Energy Efficient Carbon Capture through Electrochemical pH Swing Regeneration of Amine Solution.

Carbon capture is widely acknowledged as a promising strategy for achieving negative emissions. Electrochemical carbon capture technologies are considered a viable alternative to conventional temperature swing processes. Among these, employing the hydrogen oxidation and hydrogen evolution reactions as a redox couple, along with an ion exchange membrane, offers an effective means of establishing a pH swing for desorbing CO2 and regenerating the alkaline solvent. However, the practical scalability of this approach is impeded by challenges such as high energy demands resulting from a high pH differential between anodic and cathodic environments and operation with solutions with a low conductivity, required to obtain an acceptable current yield. To address these limitations, this study introduces an innovative anion exchange membrane (AEM)-based electrochemical process for solvent regeneration. Our research demonstrates the advantageous utilization of amines as chemical buffers. Selecting an amine solution with a favorable pKa (∼7 to 10) helps in maintaining bicarbonate as the predominant carbon species within the system, thereby ensuring a high current yield (>80%) across various operational conditions (current, load ratio, and solution concentration). Furthermore, our analysis indicates that the use of amine solutions effectively reduces the overpotential of the hydrogen evolution reaction due to a lower local pH. This results in a minimum energy requirement of 63 kJ/mol at a current density of 20 A/m2 to regenerate the solution (MDEA) while maintaining high (>99%) product (CO2) purity.

Deep learning based automatic segmentation of the Internal Pudendal Artery in definitive radiotherapy treatment planning of localized prostate cancer.

Background and purpose: Radiation-induced erectile dysfunction (RiED) commonly affects prostate cancer patients, prompting clinical trials across institutions to explore dose-sparing to internal-pudendal-arteries (IPA) for preserving sexual potency. IPA, challenging to segment, isn't conventionally considered an organ-at-risk (OAR). This study proposes a deep learning (DL) auto-segmentation model for IPA, using Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) or CT alone to accommodate varied clinical practices. Materials and methods: A total of 86 patients with CT and MRI images and noisy IPA labels were recruited in this study. We split the data into 42/14/30 for model training, testing, and a clinical observer study, respectively. There were three major innovations in this model: 1) we designed an architecture with squeeze-and-excite blocks and modality attention for effective feature extraction and production of accurate segmentation, 2) a novel loss function was used for training the model effectively with noisy labels, and 3) modality dropout strategy was used for making the model capable of segmentation in the absence of MRI. Results: Test dataset metrics were DSC 61.71 ± 7.7 %, ASD 2.5 ± .87 mm, and HD95 7.0 ± 2.3 mm. AI segmented contours showed dosimetric similarity to expert physician's contours. Observer study indicated higher scores for AI contours (mean = 3.7) compared to inexperienced physicians' contours (mean = 3.1). Inexperienced physicians improved scores to 3.7 when starting with AI contours. Conclusion: The proposed model achieved good quality IPA contours to improve uniformity of segmentation and to facilitate introduction of standardized IPA segmentation into clinical trials and practice.

Multimodal radiotherapy dose prediction using a multi-task deep learning model.

BACKGROUND: In radiation therapy (RT), accelerated partial breast irradiation (APBI) has emerged as an increasingly preferred treatment modality over conventional whole breast irradiation due to its targeted dose delivery and shorter course of treatment. APBI can be delivered through various modalities including Cobalt-60-based systems and linear accelerators with C-arm, O-ring, or robotic arm design. Each modality possesses distinct features, such as beam energy or the degrees of freedom in treatment planning, which influence their respective dose distributions. These modality-specific considerations emphasize the need for a quantitative approach in determining the optimal dose delivery modality on a patient-specific basis. However, manually generating treatment plans for each modality across every patient is time-consuming and clinically impractical. PURPOSE: We aim to develop an efficient and personalized approach for determining the optimal RT modality for APBI by training predictive models using two different deep learning-based convolutional neural networks. The baseline network performs a single-task (ST), predicting dose for a single modality. Our proposed multi-task (MT) network, which is capable of leveraging shared information among different tasks, can concurrently predict dose distributions for various RT modalities. Utilizing patient-specific input data, such as a patient's computed tomography (CT) scan and treatment protocol dosimetric goals, the MT model predicts patient-specific dose distributions across all trained modalities. These dose distributions provide patients and clinicians quantitative insights, facilitating informed and personalized modality comparison prior to treatment planning. METHODS: The dataset, comprising 28 APBI patients and their 92 treatment plans, was partitioned into training, validation, and test subsets. Eight patients were dedicated to the test subset, leaving 68 treatment plans across 20 patients to divide between the training and validation subsets. ST models were trained for each modality, and one MT model was trained to predict doses for all modalities simultaneously. Model performance was evaluated across the test dataset in terms of Mean Absolute Percent Error (MAPE). We conducted statistical analysis of model performance using the two-tailed Wilcoxon signed-rank test. RESULTS: Training times for five ST models ranged from 255 to 430 min per modality, totaling 1925 min, while the MT model required 2384 min. MT model prediction required an average of 1.82 s per patient, compared to ST model predictions at 0.93 s per modality. The MT model yielded MAPE of 1.1033 ± 0.3627% as opposed to the collective MAPE of 1.2386 ± 0.3872% from ST models, and the differences were statistically significant (p = 0.0003, 95% confidence interval = [-0.0865, -0.0712]). CONCLUSION: Our study highlights the potential benefits of a MT learning framework in predicting RT dose distributions across various modalities without notable compromises. This MT architecture approach offers several advantages, such as flexibility, scalability, and streamlined model management, making it an appealing solution for clinical deployment. With such a MT model, patients can make more informed treatment decisions, physicians gain more quantitative insight for pre-treatment decision-making, and clinics can better optimize resource allocation. With our proposed goal array and MT framework, we aim to expand this work to a site-agnostic dose prediction model, enhancing its generalizability and applicability.

Beyond conventional bounds: Surpassing system limits for stereotactic ablative (SAbR) lung radiotherapy using CBCT-based adaptive planning system.

PURPOSE: Online adaptive radiotherapy relies on a high degree of automation to enable rapid planning procedures. The Varian Ethos intelligent optimization engine (IOE) was originally designed for conventional treatments so it is crucial to provide clear guidance for lung SAbR plans. This study investigates using the Ethos IOE together with adaptive-specific optimization tuning structures we designed and templated within Ethos to mitigate inter-planner variability in meeting RTOG metrics for both online-adaptive and offline SAbR plans. METHODS: We developed a planning strategy to automate the generation of tuning structures and optimization. This was validated by retrospective analysis of 35 lung SAbR cases (total 105 fractions) treated on Ethos. The effectiveness of our planning strategy was evaluated by comparing plan quality with-and-without auto-generated tuning structures. Internal target volume (ITV) contour was compared between that drawn from CT simulation and from cone-beam CT (CBCT) at time of treatment to verify CBCT image quality and treatment effectiveness. Planning strategy robustness for lung SAbR was quantified by frequency of plans meeting reference plan RTOG constraints. RESULTS: Our planning strategy creates a gradient within the ITV with maximum dose in the core and improves intermediate dose conformality on average by 2%. ITV size showed no significant difference between those contoured from CT simulation and first fraction, and also trended towards decreasing over course of treatment. Compared to non-adaptive plans, adaptive plans better meet reference plan goals (37% vs. 100% PTV coverage compliance, for scheduled and adapted plans) while improving plan quality (improved GI (gradient index) by 3.8%, CI (conformity index) by 1.7%). CONCLUSION: We developed a robust and readily shareable planning strategy for the treatment of adaptive lung SAbR on the Ethos system. We validated that automatic online plan re-optimization along with the formulated adaptive tuning structures can ensure consistent plan quality. With the proposed planning strategy, highly ablative treatments are feasible on Ethos.

Trilobatin suppresses aging-induced cognitive impairment by targeting SIRT2: Involvement of remodeling gut microbiota to mediate the brain-gut axis.

BACKGROUND: Aging is associated with learning and memory disorder, affecting multiple brain areas, especially the hippocampus. Previous studies have demonstrated trilobatin (TLB), as a natural food additive, can extend the life of Caenorhabditis elegans and exhibit neuroprotection in Alzheimer's disease mice. However, the possible significance of TLB in anti-aging remains elusive. PURPOSE: This study aimed to delve into the physiological mechanism by which TLB ameliorated aging-induced cognitive impairment in senescence-accelerated mouse prone 8 (SAMP8) mice. METHODS: 6-month-old SAMP8 mice were administrated with TLB (5, 10, 20 mg/kg/day, i.g.) for 3 months. The therapeutic effect of TLB on aging-induced cognitive impairment was assessed in mice using behavioral tests and aging score. The gut microbiota composition in fecal samples was analyzed by metagenomic analysis. The protective effects of TLB on blood-brain barrier (BBB) and intestinal barrier were detected by transmission electron microscope, H&E staining and western blot (WB) assay. The inhibitive effects of TLB on inflammation in brain and intestine were assessed using immunofluorescence, WB and ELISA assay. Molecular docking and surface plasma resonance (SPR) assay were utilized to investigate interaction between TLB and sirtuin 2 (SIRT2). RESULTS: Herein, the findings exhibited TLB mitigated aging-induced cognitive impairment, neuron injury and neuroinflammation in hippocampus of aged SAMP8 mice. Moreover, TLB treatment repaired imbalance of gut microbiota in aged SAMP8 mice. Furthermore, TLB alleviated the damage to BBB and intestinal barrier, concomitant with reducing the expression of SIRT2, phosphorylated levels of c-Jun NH2 terminal kinases (JNK) and c-Jun, and expression of MMP9 protein in aged SAMP8 mice. Molecular docking and SPR unveiled TLB combined with SIRT2 and down-regulated SIRT2 protein expression. Mechanistically, the potential mechanism of SIRT2 in TLB that exerted anti-aging effect was validated in vitro. As expected, SIRT2 deficiency attenuated phosphorylated level of JNK in HT22 cells treated with d-galactose. CONCLUSION: These findings reveal, for the first time, SIRT2-mediated brain-gut barriers contribute to aging and aging-related diseases, and TLB can rescue aging-induced cognitive impairment by targeting SIRT2 and restoring gut microbiota disturbance to mediate the brain-gut axis. Overall, this work extends the potential application of TLB as a natural food additive in aging-related diseases.

Comprehensive analysis of juvenile idiopathic arthritis patients' immune characteristics based on bulk and single-cell sequencing data.

Background: The pathogenesis of juvenile idiopathic arthritis (JIA) is strongly influenced by an impaired immune system. However, the molecular mechanisms underlying its development and progression have not been elucidated. In this study, the computational methods TRUST4 were used to construct a T-cell receptor (TCR) and B-cell receptor (BCR) repertoire from the peripheral blood of JIA patients via bulk RNA-seq data, after which the clonality and diversity of the immune repertoire were analyzed. Results: Our findings revealed significant differences in the frequency of clonotypes between the JIA and healthy control groups in terms of the TCR and BCR repertoires. This work identified specific V genes and J genes in TCRs and BCRs that could be used to expand our understanding of JIA. After single-cell RNA analysis, the relative percentages of CD14 monocytes were significantly greater in the JIA group. Cell-cell communication analysis revealed the significant role of the MIF signaling pathway in JIA. Conclusion: In conclusion, this work describes the immune features of both the TCR and BCR repertoires under JIA conditions and provides novel insight into immunotherapy for JIA.

Master Breast Radiation Planning: Simple Guide for Radiation Oncology Residents.

This article focuses on various aspects of breast radiation treatment planning, from simulation to field design. It covers the most common techniques including tangents, mono isocentric, dual isocentric, electron-photon match, and VMAT. This can serve as a guide for radiation oncology residents and medical students to advance their understanding of key aspects of breast radiation treatment and planning processes.

Adapt-On-Demand: A Novel Strategy for Personalized Adaptive Radiation Therapy for Locally Advanced Lung Cancer.

PURPOSE: Real-time adaptation of thoracic radiation plans is compelling because offline adaptive experiences show that tumor volumes and lung anatomy can change during therapy. We present and analyze a novel adaptive-on-demand (AOD) workflow combining online adaptive radiation therapy (o-ART) on the ETHOS system with image guided radiation therapy delivery on a Halcyon unit for conventional fractionated radiation therapy of locally advanced lung cancer (LALC). METHODS AND MATERIALS: We analyzed 26 patients with LALC treated with the AOD workflow, adapting weekly. We timed segments of the workflow to evaluate efficiency in a real-world clinic. Target coverage and organ at risk (OAR) doses were compared between adaptive plans (ADP) and nonadaptive scheduled plans (SCH). Planning robustness was evaluated by the frequency of preplanning goals achieved in ADP plans, stratified by tumor volume change. RESULTS: The AOD workflow was achievable within 30 minutes for most radiation fractions. Over the course of therapy, we observed an average 26.6% ± 23.3% reduction in internal target volume (ITV). Despite these changes, with o-ART, ITV and planning target volume (PTV) coverage (V100%) was 99.2% and 93.9% for all members of the cohort, respectively. This represented a 2.9% and 6.8% improvement over nonadaptive plans (P < .05), respectively. For tumors that grew >10%, V100% was 93.1% for o-ART and 76.4% for nonadaptive plans, representing a median 17.2% improvement in the PTV coverage (P < .05). In these plans, critical OAR constraints were met 94.1% of the time, whereas in nonadaptive plans, this figure was 81.9%. This represented reductions of 1.32 Gy, 1.34 Gy, or 1.75 Gy in the heart, esophagus, and lung, respectively. The effect was larger when tumors had shrunk more than 10%. Regardless of tumor volume alterations, the PTV/ITV coverage was achieved for all adaptive plans. Exceptional cases, where dose constraints were not met, were due to large initial tumor volumes or tumor growth. CONCLUSIONS: The AOD workflow is efficient and robust in responding to anatomic changes in LALC patients, providing dosimetric advantages over standard therapy. Weekly adaptation was adequate to keep pace with changes. This approach is a feasible alternative to conventional offline replanning workflows for managing anatomy changes in LALC radiation therapy.

Benzodiazepine use in medical cannabis authorization adult patients from 2013 to 2021: Alberta, Canada.

BACKGROUND: Benzodiazepines are a class of medications that are being frequently prescribed in Canada but carry significant risk of harm. There has been increasing clinical interest on the potential "sparing effects" of medical cannabis as one strategy to reduce benzodiazepine use. The objective of this study as to examine the association of medical cannabis authorization with benzodiazepine usage between 2013 and 2021 in Alberta, Canada. METHODS: A propensity score matched cohort study with patients on regular benzodiazepine treatment authorized to use medical cannabis compared to controls who do not have authorization for medical cannabis. A total of 9690 medically authorized cannabis patients were matched to controls. To assess the effect of medical cannabis use on daily average diazepam equivalence (DDE), interrupted time series (ITS) analysis was used to assess the change in the trend of DDE in the 12 months before and 12 months after the authorization of medical cannabis. RESULTS: Over the follow-up period after medical cannabis authorization, there was no overall change in the DDE use in authorized medical cannabis patients compared to matched controls (- 0.08 DDE, 95% CI: - 0.41 to 0.24). Likewise, the sensitivity analysis showed that, among patients consuming ≤5 mg baseline DDE, there was no change immediately after medical cannabis authorization compared to controls (level change, - 0.04 DDE, 95% CI: - 0.12 to 0.03) per patient as well as in the month-to-month trend change (0.002 DDE, 95% CI: - 0.009 to 0.12) per patient was noted. CONCLUSIONS: This short-term study found that medical cannabis authorization had minimal effects on benzodiazepine use. Our findings may contribute ongoing evidence for clinicians regarding the potential impact of medical cannabis to reduce benzodiazepine use. HIGHLIGHTS: • Medical cannabis authorization had little to no effect on benzodiazepine usage among patients prescribed regular benzodiazepine treatment in Alberta, Canada. • Further clinical research is needed to investigate the potential impact of medical cannabis as an alternative to benzodiazepine medication.

In silico evaluation and feasibility of near margin-less head and neck daily adaptive radiotherapy.

OBJECTIVE: We explore the potential dosimetric benefits of reducing treatment volumes through daily adaptive radiation therapy for head and neck cancer (HNC) patients using the Ethos system/Intelligent Optimizer Engine (IOE). We hypothesize reducing treatment volumes afforded by daily adaption will significantly reduce the dose to adjacent organs at risk. We also explore the capability of the Ethos IOE to accommodate this highly conformal approach in HNC radiation therapy. METHODS: Ten HNC patients from a phase II trial were chosen, and their cone-beam CT (CBCT) scans were uploaded to the adaptive RT (ART) emulator. A new initial reference plan was generated using both a 1 mm and 5 mm planning target volume (PTV) expansion. Daily adaptive ART plans (1 mm) were simulated from the clinical CBCT taken every fifth fraction. Additionally, using physician-modified ART contours the larger 5 mm plan was recalculated on this recontoured on daily anatomy. Changes in target and OAR contours were measured using Dice coefficients as a surrogate of clinician effort. PTV coverage and organ-at-risk (OAR) doses were statistically compared, and the robustness of each ART plan was evaluated at fractions 5 and 35 to observe if OAR doses were within 3 Gy of pre-plan. RESULTS: This study involved six patients with oropharynx and four with larynx cancer, totaling 70 adaptive fractions. The primary and nodal gross tumor volumes (GTV) required the most adjustments, with median Dice scores of 0.88 (range: 0.80-0.93) and 0.83 (range: 0.66-0.91), respectively. For the 5th and 35th fraction plans, 80 % of structures met robustness criteria (quartile 1-3: 67-100 % and 70-90 %). Adaptive planning improved median PTV V100% coverage for doses of 70 Gy (96 % vs. 95.6 %), 66.5 Gy (98.5 % vs. 76.5 %), and 63 Gy (98.9 % vs. 74.9 %) (p < 0.03). Implementing ART with total volume reduction yielded median dose reductions of 7-12 Gy to key organs-at-risk (OARs) like submandibular glands, parotids, oral cavity, and constrictors (p < 0.05). CONCLUSIONS: The IOE enables feasible daily ART treatments with reduced margins while enhancing target coverage and reducing OAR doses for HNC patients. A phase II trial recently finished accrual and forthcoming analysis will determine if these dosimetric improvements correlate with improved patient-reported outcomes.

Multi-instance learning based lung nodule system for assessment of CT quality after small-field-of-view reconstruction.

Small-field-of-view reconstruction CT images (sFOV-CT) increase the pixel density across airway structures and reduce partial volume effects. Multi-instance learning (MIL) is proposed as a weakly supervised machine learning method, which can automatically assess the image quality. The aim of this study was to evaluate the disparities between conventional CT (c-CT) and sFOV-CT images using a lung nodule system based on MIL and assessments from radiologists. 112 patients who underwent chest CT were retrospectively enrolled in this study between July 2021 to March 2022. After undergoing c-CT examinations, sFOV-CT images with small-field-of-view were reconstructed. Two radiologists analyzed all c-CT and sFOV-CT images, including features such as location, nodule type, size, CT values, and shape signs. Then, an MIL-based lung nodule system objectively analyzed the c-CT (c-MIL) and sFOV-CT (sFOV-MIL) to explore their differences. The signal-to-noise ratio of lungs (SNR-lung) and contrast-to-noise ratio of nodules (CNR-nodule) were calculated to evaluate the quality of CT images from another perspective. The subjective evaluation by radiologists showed that feature of minimal CT value (p = 0.019) had statistical significance between c-CT and sFOV-CT. However, most features (all with p < 0.05), except for nodule type, location, volume, mean CT value, and vacuole sign (p = 0.056-1.000), had statistical differences between c-MIL and sFOV-MIL by MIL system. The SNR-lung between c-CT and sFOV-CT had no statistical significance, while the CNR-nodule showed statistical difference (p = 0.007), and the CNR of sFOV-CT was higher than that of c-CT. In detecting the difference between c-CT and sFOV-CT, features extracted by the MIL system had more statistical differences than those evaluated by radiologists. The image quality of those two CT images was different, and the CNR-nodule of sFOV-CT was higher than that of c-CT.

In Silico Analysis of Adjuvant Head and Neck Online Adaptive Radiation Therapy.

Purpose: Recently developed online adaptive radiation therapy (OnART) systems enable frequent treatment plan adaptation, but data supporting a dosimetric benefit in postoperative head and neck radiation therapy (RT) are sparse. We performed an in silico dosimetric study to assess the potential benefits of a single versus weekly OnART in the treatment of patients with head and neck squamous cell carcinoma in the adjuvant setting. Methods and Materials: Twelve patients receiving conventionally fractionated RT over 6 weeks and 12 patients receiving hypofractionated RT over 3 weeks on a clinical trial were analyzed. The OnART emulator was used to virtually adapt either once midtreatment or weekly based on the patient's routinely performed cone beam computed tomography. The planning target volume (PTV) coverage, dose heterogeneity, and cumulative dose to the organs at risk for these 2 adaptive approaches were compared with the nonadapted plan. Results: In total, 13, 8, and 3 patients had oral cavity, oropharynx, and larynx primaries, respectively. In the conventionally fractionated RT cohort, weekly OnART led to a significant improvement in PTV V100% coverage (6.2%), hot spot (-1.2 Gy), and maximum cord dose (-3.1 Gy), whereas the mean ipsilateral parotid dose increased modestly (1.8 Gy) versus the nonadapted plan. When adapting once midtreatment, PTV coverage improved with a smaller magnitude (0.2%-2.5%), whereas dose increased to the ipsilateral parotid (1.0-1.1 Gy) and mandible (0.2-0.7 Gy). For the hypofractionated RT cohort, similar benefit was observed with weekly OnART, including significant improvement in PTV coverage, hot spot, and maximum cord dose, whereas no consistent dosimetric advantage was seen when adapting once midtreatment. Conclusions: For head and neck squamous cell carcinoma adjuvant RT, there was a limited benefit of single OnART, but weekly adaptations meaningfully improved the dosimetric criteria, predominantly PTV coverage and dose heterogeneity. A prospective study is ongoing to determine the clinical benefit of OnART in this setting.

Simulation and pre-planning omitted radiotherapy (SPORT): a feasibility study for prostate cancer.

This study explored the feasibility of on-couch intensity modulated radiotherapy (IMRT) planning for prostate cancer (PCa) on a cone-beam CT (CBCT)-based online adaptive RT platform without an individualized pre-treatment plan and contours. Ten patients with PCa previously treated with image-guided IMRT (60 Gy/20 fractions) were selected. In contrast to the routine online adaptive RT workflow, a novel approach was employed in which the same preplan that was optimized on one reference patient was adapted to generate individual on-couch/initial plans for the other nine test patients using Ethos emulator. Simulation CTs of the test patients were used as simulated online CBCT (sCBCT) for emulation. Quality assessments were conducted on synthetic CTs (sCT). Dosimetric comparisons were performed between on-couch plans, on-couch plans recomputed on the sCBCT and individually optimized plans for test patients. The median value of mean absolute difference between sCT and sCBCT was 74.7 HU (range 69.5-91.5 HU). The average CTV/PTV coverage by prescription dose was 100.0%/94.7%, and normal tissue constraints were met for the nine test patients in on-couch plans on sCT. Recalculating on-couch plans on the sCBCT showed about 0.7% reduction of PTV coverage and a 0.6% increasing of hotspot, and the dose difference of the OARs was negligible (<0.5 Gy). Hence, initial IMRT plans for new patients can be generated by adapting a reference patient's preplan with online contours, which had similar qualities to the conventional approach of individually optimized plan on the simulation CT. Further study is needed to identify selection criteria for patient anatomy most amenable to this workflow.

ART2Dose: A comprehensive dose verification platform for online adaptive radiotherapy.

BACKGROUND: Online adaptive radiotherapy (ART) involves the development of adaptable treatment plans that consider patient anatomical data obtained right prior to treatment administration, facilitated by cone-beam computed tomography guided adaptive radiotherapy (CTgART) and magnetic resonance image-guided adaptive radiotherapy (MRgART). To ensure accuracy of these adaptive plans, it is crucial to conduct calculation-based checks and independent verification of volumetric dose distribution, as measurement-based checks are not practical within online workflows. However, the absence of comprehensive, efficient, and highly integrated commercial software for secondary dose verification can impede the time-sensitive nature of online ART procedures. PURPOSE: The main aim of this study is to introduce an efficient online quality assurance (QA) platform for online ART, and subsequently evaluate it on Ethos and Unity treatment delivery systems in our clinic. METHODS: To enhance efficiency and ensure compliance with safety standards in online ART, ART2Dose, a secondary dose verification software, has been developed and integrated into our online QA workflow. This implementation spans all online ART treatments at our institution. The ART2Dose infrastructure comprises four key components: an SQLite database, a dose calculation server, a report generator, and a web portal. Through this infrastructure, file transfer, dose calculation, report generation, and report approval/archival are seamlessly managed, minimizing the need for user input when exporting RT DICOM files and approving the generated QA report. ART2Dose was compared with Mobius3D in pre-clinical evaluations on secondary dose verification for 40 adaptive plans. Additionally, a retrospective investigation was conducted utilizing 1302 CTgART fractions from ten treatment sites and 1278 MRgART fractions from seven treatment sites to evaluate the practical accuracy and efficiency of ART2Dose in routine clinical use. RESULTS: With dedicated infrastructure and an integrated workflow, ART2Dose achieved gamma passing rates that were comparable to or higher than those of Mobius3D. Additionally, it significantly reduced the time required to complete pre-treatment checks by 3-4 min for each plan. In the retrospective analysis of clinical CTgART and MRgART fractions, ART2Dose demonstrated average gamma passing rates of 99.61 ± 0.83% and 97.75 ± 2.54%, respectively, using the 3%/2 mm criteria for region greater than 10% of prescription dose. The average calculation times for CTgART and MRgART were approximately 1 and 2 min, respectively. CONCLUSION: Overall, the streamlined implementation of ART2Dose notably enhances the online ART workflow, offering reliable and efficient online QA while reducing time pressure in the clinic and minimizing labor-intensive work.

Trilobatin attenuates cerebral ischaemia/reperfusion-induced blood-brain barrier dysfunction by targeting matrix metalloproteinase 9: The legend of a food additive.

BACKGROUND AND PURPOSE: Blood-brain barrier (BBB) breakdown is one of the crucial pathological changes of cerebral ischaemia-reperfusion (I/R) injury. Trilobatin (TLB), a naturally occurring food additive, exerts neuroprotective effects against cerebral I/R injury as demonstrated in our previous study. This study was designed to investigate the effect of TLB on BBB disruption after cerebral I/R injury. EXPERIMENTAL APPROACH: Rats with focal cerebral ischaemia caused by transient middle cerebral artery occlusion were studied along with brain microvascular endothelial cells and human astrocytes to mimic BBB injury caused by oxygen and glucose deprivation/reoxygenation (OGD/R). KEY RESULTS: The results showed that TLB effectively maintained BBB integrity and inhibited neuronal loss following cerebral I/R challenge. Furthermore, TLB increased tight junction proteins including ZO-1, Occludin and Claudin 5, and decreased the levels of apolipoprotein E (APOE) 4, cyclophilin A (CypA) and phosphorylated nuclear factor kappa B (NF-κB), thereby reducing proinflammatory cytokines. TLB also decreased the Bax/Bcl-2 ratio and cleaved-caspase 3 levels along with a reduced number of apoptotic neurons. Molecular docking and transcriptomics predicted MMP9 as a prominent gene evoked by TLB treatment. The protective effects of TLB on cerebral I/R-induced BBB breakdown was largely abolished by overexpression of MMP9, and the beneficial effects of TLB on OGD/R-induced loss of BBB integrity in human brain microvascular endothelial cells and astrocyte co-cultures was markedly reinforced by knockdown of MMP9. CONCLUSIONS AND IMPLICATIONS: Our findings reveal a novel property of TLB: preventing BBB disruption following cerebral I/R via targeting MMP9 and inhibiting APOE4/CypA/NF-κB axis.

Optimizing Online Adaptation Timing in the Treatment of Locally Advanced Cervical Cancer.

PURPOSE: Online adaptive radiation therapy (ART) has emerged as a new treatment modality for cervical cancer. Daily online adapting improves target coverage and organ-at-risk (OAR) sparing compared with traditional image guided radiation therapy (IGRT); however, the required resources may not be feasible in a busy clinical setting. Less frequent adapting may still benefit cervical cancer patients due to large volume changes of the uterocervix of the treatment course. In this study, the dosimetry from different online adapt-on-demand schedules was compared. MATERIALS AND METHODS: A retrospective cohort of 10 patients with cervical cancer treated with 260 fractions of definitive daily online ART was included. Plans with different adaptation schedules were simulated with adaptations weekly, every other week, once during treatment, and no adaptations (IGRT). These plans were applied to the synthetic computed tomography (CT) images and contours generated during the patient's delivered daily adaptive workflow. The dosimetry of the weekly replan, every-other-week replan, once replan, and IGRT plans were compared using a paired t test. RESULTS: Compared with traditional IGRT plans, weekly and every-other-week ART plans had similar clinical target volume (CTV) coverage, but statistically significant improved sparing of OARs. Weekly and every-other-week ART had reduced bowel bag V40 by 1.57% and 1.41%, bladder V40 by 3.82% and 1.64%, rectum V40 by 8.49% and 7.50%, and bone marrow Dmean by 0.81% and 0.61%, respectively. Plans with a single adaptation had statistically significantly worse target coverage, and moderate improvements in OAR sparing. Of the 18-dose metrics evaluated, improvements were seen in 15 for weekly ART, 14 for every-other-week ART, and 10 for single ART plans compared with IGRT. When every-other-week ART was compared with weekly ART, both plans had similar CTV coverage and OAR sparing with only small improvements in bone marrow dosimetry with weekly ART. CONCLUSIONS: This retrospective work compares different adapt-on-demand treatment schedules using data collected from patients treated with daily online adaptive radiation therapy. Results suggest weekly or every-other-week online ART is beneficial for reduced OAR dose compared with IGRT by exploiting the gradual changes in the uterocervix target volume.

Single patient learning for adaptive radiotherapy dose prediction.

BACKGROUND: Throughout a patient's course of radiation therapy, maintaining accuracy of their initial treatment plan over time is challenging due to anatomical changes-for example, stemming from patient weight loss or tumor shrinkage. Online adaptation of their RT plan to these changes is crucial, but hindered by manual and time-consuming processes. While deep learning (DL) based solutions have shown promise in streamlining adaptive radiation therapy (ART) workflows, they often require large and extensive datasets to train population-based models. PURPOSE: This study extends our prior research by introducing a minimalist approach to patient-specific adaptive dose prediction. In contrast to our prior method, which involved fine-tuning a pre-trained population model, this new method trains a model from scratch using only a patient's initial treatment data. This patient-specific dose predictor aims to enhance clinical accessibility, thereby empowering physicians and treatment planners to make more informed, quantitative decisions in ART. We hypothesize that patient-specific DL models will provide more accurate adaptive dose predictions for their respective patients compared to a population-based DL model. METHODS: We selected 33 patients to train an adaptive population-based (AP) model. Ten additional patients were selected, and their respective initial RT data served as single samples for training patient-specific (PS) models. These 10 patients contained an additional 26 ART plans that were withheld as the test dataset to evaluate AP versus PS model dose prediction performance. We assessed model performance using Mean Absolute Percent Error (MAPE) by comparing predicted doses to the originally delivered ground truth doses. We used the Wilcoxon signed-rank test to determine statistically significant differences in terms of MAPE between the AP and PS model results across the test dataset. Furthermore, we calculated differences between predicted and ground truth mean doses for segmented structures and determined statistical significance in the differences for each of them. RESULTS: The average MAPE across AP and PS model dose predictions was 5.759% and 4.069%, respectively. The Wilcoxon signed-rank test yielded two-tailed p-value =  2.9802 × 10 - 8 $2.9802\ \times \ {10}^{ - 8}$ , indicating that the MAPE differences between the AP and PS model dose predictions are statistically significant, and 95% confidence interval = [-2.1610, -1.0130], indicating 95% confidence that the MAPE difference between the AP and PS models for a population lies in this range. Out of 24 total segmented structures, the comparison of mean dose differences for 12 structures indicated statistical significance with two-tailed p-values < 0.05. CONCLUSION: Our study demonstrates the potential of patient-specific deep learning models in application to ART. Notably, our method streamlines the training process by minimizing the size of the required training dataset, as only a single patient's initial treatment data is required. External institutions considering the implementation of such a technology could package such a model so that it only requires the upload of a reference treatment plan for model training and deployment. Our single patient learning strategy demonstrates promise in ART due to its minimal dataset requirement and its utility in personalization of cancer treatment.

Optimization of target system for the production of 99Mo via 100Mo(γ,n)99Mo reaction.

With an increase of stopping operation of nuclear reactors worldwide, the supply of medical 99Mo becomes difficult and thus many efforts have been made to find an alternative. A process based on an electron linear accelerator (linac) system and a100Mo target via the 100Mo (γ,n)99Mo reaction receives a lot of attention due to the relatively low level of co-produced impurities. This process has been recently developed at the Institute of Modern Physics (IMP) and the Monte Carlo simulation was used to optimize the target system before operating pilot irradiation experiments. First, tungsten and tantalum, as mostly used converter materials, were tested. The yield of 99Mo was evaluated with respect to the converter thickness and the electron beam energy by means of Geant4 simulations. Besides, the specific activity of 99Mo produced from one-stage approach (100Mo target without a converter) and two-stage approach (100Mo target with a converter) was compared when varying the testing conditions. The two-stage approach was selected for the experiment due to the higher specific activity of produced 99Mo at all tested conditions. A target consisting of a 10 mm thickness of the 100Mo tablets and a 2.4 mm thick Ta converter was irradiated for 40 h (50 MeV with 0.2 µA). The Geant4-calculated specific activity of generated 99Mo at the end of bombardment agreed well with the experimental value, which proved high level of accuracy of the Geant4 simulation. In future studies, the Geant4 simulation will be used to optimize the production process when using high power linac system.

An electrochemiluminescence resonance energy transfer biosensor based on CDs/PAMAM/rGO nanocomposites and Au@Ag2S nanoparticles for PML/RARα fusion gene detection.

In recent years, electrochemiluminescence resonance energy transfer (ECL-RET) with low background signal and high specificity has attracted much attention among researchers. Herein, we established a novel ECL-RET biosensor for PML/RARα fusion gene detection. In this ECL-RET system, carbon dots (CDs) with low toxicity and prominent electrochemical activity were used as donor and Au@Ag2S core-shell nanoparticles (Au@Ag2S NPs) were employed as ECL acceptor. The Au@Ag2S NPs possessed a wide ultraviolet-visible (UV-vis) absorption spectrum between 500 nm and 700 nm, which completely overlapped with the ECL spectrum of CDs. Furthermore, the CDs-decorated poly-amidoamine/reduced graphene oxide (CDs/PAMAM/rGO) nanocomposites were prepared to improve the ECL signals and served as a substrate to stably load capture probe deoxyribonucleic acid (DNA). Based on the ECL-RET biosensing strategy, the Au@Ag2S NPs-labeled assistant probes and target DNA could pair with capture probes to form the sandwich-type DNA structure and the distance between donor and accepter was closed, leading to quenching of the ECL signal of CDs. The ECL-RET biosensor represented eminent analytical performance for PML/RARα fusion gene detection with a wide linear relationship from 5 fM to 500 pM and a low detection limit of 0.72 fM, which provided a novel technical means and theoretical basis for detection and diagnosis of acute promyelocytic leukemia.