The comparison of stress and strain between custom-designed bone plates (CDBP) and locking compression plate (LCP) for distal femur fracture.

BACKGROUND: Distal femur fracture is considered one of the most common fractures due to high-energy traumas such as car accidents or low-energy traumas such as osteoporosis. Locking plates are orthopedic implants used for stabilized femur fracture. Thus, designing a bone plate fitted exactly with the patient's bone and correctly fixing bone segments are required for better fracture healing. OBJECTIVES: This study aims to design a bone plate based on anthropometric characteristics of patients' femurs and compare performing custom-designed bone plates (CDBP) with the locking compression plate (LCP) by finite element method. MATERIALS AND METHODS: In this analytical study, a 3D model of four patients' femur and CDBP were firstly designed in MIMICS 19.0 based on the patient's femur anatomy. After designing the bone plate, the CDBPs and LCP were fixed on the bone and analyzed by finite element method (FEM) in ANSYS, and stress and strain of bone plates were also compared. RESULTS: The maximum principal stress for all 3D models of patients' fracture femur by CDBPs was stabilized better than LCP with a decrease by 39.79, 12.54, 9.49, and 20.29% in 4 models, respectively. Also, in all models, the strain of CDBPs is less than LCP. Among the different thicknesses considered, the bone plate with 5 mm thickness showed better stress and strain distribution than other thicknesses. CONCLUSION: Customized bone plate designed based on patient's femur anatomical morphology shows better bone-matching plate, resulting in increasing the quality of the fracture healing and fails to any need for additional shaping. TRIAL REGISTRATION NUMBER: Design and analysis of an implant were investigated in this study. There was no intervention in the diagnosis and treatment of patients and the study was not a clinical trial.

A Hardware-Software System for Accurate Segmentation of Phonocardiogram Signal.

Background: Phonocardiogram (PCG) signal provides valuable information for diagnosing heart diseases. However, its applications in quantitative analyses of heart function are limited because the interpretation of this signal is difficult. A key step in quantitative PCG is the identification of the first and second sounds (S1 and S2) in this signal. Objective: This study aims to develop a hardware-software system for synchronized acquisition of two signals electrocardiogram (ECG) and PCG and to segment the recorded PCG signal via the information provided in the acquired ECG signal. Material and Methods: In this analytical study, we developed a hardware-software system for real-time identification of the first and second heart sounds in the PCG signal. A portable device to capture synchronized ECG and PCG signals was developed. Wavelet de-noising technique was used to remove noise from the signal. Finally, by fusing the information provided by the ECG signal (R-peaks and T-end) into a hidden Markov model (HMM), the first and second heart sounds were identified in the PCG signal. Results: ECG and PCG signals from 15 healthy adults were acquired and analyzed using the developed system. The average accuracy of the system in correctly detecting the heart sounds was 95.6% for S1 and 93.4% for S2. Conclusion: The presented system is cost-effective, user-friendly, and accurate in identifying S1 and S2 in PCG signals. Therefore, it might be effective in quantitative PCG and diagnosing heart diseases.

Changes in Serum Alkaline Phosphatase, Calcium, and Parathyroid Hormone with Different Doses of Iodine Therapy:.

Background: Despite the benefits of radioactive iodine (RAI) therapy as an adjunctive treatment for thyroid cancer, it can be associated with several side effects. The main purpose of this study was to determine the changes in serum alkaline phosphatase (ALP), calcium (Ca), and parathyroid hormone (PTH) at different doses of RAI therapy among patients who were referred to the nuclear medicine department of Namazi Hospital, Shiraz. Materials and Methods: This cross-sectional study was conducted on 60 patients with papillary thyroid cancer who underwent RAI therapy at different doses of 100, 150, and 200 mCi. The ALP, Ca, and PTH levels of patients were measured before and 60 days after RAI therapy. Results: Our study revealed that RAI therapy at all doses significantly increased ALP level in comparison with baseline amounts (P≤0.05). However, changes in PTH and Ca levels were not significant among patients who received different doses of RAI (P˃0.05). Conclusion: RAI therapy could affect important hormones and enzymes such as ALP and PTH. This issue can be considered in diagnostic and therapeutic prescriptions of RAI for the treatment of thyroid cancer.[GMJ.2022;11:e2397].

Optimization and incorporating of green traffic for dynamic vehicle routing problem with perishable products.

In view of the significance of transportation management and logistics in the economic concept and raising the productivity of production systems, well-timed procurement of perishable materials and goods is determined as a pivotal prerequisite for economic and environmental development. Since the perishable goods produced must be made delivered to consumers as early as possible on account of the limited lifespan, thus, the vulnerability of these products is extremely high, owing to the high cost of transportation as well as the environmental impacts. So that solves this problem, this study represents a problem of dynamic green vehicle routing of perishable products in green traffic conditions that optimizes the total cost for a dynamic transportation network and minimizes environmental influences, and increases customer satisfaction. The introduced model is implemented in light of time windows as a trustworthy solution for monitoring the dynamic logistics process and attaining instantaneous information on the basis of the green traffic situation and travel duration, which is commonly known by the Logit function. Assuming the three-objective programming model, we consider a new improved algorithm developed for a novel augmented ε-constraint heuristic approach. Furthermore, robust optimization has been conducted for the established problem to tackle with uncertainties. Uncertainties are included demand and economic parameters. Eventually, to validate the proposed model, a case study was carried out at Kaleh Amol Dairy Company in Iran. The conclusions of sensitivity analysis by implementing the model in the real world indicate that the model and approach presented in various uncertainty scenarios have high flexibility.

Comparing performances of logistic regression and neural networks for predicting melatonin excretion patterns in the rat exposed to ELF magnetic fields.

Various studies have been reported on the bioeffects of magnetic field exposure; however, no consensus or guideline is available for experimental designs relating to exposure conditions as yet. In this study, logistic regression (LR) and artificial neural networks (ANNs) were used in order to analyze and predict the melatonin excretion patterns in the rat exposed to extremely low frequency magnetic fields (ELF-MF). Subsequently, on a database containing 33 experiments, performances of LR and ANNs were compared through resubstitution and jackknife tests. Predictor variables were more effective parameters and included frequency, polarization, exposure duration, and strength of magnetic fields. Also, five performance measures including accuracy, sensitivity, specificity, Matthew's Correlation Coefficient (MCC) and normalized percentage, better than random (S) were used to evaluate the performance of models. The LR as a conventional model obtained poor prediction performance. Nonetheless, LR distinguished the duration of magnetic fields as a statistically significant parameter. Also, horizontal polarization of magnetic fields with the highest logit coefficient (or parameter estimate) with negative sign was found to be the strongest indicator for experimental designs relating to exposure conditions. This means that each experiment with horizontal polarization of magnetic fields has a higher probability to result in "not changed melatonin level" pattern. On the other hand, ANNs, a more powerful model which has not been introduced in predicting melatonin excretion patterns in the rat exposed to ELF-MF, showed high performance measure values and higher reliability, especially obtaining 0.55 value of MCC through jackknife tests. Obtained results showed that such predictor models are promising and may play a useful role in defining guidelines for experimental designs relating to exposure conditions. In conclusion, analysis of the bioelectromagnetic data could result in finding a relationship between electromagnetic fields and different biological processes.

Evaluation of Short-Term Exposure to 2.4 GHz Radiofrequency Radiation Emitted from Wi-Fi Routers on the Antimicrobial Susceptibility of Pseudomonas aeruginosa and Staphylococcus aureus.

BACKGROUND: Overuse of antibiotics is a cause of bacterial resistance. It is known that electromagnetic waves emitted from electrical devices can cause changes in biological systems. This study aimed at evaluating the effects of short-term exposure to electromagnetic fields emitted from common Wi-Fi routers on changes in antibiotic sensitivity to opportunistic pathogenic bacteria. MATERIALS AND METHODS: Standard strains of bacteria were prepared in this study. Antibiotic susceptibility test, based on the Kirby-Bauer disk diffusion method, was carried out in Mueller-Hinton agar plates. Two different antibiotic susceptibility tests for Staphylococcus aureus and Pseudomonas aeruginosa were conducted after exposure to 2.4-GHz radiofrequency radiation. The control group was not exposed to radiation. RESULTS: Our findings revealed that by increasing the duration of exposure to electromagnetic waves at a frequency of 2.4 GHz, bacterial resistance increased against S. aureus and P. aeruginosa, especially after 24 hours (P<0.05). CONCLUSION: The use of electromagnetic waves with a frequency of 2.4 GHz can be a suitable method for infection control and treatment.

Investigation of the Dose-Enhancement Effects of Spherical and Rod-Shaped Gold Nanoparticles on the HeLa Cell Line.

BACKGROUND: Cervical cancer cells are known as radioresistant cells. Current treatment methods have not improved the patients' survival efficiently; thus, new therapeutic strategies are needed to enhance the efficacy of radiotherapy. Gold nanomaterials with different shapes and sizes have been explored as radiosensitizers. The present study compared the radiosensitizing effects of gold nanorods (AuNRs) with spherical gold nanoparticles (AuNPs) on the HeLa cell line irradiated with megavoltage X-rays. MATERIALS AND METHODS: The cytotoxicity of AuNRs and AuNPs on HeLa cells in the presence and absence of 6-MV X-ray was investigated using the MTT assay. For this aim, HeLa cells were incubated with and AuNPs and AuNRs at various concentrations (5, 10, and 15 µg/mL) for 6 hours. Afterward, HeLa cells were irradiated with 6-MV X-ray at a single dose of 2 Gy. RESULTS: The results showed that the addition of AuNRs and AuNPs could enhance the radiosensitivity of HeLa cells. Both AuNRs and AuNPs showed low toxicity on HeLa cells, while AuNRs were more toxic than AuNPs at the examined concentrations. Moreover, it was found that AuNRs could enhance the radiosensitivity of HeLa cells more than spherical-shaped AuNPs. CONCLUSION: This study revealed that the shape of nanoparticles is an effective factor when they are used as radiosensitizing agents during radiotherapy.

Investigating the mechanisms behind extensive death in human cancer cells following nanoparticle assisted photo-thermo-radiotherapy.

We have recently reported the synthesis and characterization of gold-coated iron oxide nanoparticle and demonstrated such a nanoparticle (Au@Fe2O3 NP) was able to significantly enhance the lethal effects of photo-thermo-radiotherapy. The purpose of this study was to determine the mechanisms behind such an enhancement by investigating the changes induced in cancer cell viability, proliferation, and morphology as well as monitoring the alteration of some genes which play important role in the process of cell death. Using MTT assay and transmission electron microscopy (TEM), the KB cells viability and morphology were assessed after treating with various combinations of NPs, photothermal therapy (PTT), and radiotherapy (RT). Clonogenic assay was used to assess the proliferation ability of treated KB cells. Nanoparticle internalization into the cells was investigated by TEM and inductively coupled plasma (ICP). During the treatment procedures, temperature changes were monitored using an IR-camera. Furthermore, the changes occurred in Bax, BCL2 and HSP70 genes expression level were measured using real-time PCR. The results showed that combination of NP, PTT, and RT caused more cell death compared to PTT or RT alone. Following such a combination therapy, massive cell injury was detected. We also observed an extensive increase in Bax/Bcl2 ratio and HSP70 expression for the KB cells treated by combination therapy procedure. Our results showed that massive cell injury and apoptosis induction are the main reasons of extensive cell death observed in cancer cells when a nanoparticle assisted photo-thermo-radiotherapy procedure is applied.

An artificial intelligence-based clinical decision support system for large kidney stone treatment.

A decision support system (DSS) was developed to predict postoperative outcome of a kidney stone treatment procedure, particularly percutaneous nephrolithotomy (PCNL). The system can serve as a promising tool to provide counseling before an operation. The overall procedure includes data collection and prediction model development. Pre/postoperative variables of 254 patients were collected. For feature vector, we used 26 variables from three categories including patient history variables, kidney stone parameters, and laboratory data. The prediction model was developed using machine learning techniques, which includes dimensionality reduction and supervised classification. A novel method based on the combination of sequential forward selection and Fisher's discriminant analysis was developed to reduce the dimensionality of the feature space and to improve the performance of the system. Multiple classifier scheme was used for prediction. The derived DSS was evaluated by running leave-one-patient-out cross-validation approach on the dataset. The system provided favorable accuracy (94.8%) in predicting the outcome of a treatment procedure. The system also correctly estimated 85.2% of the cases that required stent placement after the removal of a stone. In predicting whether the patient might require a blood transfusion during the surgery or not, the system predicted 95.0% of the cases correctly. The results are promising and show that the developed DSS could be used in assisting urologists to provide counseling, predict a surgical outcome, and ultimately choose an appropriate surgical treatment for removing kidney stones.

Elucidating the protein cold-adaptation: Investigation of the parameters enhancing protein psychrophilicity.

To investigate the role of the critical parameters in adaptation of proteins to low temperatures, a comparative systematic analysis was performed. Several parameters were proposed to have contribution to cold adaptation of proteins. Among proposed parameters, total values of residual structure states, secondary structure states and oligomeric states were alike in both psychrophilic and mesophilic proteins. In addition, our results provided new quantitative information about the trends in the substitution preference of Ile, Phe, Tyr, Lys, Arg, His, Glu and Leu with most of amino acids and substitution avoidance of Gly, Thr and Ala with most of amino acids. These findings would help future efforts propose a strategy for designing psychrophilic proteins.

Investigating the photo-thermo-radiosensitization effects of folate-conjugated gold nanorods on KB nasopharyngeal carcinoma cells.

In this article, we report a targeted cancer treatment strategy using nano-photo-thermal therapy (NPTT) and radiotherapy (RT) methods. Gold nanorods (AuNRs) without and with folic acid (FA) conjugation were firstly synthesized and then characterized. Cytotoxicity of various methods; NPTT (nanoparticles; 808 nm laser; 2 W/cm2) or RT (6 MV X-ray; 2 Gy) or combination of NPTT and RT; was separately investigated on KB nasopharyngeal carcinoma cells. The effects of different treatments were studies using MTT assay, inductively coupled plasma-mass spectrometry (ICP-MS) and transmission electron microscopy (TEM). Both TEM and ICP-MS confirmed the internalization of nanoparticles into the KB cells. ICP-MS analysis revealed that AuNR-FA cell uptake was higher than AuNRs. Viability of the cells received NPTT was lower than those cells received laser or nanoparticles alone. Furthermore, it was found that combination of NPTT and RT notably decreased the viability of KB cells. Following such a combinatorial treatment (NPTT + RT), intensive damages were identified in TEM images obtained from treated cells. It may be concluded that AuNR-FA nanoconjugate is a good candidate to be used as a targeted sensitizer agent for nano-photo-thermo-radiotherapy of head and neck cancer cells.

3D cerebral MR image segmentation using multiple-classifier system.

The three soft brain tissues white matter (WM), gray matter (GM), and cerebral spinal fluid (CSF) identified in a magnetic resonance (MR) image via image segmentation techniques can aid in structural and functional brain analysis, brain's anatomical structures measurement and visualization, neurodegenerative disorders diagnosis, and surgical planning and image-guided interventions, but only if obtained segmentation results are correct. This paper presents a multiple-classifier-based system for automatic brain tissue segmentation from cerebral MR images. The developed system categorizes each voxel of a given MR image as GM, WM, and CSF. The algorithm consists of preprocessing, feature extraction, and supervised classification steps. In the first step, intensity non-uniformity in a given MR image is corrected and then non-brain tissues such as skull, eyeballs, and skin are removed from the image. For each voxel, statistical features and non-statistical features were computed and used a feature vector representing the voxel. Three multilayer perceptron (MLP) neural networks trained using three different datasets were used as the base classifiers of the multiple-classifier system. The output of the base classifiers was fused using majority voting scheme. Evaluation of the proposed system was performed using Brainweb simulated MR images with different noise and intensity non-uniformity and internet brain segmentation repository (IBSR) real MR images. The quantitative assessment of the proposed method using Dice, Jaccard, and conformity coefficient metrics demonstrates improvement (around 5 % for CSF) in terms of accuracy as compared to single MLP classifier and the existing methods and tools such FSL-FAST and SPM. As accurately segmenting a MR image is of paramount importance for successfully promoting the clinical application of MR image segmentation techniques, the improvement obtained by using multiple-classifier-based system is encouraging.

Sensitivity to Antibiotics of Bacteria Exposed to Gamma Radiation Emitted from Hot Soils of the High Background Radiation Areas of Ramsar, Northern Iran.

BACKGROUND: Over the past several years our laboratories have investigated different aspects of the challenging issue of the alterations in bacterial susceptibility to antibiotics induced by physical stresses. OBJECTIVE: To explore the bacterial susceptibility to antibiotics in samples of Salmonella enterica subsp. enterica serovar Typhimurium (S. typhimurium), Staphylococcus aureus, and Klebsiella pneumoniae after exposure to gamma radiation emitted from the soil samples taken from the high background radiation areas of Ramsar, northern Iran. METHODS: Standard Kirby-Bauer test, which evaluates the size of the zone of inhibition as an indicator of the susceptibility of different bacteria to antibiotics, was used in this study. RESULTS: The maximum alteration of the diameter of inhibition zone was found for K. pneumoniae when tested for ciprofloxacin. In this case, the mean diameter of no growth zone in non-irradiated control samples of K. pneumoniae was 20.3 (SD 0.6) mm; it was 14.7 (SD 0.6) mm in irradiated samples. On the other hand, the minimum changes in the diameter of inhibition zone were found for S. typhimurium and S. aureus when these bacteria were tested for nitrofurantoin and cephalexin, respectively. CONCLUSION: Gamma rays were capable of making significant alterations in bacterial susceptibility to antibiotics. It can be hypothesized that high levels of natural background radiation can induce adaptive phenomena that help microorganisms better cope with lethal effects of antibiotics.

Novel paint design based on nanopowder to protection against X and gamma rays.

BACKGROUND: Lead-based shields are the standard method of intraoperative radiation protection in the radiology and nuclear medicine department. Human lead toxicity is well documented. The lead used is heavy, lacks durability, is difficult to launder, and its disposal is associated with environmental hazards. The aim of this study was to design a lead free paint for protection against X and gamma rays. MATERIALS AND METHODS: In this pilot st we evaluated several types of nano metal powder that seemed to have good absorption. The Monte Carlo code, MCNP4C, was used to model the attenuation of X-ray photons in paints with different designs. Experimental measurements were carried out to assess the attenuation properties of each paint design. RESULTS: Among the different nano metal powder, nano tungsten trioxide and nano tin dioxide were the two most appropriate candidates for making paint in diagnostic photon energy range. Nano tungsten trioxide (15%) and nano tin dioxide (85%) provided the best protection in both simulation and experiments. After this step, attempts were made to produce appropriate nano tungsten trioxide-nano tin dioxide paints. The density of this nano tungsten trioxide-nano tin dioxide paint was 4.2 g/cm(3). The MCNP simulation and experimental measurements for HVL (Half-Value Layer) values of this shield at 100 kVp were 0.25 and 0.23 mm, respectively. CONCLUSIONS: The results showed the cost-effective lead-free paint can be a great power in absorbing the X-rays and gamma rays and it can be used instead of lead.

Development of a Brain Computer Interface (BCI) Speller System Based on SSVEP Signals.

BCI is one of the most intriguing technologies among other HCI systems, mostly because of its capability of recording brain activities. Spelling BCIs, which help paralyzed people to maintain communication, are one of the striking topics in the field of BCI. In this scientific a spelling BCI system with high transfer rate and accuracy that uses SSVEP signals is proposed. In addition, we suggested that LED light sources can provide proper signals for speller BCIs and they can be used in future.

The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation.

Prediction of the amount of hospital waste production will be helpful in the storage, transportation and disposal of hospital waste management. Based on this fact, two predictor models including artificial neural networks (ANNs) and multiple linear regression (MLR) were applied to predict the rate of medical waste generation totally and in different types of sharp, infectious and general. In this study, a 5-fold cross-validation procedure on a database containing total of 50 hospitals of Fars province (Iran) were used to verify the performance of the models. Three performance measures including MAR, RMSE and R(2) were used to evaluate performance of models. The MLR as a conventional model obtained poor prediction performance measure values. However, MLR distinguished hospital capacity and bed occupancy as more significant parameters. On the other hand, ANNs as a more powerful model, which has not been introduced in predicting rate of medical waste generation, showed high performance measure values, especially 0.99 value of R(2) confirming the good fit of the data. Such satisfactory results could be attributed to the non-linear nature of ANNs in problem solving which provides the opportunity for relating independent variables to dependent ones non-linearly. In conclusion, the obtained results showed that our ANN-based model approach is very promising and may play a useful role in developing a better cost-effective strategy for waste management in future.