Journal of Radiation Research and Applied Sciences最新文献

The purpose of this study was to explore the risk factors for anastomotic leakage (AL) after laparoscopic radical resection of rectal cancer and to construct a nomogram prediction model for AL after laparoscopic radical resection of rectal cancer. We selected 366 patients with rectal cancer who underwent laparoscopic radical resection for rectal cancer in our hospital between January 2021 and December 2023 as the research subjects. Logistic regression analysis was used to screen the risk factors for AL after laparoscopic radical resection for rectal cancer, and a nomogram model for AL after radical resection for rectal cancer was constructed and validated. Our results showed among 366 patients with rectal cancer, 42 patients developed AL after surgery, and the incidence rate of AL was 11.48%. Logistic regression analysis results showed that gender, preoperative intestinal obstruction, distance between the tumor and the anal edge ≤7 cm, and diabetes were risk factors for AL after laparoscopic radical resection of rectal cancer (P < 0.05). The calibration curve of the nomogram model for AL after laparoscopic radical resection of rectal cancer showed that the model's predicted value and actual value fit well, and the area under the curve of the model was 0.859, (95%CI: 0.807–0.912). Overall, gender, preoperative intestinal obstruction, distance between the tumor and the anal verge ≤7 cm, and diabetes are risk factors for AL after laparoscopic radical resection of rectal cancer. The nomogram model of AL after laparoscopic radical resection of rectal cancer has high accuracy and has a certain guiding significance for formulating prevention and treatment strategies in advance.

Background

Advanced radiotherapy techniques necessitate the use of precise treatment planning systems (TPSs) that account for tissue density differences. Modern TPSs require electron density to be defined for every tissue type and that is usually provided from computed tomographic images.

Objective

Changing the tube voltage used during CT acquisition can affect the CT number which consequently changes the derivation of tissue electron densities. This study measures the radio-therapeutic dose deviation caused by such variations.

Methods

A phantom with different tissue equivalent densities of material plugs is used in the study. We investigated the tube voltage influence on the dose attenuation coefficients percent (DACP) and equivalent path length (EPL) within the tissue phantom plugs. Monte Carlo was used to explore the influence of the tube voltage used in imaging on the dose distributions in a pelvic phantom.

Results

Our calculations show that the attenuation percentage in each phantom plug was highest in high-density materials. The maximum difference was 3.5% when comparing the attenuation percentage of 80 KV to 140 KV, while field size had no significant impact. Our simulation of a pelvic case revealed that switching from 140 KV to 80 KV could result in a 3% difference in a patient plan. However, it is highly dependent on the proximity to high-density materials. The study demonstrated that 140 KV images yield a much closer match between measured and theoretical equivalent path lengths compared to 80 KV images in high-density materials.

Conclusions

CT tube voltage variation has an impact on the dose calculations especially for high-density structures. The CT voltage variations influence can be small in most cases, but care should be taken in high dense bone situations especially in the vicinity of critical structures.

Background

Retinal vessel segmentation is critical for ocular health assessments. Traditional methods may lack precision, prompting exploration of advanced techniques. U-Net, a deep learning architecture, shows promise in handling the intricate nature of retinal vessel segmentation.

Methodology

This study focuses on the segmentation of thermographic fundus images using the U-Net architecture. A dataset of 125 images, categorized as normal and abnormal, underwent preprocessing, normalization, and augmentation. The U-Net model, with its contracting, bottleneck, and expansive paths, was implemented for accurate segmentation. A handheld thermographic fundus imaging product was introduced, featuring with Human Computer Interaction and user-friendly interface to optimize interaction and streamline the diagnostic process.

Results

The segmentation accuracy achieved using U-Net stood at a promising 93.5%. Precision, recall, and F1-score metrics were employed for a detailed evaluation, showcasing the model's ability to identify abnormalities while minimizing false positives. The integration of a thermographic fundus imaging product significantly reduced processing time, demonstrating potential clinical utility. Leave-One-Out Cross-Validation affirmed the model's consistency, achieving an overall accuracy of 93.7%. A comparative analysis revealed U-Net's superiority over the Fully Convolutional Network (FCN) by 7%.

Conclusion

This study establishes U-Net's efficacy in thermographic fundus image segmentation, offering precision and efficiency enhancements. The proposed imaging product streamlines diagnostics, emphasizing U-Net's superiority over FCN in retinal vessel segmentation, contributing to advanced medical image analysis.

Nanoscience is a prominent scientific field with great potential and many novel and cost-effective applications. Numerous green and environmentally friendly synthesis methods have been introduced that use different plant extracts to produce silver, gold, copper, and iron antibacterial nanoparticles. Plant extracts contain rejuvenating compounds which, when exposed to metal salts (in this research, silver nitrate), facilitate their reduction to metal ions. In the present investigation, Lepidium draba was used as a regenerating factor. Silver nanoparticles (AgNPs) containing Azmak leaf extract were synthesized under optimal conditions including silver nitrate concentration, time, temperature, and pH. These nanoparticles were then evaluated for their characteristics using transmission electron microscopy (TEM), Fourier transform infrared (FT-IR) spectroscopy, X-ray diffraction (XRD), dynamic light scattering (DLS), and zeta potential analyses. Subsequently, the antibacterial properties of nanoparticles were assessed using the broth microdilution method. The 2,2-diphenyl-1-picrylhydrazyl (DPPH) method was also employed to examine the antioxidant properties of synthesized nanoparticles. Structural analyses revealed the formation of spherical, stable, and pure AgNPs with a size of 20–35 nm. The synthesized AgNPs exhibited strong antibacterial activity against Gram-negative and Gram-positive bacteria. In addition, investigation of the antioxidant activity of the nanoparticles showed 89% inhibition at a concentration of 250 μg/mL. AgNPs synthesized using the Azmak leaf extract can be employed as low-cost and efficient nanoparticles for environmental and biomedical applications and their dual antimicrobial and antioxidant characteristics could potentially position them as candidates for treating infectious wound.

In this paper, we present a new heavy-tailed distribution called the heavy-tailed linear failure rate (HTLFR) distribution. Various statistical properties of the proposed distribution are derived, including the quantile function, the median, the ordinary moments, the moment generating function, the incomplete moments and the conditional moments. Some actuarial measures such as value at risk, expected shortfall, tail value at risk, tail variance and tail variance premium are calculated. Three different methods of estimation such as the maximum likelihood method, the maximum product spacing method and the Bayesian method as well as some simulation results for the model parameters of the HTLFR distribution under complete samples are examined. The results of the real data set show that the proposed distribution has greater flexibility and has been empirically evaluated under complete data. Discrimination analysis was employed to ensure fairness, equity, and accuracy in decision-making processes for selecting the best model, comparing the proposed distribution with known distributions. A discrete analog of the HTLFR distribution is proposed.

The standard radiotherapy planning technique for locally advanced rectal cancer (LARC) was 3D Conformal Radiotherapy (3DCRT). Modern radiation delivery techniques, including Intensity-Modulated Radiotherapy (IMRT) and Volumetric Modulated Arc Therapy (VMAT) techniques, have been developed in recent years in terms of dose delivery to the target while minimizing the dose to the organ at risk (OAR). The objective of this study was to identify the most suitable treatment plan technique from the available options of 3DCRT, IMRT and VMAT for LARC using short course radiotherapy (SCRT). Therefore, twenty-five adult patients with LARC in the last five-year period (2017–2021) were involved in this study. For each patient, three radiotherapy treatment plans were generated for dosimetric comparison of short course irradiation (25 Gy/5 fractions, which is the new standard of care) using Varian Eclipse software to generate 3DCRT in comparison with IMRT and VMAT plans. The conformity index (CI), the homogeneity index (HI), and the organs at risk (OAR), including bladder, small bowel, and femoral heads, were compared between the three plans. The results revealed that VMAT and IMRT plans had a higher level of conformity for the planning target volume (PTV) with 0.996 and 0.994, respectively, compared to the 3DCRT planning of 0.947 (both p < 0.005). The PTV dose distribution was less homogeneous with 3DCRT than with VMAT or IMRT plans. VMAT offered superior homogeneity to IMRT and 3DCRT plans with 1.055, 1.066, and 1.089, respectively (p < 0.005). The findings showed that VMAT is superior to IMRT and 3DCRT in almost all parameters, including dose distribution conformity and homogeneity of the target volume, as well as providing better OAR sparing in LARC patients.

The action characteristics of power system relay protection devices can well analyze whether the relevant actions are correct. An analysis method of relay protection action characteristics in power system based on fault recording data is designed. After analyzing the relay device structure and protection action characteristics and requirements, the power system relay protection action record data recorded by the fault recorder; The second order RC low-pass filter is used to preprocess the recorded data; The filtered data is input into the analysis model of relay protection action characteristics based on SVM algorithm. The SVM algorithm classifies whether the relay protection action characteristics recorded by the filtered fault recording data meet the expectations, and completes the analysis of power system relay protection action characteristics. The experimental results show that this method can effectively analyze the operation characteristics of power system relay protection, and can accurately check whether the relay protection device can operate correctly.

Renewable and nuclear energy are seen as clean and eco-friendly substitutes for energy derived from fossil fuels. Which of the two is better, though, is still up for debate. In order to meet their combined global energy objectives and perhaps become carbon neutral or even carbon negative in the near future, a shift to additional renewable energy sources and nuclear energy will be necessary. One such method of improving energy usage through the implementation of a renewable energy management system (REMS) that will help attain a continuous and reliable source of energy, allowing for the proactive and effective management of renewable assets and dependable supply. This article investigates attitudes and opinions about nuclear and renewable energy production technologies by analyzing the dependence of carbon dioxide (CO₂) emissions on non-renewable energy sources, renewable energy sources, and nuclear energy in the group of twenty (G20) countries, over the period from 2000 to 2021. This study employs advanced panel data econometric techniques in the presence of outliers in the G20 dataset, utilizing a robust random coefficient regression (RRCR) model via the M-estimation approach to study the impacts of non-renewable, renewable, and nuclear energy use with respect to CO₂ emission mitigation in G20 countries. The overall conclusions of the econometric analysis confirm renewable and nuclear energy's importance in reducing CO₂ emissions. We can conclude that the robust M-estimate approach is the best estimation technique for the G20 dataset, as compared to the classical estimation methods in the presence of outliers. The results show that for every percentage point of energy consumption that falls into the category of non-renewable energy, CO₂ emissions will increase by 2.5737% for all G20 countries. Also, A 1% rise in renewable energy consumption reduces CO₂ emissions by 1.0566% for all G20 countries. Similarly, a 1% increase in nuclear energy consumption will reduce CO₂ emissions by 5.5703% for all G20 countries. Therefore, we can conclude that energy derived from renewable sources and nuclear energy greatly cuts CO₂ emissions in all G20 countries. Total energy consumption, on the other hand, increases CO₂ emissions in the long run. According to these findings, it is pivotal for the G20 countries to increase nuclear energy use and employ REMS to reduce the majority of G20 country's reliance on fossil fuels in order to minimize CO₂ emissions. These findings are vital for understanding the relationship between these variables and the implementation of new or revised policies and initiatives supporting carbon neutrality at the G20 countries and national levels.

In thyroid autoimmune diseases diagnosis, there is growing evidence that thyroid ultrasonography (US) can be helpful without accurate differentiation of Graves’ disease GD from other autoimmune diseases. Using histopathological findings as a reference standard, this study aimed to determine the diagnostic accuracy of ^99mTc and ¹²³I in comparison with US for the differential diagnosis of GD in Saudi patients. Results demonstrated that thyroid scintigraphy using ^99mTc (sensitivity = 96.1 vs. 23.5%) and ¹²³I (sensitivity = 83.33 vs. 25%) was superior to US in detection of GD in Saudi patients. All GD patients detected by US were detected by both ^99mTc and ¹²³I scans, whereas, ^99mTc (37/39) and ¹²³I (14/18) scans detected many patients from GD cases that US failed to detect. This superiority was found regardless of gender and age particularly in ^99mTc scan. In the same group of patients, both ^99mTc and ¹²³I scans had the same sensitivity (80%) (P = 0.151). In conclusion, the conducting study findings proved that ^99mTc and ¹²³I thyroid scintigraphy scans could be used in the differential diagnosis of GD from other thyroiditis disorders with a higher degree of diagnostic accuracy.

Objective

To compare the diagnostic accuracy of radiology trainees, CT technicians, and radiologists in interpreting chest CT scans for the detection of COVID-19.

Methods

Retrospective data were analyzed from 269 reading assessments of chest CT scans for COVID-19. These were conducted by 160 radiologists, 41 radiology residents, and 68 CT technicians from diverse countries and performed online using DetectedX software between March 30, 2020, and January 1, 2022. The readers examined and assessed realistic, de-identified, digital chest CT images—15 positive and 15 negative for COVID-19. Information about the readers' clinical roles (radiologist, radiology resident, or CT technician) was collected alongside measures of their performance in distinguishing between positive and negative cases (sensitivity, specificity, and ROC AUC). The data were evaluated with Kruskal–Wallis tests to identify any differences in performance between the roles. Post hoc comparisons using Bonferroni-corrected Dunn's tests were then used to identify any significant pairwise differences.

Results

Statistically significant differences were observed in specificity (p < 0.001) and ROC AUC (p < 0.001) when comparing the three groups. In the pairwise comparisons, differences in specificity were only found between CT technicians and radiologists (p = 0.001), while ROC AUC was significantly different between CT technicians and radiologists (p = 0.001) and between CT technicians and radiology trainees (p = 0.016). No statistically significant differences were observed in sensitivity (p = 0.309) between the groups.

Conclusions

The study identifies both diagnostic potential and areas for improvement for radiology residents and CT technicians. Notably, the performance of residents mirrored that of radiologists, underscoring their crucial role in early case management. However, while CT technicians demonstrated comparable sensitivity, their specificity and ROC AUC were lower, highlighting an opportunity for targeted training to enhance their ability to differentiate COVID-19 from other pathologies.