Karpagam Academy of Higher Education

Microalgae are one of the important components in food chains of aquatic ecosystems and have been used for human consumption as food and as medicines. The wide diversity of compounds synthesized from different metabolic pathways of fresh and marine water algae provide promising sources of fatty acids, steroids, carotenoids, polysaccharides, lectins, mycosporine-like amino acids, halogenated compounds, polyketides, toxins, agar agar, alginic acid and carrageenan. This review discusses microalgae used to produce biological substances and its economic importance in food science, the pharmaceutical industry and public health.

Zinc oxide nanoparticles are known to be one of the multifunctional inorganic nanoparticles with effective antibacterial activity. This study aims to determine the antimicrobial efficacy of green and chemical synthesized ZnO nanoparticle against various bacterial and fungal pathogens. Various microbiological tests were performed using varying concentrations of green and chemical ZnO NPs with sizes 40 and 25 nm respectively. Results prove that green ZnO nanoparticles show more enhanced biocidal activity against various pathogens when compared to chemical ZnO nanoparticles. Also effectiveness of nanoparticles increases with increasing particle dose, treatment time and synthesis method. In addition, the current study has clearly demonstrated that the particle size variation and surface area to volume ratio of green ZnO nanoparticle are responsible for significant higher antimicrobial activity. From the results obtained it is suggested that green ZnO NPs could be used effectively in agricultural and food safety applications and also can address future medical concerns.

Theepithelial-mesenchymal transition (EMT) is an essential event during cell development, in which epithelial cells acquire mesenchymal fibroblast-like features including reduced intercellular adhesion and increased motility. EMT also plays a key role in wound healing processes, which are mediated by inflammatory cells and fibroblasts. These cells secrete specific factors that interact with molecules of the extracellular matrix (ECM) such as collagens, laminins, elastin and tenascins. Wound healing follows four distinct and successive phases characterized by haemostasis, inflammation, cell proliferation and finally tissue remodeling. EMT is classified into three diverse subtypes: type-1 EMT, type-2 EMT and type-3 EMT. Type-1 EMT is involved in embryogenesis and organ development. Type-2 EMT is associated with wound healing, tissue regeneration and organ fibrosis. During organ fibrosis, type-2 EMT occurs as a reparative-associated process in response to ongoing inflammation and eventually leads to organ destruction. Type-3 EMT is implicated in cancer progression, which is linked to the occurrence of genetic and epigenetic alterations, in detail the ones promoting clonal outgrowth and the formation of localized tumors. The current review aimed at exploring the role of EMT process with particular focus on type-2 EMT in wound healing, fibrosis and tissue regeneration, as well as some recent progresses in the EMT and tissue regeneration field, including the modulation of EMT by biomaterials.

BACKGROUND: For more than three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has provided a framework to quantify health loss due to diseases, injuries, and associated risk factors. This paper presents GBD 2023 findings on disease and injury burden and risk-attributable health loss, offering a global audit of the state of world health to inform public health priorities. This work captures the evolving landscape of health metrics across age groups, sexes, and locations, while reflecting on the remaining post-COVID-19 challenges to achieving our collective global health ambitions. METHODS: The GBD 2023 combined analysis estimated years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 375 diseases and injuries, and risk-attributable burden associated with 88 modifiable risk factors. Of the more than 310 000 total data sources used for all GBD 2023 (about 30% of which were new to this estimation round), more than 120 000 sources were used for estimation of disease and injury burden and 59 000 for risk factor estimation, and included vital registration systems, surveys, disease registries, and published scientific literature. Data were analysed using previously established modelling approaches, such as disease modelling meta-regression version 2.1 (DisMod-MR 2.1) and comparative risk assessment methods. Diseases and injuries were categorised into four levels on the basis of the established GBD cause hierarchy, as were risk factors using the GBD risk hierarchy. Estimates stratified by age, sex, location, and year from 1990 to 2023 were focused on disease-specific time trends over the 2010-23 period and presented as counts (to three significant figures) and age-standardised rates per 100 000 person-years (to one decimal place). For each measure, 95% uncertainty intervals [UIs] were calculated with the 2·5th and 97·5th percentile ordered values from a 250-draw distribution. FINDINGS: Total numbers of global DALYs grew 6·1% (95% UI 4·0-8·1), from 2·64 billion (2·46-2·86) in 2010 to 2·80 billion (2·57-3·08) in 2023, but age-standardised DALY rates, which account for population growth and ageing, decreased by 12·6% (11·0-14·1), revealing large long-term health improvements. Non-communicable diseases (NCDs) contributed 1·45 billion (1·31-1·61) global DALYs in 2010, increasing to 1·80 billion (1·63-2·03) in 2023, alongside a concurrent 4·1% (1·9-6·3) reduction in age-standardised rates. Based on DALY counts, the leading level 3 NCDs in 2023 were ischaemic heart disease (193 million [176-209] DALYs), stroke (157 million [141-172]), and diabetes (90·2 million [75·2-107]), with the largest increases in age-standardised rates since 2010 occurring for anxiety disorders (62·8% [34·0-107·5]), depressive disorders (26·3% [11·6-42·9]), and diabetes (14·9% [7·5-25·6]). Remarkable health gains were made for communicable, maternal, neonatal, and nutritional (CMNN) diseases, with DALYs falling from 874 million (837-917) in 2010 to 681 million (642-736) in 2023, and a 25·8% (22·6-28·7) reduction in age-standardised DALY rates. During the COVID-19 pandemic, DALYs due to CMNN diseases rose but returned to pre-pandemic levels by 2023. From 2010 to 2023, decreases in age-standardised rates for CMNN diseases were led by rate decreases of 49·1% (32·7-61·0) for diarrhoeal diseases, 42·9% (38·0-48·0) for HIV/AIDS, and 42·2% (23·6-56·6) for tuberculosis. Neonatal disorders and lower respiratory infections remained the leading level 3 CMNN causes globally in 2023, although both showed notable rate decreases from 2010, declining by 16·5% (10·6-22·0) and 24·8% (7·4-36·7), respectively. Injury-related age-standardised DALY rates decreased by 15·6% (10·7-19·8) over the same period. Differences in burden due to NCDs, CMNN diseases, and injuries persisted across age, sex, time, and location. Based on our risk analysis, nearly 50% (1·27 billion [1·18-1·38]) of the roughly 2·80 billion total global DALYs in 2023 were attributable to the 88 risk factors analysed in GBD. Globally, the five level 3 risk factors contributing the highest proportion of risk-attributable DALYs were high systolic blood pressure (SBP), particulate matter pollution, high fasting plasma glucose (FPG), smoking, and low birthweight and short gestation-with high SBP accounting for 8·4% (6·9-10·0) of total DALYs. Of the three overarching level 1 GBD risk factor categories-behavioural, metabolic, and environmental and occupational-risk-attributable DALYs rose between 2010 and 2023 only for metabolic risks, increasing by 30·7% (24·8-37·3); however, age-standardised DALY rates attributable to metabolic risks decreased by 6·7% (2·0-11·0) over the same period. For all but three of the 25 leading level 3 risk factors, age-standardised rates dropped between 2010 and 2023-eg, declining by 54·4% (38·7-65·3) for unsafe sanitation, 50·5% (33·3-63·1) for unsafe water source, and 45·2% (25·6-72·0) for no access to handwashing facility, and by 44·9% (37·3-53·5) for child growth failure. The three leading level 3 risk factors for which age-standardised attributable DALY rates rose were high BMI (10·5% [0·1 to 20·9]), drug use (8·4% [2·6 to 15·3]), and high FPG (6·2% [-2·7 to 15·6]; non-significant). INTERPRETATION: Our findings underscore the complex and dynamic nature of global health challenges. Since 2010, there have been large decreases in burden due to CMNN diseases and many environmental and behavioural risk factors, juxtaposed with sizeable increases in DALYs attributable to metabolic risk factors and NCDs in growing and ageing populations. This long-observed consequence of the global epidemiological transition was only temporarily interrupted by the COVID-19 pandemic. The substantially decreasing CMNN disease burden, despite the 2008 global financial crisis and pandemic-related disruptions, is one of the greatest collective public health successes known. However, these achievements are at risk of being reversed due to major cuts to development assistance for health globally, the effects of which will hit low-income countries with high burden the hardest. Without sustained investment in evidence-based interventions and policies, progress could stall or reverse, leading to widespread human costs and geopolitical instability. Moreover, the rising NCD burden necessitates intensified efforts to mitigate exposure to leading risk factors-eg, air pollution, smoking, and metabolic risks, such as high SBP, BMI, and FPG-including policies that promote food security, healthier diets, physical activity, and equitable and expanded access to potential treatments, such as GLP-1 receptor agonists. Decisive, coordinated action is needed to address long-standing yet growing health challenges, including depressive and anxiety disorders. Yet this can be only part of the solution. Our response to the NCD syndemic-the complex interaction of multiple health risks, social determinants, and systemic challenges-will define the future landscape of global health. To ensure human wellbeing, economic stability, and social equity, global action to sustain and advance health gains must prioritise reducing disparities by addressing socioeconomic and demographic determinants, ensuring equitable health-care access, tackling malnutrition, strengthening health systems, and improving vaccination coverage. We live in times of great opportunity. FUNDING: Gates Foundation and Bloomberg Philanthropies.

BACKGROUND: Translating the conventional scientific concepts into a new robust invention is a much needed one at a present scenario to develop some novel materials with intriguing properties. Particles in nanoscale exhibit superior activity than their bulk counterpart. This unique feature is intensively utilized in physical, chemical, and biological sectors. Each metal is holding unique optical properties that can be utilized to synthesize metallic nanoparticles. At present, versatile nanoparticles were synthesized through chemical and biological methods. Metallic nanoparticles pose numerous scientific merits and have promising industrial applications. But concerning the pros and cons of metallic nanoparticle synthesis methods, researchers elevate to drive the synthesis process of nanoparticles through the utilization of plant resources as a substitute for use of chemicals and reagents under the theme of green chemistry. These synthesized nanoparticles exhibit superior antimicrobial, anticancer, larvicidal, leishmaniasis, wound healing, antioxidant, and as a sensor. Therefore, the utilization of such conceptualized nanoparticles in treating infectious and environmental applications is a warranted one. CONCLUSION: Green chemistry is a keen prudence method, in which bioresources is used as a template for the synthesis of nanoparticles. Therefore, in this review, we exclusively update the context of plant-based metallic nanoparticle synthesis, characterization, and applications in detailed coverage. Hopefully, our review will be modernizing the recent trends going on in metallic nanoparticles synthesis for the blooming research fraternities.

Agriculture is the primary source of income in developing countries like India. Agriculture accounts for 17 percent of India’s total GDP, with almost 60 percent of the people directly or indirectly employed. While researchers and planters focus on a variety of elements to boost productivity, crop loss due to disease is one of the most serious issues they confront. Crop growth monitoring and early detection of pest infestations are still a problem. With the expansion of cultivation to wider fields, manual intervention to monitor and diagnose insect and pest infestations is becoming increasingly difficult. Failure to apply on time fertilizers and pesticides results in more crop loss and so lower output. Farmers are putting in greater effort to conserve crops, but they are failing most of the time because they are unable to adequately monitor the crops when they are infected by pests and insects. Pest infestation is also difficult to predict because it is not evenly distributed. In the recent past, modern equipment, tools, and approaches have been used to replace manual involvement. Unmanned aerial vehicles serve a critical role in crop disease surveillance and early detection in this setting. This research attempts to give a review of the most successful techniques to have precision-based crop monitoring and pest management in agriculture fields utilizing unmanned aerial vehicles (UAVs) or unmanned aircraft. The researchers’ reports on the various types of UAVs and their applications to early detection of agricultural diseases are rigorously assessed and compared. This paper also discusses the deployment of aerial, satellite, and other remote sensing technologies for disease detection, as well as their Quality of Service (QoS).

Medical expert systems are part of the portable and smart healthcare monitoring devices used in day-to-day life. Arrhythmic beat classification is mainly used in electrocardiogram (ECG) abnormality detection for identifying heart related problems. In this paper, ECG signal preprocessing and support vector machine-based arrhythmic beat classification are performed to categorize into normal and abnormal subjects. In ECG signal preprocessing, a delayed error normalized LMS adaptive filter is used to achieve high speed and low latency design with less computational elements. Since the signal processing technique is developed for remote healthcare systems, white noise removal is mainly focused. Discrete wavelet transform is applied on the preprocessed signal for HRV feature extraction and machine learning techniques are used for performing arrhythmic beat classification. In this paper, SVM classifier and other popular classifiers have been used on noise removed feature extracted signal for beat classification. Results indicate that the performance of SVM classifier is better than other machine learning-based classifiers.

spp. using different agro-waste as growth substances paying attention to their effects on the growth and chemical composition.

Oxidative stress is a pathological condition occurring due to an imbalance between the oxidants and antioxidant defense systems in the body. Nuclear factor E2-related factor 2 (NRF2), encoded by the gene NFE2L2, is the master regulator of phase II antioxidant enzymes that protect against oxidative stress and inflammation. NRF2/ARE signaling has been considered as a promising target against oxidative stress-mediated diseases like diabetes, fibrosis, neurotoxicity, and cancer. The consumption of dietary phytochemicals acts as an effective modulator of NRF2/ARE in various acute and chronic diseases. In the present review, we discussed the role of NRF2 in diabetes, Alzheimer’s disease (AD), Parkinson’s disease (PD), cancer, and atherosclerosis. Additionally, we discussed the phytochemicals like curcumin, quercetin, resveratrol, epigallocatechin gallate, apigenin, sulforaphane, and ursolic acid that have effectively modified NRF2 signaling and prevented various diseases in both in vitro and in vivo models. Based on the literature, it is clear that dietary phytochemicals can prevent diseases by (1) blocking oxidative stress-inhibiting inflammatory mediators through inhibiting Keap1 or activating Nrf2 expression and its downstream targets in the nucleus, including HO-1, SOD, and CAT; (2) regulating NRF2 signaling by various kinases like GSK3beta, PI3/AKT, and MAPK; and (3) modifying epigenetic modulation, such as methylation, at the NRF2 promoter region; however, further investigation into other upstream signaling molecules like NRF2 and the effect of phytochemicals on them still need to be investigated in the near future.

Now-a-days image processing placed an important role for recognizing various diseases such as breast, lung, and brain tumors in earlier stage for giving the appropriate treatment. Presently, most cancer diagnosis worked according to the visual examination process with effectively. Human visual reviewing of infinitesimal biopsy pictures is exceptionally tedious, subjective, and conflicting due to between and intra-onlooker varieties. In this manner, the malignancy and it’s compose will be distinguished in a beginning time for finish treatment and fix. This brain tumor classification system using machine learning-based back propagation neural networks (MLBPNN) causes pathologists to enhance the exactness and proficiency in location of threat and to limit the entomb onlooker variety. Moreover, the technique may assist doctors with analyzing the picture cell by utilizing order and bunching calculations by recoloring qualities of the phones. The different picture preparing steps required for disease location from biopsy pictures incorporate procurement, upgrade, and division; include extraction, picture portrayal, characterization, and basic leadership. In this paper, MLBPNN is analyzed with the help of infra-red sensor imaging technology. Then, the computational multifaceted nature of neural distinguishing proof incredibly diminished when the entire framework is deteriorated into a few subsystems. The features are extracted using fractal dimension algorithm and then the most significant features are selected using multi fractal detection technique to reduce the complexity. This imaging sensor is integrated via wireless infrared imaging sensor which is produced to transmit the tumor warm data to a specialist clinician to screen the wellbeing condition and for helpful control of ultrasound measurements level, especially if there should arise an occurrence of elderly patients living in remote zones.

In recent years, the underwater wireless sensor network (UWSN) has received a significant interest among research communities for several applications, such as disaster management, water quality prediction, environmental observance, underwater navigation, etc. The UWSN comprises a massive number of sensors placed in rivers and oceans for observing the underwater environment. However, the underwater sensors are restricted to energy and it is tedious to recharge/replace batteries, resulting in energy efficiency being a major challenge. Clustering and multi-hop routing protocols are considered energy-efficient solutions for UWSN. However, the cluster-based routing protocols for traditional wireless networks could not be feasible for UWSN owing to the underwater current, low bandwidth, high water pressure, propagation delay, and error probability. To resolve these issues and achieve energy efficiency in UWSN, this study focuses on designing the metaheuristics-based clustering with a routing protocol for UWSN, named MCR-UWSN. The goal of the MCR-UWSN technique is to elect an efficient set of cluster heads (CHs) and route to destination. The MCR-UWSN technique involves the designing of cultural emperor penguin optimizer-based clustering (CEPOC) techniques to construct clusters. Besides, the multi-hop routing technique, alongside the grasshopper optimization (MHR-GOA) technique, is derived using multiple input parameters. The performance of the MCR-UWSN technique was validated, and the results are inspected in terms of different measures. The experimental results highlighted an enhanced performance of the MCR-UWSN technique over the recent state-of-art techniques.

According to the survey on various health centres, smart log-based multi access physical monitoring system determines the health conditions of humans and their associated problems present in their lifestyle. At present, deficiency in significant nutrients leads to deterioration of organs, which creates various health problems, particularly for infants, children, and adults. Due to the importance of a multi access physical monitoring system, children and adolescents' physical activities should be continuously monitored for eliminating difficulties in their life using a smart environment system. Nowadays, in real-time necessity on multi access physical monitoring systems, information requirements and the effective diagnosis of health condition is the challenging task in practice. In this research, wearable smart-log patch with Internet of Things (IoT) sensors has been designed and developed with multimedia technology. Further, the data computation in that smart-log patch has been analysed using edge computing on Bayesian deep learning network (EC-BDLN), which helps to infer and identify various physical data collected from the humans in an accurate manner to monitor their physical activities. Then, the efficiency of this wearable IoT system with multimedia technology is evaluated using experimental results and discussed in terms of accuracy, efficiency, mean residual error, delay, and less energy consumption. This state-of-the-art smart-log patch is considered as one of evolutionary research in health checking of multi access physical monitoring systems with multimedia technology.

Abstract A novel hybrid composite was developed from natural fibers and the mechanical properties were investigated in this work. The palm sheath and sugarcane bagasse fibres were the natural fibers used and epoxy resin was the matrix. By using compression‐molding machine, various samples were prepared by varying the weight proportions of fibers. The performance of fibers was investigated under untreated and NaOH treated conditions. The tensile properties, flexural properties, hardness, and impact properties were evaluated using ASTM standards. The best sample was determined based on the experimental results. The best sample had the tensile strength of 19.80 ± 0.78 MPa, Young's Modulus of 0.953 ± 0.076 GPa, flexural strength of 28.79 MPa, impact strength of 2 kJ/m 2 , and the hardness value of 38.02 HD. The best sample was used to develop an automobile dashboard to justify its application.

This Proposed Work exposes, a advance computing technology that has been developed to help the farmer to take superior decision about many aspects of crop development process. Suitable evaluation and diagnosis of crop disease in the field is very critical for the increased production. Foliar is the major important fungal disease of cotton and occurs in all growing Indian regions. In this work we express new technological strategies using mobile captured symptoms of cotton leaf spot images and categorize the diseases using HPCCDD Proposed Algorithm. The classifier is being trained to achieve intelligent farming, including early Identification of diseases in the groves, selective fungicide application, etc. This proposed work is based on Image RGB feature ranging techniques used to identify the diseases (using Ranging values) in which, the captured images are processed for enhancement first. Then color image segmentation is carried out to get target regions (disease spots). Next Homogenize techniques like Sobel and Canny filter are used to Identify the edges, these extracted edge features are used in classification to identify the disease spots. Finally, pest recommendation is given to the farmers to ensure their crop and reduce the yeildloss.

With the tremendous growth of the usage of computers over network and development in application running on various platform captures the attention toward network security. This paradigm exploits security vulnerabilities on all computer systems that are technically difficult and expensive to solve. Hence intrusion is used as a key to compromise the integrity, availability and confidentiality of a computer resource. The Intrusion Detection System (IDS) plays a vital role in detecting anomalies and attacks in the network. In this work, data mining concept is integrated with an IDS to identify the relevant, hidden data of interest for the user effectively and with less execution time. Four issues such as Classification of Data, High Level of Human Interaction, Lack of Labeled Data, and Effectiveness of Distributed Denial of Service Attack are being solved using the proposed algorithms like EDADT algorithm, Hybrid IDS model, Semi-Supervised Approach and Varying HOPERAA Algorithm respectively. Our proposed algorithm has been tested using KDD Cup dataset. All the proposed algorithm shows better accuracy and reduced false alarm rate when compared with existing algorithms.

Breast cancer is the most lethal type of cancer for all women worldwide. At the moment, there are no effective techniques for preventing or curing breast cancer, as the source of the disease is unclear. Early diagnosis is a highly successful means of detecting and managing breast cancer, and early identification may result in a greater likelihood of complete recovery. Mammography is the most effective method of detecting breast cancer early. Additionally, this instrument enables the detection of additional illnesses and may provide information about the nature of cancer, such as benign, malignant, or normal. This article discusses an evolutionary approach for classifying and detecting breast cancer that is based on machine learning and image processing. This model combines image preprocessing, feature extraction, feature selection, and machine learning techniques to aid in the classification and identification of skin diseases. To enhance the image’s quality, a geometric mean filter is used. AlexNet is used for extracting features. Feature selection is performed using the relief algorithm. For disease categorization and detection, the model makes use of the machine learning techniques such as least square support vector machine, KNN, random forest, and Naïve Bayes. The experimental investigation makes use of MIAS data collection. This proposed technology is advantageous for accurately identifying breast cancer disease using image analysis.

The search for novel bio-fibers in the field of the green composite can rise the invention of natural fiber composite and applications. In this work, the physical, chemical, structural, thermal, tensile and surface morphology properties of Dracaena reflexa fiber (DRF) are investigated. The chemical analysis results authorized the higher cellulose (70.32%) and lesser hemicelluloses (11.02%) and lignin (11.35%) existing in DRF. XRD analysis proved that DRF has a relatively higher crystallinity index of 57.32%. The free chemical functional groups presented in DRFs were determined by FT-IR. The DRF is thermally stable up to 230 °C which is greater than the processing temperature of thermoplastics resin. The C2, C3, and C5 peaks intensity of CP/MAS C13 NMR spectra once again confirmed that maximum cellulose present in DRFs. The lower density (790 kg/m3) and higher tensile properties of DRF show the DRF is a suitable alternative to the synthetic fibers.

The synthesis, characterization and application of biologically synthesized nanomaterials are an important aspect in nanotechnology. The present study deals with the synthesis of silver nanoparticles (Ag-NPs) using the aqueous extract of red seaweed Gelidiella acerosa as the reducing agent to study the antifungal activity. The formation of Ag-NPs was confirmed by UV-Visible Spectroscopy, X-Ray Diffraction (XRD) pattern, Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM). The synthesized Ag-NPs was predominately spherical in shape and polydispersed. Fourier Transform Infra-Red (FT-IR) spectroscopy analysis showed that the synthesized nano-Ag was capped with bimolecular compounds which are responsible for reduction of silver ions. The antifungal effects of these nanoparticles were studied against Humicola insolens (MTCC 4520), Fusarium dimerum (MTCC 6583), Mucor indicus (MTCC 3318) and Trichoderma reesei (MTCC 3929). The present study indicates that Ag-NPs have considerable antifungal activity in comparison with standard antifungal drug, and hence further investigation for clinical applications is necessary.

BACKGROUND: Snakebite represents a significant health issue worldwide, affecting several million people each year with as many as 95,000 deaths. India is considered to be the country most affected, but much remains unknown about snakebite incidence in this country, its socio-economic impact and how snakebite management could be improved. METHODS/PRINCIPAL FINDINGS: We conducted a study within rural villages in Tamil Nadu, India, which combines a household survey (28,494 people) of snakebite incidence with a more detailed survey of victims in order to understand the health and socio-economic effects of the bite, the treatments obtained and their views about future improvements. Our survey suggests that snakebite incidence is higher than previously reported. 3.9% of those surveyed had suffered from snakebite and the number of deaths corresponds to 0.45% of the population. The socio-economic impact of this is very considerable in terms of the treatment costs and the long-term effects on the health and ability of survivors to work. To reduce this, the victims recommended improvements to the accessibility and affordability of antivenom treatment. CONCLUSIONS: Snakebite has a considerable and disproportionate impact on rural populations, particularly in South Asia. This study provides an incentive for researchers and the public to work together to reduce the incidence and improve the outcomes for snake bite victims and their families.

Wearable devices with 5G technology are currently more ingrained in our daily lives, and they will now be a part of our bodies too. The requirement for personal health monitoring and preventive disease is increasing due to the predictable dramatic increase in the number of aging people. Technologies with 5G in wearables and healthcare can intensely reduce the cost of diagnosing and preventing diseases and saving patient lives. This paper reviewed the benefits of 5G technologies, which are implemented in healthcare and wearable devices such as patient health monitoring using 5G, continuous monitoring of chronic diseases using 5G, management of preventing infectious diseases using 5G, robotic surgery using 5G, and 5G with future of wearables. It has the potential to have a direct effect on clinical decision making. This technology could improve patient rehabilitation outside of hospitals and monitor human physical activity continuously. This paper draws the conclusion that the widespread adoption of 5G technology by healthcare systems enables sick people to access specialists who would be unavailable and receive correct care more conveniently.