Medical Sector Research Articles

Beneficial applications of biofilms.

Many microorganisms live in the form of a biofilm. Although they are feared in the medical sector, biofilms that are composed of non-pathogenic organisms can be highly beneficial in many applications, including the production of bulk and fine chemicals. Biofilm systems are natural retentostats in which the biocatalysts can adapt and optimize their metabolism to different conditions over time. The adherent nature of biofilms allows them to be used in continuous systems in which the hydraulic retention time is much shorter than the doubling time of the biocatalysts. Moreover, the resilience of organisms growing in biofilms, together with the potential of uncoupling growth from catalytic activity, offers a wide range of opportunities. The ability to work with continuous systems using a potentially self-advancing whole-cell biocatalyst is attracting interest from a range of disciplines, from applied microbiology to materials science and from bioengineering to process engineering. The field of beneficial biofilms is rapidly evolving, with an increasing number of applications being explored, and the surge in demand for sustainable and biobased solutions and processes is accelerating advances in the field. This Review provides an overview of the research topics, challenges, applications and future directions in beneficial and applied biofilm research.

On Topp-Leone-G Power Series: Saturation in the Hausdorff Sense and Applications

This paper discusses the Topp-Leone-G power series class of distributions. The greatest attention is paid to the investigation of intrinsic characteristic “saturation” to the horizontal asymptote in the Hausdorff sense. Some estimates for the value of the Hausdorff distance are obtained. We present a new family of recurrence generated adaptive functions with corresponding applications. The usefulness of the obtained results is demonstrated in a simulation study of some real data sets from the medical sector and insurance. Some suitable software modules within the programming environment CAS MATHEMATICA are proposed.

HAE: A Hybrid Cryptographic Algorithm for Blockchain Medical Scenario Applications

The integration of cryptographic algorithms like Advanced Encryption Standard (AES) and Elliptic Curve Cryptography (ECC) is pivotal in bolstering the core attributes of blockchain technology, especially in achieving decentralization, tamper resistance, and anonymization within the realm of medical applications. Despite their widespread utilization, the conventional AES and ECC face significant hurdles in security and efficiency when dealing with expansive medical data, posing a challenge to the effective preservation of patient privacy. In light of these challenges, this study introduces HAE (hybrid AES and ECC), an innovative hybrid cryptographic algorithm that ingeniously amalgamates the robustness of AES with the agility of ECC. HAE is designed to symmetrically encrypt original data with AES while employing ECC for the asymmetric encryption of the initial AES key. This strategy not only alleviates the complexities associated with AES key management but also enhances the algorithm’s security without compromising its efficiency. We provide an in-depth exposition of HAE’s deployment within a framework tailored for medical scenarios, offering empirical insights into its enhanced performance metrics. Our experimental outcomes underscore HAE’s exemplary security, time efficiency, and optimized resource consumption, affirming its potential as a breakthrough advancement for augmenting blockchain applications in the medical sector, heralding a new era of enhanced data security and privacy within this critical domain.

Design and Manufacturing Ankle Foot Prosthetics for Patients with Transtibial Amputation

Rapid Prototyping (RP) is a technology used to incrementally shape products or add material using 3D printing equipment. 3D printing is extensively utilized throughout several industries, particularly in the medical sector. This study aims to make a valuable contribution to the field by specifically focusing on the manufacturing of foot prosthetics for patients who have undergone below-knee amputations. The design step on Solidwork software is initiated, followed by the realization of the chosen design utilizing a 3D printing machine, which utilizes PLA as the preferred material. The main focus of this study is to optimize the manufacturing process in order to determine the parameters that result in the most effective production of foot prosthesis, while minimizing the time required for machining. Optimization analysis revealed that the most effective parameters for producing PLA foot prosthesis using a 3D printing machine are a layer height of 0.1 mm, infill density of 100%, print speed of 100 mm/s, and nozzle temperature of 210°C. These adjusted parameters are essential benchmarks for the production sector of foot prostheses.

Investigation of the performance of integrated intelligent models to predict the roughness of Ti6Al4V end-milled surface with uncoated cutting tool

Abstract Titanium alloys are broadly used in the medical and aerospace sectors. However, they are categorized within the hard-to-machine alloys ascribed to their higher chemical reactivity and lower thermal conductivity. This aim of this research was to study the impact of the dry-end-milling process with an uncoated tool on the produced surface roughness of Ti6Al4V alloy. This research aims to study the impact of the dry-end milling process with an uncoated tool on the produced surface roughness of Ti6Al4V alloy. Also, it seeks to develop a new hybrid neural model based on the training back propagation neural network (BPNN) with swarm optimization-gravitation search hybrid algorithms (PSO-GSA). Full-factorial design of the experiment with L27 orthogonal array was applied, and three end-milling parameters (cutting speed, feed rate, and axial depth of cut) with three levels were selected (50, 77.5, and 105 m/min; 0.1, 0.15, and 0.2 mm/tooth; and 1, 1.5, and 2 mm) and investigated to show their influence on the obtained surface roughness. The results revealed that the surface roughness is significantly affected by the feed rate followed by the axial depth. A 0.49 µm was produced as a minimum surface roughness at the optimized parameters of 105 m/min, 0.1 mm/tooth, and 1 mm. On the other hand, a neural network having a single hidden layer with 1–20 hidden neurons, 3 input neurons, and 1 output neuron was trained with both PSO and PSO–GSA algorithms. The hybrid BPNN–PSO–GSA model showed its superiority over the BPNN–PSO model in terms of the minimum mean square error (MSE) that was calculated during the testing stage. The best BPNN–PSO–GSA hybrid model was the 3–18–1 structure, which reached the best testing MSE of 3.8 × 10−11 against 2.42 × 10−5 of the 3–8–1 BPNN–PSO hybrid model.

Exploring the barriers in medical additive manufacturing from an emerging economy

Sustainable medical additive manufacturing (SMAM) is becoming the next Industry 4.0 technology to revolutionise the medical industry. The adoption of SMAM offers several advantages and brings a paradigm shift in complex manufacturing geometry with improved quality, speed, cost, and sustainability in medical sectors. This research aims to identify the adoption barriers of SMAM in the Indian context. The research design involves two-step procedures: First, a literature review was conducted to determine the barriers to SMAM technology adoption. Later, these were validated by a panel of experts from industry and academia. Second, a hybrid ISM-DEMATEL methodology was deployed to establish and evaluate the cause-effect relationship between the validated barriers. Our findings suggest that among the identified barriers, infrastructural barriers were the most important for adopting SMAM in India, followed by a lack of long-term planning, operational barriers, and supply-demand barriers. Further, it also identified net cause driving barriers, including financial barriers, legal and policy barriers, technological barriers, and management barriers to SMAM adoption. As the adoption of SMAM offers several advantages, including a shift in complex manufacturing geometry with improved quality, speed, cost, and sustainability in medical sectors, these findings assume significance and will help decision-makers overcome complex barriers to SMAM adoption.

Sustainable natural biopolymers for biomedical applications

Biopolymers are materials specifically engineered to interact with biological systems. They can be derived from either natural or synthetic sources, depending on the biological resources used or the manufacturing process employed. Over recent decades, these materials have gained significant popularity within the medical field due to their remarkable attributes such as biodegradability, bioactivity, and compatibility with human tissue. One notable application is their use as scaffolds for bone regeneration. Biopolymers, being renewable biomaterials, provide opportunities for continuous manufacturing and technological progress across various industries. These biomaterials have demonstrated great promise in medical sectors, including nerve regeneration and the production of surgical devices. Additionally, their versatility extends to non-biomedical applications, like food packaging. This paper aims to provide a comprehensive overview of different biopolymers, elucidating their properties, showcasing their latest applications, and delving into the state-of-the-art manufacturing technologies used in their production. Special emphasis is placed on their suitability as bone tissue repair and regeneration scaffolds, owing to their unique properties, which render them an ideal choice for this specific application.

Electronic Health File System based on Fingerprint Sensor Technology

In medical sector, the use of computers and information technology helps in diagnosis, especially the computer-based patient record (CPR). As the traditional method of storing patient files is not feasible to obtain the health file, especially those who are in critical conditions, such as accidents, loss of consciousness and memory at times, due to the consumption of a lot of time and effort in the process of manual searching in traditional files. In order to help health care centers quickly identify patient personal data and make accurate decisions, an automated electronic system for patient health files based on a fingerprint sensor was built using special methods and technologies such as Content-Based Image Retrieval (CBIR) system, which is a retrieval technology based on the similarity of the images has the potential to facilitate identification and diagnosis of the patient's condition when he or she is unconscious. This study aims to integrate different technologies and discuss issues related to the electronic health file system based on the patient's fingerprint data, the use of (fingerprint sensor) technology, linking to the patient file data system, storage in fingerprint databases, and health data for each. sick.; To help critical cases, in addition to applying this technology and employing it in the ambulance and diagnostic system in order to improve the level of medical services and reach the desired results. The results showed that the electronic health profile was successfully built, designed and implemented for its intended purpose, the system and the destinations that connect users to the system as well as the fingerprint sensor. Finally, the system is efficient and gives reliable results with up to 100% tested accuracy based on the states it was stored.

Rhodotorula paludigena CM33 cultivation process development for high β-carotene single cell protein production

Rhodotorula yeast has potential in terms of biotechnology for the production of various compounds for the environmental, industrial, and medical sectors. It is well known to generate carotenoid pigments that are beneficially distinguished. This investigation aimed to optimize the growth of Rhodotorula paludigena CM33 and its production of β-carotene. Response Surface Methodology was employed to optimize temperature, ammonium sulfate, and ferric chloride concentrations in a 5L-bioreactor batch culture. The optimal conditions of 30 °C, 6.2 g/L of ammonium sulfate, and 75.0 mg/L of ferric chloride provided biomass of 17.71 g/L, a specific growth rate of 0.22 h−1, and β-carotene concentration of 40.29 mg/L. Upon scaling up the cultivation process, the 22L-bioreactor displayed similar growth patterns to those of the 5L-bioreactor. However, the 22L-bioreactor exhibited higher product yields in a shorter time period. The maximum biomass production of 18.96 ± 1.31 g/L and the maximum β-carotene production of 42.81 ± 0.61 mg/L were achieved at 28 h. This scaling-up strategy resulted in improved production efficiency. Additionally, the nutritional value of the cells was evaluated, suggesting their potential as animal feed supplements. R. paludigena CM33 demonstrated a β-carotene single-cell protein content, making it a valuable source for aquaculture and livestock feed. These findings highlight the potential of this yeast strain for large-scale production of β-carotene and single-cell protein, offering nutritional benefits for animal feed.

Surfactants in waste water: Development, current status and associated challenges

Using soaps, disinfectants, sanitizers, surface cleaners, and detergents on a large scale has increased during COVID-19, making it apparent again to review their scale of usage. Increasing consumption of disinfectants, soaps, and detergents for hygiene enhancement in medical, domestic, and corporate sectors triggers a significant increase in the contaminants in wastewater. Surfactants facilitate cleansing by lowering the surface tension. Surfactants, the main ingredients of soaps, detergents, and cleansing agents, impart different properties to the cleansing systems. Surfactants are used as substrates in the synthesis of various products, including dyes, pigments, pesticides, exchange resins, plasticizers, and pharmaceuticals, along with their primary use in household and laundry detergents and cleaners, cosmetics, textile and tanning industry, paints, preservative coatings, pulp and paper, and petroleum products. A significant portion of surfactants is let into the water bodies through laundry detergents. The detergent formulations constitute almost 40% of surfactants. The quest for effective detergents and disinfecting properties has led to the pursuit of newer alternatives for surfactant incorporation.The current manuscript represents the journey of surfactants in terms of their development over time, the modifications carried out to impart desired traits, and their hazardous implications. A study on species of Anabas testudineus showed that the fishes are fatally affected by detergent concentrations above 200 mg/L. It further encompasses the proposed alterations to combat the harmful environmental effects. Cationic surfactants were observed to have a more hazardous impacts than anionic and non-ionic surfactants. The manuscript emphasizes the utilization of more environment-friendly substitutes for surfactant molecules. It presents an overview of different molecules which are a part of surfactants, hazardous traits, and prospects associated with different surfactant entities.

Experimental investigation and optimization of process parameters in micro EDD for Mg1Fe3O4 nanocomposite

Electric discharge drilling (EDD) is a spark erosion process used for generating micro-holes in conductive materials. The process has wide range of applications in aerospace, medical, dental and automobile sectors. The input parameters selected in the present study are discharge current, pulse-on-time, pulse-off-time and gap voltage, while the responses are Drilling Speed and Taper Angle. Magnesium based nanocomposite (Mg1Fe3O4) is used as the work material and a tubular brass electrode of 1 mm dia. as tool. The micro-holes are drilled as per Taguchi L9 design of experiments, optimized and analyzed using ANOVA to understand the parameters significance and contribution in the responses.

Computer-aided Detection of Tuberculosis from Microbiological and Radiographic Images

ABSTRACT Tuberculosis caused by Mycobacterium tuberculosis have been a major challenge for medical and healthcare sectors in many underdeveloped countries with limited diagnosis tools. Tuberculosis can be detected from microscopic slides and chest X-ray but as a result of the high cases of tuberculosis, this method can be tedious for both Microbiologists and Radiologists and can lead to miss-diagnosis. These challenges can be solved by employing Computer-Aided Detection (CAD)via AI-driven models which learn features based on convolution and result in an output with high accuracy. In this paper, we described automated discrimination of X-ray and microscope slide images into tuberculosis and non-tuberculosis cases using pretrained AlexNet Models. The study employed Chest X-ray dataset made available on Kaggle repository and microscopic slide images from both Near East University Hospital and Kaggle repository. For classification of tuberculosis using microscopic slide images, the model achieved 90.56% accuracy, 97.78% sensitivity and 83.33% specificity for 70: 30 splits. For classification of tuberculosis using X-ray images, the model achieved 93.89% accuracy, 96.67% sensitivity and 91.11% specificity for 70:30 splits. Our result is in line with the notion that CNN models can be used for classifying medical images with higher accuracy and precision.

A blockchain-based federated learning mechanism for privacy preservation of healthcare IoT data

The Corona virus outbreak sped up the process of digitalizing healthcare. The ubiquity of IoT devices in healthcare has thrust the Healthcare Internet of Things (HIoT) to the forefront as a viable answer to the shortage of healthcare professionals. However, the medical field’s ability to utilize this technology may be constrained by rules governing the sharing of data and privacy issues. Furthermore, endangering human life is what happens when a medical machine learning system is tricked or hacked. As a result, robust protections against cyberattacks are essential in the medical sector. This research uses two technologies, namely federated learning and blockchain, to solve these problems. The ultimate goal is to construct a trusted federated learning system on the blockchain that can predict people who are at risk for developing diabetes. The study’s findings were deemed satisfactory as it achieved a multilayer perceptron accuracy of 97.11% and an average federated learning accuracy of 93.95%.

Isolation, screening, and identification of chitinase-producing bacterial strains from riverbank soils at Ambo, Western Ethiopia

Chitinases are hydrolytic enzymes that dissolve the glycosidic linkages in chitin. Chitin is a cell wall component of fungi and fund in exoskeleten of worms and arthropods. Chitinase has been applied in agriculture, as a biopesticide for the control of plant fungal infections, in medicine, and in waste management. This research aimed to isolate, screen, and identification of chitinase-producing bacteria from riverbank soils. Twenty nine chitinolytic bacteria were isolated from the river bank soil samples, from which 9 of them had strong chitinolytic properties. Chitinase production was determined by zones of hydrolysis produced after 96 h of incubation at 37 °C. The different bacterial isolates were characterized morphologically, microscopically, and biochemically and finally eight strain were identified at species level by Matrix Assisted Laser Desorption Ionization - Time of Flight Mass Spectrometry (MALDI–TOF MS). From the eight, bacterial isolates investigated in this study Stenotrophomonas maltophilia showed the highest chitinase enzyme activity (625 μg/mL) followed by Pseudomonas putida with the enzyme activity of (553 μg/mL) and the least enzyme activity was recorded for Lilliottia amnigena (80 μg/mL). An incubation temperature of 45 °C, neutral pH and an incubation period of 96 h are found to be the optimum condition for the chitinase enzyme production from Stenotrophomonas maltophilia. The results of this study indicated the possibility of the production of chitinase from the chitinolytic bacterial isolates, which was highly useful for a variety of applications, including biocontrol of harmful insects and pathogenic fungi as well as in the biochemical, pharmaceutical, and medical sectors.

Control of Escherichia coli in Fresh-Cut Mixed Vegetables Using a Combination of Bacteriophage and Carvacrol.

The continual emergence of antibiotic-resistant bacteria and the slow development of new antibiotics has driven the resurgent interest in the potential application of bacteriophages as antimicrobial agents in different medical and industrial sectors. In the present study, the potential of combining phage biocontrol and a natural plant compound (carvacrol) in controlling Escherichia coli on fresh-cut mixed vegetable was evaluated. Four coliphages, designated Escherichia phage SUT_E420, Escherichia phage SUT_E520, Escherichia phage SUT_E1520 and Escherichia phage SUT_E1620, were isolated from raw sewage. Biological characterization revealed that all four phages had a latent period of 20-30 min and a burst size ranging from 116 plaque-forming units (PFU)/colony forming units (CFU) to 441 PFU/CFU. The phages effectively inhibited the growth of respective host bacteria in vitro, especially when used at a high multiplicity of infection (MOI). Based on transmission electron microscopy analysis, all phages were classified as tailed phages in the class of Caudoviricetes. Additionally, next generation sequencing indicated that none of the selected coliphages contained genes encoding virulence or antimicrobial resistance factors, highlighting the suitability of isolated phages as biocontrol agents. When a phage cocktail (~109 PFU/mL) was applied alone onto fresh-cut mixed vegetables artificially contaminated with E. coli, no bacteria were recovered from treated samples on Day 0, followed by a gradual increase in the E. coli population after 24 h of incubation at 8 °C. On the other hand, no significant differences (p < 0.05) were observed between treated and non-treated samples in terms of E. coli viable counts when carvacrol at the minimum inhibitory concentration (MIC) of 6.25 μL/mL was applied alone. When a phage cocktail at an MOI of ~1000 and MIC carvacrol were applied in combination, no E. coli were recovered from treated samples on Day 0 and 1, followed by a slight increase in the E. coli population to approximately 1.2-1.3 log CFU/mL after 48 h of incubation at 8 °C. However, total elimination of E. coli was observed in samples treated with a phage cocktail at a higher MOI of ~2000 and carvacrol at MIC, with a reduction of approximately 4 log CFU/mL observed at the end of Day 3. The results obtained in this study highlight the potential of combined treatment involving phage biocontrol and carvacrol as a new alternative method to reduce E. coli contamination in minimally processed ready-to-eat foods.

Brain Tumor Classification, Segmentation, and Detection using Deep Learning - A Review

V.Vapnik in 1965 proposed Vector methods. Kimeldorf presented a technique for creating kernel space based on support vectors in 1971. Support Vector Machine (SVM) techniques were initially presented in the 1990s by V. Vapnik in the field of statistical learning. Since then, pattern recognition, natural language processing, image processing and other areas have seen extensive use of SVM. By converting non-linear sample space into linear space via a kernel approach, the algorithm's complexity is reduced. Image classification is a well-known issue in image processing. Predicting the input image categories using the features is the main objective of image classification. There are several different classifiers, including Artificial Neural Networks, Support Vector Machines, and Random Forests, Decision Forests, k-NNs (k Nearest Neighbors), and Adaptive Boost. SVM is one of the best techniques for categorizing any image or pattern. A common non-invasive technique used in the medical sector for the analysis, diagnosis, treatment of brain tissues is magnetic resonance imaging. When a brain tumor is discovered early, the patient's life can be saved by receiving the appropriate care. It becomes difficult to accurately identify tumors in the MRI slices, which requires fussy work..

APPLICATION OF TECHNOLOGIES BASED ON ARTIFICIAL INTELLIGENCE IN THE HEALTHCARE SECTOR OF GEORGIA

Interest in artificial intelligence (AI) is rapidly growing in the modern world, and Georgia is no exception. Artificial intelligence has transformed many fields in recent years, including healthcare. Artificial intelligence has the ability to analyze large amounts of data, make predictions, and provide personalized experiences, making it a powerful tool for medical professionals. The paper examines the impact of artificial intelligence on the medical sector. The study results show that hospitals in many countries around the world are already using AI-supported systems to increase the capabilities of medical staff in the process of diagnosing and treating patients for a wide range of diseases. In addition, artificial intelligence systems have an impact on improving the efficiency of hospital management, reducing medical costs, and more. Considering the risks associated with the use of artificial intelligence in the healthcare sector, it is necessary to plan it effectively and develop an appropriate strategy to maximize the benefits.

Autism Spectrum Disorder Classification Using Machine Learning and Deep Learning- A Survey

Modern, highly developed technology has impacted reputable procedures in the medical and healthcare industries. Smart healthcare prediction to the senior sick patient is not only for quick access to data but also to get dependable treatment in an accurate prediction by healthcare service provider. smart health prediction helps in the identification of numerous diseases. Based on patient experience, Deep learning technology provides a robust application space in the medical sector for health disease prediction problems by applying deep learning techniques to analyze various symptoms. In order to classify things and make precise predictions about diseases, deep learning techniques are utilized. people's health will be more secure, medical care will be of a higher caliber, and personal information will be kept more secret. As deep learning algorithms become more widely used to construct an interactive smart healthcare prediction and evaluation model on the basis of the deep learning model, CNN is upgraded. Advanced deep learning algorithms combined with multi-mode approaches and resting-state functional magnetic resonance represent an innovative approach that researchers have taken. A DL structure for the programmed ID ASD using highlights separated from the corpus callosum and cerebrum volume from the Stand dataset is proposed. Imaging is used to reveal hidden diseased brain connectome patterns to find diagnostic and prognostic indicators.

Building a European One Health workforce for prevention and preparedness to health threats

Abstract Background The Quadripartite has elaborated the One Health Joint Plan of Action (2022-2026) (OH JPA) to promote health sustainably and prevent the next pandemic through the One Health (OH) approach. One of the action track of the OH JPA is to create a training program for environment, medical and veterinary sector professionals. Objectives The Italian National Institute of Health is developing a training curriculum about OH prevention and preparedness to health threats within the European Region to promote the OH workforce capacities and capabilities and to support the OH JPA execution. The training curriculum would be developed through a literature review and a stakeholder consultation process, validated by a steering committee and presented during a workshop. Results A modular training curriculum about OH prevention and preparedness to health threats within the European Region is developed and it includes modules to strengthen soft and hard skills around 5 OH domains (OH thinking, OH design, OH implementation, OH evaluation, OH cross-cutting competences) with case studies encompassing different threats. The curriculum would be further developed into a training hybrid course for National Public Health Institutes, decision and policy-makers at all level, International and European organizations and agencies, Academia, professionals working in the area of environmental and agricultural sciences, social sciences, human and animal health. Conclusions The training curriculum would contribute to build a European OH workforce by strengthening OH prevention and preparedness skills and competences and networking opportunities. It would support OH action and practice at national, regional and global level and the execution of the OH JPA. Key messages • A training curriculum for OH prevention and preparedness to health threats within Europe has been elaborated. • The OH workforce, OH action, and the OH JPA execution are strengthened and supported.

A Review: Optimum Conditions for Magnetite Synthesis (Fe3O4)

Magnetite, chemically represented as Fe3O4, is an opaque substance characterized by its black coloration. It possesses a significant saturation magnetization value, denoting a substantial magnetic strength compared to alternative materials. Magnetite (Fe3O4) has significant use across several domains, including its deployment as a catalyst and sensor in the medical sector, such as drug delivery, hyperthermia therapy, and magnetic resonance imaging (MRI). The synthesis of magnetite (Fe3O4) can be achieved using several methodologies, such as solvothermal, sol-gel, solid state, reverse micelle, microwave plasma synthesis, freeze drying, ultrasound irradiation, hydrothermal, and coprecipitation techniques. The co-preparation technique is extensively employed for magnetite synthesis due to its simplicity, ease of usage, and ability to generate magnetic particles at the nanoscale scale without elevated temperatures. The outcome of the coprecipitation process is influenced by various factors such as the nature of the salt, molar ratio, pH level, stirring rate, and reaction temperature. This article aims to examine the impact of many factors, including the salt type, molar ratio, pH level, stirring rate, and reaction temperature, on the production of Magnetite (Fe3O4) through coprecipitation. It was found that Magnetite (Fe3O4) synthesis can produce nanoparticle size material with great magnetic power, namely at pH 8, temperature 70°C, and stirring rate of 10,000 rpm by using FeCl3 and FeCl2 as precursors Fe3+ and Fe2+ with a ratio of 1.5:1 and using precipitating base NH4OH.