Co-dependent regulation of p-BRAF and potassium channel KCNMA1 levels drives glioma progression.

BRAF mutations have been found in gliomas which exhibit abnormal electrophysiological activities, implying their potential links with the ion channel functions. In this study, we identified the Drosophila potassium channel, Slowpoke (Slo), the ortholog of human KCNMA1, as a critical factor involved in dRafGOF glioma progression. Slo was upregulated in dRafGOF glioma. Knockdown of slo led to decreases in dRafGOF levels, glioma cell proliferation, and tumor-related phenotypes. Overexpression of slo in glial cells elevated dRaf expression and promoted cell proliferation. Similar mutual regulations of p-BRAF and KCNMA1 levels were then recapitulated in human glioma cells with the BRAF mutation. Elevated p-BRAF and KCNMA1 were also observed in HEK293T cells upon the treatment of 20 mM KCl, which causes membrane depolarization. Knockdown KCNMA1 in these cells led to a further decrease in cell viability. Based on these results, we conclude that the levels of p-BRAF and KCNMA1 are co-dependent and mutually regulated. We propose that, in depolarized glioma cells with BRAF mutations, high KCNMA1 levels act to repolarize membrane potential and facilitate cell growth. Our study provides a new strategy to antagonize the progression of gliomas as induced by BRAF mutations.

Development of a virtual source model for Monte Carlo-based independent dose calculation for varian linac.

Monte Carlo (MC) independent dose calculations are often based on phase-space files (PSF), as they can accurately represent particle characteristics. PSF generally are large and create a bottleneck in computation time. In addition, the number of independent particles is limited by the PSF, preventing further reduction of statistical uncertainty. The purpose of this study is to develop and validate a virtual source model (VSM) to address these limitations. Particles from existing PSF for the Varian TrueBeam medical linear accelerator 6X, 6XFFF, 10X, and 10XFFF beam configurations were tallied, analyzed, and used to generate a dual-source photon VSM that includes electron contamination. The particle density distribution, kinetic energy spectrum, particle direction, and the correlations between characteristics were computed. The VSM models for each beam configuration were validated with water phantom measurements as well as clinical test cases against the original PSF. The new VSM requires 67 MB of disk space for each beam configuration, compared to 50 GB for the PSF from which they are based and effectively remove the bottleneck set by the PSF. At 3% MC uncertainty, the VSM approach reduces the calculation time by a factor of 14 on our server. MC doses obtained using the VSM approach were compared against PSF-generated doses in clinical test cases and measurements in a water phantom using a gamma index analysis. For all tests, the VSMs were in excellent agreement with PSF doses and measurements (>90% passing voxels between doses and measurements). Results of this study indicate the successful derivation and implementation of a VSM model for Varian Linac that significantly saves computation time without sacrificing accuracy for independent dose calculation.

Enhancing Precision in Radiation Therapy for Locally Advanced Lung Cancer: A Case Study of Cone-Beam Computed Tomography (CBCT)-Based Online Adaptive Techniques and the Promise of HyperSight™ Iterative CBCT.

This study explores the dosimetric benefits of cone-beam computed tomography (CBCT)-based online adaptive radiation therapy (oART) for a non-small-cell lung cancer (NSCLC) patient exhibiting significant tumor shrinkage during ChemoRT. The patient was prescribed 60 Gray (Gy) in 30 fractions and was initially treated with conventional RT. After the delivery of the first four treatment fractions, the patient's treatment course was converted to oART due to tumor shrinkage seen on CBCT. Current oART dose calculations use a synthetic CT (sCT) image derived from deformable image registration (DIR) of the planning CT to the daily CBCT, and, as the tumor regressed, the discrepancy between the CBCT and the sCT increased, leading to a re-simulation after the delivery of the ninth fraction. In this case report, we first investigated dosimetric differences leveraged by converting this patient from conventional RT to oART. With oART using sCT, the patient's target coverage remained consistent with the reference plan while simultaneously changing lung V20 by 7.8 ± 1.4% and heart mean by 3.4 ± 1.5 Gy. Then, using this new simulation CT and comparing it with iterative CBCT (iCBCT) images acquired with the new HyperSight™ (HS) (Varian Medical Systems, Inc., Palo Alto, CA, USA) imaging system on the Ethos, we investigated the impact of direct dose calculation on HS-iCBCT as compared to sCT. The HS-iCBCT generated a dose distribution similar to the CT reference, achieving a 96.01% gamma passing rate using Task Group-218 (TG-218) criteria. Results indicate that HS-iCBCT has the potential to better reflect daily anatomical changes, resulting in improved dosimetric accuracy. This study highlights the advantages of oART in the presence of tumor response to therapy and underscores HS-iCBCT's potential to provide CT-level dose calculation accuracy in oART for NSCLC patients.

Early Enteral Nutrition Could Be Associated with Improved Survival Outcome in Cardiac Arrest.

Background: Although the latest European and US guidelines recommend that early enteral nutrition (EN) be attempted in critically ill patients, there is still a lack of research on feeding strategies for patients after cardiac arrest (CA). Due to the unique pathophysiology following CA, it remains unknown whether evidence from other diseases can be applied in this condition. Objective: We aimed to explore the relationship between the timing of EN (within 48 hours or after 48 hours) and clinical outcomes and safety in CA. Method: From the MIMIC-IV (version 2.2) database, we conducted this retrospective cohort study. A 1 : 1 propensity score matching (PSM) analysis was also conducted to prevent potential interference from confounders. Moreover, adjusted proportional hazards model regression models were used to adjust for prehospital and hospitalization characteristics to verify the independence of the association between early EN initiation and patient outcomes. Results: Of the initial 1286 patients, 670 were equally assigned to the early EN or delayed EN group after PSM. Patients in the early EN group had improved survival outcomes than those in the delayed EN group within 30 days (HR = 0.779, 95% confidence interval [CI] [0.611-0.994], p = 0.041). Similar results were shown at 90 and 180 days. However, there was no significant difference in neurological outcome between the two groups at 30 days (51% vs. 57%, odds ratio [OR] = 0.786, 95% CI [0.580-1.066], p = 0.070). Patients who underwent early EN had a lower risk of ileus than patients who underwent delayed EN (4% vs. 8%, OR = 0.461, 95% CI [0.233-0.909], p = 0.016). Moreover, patients who underwent early EN had shorter hospital stays. Conclusion: Early EN could be associated with improved survival outcomes for patients after CA. Further studies are needed to verify it. However, at present, we might consider early EN to be a more suitable feeding strategy for CA.

Cardiac Arrest-Associated Coagulopathy Could Predict 30-day Mortality: A Retrospective Study from Medical Information Mart for Intensive Care IV Database.

BACKGROUND: Cardiac arrest (CA) can activate the coagulation system. Some coagulation-related indicators are associated with clinical outcomes. Early evaluation of patients with cardiac arrest-associated coagulopathy (CAAC) not only predicts clinical outcomes, but also allows for timely clinical intervention to prevent disseminated intravascular coagulation. OBJECTIVE: To assess whether CAAC predicts 30-day cumulative mortality. METHODS: From the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, we conducted a retrospective cohort study from 2008 to 2019. Based on international normalized ratio (INR) value and platelet count, we diagnosed CAAC cases and made the following stratification of severity: mild CAAC was defined as 1.4 > INR≧1.2 and 100,000/µL < platelet count≦150,000/µL; moderate CAAC was defined with either 1.6 > INR≧1.4 or 80,000/µL < platelet count≦100,000/µL; severe CAAC was defined as an INR≧1.6 and platelet count≦80,000/µL. RESULTS: A total of 1485 patients were included. Crude survival analysis showed that patients with CAAC had higher mortality risk than those without CAAC (33.0% vs 52.0%, P < 0.001). Unadjusted survival analysis showed an incremental increase in the risk of mortality as the severity of CAAC increased. After adjusting confounders (prehospital characteristics and hospitalization characteristics), CAAC was independently associated with 30-day mortality (hazard rate [HR] 1.77, 95% confidence interval [CI] 1.41-2.25; P < 0.001); moderate CAAC (HR 1.48, 95% CI 1.09-2.10; P = 0.027) and severe CAAC (HR 2.22, 95% CI 1.64-2.97; P < 0.001) were independently associated with 30-day mortality. CONCLUSION: The presence of CAAC identifies a group of CA at higher risk for mortality, and there is an incremental increase in risk of mortality as the severity of CAAC increases. However, the results of this study should be further verified by multicenter study.

Achieving accurate prostate auto-segmentation on CT in the absence of MR imaging.

BACKGROUND: Magnetic resonance imaging (MRI) is considered the gold standard for prostate segmentation. Computed tomography (CT)-based segmentation is prone to observer bias, potentially overestimating the prostate volume by âˆ¼ 30 % compared to MRI. However, MRI accessibility is challenging for patients with contraindications or in rural areas globally with limited clinical resources. PURPOSE: This study investigates the possibility of achieving MRI-level prostate auto-segmentation accuracy using CT-only input via a deep learning (DL) model trained with CT-MRI registered segmentation. METHODS AND MATERIALS: A cohort of 111 definitive prostate radiotherapy patients with both CT and MRI images was retrospectively grouped into training (n = 37) and validation (n = 20) (where reference contours were derived from CT-MRI registration), and testing (n = 54) sets. Two commercial DL models were benchmarked against the reference contours in the training and validation sets. A custom DL model was incrementally retrained using the training dataset, quantitatively evaluated on the validation dataset, and qualitatively assessed by two different physician groups on the validation and testing datasets. A contour quality assurance (QA) model, established from the proposed model on the validation dataset, was applied to the test group to identify potential errors, confirmed by human visual inspection. RESULTS: Two commercial models exhibited large deviations in the prostate apex with CT-only input (median: 0.77/0.78 for Dice similarity coefficient (DSC), and 0.80 cm/0.83 cm for 95 % directed Hausdorff Distance (HD95), respectively). The proposed model demonstrated superior geometric similarity compared to commercial models, particularly in the apex region, with improvements of 0.05/0.17 cm and 0.06/0.25 cm in median DSC/HD95, respectively. Physician evaluation on MRI-CT registration data rated 69 %-78 % of the proposed model's contours as clinically acceptable without modifications. Additionally, 73 % of cases flagged by the contour quality assurance (QA) model were confirmed via visual inspection. CONCLUSIONS: The proposed incremental learning strategy based on CT-MRI registration information enhances prostate segmentation accuracy when MRI availability is limited clinically.

Microstructure evolution of medium carbon steel during heat-assisted 3D ultrasonic vibration-assisted turning.

Medium carbon steel is an excellent carbon structural steel, and is one of the most common materials for metal cutting. Little research has been done on the microstructural changes induced by thermal-force coupling. In this paper, a finite element simulation method based on the improved J-C model is used to predict the grain size, microstructure change depth and surface hardness of medium carbon steel surface induced by heat-assisted 3D-UVAT are studied. The numerical simulation results are compared with the experimental results, and the significant influence of turning conditions on them is analyzed. The results show that heat-assisted 3D-UVAT lowered the grain size of machined induced deformation zone. Numerical model foresees this case with a mean error of 9.4%. Microstructure and hardness measurements under different turning conditions show that the turning speed and feed rate contribute significantly to grain size and grain refinement layer depth in the area being machined.

NKAP acts with HDAC3 to prevent R-loop associated genome instability.

Persistent R-loop accumulation can cause DNA damage and lead to genome instability, which contributes to various human diseases. Identification of molecules and signaling pathways in controlling R-loop homeostasis provide important clues about their physiological and pathological roles in cells. Here, we show that NKAP (NF-κB activating protein) is essential for preventing R-loop accumulation and maintaining genome integrity through forming a protein complex with HDAC3. NKAP depletion causes DNA damage and genome instability. Aberrant accumulation of R-loops is present in NKAP-deficient cells and leads to DNA damage and DNA replication fork progression defects. Moreover, NKAP depletion induced R-loops and DNA damage are dependent on transcription. Consistently, the NKAP interacting protein HDAC3 exhibits a similar role in suppressing R-loop associated DNA damage and replication stress. Further analysis uncovers that HDAC3 functions to stabilize NKAP protein, independent of its deacetylase activity. In addition, NKAP prevents R-loop formation by maintaining RNA polymerase II pausing. Importantly, R-loops induced by NKAP or HDAC3 depletion are processed into DNA double-strand breaks by XPF and XPG endonucleases. These findings indicate that both NKAP and HDAC3 are novel key regulators of R-loop homeostasis, and their dysregulation might drive tumorigenesis by causing R-loop associated genome instability.

Contour subregion error detection methodology using deep learning auto-segmentation.

BACKGROUND: Inaccurate manual organ delineation is one of the high-risk failure modes in radiation treatment. Numerous automated contour quality assurance (QA) systems have been developed to assess contour acceptability; however, manual inspection of flagged cases is a time-consuming and challenging process, and can lead to users overlooking the exact error location. PURPOSE: Our aim is to develop and validate a contour QA system that can effectively detect and visualize subregional contour errors, both qualitatively and quantitatively. METHODS/MATERIALS: A novel contour subregion error detection (CSED) system was developed using subregional surface distance discrepancies between manual and deep learning auto-segmentation (DLAS) contours. A validation study was conducted using a head and neck public dataset containing 339 cases and evaluated according to knowledge-based pass criteria derived from a clinical training dataset of 60 cases. A blind qualitative evaluation was conducted, comparing the results from the CSED system with manual labels. Subsequently, the CSED-flagged cases were re-examined by a radiation oncologist. RESULTS: The CSED system could visualize the diverse types of subregional contour errors qualitatively and quantitatively. In the validation dataset, the CSED system resulted in true positive rates (TPR) of 0.814, 0.800, and 0.771; false positive rates (FPR) of 0.310, 0.267, and 0.298; and accuracies of 0.735, 0.759, and 0.730, for brainstem and left and right parotid contours, respectively. The CSED-assisted manual review caught 13 brainstem, 19 left parotid, and 21 right parotid contour errors missed by conventional human review. The TPR/FPR/accuracy of the CSED-assisted manual review improved to 0.836/0.253/0.784, 0.831/0.171/0.830, and 0.808/0.193/0.807 for each structure, respectively. Further, the time savings achieved through CSED-assisted review improved by 75%, with the time for review taking 24.81 ± 12.84, 26.75 ± 10.41, and 28.71 ± 13.72 s for each structure, respectively. CONCLUSIONS: The CSED system enables qualitative and quantitative detection, localization, and visualization of manual segmentation subregional errors utilizing DLAS contours as references. The use of this system has been shown to help reduce the risk of high-risk failure modes resulting from inaccurate organ segmentation.

Contouring quality assurance methodology based on multiple geometric features against deep learning auto-segmentation.

BACKGROUND: Contouring error is one of the top failure modes in radiation treatment. Multiple efforts have been made to develop tools to automatically detect segmentation errors. Deep learning-based auto-segmentation (DLAS) has been used as a baseline for flagging manual segmentation errors, but those efforts are limited to using only one or two contour comparison metrics. PURPOSE: The purpose of this research is to develop an improved contouring quality assurance system to identify and flag manual contouring errors. METHODS AND MATERIALS: DLAS contours were used as a reference to compare with manually segmented contours. A total of 27 geometric agreement metrics were determined from the comparisons between the two segmentation approaches. Feature selection was performed to optimize the training of a machine learning classification model to identify potential contouring errors. A public dataset with 339 cases was used to train and test the classifier. Four independent classifiers were trained using five-fold cross validation, and the predictions from each classifier were ensembled using soft voting. The trained model was validated on a held-out testing dataset. An additional independent clinical dataset with 60 cases was used to test the generalizability of the model. Model predictions were reviewed by an expert to confirm or reject the findings. RESULTS: The proposed machine learning multiple features (ML-MF) approach outperformed traditional nonmachine-learning-based approaches that are based on only one or two geometric agreement metrics. The machine learning model achieved recall (precision) values of 0.842 (0.899), 0.762 (0.762), 0.727 (0.842), and 0.773 (0.773) for Brainstem, Parotid_L, Parotid_R, and mandible contours, respectively compared to 0.526 (0.909), 0.619 (0.765), 0.682 (0.882), 0.773 (0.568) for an approach based solely on Dice similarity coefficient values. In the external validation dataset, 66.7, 93.3, 94.1, and 58.8% of flagged cases were confirmed to have contouring errors by an expert for Brainstem, Parotid_L, Parotid_R, and mandible contours, respectively. CONCLUSIONS: The proposed ML-MF approach, which includes multiple geometric agreement metrics to flag manual contouring errors, demonstrated superior performance in comparison to traditional methods. This method is easy to implement in clinical practice and can help to reduce the significant time and labor costs associated with manual segmentation and review.

First Report On Physician Assessment and Clinical Acceptability of Custom-Retrained Artificial Intelligence Models for Clinical Target Volume and Organs-at-Risk Auto-Delineation for Postprostatectomy Patients.

PURPOSE: To assess the clinical acceptability of a commercial deep-learning-based auto-segmentation (DLAS) prostate model that was retrained using institutional data for delineation of the clinical target volume (CTV) and organs-at-risk (OARs) for postprostatectomy patients, accounting for clinical and imaging protocol variations. METHODS AND MATERIALS: CTV and OARs of 109 prostate-bed patients were used to evaluate the performance of the vendor-trained model and custom retrained DLAS models using different training quantities. Two new models for OAR structures were retrained (n = 30, 60 data sets), while separate models were trained for a new CTV structure (n = 30, 60, 90 data sets), with the remaining data sets used for testing (n = 49, 19). The dice similarity coefficient (DSC), Hausdorff distance, and mean surface distance were evaluated. Six radiation oncologists performed a qualitative evaluation scoring both preference and clinical utility for blinded structure sets. Physician consensus data sets identified from the qualitative evaluation were used toward a separate CTV model. RESULTS: Both the 30- and 60-case retrained OAR models had median DSC values between 0.91 to 0.97, improving significantly over the vendor-trained model for all OARs except the penile bulb. The brand new 60-case CTV model had a median DSC of 0.70 improving significantly over the 30-case model. DLAS (60-case model) and manual contours were blinded and evaluated by physicians with contours deemed acceptable or precise for 87% and 94% of cases for DLAS and manual delineations, respectively. DLAS-generated CTVs were scored precise or acceptable in 54% of cases, compared with the manual delineation value of 73%. The 30-case physician consensus CTV model did not show a significant difference compared with the randomly selected models. CONCLUSIONS: Custom retraining using institutional data leads to performance improvement in the clinical utility and accuracy of DLAS for postprostatectomy patients. A small number of data sets are sufficient for building an institutional site-specific DLAS OAR model, as well as for training new structures. Data indicates the workload for identifying training data sets could be shared among groups for the male pelvic region, making it accessible to clinics of all sizes.

Impaired dNKAP function drives genome instability and tumorigenic growth in Drosophila epithelia.

Mutations or dysregulated expression of NF-kappaB activating protein (NKAP) family genes have been found in human cancers. How NKAP family gene mutations promote tumor initiation and progression remains to be determined. Here, we characterized dNKAP, the Drosophila homolog of NKAP, and showed that impaired dNKAP function causes genome instability and tumorigenic growth in a Drosophila epithelial tumor model. dNKAP-knockdown wing imaginal discs exhibit tumorigenic characteristics, including tissue overgrowth, cell invasive behavior, abnormal cell polarity, and cell adhesion defects. dNKAP knockdown causes both R-loop accumulation and DNA damage, indicating the disruption of genome integrity. Further analysis showed that dNKAP knockdown induces c-Jun N-terminal kinase (JNK)-dependent apoptosis and causes changes in cell proliferation in distinct cell populations. Activation of the Notch and JAK/STAT signaling pathways contributes to the tumorigenic growth of dNKAP-knockdown tissues. Furthermore, JNK signaling is essential for dNKAP depletion-mediated cell invasion. Transcriptome analysis of dNKAP-knockdown tissues confirmed the misregulation of signaling pathways involved in promoting tumorigenesis and revealed abnormal regulation of metabolic pathways. dNKAP knockdown and oncogenic Ras, Notch, or Yki mutations show synergies in driving tumorigenesis, further supporting the tumor-suppressive role of dNKAP. In summary, this study demonstrates that dNKAP plays a tumor-suppressive role by preventing genome instability in Drosophila epithelia and thus provides novel insights into the roles of human NKAP family genes in tumor initiation and progression.

Incremental retraining, clinical implementation, and acceptance rate of deep learning auto-segmentation for male pelvis in a multiuser environment.

BACKGROUND: Deep learning auto-segmentation (DLAS) models have been adopted in the clinic; however, they suffer from performance deterioration owing to the clinical practice variability. Some commercial DLAS software provide an incremental retraining function that enables users to train a custom model using their institutional data to account for clinical practice variability. PURPOSE: This study was performed to evaluate and implement the commercial DLAS software with the incremental retraining function for definitive treatment of patients with prostate cancer in a multi-user environment. METHODS: CT-based target organs and organs-at-risk (OAR) delineation of 215 prostate cancer patients were utilized. The performance of three commercial DLAS software built-in models was validated with 20 patients. A retrained custom model was developed using 100 patients and evaluated on the remaining data (n = 115). Dice similarity coefficient (DSC), Hausdorff distance (HD), mean surface distance (MSD), and surface DSC (SDSC) were utilized for quantitative evaluation. A multi-rater qualitative evaluation was blindly performed with a five-level scale. Visual inspection was performed in consensus and non-consensus unacceptable cases to identify the failure modes. RESULTS: Three commercial DLAS vendor built-in models achieved sub-optimal performance in 20 patients. The retrained custom model had a mean DSC of 0.82 for prostate, 0.48 for seminal vesicles (SV), and 0.92 for rectum, respectively. This represents a significant improvement over the built-in model with DSC of 0.73, 0.37, and 0.81 for the corresponding structures. Compared to the acceptance rate of 96.5% and consensus unacceptable rate (i.e., both reviewers rated as unacceptable) of 3.5% achieved by manual contours, the custom model achieved a 91.3% acceptance rate and 8.7% consensus unacceptable rate. The failure modes of retrained custom model were attributed to the following: cystogram (n = 2), hip prosthesis (n = 2), low dose rate brachytherapy seeds (n = 2), air in endorectal balloon(n = 1), non-iodinated spacer (n = 2), and giant bladder(n = 1). CONCLUSION: The commercial DLAS software with the incremental retraining function was validated and clinically adopted for prostate patients in a multi-user environment. AI-based auto-delineation of the prostate and OARs is shown to achieve improved physician acceptance, overall clinical utility, and accuracy.

Evaluating the clinical acceptability of deep learning contours of prostate and organs-at-risk in an automated prostate treatment planning process.

BACKGROUND: Radiation treatment is considered an effective and the most common treatment option for prostate cancer. The treatment planning process requires accurate and precise segmentation of the prostate and organs at risk (OARs), which is laborious and time-consuming when contoured manually. Artificial intelligence (AI)-based auto-segmentation has the potential to significantly accelerate the radiation therapy treatment planning process; however, the accuracy of auto-segmentation needs to be validated before its full clinical adoption. PURPOSE: A commercial AI-based contouring model was trained to provide segmentation of the prostate and surrounding OARs. The segmented structures were input to a commercial auto-planning module for automated prostate treatment planning. This study comprehensively evaluates the performance of this contouring model in the automated prostate treatment planning process. METHODS AND MATERIALS: A 3D U-Net-based model (INTContour, Carina AI) was trained and validated on 84 computed tomography (CT) scans and tested on an additional 23 CT scans from patients treated in our local institution. Prostate and OARs contours generated by the AI model (AI contour) were geometrically evaluated against reference contours. The prostate contours were further evaluated against AI, reference, and two additional observer contours for comparison using inter-observer variation (IOV) and 3D boundaries discrepancy analyses. A blinded evaluation was introduced to assess subjectively the clinical acceptability of the AI contours. Finally, treatment plans were created from an automated prostate planning workflow using the AI contours and were evaluated for their clinical acceptability following the Radiation Therapy Oncology Group-0815 protocol. RESULTS: The AI contours demonstrated good geometric accuracy on OARs and prostate contours, with average Dice similarity coefficients (DSC) for bladder, rectum, femoral heads, seminal vesicles, and penile bulb of 0.93, 0.85, 0.96, 0.72, and 0.53, respectively. The DSC, 95% directed Hausdorff distance (HD95), and mean surface distance for the prostate were 0.83 ± 0.05, 6.07 ± 1.87 mm, and 2.07 ± 0.73 mm, respectively. No significant differences were found when comparing with IOV. In the double-blinded evaluation, 95.7% of the AI contours were scored as either "perfect" (34.8%) or "acceptable" (60.9%), while only one case (4.3%) was scored as "unacceptable with minor changes required." In total, 69.6% of the AI contours were considered equal to or better than the reference contours by an independent radiation oncologist. Automated treatment plans created from the AI contours produced similar and clinically acceptable dosimetric distributions as those from plans created from reference contours. CONCLUSIONS: The investigated AI-based commercial model for prostate segmentation demonstrated good performance in clinical practice. Using this model, the implementation of an automated prostate treatment planning process is clinically feasible.

Clinical Practice Evolvement for Post-Operative Prostate Cancer Radiotherapy-Part 2: Feasibility of Margin Reduction for Fractionated Radiation Treatment with Advanced Image Guidance.

Purpose: Planning target volume (PTV) expansion for post-prostatectomy radiotherapy is typically ≥5 mm. Recent clinical trials have proved the feasibility of a reduced margin of 2−3 mm for treatments on MRI-linac. We aim to study the minimum PTV margin needed using iterative cone-beam CT (iCBCT) as image guidance on conventional linacs. Materials/Methods: Fourteen patients who received post-prostatectomy irradiation (8 with an endorectal balloon and 6 without a balloon) were included in this study. Treatment was delivered with volumetric modulated radiation therapy (VMAT). Fractional dose delivery was evaluated in 165 treatment fractions. The bladder, rectal wall, femoral heads, and prostate bed clinical tumor volume (CTV) were contoured and verified on daily iCBCT. PTV margins (0 mm, 2 mm, and 4 mm) were evaluated on daily iCBCT. CTV coverage and OAR dose parameters were assessed with each PTV margin. Results: CTV D100% was underdosed with a 0 mm margin in 32% of fractions in comparison with 2 mm (6%) and 4 mm (6%) PTV margin (p ≤ 0.001). CTV D95% > 95% was met in 93−94% fractions for all PTV expansions. CTV D95% > 95% was achieved in more patients with an endorectal balloon than those without: 0 mm­90/91 (99%) vs. 63/74 (85%); 2 mm­90/91 (99%) vs. 65/75 (87%); 4 mm­90/90 (100%) vs. 63/73 (86%). There was no difference in absolute median change in CTV D95% (0.32%) for 0-, 2-, and 4 mm margins. The maximum dose remained under 108% for 100% (0 mm), 97% (2 mm), and 98% (4 mm) of images. Rectal wall maximum dose remained under 108% for 100% (0 mm), 100% (2 mm), and 98% (4 mm) of images. Conclusions: With high-quality iCBCT image guidance, PTV margin accounting for inter-fractional uncertainties can be safely reduced for post-prostatectomy radiotherapy. For fractionated radiotherapy, an isotropic expansion of 2 mm and 4 mm may be considered for margin expansion with and without the endorectal balloon. Future application for margin reduction needs to be further evaluated and considered with the advent of shorter post-prostatectomy radiation courses.

eCPR Combined With Therapeutic Hypothermia Could Improve Survival and Neurologic Outcomes for Patients With Cardiac Arrest: A Meta-Analysis.

Background: Extracorporeal membrane oxygenation with CPR (eCPR) or therapeutic hypothermia (TH) seems to be a very effective CPR strategy to save patients with cardiac arrest (CA). Furthermore, the subsequent post-CA neurologic outcomes have become the focus. Therefore, there is an urgent need to find a way to improve survival and neurologic outcomes for CA. Objective: We conducted this meta-analysis to find a more suitable CPR strategy for patients with CA. Method: We searched four online databases (PubMed, Embase, CENTRAL, and Web of Science). From an initial 1,436 articles, 23 studies were eligible into this meta-analysis, including a total of 2,035 patients. Results: eCPR combined with TH significantly improved the short-term (at discharge or 28 days) survival [OR = 2.27, 95% CIs (1.60-3.23), p < 0.00001] and neurologic outcomes [OR = 2.60, 95% CIs (1.92-3.52), p < 0.00001). At 3 months of follow-up, the results of survival [OR = 3.36, 95% CIs (1.65-6.85), p < 0.0008] and favorable neurologic outcomes [OR = 3.02, 95% CIs (1.38-6.63), p < 0.006] were the same as above. Furthermore, there was no difference in any bleeding needed intervention [OR = 1.33, 95% CIs (0.09-1.96), p = 0.16] between two groups. Conclusions: From this meta-analysis, we found that eCPR combined with TH might be a more suitable CPR strategy for patients with CA in improving survival and neurologic outcomes, and eCPR with TH did not increase the risk of bleeding. Furthermore, single-arm meta-analyses showed a plausible way of temperature and occasion of TH.

Short-Term Efficacy and Safety of Different Mechanical Hemodynamic Support Devices for Cardiogenic Shock or High-Risk Pci: a Network Meta-Analysis of Thirty-Seven Trials.

BACKGROUND: With more advanced mechanical hemodynamic support for patients with cardiogenic shock (CS) or high-risk percutaneous coronary intervention (HS-PCI), the morality rate is now significantly lower than before. While previous studies showed that intra-aortic balloon pumping (IABP) did not reduce the risk of mortality in patients with CS compared to conservative treatment, the efficacy in other mechanical circulatory support (MCS) trials was inconsistent. OBJECTIVE: We conducted this network meta-analysis to assess the short-term efficacy and safety of different intervention measures for patients with CS or who underwent HS-PCI. METHODS: Four online databases were searched. From the initial 1,550 articles, we screened 38 studies (an extra 14 studies from references) into this analysis, including a total of 11,270 patients from five interventions (pharmacotherapy, IABP, pMCS, ECMO alone, and ECMO+IABP). RESULT: The short-term efficacy was determined by 30-day or in-hospital mortality. ECMO+IABP significantly reduced mortality compared with pMCS and ECMO alone (OR = 1.85, 95% CrI [1.03-3.26]; OR = 1.89, 95% CrI [1.19-3.01], respectively). ECMO+IABP did not show reduced mortality when compared with pharmacotherapy and IABP (OR = 1.73, 95% CrI [0.97-3.82]; OR = 1.67, 95% CrI [0.98-2.89], respectively). The rank probability, however, supported that ECMO+IABP might be a more suitable intervention in improving mortality for patients with CS or who underwent HS-PCI. Regarding bleeding, compared with other invasive intervention measures, IABP showed a trend of reduced bleeding (with pMCS OR = 3.86, 95% CrI [1.53-10.66]; with ECMO alone OR = 3.74, 95% CrI [1.13-13.78]; with ECMO+IABP OR = 4.80, 95% CrI [1.61-18.53]). No difference was found in stroke, myocardial infarction, limb ischemia, and hemolysis among the invasive therapies evaluated. CONCLUSION: Following this analysis, ECMO+IABP might be a more suitable intervention measure in improving short-term mortality for patients with CS and who underwent HS-PCI. However, the result was limited by the lack of sufficient direct comparisons and evidence from randomized controlled trials. Moreover, bleeding and other device-related complications should be considered in clinical applications.

Cocktail Therapy of Fosthiazate and Cupric-Ammoniun Complex for Citrus Huanglongbing.

Huanglongbing (HLB) is a destructive citrus bacterial disease caused by Candidatus Liberibacter asiaticus (Ca.Las) and cannot be cured by current pesticides. Root lesion and Tylenchulus semipenetrans juveniles were observed in HLB-affected citrus tree roots. We hypothesize that root treatment with fosthiazate (FOS) and Cupric-Ammonium Complex (CAC) will improve the root growth and inhibit HLB. CAC is a broad spectrum fungicide and can promote growth of crops. FOS kills Tylenchulus semipenetrans and protects roots from damage by harmful bacteria such as Ca.Las. After 90 days of combination treatment of FOS and CAC through root drenches, the citrus grew new roots and its leaves changed their color to green. The inhibition rate of Ca.Las reached more than 90%. During treatment process, the chlorophyll content and the root vitality increased 396 and 151%, respectively, and starch accumulation decreased by 88%. Transmission electron microscopy (TEM) and plant tissue dyeing experiments showed that more irregular swollen starch granules existed in the chloroplast thylakoid system of the HLB-infected leaves. This is due to the blocking of their secretory tissue by starch. TEM and flow cytometry experiments in vitro showed the synergistic effects of FOS and CAC. A transcriptome analysis revealed that the treatment induced the differential expression of the genes which involved 103 metabolic pathways. These results suggested that the cocktail treatment of FOS and CAC may effectively kill various pathogens including Ca.Las on citrus root and thus effectively control HLB.

Optimal Time of Collapse to Return of Spontaneous Circulation to Apply Targeted Temperature Management for Cardiac Arrest: A Bayesian Network Meta-Analysis.

Background: Both the American Heart Association (AHA) and European Resuscitation Council (ERC) have strongly recommended targeted temperature management (TTM) for patients who remain in coma after return of spontaneous circulation (ROSC). However, the role of TTM, especially hypothermia, in cardiac arrest patients after TTM2 trials has become much uncertain. Methods: We searched four online databases (PubMed, Embase, CENTRAL, and Web of Science) and conducted a Bayesian network meta-analysis. Based on the time of collapse to ROSC and whether the patient received TTM or not, we divided this analysis into eight groups (<20 min + TTM, <20 min, 20-39 min + TTM, 20-39 min, 40-59 min + TTM, 40-59 min, ≥60 min + TTM and ≥60 min) to compare their 30-day and at-discharge survival and neurologic outcomes. Results: From an initial search of 3,023 articles, a total of 9,005 patients from 42 trials were eligible and were included in this network meta-analysis. Compared with other groups, patients in the <20 min + TTM group were more likely to have better survival and good neurologic outcomes (probability = 46.1 and 52.5%, respectively). In comparing the same time groups with and without TTM, only the survival and neurologic outcome of the 20-39 min + TTM group was significantly better than that of the 20-39 min group [odds ratio = 1.41, 95% confidence interval (1.04-1.91); OR = 1.46, 95% CI (1.07-2.00) respectively]. Applying TTM with <20 min or more than 40 min of collapse to ROSC did not improve survival or neurologic outcome [ <20 min vs. <20 min + TTM: OR = 1.02, 95% CI (0.61-1.71)/OR = 1.03, 95% CI (0.61-1.75); 40-59 min vs. 40-59 min + TTM: OR = 1.50, 95% CI (0.97-2.32)/OR = 1.40, 95% CI (0.81-2.44); ≧60 min vs. ≧60 min + TTM: OR = 2.09, 95% CI (0.70-6.24)/OR = 4.14, 95% CI (0.91-18.74), respectively]. Both survival and good neurologic outcome were closely related to the time from collapse to ROSC. Conclusion: Survival and good neurologic outcome are closely associated with the time of collapse to ROSC. These findings supported that 20-40 min of collapse to ROSC should be a more suitable indication for TTM for cardiac arrest patients. Moreover, the future trials should pay more attention to these patients who suffer from moderate injury. Systematic Review Registration: [https://inplasy.com/?s=202180027], identifier [INPLASY202180027].

The Predictive Value of Fragmented QRS for Cardiovascular Events in Acute Myocardial Infarction: A Systematic Review and Meta-Analysis.

Objective: Fragmented QRS (fQRS) have been reported as a predictor of major adverse cardiac events (MACE) and mortality in several studies on cardiovascular disease. However, most studies have yielded discrepant results. This study aimed to explore the correlation between fQRS and cardiovascular events in patients with acute myocardial infarction (AMI) during their hospital stay and follow-up period, and the predictive value of fQRS in the prognosis of AMI. Methods: We searched for relevant studies in four databases, Medline, Embase, PubMed, and the Cochrane Library from January 2010 to March 2020. Our initial search yielded 585 articles. Of these, we screened 19 studies, and finally included a total of 6,914 patients in this analysis, comparing death events or MACE in AMI patients with or without fQRS. Results: Fragmented QRS was significantly associated with a higher risk of in-hospital mortality (OR, 3.97; 95% CI, 2.45-6.44; p < 0.00001), long-term mortality (OR, 2.93; 95% CI, 1.76-4.88; p < 0.0001), in-hospital MACE (OR, 2.48; 95% CI, 1.62-3.80; p < 0.0001), and long-term MACE (OR, 3.81; 95% CI, 2.21-6.57; p < 0.00001). In particular, it demonstrated a higher predictive value for in-hospital cardiovascular mortality and long-term all-cause mortality in AMI patients and in-hospital mortality in patients with ST-segment elevation myocardial infarction (STEMI). Moreover, fQRS was also associated with an increased risk of ventricular arrhythmias (OR, 2.76; 95% CI, 1.72-4.43; p < 0.0001) and heart failure (OR, 1.65; 95% CI, 1.02-2.66; p = 0.04). Fragmented QRS was negatively associated with left ventricular ejection function (LVEF) (MD, -5.47; CI, [-7.03, -3.91]; p < 0.00001) and positively associated with a high incidence of coronary artery triple vessel lesions (OR, 2.14; 95% CI, 1.31-3.51; p = 0.002) in AMI patients. Conclusion: Fragmented QRS is significantly associated with in-hospital and long-term mortality and MACE in patients with AMI, as well as ventricular arrhythmias and heart failure. Furthermore, it may be a marker of mortality and MACE risk. Moreover, fQRS also indicates a reduced LVEF and a high incidence of coronary artery triple vessel lesions in AMI patients. Meta-analysis Registration: https://www.crd.york.ac.uk/prospero; ID: CRD42020171668.