airflow

Elimination of inflammation and re-osseointegration are the major objectives of peri-implantitis therapy. Existing data, however, do not support any decontamination approach. Thus, the present in vitro study aims to assess whether the air-debriding decontamination method with erythritol powder restores the biocompatibility of infected titanium discs and to investigate the potent biomodulatory ability of diode laser (810 nm) irradiation to promote cell proliferation and differentiation of premature osteoblast-like cells (MG63) towards osteocytes.

The experimental groups consisted of cells seeded on titanium discs exposed or not in a peri-implantitis environment with or without biomodulation. Infected discs were cleaned with airflow with erythritol powder. Cell cultures seeded on tricalcium phosphate (TCP) surfaces with or without biomodulation with a laser (810 nm) were used as controls. The study evaluated cell viability, proliferation, adhesion (SEM) at 24, 48 and 72 hours, and surface roughness changes (profilometry), as well as the effects of low-level laser therapy (LLLT) on ALP, OSC, TGF-b1, Runx-2, and BMP-7 expression in MG63 cells’ genetic profile on days 7, 14, and 21.

The MTT assay as well as the FDA/PI method revealed that cell proliferation did not show significant differences between sterile and decontaminated discs at any timepoint. SEM photographs on day 7 showed that osteoblast-like cells adhered to both sterile and disinfected surfaces, while surface roughness did not change based on amplitude parameters. The combination of airflow and LLLT revealed a biomodulated effect on the differentiation of osteoblast-like cells with regard to the impact of laser irradiation on the genetic profile of the MG63 cells.

In all groups tested, osteoblast-like cells were able to colonize, proliferate, and differentiate, suggesting a restoration of biocompatibility of infected discs using airflow. Furthermore, photomodulation may promote the differentiation of osteoblast-like cells cultured on both sterile and disinfected titanium surfaces.

Sleep apnea syndrome can be considered as one of the most serious risk factors of sleep disorder. Due to the lack of information about this disease, many causes of unexpected deaths have been identified. With increasing the number of patients with this disease around the world, many patients suffer apnea complications. Most of them are not treated because of the complex and costly and time - c onsuming polysomnography (PSG) diagnostic procedure.

This descriptive - analytical study was performed on 50 patients referred to sleep clinic of Imam Khomeini Hospital in Tehran, Attempts to design, and develop a system for detection of sleep apnea and its severity using ECG signals, RR intervals and airflow. The random forest algorithm and MATLAB2016 were used in the design of the system that the algorithm inputs are extracted 8 features nonlinear in time - frequency domain from airflow and ECG signals and 10 nonlinear features of RR intervals.

The accuracy for normal, obstructive, central and mixed apnea was obtained at 95.3%, 97.92%, 99.60%, and 97.29%, respectively, and the accuracy For detection of normal, mild, moderate and severe apnea was obtained 96%, 94%, 94%, 96% respectively. According to the results, the proposed system can correctly classify the types of sleep apnea and its severity.

The proposed system, which has high performance capability in addition t o increasing the physician speed and accuracy in the diagnosis of apnea can be used in home systems and the areas where healthcare facilities are not sufficient.

Among sleep-related disorders, Sleep apneahas been under more attention and it’s the most common respiratory disorder in which respirationceases frequently which can lead to serious health disorders and even mortality. Polysomnography is the standard method for diagnosing this disease at the moment which is costly and time-consuming. The present study aimed at analyzing vital signals to diagnose Sleep apneausing machine learning algorithms.

This analytical–descriptive was conducted on 50 patients (11 normal, 13 mild, 17 moderate and 9 severe patients) in the sleep clinic of Imam Khomeini hospital. Initially, data pre-processing was carried out in two steps(noise elimination and moving average algorithm). Next, using thesingular value decompositionmethod, 12 features were extracted for airflow. Finally, to classify data, SVM with quadratic, polynomialand RBF kernels were trained and tested.

After applying different kernel functions on SVM, the RBF kernel showed the most efficient performance.After 10 fold cross validation method for evaluation, the mean accuracy obtained for normal, apnea, and hypopnea modes were 92.74%, 91.70%, 93.26%.

The results show that in online applications or applications where the volume and time of calculations and at the same time the accuracy of the result is very important, The disease can be diagnosed with acceptable accuracy using machine learning algorithms.