Design and Implementation of a Specialised Millimetre-Wave Exposure System for Investigating the Radiation Effects of 5G and Future Technologies

As the fifth-generation (5G) network is introduced in the millimetre-wave (mmWave) spectrum, and the widespread deployment of 5G standalone (SA) is approaching, it becomes essential to establish scientifically grounded exposure limits in the mmWave frequency band. To achieve this, conducting experiments at specific frequencies is crucial for obtaining reliable evidence of potential biological impacts. However, there is a literature gap where experimental research either does not utilise the mmWave high band (e.g., the 26 Gigahertz (GHz) band) or most studies mainly rely on computational approaches. Moreover, some experimental studies do not establish reproducible test environment and exposure systems. Addressing these gaps is vital for a comprehensive exploration of the biological implications associated with mmWave exposure. This study was designed to develop and implement a mmWave exposure system operating at 26 GHz. The step-by-step design and development of the system are explained. This specialised system was designed and implemented within an anechoic chamber to minimise external electromagnetic (EM) interference, creating a controlled and reproducible environment for experiments involving high-frequency EM fields. The exposure system features a 1 cm radiation spot size, enabling highly localised exposure for various biological studies. This configuration facilitates numerous dosimetry studies related to mmWave frequencies.

ArtEMon: Artificial Intelligence and Internet of Things Powered Greenhouse Gas Sensing for Real-Time Emissions Monitoring

Greenhouse gas (GHG) emissions reporting and sustainability are increasingly important for businesses around the world. Yet the lack of a single standardised method of measurement, when coupled with an inability to understand the true state of emissions in complex logistics activities, presents enormous barriers for businesses to understanding the extent of their emissions footprint. One of the traditional approaches to accurately capturing and monitoring gas emissions in logistics is through using gas sensors. However, connecting, maintaining, and operating gas sensors on moving vehicles in different road and weather conditions is a large and costly challenge. This paper presents the development and evaluation of a reliable and accurate sensing technique for GHG emissions collection (or monitoring) in real-time, employing the Internet of Things (IoT) and Artificial Intelligence (AI) to eliminate or reduce the usage of gas sensors, using reliable and cost-effective solutions.

The Effects of mmW and THz Radiation on Dry Eyes: A Finite-Difference Time-Domain (FDTD) Computational Simulation Using XFdtd

The importance of investigating the health effects of RF radiation on the cornea cannot be overstated. This study aimed to address this need by utilizing a mathematical simulation to examine the absorption of millimeter wave (mmW) and terahertz (THz) waves by the cornea, considering both normal and pathological conditions. The simulation incorporated variations in tear film thickness and hydration levels, as these factors play a crucial role in corneal health. To assess the impact of RF radiation on the cornea, the study calculated temperature rises, which indicate heating effects for both dry and normal eyes. XFdtd, a widely used commercial software based on the Finite-Difference Time Domain (FDTD) method, was employed to evaluate the radiation absorption and resulting temperature changes. The outcomes of this study demonstrated a crucial finding, i.e., that changes in the water ratio and thickness of the tear film, which are associated with an increased risk of dry eye syndrome, directly impact the absorption of mmW and THz waves by the cornea. This insight provides valuable evidence supporting the interconnection between tear film properties and the vulnerability of the cornea to RF radiation.

Sazgar IoT: A Device-Centric IoT Framework and Approximation Technique for Efficient and Scalable IoT Data Processing

The Internet of Things (IoT) plays a fundamental role in monitoring applications; however, existing approaches relying on cloud and edge-based IoT data analysis encounter issues such as network delays and high costs, which can adversely impact time-sensitive applications. To address these challenges, this paper proposes an IoT framework called Sazgar IoT. Unlike existing solutions, Sazgar IoT leverages only IoT devices and IoT data analysis approximation techniques to meet the time-bounds of time-sensitive IoT applications. In this framework, the computing resources onboard the IoT devices are utilised to process the data analysis tasks of each time-sensitive IoT application. This eliminates the network delays associated with transferring large volumes of high-velocity IoT data to cloud or edge computers. To ensure that each task meets its application-specific time-bound and accuracy requirements, we employ approximation techniques for the data analysis tasks of time-sensitive IoT applications. These techniques take into account the available computing resources and optimise the processing accordingly. To evaluate the effectiveness of Sazgar IoT, experimental validation has been conducted. The results demonstrate that the framework successfully meets the time-bound and accuracy requirements of the COVID-19 citizen compliance monitoring application by effectively utilising the available IoT devices. The experimental validation further confirms that Sazgar IoT is an efficient and scalable solution for IoT data processing, addressing existing network delay issues for time-sensitive applications and significantly reducing the cost related to cloud and edge computing devices procurement, deployment, and maintenance.

The thoracoacromial artery as the lifeboat in recipient artery deficiency in complex chest wall defect reconstruction

INTRODUCTION AND IMPORTANCE

Reconstruction of chest wall defects is a complex procedure requiring an accurate understanding of the complete anatomy of the chest wall to deal with challenging defects. This report investigates the use of the thoracoacromial artery and cephalic vein as recipient vessels in a musculocutaneous latissimus dorsi free flap to cover the large chest wall defect resulting from post-radiation necrosis for breast cancer.

CASE PRESENTATION

A 25-year-old woman with established necrotic osteochondritis of the left side ribs following radiotherapy in breast cancer management was admitted for reconstructing the violated chest wall. The contralateral latissimus dorsi muscle was selected as an alternative to the previously used ipsilateral muscle. The thoracoacromial artery was the only one available as a recipient artery with a successful outcome.

CLINICAL DISCUSSION

Breast cancer is the most common indication for radiotherapy. Osteoradionecrosis can present months to years after radiation with deep ulcers and major bone destruction with soft tissue necrosis. Large defect reconstruction is sometimes challenging due to lack of recipient artery and vein because of previous unsuccessful interventions. Thoracoacromial artery and its branches can be recommended as a good alternative recipient artery.

CONCLUSION

The Thoracoacromial artery may aid surgeons in achieving successful anastomoses in difficult thoracic defects.

Cyber-Physical-Social Awareness Platform for Comprehensive Situation Awareness

Cyber-physical-social computing system integrates the interactions between cyber, physical, and social spaces by fusing information from these spaces. The result of this fusion can be used to drive many applications in areas such as intelligent transportation, smart cities, and healthcare. Situation Awareness was initially used in military services to provide knowledge of what is happening in a combat zone but has been used in many other areas such as disaster mitigation. Various applications have been developed to provide situation awareness using either IoT sensors or social media information spaces and, more recently, using both IoT sensors and social media information spaces. The information from these spaces is heterogeneous and, at their intersection, is sparse. In this paper, we propose a highly scalable, novel Cyber-physical-social Awareness (CPSA) platform that provides situation awareness by using and intersecting information from both IoT sensors and social media. By combining and fusing information from both social media and IoT sensors, the CPSA platform provides more comprehensive and accurate situation awareness than any other existing solutions that rely only on data from social media and IoT sensors. The CPSA platform achieves that by semantically describing and integrating the information extracted from sensors and social media spaces and intersects this information for enriching situation awareness. The CPSA platform uses user-provided situation models to refine and intersect cyber, physical, and social information. The CPSA platform analyses social media and IoT data using pretrained machine learning models deployed in the cloud, and provides coordination between information sources and fault tolerance. The paper describes the implementation and evaluation of the CPSA platform. The evaluation of the CPSA platform is measured in terms of capabilities such as the ability to semantically describe and integrate heterogenous information, fault tolerance, and time constraints such as processing time and throughput when performing real-world experiments. The evaluation shows that the CPSA platform can reliably process and intersect with large volumes of IoT sensor and social media data to provide enhanced situation awareness.

The Effects of Zataria multiflora Cream on Split-Thickness Skin Graft Donor-Site Management: A Randomized, Blinded, Placebo-Controlled Study

Purpose: The wound healing process involves a complex series of biological events. Skin grafts have several uses as a reconstructive method. There are several dressings for the skin graft donor site, but the optimum dressing agents that provide all the requirements at the same time are unclear. This prospective, randomized, placebo-controlled clinical trial aimed to evaluate the therapeutic effect of Zataria multiflora cream in the wound healing process of partial-thickness skin graft donor sites and compared it with a placebo. Materials and Methods: This clinical trial study was performed on patients who underwent split-thickness skin grafts. Enrolled patients applied Z. multiflora cream and placebo controlled (petrolatum ointment) twice a day, from the day of intervention at the skin graft donor sites in two parts, separately. On 7, 14, 21, and 28 days after surgery, the wound healing process was evaluated, photographed, and scored according to the Bates-Jensen assessment tool. Evidence of infection was evaluated. The main agent and placebo were compared during the wound healing process. Results: Decreases in wound surface area and total score were significantly greater in the Z. multiflora group (p < 0.05). The wounds of 30% of patients in the second week and 90% of patients in the third week were completely epithelialized in the Z. multiflora group. These values were 3.3% and 36.7% for the control group, respectively, and so, the healing rate was ∼9-fold in the second week and 2.45-fold in the third week in the Z. multiflora group compared with the control group (p < 0.05). Conclusion: Wound healing and reepithelialization accelerated significantly in the first, second, third, and fourth week after intervention in the Z. multiflora treatment group, due to modulating the inflammatory phase and improving the proliferative phase. Clinical Trial Registration Number: IRCT20210624051695N1.

Investigating the Impact of Synchrotron THz Radiation on the Corneal Hydration Using Synchrotron THz-Far Infrared Beamline

Due to increasing interest in imaging, industrial, and the development of wireless communication operating at THz frequencies, it is crucial to ascertain possible health impacts arising from exposure to THz radiation. This paper reports on the pilot study of transmittance and absorbance spectra of the porcine cornea following THz frequency irradiation at a synchrotron THz/Far-IR beamline. The exposure period was 4 hours. One cornea was exposed to the radiation, with a second cornea acting as a control. An Attenuated Total Reflection (ATR) apparatus was used, and the frequency range of 2.4 to 8 THz was selected to evaluate any changes. It was found that the synchrotron THz radiation intensities are too low to produce induced corneal injury, but may lead to subtle changes in the state of water. Our results suggest that THz spectroscopy is a promising modality for corneal tissue hydration sensing.

Improving Situation Awareness via a Situation Model-Based Intersection of IoT Sensor and Social Media Information Spaces

Existing techniques for distilling situation awareness currently focus on information harvested from either IoT sensors or social media. While the benefits of fusing information from these two distinct information spaces for achieving enhanced situation awareness are well understood, existing techniques and related systems for fusing the IoT sensors and social media information spaces are currently embryonic. Key challenges in intersecting, combining, and fusing these information spaces to distil high-value situation awareness include devising situation models and related techniques for filtering, integrating, and fusing sparse and heterogeneous IoT sensor data and social media postings to provide a richer and more accurate situation awareness. This paper proposes novel, semantically based techniques fusing social media and IoT sensor information spaces and a comprehensive, fully implemented system that utilizes these to provide enhanced situation awareness. More specifically, this paper proposes the design of semantic-based situation models for fusing sensor and social media information spaces and presents techniques for finding similarities across these information spaces and fusing social media posting and IoT sensor data to generate an enhanced situation awareness. Furthermore, the paper presents the design and implementation of a complete system that uses the proposed models and techniques and uses that in an experimental evaluation that illustrates improvements in situation awareness from fusing the IoT sensor and social media information spaces.

A phase 2a trial investigating ninerafaxstat – a novel cardiac mitotrope for the treatment of diabetic cardiomyopathy (IMPROVE-DiCE)

Background

Type 2 diabetes (T2D) is a significant, independent contributor to the development of heart failure (HF), driven by energetic, metabolic, structural and functional myocardial changes. The T2D heart is characterised by over-reliance on fatty acid utilisation, shows reduced glucose oxidation and inhibition of pyruvate dehydrogenase (PDH). This results in a diminished myocardial energy reserve and blunted adenosine triphosphate (ATP) generation as well as cardiac steatosis, contributing to lipotoxicity, and diastolic dysfunction.

Purpose

We assessed the effects of ninerafaxstat – a novel cardiac mitotrope designed to shift myocardial substrate utilisation in favour of glucose and thus, restore myocardial energy homeostasis – on cardiac metabolism & diastolic function in patients with T2D and obesity.

Methods

In this open-label, mechanistic phase 2a trial, we enrolled 21 patients with T2D & obesity (HbA1c median 7.0% (IQR 6.6, 7.8), weight 97kg (90, 102)) and subsequently treated them with 200mg ninerafaxstat twice daily for 4 or 8 weeks; (Fig. 1). Cardiac metabolism and function were assessed pre- & post-treatment using magnetic resonance imaging (MRI), 31P-, 1H- and, in a subset of n=9, hyperpolarized [1-13C]pyruvate MR spectroscopy.

Results

T2D patients at baseline presented with impaired myocardial energetics with a markedly reduced PCr/ATP (1.6 [1.4, 2.1]), myocardial steatosis (myocardial triglycerides 2.2% [1.5, 3.2]) left ventricular (LV) hypertrophy (LV mass 130g [98, 152]), and diastolic dysfunction (peak diastolic strain rate 0.86 1/s [0.82, 1.06]). Ninerafaxstat significantly improved myocardial energetics (PCr/ATP median by 32%, p&lt;0.01), reduced myocardial triglyceride content (by 34%, p=0.03) and improved LV diastolic function (peak circumferential diastolic strain rate by 10%, peak LV filling rate by 11%, both p&lt;0.05) (Fig. 2). PDH flux was increased in 7/9 subjects (mean 45%, p=0.08), consistent with improved glucose utilisation. Left ventricular volumes and mass, heart rate and blood pressure remained unchanged.

Conclusions

Treatment with ninerafaxstat significantly improves myocardial energetics, reduces myocardial steatosis and improves diastolic function in patients with T2D and obesity.

Funding Acknowledgement

Type of funding sources: Private company. Main funding source(s): Imbria Pharmaaceuticals

ParcEMon: IoT Platform for Real-Time Parcel Level Last-Mile Delivery Greenhouse Gas Emissions Reporting and Management

Transport is Australia's third-largest source of greenhouse gases accounting for around 17% of emissions. In recent times, and particularly as a result of the global pandemic, the rapid growth within the e-commerce sector has contributed to last-mile delivery becoming one of the main emission sources. Delivery vehicles operating at the last-mile travel long routes to deliver to customers an array of consignment parcels in varying numbers and weights, and therefore these vehicles play a major role in increasing emissions and air pollutants. The work reported in this paper aims to address these challenges by developing an IoT platform to measure and report on real-world last-mile delivery emissions. Such evaluations help to understand the factors contributing to freight emissions so that appropriate mitigation measures are implemented. Unlike previous research that was completed in controlled laboratory settings, the data collected in this research were from a delivery vehicle under real-world traffic and driving conditions. The IoT platform was tested to provide contextualised reporting by taking into account three main contexts including vehicle, environment and driving behaviours. This approach to data collection enabled the analysis of parcel level emissions and correlation of the vehicle characteristics, road conditions, ambient temperature and other environmental factors and driving behaviour that have an impact on emissions. The raw data collected from the sensors were analysed in real-time in the IoT platform, and the results showed a trade-off between parcel weight and total distance travelled which must be considered when selecting the best delivery order for reducing emissions. Overall, the study demonstrated the feasibility of the IoT platform in collecting the desired levels of data and providing detailed analysis of emissions at the parcel level. This type of micro-level understanding provides an important knowledge base for the enhancement of delivery processes and reduction of last-mile delivery emissions.

The Impact of Base Cell Size Setup on the Finite Difference Time Domain Computational Simulation of Human Cornea Exposed to Millimeter Wave Radiation at Frequencies above 30 GHz

Mobile communication has achieved enormous technology innovations over many generations of progression. New cellular technology, including 5G cellular systems, is being deployed and making use of higher frequencies, including the Millimetre Wave (MMW) range (30-300 GHz) of the electromagnetic spectrum. Numerical computational techniques such as the Finite Difference Time Domain (FDTD) method have been used extensively as an effective approach for assessing electromagnetic fields' biological impacts. This study demonstrates the variation of the accuracy of the FDTD computational simulation system when different meshing sizes are used, by using the interaction of the critically sensitive human cornea with EM in the 30 to 100 GHz range. Different approaches of base cell size specifications were compared. The accuracy of the computation is determined by applying planar sensors showing the detail of electric field distribution as well as the absolute values of electric field collected by point sensors. It was found that manually defining the base cell sizes reduces the model size as well as the computation time. However, the accuracy of the computation decreases in an unpredictable way. The results indicated that using a cloud computing capacity plays a crucial role in minimizing the computation time.

Central diabetes insipidus secondary to COVID-19 infection: a case report

BACKGROUND

Novel coronavirus disease 2019 (COVID-19) mainly affects the lungs, but can involve several other organs. The diagnosis of acute and chronic sequelae is one of the challenges of COVID-19. The current literature proposes that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may involve the hypothalamic-pituitary axis. In this case report, we present a unique case of new-onset central diabetes insipidus secondary to the COVID-19 disease in a 54-year-old woman.

CASE PRESENTATION

A 54-year-old woman presented with the history of excessive thirst, polyuria, and polydipsia, six weeks after being infected by COVID-19. Laboratory tests revealed low urine osmolarity and increased serum osmolarity, and the patient was diagnosed with central diabetes insipidus. After administration of nasal desmopressin, urinary osmolarity increased, and the patient's symptoms improved. However, to stabilize her condition, desmopressin treatment was required.

CONCLUSIONS

We reported a unique case of diabetes insipidus in a COVID-19 patient. Central diabetes insipidus may be included in clinical manifestations of the COVID-19, in case of new-onset polyuria and polydipsia following COVID-19 disease. Nevertheless, a causal relationship has not been established between the symptoms of the patient and the SARS-CoV-2 infection.

Design, Synthesis, In Vitro, and In silico Evaluation of N-phenylacetamide-oxindole-thiosemicarbazide hybrids as New Potential Tyrosinase Inhibitors

A novel series of N-phenylacetamide-oxindole-thiosemicarbazide hybrids were synthesized and evaluated for their tyrosinase inhibitory activity. According to tyrosinase inhibition results, all the synthesized compounds showed high tyrosinase inhibitory activity with IC50 values ranging from 0.8 to 3.88 µM in comparison to positive control kojic acid with IC50 value of 36.32 µM. Among tested compounds, analog 7o, containing the 2-methyl-4-nitrophenyl on N-phenylacetamide moiety displayed superior tyrosinase inhibition. This compound was around 45-fold more potent than kojic acid. The kinetic analysis of compound 7o demonstrated that this compound is a competitive inhibitor against tyrosinase. Docking study of this compound demonstrated that compound 7o interacted with critical histidine residues within tyrosinase active site.

Design, synthesis, and α-glucosidase-inhibitory activity of phenoxy-biscoumarin-N-phenylacetamide hybrids

Thirteen new phenoxy-biscoumarin-N-phenylacetamide derivatives (7a-m) were designed based on a molecular hybridization approach as new α-glucosidase inhibitors. These compounds were synthesized with high yields and evaluated in vitro for their inhibitory activity against yeast α-glucosidase. The obtained results revealed that a significant proportion of the synthesized compounds showed considerable α-glucosidase-inhibitory activity in comparison to acarbose as a positive control. Representatively, 2-(4-(bis(4-hydroxy-2-oxo-2H-chromen-3-yl)methyl)phenoxy)-N-(4-bromophenyl)acetamide (7f), with IC₅₀  = 41.73 ± 0.38 µM against α-glucosidase, was around 18 times more potent than acarbose (IC₅₀  = 750.0 ± 10.0 µM). This compound was a competitive α-glucosidase inhibitor. Molecular modeling and dynamic simulation of these compounds confirmed the obtained results through in vitro experiments. Prediction of the druglikeness/ADME/toxicity of the compound 7f and comparison with the standard drug acarbose showed that the new compound 7f was probably better than the standard drug in terms of toxicity.

Profile-based assessment of diseases affective factors using fuzzy association rule mining approach: A case study in heart diseases

The existing data mining solutions to identify risk factors associated with diseases are burdened with quite a few shortcomings. They usually use crisp partitions for numerical features and also do not use patient-specific profiles. These shortcomings create limitations for solving real problems. Discretizing a numerical feature through crisp partitions can also generate substantial partitioning errors, particularly for features whose values are closer to crisp boundaries. Since the normal range of each numerical feature varies according to the age, gender, and medical conditions of the patients, then ignoring these differences can undermine the accuracy of the extracted itemsets and rules. This paper presents a profile-based fuzzy association rule mining (PB-FARM) approach for the assessment of risk factors highly correlated with diseases. The proposed approach has three phases. Phase I involves creating profiles for patients based on their age, gender, and medical conditions, to determine a normal range of each numerical feature. Then fuzzy partitioning is done for all features (namely, numerical and categorical), and consequently, a structure, called FirstScan, is created. In Phase II, the FirstScan structure is utilized to mine for large fuzzy k-itemsets. Ultimately, in Phase III, the given k-itemsets are employed to generate fuzzy rules for associations between risk factors and diseases. To evaluate the performance of the proposed method the Z-Alizadeh Sani coronary artery disease (CAD) dataset, containing 303 records and 54 features, was used. The results show a positive correlation between typical chest pain and old age with the incidence of CAD. The comparisons made in this study showed that, firstly, the proposed algorithm has a higher partitioning accuracy than other methods, and secondly, it has a reasonably short execution time.

Internet of Things Platform for Smart Farming: Experiences and Lessons Learnt

Improving farm productivity is essential for increasing farm profitability and meeting the rapidly growing demand for food that is fuelled by rapid population growth across the world. Farm productivity can be increased by understanding and forecasting crop performance in a variety of environmental conditions. Crop recommendation is currently based on data collected in field-based agricultural studies that capture crop performance under a variety of conditions (e.g., soil quality and environmental conditions). However, crop performance data collection is currently slow, as such crop studies are often undertaken in remote and distributed locations, and such data are typically collected manually. Furthermore, the quality of manually collected crop performance data is very low, because it does not take into account earlier conditions that have not been observed by the human operators but is essential to filter out collected data that will lead to invalid conclusions (e.g., solar radiation readings in the afternoon after even a short rain or overcast in the morning are invalid, and should not be used in assessing crop performance). Emerging Internet of Things (IoT) technologies, such as IoT devices (e.g., wireless sensor networks, network-connected weather stations, cameras, and smart phones) can be used to collate vast amount of environmental and crop performance data, ranging from time series data from sensors, to spatial data from cameras, to human observations collected and recorded via mobile smart phone applications. Such data can then be analysed to filter out invalid data and compute personalised crop recommendations for any specific farm. In this paper, we present the design of SmartFarmNet, an IoT-based platform that can automate the collection of environmental, soil, fertilisation, and irrigation data; automatically correlate such data and filter-out invalid data from the perspective of assessing crop performance; and compute crop forecasts and personalised crop recommendations for any particular farm. SmartFarmNet can integrate virtually any IoT device, including commercially available sensors, cameras, weather stations, etc., and store their data in the cloud for performance analysis and recommendations. An evaluation of the SmartFarmNet platform and our experiences and lessons learnt in developing this system concludes the paper. SmartFarmNet is the first and currently largest system in the world (in terms of the number of sensors attached, crops assessed, and users it supports) that provides crop performance analysis and recommendations.

Effect of a long-term regular physical activity on hypertension and body mass index in type 2 diabetes patients

AIM

Hypertension and overweight are prevalent problems in type 2 diabetic patients. The purpose of this study was to assess the effects of long term regular aerobic exercise training on blood pressure (BP) as well as body mass index (BMI) in type 2 diabetes mellitus (T2DM).

METHODS

In a randomized, controlled trial, 60 patients with T2DM aged 40-65 years were randomly divided in two groups (30 subjects in exercise group and 30 subjects in control group). Supervised exercise program was performed three times per week for 3 years. Finally, 28 patients who completed the optimal sessions, entered in statistical analysis.

RESULTS

Systolic and diastolic blood pressure (SBP, DBP) had no significant changes in control group (respectively 127.48±12.79 to 129.28±8.52 and 78.90±8.06 to 77.55±5.95). These changes were statistically significant in the exercise group (respectively 132.57±14.82 to 118.00±12.48 and 82.89±9.19 to 74.00±6.57; P<0.001). Furthermore, the BMI of exercise group reduced significantly (28±5.26 to 26.42±4.36; P<0.001) in comparison with control group that showed a significant increase in terms of this variable (30.02±4.79 to 31.05±4.84; P<0.05).

CONCLUSION

The present study suggests that long term aerobic exercise has positive effects on systolic and diastolic blood pressure as well as heart rate of T2DM patients. We observed significant differences in levels of BMI in two groups after intervention.

The effect of aerobic exercise on glycosylated hemoglobin values in type 2 diabetes patients

AIM

The changes of glycosylated hemoglobin A1c values have been considered as an important marker of glucose control over time. On the other hand, benefit of exercise in the control of glycemia is well known. The purpose of the present study was to investigate the influence of aerobic exercise program on A1c values and glycemic control in type 2 diabetic patients.

METHODS

In this randomized, controlled trial, 65 participants aged 40 to 65 years with type 2 diabetes were randomly divided into 2 groups (30 controls and 35 exercises). The exercise training group performed aerobic training for 16 weeks (3 days/week, 90 min, 50-80%VO2max) and the control group did not perform any exercise. Finally, 60 subjects completed the program, and their results were analyzed to assess the A1c changes.

RESULTS

After a 16-week aerobic exercise program the mean A1c value significantly reduced in the exercise training group in comparison with the control group (-0.73±1.4% vs. +0.28±0.60%, P<0.001). No serious adverse event was observed in the training sessions. As secondary outcomes, changes of baseline parameters (i.e. fasting blood glucose, body mass index, and blood pressure) were statistically significant. The reduction of blood pressure in control group was also significant.

CONCLUSION

In patients with type 2 diabetes, significant improvement in A1c value and better glycemic control could be achieved by a regular exercise program as an intervention.