Human Health Conditions Research Articles

An integrated method for assessing passenger evacuation performance in ship fires

In order to assess effectively the passenger evacuation performance in ship fires, a quantitative method combining the computational fluid dynamics (CFD)-derived fire data and AnyLogic evacuation simulation is proposed in this paper. In this developed method, the speed reduction factor, instantaneous untenable conditions and fractional effective dose (FED) model are utilized to consider the effects of smoke visibility, heat and toxic gases in ship fires on the passenger walking speed, activity space and human health conditions. In order to demonstrate this proposed method, passenger evacuation analysis is performed in a hypothetical zone of the ship with obstacles for fast t-squared fires. The results indicate that the relationship between the required safe egress time (RSET) and fire heat release rate (HRR) is non-monotonic. However, the total FED increases with the increase of the fire HRR and the sequence of survivors. Compared with two existing methods, this proposed method is able to assess more rigorously the passenger evacuation performance in ship fires. The existing ASET/RSET method is more conservative, and the existing FED method with the Pathfinder underestimates the FED in ship fires. This work will provide a more accurate method for ship fire safety assessment and performance-based fire protection design.

Assessment of Heavy Metal Concentration in Soil and Plant and Evaluation of Bioconcentration Factor at Loumbila Market Gardening Perimeters, Burkina Faso

Agricultural soil quality deterioration resulting from an increase in the level of heavy metals is becoming more and more pronounced, thus raising the question on the safety status of human health and the environment. Determination of heavy metal concentration in soils and plants from Loumbila market gardening (Burkina Faso), and calculation of bioconcentration factor were undertaken. The heavy metals such as copper (Cu), nickel (Ni), lead (Pb), chromium (Cr), cadmium (Cd) and zinc (Zn) were measured using an atomic absorption spectrometer, model PERKIN ELMER AANALYST 200.
 In the soil where the pepper was grown, the concentration of chromium was 175 mg/kg, which is higher than the concentration limit which is 150 mg/kg. Also the concentrations of lead in the soils of onion (118 mg/kg), green bean (118 mg/kg), carrot (178 mg/kg) and pepper (135 mg/kg) were above the limit which is 100 mg/kg. The concentrations of chromium in Onion leaves (3.72 mg/kg), onions (4.65 mg/kg), lettuce (4.89 mg/kg), green beans (5.89 mg/kg) and bell peppers (3.56 mg/kg) were concentrations above the limit established by the FAO/WHO, which is 2.3 mg/Kg. The concentrations of lead in carrot, onion leaf, onion, lettuce, green bean and bell pepper were above the limit proposed by FAO/WHO.
 The bioconcentration factors show that the onion leaf (0.731), onion (0.929), lettuce (0.876), green bean (0.987) and bell pepper (0.858) are the plants that accumulate zinc and the carrot (0.524) accumulates nickel. The bioaccumulation of the metals in the vegetable from Loumbila market gardening decreased in the order of onion > lettuce > green bean > bell pepper > onion leaves > carrot.

A machine-vision-based frailty index for mice.

Heterogeneity in biological aging manifests itself in health status and mortality. Frailty indices (FIs) capture health status in humans and model organisms. To accelerate our understanding of biological aging and carry out scalable interventional studies, high-throughput approaches are necessary. Here we introduce a machine-learning-based visual FI for mice that operates on video data from an open-field assay. We use machine vision to extract morphometric, gait and other behavioral features that correlate with FI score and age. We use these features to train a regression model that accurately predicts the normalized FI score within 0.04 ± 0.002 (mean absolute error). Unnormalized, this error is 1.08 ± 0.05, which is comparable to one FI item being mis-scored by 1 point or two FI items mis-scored by 0.5 points. This visual FI provides increased reproducibility and scalability that will enable large-scale mechanistic and interventional studies of aging in mice.

Precision targeting of food biofilm-forming genes by microbial scissors: CRISPR-Cas as an effective modulator.

The abrupt emergence of antimicrobial resistant (AMR) bacterial strains has been recognized as one of the biggest public health threats affecting the human race and food processing industries. One of the causes for the emergence of AMR is the ability of the microorganisms to form biofilm as a defense strategy that restricts the penetration of antimicrobial agents into bacterial cells. About 80% of human diseases are caused by biofilm-associated sessile microbes. Bacterial biofilm formation involves a cascade of genes that are regulated via the mechanism of quorum sensing (QS) and signaling pathways that control the production of the extracellular polymeric matrix (EPS), responsible for the three-dimensional architecture of the biofilm. Another defense strategy utilized commonly by various bacteria includes clustered regularly interspaced short palindromic repeats interference (CRISPRi) system that prevents the bacterial cell from viral invasion. Since multigenic signaling pathways and controlling systems are involved in each and every step of biofilm formation, the CRISPRi system can be adopted as an effective strategy to target the genomic system involved in biofilm formation. Overall, this technology enables site-specific integration of genes into the host enabling the development of paratransgenic control strategies to interfere with pathogenic bacterial strains. CRISPR-RNA-guided Cas9 endonuclease, being a promising genome editing tool, can be effectively programmed to re-sensitize the bacteria by targeting AMR-encoding plasmid genes involved in biofilm formation and virulence to revert bacterial resistance to antibiotics. CRISPRi-facilitated silencing of genes encoding regulatory proteins associated with biofilm production is considered by researchers as a dependable approach for editing gene networks in various biofilm-forming bacteria either by inactivating biofilm-forming genes or by integrating genes corresponding to antibiotic resistance or fluorescent markers into the host genome for better analysis of its functions both in vitro and in vivo or by editing genes to stop the secretion of toxins as harmful metabolites in food industries, thereby upgrading the human health status.

Forecasting of Total Column Ozone using Regression Analysis and LSTM-RNN Machine Learning Approach

Objectives: Atmospheric Ozone plays an important role in global climate change, human health and environmental conditions. Accurate and timely prediction of variation in Total Column Ozone (TCO) concentration is very important for both climatology and environment. In this context, the present study aims to utilize the advancement of Artificial Intelligence in Machine learning and a conventional method to develop models for the prediction of TCO concentration using historical time series data over a tropical region, India. Methods: Long Short-Term Memory (LSTM) deep learning networks are very useful in classifying, processing and making predictions based on time series data. In this work, Multiple Linear Regression (MLR) model, Long Short-Term Memory (LSTM) models are developed for forecasting TCO over a tropical region. The Statistical Parameters such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were used to analyze and evaluate the performance of the proposed models. Findings: In the present study the TCO concentration varied between 217.4 DU and 288.6 DU. The performances of MLR and LSTM model were compared. From the results, it is evident that the values of RMSE, MAE and MAPE of the MLR model are 3.807924, 2.93766, 1.15 respectively whereas the values for LSTM model are 3.074492, 2.574, 1.01 respectively. LSTM was found to be the accurate model for the study data set and the predicted TCO concentration has a very good correlation with the actual observations and it does not rely on the meteorological parameters. Novelty: The LSTM models are usually employed in the field of machine translation, speech recognition and for the prediction of traffic levels, stock levels and air pollutants. This work utilizes the fast numerical machine learning computing library Tensorflow and a high level neural network library Keras that runs on top of Tensorflow to develop a LSTM network model for the prediction of the concentration levels of TCO. Keywords: Total Column Ozone; Regression; Forecasting; Climate Change; Long Short-Term Memory

Dielectric property measurement of human sweat using attenuated total reflection terahertz time domain spectroscopy.

Sweat is one of the essential biofluids produced by the human body, and it contains various physiological biomarkers. These biomarkers can indicate human health conditions such as disease and illness. In particular, imbalances in the concentration of electrolytes can indicate the onset of disease. These same imbalances affect the dielectric properties of sweat. In this study, we used attenuated total reflection terahertz time domain spectroscopy to obtain the frequency-dependent dielectric properties of human sweat in a frequency range from 200 GHz to 2.5 THz. We have investigated the variation of dielectric properties of sweat collected from different regions of the human body, and we have observed that the real and imaginary part of dielectric permittivity decreases with the increase in frequency. A combination of left-hand Jonscher and Havriliak-Negami processes is used to model the results and reveal the presence of relaxation processes related to sodium and calcium ions concentrations. This information may help design novel biosensors to understand the human health condition and provide a hydration assessment.

The Study of Protein Hydrolysis and Peptide Antioxidants Activity of Chicken Slaughterhouse Waste and Its Potential for Feed Additives

Ensuring food safety in livestock requires specific feeding technology in agriculture by using feed additives in the form of antibiotics, prebiotics, probiotics, acidifiers, hormones and enzymes. Bioactive peptides improve the health status of humans and animals. Protein hydrolysis produce peptides that are safe, quickly metabolizable, less risky for livestock products to have contaminant residue. Bioactive peptides are still bound to the original protein, so they need to be released through an enzymatic process. This research explored the potential of chicken slaughterhouse waste to produce biopeptides by hydrolyzing proteins using various proteolytic enzymes. The slaughterhouse waste included chicken feet, intestines, filleting waste and blood plasma. The proteolytic enzymes used were papain, bromelain, protease by Rhizopus oligosphorus, probiotic protease. The observed variables were dissolved protein content with and without precipitation, protein hydrolyzate content, and the degree of enzyme hydrolysis. The research was conducted using exploratory methods. The results showed that the dissolved protein content in the chicken slaughterhouse waste protein concentrate was 1,585 mg/ml (feet), 2,361 mg/ml (intestines), 1,787 (filleting waste) and 2,372 mg/ml (blood plasma). Blood plasma protein concentrate showed the highest yield among other chicken slaughterhouse waste protein concentrates, namely 0.14 mg/ml (hydrolysis of papain), 0.18 mg/ml (hydrolysis of bromelain), 0.56 mg/ml (hydrolysis of R.oligosphorus protease) 0.68 mg/ml (hydrolysis of probiotic proteases). The highest degree of hydrolysis was shown in blood plasma hydrolyzates using probiotic protease enzymes, namely 28.72%. The highest antioxidant activity was 92.92% as observed in chicken feet protein hydrolyzate which was hydrolyzed using papain. Therefore, chicken feet, intestines and fillet waste can produce protein concentrates through precipitation using ammonium sulfate, and plasma using acetone. The highest protein concentration was in blood plasma protein which also produced the highest hydrolysis from hydrolyzing blood plasma proteins with hydrolyzed probiotic protease. The highest antioxidant activity was observed in chicken feet protein hydrolyzate which was hydrolyzed using papain enzyme and incubated for 6h.

Alternative Paradigms in Animal Health Decisions: A Framework for Treating Animals Not Only as Commodities.

Simple SummarySince zoonotic diseases can be transmitted from animals to humans, more comprehensive measures are needed when preventing and controlling these diseases. Because the value of animals is mainly based on monetary terms, animals are typically treated as commodities, impacting public health decisions. Therefore, a framework is proposed to value the health of animals beyond money for public health decision-making with a “One Health” approach. The aim is to have more comprehensive animal values based on the opinion of societies. However, tackling the dilemmas related to animal diseases, public health, and welfare still represents a challenge and a work in progress.Zoonoses are diseases transmitted from (vertebrate) animals to humans in the environment. The control and prevention of these diseases require an appropriate way to measure health value for prudent and well-balanced decisions in public health, production costs, and market values. Currently, the impact of diseases and animal disease control measures are typically assessed in monetary values, thus lacking consideration of other values such as emotional, societal, ecological, among others. Therefore, a framework is proposed that aims to explore, understand, and open up a conversation about the non-monetary value of animals through environmental and normative ethics. This method might help us complement the existing metrics in health, which are currently DALY and zDALY, adding more comprehensive values for animal and human health to the “One Health” approach. As an example of this framework application, participants can choose what they are willing to give in exchange for curing an animal in hypothetical scenarios selecting a human health condition to suffer, the amount of money, and lifetime as a tradeoff. Considering animals beyond their monetary value in public health decisions might contribute to a more rigorous assessment of the burden of zoonotic diseases, among other health decisions. This study is structured as follows: after a brief introduction of zoonoses, animal health, and health metrics, briefly, different environmental health perspectives are presented. Based on this, a framework for animal health decisions is proposed. This framework introduces the “anthropozoocentric interface” based on anthropocentrism and zoocentrism perspectives.

Selenium as an important factor in various disease states - a review.

Selenium (Se) is an element that has a pro-health effect on humans and animals. However, both the deficiency of this element and its excess may prove harmful to the body depending on the chemical form of the selenium, the duration of supplementation, and the human health condition. Many data indicate insufficient coverage of the demand for selenium in humans and animals due to its low content in soils and food products. A balance in the physiological process of the body can be achieved via the proper percentage of organically active minerals in the feed of animals as well as human beings. Selenium is a trace mineral of great importance to the body, required for the maintenance of a variety of its processes; primarily, selenium maintains immune endocrine, metabolic, and cellular homeostasis. Recently, this element has been emerging as a most promising treatment option for various disorders. Therefore, research based on Se has been increasing in recent times. The present review is designed to provide up-to-date information related to Se and its different forms as well as its effects on health.

Mobile Health With Head-Worn Devices: Challenges and Opportunities

Monitoring human behavior and health status using mobile devices, a.k.a. Mobile Health, has gained increasing attention from both academia and industry in recent years. It allows imperceptible health tracking from the users and remote health management from the healthcare service providers. Head-worn devices, such as earbuds, glasses, and brain–computer interfaces, exhibit great potential for mobile health due to their advantageous wearing position, the human head, which is motion-resilient and full of human biosignals. Although initial attempts have been conducted for different healthcare applications with head-worn devices, this fast-growing area is still underexplored and retains great promises. With this work, we investigate the most pressing challenges to fully exploit the potential of head-worn devices for mobile health, from the perspective of sensing, computing, and system design. Our exploration reveals key guidelines and lessons to inform future efforts in this space.

Gut microbiota–derived metabolite trimethylamine-N-oxide and multiple health outcomes: an umbrella review and updated meta-analysis

ABSTRACTBackgroundTrimethylamine-N-oxide (TMAO) is a gut microbiota–derived metabolite produced from dietary nutrients. Many studies have discovered that circulating TMAO concentrations are linked to a wide range of health outcomes.ObjectivesThis study aimed to summarize health outcomes related to circulating TMAO concentrations.MethodsWe searched the Embase, Medline, Web of Science, and Scopus databases from inception to 15 February, 2022 to identify and update meta-analyses examining the associations between TMAO and multiple health outcomes. For each health outcome, we estimated the summary effect size, 95% prediction CI, between-study heterogeneity, evidence of small-study effects, and evidence of excess-significance bias. These metrics were used to evaluate the evidence credibility of the identified associations.ResultsThis umbrella review identified 24 meta-analyses that investigated the association between circulating TMAO concentrations and health outcomes including all-cause mortality, cardiovascular diseases (CVDs), diabetes mellitus (DM), cancer, and renal function. We updated these meta-analyses by including a total of 82 individual studies on 18 unique health outcomes. Among them, 14 associations were nominally significant. After evidence credibility assessment, we found 6 (33%) associations (i.e., all-cause mortality, CVD mortality, major adverse cardiovascular events, hypertension, DM, and glomerular filtration rate) to present highly suggestive evidence.ConclusionsTMAO might be a novel biomarker related to human health conditions including all-cause mortality, hypertension, CVD, DM, cancer, and kidney function. Further studies are needed to investigate whether circulating TMAO concentrations could be an intervention target for chronic disease.This review was registered at www.crd.york.ac.uk/prospero/ as CRD42021284730.

PARTICULATE MATTER EMISSION SOURCES AND THEIRCONTROL TECHNOLOGIES

Particulate matter (PM) is a complex form of air pollution that includes all types of solid and liquid droplets present in the atmosphere. Primary PM Emission rates, sizes, and composition are difficult to predict since they rely not only on the industry, but also on the fuel properties, infrastructure, and other aspects of the emission process. Normal (volcanic eruptions, forest fires, and weathering of parent material and sea salt) and anthropogenic (power plants, internal combustion engines, and the thermo-degradation process) sources of airborne particulate matter exist. In order to uphold human health conditions and a clean climate; it has become important to reduce PM concentrations. Given the negative effects of particulates, their regulation is becoming increasingly necessary. In this article, we look at all of the origins of particulate matter, their negative consequences, and the monitoring systems that are used to minimize PM emissions. However, manufacturing and plant operations will get the most attention because they are the backbone of a country’s economy as well as one of the largest causes of PM pollution and traffic regulation.

Numerical dynamics and fractional modeling of hepatitis B virus model with non-singular and non-local kernels

A fractional model for the transmission dynamics of Hepatitis B was designed. Hepatitis B disease has a great impact on global human health conditions and economical systems. The spreading of HBV disease has several phases, i.e., acute and chronicle carriers phases have a key role. The chronicle carrier’s cases do not have any signs and are capable of transmitting the Hepatitis B infection. In this article, we investigated the transmission due to different infection phases of the Hepatitis B virus and constructed a nonlinear epidemic. Next, a fractional hepatitis B virus model with Atangana–Baleanu derivative (AB derivative) is formulated with vaccine effects. Firstly, we calculated the basic reproductive value and equilibria of the proposed model. Qualitative analysis of the approximate root of the said problem has been derived with the help of Fixed Point Theory. The Iterative approximate technique with the help of the Adams–Bashforth predictor–corrector scheme for evaluating the considered fractional system having the ABC derivative is expressed. In the last, a graphical representation is established to show the obtained scheme findings and compare different non-integer orders of Ψ.

Impact of magnesium addition in zinc oxide matrix for enhanced structural, optical, antimicrobial and anticancer properties

In the present work, low cost, high efficiency and multifunctional inorganic biocidal (antibacterial and anticancer) materials such as ZnO, MgO and MgZnO nanocomposites (NCs) have been developed with a facile precipitation process. XRD studies confirmed that the prepared NCs exhibit hexagonal, face-centered cubic and hexagonal structures respectively. Transmission electron microscopy (TEM) images revealed spherical shaped ZnO, MgO and MgZnO NCs. The elemental (Zn, Mg and O) compositions of the prepared NMs were identified using EDAX spectra. The photoluminescence (PL) spectra of the prepared NMs revealed oxygen vacancies at 487 nm, 500 nm and 524 nm respectively. They correspond to the active free radicals (OH, O2 and H2O2) production and these results were responsible for their biocidal activities. Furthermore, the biocidal effects of the MgZnO NCs were studied on the human breast cancer cell line and microbes. The cell viability studies proved that MgZnO NCs possess higher anticancer properties than ZnO and MgO NMs. Overall results displayed that the MgZnO NCs exposed better biocidal effects than ZnO and MgO NMs which can be used for biomedical and industrial applications to improve human health conditions.

Evolutionary Insights Into Microbiota Transplantation in Inflammatory Bowel Disease.

The intestinal microbiome plays an essential role in human health and disease status. So far, microbiota transplantation is considered a potential therapeutic approach for treating some chronic diseases, including inflammatory bowel disease (IBD). The diversity of gut microbiota is critical for maintaining resilience, and therefore, transplantation with numerous genetically diverse gut microbiota with metabolic flexibility and functional redundancy can effectively improve gut health than a single probiotic strain supplement. Studies have shown that natural fecal microbiota transplantation or washing microbiota transplantation can alleviate colitis and improve intestinal dysbiosis in IBD patients. However, unexpected adverse reactions caused by the complex and unclear composition of the flora limit its wider application. The evolving strain isolation technology and modifiable pre-existing strains are driving the development of microbiota transplantation. This review summarized the updating clinical and preclinical data of IBD treatments from fecal microbiota transplantation to washing microbiota transplantation, and then to artificial consortium transplantation. In addition, the factors considered for strain combination were reviewed. Furthermore, four types of artificial consortium transplant products were collected to analyze their combination and possible compatibility principles. The perspective on individualized microbiota transplantation was also discussed ultimately.

Microencapsulation of Bacteriophages for the Delivery to and Modulation of the Human Gut Microbiota through Milk and Cereal Products

There is a bidirectional interaction between the gut microbiota and human health status. Disturbance of the microbiota increases the risk of pathogen infections and other diseases. The use of bacteriophages as antibacterial therapy or prophylaxis is intended to counteract intestinal disorders. To deliver bacteriophages unharmed into the gut, they must be protected from acidic conditions in the stomach. Therefore, an encapsulation method based on in situ complexation of alginate (2%), calcium ions (0.5%), and milk proteins (1%) by spray drying was investigated. Powdered capsules with particle sizes of ~10 µm and bacteriophage K5 titers of ~107 plaque-forming units (pfu) g−1 were obtained. They protected the bacteriophages from acid (pH 2.5) in the stomach for 2 h and released them within 30 min under intestinal conditions (in vitro). There was no loss of viability during storage over two months (4 °C). Instead of consuming bacteriophage capsules in pure form (i.e., as powder/tablets), they could be inserted into food matrices, as exemplary shown in this study using cereal cookies as a semi-solid food matrix. By consuming bacteriophages in combination with probiotic organisms (e.g., via yoghurt with cereal cookies), probiotics could directly repopulate the niches generated by bacteriophages and, thus, contribute to a healthier life.

The Urobiomes of Adult Women With Various Lower Urinary Tract Symptoms Status Differ: A Re-Analysis.

The discovery of the urinary microbiome (urobiome) has created opportunities for urinary health researchers who study a wide variety of human health conditions. This manuscript describes an analysis of catheterized urine samples obtained from 1,004 urobiome study participants with the goal of identifying the most abundant and/or prevalent (common) taxa in five clinically relevant cohorts: unaffected adult women (n=346, 34.6%), urgency urinary incontinence (UUI) (n=255, 25.5%), stress urinary incontinence (SUI) (n=50, 5.0%), urinary tract infection (UTI) (n=304, 30.4%), and interstitial cystitis/painful bladder syndrome (IC/PBS) (n=49, 4.9%). Urine was collected via transurethral catheter and assessed for microbes with the Expanded Quantitative Urine Culture (EQUC) technique. For this combined analytic cohort, the mean age was 59 ± 16; most were Caucasian (n=704, 70.2%), Black (n=137, 13.7%), or Hispanic (n=130, 13.0%), and the mean BMI was 30.4 ± 7.7. Whereas many control or IC/PBS cohort members were EQUC-negative (42.4% and 39.8%, respectively), members of the other 3 cohorts were extremely likely to have detectable microbes. The detected urobiomes of the controls and IC/PBS did not differ by alpha diversity or genus level composition and differed by only a few species. The other 3 cohorts differed significantly from the controls. As expected, Escherichia was both prevalent and highly abundant in the UTI cohort, but other taxa also were prevalent at more moderate abundances, including members of the genera Lactobacillus, Streptococcus, Staphylococcus, Corynebacterium, Actinomyces, and Aerococcus. Members of these genera were also prevalent and highly abundant in members of the UUI cohort, especially Streptococcus anginosus. Intriguingly, these taxa were also detected in controls but at vastly lower levels of both prevalence and abundance, suggesting the possibility that UUI-associated symptoms could be the result of an overabundance of typical urobiome constituents. Finally, prevalence and abundance of microbes in the SUI cohort were intermediate to those of the UUI and control cohorts.These observations can inform the next decade of urobiome research, with the goal of clarifying the mechanisms of urobiome community composition and function. There is tremendous potential to improve diagnosis, evaluation and treatment for individuals affected with a wide variety of urinary tract disorders.

Analysis of patient health condition based on hybrid machine learning algorithm

In data mining, the classification methods are used to determine the relationships between the various objects of the interactions database. In this research, the objective is predominantly focused on the prediction of three types of functioning test level. The main purpose of the research work is to analyze human health condition, realised by fluctuation of specific ranges such as Bilirubin, Albumin, Prothrombin time (INR), Ascites, Encephalopathy , Bicarbonate, Calcium etc. It can improve the disease by taking medical diagnosis based on Apriori algorithm by generating rule for the most significant parameters of all three functioning test level. In this article, the experiment can be carried out by using a variety of different sizes of support count based on the association factor of attributes in the kidney and liver functional test data from a wide range of patients. The goal of the experiment is to gain an understanding of the effect that the Apriori algorithm has on the amount of time it takes for the execution, the precision of the best rule discovered from the mining of frequent patterns, and the number of association rules that are generated.

Heart Disease Prediction Using ML Algorthim

Abstract: Cardiac disease could be a major international unhealthiness in fashionable drugs. The twenty-first century saying that by taking unusual or junk food cause cardio attack. The criticality of viscus diseases square measure a lot of crucial and may even result in vulnerable consequences if it is not detected at associate degree earlier stage. The techniques such as electronic health records, blood pressure measure machine are continuously monitoring human health condition through injection of major wearable devices. Since the information generated from the body space networks square measure continuous and tremendous in volume, the machine learning techniques square measure used for economical health information classification processes. This research paper is related to heart disease prediction. The outcomes are analyzed regarding factors such as accuracy, sensitivity, precision, and specificity. It has been found from the analysis that the proposed system provides comparatively better accuracy and prediction measures than the existing techniques. Keywords: Heart disease, logistic regression ,supervised learning

Integrating MXene waste materials into value-added products for smart wearable self-powered healthcare monitoring

MXenes have gained significant attention for development of flexible or wearable electronics. However, intrinsic sediment waste from the MXene production process is usually discarded, greatly decreasing efficiency. Rational use of MXene waste (trash) in wearables may improve economic benefits. Here, a strategy that integrates MXene nanosheets (treasure) with sediment trash is employed to construct a smart wearable self-powered health monitoring system. A flexible solid-state supercapacitor (FSSC) consisting of a robust MXene/polyamide (MX-PA) film delivers good specific capacitance and mechanical properties. An MXene trash-based pressure sensor realizes effective monitoring of physiological signals. By integrating the FSSC and pressure sensor, the self-powered smart system can remotely receive physiological feedback for evaluating human health status. This work explores a feasible strategy to take full advantage of MXene starting materials and may expand scalable fabrication of MXene-based smart wearables in healthcare fields.