seyedeh zohreh hosseini

Myofascial pain syndrome is a common musculoskeletal injury, especially among athletes, typically treated with symptomatic invasive and non-invasive methods. This study compares the effects of massage guns and radial extracorporeal shock wave therapies on myofascial pain syndrome symptoms in amateur athletes.

In this clinical trial, 45 amateur athletes (18-30 years old) were recruited, who were randomly assigned to 15-member groups of shock wave, massage gun, and control (routine treatment included electrotherapy and stretching, which was applied on three groups). After initial evaluations of the pain intensity, pain pressure threshold (PPT), isometric muscle strength, and range of motion (ROM), the patients received single-session treatment and were immediately reassessed.

The results showed pain relief and improved PPT following shock wave plus routine treatment (P=0.03). The control group had less pain, while pain intensity and PPT did not change. The variables were not significantly different between the groups (P=0.12). Shock wave along with routine treatment increased plantar flexion ROM (P=0.00), unlike the massage gun. Additionally, dorsiflexion ROM (P=0.63) and maximal isometric gastrocnemius muscle strength (P=0.95) remained unchanged in all groups.

One session of massage gun therapy immediately reduced gastrocnemius muscle pain, while it failed to change PPT, maximal isometric gastrocnemius muscle strength, or dorsiflexion and plantar flexion ROM. However, shock wave therapy immediately increased plantar flexion ROM and PPT, and reduced pain intensity. These modalities led to limited changes, suggesting the need for repeated sessions and supplementary treatments.

This study aimed to develop IoT-based machine learning algorithms care and improvement while detecting and predicting real-time epidemics.The target disease is COVID-19 due to its importance and epidemic.The research method is based on design science. The research approach is forward-looking, so the mechanism of disease transmission and its effective characteristics enable us to make predictions about the disease and thus design disease control strategies and health care.The research was carried out in a seven-step process. IoT features were extracted in the present study with experts' opinions. The features obtained in the experiment of two different algorithms, "k nearest neighbor" and "decision tree," were created on the data to determine the best model.After selecting the best depth validation of the model were performed by confusion matrix analysis.The results of running k-nearest neighborhood and Decision Tree algorithms for the prediction of COVID-19 indicated an accuracy of > 98%. Higher sensitivity (99%) was obtained in the Decision Tree algorithm, which is very important diagnosing COVID-19 and indicates the minimum number of false negatives in the test results.

The development of information technology and its use in the health system has taken many measures to protect and promote human health, however, the world still faces long-term threats and recurrence of infectious diseases. Understanding the dynamics of infectious diseases is important in controlling the disease because the network and the mode of impact of infectious diseases are very complex. The management of infectious diseases can also be considered as a complex social system due to the fact that has many complexities (such as dimensions, parameters, interactions, behaviors and rules), for this reason, the approach of the present study is a multifaceted understanding of the spread of infectious diseases. To design the present model, an intelligent system with a combination of mathematical, machine learning and epidemiological dimensions is proposed. The disease studied in this study, due to its importance and prevalence, is Covid 19. In this study, with the approach of complex systems and using the Internet of Things and machine learning methods, an algorithm was presented that uses environmental and individual variables to predict the probability of disease in an individual. Therefore, this research can improve the prevention of infectious diseases by filling some of the gaps in 3 sections: 1- Re-emergence of infectious diseases and the potential of IoT and AI, 2- Speed of dissemination and importance of real-time tracking, and 3- Budget and cost. The evaluation of the algorithm in this study was determined by two criteria of sensitivity and specificity. The results of the proposed algorithm for predicting Covid 19 disease showed an accuracy of more than 98%. Sensitivity above 98% was also obtained. Which is very important for the diagnosis of Covid disease 19 and shows the low number of false negatives in the test results. Therefore, the proposed model, combined with the Internet of Things and machine learning, can cause early diagnosis and prevent the spread of the Covid-19 disease with high specificity and sensitivity.

The development of information technology and its use in the health system have undertaken numerous measures for the protection and promotion of human health. However, long-term threats and the recurrence of infectious diseases are still the challenges in the world. Management of infectious diseases is of paramount importance in the global health system and is very complicated. Therefore, the aim of this research is designing a model to predict the covid-19 using artificial intelligence, which will ultimately be useful for rapid detection and control of Covid-19.

To design a proposed model to analyze the spread of infectious diseases, an intelligent system is presented by combining mathematical dimensions, machine learning, and epidemiology. In this research, the target disease is COVID-19 due to its importance and epidemic. Despite extensive research on this disease, each study focused on a specific area due to its extensive dimension, and no detailed study is available on the main aspects of the disease. The research method is interpretive and exploratory in terms of paradigm and strategy, respectively, and is classified as descriptive-prescriptive in terms of implementation. The research was carried out in a seven-step process.

The results of the systematic review showed the rapid growth of documents since 2015, which can indicate the application of various fields of information technology such as the Internet of Things and machine learning in the field of public health and prevention of infectious diseases.In the algorithm, K values from 1 to 20 neighborhoods were calculated and the best accuracy was obtained at K equal to 2. Therefore, the accuracy of the algorithm is higher than 98% to predict the disease of Covid-19. After calculating the accuracy, the analysis of the confusion matrix showed that in K equal to 2, the sensitivity is 99% and the specificity is 92%.

A comparison of algorithm results shows that in addition to accuracy, sensitivity and specificity obtained for algorithm are higher than traditional methods of diagnosing infectious diseases. Also, due to not having unnecessary complex features that only increase the model implementation time, the algorithm runs in a few minutes, and therefore the detection speed is completely acceptable. High sensitivity (99%) was obtained in the DT algorithm, which is very important for the diagnosis of COVID-19 and indicates the minimum number of false negatives in the test results. Therefore, the proposed algorithm is practical for the purpose of identifying the maximum number of people infected with Covid-19.

Cutaneous leishmaniasis is an ancient endemic disease in Iran and continues to be a growing health threat to community development and the environment. This paper explains how to use the facilities of health centers for developing a laboratory network on vectors and reservoir hosts of cutaneous leishmaniasis in Iran.

A literature search was performed of the relevant multiple databases to include studies on vectors and reservoirs of cutaneous leishmaniasis in Iran. A team of experienced experts was performed. After holding several meetings, field visits and organizing workshops, the activities of laboratories were determined at three levels.

Entomological studies showed that 5 species of the genus Phlebotomus and 10 species of the genus Sergentomyia are active in the south, 4 species of the genus Phlebotomus and one species of the genus Sergentomyia in the central part and 5 species of the genus Phlebotomus and 2 species of the genus Sergentomyia in the north east. Reservoir hosts were identified in the study areas. The tasks of laboratories were regulated at different levels.

The Iranian Ministry of Health and Medical Education should prioritize the employment of capable persons in the field of Medical Entomology and Vector Control. The survival of this laboratory network depends on hiring and employing interested and persistent people. The universities of Medical Sciences that have the facilities to set up this network will be a very effective partner in the control of the disease in high risk areas. The results can be used in neighboring countries.

This study aimed to compare the effects of Low-Level Laser Therapy (LLLT) combined with Dry Needling (DN) with DN alone on pain and neck disability index following myofascial pain syndrome.

Sixteen women with active Trigger Points (TrPs) in their upper trapezius muscles participated in this study. They were divided into two groups: Experimental and control. The experimental group received one session of the DN plus the LLLT with 6 j/cm2 energy at their TrPs. The patients in the control group were under a similar procedure, but they did not receive any energy by the LLLT (placebo). The pain score was assessed before, immediately, and 48 hours after the treatment. Neck Disability Index (NDI) was assessed before and 48 hours after the treatment.

There was a significant improvement in pain intensity and NDI scores 48 hours after the treatment in both groups compared with the baseline scores (P<0.05). The pain was also significantly reduced at the patients following laser therapy immediately after the treatment (P=0.01).

A combination of the LLLT and DN might be more effective compared with using DN alone, and reduce immediate pain at the patients with the active TrPs. There was no difference between the groups 48 hours after the treatment. It seems that LLLT has no considerable effect on NDI and pain intensity 48 hours after the treatment.