long-term efficacy in clinical trials. AAV-delivery of CRISPR/Cas9 may potentially correct disease-causing mutations following a single treatment compared to the required continuous AO/PMO-delivery of exon skipping drugs. The latter has the potential to increase the dystrophin expression in skeletal/heart muscle with sustained effects. However, therapeutic challenges including the need for optimised delivery must be overcome in to advance current clinical data.

The micromechanical properties of soft tissue can be used as markers for the physiological state and function of the tissue. Deep understanding of the micromechanics of soft tissue, such as the oesophagus, is of great significance to the design of artificial oesophagi, endoscope materials and coatings for medical devices. Here, the micromechanical properties of oesophageal mucosa were studied under different loading rates, deflections, and dwell time by using atomic force microscopy. The micromechanical properties of soft tissue included elastic modulus, plasticity and adhesion force. Results showed that the micromechanical properties changed with increasing loading rate, deflection and dwell time. The micromechanical properties of oesophageal mucosa were related to time-dependent behaviours, such as contact stress, energy transformation, and strain gradient plasticity. Furthermore, the heterogeneity of oesophageal mucosa affected the micromechanical properties. The force mapping mode was a reliable and effective means to study the micromechanical properties of soft tissue. The results can provide a basis and technical support for the diagnosis of oesophageal diseases from a microscale as well as a material design perspective.

Multiple Sclerosis (MS) causes the central nervous system to malfunction due to inflammation surrounding nerve cells. Detection of MS at an early stage is very important to prevent progressive MS attacks. Clinical findings, cerebrospinal fluid examinations, the evoked potentials, magnetic resonance imaging (MRI) findings have an important role in the diagnosis and follow‐up of MS. However, many of the findings on MRI may indicate brain disorders other than MS. In addition, the clinical practices accepted by physicians for MS detection are very limited. In this study, a Mask R‐CNN based method in two dataset is proposed for the automatic detection of MS lesions on magnetic resonance scans. We also improved the ROI detection stage with RPN in the Mask R‐CNN to easily adapt for different lesion sizes. MS lesions in different sizes in the dataset are successfully detected with 84.90% Dice similarity rate and 87.03% precision rates using the proposed method. In addition, volumetric overlap error and lesion‐wise true positive rate are obtained as 12.97% and 73.75%, respectively. Moreover, performance tests of the use of different numbers of GPU hardware structures are also performed and the evaluation of its effects on processing speed is performed on experimental studies.. [ABSTRACT FROM AUTHOR]

The novel coronavirus has spread quite rapidly across the globe. The current testing rate is failing to match the exponential rate of rising cases. Moreover, the available testing methodologies are expensive and time‐consuming. A sensitive automated diagnosis is one of the biggest need of the hour. In the proposed work, the authors analyse the chest X‐ray images of normal, pneumonia and coronavirus disease‐2019 (COVID‐19) patients and process them to boost the COVID‐specific features (opacities etc.), which enable to perform sensitive identification of COVID‐19 patients. The sets of original and processed images are used with a stack of pre‐trained deep models for ensemble learning. They used VGG‐16 as base‐learners, trained with a diverse set of inputs followed by a logistic regression model, the meta learner, to combine the base‐learner predictions. The proposed fusion‐based model is trained and tested for three types of classification, TYPE‐I: binary (NORMAL/ABNORMAL), TYPE‐II: binary (PNEUMONIA/COVID‐19) and TYPE‐III: multi‐class (NORMAL/PNEUMONIA/COVID‐19). The diagnosis results are quite promising, with high accuracy and sensitivity values for all the cases. The proposed algorithm can be used to assist the medical experts for quick identification and isolation of COVID‐19 patients and thereby mitigating the effect of the virus. [ABSTRACT FROM AUTHOR]

The diagnosis and prevention of Alzheimer's disease plays an important role in improving patient cognition. It can be seen from the current situation that the diagnosis of Alzheimer's disease is still poor because it is affected by many factors. Based on this, combined with the symptoms of Alzheimer's disease, this study used computer‐aided to diagnose the symptoms of patients. First of all, this study analyses classical machine learning and chooses the appropriate model for diagnosis. Next, this study constructs a diagnostic system based on a mixed Gaussian model and uses a mixed Gaussian model to predict the probability of different distribution methods. Finally, this study designs experiments to analyse the performance of diagnostic models. Studies have shown that the mixed Gaussian model has a good effect on the automatic diagnosis of Alzheimer's disease, and can provide a theoretical reference for subsequent related research. [ABSTRACT FROM AUTHOR]

This study was to identify important circRNA–miRNA–mRNA (ceRNAs) regulatory mechanisms in hepatocellular carcinoma (HCC). The circRNA dataset GSE97332 and miRNA dataset GSE57555 were used for analyses. Functional enrichment analysis for miRNA and target gene was conducted using cluster Profiler. Survival analysis was conducted through R package Survival. The ceRNAs and drug–gene interaction networks were constructed. The ceRNAs network contained five miRNAs including hsa‐miR‐25‐3p, hsa‐miR‐3692‐5p, hsa‐miR‐4270, hsa‐miR‐331‐3p, and hsa‐miR‐125a‐3p. Among the network, hsa‐miR‐25‐3p targeted the most genes, hsa‐miR‐3692‐5p and hsa‐miR‐4270 were targeted by more circRNAs than other miRNAs, hsa‐circ‐0034326 and hsa‐circ‐0011950 interacted with three miRNAs. Furthermore, target genes, including NRAS, ITGA5, SLC7A1, SEC14L2, SLC12A5, and SMAD2 were obtained in drug–gene interaction network. Survival analysis showed NRAS, ITGA5, SLC7A1, SEC14L2, SLC12A5, and SMAD2 were significantly associated with prognosis of HCC. NRAS, ITGA5, and SMAD2 were significantly enriched in proteoglycans in cancer. Moreover, hsa‐circ‐0034326 and hsa‐circ‐0011950 might function as ceRNAs to play key roles in HCC. Furthermore, miR‐25‐3p, miR‐3692‐5p, and miR‐4270 might be significant for HCC development. NRAS, ITGA5, SEC14L2, SLC12A5, and SMAD2 might be prognostic factors for HCC patients via proteoglycans in cancer pathway. Taken together, the findings will provide novel insight into pathogenesis, selection of therapeutic targets and prognostic factors for HCC. [ABSTRACT FROM AUTHOR]

Duchenne muscular dystrophy (DMD) is an X‐linked genetic disease affecting 1 in 5000 young males worldwide annually. Patients experience muscle weakness and loss of ambulation at an early age, with ∼75% reduced life expectancy. Recently developed genetic editing strategies aim to convert severe DMD phenotypes to a milder disease course. Among these, the antisense oligonucleotide (AO)‐mediated exon skipping and the adeno‐associated viral‐delivered clustered regularly interspaced short palindromic repeat (CRISPR) associated protein 9 (adeno‐associated viral (AAV)‐delivered CRISPR/Cas9) gene editing have shown promising results in restoring dystrophin protein expression and functionality in skeletal and heart muscle in both animals and human cells in vivo and in vitro. However, therapeutic benefits currently remain unclear. The aim of this review is to compare the potential therapeutic benefits, efficacy, safety, and clinical progress of AO‐mediated exon skipping and CRISPR/Cas9 gene‐editing strategies. Both techniques have demonstrated therapeutic benefit and long‐term efficacy in clinical trials. AAV‐delivery of CRISPR/Cas9 may potentially correct disease‐causing mutations following a single treatment compared to the required continuous AO/PMO‐delivery of exon skipping drugs. The latter has the potential to increase the dystrophin expression in skeletal/heart muscle with sustained effects. However, therapeutic challenges including the need for optimised delivery must be overcome in to advance current clinical data. [ABSTRACT FROM AUTHOR]

Echography imaging is an important technique frequently used in medical diagnostics due to low-cost, non-ionising characteristics, and pragmatic convenience. Due to the shortage of skilful technicians and injuries of physicians sustained from diagnosing several patients, robot-assisted echography (RAE) system is gaining great attention in recent decades. A thorough study of the recent research advances in the field of perception, control and cognition techniques used in RAE systems is presented in this study. This survey introduces the representative system structure, applications and projects, and products. Challenges and key technological issues faced by the traditional RAE system and how the current artificial intelligence and cobots attempt to overcome these issues are summarised. Furthermore, significant future research directions in this field have been identified by this study as cognitive computing, operational skills transfer, and commercially feasible system design.

Hand foot and mouth disease (HFMD) and tetanus are serious infectious diseases in low- and middle-income countries. Tetanus, in particular, has a high mortality rate and its treatment is resource-demanding. Furthermore, HFMD often affects a large number of infants and young children. As a result, its treatment consumes enormous healthcare resources, especially when outbreaks occur. Autonomic nervous system dysfunction (ANSD) is the main cause of death for both HFMD and tetanus patients. However, early detection of ANSD is a difficult and challenging problem. The authors aim to provide a proof-of-principle to detect the ANSD level automatically by applying machine learning techniques to physiological patient data, such as electrocardiogram waveforms, which can be collected using low-cost wearable sensors. Efficient features are extracted that encode variations in the waveforms in the time and frequency domains. The proposed approach is validated on multiple datasets of HFMD and tetanus patients in Vietnam. Results show that encouraging performance is achieved. Moreover, the proposed features are simple, more generalisable and outperformed the standard heart rate variability analysis. The proposed approach would facilitate both the diagnosis and treatment of infectious diseases in low- and middle-income countries, and thereby improve patient care.

In pathological diagnosis of breast cancer, there are problems such as shortage of pathologists, difficulties in sample labeling, and huge workload of manual diagnosis. Therefore, deep learning-based computer-assisted pathology analysis systems have been developed to diagnose breast cancer and have achieved impressive results. However, it is difficult to obtain a large number of training sets due to the scarcity of pathological images and the huge labeling costs. Therefore, the size of the training set should be planned before building the pathology computer-assisted breast cancer analysis system. Here, the authors present a study to determine the optimal size of the training data set needed to achieve high classification accuracy when developing a pathology computer-assisted breast cancer analysis system. The authors trained two kind of CNNs using six different sizes of training data set and then tested the resulting system with a total of 10,000 images. All images were acquired from the Camelyon17 challenge. Here, the authors propose a scheme for determining the size of the training set and the size of the model in developing the pathology computer-assisted breast cancer analysis systems, which can be easily applied to develop systems for other different pathological images.

The skin is a complex biological tissue whose impedance varies with frequency. The properties and structure of skin changes with the location on the body, age, geographical location and other factors. Considering these factors, skin impedance analysis is a sophisticated data analysis. However, despite all these variations, various researchers have always worked to develop an equivalent electrical model of the skin. The two most important categories of electrical models are RC‐based model and CPE‐based model which focus on the physiological stratification and biological properties of skin, respectively. In this work, experimental skin impedance data is acquired from ten sites on the body to find the fitting model. It is observed that a hybrid of fractional‐order CPE‐based model and higher‐order RC layered‐based model can provide the best fitting electrical model of skin. A new model is developed with this hybrid orders. Genetic algorithm is used for the extraction of parameter components. Least error of fitting has been observed for the proposed model as compared with the other models. This model can be used in correlating many skin problems and in the development of diagnostic tools. It will offer an additional supportive tool in‐vitro to the medical specialist. [ABSTRACT FROM AUTHOR]

Echography imaging is an important technique frequently used in medical diagnostics due to low‐cost, non‐ionising characteristics, and pragmatic convenience. Due to the shortage of skilful technicians and injuries of physicians sustained from diagnosing several patients, robot‐assisted echography (RAE) system is gaining great attention in recent decades. A thorough study of the recent research advances in the field of perception, control and cognition techniques used in RAE systems is presented in this study. This survey introduces the representative system structure, applications and projects, and products. Challenges and key technological issues faced by the traditional RAE system and how the current artificial intelligence and cobots attempt to overcome these issues are summarised. Furthermore, significant future research directions in this field have been identified by this study as cognitive computing, operational skills transfer, and commercially feasible system design. [ABSTRACT FROM AUTHOR]

Precise diagnosis of brain tumour by experienced radiologists involves a complex set of processes including magnetic resonance imaging, magnetic resonance spectroscopy (MRS) data and histopathological evaluations. In this study, a new hybrid feature extraction method, called as aiMRS, based on the negative selection algorithm and clonal selection algorithm of artificial immune systems is developed on MRS data for the detection and classification of brain tumours. In the study, differentiation of benign and malignant brain tumours, classification of normal brain tissue and brain tumour, and detection of metastasis and primary brain tumours are performed with high precision using pattern recognition methods based on the proposed aiMRS method. According to the experimental results performed on a large data set created with the MRS data obtained from INTERPRET database, when the proposed feature extraction method applied, classification of normal brain tissue and brain tumours, benign and malignant brain tumours and metastasis and primary brain tumours is achieved with 100, 98.58 and 98.94% accuracy, respectively. These results show that this proposed system can be used as a secondary tool in physicians' decision‐making processes for the classification of brain tumours. [ABSTRACT FROM AUTHOR]

Pulmonary nodules risk classification in adenocarcinoma is essential for early detection of lung cancer and clinical treatment decision. Improving the level of early diagnosis and the identification of small lung adenocarcinoma has been always an important topic for imaging studies. In this study, the authors propose a deep convolutional neural network (CNN) with scale‐transfer module (STM) and incorporate multi‐feature fusion operation, named STM‐Net. This network can amplify small targets and adapt to different resolution images. The evaluation data were obtained from the computed tomography (CT) database provided by Zhongshan Hospital Fudan University (ZSDB). All data have a pathological label and their lung adenocarcinomas risk are classified into four categories: atypical adenomatous hyperplasia, adenocarcinoma in situ, minimally invasive adenocarcinoma, and invasive adenocarcinoma. The authors' deep learning network STM‐Net was trained and tested for the risk stage prediction. The accuracy and the average area under the receiver operating characteristic curve achieved by their method are 95.455% and 0.987 for the ZSDB dataset. The experimental results show that STM‐Net largely boosts classification accuracy on the pulmonary nodules classification compared with state‐of‐the‐art approaches. The proposed method will be an effective auxiliary to help physicians diagnosis pulmonary nodules risk classification in adenocarcinoma in early‐stage. [ABSTRACT FROM AUTHOR]

In this research, the authors investigate the feasibility of selecting three‐dimensional thigh and shank angles as the features of machine learning methods. Four common machine learning techniques, i.e. random forest, k‐nearest neighbour, support vector machine and perceptron, were compared in terms of accuracy and memory usage so that a real‐time standalone gait diagnosis device can be constructed using low‐end inertial measurement units (IMUs). With proper re‐sampling and normalisation, they discovered that the support vector machine and perceptron resulted in the top two highest accuracies (96–99%) among the four machine learning methods. The memory requirement of the perceptron is the lowest among the machine learning methods. Therefore, perceptron was selected as the classification algorithm for the standalone gait diagnosis device. The trained perceptron was transferred to the thigh and shank's IMUs to process the data locally in real‐time. The constructed standalone gait diagnosis device lit up green or red light emitting diodes when normal or abnormal gaits were detected, respectively. This standalone device was further tested in real‐life and achieved a mean classification accuracy of 96.50%. [ABSTRACT FROM AUTHOR]

When performing paediatric pancreatic cancer (PC) diagnosis, magnetic resonance imaging (MRI) will be interfered by many factors, resulting in poor diagnosis. In order to improve the diagnostic effect of MRI on PC, this study is based on machine learning and combines SVM (support vector machine) and MRI detection with the support of image processing technology and improves the diagnostic accuracy of MRI for PC by the computer‐assisted method. At the same time, in the research, this study combines the characteristics of MRI detection to construct an MRI detection model based on SVM. In addition, this study obtains test samples through data collection, analyses the performance of the algorithm model through actual cases, and combines image processing to improve the detection effect. Studies have shown that the algorithm model proposed in this study can effectively improve the accuracy of MRI diagnosis of PC and provide a theoretical reference for subsequent related research. [ABSTRACT FROM AUTHOR]

At present, the rate of missed diagnosis of lung cancer is high. The reason is that the pulmonary nodule phenomenon cannot be effectively monitored due to various interference factors in the actual detection process. In order to improve the detection accuracy, this study combined with the actual situation to analyse the diversity of nodular shape and constructed a deep belief network‐based diagnosis model for pulmonary nodules. At the same time, in order to improve the detection effect, this study sets the model to have multi‐layer non‐linear structure and analyses the previous clinical data to improve the model learning rate and training effect. In addition, in order to verify the performance of the model, the diagnostic effect of the model is studied by comparative experiments. The research shows that the model proposed in this study is higher than the traditional algorithm in detection accuracy, which can provide theoretical reference for subsequent related research. [ABSTRACT FROM AUTHOR]

Microfluidics is an upcoming field of science that is going to be used widely in many safety‐critical applications including healthcare, medical research and defence. Hence, technologies for fault testing and fault diagnosis of these chips are of extreme importance. In this study, the authors propose a scalable pseudo‐exhaustive testing and diagnosis methodology for flow‐based microfluidic biochips. The proposed approach employs a divide‐and‐conquer based technique wherein, large architectures are split into smaller sub‐architectures and each of these are tested and diagnosed independently. [ABSTRACT FROM AUTHOR]

Human autoantibodies targeting myelin oligodendrocyte glycoprotein (MOG Ab) have become a useful clinical biomarker for the diagnosis of a spectrum of inflammatory demyelinating disorders. Live cell-based assays that detect MOG Ab against conformational MOG are currently the gold standard. Flow cytometry, in which serum binding to MOG-expressing cells and control cells are quantitively evaluated, is a widely used observer-independent, precise, and reliable detection method. However, there is currently no consensus on data analysis; for example, seropositive thresholds have been reported using varying standard deviations above a control cohort. Herein, we used a large cohort of 482 sera including samples from patients with monophasic or relapsing demyelination phenotypes consistent with MOG antibody-associated demyelination and other neurological diseases, as well as healthy controls, and applied a series of published analyses involving a background subtraction (delta) or a division (ratio). Loss of seropositivity and reduced detection sensitivity were observed when MOG ratio analyses or when 10 standard deviation (SD) or an arbitrary number was used to establish the threshold. Background binding and MOG ratio value were negatively correlated, in which patients seronegative by MOG ratio had high non-specific binding, a characteristic of serum that must be acknowledged. Most MOG Ab serostatuses were similar across analyses when optimal thresholds obtained by ROC analyses were used, demonstrating the robust nature and high discriminatory power of flow cytometry cell-based assays. With increased demand to identify MOG Ab-positive patients, a consensus on analysis is vital to improve patient diagnosis and for cross-study comparisons to ultimately define MOG Ab-associated disorders. [ABSTRACT FROM AUTHOR]

A highly sensitive, non‐invasive, and rapid HBV (Hepatitis B virus) screening method combining membrane protein purification with silver nanoparticle‐based surface‐enhanced Raman scattering (SERS) spectroscopy was developed in this study. Reproducible serum protein SERS spectra were obtained from cellulose acetate membrane‐purified human serum from 94 HBV patients and 89 normal groups. Tentative assignments of serum protein SERS spectra showed that the HBV patients primarily led to specific biomedical changes of serum protein. Principal components analysis and linear discriminate analysis were introduced to analyse the obtained spectra, with the diagnostic sensitivity of 92.6% and specificity of 77.5% were achieved for differentiating HBV patients from normal groups. [ABSTRACT FROM AUTHOR]

Ureaplasma urealyticum (uu) is one of the most common agents of urogenital infections and is associated with complications such as infertility, spontaneous abortion and other sexually transmitted diseases. Here, a DNA sensor based on oligonucleotide target‐specific gold nanoparticles (AuNPs) was developed, in which the dispersed and aggregated states of oligonucleotide‐functionalised AuNPs were optimised for the colorimetric detection of a polymerase chain reaction (PCR) amplicon of U. urealyticum DNA. A non‐cross‐linking approach utilising a single Au‐nanoprobe specific of the urease gene was utilised and the effect of a PCR product concentration gradient evaluated. Results from both visual and spectral analyses showed that target–Au‐nanoprobe hybrids were stable against aggregation after adding the inducer. Furthermore, when a non‐target PCR product was used, the peak position shifted and salt‐induced aggregation occurred. The assay's limit of detection of the assay was 10 ng with a dynamic range of 10–60 ng. This procedure provides a rapid, facile and low‐cost detection format, compared to methods currently used for the identification of U. urealyticum. [ABSTRACT FROM AUTHOR]

Wireless capsule endoscopy (WCE) is a non‐invasive diagnosis method that allows recording a video as the capsule travels through the gastrointestinal (GI) tract. The practical drawback is producing a long clinical video in which the review process by an experienced specialist is tedious. Automated summarisation methods can reduce the evaluation time by experts as well as errors in manual interpretation. The proposed approach consists of three main steps as follows: First, an adaptive sliding window singular value decomposition is employed to extract representative video frames. Then, adaptive contrast diffusion is utilised to increase the visibility of WCE frames. At the end stage, a novel knowledge‐based method is developed to segment video frames into four topographic zones of GI tract, which are oesophagus, stomach, small intestine and large intestine. The authors have evaluated the proposed framework in the presence of 30 local datasets as well as publicly available KID database. The average recall and precision were estimated by 0.86 and 0.83, and by 0.82 and 0.83 for KID database, respectively. Their results reveal that significant reduction in the review time is feasible using the proposed technique. Quantitative results of summarisation show that the proposed method is more effective than three methods in the literature. [ABSTRACT FROM AUTHOR]

Nasopharyngeal carcinoma (NPC), a kind of squamous cell carcinoma, occurs in the top and the side wall of nasopharyngeal, which harms human health and life. In this study, a novel blood test (SERS) was carried out for 30 NPC patients and 30 normal ones. Using multi‐variate statistical analysis for spectral data, the diagnostic sensitivities of 89.3% (50/56) and 85.7% (48/56) can be achieved for 633 and 785 nm exciting wavelength, respectively. Also corresponding specificities are 71.4% (41/56) and 78.6% (44/56), respectively. These results demonstrated that the two kinds of excitation wavelength all have the feasibility of obtaining high‐quality SERS spectra to differentiate cancer from normal samples. Furthermore, the performance of the SERS test with 785 nm wavelength excitation is nearly equal to the SERS experimental effect under 633 nm wavelength excitation for NPC detection. [ABSTRACT FROM AUTHOR]

In recent years, nanoparticles especially with gold and silver nanoparticles based point of care diagnostic methods is being developed for the lethal diseases like dengue. This study focused to work on the dengue virus detection in a simplest method using gold nanoparticles probe (AuNPs) with thiol tagged single strand DNA (ss‐DNA). A sensitive, fluorescence‐based detection strategy was designed to examine and quantified the hybridisation process and also elucidated the behaviour of AuNPs before and after interaction of biomolecule. The detection process was focused on aggregation of gold nanoprobe in the presence of complementary strand (target region). Hence the percentage of aggregation was measured and as a result, the limit of detection was found to be 10−6 dilutions. Current detection method was highly sensitive, easy to perform and the reaction timing is rapid between 5 and 10 min, and it can be observed through naked eye. [ABSTRACT FROM AUTHOR]

In pathological diagnosis of breast cancer, there are problems such as shortage of pathologists, difficulties in sample labeling, and huge workload of manual diagnosis. Therefore, deep learning-based computer-assisted pathology analysis systems have been developed to diagnose breast cancer and have achieved impressive results. However, it is difficult to obtain a large number of training sets due to the scarcity of pathological images and the huge labeling costs. Therefore, the size of the training set should be planned before building the pathology computer-assisted breast cancer analysis system. Here, the authors present a study to determine the optimal size of the training data set needed to achieve high classification accuracy when developing a pathology computer-assisted breast cancer analysis system. The authors trained two kind of CNNs using six different sizes of training data set and then tested the resulting system with a total of 10,000 images. All images were acquired from the Camelyon17 challenge. Here, the authors propose a scheme for determining the size of the training set and the size of the model in developing the pathology computer-assisted breast cancer analysis systems, which can be easily applied to develop systems for other different pathological images. [ABSTRACT FROM AUTHOR]

Esophagogastroduodenoscopy (EGD) has been widely applied for gastrointestinal (GI) examinations. However, there is a lack of mature technology to evaluate the quality of the EGD inspection process. In this Letter, the authors design a multi-task anatomy detection convolutional neural network (MT-AD-CNN) to evaluate the EGD inspection quality by combining the detection task of the upper digestive tract with ten anatomical structures and the classification task of informative video frames. The authors’ model is able to eliminate non-informative frames of the gastroscopic videos and detect the anatomies in real time. Specifically, a sub-branch is added to the detection network to classify NBI images, informative and non-informative images. By doing so, the detected box will be only displayed on the informative frames, which can reduce the false-positive rate. They can determine the video frames on which each anatomical location is effectively examined, so that they can analyse the diagnosis quality. Their method reaches the performance of 93.74% mean average precision for the detection task and 98.77% accuracy for the classification task. Their model can reflect the detailed circumstance of the gastroscopy examination process, which shows application potential in improving the quality of examinations.

Fretting corrosion of metal implants has been associated with implant failure and revision surgeries. This report describes the fixation failure of a femoral neck fracture in a 61-year-old male patient due to corrosion of three cannulated screws. Radiographic evaluation at the time of primary surgery demonstrated well-positioning of the cannulated screws. The patient had no significant medical comorbidities at the time of surgery. However, screw loosening and avascular necrosis were diagnosed after 5 years. At the revision surgery, inflammatory serological markers, C-reactive protein and erythrocyte sedimentation rate showed no signs of infections, and screws were retrieved. Scanning electron microscopy observations showed that all screws were subjected to fretting corrosion which led to discolouration, pitting attack, and cracking. Thus, Fretting corrosion may have contributed to the failure of the fixation of screws.

A complete blood cell count is an important test in medical diagnosis to evaluate overall health condition. Traditionally blood cells are counted manually using haemocytometer along with other laboratory equipment's and chemical compounds, which is a time-consuming and tedious task. In this work, the authors present a machine learning approach for automatic identification and counting of three types of blood cells using ‘you only look once’ (YOLO) object detection and classification algorithm. YOLO framework has been trained with a modified configuration BCCD Dataset of blood smear images to automatically identify and count red blood cells, white blood cells, and platelets. Moreover, this study with other convolutional neural network architectures considering architecture complexity, reported accuracy, and running time with this framework and compare the accuracy of the models for blood cells detection. They also tested the trained model on smear images from a different dataset and found that the learned models are generalised. Overall the computer-aided system of detection and counting enables us to count blood cells from smear images in less than a second, which is useful for practical applications.

Diabetes is a metabolic disorder that affects more than 400 million people worldwide. Most existing approaches for measuring fasting blood glucose levels (FBGLs) are invasive. This work presents a proof-of-concept study in which saliva is used as a proxy biofluid to estimate FBGL. Saliva collected from 175 volunteers was analysed using portable, handheld sensors to measure its electrochemical properties such as conductivity, redox potential, pH and K^+, Na^+ and Ca^2+ ionic concentrations. These data, along with the person's gender and age, were trained and tested after casewise annotation with their true FBGL values using a set of mathematical algorithms. An accuracy of 87.4 ± 1.7% and a mean relative deviation of 14.1% (R^2 = 0.76) was achieved using a mathematical algorithm. All parameters except the gender were found to play a key role in the FBGL determination process. Finally, the individual electrochemical sensors were integrated into a single platform and interfaced with the authors’ algorithm through a simple graphical user interface. The system was revalidated on 60 new saliva samples and gave an accuracy of 81.67 ± 2.53% (R^2 = 0.71). This study paves the way for rapid, efficient and painless FBGL estimation from saliva.

To predict diabetes mellitus model data mining (DM) based approaches on the dataset collected from the seven northwestern states of Nigeria. Data were collected from both primary and secondary sources through questionnaires and verbal interviews from patients with diabetic mellitus and other chronic diseases. Some hospital data were also used from the records of patients involved in this work. The dataset comprises 281 instances with 8 attributes. R programming software (version 5.3.1) was used in the experiments. The DM techniques used in this research were binomial logistic regression, classification, confusion matrix and correlation coefficient. The data were partitioned into training and testing sets. Training data were used in building the model while testing data were used to validate the model. The algorithm for the best-fitted model converges with null deviance: 281.951, residual deviance: 16.476 and AIC: 30.476. The significance variables are AGE, GLU, DBP and KDYP with 0.025, 0.01, 0.05 and 0.025 P values, respectively. The predicted model accounted for the accuracy of ∼97.1%. The correlation analysis results revealed that diabetic patients are more likely to be hypertensive than patients with other chronic diseases considered in the research.

The mortality rate has risen due to the increase in number of cardiac patients in recent times due to the lack of unawareness of the symptoms. This work mainly aims to detect the anomalies of the rhythmic conditions of the pulse derived from the electrocardiogram (ECG) pattern based on correlation and the method of mapping. As this device is a programmable one and a real-time application wearable system on the wrist which is physically connected to the veins, it continuously monitors the photoplethysmography (PPG) pattern based on certain parameters and rhythmic conditions, it ensures whether the patient is under the safe condition or not. The salient features of PPG waveform are extracted with respect to various abnormal categories of ECG beats subdivided into various time durations of one, two and three. The PPG pattern using various feature extraction and the correlation transforms with the signal processing application. The extracted features help to find the skipped beat with irregularities of the rhythm will activate the emergency condition protocol in the device. The location of the patient with a critical condition is sent to the nearest health centre. This innovation is a portable one and a user-friendly application which can save many lives in the society.

In China, several problems were common in the telemedicine systems, such as the poor network stability and difficult interconnection. A new telemedicine system jointly driven by multinetwork integration and remote control has been designed to address these problems. A multilink aggregation algorithm and an overlay network for telemedicine system (ONTMS) were developed to improve network stability, and a non-intervention remote control method was designed for Internet of Things (IoT) devices/systems. The authors monitored the network parameters, and distributed the questionnaire to participants, for evaluating the telemedicine system and services. Under a detection bandwidth of 8 Mbps, the aggregation parameters of Unicom 4G, Telecom 4G, and China Mobile 4G were optimal, with an uplink bandwidth, delay, and packet loss ratio (PLR) of 7.93 Mbps, 58.80 ms, and 0.06%, respectively. These parameters were significantly superior to those of China Mobile 4G, the best single network ( p < 0.001). Through the ONTMS, the mean round-trip transporting delay from Beijing to Sanya was 76 ms, and the PLR was 0 at vast majority of time. A total of 1988 participants, including 1920 patients and 68 doctors, completed the questionnaires. More than 97% of participants felt that the audio and video transmission and remote control were fluent and convenient. 96% of patients rated the telemedicine services with scores of 4 or 5. This system has shown robust network property and excellent interaction ability, and satisfied the needs of patients and doctors., Competing Interests: The authors declare no conflict of interest., (© 2023 The Authors. Healthcare Technology Letters published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.)

Tongue diagnosis is an important concept in Traditional Chinese Medicine (TCM). The tongue colour and coating can aid understanding of the body's physiological mechanisms, as well as the pathology of diseases. Existing research has focused on using digital images and tongue colour classification, without considering the other visible bands of information in the tongue. In this study, a visible hyperspectral image system, with an approximate spectral range of 400–1000 nm, was used to predict the tongue colour values and the coating position in TCM, and a stacked autoencoder (SAE) predict model based on spectral–spatial feature was performed to digital the tongue colour space and the coating. The experimental results show the effectiveness of the spectral–spatial feature with SAE model in predicting the CIELAB values of L, a, and coating position, thus the authors provide a new technique for the objective and digitising development of TCM. [ABSTRACT FROM AUTHOR]

The alarming increase in global burden of various corneal diseases in general and mycotic keratitis in particular has raised up a major concern for its treatment. Mycotic keratitis is one of the most serious infections among the various ocular diseases. The proper diagnosis and effective treatment strategies remain a great challenge for ophthalmologists. The inefficacy and failure of conventional treatments have generated need to develop alternative approaches for the treatment of mycotic keratitis. Considering the promising applications of nanotechnology in biomedical area, it is believed that various nanomaterials can be effectively used in the management of mycotic keratitis. This review focuses on worldwide burden of various corneal diseases including mycotic keratitis and the role of nanotechnology in its treatment. In addition, safety and toxicological issues are also discussed. [ABSTRACT FROM AUTHOR]

This study presents a diagnostic quality assured electrocardiogram (ECG) signal compression algorithm which uses discrete wavelet transform with the selection of appropriate mother wavelet. Since distortion of reconstructed ECG signal depends on the type of mother wavelet used for decomposition. Therefore, appropriate mother wavelet is selected first which produces minimal distortion. Small valued wavelet transform coefficients are discarded using dead‐zone quantisation. Further integer conversion of coefficients is performed to improve compression at the cost of very less error. The processed transform coefficients obtained at this stage contain approximate coefficients and detail coefficients, where approximate coefficients consist very less repetition of data instances while detail coefficients are much repetitive. The repetition of detail coefficients is exploited by run–length encoding which represents the data as run and length. Compression performance of the proposed algorithm is evaluated using single‐channel ECG records taken from the Massachusetts Institute of Technology‐Beth Israel Hospital (MIT‐BIH) arrhythmia database, MIT‐BIH ECG compression test database and Physikalisch‐Technische Bundesanstalt (PTB) diagnostic ECG database. Compression results exhibit better performance than other existing techniques. Subjective evaluation of reconstructed ECG signals is also performed which ensures effective working of the proposed algorithm on the different morphology of ECG signals. [ABSTRACT FROM AUTHOR]