A comprehensive survey on investigation techniques of exhaled breath (EB) for diagnosis of diseases in human body

High-precision Helicobacter pylori infection diagnosis using a dual-element multimodal gas sensor array

Helicobacter pylori (H. pylori) is a globally widespread bacterial infection. Early diagnosis of this infection is vital for public and individual health. Prevalent diagnosis methods like the isotope 13C or 14C labelled urea breath test (UBT) are not convenient and may do harm to the human body. The use of cross-response gas sensor arrays (GSAs) is an alternative way for label-free detection of metabolite changes in exhaled breath (EB). However, conventional GSAs are complex to prepare, lack reliability, and fail to discriminate subtle changes in EB due to the use of numerous sensing elements and single dimensional signal. This work presents a dual-element multimodal GSA empowered with multimodal sensing signals including conductance (G), capacitance (C), and dissipation factor (DF) to improve the ability for gas recognition and H. pylori-infection diagnosis. Sensitized by poly(diallyldimethylammonium chloride) (PDDA) and the metal-organic framework material NH2-UiO66, the dual-element graphene oxide (GO)-composite GSAs exhibited a high specific surface area and abundant adsorption sites, resulting in high sensitivity, repeatability, and fast response/recovery speed in all three signals. The multimodal sensing signals with rich sensing features allowed the GSA to detect various physicochemical properties of gas analytes, such as charge transfer and polarization ability, enhancing the sensing capabilities for gas discrimination. The dual-element GSA could differentiate different typical standard gases and non-dehumidified EB samples, demonstrating the advantages in EB analysis. In a case-control clinical study on 52 clinical EB samples, the diagnosis model based on the multimodal GSA achieved an accuracy of 94.1%, a sensitivity of 100%, and a specificity of 90.9% for diagnosing H. pylori infection, offering a promising strategy for developing an accurate, non-invasive and label-free method for disease diagnosis.

Tuning phosphorene and MoS2 2D materials for detecting volatile organic compounds associated with respiratory diseases

Efficient identification of volatile organic compounds (VOCs) is essential for the rapid diagnostication of respiratory diseases. By detecting specific biomarkers associated with different pathologies one may distinguish between tuberculosis, nosocomial pneumonia, Aspergillus fumigatus, influenza and SARS-CoV-2 virus infections. Phosphorene and MoS2 are potential candidates from the class of 2D graphene-like materials, which can be used as active layers for sensing elements. However, as the target molecules poorly adhere to the pristine layers, binding centers are created by introducing substitutional impurities. The adsorbed VOCs induce modifications in the electrical properties of the customized active layers. For each biomarker and a sequence of substitutional impurities, a pattern of conductivities is obtained, which enables the detection of an unknown test specimen. Exploring multiple biosensor configurations we find an optimal design yielding a considerable selectivity for the five biomarker compounds.

An Investigation on the Acetone and Ethanol Vapor-Sensing Behavior of Sol-Gel Electrospun ZnO Nanofibers Using an Indigenous Setup

The calibration is essential for accuracy, repeatability, and continuous trouble-free operation of gas sensors with safety. Most gas sensors are fabricated using metal oxide nanomaterials in different structures such as films, coating, or nanofibers. Therefore, a device in the sensor manufacturing industry is necessary to test, calibrate, and optimize metal oxide structures. In this point of view, a simple device is developed to test and estimate the sensing response, response time, and recovery time of nanostructures. The sol-gel method was used to produce nanofibers through electrospinning. An average fiber diameter of 245 nm was obtained after pyrolysis at 600 °C. The structure and composition of ZnO nanofibers are confirmed by X-ray diffraction, scanning electron microscopy, and Brunauer-Emmett-Teller. The trials were taken using ZnO nanofibers in the presence of acetone and ethanol vapor, and the results were reported. High response (31.74), rapid response (40 s), and recovery (30 s) times have been achieved for ethanol gas to 50 ppm concentration test gas at an optimal temperature of 260 °C. The results obtained from the trials are compared with the literature results, which are in line with the values presented by the various researchers. Due to the low cost, easy maintenance, and accuracy, this device is recommended in metal oxide sensor development industries and laboratories.

Gold Nanoparticles-Functionalized Cotton as Promising Flexible and Green Substrate for Impedometric VOC Detection

This work focuses on the possible application of gold nanoparticles on flexible cotton fabric as acetone- and ethanol-sensitive substrates by means of impedance measurements. Specifically, citrate- and polyvinylpyrrolidone (PVP)-functionalized gold nanoparticles (Au NPs) were synthesized using green and well-established procedures and deposited on cotton fabric. A complete structural and morphological characterization was conducted using UV-VIS and Fourier transform infrared (FT-IR) spectroscopy, atomic force microscopy (AFM), and scanning electron microscopy (SEM). A detailed dielectric characterization of the blank substrate revealed interfacial polarization effects related to both Au NPs and their specific surface functionalization. For instance, by entirely coating the cotton fabric (i.e., by creating a more insulating matrix), PVP was found to increase the sample resistance, i.e., to decrease the electrical interconnection of Au NPs with respect to citrate functionalized sample. However, it was observed that citrate functionalization provided a uniform distribution of Au NPs, which reduced their spacing and, therefore, facilitated electron transport. Regarding the detection of volatile organic compounds (VOCs), electrochemical impedance spectroscopy (EIS) measurements showed that hydrogen bonding and the resulting proton migration impedance are instrumental in distinguishing ethanol and acetone. Such findings can pave the way for the development of VOC sensors integrated into personal protective equipment and wearable telemedicine devices. This approach may be crucial for early disease diagnosis based on nanomaterials to attain low-cost/low-end and easy-to-use detectors of breath volatiles as disease markers.

Volatile Markers for Cancer in Exhaled Breath-Could They Be the Signature of the Gut Microbiota?

It has been shown that the gut microbiota plays a central role in human health and disease. A wide range of volatile metabolites present in exhaled breath have been linked with gut microbiota and proposed as a non-invasive marker for monitoring pathological conditions. The aim of this study was to examine the possible correlation between volatile organic compounds (VOCs) in exhaled breath and the fecal microbiome by multivariate statistical analysis in gastric cancer patients (n = 16) and healthy controls (n = 33). Shotgun metagenomic sequencing was used to characterize the fecal microbiota. Breath-VOC profiles in the same participants were identified by an untargeted gas chromatography-mass spectrometry (GC-MS) technique. A multivariate statistical approach involving a canonical correlation analysis (CCA) and sparse principal component analysis identified the significant relationship between the breath VOCs and fecal microbiota. This relation was found to differ between gastric cancer patients and healthy controls. In 16 cancer cases, 14 distinct metabolites identified from the breath belonging to hydrocarbons, alcohols, aromatics, ketones, ethers, and organosulfur compounds were highly correlated with 33 fecal bacterial taxa (correlation of 0.891, p-value 0.045), whereas in 33 healthy controls, 7 volatile metabolites belonging to alcohols, aldehydes, esters, phenols, and benzamide derivatives correlated with 17 bacterial taxa (correlation of 0.871, p-value 0.0007). This study suggested that the correlation between fecal microbiota and breath VOCs was effective in identifying exhaled volatile metabolites and the functional effects of microbiome, thus helping to understand cancer-related changes and improving the survival and life expectancy in gastric cancer patients.

A Study on THE Mechanism of Electroacupuncture to Alleviate Visceral Pain and NGF Expression

Visceral pain is unbearable, and natural methods are needed to relieve it. Electroacupuncture is a relatively new technique that helps relieve visceral pain by improving blood circulation and providing energy to clogged parts of the body. However, its analgesic effect and mechanism in colorectal pain are still unknown. In this study, the visceral pain models of electroacupuncture in rats were compared and discussed, using nanocomponents to stimulate the expression and mechanism of the nerve growth factor in colorectal pain and electroacupuncture and to observe the expression and mechanism of nerve growth factor in visceral pain relief rats induced by nanocomponents and electroacupuncture. The results show that nanocomponents can effectively relieve visceral pain under the action of electroacupuncture. NGF can activate endogenous proliferation, migration, differentiation, and integration. NSC can promote nerve regeneration and recovery after injury.

Computational Analysis of Influencing Factors and Multiple Scoring Systems of Stone Clearance Rate after Flexible Ureteroscopic Lithotripsy

Our research aims at the analysis of various stone scoring systems which are referred to as STONE scoring system (SSS) in this study. GUY's scoring system and RUSS scoring system (RSS) are utilized to predict stone-free status (SFS) after surgery and problems after percutaneous nephrolithotomy (PCNL) for harder stones. The data of 68 patients with renal calculi who received FURL in Ren Ji Hospital from Jan 2020 to Mar 2021 are collected as the study subjects. There were 44 male and 24 female patients, with an average age of 55.6 ± 11.4 years. Reliability analysis of related influencing factors (IF) of stone clearance rate (SCR) and multiple scoring systems after flexible ureteroscopic lithotripsy (FURL) was performed. Relevant factors with statistical significance for postoperative SCR were selected for logistic regression analysis (RA). According to the SSS score, GSS classification, and RUSS score, the SCR after FURL was statistically analyzed. The results showed that the P values corresponding to stone position (lower caliceal), cumulative stone diameter (CSD), urinary tract infection, and external physical vibration lithecbole (EPVL) were less than 0.05. The area under the ROC curve of RUSS score, SSS score, and GSS grading was 0.932, 0.841, and 0.533, respectively. The main IF of SCR after FURL were stone location (lower caliceal), CSD, urinary tract infection, and EPVL. The RUSS score system was the best in the evaluation of SCR after FURL. In the previous research, the score systems such as CROES (CRS), SSS, S-ReS, C, and GSS for the prediction of SFS were compared. In our analysis, we have compared the RUSS scoring system which has proven to be giving better results as compared to SSS and GSS. We also performed the regression analysis and found that the stone location shows the strongest correlation of all the other factors for stone clearing rate.

A Comprehensive Study on Epidemiology Case Studies Using Computational Analysis

Health-related issues and occurrences with regard to a particular population are the subject of an epidemiology study. This paper presents the results of a retrospective epidemiological investigation on 15922 hospitalized hand trauma patients from Central China between 2011 and 2020. Gender, age, onset season, injury mechanism, injury environment, injury location, and clinical characteristics are among the characteristics of the data gathered. The study is using computational analysis to draw inferences from the case studies collected in the databases of the hospitals. The types and characteristics of occupational injuries at home and outdoor are compared and analyzed. The purpose of the study is to present the findings from recent case studies for future reference and to recommend useful roles for the industrial sector in the care of patients with hand trauma in order to lower occupational harm. The injuries of preschool children are also analyzed. The incidence rate of hand injuries in infants has been increasing year by year which is directly related to the inefficient growth of children in rural areas. The data are collected from hospitals, then the data analytical tools are applied to draw conclusions. The suggested model is intelligently learned through the application of computational techniques, which are also used to suggest treatments to trauma victims. According to this study, males are more likely than females to sustain hand trauma; occupational injuries are more common than living injuries; males between the ages of 20 and 50 are at an increased risk of suffering an occupational injury. This study showed that the proportion of hand trauma in preschool children was higher (12.27%), and the 2-3-year-old group was the main injury target of preschool children (45.70%). The accidental injuries of newborns and young children can be reduced by government assistance, social support, and tighter monitoring.

The Relationship between Physical Activity and Academic Achievement in Multimodal Environment Using Computational Analysis

Health has always been recognized as the imperative parameter to excel in any field whether professional or personal. People with sound health having regular habit of physical activities show more potential in their professional and personal lives than the people who do not participate in the physical activities. Many state-of-the-art studies exist in the literature where researchers have proved the significance of physical activities as supportive treatment for the existing ailments and in improving the overall health of human beings. Our research aims at accessing the correlation between physical activities carried out by the students and its impact on the success rate of academic achievements. The study is using computational techniques to investigate the relevance of physical activities on the academic achievements of middle school children. The study employs data mining techniques for processing the data. The computational methods are used in a multimodal environment where the surrounding parameters of the environment are considered before performing computational techniques on the subjects (participants in terms of sample for the study). In this cross-sectional study, we have considered the data on various physical activities such as aerobic fitness, running, playing, and participation in extracurricular activities. After collection of data in a real multimodal environment from middle school students, the data preprocessing is performed to handle the missing values. Then, the computational techniques are applied in a step-by-step approach using regression and the bootstrap methods to examine the data and predict the outcome. The correlation is assessed between academic achievements and physical activities. The outcome predicts that physical activities promote the success rate of academic achievements including extra-curricular activities.

Influence of Autologous Bone Marrow Stem Cell Therapy on the Levels of Inflammatory Factors and Conexin43 of Patients with Moyamoya Disease

Moyamoya disease is a medical condition that shows the typical characteristics like continuous and chronic thickening of the walls and the contraction of the internal carotid artery; as a result, the internal diameter of the artery gets narrowed. There are six phases of the disease ranging from I to VI (moyamoya vessels completely disappear, followed by the complete blockage of the arteries). Surgery is a commonly recommended treatment for the moyamoya disease. Our research study identifies the effect of autologous bone marrow stem cell therapy (ABMSCT) on the levels of inflammatory factors and Conexin43 (Cx43) protein in patients suffering from moyamoya. In our study, we have selected 52 moyamoya patients admitted to our hospital from 30 July 2019 to 10 February 2020. The control group (CG) was treated with superficial temporal artery to a middle cerebral artery (STA-MCA) bypass + encephalo-duro-myosinangiosis (EDMS). The experimental group (Exp. Grp) was treated with ABMSC. The cerebral vascular tissue of the patients was treated with hematoxylin-eosin (HE) staining. Immunohistochemical staining was used to identify the levels of Cx43 protein. The concentrations of vascular endothelial growth factor (VEGF), inflammatory factor interleukin-6 (IL6), interleukin-1β (IL1β), tumor necrosis factor (TNFα), and anti-inflammatory factor interleukin-1β (IL1β) were determined by enzyme-linked immunosorbent assay (ELISA). We have found that after treatment of the expression of Cx43 protein, the proportions of grade IV (7.7%), grade III (311.5%), and grade II (3.8%) patients in the Exp. Grp were lower than those in the CG. The proportion of grade I patients in the Exp. Grp (77%) was higher than that in the CG (38.5%). After treatment, the inflammatory factors IL6 (0.97 ± 0.82 pg/mL), IL1β (8.33 ± 1.21 pg/mL), and TNFα (1.73 ± 0.71 pg/mL) in the Exp. Grp were lower than those in the CG. The anti-inflammatory factor IL1β (15.09 ± 4.72 pg/mL) increased in the Exp. Grp compared with the CG (11.25 ± 3.48 pg/mL) post treatment. Intracranial infection, hydrocephalus, hemiplegia, and transient neurological dysfunction in the Exp. Grp were lower than those in the CG, with statistical differences (P < 0.05). Our study suggests that the treatment of autologous bone marrow stem cells (ABMSC) was beneficial to balance the inflammatory response of disorders, reduce the damage of vascular tissue in the brain, and regulate tissue repair by co-acting with various inflammatory factors as compared to traditional surgery. We conclude that the involvement of Cx43 in the occurrence and development of moyamoya. We also have found that the risk factors of intracranial infection after ABMSCT were less as compared to those after conventional surgery.

Diagnostic Significance of 3D Automated Breast Volume Scanner in a Combination with Contrast-Enhanced Ultrasound for Breast Cancer

The incidence of cancer is increasing today, particularly lung and chest cancer. Employing novel methods to detect cancer in its earliest stages and discover painless, noninvasive treatments are urgently needed. The goal of the proposed study is to investigate the value of automated breast volume scanning (ABUS) in conjunction with contrast-enhanced ultrasonography (CEUS) in properly diagnosing breast cancer in its early stages and the effectiveness of neoadjuvant chemotherapy (NAC) in treating the disease. For the research study, information on 98 patients who had NAC and surgery in the breast surgery department of the Shaanxi Provincial Cancer Hospital has been gathered. All patients have received four cycles of NAC and underwent conventional ultrasound (HUSS), CEUS, ABUS, and pathological examination. At the same time, receiver operating characteristic (ROC) curve analysis, single factor, multiple linear regression, and other methods have also been used to analyze the diagnostic efficacy of breast cancer and NAC efficacy evaluation results. The study of this paper is totally based on the data collected from Shaanxi Provincial Cancer Hospital. The statistical and computational analyses are performed on the data collected for drawing inferences. When the findings are compared to the results of the pathological examination, HUSS has demonstrated a significant distinction between benign and malignant diagnoses with a statistical value of P < 0.05.ABUS combined with CEUS has shown no considerable differences in correlation study. Except for negative likelihood ratio, the diagnostic performance indexes of CEUS+ ABUS are substantially higher than HHUS with P < 0.05. ROC curve analysis is also performed which shows that CEUS and ABUS combination has higher precision in the analysis of breast cancer. ABUS pooled with CEUS shows great application value in the judgment of breast cancer as per the results obtained from the statistical analysis on data of 98 patients.

Image Super-Resolution Reconstruction Method for Lung Cancer CT-Scanned Images Based on Neural Network

The super-resolution (SR) reconstruction of a single image is an important image synthesis task especially for medical applications. This paper is studying the application of image segmentation for lung cancer images. This research work is utilizing the power of deep learning for resolution reconstruction for lung cancer-based images. At present, the neural networks utilized for image segmentation and classification are suffering from the loss of information where information passes through one layer to another deep layer. The commonly used loss functions include content-based reconstruction loss and generative confrontation network. The sparse coding single-image super-resolution reconstruction algorithm can easily lead to the phenomenon of incorrect geometric structure in the reconstructed image. In order to solve the problem of excessive smoothness and blurring of the reconstructed image edges caused by the introduction of this self-similarity constraint, a two-layer reconstruction framework based on a smooth layer and a texture layer is proposed for a medical application of lung cancer. This method uses a global nonzero gradient number constrained reconstruction model to reconstruct the smooth layer. The proposed sparse coding method is used to reconstruct high-resolution texture images. Finally, a global and local optimization models are used to further improve the quality of the reconstructed image. An adaptive multiscale remote sensing image super-division reconstruction network is designed. The selective core network and adaptive gating unit are integrated to extract and fuse features to obtain a preliminary reconstruction. Through the proposed dual-drive module, the feature prior drive loss and task drive loss are transmitted to the super-resolution network. The proposed work not only improves the subjective visual effect but the robustness has also been enhanced with more accurate construction of edges. The statistical evaluators are used to test the viability of the proposed scheme.

The Application of Artificial Intelligence in Football Risk Prediction

Football is the most popular sports in the World, with an estimated global following of 4.0 billion fans worldwide. Football draws attention from people of various age groups. The result of the game only decides the performance of the team and individual players. The player has to train smarter to avoid a career-ending injury. Sports have also entered into the new era of artificial intelligence as any industry. Artificial intelligence (AI) in football acts like a teammate to the players and also plays the role of an assistant coach. The coach uses artificial intelligence and incorporates it into the traditional way of training. The Football Associations have already implemented sensors to collect data in the form of technologies such as Video Assistant Referee and Goal Line Technology. Additionally, the quality of the players and the coaches is improved with smart technological implementation. This technology itself incorporates the utilization of smart technologies for data acquisition using sensor networks and an intelligent data analysis. The proposed algorithm is compared with the fuzzy logic model (FLM) and found that it is 7.2% of higher risk predication by the proposed model than the existing.

Early Stroke Prediction Methods for Prevention of Strokes

The emergence of the latest technologies gives rise to the usage of noninvasive techniques for assisting health-care systems. Amongst the four major cardiovascular diseases, stroke is one of the most dangerous and life-threatening disease, but the life of a patient can be saved if the stroke is detected during early stage. The literature reveals that the patients always experience ministrokes which are also known as transient ischemic attacks (TIA) before experiencing the actual attack of the stroke. Most of the literature work is based on the MRI and CT scan images for classifying the cardiovascular diseases including a stroke which is an expensive approach for diagnosis of early strokes. In India where cases of strokes are rising, there is a need to explore noninvasive cheap methods for the diagnosis of early strokes. Hence, this problem has motivated us to conduct the study presented in this paper. A noninvasive approach for the early diagnosis of the strokes is proposed. The cascaded prediction algorithms are time-consuming in producing the results and cannot work on the raw data and without making use of the properties of EEG. Therefore, the objective of this paper is to devise mechanisms to forecast strokes on the basis of processed EEG data. This paper is proposing time series-based approaches such as LSTM, biLSTM, GRU, and FFNN that can handle time series-based predictions to make useful decisions. The experimental research outcome reveals that all the algorithms taken up for the research study perform well on the prediction problem of early stroke detection, but GRU performs the best with 95.6% accuracy, whereas biLSTM gives 91% accuracy and LSTM gives 87% accuracy and FFNN gives 83% accuracy. The experimental outcome is able to measure the brain waves to predict the signs of strokes. The findings can certainly assist the physicians to detect the stroke at early stages to save the lives of the patients.

Investigation and Strategy Research on Dietary Nutrition Knowledge, Attitude, and Behavior of Athletes

The goal of this paper is to learn more about the nutritional knowledge, attitude, and behavior of teenager trampoline athletes to improve their dietary nutritional levels as well as their sports performance. The questionnaires were designed on athletes’ diet nutrition knowledge, and the questionnaire surveys were conducted on the selected teenager trampoline athletes aged 10–17 as the survey object. Dietary nutrition knowledge, attitude, and behavior are significantly correlated with the total score $\left(P<0.05\right)$ ; dietary nutrition knowledge and dietary nutrition behavior are significantly correlated $\left(P<0.05\right)$ ; dietary nutrition attitude and dietary nutrition behavior are also significantly correlated $\left(P<0.05\right)$ ; dietary nutrition knowledge and dietary nutrition attitude are not strongly correlated $\left(P>0.05\right)$ . The average score of dietary nutrition knowledge of teenager trampoline athletes is $6.5\pm 1.7$ (failing). The nutritional knowledge of teenager trampoline athletes is not systematic and the quality of nutrition information obtained by teenager trampoline athletes is low. The average score of dietary nutrition attitude of teenager trampoline athletes is $7.1\pm 1.8$ (good). The average score of dietary nutrition behavior is $13.4\pm 3.1$ (moderate). Teenager trampoline athletes mainly rely on coaches, relatives, and friends to obtain nutritional knowledge. Therefore, the correct eating habits need to be strengthened and their dietary nutrition behaviors need to be reasonably monitored. Suggestions will include providing relevant nutrition courses, increasing athletes’ awareness of the importance of dietary nutrition, and using professional dietitians for dietary nutrition matching and monitoring.

Walking and Activeness: The First Step toward the Prevention of Strokes and Mental Illness

Physical activity, especially routine walking, is an imperative factor for the prevention of strokes, mental illness, and cardiovascular diseases (CVDs). The NIH (National Institute of Health) has also acknowledged walking as the most important factor of the stroke rehabilitation program. Many research studies are presented by physicians and researchers in the literature that highlight the positive impacts of walking on human health (physical and mental). This paper has the objective of studying the impact of regular walking, especially on mental illness, CVDs, and strokes. The C-reactive protein (CRP), P-selectin protein, and homocysteine biomarkers are considered to decide the improvement in the health of an individual with respect to CVDs and strokes. The other parameters considered for the recommendations of physicians and healthcare experts for mental health are PSS (perceived stress score) and ESS (Epworth sleepiness score) that control mental illness. The values are measured for the participating subjects before participating in the walking activity and after the end of the walking schedule to see the impact on individuals. The overall mental and physical health of an individual contributes to the chances of occurrence of CVDs, mental illness, and strokes in individuals aged between 40 years and 55 years, as per the study presented in this paper. The results show that the PSS and ESS scores are improved after the walking activity. Eventually, it improved recovery from many kinds of mental illness and also reduced the chances of strokes. Similarly, the levels of the biomarkers that determine the chances of an individual having CVD or stroke also improved. Walking can impact our overall health in many ways, however, in this paper, the focus is given to ailments, such as strokes, CVDs, and mental illness. The results show that stress and improper sleepiness can impact mental health negatively. The research outcome is measured by adding walking in a routine life so that every individual can get rid of many physical and mental ailments. The results presented in the paper reveal that the 90-day walking program has created a good impact on the health of individuals by improving their physical and mental health.

Analyzing the Treatment of Patients with Acute Exacerbation of COPD with the Aid of Intelligent Diagnosis Method

To observe the clinical efficacy of heat clearing phlegm mixture combined with vibration sputum excretion instrument in the treatment of patients with acute exacerbation of COPD with phlegm-heat obstructing lung, 90 patients with acute exacerbation of COPD are selected and divided into three groups, namely, control group, traditional medicine group, and combined group: the control group (conventional western medicine treatment), traditional medicine group (heat clearing and phlegm mixture), and combined group (heat clearing and phlegm mixture + vibratory sputum excretion instrument) with 30 cases each. All the patients in the three groups were given conventional western medicine treatment. On this basis, the traditional medicine group was given the oral administration of the heat-clearing and phlegm-clearing mixture, and the combined group was given the oral administration of the heat-clearing and phlegm-clearing mixture and the vibratory sputum discharge apparatus. Machine learning is used to classify the patients into three groups based on the characteristics of their biomarkers, physical attributes, and medical history. The TCM syndrome score, blood gas analysis, lung function, and inflammatory indexes of the three groups were compared. TCM syndrome scores of the three groups were all lower than before; both the combined group and the TCM group were better than the control group ( $P$  < 0.05). Although the improvement degree of the combined group was better than that of the TCM group, the difference was not statistically significant ( $P$  > 0.05). TCM syndrome effect is seen to be 96.55% in the combined group, 89.29% in the TCM group, and 63.33% in the control group. Blood gas analysis is also performed; PO2 and PCO2 of the three groups were significantly improved after treatment. The combination group was superior to the traditional medicine group and the control group ( $P$  < 0.05), and the traditional medicine group was superior to the control group ( $P$  < 0.05). It is concluded that the combination of heat clearing phlegm mixture and vibration sputum excretion instrument can improve TCM syndrome score, CAT score, blood gas analysis, lung function, and inflammatory indicators in patients with acute exacerbation of COPD with phlegm-heat obstructing lung.

Evaluation Algorithm for Team Strength Based on the Collected Healthcare Data through IoT and Smart Devices

In the past, the fans used to evaluate the strength of the team according to the victory and defeat ranking or according to their own intuition and preferences, however, the strength of the team is difficult to measure in analytical figures. The team's winning rate is not the only factor to be considered to determine the strength of the team. There are many factors to be considered for determining the strength of the team. According to the variation coefficient of basketball scoring frequency, the paper designs the principal model of basketball players' pitching target system. The data is captured by IoT devices and smart devices. The algorithm sets the number of the frequency of Gabor filter transformation features, controls the error accumulation, extracts the cascade features of basketball score video, constructs the video conversion discrimination rules, detects the basketball target, and obtains the tracking target contour to frame information. Finally, it realizes the target tracking detection of the team based on the team strength using an evaluation algorithm. The aim of this research work is to determine the strength of the team based on the healthcare data, team cohesiveness, and variance coefficient of basketball score frequency. The study on the coefficient of variation for basketball score frequency in teams can provide a theoretical research direction for team strength evaluation and meet the real-time needs of the coefficient of variation of basketball score frequency in teams. The empirical results show that the designed algorithm has the optimal execution time, more successful evaluation targets, high efficiency, and more reliability in evaluating the strength of the team.

Commercial and Scientific Solutions for Blood Glucose Monitoring-A Review

Diabetes is a chronic and, according to the state of the art, an incurable disease. Therefore, to treat diabetes, regular blood glucose monitoring is crucial since it is mandatory to mitigate the risk and incidence of hyperglycemia and hypoglycemia. Nowadays, it is common to use blood glucose meters or continuous glucose monitoring via stinging the skin, which is classified as invasive monitoring. In recent decades, non-invasive monitoring has been regarded as a dominant research field. In this paper, electrochemical and electromagnetic non-invasive blood glucose monitoring approaches will be discussed. Thereby, scientific sensor systems are compared to commercial devices by validating the sensor principle and investigating their performance utilizing the Clarke error grid. Additionally, the opportunities to enhance the overall accuracy and stability of non-invasive glucose sensing and even predict blood glucose development to avoid hyperglycemia and hypoglycemia using post-processing and sensor fusion are presented. Overall, the scientific approaches show a comparable accuracy in the Clarke error grid to that of the commercial ones. However, they are in different stages of development and, therefore, need improvement regarding parameter optimization, temperature dependency, or testing with blood under real conditions. Moreover, the size of scientific sensing solutions must be further reduced for a wearable monitoring system.

Healthcare Data-Based Prediction Algorithm for Potential Knee Joint Injury of Football Players

It is important to predict the potential harm to the knee joint in order to prevent football players from inflicting numerous injuries to the knee during activity. Numerous professionals have been drawn to this subject, and many viable prediction systems have been developed. Prediction of potential knee joint injury is critical to effectively avoid knee joint injury during exercise. The current prediction algorithms are mainly implemented through expert interviews, medical reports, and historical documents. The algorithms have problems with low prediction accuracy or precision values. There is a need to understand more knee injury factors and improve the prediction accuracy; hence, the intelligent prediction algorithm for potential injury of knee joints of football players is proposed in this paper. Firstly, the characteristics of the knee joint injury and the injury factors of the football players are gathered and analyzed. Then, the damage is predicted by the similarity measurement. The experimental results show that the proposed algorithm has higher prediction accuracy and shorter time. According to the findings of a survey that collected healthcare data, several key factors contribute to football knee injuries. To a degree, this algorithm can predict the likelihood of a football player's knee injury.

Retrospection of Analytical Data Collected through Smart Devices for Diseases and Disability Caused by Physical Inactivity

The basic meaning of inactive lifestyle is doing sedentary activities. This includes playing video games, watching TV, operating computers, and sitting in a particular place for a long time. It may be sitting on the train or in bus, car, or office. This has many adverse effects on our bodies. This paper emphasizes the importance of physical activity, and a case study is presented to support the hypothesis that physical activity can certainly help improve human health. The diseases caused by inactive life are discussed in our paper. In this paper, a case study is described in which 200 individuals have participated in an activity called "Activity is Life," with the goal of improving the sedentary lifestyle and getting rid of physical ailments. The usage of analytical tools is made to analyse the data collected from the empirical research study, and IoT-based smart devices are used to capture the runtime data. The physicians and nurses have odd working hours, they have to go through many stress-induced situations, and most of them are suffering from life-threatening diseases such as depression, hypertension, cancer, and cardiovascular diseases. The main reason is inactivity in their lifestyles. To carry out an experimental study and to see the impact of regular activities on the health of doctors and nurses, a program was designed where 200 participants have participated. It is found that the activity-based 30-day program yielded great health benefits, including reduction in stress level, improvement in sleep quality index, and improvement in blood pressure values.

Association of Lower Extremity Vascular Disease, Coronary Artery, and Carotid Artery Atherosclerosis in Patients with Type 2 Diabetes Mellitus

The motive of this article is to present the case study of patients to investigate the association between the ultrasonographic findings of lower extremity vascular disease (LEAD) and plaque formation. Secondly, to examine the association between the formation of coronary artery and carotid artery atherosclerosis in patients with type 2 diabetes mellitus. 124 patients with type 2 diabetes (64 males and 60 females with the age group 25-78 years) are considered for the research studies who have registered themselves in the Department of Endocrinology and Metabolism from April 2017 to February 2019. All participants have reported their clinical information regarding diabetes, alcohol consumption, smoking status, and medication. The blood samples from subjects are collected for measurement of HbA_1c, total cholesterol, triglycerides, HDL-c, and LDL-c levels. Two-dimensional ultrasound has been used to measure the inner diameter, peak flow velocity, blood flow, and spectral width of the femoral artery, pop artery, anterior iliac artery, posterior tibial artery, and dorsal artery and to calculate the artery stenosis degree. Independent factors of atherosclerosis are determined by multivariate logistic regression analysis. The results are evaluated within the control group and it is found that there is no significant impact of gender, age, and body mass index (P > 0.05) on the lower extremity vascular diseases. Those with smoking, alcohol consumption, hypertension, and dyslipidemia have higher positive rate (P < 0.05). The type 2 diabetes mellitus group has higher diastolic blood pressure and lower triglyceride (P < 0.05). Diastolic blood pressure, HbA_1C, total cholesterol, HDL-c, and LDL-C are not remarkably dissimilar between the type 2 diabetes mellitus group and the control group (P > 0.05). Compared with the control group, the type 2 diabetes mellitus group has higher frequency of lower extremity vascular diseases in the dorsal artery than in the pop artery (P < 0.05). The blood flow of type 2 diabetes mellitus group is found to be lower than that of the control group, especially in the dorsal artery (P < 0.05). The blood flow velocity of the dorsal artery is accelerated (P < 0.01). Among 117 patients of type 2 diabetes mellitus (94.35%) with a certain degree of injury, there are 72 cases of type I carotid stenosis (58.06%), 30 cases of type II carotid stenosis (24.19%), and 15 cases of type III carotid stenosis (12.10%). Out of 108 subjects in the control group, there are 84 cases of type 0 carotid stenosis (77.78%), 19 cases of type I carotid stenosis (17.59%), 5 cases of type II carotid stenosis (4.63%), and 0 case of type III carotid stenosis (0.00%). Compared with the control group, carotid stenosis is more common in patients with type 2 diabetes mellitus (P < 0.05). Age, smoking, duration of diseases, systolic blood pressure, and degree of carotid stenosis are found to be associated with atherosclerosis. The findings suggest that the color Doppler ultrasonography can give early warning when applied in patients with carotid and lower extremity vascular diseases to delay the incidence of diabetic macroangiopathy and to control the development of cerebral infarction, thus providing an important basis for clinical diagnosis and treatment.

Impact of HSP90α, CEA, NSE, SCC, and CYFRA21-1 on Lung Cancer Patients

Lung cancer is a lethal disease, and early diagnosis with the aid of biomarkers such as HSP90α protein can certainly assist the doctors to start treatment of patient at the earliest and can save their lives. To analyse the diagnostic value of HSP90α expression in lung cancer patients by collecting data of patients through IoT devices to avoid delay in treatments, a study has been presented in this paper where the significance of HSP90α biomarker is highlighted in early diagnosis of patients suffering from lung cancer. The second objective of the research study is to examine the correlation between the appearance level of HSP90α biomarker and the clinicopathological features of lung cancer. It is also evaluated whether the changes in HSP90α index are indicative or noteworthy before and after surgery of lung cancer patients. An observatory study of 78 patients with lung cancer in Qinhuangdao Hospital is presented in this paper where the samples were collected from June 2018 to March 2020. Their data were collected through IoT devices used in the latest healthcare facilities of the hospital. The ELISA method was utilized to identify the level of plasma HSP90 and to analyse HSP90 levels between the lung cancer group and healthy group of people. The relationship between HSP90 and the clinical pathological features of 78 patients suffering from lung cancer was analysed. An electrochemical luminescence method was used to detect CEA, NSE, SCC, and CYFRA21-1 levels. ROC curve and box plots were used to determine the analytic value of HSP90 and other biomarkers used in lung cancer diagnosis. Forty-two patients with moderate to early stage lung cancer with surgical correction were selected, and paired sample T test was used to analyse HSP90 levels before and after surgery. The plasma HSP90 level of lung cancer patients was quite higher as compared to the group of healthy people as per the values depicted in the research study. Second, HSP90 levels are substantially higher in pathologic type, differentiation degree, stage, and the existence of the lung, liver, and bone metastases (P < 0.05). The level of HSP90 expression was largely impacted by a few factors such as sex, age, smoking, and tumour location (P > 0.05). The ROC value for HSP90 was 0.599, while the area under the curve of HSP90 combined with other four tumour markers was 0.915 in the presented case study, indicating the presence of lung cancer. Patients with lung cancer had statistically significant differences in HSP90 expression levels before and after surgery (P < 0.05). It is concluded that the expression level of plasma HSP90α in lung cancer patients increases remarkably; therefore, HSP90 can be used to monitor presence of lung cancer before and after surgery in the patients.