Human-like intelligent automatic treatment planning of head and neck cancer radiation therapy.

Automatic treatment planning of radiation therapy (RT) is desired to ensure plan quality, improve planning efficiency, and reduce human errors. We have proposed an Intelligent Automatic Treatment Planning framework with a virtual treatment planner (VTP), an artificial intelligence robot built using deep reinforcement learning (DRL), autonomously operating a treatment planning system (TPS). This study extends our previous successes in relatively simple prostate cancer RT planning to head-and-neck (H&N) cancer, a more challenging context even for human planners due to multiple prescription levels, proximity of targets to critical organs, and tight dosimetric constraints. We integrated VTP with a real clinical TPS to establish a fully automated planning workflow guided by VTP. This integration allowed direct model training and evaluation using the clinical TPS. We designed the VTP network structure to approach the decision-making process in RT planning in a hierarchical manner that mirrors human planners. The VTP network was trained via the Q-learning framework. To assess the effectiveness of VTP, we conducted a prospective evaluation in the 2023 Planning Challenge organized by the American Association of Medical Dosimetrists (AAMD). We extended our evaluation to include 20 clinical H&N cancer patients, comparing the plans generated by VTP against their clinical plans. In the prospective evaluation for the AAMD Planning Challenge, VTP achieved a plan score of 139.08 in the initial phase evaluating plan quality, and 15 min of planning time with the first place ranking in the adaptive phase competing for planning efficiency while meeting all plan quality requirements. For clinical cases, VTP-generated plans achieved an average VTP score of 125.33±11.12, which outperformed the corresponding clinical plans with an average score of117.76±13.56. We successfully integrated VTP with the clinical TPS to achieve a fully automated treatment planning workflow. The compelling performance of VTP demonstrated its potential in automating H&N RT planning.

Trial characteristics and treatment effect estimates in randomized controlled trials of Chinese herbal medicine: A meta-epidemiological study.

BACKGROUND: Previously published meta-epidemiological studies focused on Western medicine have identified some trial characteristics that impact the treatment effect of randomized controlled trials (RCTs). Nevertheless, it remains unclear if similar associations exist in RCTs on Chinese herbal medicine (CHM). Further, Chinese medicine-related characteristics have not been explored yet. OBJECTIVE: To investigate trial characteristics related to treatment effect estimates on CHM RCTs. SEARCH STRATEGY: This meta-epidemiological study searched 5 databases for systematic reviews on CHM treatment published between January 2011 and July 2021. INCLUSION CRITERIA: An eligible systematic review should only include RCTs of CHM and conduct at least one meta-analysis. DATA EXTRACTION AND ANALYSIS: Two reviewers independently conducted data extraction on general characteristics of systematic reviews, meta-analyses and included RCTs. They also assessed the risk of bias of RCTs using the Cochrane risk of bias tool. A two-step approach was used for data analyses. The ratio of odds ratios (ROR) and difference in standardized mean differences (dSMD) with 95% confidence interval (CI) were applied to present the difference in effect estimates for binary and continuous outcomes, respectively. RESULTS: Ninety-one systematic reviews, comprising 1338 RCTs were identified. For binary outcomes, RCTs incorporated with syndrome differentiation (ROR: 1.23; 95 % CI: [1.07, 1.39]), adopting Chinese medicine formula (ROR: 1.19; 95% CI: [1.03, 1.34]), with low risk of bias on incomplete outcome data (ROR: 1.29; 95% CI: [1.06, 1.52]) and selective outcome reporting (ROR: 1.12; 95% CI: [1.01, 1.24]), as well as a trial size ≥ 100 (ROR: 1.23; 95% CI: [1.04, 1.42]) preferred to show larger effect estimates. As for continuous outcomes, RCTs with Chinese medicine diagnostic criteria (dSMD: 0.23; 95% CI: [0.06, 0.41]), judged as high/unclear risk of bias on allocation concealment (dSMD: -0.70; 95% CI: [-0.99, -0.42]), with low risk of bias on incomplete outcome data (dSMD: 0.30; 95% CI: [0.18, 0.43]), conducted at a single center (dSMD: -0.33; 95% CI: [-0.61, -0.05]), not using intention-to-treat analysis (dSMD: -0.75; 95% CI: [-1.43, -0.07]), and without funding support (dSMD: -0.22; 95% CI: [-0.41, -0.02]) tended to show larger effect estimates. CONCLUSION: This study provides empirical evidence for the development of a specific critical appraisal tool for risk of bias assessments on CHM RCTs. Please cite this article as: Wang BH, Lin YL, Gao YY, Song JL, Qin L, Li LQ, et al. Trial characteristics and treatment effect estimates in randomized controlled trials of Chinese herbal medicine: A meta-epidemiological study. J Integr Med. 2024; Epub ahead of print.

Natural Allelic Variations of Bch10G006400 Controlling Seed Size in Chieh-qua (Benincasa hispida Cogn. var. Chieh-qua How).

Seeds are the most important reproductive organs of higher plants, the beginning and end of a plant's lifecycle. They are very important to plant growth and development, and also an important factor affecting yield. In this study, genetic analysis and BSA-seq of the F2 population crossed with the large-seeded material 'J16' and small-seeded material 'FJ5' were carried out, and the seed size locus was initially located within the 1.31 Mb region on chr10. In addition, 2281 F2 plants were used to further reduce the candidate interval to 48.8 Kb. This region contains only one gene encoding the N-acetyltransferase (NAT) protein (Bch10G006400). Transcriptome and expression analysis revealed that the gene was significantly more highly expressed in 'J16' than in 'FJ5'. Variation analysis of Bch10G006400 among parents and 50 chieh-qua germplasms revealed that as well as a nonsynonymous mutation (SNP_314) between parents, two mutations (SNP_400 and InDel_551) were detected in other materials. Combining these three mutations completely distinguished the seed size of the chieh-qua. GO and KEGG enrichment analyses revealed that DGEs played the most important roles in carbohydrate metabolism and plant hormone signal transduction, respectively. The results of this study provide important information for molecular marker-assisted breeding and help to reveal the molecular mechanism of seed size.

Predicting treatment plan approval probability for high-dose-rate brachytherapy of cervical cancer using adversarial deep learning.

Objective.Predicting the probability of having the plan approved by the physician is important for automatic treatment planning. Driven by the mathematical foundation of deep learning that can use a deep neural network to represent functions accurately and flexibly, we developed a deep-learning framework that learns the probability of plan approval for cervical cancer high-dose-rate brachytherapy (HDRBT).Approach.The system consisted of a dose prediction network (DPN) and a plan-approval probability network (PPN). DPN predicts organs at risk (OAR)D2ccand CTVD90%of the current fraction from the patient's current anatomy and prescription dose of HDRBT. PPN outputs the probability of a given plan being acceptable to the physician based on the patients anatomy and the total dose combining HDRBT and external beam radiotherapy sessions. Training of the networks was achieved by first training them separately for a good initialization, and then jointly via an adversarial process. We collected approved treatment plans of 248 treatment fractions from 63 patients. Among them, 216 plans from 54 patients were employed in a four-fold cross validation study, and the remaining 32 plans from other 9 patients were saved for independent testing.Main results.DPN predicted equivalent dose of 2 Gy for bladder, rectum, sigmoidD2ccand CTVD90%with a relative error of 11.51% ± 6.92%, 8.23% ± 5.75%, 7.12% ± 6.00%, and 10.16% ± 10.42%, respectively. In a task that differentiates clinically approved plans and disapproved plans generated by perturbing doses in ground truth approved plans by 20%, PPN achieved accuracy, sensitivity, specificity, and area under the curve 0.70, 0.74, 0.65, and 0.74.Significance.We demonstrated the feasibility of developing a novel deep-learning framework that predicts a probability of plan approval for HDRBT of cervical cancer, which is an essential component in automatic treatment planning.

Association of healthy lifestyle behaviours with incident irritable bowel syndrome: a large population-based prospective cohort study.

OBJECTIVES: To evaluate the association between healthy lifestyle behaviours and the incidence of irritable bowel syndrome (IBS). DESIGN: Population-based prospective cohort study. SETTING: The UK Biobank. PARTICIPANTS: 64 268 adults aged 37 to 73 years who had no IBS diagnosis at baseline were enrolled between 2006 and 2010 and followed up to 2022. MAIN EXPOSURE: The five healthy lifestyle behaviours studied were never smoking, optimal sleep, high level of vigorous physical activity, high dietary quality and moderate alcohol intake. MAIN OUTCOME MEASURE: The incidence of IBS. RESULTS: During a mean follow-up of 12.6 years, 961 (1.5%) incident IBS cases were recorded. Among the 64 268 participants (mean age 55.9 years, 35 342 (55.0%) female, 7604 (11.8%) reported none of the five healthy lifestyle behaviours, 20 662 (32.1%) reported 1 behaviour, 21 901 (34.1%) reported 2 behaviours and 14 101 (21.9%) reported 3 to 5 behaviours at baseline. The multivariable adjusted hazard ratios associated with having 1, 2 and 3 to 5 behaviours for IBS incidence were 0.79 (95% confidence intervals 0.65 to 0.96), 0.64 (0.53 to 0.78) and 0.58 (0.46 to 0.72), respectively (P for trend <0.001). Never smoking (0.86, 0.76 to 0.98, P=0.02), high level of vigorous physical activity (0.83, 0.73 to 0.95, P=0.006) and optimal sleep (0.73, 0.60 to 0.88, P=0.001) demonstrated significant independent inverse associations with IBS incidence. No significant interactions were observed between these associations and age, sex, employment status, geographic location, gastrointestinal infection, endometriosis, family history of IBS or lifestyle behaviours. CONCLUSIONS: Adhering to a higher number of healthy lifestyle behaviours is significantly associated with a lower incidence of IBS in the general population. Our findings suggest the potential of lifestyle modifications as a primary prevention strategy for IBS.

Definitive radiation for advanced cervix cancer is not associated with vaginal shortening-a prospective vaginal length and dose correlation.

PURPOSE: Prospectively measure change in vaginal length after definitive chemoradiation (C-EBRT) with Intracavitary Brachytherapy (ICBT) for locally advanced cervix cancer (LACC) and correlate with vaginal dose (VD). MATERIALS AND METHODS: Twenty one female patients with LACC receiving C-EBRT and ICBT underwent serial vaginal length (VL) measurements. An initial measurement was made at the time of the first ICBT procedure and subsequently at 3 month intervals up to 1 year post radiation. The vagina was contoured as a 3-dimensional structure for each brachytherapy plan. The difference in VL before and at least 6 months after the last fraction of brachytherapy was considered as an indicator of toxicity. RESULTS: The mean initial VL was 8.7 cm (6.5-12) with median value of 8.5 cm. The mean VL after 6 months was 8.6 cm (6.5-12) and VL change was not found to be statistically significant. The median values (interquartile ranges) for vaginal D0.1cc, D1cc, and D2cc were 129.2 Gy (99.6-252.2), 96.9 Gy (84.2-114.9), and 89.6 Gy (82.4-102.2), respectively. No significant correlation was found between vaginal length change and the dosimetric parameters calculated for all patients. CONCLUSION: Definitive C-EBRT and ICBT did not significantly impact VL in this prospective cohort probably related to acceptable doses per ICRU constraints. Estimate of vaginal stenosis and sexual function was not performed in this cohort which is a limitation of this study and which we hope to study prospectively going forward.

Development of a radiomics-based model to predict occult liver metastases of pancreatic ductal adenocarcinoma: a multicenter study.

BACKGROUND: Undetectable occult liver metastases block the long-term survival of pancreatic ductal adenocarcinoma (PDAC). This study aimed to develop a radiomics-based model to predict occult liver metastases and assess its prognostic capacity for survival. MATERIALS AND METHODS: Patients who underwent surgical resection and were pathologically proven with PDAC were recruited retrospectively from five tertiary hospitals between January 2015 and December 2020. Radiomics features were extracted from tumors, and the radiomics-based model was developed in the training cohort using LASSO-logistic regression. The model's performance was assessed in the internal and external validation cohorts using the area under the receiver operating curve (AUC). Subsequently, the association of the model's risk stratification with progression-free survival (PFS) and overall survival (OS) was then statistically examined using Cox regression analysis and the log-rank test. RESULTS: A total of 438 patients [mean (SD) age, 62.0 (10.0) years; 255 (58.2%) male] were divided into the training cohort ( n =235), internal validation cohort ( n =100), and external validation cohort ( n =103). The radiomics-based model yielded an AUC of 0.73 (95% CI: 0.66-0.80), 0.72 (95% CI: 0.62-0.80), and 0.71 (95% CI: 0.61-0.80) in the training, internal validation, and external validation cohorts, respectively, which were higher than the preoperative clinical model. The model's risk stratification was an independent predictor of PFS (all P <0.05) and OS (all P <0.05). Furthermore, patients in the high-risk group stratified by the model consistently had a significantly shorter PFS and OS at each TNM stage (all P <0.05). CONCLUSION: The proposed radiomics-based model provided a promising tool to predict occult liver metastases and had a great significance in prognosis.

Expanding the therapeutic potential of Salvia miltiorrhiza: a review of its pharmacological applications in musculoskeletal diseases.

Salvia miltiorrhiz, commonly known as "Danshen" in Chinese medicine, has longstanding history of application in cardiovascular and cerebrovascular diseases. Renowned for its diverse therapeutic properties, including promoting blood circulation, removing blood stasis, calming the mind, tonifying the blood, and benefiting the "Qi", recent studies have revealed its significant positive effects on bone metabolism. This potential has garnered attention for its promising role in treating musculoskeletal disorders. Consequently, there is a high anticipation for a comprehensive review of the potential of Salvia miltiorrhiza in the treatment of various musculoskeletal diseases, effectively introducing an established traditional Chinese medicine into a burgeoning field. AIM OF THE REVIEW: Musculoskeletal diseases (MSDs) present significant challenges to healthcare systems worldwide. Previous studies have demonstrated the high efficacy and prospects of Salvia miltiorrhiza and its active ingredients for treatment of MSDs. This review aims to illuminate the newfound applications of Salvia miltiorrhiza and its active ingredients in the treatment of various MSDs, effectively bridging the gap between an established medicine and an emerging field. METHODS: In this review, previous studies related to Salvia miltiorrhiza and its active ingredients on the treatment of MSD were collected, the specific active ingredients of Salvia miltiorrhiza were summarized, the effects of Salvia miltiorrhiza and its active ingredients for the treatment of MSDs, as well as their potential molecular mechanisms were reviewed and discussed. RESULTS: Based on previous publications, Salvianolic acid A, salvianolic acid B, tanshinone IIA are the representative active ingredients of Salvia miltiorrhiza. Their application has shown significant beneficial outcomes in osteoporosis, fractures, and arthritis. Salvia miltiorrhiza and its active ingredients protect against MSDs by regulating different signaling pathways, including ROS, Wnt, MAPK, and NF-κB signaling. CONCLUSION: Salvia miltiorrhiza and its active ingredients demonstrate promising potential for bone diseases and have been explored across a wide variety of MSDs. Further exploration of Salvia miltiorrhiza's pharmacological applications in MSDs holds great promise for advancing therapeutic interventions and improving the lives of patients suffering from these diseases.

Low oil Pickering emulsion gels stabilized by bacterial cellulose nanofiber/soybean protein isolate: An excellent fat replacer for ice cream.

Food-grade Pickering emulsion gels with different oil phase fractions stabilized by Bacterial cellulose nanofibers/Soy protein isolate complex colloidal particles were prepared by one-step method. The properties of Pickering emulsion gels with different oil phase fractions (5 %, 10 %, 20 %, 40 %, 60 %, 75 %, v/v) and their applications in ice cream were investigated in the present study. The microstructural results showed that Pickering emulsion gels with the low oil phase fractions (5 %-20 %) were an emulsion droplet-filled gel, where the oil droplets were embedded in the network structure of cross-linked polymer, while Pickering emulsion gels with higher oil phase fractions (40 %-75 %) were an emulsion droplet-aggregated gel, which formed a network structure by flocculated oil droplets. The rheology result showed that the low oil Pickering emulsion gels had the same excellent performance as the high oil Pickering emulsion gels. Furthermore, the low oil Pickering emulsion gels showed good environmental stability under harsh conditions. Consequently, Pickering emulsion gels with 5 % oil phase fraction were used as fat replacers in ice cream and ice cream with different fat replacement rates (30 %, 60 % and 90 %, w/w) was prepared in this work. The results showed the appearance and texture of the ice cream with low oil Pickering emulsion gels as fat replacers was similar to that of the ice cream with no fat replacers, and the melting rate of the ice cream with low oil Pickering emulsion gels as fat replacers showed the lowest value of 21.08 % during the 45 min of melting experiment, as the fat replacer rate in the ice cream reached to 90 %. Therefore, this study demonstrated that low oil Pickering emulsion gels were excellent fat replacers and had great potential application in low calorie food production.

An imaging-based machine learning model outperforms clinical risk scores for prognosis of cirrhotic variceal bleeding.

OBJECTIVES: To develop and validate a machine learning model based on contrast-enhanced CT to predict the risk of occurrence of the composite clinical endpoint (hospital-based intervention or death) in cirrhotic patients with acute variceal bleeding (AVB). METHODS: This retrospective study enrolled 330 cirrhotic patients with AVB between January 2017 and December 2020 from three clinical centers. Contrast-enhanced CT and clinical data were collected. Centers A and B were divided 7:3 into a training set and an internal test set, and center C served as a separate external test set. A well-trained deep learning model was applied to segment the liver and spleen. Then, we extracted 106 original features of the liver and spleen separately based on the Image Biomarker Standardization Initiative (IBSI). We constructed the Liver-Spleen (LS) model based on the selected radiomics features. The performance of LS model was evaluated by receiver operating characteristics and calibration curves. The clinical utility of models was analyzed using decision curve analyses (DCA). RESULTS: The LS model demonstrated the best diagnostic performance in predicting the composite clinical endpoint of AVB in patients with cirrhosis, with an AUC of 0.782 (95% CI 0.650-0.882) and 0.789 (95% CI 0.674-0.878) in the internal test and external test groups, respectively. Calibration curves and DCA indicated the LS model had better performance than traditional clinical scores. CONCLUSION: A novel machine learning model outperforms previously known clinical risk scores in assessing the prognosis of cirrhotic patients with AVB CLINICAL RELEVANCE STATEMENT: The Liver-Spleen model based on contrast-enhanced CT has proven to be a promising tool to predict the prognosis of cirrhotic patients with acute variceal bleeding, which can facilitate decision-making and personalized therapy in clinical practice. KEY POINTS: • The Liver-Spleen machine learning model (LS model) showed good performance in assessing the clinical composite endpoint of cirrhotic patients with AVB (AUC ≥ 0.782, sensitivity ≥ 80%). • The LS model outperformed the clinical scores (AUC ≤ 0.730, sensitivity ≤ 70%) in both internal and external test cohorts.

The generation of 5-N-glycolylneuraminic acid as a consequence of high levels of reactive oxygen species.

The presence of N-glycolylneuraminic acid (Neu5Gc), a non-human sialic acid in cancer patients, is currently attributed to the consumption of red meat. Excess dietary red meat has been considered a risk factor causing chronic inflammation and for the development of cancers. However, it remains unknown whether Neu5Gc can be generated via a chemical reaction rather than via a metabolic pathway in the presence of high levels of reactive oxygen species (ROS) found in the inflammatory and tumor environments. In this study, the conversion of N-acetylneuraminic acid (Neu5Ac) to Neu5Gc has been assessed in vitro under conditions mimicking the hydroxyl radical-rich humoral environment found in inflammatory and cancerous tissues. As a result, Neu5Gc has been detected via liquid chromatography-multiple reaction monitoring mass spectrometry. Furthermore, this conversion has also been found to take place in serum biomatrix containing ROS and in cancer cell cultures with induced ROS production.

Cooperative Bimetallic Catalysis via One-Metal/Two-Ligands: Mechanistic Insights of Polyfluoroarylation-Allylation of Diazo Compounds.

Metal/ligand in situ assembly is crucial for tailoring the reactivity & selectivity in transition metal catalysis. Cooperative catalysis via a single metal/two ligands is still underdeveloped, since it is rather challenging to harness the distinct reactivity profiles of the species generated by self-assembly of a single metal precursor with a mixture of different ligands. Herein, we report a catalytic system composed of a single metal/two ligands for a three-component reaction of polyfluoroarene, α-diazo ester, and allylic electrophile, leading to highly efficient construction of densely functionalized quaternary carbon centers, that are otherwise hardly accessible. Mechanistic studies suggest this reaction follows a cooperative bimetallic pathway via two catalysts with distinct reactivity profiles, which are assembled in situ from a single metal precursor and two ligands and work in concert to escort the transformation.

Metformin attenuates diabetes-induced osteopenia in rats is associated with down-regulation of the RAGE-JAK2-STAT1 signal axis.

Background: Osteopenia and fragile fractures are diabetes-associated complications. Many hypoglycemic drugs have effects on bone metabolism. Metformin, as is a prescribed medication for type 2 diabetes mellitus (T2DM), had been reported to have osteoprotective effects beyond its hypoglycemic effect, however the potential mechanism behind these effects remains unclear. In this study, we aimed to investigate the comprehensive effects of metformin on bone metabolism in T2DM rat model and elucidate the potential mechanism. Methods: Goto-Kakizaki spontaneous T2DM rats with significant hyperglycemia were treated with/without metformin for 20 weeks. Glucose tolerance was tested and all rats were weighed every two weeks. The osteoprotective effects of metformin in diabetic rats were determined by quantifying serum bone biomarkers, µ-CT imaging, histological staining, bone histomorphometry, and biomechanical properties analyses. Potential targets of metformin in the treatment of T2DM and osteoporosis were predicted using network pharmacology. The effects of metformin on mesenchymal stem cells (C3H10) cultured in high glucose medium were evaluated by CCK-8 assay, alkaline phosphatase (ALP) staining, qPCR and western blotting. Results: This study demonstrated that metformin significantly attenuated osteopenia, decreased serum glucose and glycated serum protein (GSP) levels, improved bone microarchitecture, and biomechanical properties in GK rats with T2DM. Metformin significantly increased biomarkers of bone formation, and significantly decreased muscle ubiquitin C (Ubc) expression. Network pharmacology analysis found that signal transducer and activator of transcription1 (STAT1) would be a potential target of metformin for regulating bone metabolism. Metformin increased C3H10 â€‹cell viability in vitro, alleviated ALP inhibition caused by hyperglycemia, increased the osteogenic gene expression of runt-related transcription factor 2 (RUNX2), collagen type I alpha 1 (Col1a1), osteocalcin (OCN), and ALP, while suppressing RAGE and STAT1 expression. Metformin also increased the protein expression of Osterix and decreased that of RAGE, p-JAK2, and p-STAT1. Conclusions: Our results demonstrate that metformin attenuated osteopenia and improved bone microarchitecture in GK rats with T2DM and significantly promoted stem cell osteogenic differentiation under high glucose condition. The effects of metformin on bone metabolism are closely associated with the suppression of RAGE-JAK2-STAT1 signaling axis. The translational potential of this article: Our research provides experiment evidence and potential mechanistic rationale for the use of metformin as an effective candidate for diabetes-induced osteopenia treatment.

Implementation and evaluation of an intelligent automatic treatment planning robot for prostate cancer stereotactic body radiation therapy.

PURPOSE: We previously developed a virtual treatment planner (VTP), an artificial intelligence robot, operating a treatment planning system (TPS). Using deep reinforcement learning guided by human knowledge, we trained the VTP to autonomously adjust relevant parameters in treatment plan optimization, similar to a human planner, to generate high-quality plans for prostate cancer stereotactic body radiation therapy (SBRT). This study describes the clinical implementation and evaluation of VTP. MATERIALS AND METHODS: We integrate VTP with Eclipse TPS using scripting Application Programming Interface. VTP observes dose-volume histograms of relevant structures, decides how to adjust dosimetric constraints, including doses, volumes, and weighting factors, and applies the adjustments to the TPS interface to launch the optimization engine. This process continues until a high-quality plan is achieved. We evaluated VTP's performance using the prostate SBRT case from the 2016 American Association of Medical Dosimetrist/Radiosurgery Society plan study with its plan scoring system, and compared to human-generated plans submitted to the challenge. Using the same scoring system, we also compared the plan quality of 36 prostate SBRT cases (20 planned with IMRT and 16 planned with VMAT) treated at our institution for both VTP and human-generated plans. RESULTS: In the plan study case, VTP achieved a score of 142.1/150.0, ranking the third in the competition (median 134.6). For the clinical cases, VTP achieved 110.6 ± 6.5 for 20 IMRT plans and 126.2 ± 4.7 for 16 VMAT plans, similar to scores of human-generated plans with 110.4 ± 7.0 for IMRT plans and 125.4 ± 4.4 for VMAT plans. The workflow, plan quality and planning time of VTP were reviewed to be satisfactory by experienced physicists. CONCLUSION: We successfully implemented VTP to operate a TPS for autonomous human-like treatment planning for prostate SBRT.

Calpain-TRPC6 signaling pathway contributes to propofol-induced developmental neurotoxicity in rats.

Growing animal studies suggest a risk of neuronal damage following early childhood exposure to anesthesia and sedation drugs including propofol. Inhibition of transient receptor potential canonical 6 (TRPC6) degradation has been shown to protect neurons from neuronal damage induced by multiple brain injury models. Our aim was to investigate whether calpain-TRPC6 pathway is a target in propofol-induced neurotoxicity. Postnatal day (PND) 7 rats were exposed to five bolus injections of 25 mg/kg propofol or 10 % intralipid at hourly intervals. Neuronal injury was assessed by the expression pattern of TUNEL staining and cleaved-caspase-3. The Morris water maze test was used to evaluate learning and memory functions in later life. Pretreatments consisting of intracerebroventricular injections of a TRPC6 agonist, TRPC6 inhibitor, or calpain inhibitor were used to confirm the potential role of the calpain-TRPC6 pathway in propofol-induced neurotoxicity. Prolonged exposure to propofol induced neuronal injury, downregulation of TRPC6, and enhancement of calpain activity in the cerebral cortex up to 24 h after anesthesia. It also induced long-term behavioral disorders, manifesting as longer escape latency at PND40 and PND41 and as fewer platform-crossing times and less time spent in the target quadrant at PND42. These propofol-induced effects were attenuated by treatment with the TRPC6 agonist and exaggerated by the TRPC6 inhibitor. Pretreatment with the calpain inhibitor alleviated the propofol-induced TRPC6 downregulation and neuronal injury in the cerebral cortex. In conclusion, our data suggest that a calpain-TRPC6 signaling pathway contributes to propofol-induced acute cortical neuron injury and long-term behavioral disorders in rats.

MR image reconstruction from undersampled data for image-guided radiation therapy using a patient-specific deep manifold image prior.

Introduction: Recent advancements in radiotherapy (RT) have allowed for the integration of a Magnetic Resonance (MR) imaging scanner with a medical linear accelerator to use MR images for image guidance to position tumors against the treatment beam. Undersampling in MR acquisition is desired to accelerate the imaging process, but unavoidably deteriorates the reconstructed image quality. In RT, a high-quality MR image of a patient is available for treatment planning. In light of this unique clinical scenario, we proposed to exploit the patient-specific image prior to facilitate high-quality MR image reconstruction. Methods: Utilizing the planning MR image, we established a deep auto-encoder to form a manifold of image patches of the patient. The trained manifold was then incorporated as a regularization to restore MR images of the same patient from undersampled data. We performed a simulation study using a patient case, a real patient study with three liver cancer patient cases, and a phantom experimental study using data acquired on an in-house small animal MR scanner. We compared the performance of the proposed method with those of the Fourier transform method, a tight-frame based Compressive Sensing method, and a deep learning method with a patient-generic manifold as the image prior. Results: In the simulation study with 12.5% radial undersampling and 15% increase in noise, our method improved peak-signal-to-noise ratio by 4.46dB and structural similarity index measure by 28% compared to the patient-generic manifold method. In the experimental study, our method outperformed others by producing reconstructions of visually improved image quality.

Changes and Influencing Factors of Stress Disorder in Patients with Mild Traumatic Brain Injury Stress Disorder.

Traumatic brain injury (TBI) is a brain injury caused by motor vehicle accidents, falls from heights, sports, and combat. Posttraumatic stress disorder (PTSD) is a complex mental disorder caused by physical and psychological trauma, which manifests itself with symptoms such as anxiety, depression, and cognitive dysfunction. How its symptoms arise and what factors influence it are not fully understood nor can it be predicted. In order to better understand the changes after stress disorder in TBI patients and the influencing factors of PTSD, this paper analyzed the changes and influencing factors of stress disorder in patients with mild traumatic brain injury stress disorder. In this paper, the Wechsler Memory Scale and functional magnetic resonance imaging were first used to study the memory impairment and functional changes of corresponding brain regions in patients with TBI stress disorder, and then, the Pittsburgh Sleep Quality Index Scale and the pain Visual Analogue Scale were used to study the influencing factors of PTSD. The results of the study showed that PTSD patients reduced and enhanced regional brain functional activity and impaired memory function in the resting state. Male gender, age under 45 years, no hemiplegia, and good sleep quality were protective factors for PTSD in TBI patients. The need for drug-assisted sleep, severe headache, and moderate headache was the risk factor for PTSD in TBI patients.

Collaborative variable neighborhood search for multi-objective distributed scheduling in two-stage hybrid flow shop with sequence-dependent setup times.

Distributed scheduling is seldom investigated in hybrid flow shops. In this study, distributed two-stage hybrid flow shop scheduling problem (DTHFSP) with sequence-dependent setup times is considered. A collaborative variable neighborhood search (CVNS) is proposed to simultaneously minimize total tardiness and makespan. DTHFSP is simplified by incorporating factory assignment into machine assignment of a prefixed stage, and its solution is newly represented with a machine assignment string and a scheduling string. CVNS consists of two cooperated variable neighborhood search (VNS) algorithms, and neighborhood structures and global search have collaborated in each VNS. Eight neighborhood structures and two global search operators are defined to produce new solutions. The current solution is periodically replaced with a member of the archive farthest from it. Experiments are conducted , and the computational results validate that CVNS has good advantages over the considered DTHFSP.

Novel functionalized selenium nanowires as antibiotic adjuvants in multiple ways to overcome drug resistance of multidrug-resistant bacteria.

Methicillin-resistant Staphylococcus (MRS) is a multi-drug resistant bacteria that pose a serious threat to human health. Antibacterial nanomaterials are becoming a promising antibiotic substitute or antibiotic adjuvants. In this work, selenium nanowires were modified with nano­silver (Ag NPs) with antibacterial activity and [Ru(bpy)2dppz]2+ with fluorescent labeling of DNA (SRA), and the antibacterial activity, antibacterial mechanism and biological toxicity of SRA synergistic antibiotics were studied. In vitro, antibacterial results show that SRA (12 µg/mL) improves the antibacterial activity of various antibiotics against resistant bacteria and significantly slows the development of bacterial resistance to antibiotics. Studies on antibacterial mechanisms have shown that SRA synergistic antibiotics destroy drug-resistant bacteria through a combination of physical (physical damage) and chemical pathways (destruction of biofilm, membrane depolarization, cell membrane destruction, adenosine triphosphate consumption and reactive oxygen species production). Transcriptomics analysis found that SRA affects bacterial activity by affecting bacterial biosynthesis, ATP synthesis and biofilm formation. Furthermore, SRA synergistic antibiotics can accelerate wound healing of bacterial infection by reducing the inflammatory response. The toxicity evaluation results show that SRA has extremely low cellular and in vivo toxicity. SRA has the potential of clinical application as multiple antibiotic adjuvants to deal with resistant bacterial infections.