Melatonin alleviates palmitic acid-induced mitochondrial dysfunction by reducing oxidative stress and enhancing autophagy in bovine endometrial epithelial cells.

BACKGROUND: Negative energy balance (NEB) typically occurs in dairy cows after delivery. Cows with a high yield are more likely to experience significant NEB. This type of metabolic imbalance could cause ketosis, which is often accompanied by a decline in reproductive performance. However, the molecular mechanisms underlying NEB have yet to be fully elucidated. During excessive NEB, the body fat is extensively broken down, resulting in the abnormal accumulation of non-esterified fatty acids (NEFAs), represented by palmitic acid (PA), within the uterus. Such an abnormal accumulation has the potential to damage bovine endometrial epithelial cells (BEECs), while the molecular mechanisms underlying its involvement in the PA-induced injury of BEECs remains poorly understood. Melatonin (MT) is recognized for its regulatory role in maintaining the homeostasis of mitochondrial reactive oxygen species (mitoROS). However, little is known as to whether MT could ameliorate the damage incurred by BEECs in response to PA and the molecular mechanism involved. RESULTS: Analysis showed that 0.2 mmol/L PA stress increased the level of cellular and mitochondrial oxidative stress, as indicated by increased reactive oxygen species (ROS) level. In addition, we observed mitochondrial dysfunction, including abnormal mitochondrial structure and respiratory function, along with a reduction in mitochondrial membrane potential and mitochondrial copy number, and the induction of apoptosis. Notably, we also observed the upregulation of autophagy proteins (PINK, Parkin, LC3B and Ubiquitin), however, the P62 protein was also increased. As we expected, 100 µmol/L of MT pre-treatment attenuated PA-induced mitochondrial ROS and restored mitochondrial respiratory function. Meanwhile, MT pretreatment reversed the upregulation of P62 induced by PA and activated the AMPK-mTOR-Beclin-1 pathway, contributing to an increase of autophagy and decline apoptosis. CONCLUSIONS: Our findings indicate that PA can induce mitochondrial dysfunction and enhance autophagy in BEECs. In addition, MT is proved to not only reduce mitochondrial oxidative stress but also facilitate the clearance of damaged mitochondria by upregulating autophagy pathways, thereby safeguarding the mitochondrial pool and promoting cellular viability. Our study provides a better understanding of the molecular mechanisms underlying the effect of an excess of NEB on the fertility outcomes of high yielding dairy cows.

Applying mixed-integer linear programming to the non-coplanar beam angle optimization of intensity-modulated radiotherapy for liver cancer.

Background: Currently, intensity-modulated radiation therapy (IMRT) is commonly used in radiotherapy clinics. However, designing a treatment plan with multiple beam angles depends on the experience of human planners, and is mostly achieved using a trial-and-error approach. It is preferrable but challenging to solve this issue automatically and mathematically using an optimization approach. The goal of this study is to develop a mixed-integer linear programming (MILP) approach for the beam angle optimization (BAO) of non-coplanar IMRT for liver cancer. Methods: MILP models for the BAO of both coplanar and non-coplanar IMRT treatment plans were developed. The beam angles of the IMRT plans were first selected by the MILP model built using mathematical optimization software. Next, the IMRT plans with the selected beam angles was created in a commercial treatment planning system. Finally, the fluence map and dose distribution of the IMRT plans were generated under pre-defined dose-volume constraints. The IMRT plans of 10 liver cancer patients previously treated at our institute were used to assessed the proposed MILP models. For each patient, both coplanar and non-coplanar IMRT plans with beam angles optimized by the MILP models were compared with the IMRT plan clinically approved by physicians. Results: The MILP model-guided IMRT plans showed reduced doses for most of the organs at risk (OARs). Compared with the IMRT plans clinically approved by physicians, the doses for the spinal cord (28.5 vs. 36.1, P=0.001<0.05) and liver (27.6 vs. 29.1, P=0.005<0.05) decreased significantly in the IMRT plans with non-coplanar beams selected by the MILP models. Conclusions: The MILP model is an effective tool for the BAO in coplanar and non-coplanar IMRT treatment planning. It facilitates the automation of IMRT treatment planning for current high-precision radiotherapy.

Occurrence and removal technologies of perchlorate in water: A systematic review and bibliometric analysis.

The pollution resulting from the emergence of the contaminant perchlorate is anticipated to have a substantial effect on the water environment in the foreseeable future. Considerable research efforts have been devoted to investigating treatment technologies for addressing perchlorate contamination, garnering widespread international interest in recent decades. A systematic review was conducted utilizing the Web of Science, Scopus, and Science Direct databases to identify pertinent articles published from 2000 to 2024. A total of 551 articles were chosen for in-depth examination utilizing VOS viewer. Bibliometric analysis indicated that countries such as China, the United States, Chile, India, Japan, and Korea have been prominent contributors to the research on this topic. The order of ClO4- occurrence was as follows: surface water > groundwater > drinking water. Various remediation methods for perchlorate contamination, such as adsorption, ion-exchange, membrane filtration, chemical reduction, and biological reduction, have been suggested. Furthermore, the research critically evaluated the strengths and weaknesses of each approach and offered recommendations for addressing their limitations. Advanced technologies have shown the potential to achieve notably enhanced removal of perchlorate and co-contaminants from water sources. However, the low concentration of perchlorate in natural water sources and the high energy consumption related to these technologies need to be solved in order to effectively remove perchlorate from water.

Applying the six-sigma methodology to determine the limits of quality control (QC) tests for a specific linear accelerator.

PURPOSE: We aimed to show the framework of the six-sigma methodology (SSM) that can be used to determine the limits of QC tests for the linear accelerator (Linac). Limits for QC tests are individually determined using the SSM. METHODS AND MATERIALS: The SSM is based on the define-measure-analyze-improve-control (DMAIC) stages to improve the process. In the "define" stage, the limits of QC tests were determined. In the "measure" stage, a retrospective collection of daily QC data using a Machine Performance Check platform was performed from January 2020 to December 2022. In the "analyze" stage, the process of determining the limits was proposed using statistical analyses and process capability indices. In the "improve" stage, the capability index was used to calculate the action limits. The tolerance limit was established using the larger one of the control limits in the individual control chart (I-chart). In the "control" stage, daily QC data were collected prospectively from January 2023 to May 2023 to monitor the effect of action limits and tolerance limits. RESULTS: A total of 798 sets of QC data including beam, isocenter, collimation, couch, and gantry tests were collected and analyzed. The Collimation Rotation offset test had the min-Cp, min-Cpk, min-Pp, and min-Ppk at 2.53, 1.99, 1.59, and 1.25, respectively. The Couch Rtn test had the max-Cp, max-Cpk, max-Pp, and max-Ppk at 31.5, 29.9, 23.4, and 22.2, respectively. There are three QC tests with higher action limits than the original tolerance. Some data on the I-chart of the beam output change, isocenter KV offset, and jaw X1 exceeded the lower tolerance and action limit, which indicated that a system deviation occurred and reminded the physicist to take action to improve the process. CONCLUSIONS: The SSM is an excellent framework to use in determining the limits of QC tests. The process capability index is an important parameter that provides quantitative information on determining the limits of QC tests.

New sagittal abdominal diameter and transverse abdominal diameter based equations to estimate visceral fat area in type 2 diabetes patients.

OBJECTIVE: Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) are considered gold standards for measuring visceral fat area (VFA). However, their relatively high prices and potential radiation exposure limit their widespread use in clinical practice and everyday life. Therefore, our study aims to develop a VFA estimated equation based on sagittal abdominal diameter (SAD) and transverse abdominal diameter (TAD) using anthropometric indexes. To the best of our knowledge, there have been limited studies investigating this aspect thus far. METHODS: This study was designed as a cross-sectional, retrospective cohort survey. A total of 288 patients (167 males and 121 females) aged 18-80 with type 2 diabetes (T2D) were consecutively collected from a multicenter hospital, and VFA was measured by CT. Subsequently, variables highly correlated with VFA were screened through general linear correlation analysis. A stepwise regression analysis was then conducted to develop a VFA estimated equation. Discrepancies between the estimated and actual VFA values were assessed using the Bland-Altman method to validate the accuracy of the equation. RESULTS: In the female T2D population, triglyceride (TG), SAD, TAD were found to be independently correlated with VFA; in the male T2D population, BMI, TG, SAD and TAD showed independent correlations with VFA. Among these variables, SAD exhibited the strongest correlation with VFA (r = 0.83 for females, r = 0.88 for males), followed by TAD (r = 0.69 for females, r = 0.79 for males). Based on these findings, a VFA estimated equation was developed for the T2D population: VFA (male) =-364.16 + 15.36*SAD + 0.77*TG + 9.41*TAD - 5.00*BMI (R2 = 0.75, adjusted R2 = 0.74); VFA(female)=-170.87 + 9.72*SAD-24.29*(TG^-1) + 3.93*TAD (R2 = 0.69, adjusted R2 = 0.68). Both models demonstrated a good fit. The Bland-Altman plot indicated a strong agreement between the actual VFA values and the estimated values, the mean differences were close to 0, and the majority of differences fell within the 95% confidence interval. CONCLUSIONS: In the T2D population, a VFA estimated equation is developed by incorporating SAD and TAD along with other measurement indices. This equation demonstrates a favorable estimated performance, suggesting to the development of novel and practical VFA estimation models in the future study.

Serum SERCA2a levels in heart failure patients are associated with adverse events after discharge.

Calcium homeostasis imbalance is one of the important pathological mechanisms in heart failure. Sarco/endoplasmic reticulum Ca2+-ATPase (SERCA2a), a calcium ATPase on the sarcoplasmic reticulum in cardiac myocytes, is a myocardial systolic-diastolic Ca2 + homeostasis regulating enzyme that is not only involved in cardiac diastole but also indirectly affects cardiac myocyte contraction. SERCA2a expression was found to be decreased in myocardial tissue in heart failure, however, there are few reports on serum SERCA2a expression in patients with heart failure, and this study was designed to investigate whether serum SERCA2a levels are associated with the occurrence of adverse events after discharge in patients hospitalized with heart failure. Patients with heart failure hospitalized in the cardiovascular department of the Second Affiliated Hospital of Guangdong Medical University, China, from July 2018 to July 2019 were included in this study, and serum SERCA2a concentrations were measured; each enrolled patient was followed up by telephone after 6 months (6 ±â€…1 months) for general post-discharge patient status. The correlation between serum SERCA2a levels and the occurrence of adverse events (death or readmission due to heart failure) after hospital discharge was assessed using multiple analysis and trend analysis. Seventy-one patients with heart failure were finally included in this study, of whom 38 (53.5%) were men and 33 (46.5%) were women (All were postmenopausal women). Multiple analysis revealed no correlation between serum SERCA2a levels and the occurrence of adverse events in the total study population and in male patients, but serum SERCA2a levels were associated with the occurrence of adverse outcome events after hospital discharge in female patients (OR = 1.02, P = .047). Further analysis using a trend analysis yielded a 4.0% increase in the risk of adverse outcomes after hospital discharge for each unit increase in SERCA2a in female patients (OR = 1.04; P = .02), while no significant difference was seen in men. This study suggests that serum SERCA2a levels at admission are associated with the occurrence of post-discharge adverse events in postmenopausal female patients hospitalized with heart failure.

Credit risk assessment of small and micro enterprise based on machine learning.

Small and micro enterprises are pivotal in national economic and social development. To foster their growth, managing their credit risks scientifically is crucial. This study starts by examining the credit information of these enterprises. We use imbalanced sample processing algorithms to ensure a balanced representation of minority-class samples. Then, a machine learning classifier is employed to identify key factors contributing to these enterprises' low credibility. Based on these factors, an XGBoost scoring card model is developed. The study reveals: firstly, the integration of the SMOTE algorithm with the XGBoost model exhibits certain performance advantages in handling imbalanced datasets; secondly, trustworthy financial information remains at the heart of crucial risk determinants; thirdly, the XGBoost scoring card model based on significant features effectively enhances the accuracy of credit risk assessment. These insights provide both theoretical references and practical tools for enhancing the robustness of small and micro enterprises, facilitating early warnings on credit risks, and refining financing efficiency.

Insight into roles of carbon anodes for removal of refractory organic contaminants in electro-peroxone system: Mechanism, performance and stability.

Electro-peroxone (EP) is a novel technique for the removal of refractory organic contaminants (ROCs), while the role of anode in this system is neglected. In this work, the EP system with graphite felt anode (EP-GF) and activated carbon fiber anode (EP-ACF) was developed to enhance ibuprofen (IBP) removal. The results showed that 91.2% and 98.6% of IBP was removed within 20 min in EP-GF and EP-ACF, respectively. Hydroxy radical (O⋅H) was identified as the dominant reactive species, contributing 80.9% and 54.0% of IBP removal in EP-ACF and EP-GF systems, respectively. The roles of adsorption in EP-ACF and direct electron transfer in EP-GF cannot be ignored. Due to the differences in mechanism, EP-GF and EP-ACF systems were suitable for the removal of O⋅H-resistant ROCs (e.g., oxalic acid and pyruvic acid) and non-O⋅H-resistant ROCs (e.g., IBP and nitrobenzene), respectively. Both systems had excellent stability relying on the introduction of oxygen functional groups on the anode, and their electrolysis energy consumption was significantly lower than that of EP-Pt system. The three degradation pathways of IBP were proposed, and the toxicity of intermediates were evaluated. In general, carbon anodes have a good application prospect in the removal of ROCs in EP systems.

A highly accurate and semi-automated method for quantifying spherical microplastics based on digital slide scanners and image processing.

Microplastics (MPs), the emerging pollutants appeared in water environment, have grabbed significant attention from researchers. The quantitative method of spherical MPs is the premise and key for the study of MPs in laboratory researches. However, the manual counting is time-consuming, and the existing semi-automated analysis lacked of robustness. In this study, a highly accurate quantification method for spherical MPs, called VS120-MC was proposed. VS120-MC consisted of the digital slide scanner VS120 and the MPs image processing software, MPs-Counter. The full-area scanning photography was employed to fundamentally avoid the error caused by random or partition sampling modes. To accomplish high-performance batch recognition, the Weak-Circle Elimination Algorithm (WEA) and the Variable Coefficient Threshold (VCT) was developed. Finally, lower than 0.6% recognition error rate of simulated images with different aggregated indices was achieved by MPs-Counter with fast processing speed (about 2 s/image). The smallest size for VS120-MC to detect was 1 µm. And the applicability of VS120-MC in real water body was investigated. The measured value of 1 µm spherical MPs in ultra-pure water and two kinds of polluted water after digestion showed a good linear relationship with the Manual measurements (R2 = 0.982,0.987 and 0.978, respectively). For 10 µm spherical MPs, R2 reached 0.988 for ultra-pure water and 0.984 for both of the polluted water. MPs-Counter also showed robustness when using the same set of parameters processing the images with different conditions. Overall, VS120-MC eliminated the error caused by traditional photography and realized an accurate, efficient, stable image processing tool, providing a reliable alternative for the quantification of spherical MPs.

Predicting voxel-level dose distributions of single-isocenter volumetric modulated arc therapy treatment plan for multiple brain metastases.

Purpose: Brain metastasis is a common, life-threatening neurological problem for patients with cancer. Single-isocenter volumetric modulated arc therapy (VMAT) has been popularly used due to its highly conformal dose and short treatment time. Accurate prediction of its dose distribution can provide a general standard for evaluating the quality of treatment plan. In this study, a deep learning model is applied to the dose prediction of a single-isocenter VMAT treatment plan for radiotherapy of multiple brain metastases. Method: A U-net with residual networks (U-ResNet) is employed for the task of dose prediction. The deep learning model is first trained from a database consisting of hundreds of historical treatment plans. The 3D dose distribution is then predicted with the input of the CT image and contours of regions of interest (ROIs). A total of 150 single-isocenter VMAT plans for multiple brain metastases are used for training and testing. The model performance is evaluated based on mean absolute error (MAE) and mean absolute differences of multiple dosimetric indexes (DIs), including (D max and D mean) for OARs, (D 98, D 95, D 50, and D 2) for PTVs, homogeneity index, and conformity index. The similarity between the predicted and clinically approved plan dose distribution is also evaluated. Result: For 20 tested patients, the largest and smallest MAEs are 3.3% ± 3.6% and 1.3% ± 1.5%, respectively. The mean MAE for the 20 tested patients is 2.2% ± 0.7%. The mean absolute differences of D 98, D 95, D 50, and D2 for PTV60, PTV52, PTV50, and PTV40 are less than 2.5%, 3.0%, 2.0%, and 3.0%, respectively. The prediction accuracy of OARs for D max and D mean is within 3.2% and 1.2%, respectively. The average DSC ranges from 0.86 to 1 for all tested patients. Conclusion: U-ResNet is viable to produce accurate dose distribution that is comparable to those of the clinically approved treatment plans. The predicted results can be used to improve current treatment planning design, plan quality, efficiency, etc.

Glycyrrhizin regulates antioxidation through Nrf2 signaling pathway in rat temporomandibular joint osteoarthritis.

BACKGROUND: Regulation of redox homeostasis could reduce osteoarthritis severity and limit disease progression, while glycyrrhizin (GL) shows great antioxidant and anti-inflammatory capacity. OBJECTIVE: The aim of this study was to investigate the role of GL on oxidative stress and the potential regulatory mechanism in rat temporomandibular joint (TMJ) chondrocytes under oxidative stress, and investigate the effect of GL in the rat temporomandibular joint osteoarthritis (TMJOA) model. METHODS: Rat TMJ chondrocytes were cultured in oxidative stress with different doses of GL. The effect of glycyrrhizin on the nuclear factor-erythroid 2-related factor 2 (Nrf2) in oxidative stress was evaluated by western blot and immunofluorescence staining. A rat model of TMJOA was treated with GL. Micro-computed tomography, histological and immunohistochemical analysis were used to assess the pathological change of TMJOA. RESULTS: The expression of superoxide dismutase 1 (SOD1), heme oxygenase-1 (HO-1), and peroxiredoxin 6 (PRDX6) were decreased, and intracellular Nrf2 signaling pathway was activated in chondrocytes in oxidative stress. GL upregulates the expression of antioxidants, especially PRDX6, as well as increases Nrf2 expression and nuclear translocation in rat condylar chondrocytes. Administration of GL attenuates condylar bone destruction, cartilage degeneration, and synovitis in rats TMJOA. Meanwhile, GL alleviated oxidative stress and enhanced the antioxidant capacity of TMJOA cartilage. CONCLUSION: This study suggested that GL alleviates rat TMJOA by regulating oxidative stress in condylar cartilage.

Preliminary study of cine-MRI compression in MR-guided radiotherapy.

Background: Cine-magnetic resonance imaging (MRI) is currently used in real-time tumor tracking during magnetic resonance (MR)-guided radiotherapy. As a type of MRI specified for motion tracking, a few minutes' acquisition results in thousands of 2-dimensional (2D) images. For MR-guided radiotherapy consisting of multiple treatment fractions, the large number of cine-MRI images would be disproportionate to the tight clinical data storage available. To alleviate this issue, the feasibility of compression of cine-MRI via video encoders was investigated in this study. Methods: The cine-MRI images were first sorted into 3 sequences according to their plane orientations. Then, each sequence was reordered according to their acquisition times [time-based (TB)] or content similarities [similarity-based (SB)]. As a result, 3 sequences were obtained for 3 plan orientations. Next, the obtained sequences were processed by a video encoder and the corresponding 3 video files were achieved. We employed 3 popular video encoders: Motion JPEG (M-JPEG), Advanced Video Coding (AVC), and High Efficiency Video Coding (HEVC). The performances of the sequence reordering methods and video encoders were evaluated based on a total of 150 image sets. Results: The mean correlation quantities for SB sequences were higher than those for TB sequences by 3% (sagittal), 2% (coronal), and 1% (transverse), respectively. The average compression ratio (CR) yielded by the SB sequences was higher than that achieved by the TB sequences. Comparing with M-JPEG, the CRs obtained by AVC and HEVC were increased by 58% and 62% (sagittal), 16% and 23% (coronal), and 48% and 56% (transverse), respectively. Among the 3 video encoders, the highest CRs and restoration accuracy were achieved by HEVC. Conclusions: HEVC with inter-frame coding is more effective in reducing the redundant information in consecutive images. It is feasible to implement the video encoder for high-performance cine-MRI compression.

Quality assurance in a phase III, multicenter, randomized trial of POstmastectomy radioThErapy in Node posiTive breast cancer with or without Internal mAmmary nodaL irradiation (POTENTIAL): a planning benchmark case.

PURPOSE: To report the planning benchmark case results of the POTENTIAL trial-a multicenter, randomized, phase 3 trial-to evaluate the value of internal mammary nodal (IMN) irradiation for patients with high-risk breast cancer. METHODS: All participating institutions were provided the outlines of one benchmark case, and they generated radiation therapy plans per protocol. The plans were evaluated by a quality assurance team, after which the institutions resubmitted their revised plans. The information on beams arrangement, skin flash, inhomogeneity corrections, and protocol compliance was assessed in the first and final submission. RESULTS: The plans from 26 institutions were analyzed. Some major deviations were found in the first submission. The protocol compliance rates of dose coverage for the planning target volume of chest wall, supraclavicular fossa plus axilla, and IMN region (PTVim) were all significantly improved in the final submission, which were 96.2% vs. 69.2%, 100% vs. 76.9%, and 88.4% vs. 53.8%, respectively. For OARs, the compliance rates of heart Dmean, left anterior descending coronary artery V40Gy, ipsilateral lung V5Gy, and stomach V5Gy were significantly improved. In the first and final submission, the mean values of PTVim V100% were 79.9% vs. 92.7%; the mean values of heart Dmean were 11.5 Gy vs. 9.7 Gy for hypofractionated radiation therapy and 11.5 Gy vs. 11.0 Gy for conventional fractionated radiation therapy, respectively. CONCLUSION: The major deviations were corrected and protocol compliance was significantly improved after revision, which highlighted the importance of planning benchmark case to guarantee the planning quality for multicenter trials.

Association between the triglyceride-glucose index and calcified aortic stenosis in elderly patients: a cross-sectional study.

Insulin resistance (IR) is associated with a variety of cardiovascular diseases, but there are few studies on the correlation between IR and calcified aortic stenosis (CAS). In this study, the triglyceride-glucose (TyG) index, which reflects IR, was used to investigate the correlation between IR and CAS. The study included 183 elderly patients who were diagnosed with CAS by transthoracic echocardiography. The patients were matched 1:1 according to age and sex, and elderly patients who were hospitalized during the same period and underwent transthoracic echocardiography without aortic stenosis were included as the control group. The relationship between the TyG index and CAS was analyzed by a multivariable logistic regression model, curve fitting and trend test. Multivariate logistic regression analysis showed that the TyG index as a continuous variable was negatively associated with CAS (P < 0.001); trend tests and curve fitting further supported this association. Our study showed that the TyG index was negatively associated with CAS in elderly patients, which may be related to the impairment of insulin receptors and signaling pathways in IR.

Pyroptosis of chondrocytes activated by synovial inflammation accelerates TMJ osteoarthritis cartilage degeneration via ROS/NLRP3 signaling.

OBJECTIVES: Synovial inflammation and chondrocyte death have been widely acknowledged as key contributors to the pathological progression of temporomandibular joint osteoarthritis (TMJ-OA), a degenerative joint disease currently lacking definitive treatments. This study aims to understand the regulatory role of chondrocyte pyroptosis in condylar cartilage degradation during TMJ-OA. METHODS: The levels of cytokines, cartilage degeneration markers, and pyroptotic biomarkers in the synovium and synovial fluid of temporomandibular disorders (TMD) patients were examined. The synovitis, cartilage degradation, and chondrocyte pyroptosis in wild-type and alpha-kinase 1 (ALPK1)-deficient TMJ-OA mice were then compared following monosodium iodoacetate (MIA) induction. Subsequently, we investigated the downstream mechanisms of cytokines- or macrophage supernatants-induced metabolic disorders and pyroptosis in chondrocytes using primary TMJ chondrocytes and ATDC5 chondrocyte cultures. RESULTS: We found a positive correlation between pyroptotic biomarkers and cartilage degradation mediators and cytokines in the synovial fluid of TMD patients. MIA-induced TMJ-OA mice demonstrated significant synovitis, cartilage degradation, and chondrocyte pyroptosis, which were mitigated in ALPK1-deficient TMJ-OA mice, inflammation-restrained mice. Ex-vivo study revealed the contribution of reactive oxygen species (ROS) to inflammation-irritated macrophage supernatants-induced pyroptosis and metabolic disorders in chondrocytes. Targeting NOD-like receptor protein 3 (NLRP3) alleviated cytokines- or ROS-induced pyroptosis and metabolic disorders in chondrocytes by inhibiting caspase-1 activation and interleukin-1ß (IL-1ß) secretion. CONCLUSION: Our findings offer novel insight into the role of synovial inflammation-induced chondrocyte pyroptosis in promoting cartilage degradation during TMJ-OA via the ROS and NLRP3 signaling pathway.

Improving accelerated 3D imaging in MRI-guided radiotherapy for prostate cancer using a deep learning method.

PURPOSE: This study was to improve image quality for high-speed MR imaging using a deep learning method for online adaptive radiotherapy in prostate cancer. We then evaluated its benefits on image registration. METHODS: Sixty pairs of 1.5 T MR images acquired with an MR-linac were enrolled. The data included low-speed, high-quality (LSHQ), and high-speed low-quality (HSLQ) MR images. We proposed a CycleGAN, which is based on the data augmentation technique, to learn the mapping between the HSLQ and LSHQ images and then generate synthetic LSHQ (synLSHQ) images from the HSLQ images. Five-fold cross-validation was employed to test the CycleGAN model. The normalized mean absolute error (nMAE), peak signal-to-noise ratio (PSNR), structural similarity index measurement (SSIM), and edge keeping index (EKI) were calculated to determine image quality. The Jacobian determinant value (JDV), Dice similarity coefficient (DSC), and mean distance to agreement (MDA) were used to analyze deformable registration. RESULTS: Compared with the LSHQ, the proposed synLSHQ achieved comparable image quality and reduced imaging time by ~ 66%. Compared with the HSLQ, the synLSHQ had better image quality with improvement of 57%, 3.4%, 26.9%, and 3.6% for nMAE, SSIM, PSNR, and EKI, respectively. Furthermore, the synLSHQ enhanced registration accuracy with a superior mean JDV (6%) and preferable DSC and MDA values compared with HSLQ. CONCLUSION: The proposed method can generate high-quality images from high-speed scanning sequences. As a result, it shows potential to shorten the scan time while ensuring the accuracy of radiotherapy.

Combining autoencoder with clustering analysis for anomaly detection in radiotherapy plans.

Background: To develop an unsupervised anomaly detection method to identify suspicious error-prone treatment plans in radiotherapy. Methods: A total of 577 treatment plans of breast cancer patients were used in this study. They were labeled as either normal or abnormal plans by experienced clinicians. Multiple features of each plan were extracted and selected by the learning algorithms. The training set consisted of feature samples from 400 normal plans and the testing set consisted of feature samples from 158 normal plans and 19 abnormal plans. Using the k-means clustering algorithm in the training stage, 4 normal plan clusters were formed. The distance between the samples in the testing set and the cluster centers were then determined. To evaluate the effect of dimensionality reduction (DR) on detection accuracy, principal component analysis (PCA) and autoencoder (AE) methods were compared. Results: The sensitivity of the anomaly detection model based on PCA and AE methods were 84.2% (16/19) and 94.7% (18/19), respectively. The specificity of the anomaly detection model based on PCA and AE methods were 64.6% (102/158) and 69.0% (109/158), respectively. The areas under the receiver operating characteristic (ROC) curve (AUCs) based on PCA and AE methods were 0.81 and 0.90, respectively. Conclusions: The unsupervised learning method was effective for detecting anomalies from the feature samples. Accuracy could be improved with the introduction of AE-based DR technique. The combination of AE and k-means clustering methods provides an automated way to identify abnormal plans among clinical treatment plans in radiotherapy.

A method for adjusting MLC leaf positions outside a reference region for VMAT plans in a commercial treatment planning system.

Multileaf collimators (MLC) leaf positions in a volumetric modulated arc therapy (VMAT) plan is determined from inverse treatment planning process. On the isocenter plane, leaf projections should not be set too far out of projected PTV boundary. In this study we developed an automatic method of adjusting leaf positions outside a reference region of interest (ROI) for VMAT plans generated in Pinnacle treatment planning system (TPS). The proposed method consisted of a Pinnacle script and a Python program. It checked each MLC leaf position for all control points in a VMAT arc relative to a reference ROI created by adding a small margin to PTV. For leaves opened outside the reference ROI, the method adjusted their positions to reduce dose to normal tissue while maintaining PTV coverage and satisfying leaf position constraints. The deliverability and dose accuracy of the method was verified by applying it to 15 VMAT plans which were delivered in five different linacs in our department. Dosimetric improvement of the proposed method was analyzed for another group of 16 randomly selected VMAT plans. The average gamma passing rate using a 3%/3 mm criteria for the verification group of VMAT plans was 98.3% and all passing rates were above our internal passing threshold. Dosimetric improvement was observed for the evaluation group of VMAT plans. The method can improve normal tissue protection for VMAT plans. It can be safely applied in routine clinic work.

Anomaly detection in radiotherapy plans using deep autoencoder networks.

Purpose: Treatment plans are used for patients under radiotherapy in clinics. Before execution, these plans are checked for safety and quality by human experts. A few of them were identified with flaws and needed further improvement. To automate this checking process, an unsupervised learning method based on an autoencoder was proposed. Methods: First, features were extracted from the treatment plan by human experts. Then, these features were assembled and used for model learning. After network optimization, a reconstruction error between the predicted and target signals was obtained. Finally, the questionable plans were identified based on the value of the reconstruction error. A large value of the reconstruction error indicates a longer distance from the standard distribution of normal plans. A total of 576 treatment plans for breast cancer patients were used for the test. Among them, 19 were questionable plans identified by human experts. To evaluate the performance of the autoencoder, it was compared with four baseline detection algorithms, namely, local outlier factor (LOF), hierarchical density-based spatial clustering of applications with noise (HDBSCAN), one-class support vector machine (OC-SVM), and principal component analysis (PCA). Results: The results showed that the autoencoder achieved the best performance than the other four baseline algorithms. The AUC value of the autoencoder was 0.9985, while the second one was 0.9535 (LOF). While maintaining 100% recall, the average accuracy and precision of the results by the autoencoder were 0.9658 and 0.5143, respectively. While maintaining 100% recall, the average accuracy and precision of the results by LOF were 0.8090 and 0.1472, respectively. Conclusion: The autoencoder can effectively identify questionable plans from a large group of normal plans. There is no need to label the data and prepare the training data for model learning. The autoencoder provides an effective way to carry out an automatic plan checking in radiotherapy.

Sirtuin 3 regulation: a target to alleviate ß-hydroxybutyric acid-induced mitochondrial dysfunction in bovine granulosa cells.

BACKGROUND: During the transition period, the insufficient dry matter intake and a sharply increased in energy consumption to produce large quantities of milk, high yielding cows would enter a negative energy balance (NEB) that causes an increase in ketone bodies (KBs) and decrease in reproduction efficiency. The excess concentrations of circulating KBs, represented by ß-hydroxybutyric acid (BHBA), could lead to oxidative damage, which potentially cause injury to follicular granulosa cells (fGCs) and delayed follicular development. Sirtuin 3 (Sirt3) regulates mitochondria reactive oxygen species (mitoROS) homeostasis in a beneficial manner; however, the molecular mechanisms underlying its involvement in the BHBA-induced injury of fGCs is poorly understood. The aim of this study was to explore the protection effects and underlying mechanisms of Sirt3 against BHBA overload-induced damage of fGCs. RESULTS: Our findings demonstrated that 2.4 mmol/L of BHBA stress increased the levels of mitoROS in bovine fGCs. Further investigations identified the subsequent mitochondrial dysfunction, including an increased abnormal rate of mitochondrial architecture, mitochondrial permeability transition pore (MPTP) opening, reductions in mitochondrial membrane potential (MMP) and Ca2+ release; these dysfunctions then triggered the caspase cascade reaction of apoptosis in fGCs. Notably, the overexpression of Sirt3 prior to treatment enhanced mitochondrial autophagy by increasing the expression levels of Beclin-1, thus preventing BHBA-induced mitochondrial oxidative stress and mitochondrial dysfunction in fGCs. Furthermore, our data suggested that the AMPK-mTOR-Beclin-1 pathway may be involved in the protective mechanism of Sirt3 against cellular injury triggered by BHBA stimulation. CONCLUSIONS: These findings indicate that Sirt3 protects fGCs from BHBA-triggered injury by enhancing autophagy, attenuating oxidative stress and mitochondrial damage. This study provides new strategies to mitigate the fGCs injury caused by excessive BHBA stress in dairy cows with ketosis.