Cone-Beam Computed Tomography (CBCT)-Guided Adaptive Boost Radiotherapy for a Patient With Locally Advanced Cervical Cancer Ineligible for Brachytherapy.

Brachytherapy is a critical component of locally advanced cervical cancer treatment, and patients ineligible for brachytherapy historically have poor outcomes. Delivery of boost with stereotactic body radiation therapy (SBRT) has been studied, though toxicity is a concern. Recent case reports have explored adaptive radiation boost, which can adjust plans for inter-fraction motion using magnetic resonance guidance. Herein, we report the first patient with locally advanced cervical cancer ineligible for brachytherapy who was treated with a cone-beam computed tomography (CBCT)-guided adaptive boost following completion of chemoradiation. A 71-year-old female with locally advanced cervical cancer was treated with chemoradiation and was deemed ineligible for a brachytherapy boost due to tumor size, geometry, and a fistula with a tumor in the bladder. She was prescribed a boost to the primary tumor of 25 Gy in five fractions using CBCT-guided adaptive radiation following the completion of chemoradiation. A simulation was performed using a non-contrast CT fused with a mid-chemoradiation magnetic resonance imaging (MRI) scan to create an initial plan. For each treatment fraction, kilovoltage CBCTs were acquired, contours of organs at risk (OARs) were adjusted to reflect anatomy-of-the-day, and an adapted plan was generated. The initial and adapted plans were compared using dose-volume histogram objectives, and the adapted plan was used if it resolved OAR constraint violations or improved target coverage. The use of the initial treatment plan would have resulted in constraint violations for the rectum, sigmoid, and bladder in all fractions. The adapted plans achieved hard constraints in all fractions for all four critical OARs. The mean total treatment time across all five fractions was 58 minutes. This case demonstrates the feasibility of a CBCT-guided adaptive boost approach and the dosimetric benefits of plan adaptation in this setting. Though larger-scale and longer-term data are needed, CBCT-guided adaptive radiation may present a feasible alternative modality to deliver boost doses for brachytherapy-ineligible patients.

Self-adaptive selection graph pooling based fault diagnosis method under few samples and noisy environment.

Neural network (NN)-based methods are extensively used for intelligent fault diagnosis in industrial systems. Nevertheless, due to the limited availability of faulty samples and the presence of noise interference, most existing NN-based methods perform limited diagnosis performance. In response to these challenges, a self-adaptive selection graph pooling method is proposed. Firstly, graph encoders with sharing parameters are designed to extract local structure-feature information (SFI) of multiple sensor-wise sub-graphs. Then, the temporal continuity of the SFI is maintained through time-by-time concatenation, resulting in a global sensor graph and reducing the dependency on data volume from the perspective of adding prior knowledge. Subsequently, leveraging a self-adaptive node selection mechanism, the noise interference of redundant and noisy sensor-wise nodes in the graph is alleviated, allowing the networks to concentrate on the fault-attention nodes. Finally, the local max pooling and global mean pooling of the node-selection graph are incorporated in the readout module to get the multi-scale graph features, which serve as input to a multi-layer perceptron for fault diagnosis. Two experimental studies involving different mechanical and electrical systems demonstrate that the proposed method not only achieves superior diagnosis performance with limited data, but also maintains strong anti-interference ability in noisy environments. Additionally, it exhibits good interpretability through the proposed self-adaptive node selection mechanism and visualization methods.

Macular vascular and photoreceptor changes for diabetic macular edema at early stage.

This study was intended to investigate the macular vascular and photoreceptor changes for diabetic macular edema (DME) at the early stage. A total of 255 eyes of 134 diabetes mellitus patients were enrolled and underwent an ophthalmological and systemic evaluation in this cross-sectional study. Early DME was characterized by central subfoveal thickness (CST) value between 250 and 325 µm, intact ellipsoid zone, and an external limiting membrane. While non-DME was characterized by CST < 250 µm with normal retinal morphology and structure. Foveal avascular zone (FAZ) area ≤ 0.3 mm2 (P < 0.001, OR = 0.41, 95% CI 0.26-0.67 in the multivariate analysis) and HbA1c level ≤ 8% (P = 0.005, OR = 0.37, 95% CI 0.19-0.74 in multivariate analysis) were significantly associated with a higher risk of early DME. Meanwhile, no significant differences exist in cone parameters between non-DME and early DME eyes. Compared with non-DME eyes, vessel diameter, vessel wall thickness, wall-to-lumen ratio, the cross-sectional area of the vascular wall in the upper side were significantly decreased in the early DME eyes (P = 0.001, P < 0.001, P = 0.005, P = 0.003 respectively). This study suggested a vasospasm or vasoconstriction with limited further photoreceptor impairment at the early stage of DME formation. CST ≥ 250 µm and FAZ ≤ 0.3 mm2 may be the indicator for early DME detection.

A novel multi-objective dung beetle optimizer for Multi-UAV cooperative path planning.

Path planning for multiple unmanned aerial vehicles (UAVs) is crucial in collaborative operations and is commonly regarded as a complicated, multi-objective optimization problem. However, traditional approaches have difficulty balancing convergence and diversity, as well as effectively handling constraints. In this study, a directional evolutionary non-dominated sorting dung beetle optimizer with adaptive stochastic ranking (DENSDBO-ASR) is developed to address these issues in collaborative multi-UAV path planning. Two objectives are initially formulated: the first one represents the total cost of length and altitude, while the second represents the total cost of threat and time. Additionally, an improved multi-objective dung beetle optimizer is introduced, which integrates a directional evolutionary strategy including directional mutation and crossover, thereby accelerating convergence and enhancing global search capability. Furthermore, an adaptive stochastic ranking mechanism is proposed to successfully handle different constraints by dynamically adjusting the comparison probability. The effectiveness and superiority of DENSDBO-ASR are demonstrated by the constrained problem functions (CF) test, the Wilcoxon rank sum test, and the Friedman test. Finally, three sets of simulated tests are carried out, each including different numbers of UAVs. In the most challenging scenario, DENSDBO-ASR successfully identifies feasible paths with average values of the two objective functions as low as 637.26 and 0. The comparative results demonstrate that DENSDBO-ASR outperforms the other five algorithms in terms of convergence accuracy and population diversity, making it an exceptional optimization approach to path planning challenges.

Experimental investigation of the multianticipation mechanism in commercial SAE level 2 automated driving vehicles and associated safety impact.

Extensive experimental analyses concerned with Adaptive Cruise Control (ACC) have clearly shown that such systems have failed to deliver the promise of safe and traffic-flow effective car-following. On the contrary, large reaction times and poor string stability performances characterize commercial ACCs. While a huge research line is investigating the introduction of communication among vehicles to overcome the mentioned limitation, market adoption of connectivity-enhanced vehicles is struggling. In this context, an alternative approach based on multiple vehicle anticipation using RADAR only has emerged. Multianticipation is definitely not a new concept within the transportation community. However, until now, it was mainly associated with human driving. In the present manuscript, we demonstrate instead how, at least, one vehicle manufacturer has implemented multianticipation on a commercial vehicle. Following an in-house carried out testing campaign, we give an experimental characterization of the functioning of such a system including the potential impact on the flow and safety using a state-of-the-art fuzzy-logic safety performance model. The first results demonstrate that the vehicle under test reacted to one additional vehicle in front of the leader vehicle. Moreover, the actual realization appears to mainly target safety applications whereas there is only a marginal benefit on the string stability characteristics of the system. While we recorded a marginal string stability improvement (about 10 %), the minimum TTC was twice as large when multianticipation occurred with respect to the cases when that was not activated. Relevant Fuzzy Surrogate Safety Metrics further supported the safety argument.

Physical extraction of antigen and information.

To respond and adapt, cells use surface receptors to sense environmental cues. While biochemical signal processing inside the cell is studied in depth, less is known about how physical processes during cell-cell contact impact signal acquisition. New experiments found that fast-evolving immune B cells in germinal centers (GCs) apply force to acquire antigen clusters prior to internalization, suggesting adaptive benefits of physical information extraction. We present a theory of stochastic antigen transfer and show that maximizing information gain via physical extraction can explain the dramatic phenotypic transition from naive to GC B cells-attenuated receptor signaling, enhanced force usage, and decentralized contact architecture. Our model suggests that binding-lifetime measurement and physical extraction serve as complementary modes of antigen recognition, greatly extending the dynamic range of affinity discrimination when combined. This physical-information framework further predicts that the optimal size of receptor clusters decreases as affinity improves, rationalizing the use of a multifocal synaptic pattern seen in GC B cells. By linking extraction dynamics to selection fidelity via discriminatory performance, we propose that cells may physically enhance information acquisition to sustain adaptive evolution.

2D spatiotemporal passive cavitation imaging and evaluation during ultrasound thrombolysis based on diagnostic ultrasound platform.

Acoustic cavitation plays a critical role in various biomedical applications. However, uncontrolled cavitation can lead to undesired damage to healthy tissues. Therefore, real-time monitoring and quantitative evaluation of cavitation dynamics is essential for understanding underlying mechanisms and optimizing ultrasound treatment efficiency and safety. The current research addressed the limitations of traditionally used cavitation detection methods by developing introduced an adaptive time-division multiplexing passive cavitation imaging (PCI) system integrated into a commercial diagnostic ultrasound platform. This new method combined real-time cavitation monitoring with B-mode imaging, allowing for simultaneous visualization of treatment progress and 2D quantitative evaluation of cavitation dosage within targeted area. An improved delay-and-sum (DAS) algorithm, optimized with a minimum variance (MV) beamformer, is utilized to minimize the side lobe effect and improve the axial resolution typically associated with PCI. In additional to visualize and quantitatively assess the cavitation activities generated under varied acoustic pressures and microbubble concentrations, this system was specifically applied to perform 2D cavitation evaluation for ultrasound thrombolysis mediated by different solutions, e.g., saline, nanodiamond (ND) and nitrogen-annealed nanodiamond (N-AND). This research aims to bridge the gap between laboratory-based research systems and real-time spatiotemporal cavitation evaluation demands in practical uses. Results indicate that this improved 2D cavitation monitoring and evaluation system could offer a useful tool for comprehensive evaluating cavitation-mediated effects (e.g., ultrasound thrombolysis), providing valuable insights into in-depth understanding of cavitation mechanisms and optimization of cavitation applications.

Effectiveness of Family Coping Interventions in Improving Problem-Solving Skills in the Care of Children and Adolescent Cancer Survivors during and after Treatment: A Scoping Review.

(1) Context: Cancer triggers significant changes in family dynamics. It is noteworthy that coping and problem-solving skills, particularly in situations involving cancer in children and adolescents, have not been adequately explored in the context of family adaptation. This study aims to analyze the effectiveness of family interventions in coping to improve problem-solving skills in parents and/or caregivers of children and adolescents during and after oncological treatment. (2) Methods: This is a scoping review following the recommendations of the Joanna Briggs Institute and the Systematic Reviews and Meta-Analyses Extension for Scoping Review (PRISMA-ScR), from 2014 to 2024, in the databases LILACS, CINAHL, SCOPUS, Web of Science, and PUBMED. (3) Results: Forty-five studies were eligible. Coping strategies were categorized as follows: (1) positive attitudes (including a sense of courage and hope, family support to enhance resilience, and future planning), (2) caregiver empowerment (involving acceptance of diagnosis, emotional distancing, and coping through religiosity), and (3) communication skills (encompassing professional communication, horizontal dialogue with healthcare teams, and sincere communication with friends and family). (4) Conclusions: Over time, families develop coping and problem-solving strategies that influence changes in family functioning patterns, aiding them in accepting, reinterpreting, and reframing ideas and feelings associated with neoplasia.

Validating alternative oxidase (AOX) gene family as efficient marker consortium for multiple-resilience in Xylella fastidiosa-infected Vitis holobionts.

KEY MESSAGE: AOX gene family in motion marks in-born efficiency of respiration adjustment; can serve for primer screening, genotype ranking, in vitro-plant discrimination and a SMART perspective for multiple-resilient plant holobiont selection. The bacteria Xylella fastidiosa (Xf) is a climate-dependent, global threat to many crops of high socio-economic value, including grapevine. Currently designed breeding strategies for Xf-tolerant or -resistant genotypes insufficiently address the danger of biodiversity loss by focusing on selected threats, neglecting future environmental conditions. Thus, breeding strategies should be validated across diverse populations and acknowledge temperature changes and drought by minimizing the metabolic-physiologic effects of multiple stress-induced oxygen shortages. This research hypothesizes that multiple-resilient plant holobionts achieve lifelong adaptive robustness through early molecular and metabolic responses in primary stress target cells, which facilitate efficient respiration adjustment and cell cycle down-regulation. To validate this concept open-access transcriptome data were analyzed of xylem tissues of Xf-tolerant and -resistant Vitis holobionts from diverse trials and genetic origins from early hours to longer periods after Xf-inoculation. The results indicated repetitive involvement of alternative oxidase (AOX) transcription in episodes of down-regulated transcripts of cytochrome c oxidase (COX) at various critical time points before disease symptoms emerged. The relation between transcript levels of COX and AOX ('relCOX/AOX') was found promising for plant discrimination and primer screening. Furthermore, transcript levels of xylem-harbored bacterial consortia indicated common regulation with Xf and revealed stress-induced early down-regulation and later enhancement. LPS priming promoted the earlier increase in bacterial transcripts after Xf-inoculation. This proof-of-principle study highlights a SMART perspective for AOX-assisted plant selection towards multiple-resilience that includes Xf-tolerance. It aims to support timely future plant diagnostics and in-field substitution, sustainable agro-management, which protects population diversity and strengthens both conventional breeding and high-tech, molecular breeding research. Furthermore, the results suggested early up-regulation of bacterial microbiota consortia in vascular-enriched tissues as a novel additional trait for future studies on Xf-tolerance.

Design of compensation algorithms for zero padding and its application to a patch based deep neural network.

In this article, compensation algorithms for zero padding are suggested to enhance the performance of deep convolutional neural networks. By considering the characteristics of convolving filters, the proposed methods efficiently compensate convolutional output errors due to zero padded inputs in a convolutional neural network. Primarily the algorithms are developed for patch based SRResNet for Single Image Super Resolution and the performance comparison is carried out using the SRResNet model but due to generalized nature of the padding algorithms its efficacy is tested in U-Net for Lung CT Image Segmentation. The proposed algorithms show better performance than the existing algorithm called partial convolution based padding (PCP), developed recently.

An intranasally administered adenovirus-vectored SARS-CoV-2 vaccine induces robust mucosal secretory IgA.

BACKGROUNDThe level of nasal spike-specific secretory IgA (sIgA) is inversely correlated with the risk of SARS-CoV-2 Omicron infection. This study aimed to evaluate the safety and immunogenicity of intranasal vaccination using Ad5-S-Omicron (NB2155), a replication-incompetent human type 5 adenovirus carrying Omicron BA.1 spike.METHODSAn open-label, single-center, investigator-initiated trial was carried out on 128 health care workers who had never been infected with SARS-CoV-2 and had previously received 2 or 3 injections of inactivated whole-virus vaccines, with the last dose given 3-19 months previously (median 387 days, IQR 333-404 days). Participants received 2 intranasal sprays of NB2155 at 28-day intervals between November 30 and December 30, 2022. Safety was evaluated by solicited adverse events and laboratory tests. The elevation of nasal mucosal spike-specific sIgA and serum neutralizing activities were assessed. All participants were monitored for infection by antigen tests, disease symptoms, and the elevation of nucleocapsid-specific sIgA in the nasal passage.RESULTSThe vaccine-related solicited adverse events were mild. Nasal spike-specific sIgA against 10 strains had a mean geometric mean fold increase of 4.5 after the first dose, but it increased much higher to 51.5 after the second dose. Serum neutralizing titers also increased modestly to 128.1 (95% CI 74.4-220.4) against authentic BA.1 and 76.9 (95% CI 45.4-130.2) against BA.5 at 14 days after the second dose. Due to the lifting of the zero-COVID policy in China on December 7, 2022, 57.3% of participants were infected with BA.5 between days 15 and 28 after the first dose, whereas no participants reported having any symptomatic infections between day 3 and day 90 after the second dose. The elevation of nasal nucleocapsid-specific sIgA on days 0, 14, 42, and 118 after the first dose was assessed to verify that these 2-dose participants had no asymptomatic infections.CONCLUSIONA 2-dose intranasal vaccination regimen using NB2155 was safe, was well tolerated, and could dramatically induce broad-spectrum spike-specific sIgA in the nasal passage. Preliminary data suggested that the intranasal vaccination may establish an effective mucosal immune barrier against infection and warranted further clinical studies.TRIAL REGISTRATIONChinese Clinical Trial Registry (ChiCTR2300070346).FUNDINGNatural Science Foundation of China, Guangzhou Laboratory, The First Affiliated Hospital of Guangzhou Medical University.

Overview on tyrosinases: Genetics, molecular biology, phylogenetic relationship.

Tyrosinases (TYRs) are enzymes found in various organisms that are crucial for melanin biosynthesis, coloration, and UV protection. They play vital roles in insect cuticle sclerotization, mollusk shell formation, fungal and bacterial pigmentation, biofilm formation, and virulence. Structurally, TYRs feature copper-binding sites that are essential for catalytic activity, facilitating substrate oxidation via interactions with conserved histidine residues. TYRs exhibit diversity across animals, plants, fungi, mollusks, and bacteria, reflecting their roles and function. Eukaryotic TYRs undergo post-translational modifications, such as glycosylation, which affect protein folding and activity. Bacterial TYRs are categorized into five types based on their structural variation, domain organization and enzymatic properties, showing versatility across bacterial species. Moreover, bacterial TYRs, akin to fungal TYRs, have been implicated in the synthesis of secondary metabolites with antimicrobial properties. TYRs share significant sequence homology with hemocyanins, oxygen-carrier proteins in mollusks and arthropods, highlighting their evolutionary relationships. The evolution of TYRs underscores the dynamic nature of these enzymes and reflects adaptive strategies across diverse taxa.

ADP-based online compensation hierarchical sliding-mode control for partially unknown switched nonlinear systems with actuator failures.

This article investigates an adaptive dynamic programming-based online compensation hierarchical sliding-mode control problem for a class of partially unknown switched nonlinear systems with actuator failures and uncertain perturbations under an identifier-critic neural networks architecture. Firstly, by introducing a cost function related to hierarchical sliding-mode surfaces for the nominal system, the original control problem is equivalently converted into an optimal control problem. To obtain this optimal control policy, the Hamilton-Jacobi-Bellman equation is solved through an adaptive dynamic programming method. Compared with conventional adaptive dynamic programming methods, the identifier-critic network architecture not only overcomes the limitation on the unknown internal dynamic but also eliminates the approximation error arising from the actor network. The weights in the critic network are tuned via the gradient descent approach and the experience replay technology, such that the persistence of excitation condition can be relaxed. Then, a compensation term containing hierarchical sliding-mode surfaces is used to offset uncertain actuator failures without the fault detection and isolation unit. Based on the Lyapunov stability theory, all states of the closed-loop nonlinear system are stable in the sense of uniformly ultimately boundedness. Finally, numerical and practical examples are given to demonstrate the effectiveness of our presented online compensation control strategy.

Assessing agricultural vulnerability to climate change through dynamic indexing approach.

The present study aims to assess agricultural vulnerability in the context of climate change, focusing on the diverse districts of Odisha. Acknowledging that vulnerability is influenced by exposure, sensitivity, and adaptive capacity, our research incorporates the growth rate and instability of vital performance indicators to evaluate the relative vulnerability of each district. A key strength of this approach is the use of normalized indicators, weighted in accordance with the proportional acreage of major crops in each district relative to the state, culminating in a comprehensive vulnerability index through the aggregation of these weighted components. Our findings reveal significant variability in the vulnerability profiles across districts, thereby necessitating state-level intervention through tailored "Location Performance Vulnerability" based adaptation strategies. These strategies, including early weather warning systems, development of new and early maturing crop varieties, and adjustment of crop planting dates, are crucial for mitigating the adverse effects of climate change on agriculture. The study's methodology and findings offer significant contributions to the field, providing policymakers and stakeholders with a district-specific framework for climate change adaptation. This approach is especially relevant for the international academic and policy-making communities, as it highlights the importance of localized adaptation strategies in the broader context of global climate change resilience.

Mapping ecoregional vulnerability to climate change for Africa.

African ecosystems are expected to be significantly affected by climate change, making it crucial to understand these changes for effective management. We provide a framework and application to assess ecoregional vulnerability to climate change, considering environmental exposure, sensitivity, and adaptive capacity. We assessed environmental exposure using projections for consecutive dry days, precipitation, and temperature changes. Sensitivity was determined based on forest fragmentation and grassland degradation. Adaptive capacity was represented by protected areas network and biodiversity intactness. These factors were combined to create overall vulnerability index and specific categories to guide management decisions. Under the SSP5 8.5 scenario, 16 % of ecoregions will be highly impacted by 2050, with vulnerable areas emerging in Montane forest-grassland and flooded savanna. Impacted ecoregions are disproportionately biodiverse. By intersecting the likely climate impacts and adaptive capacity, we highlight where conservation actions are needed to enhance the resilience of ecoregions to climate change.

Improved complete ensemble empirical mode decomposition with adaptive noise and composite multiscale permutation entropy for denoising blast vibration signal.

Monitoring the building blast vibration signal is an efficient way to determine the power of blast vibration hazards. Due to the harsh measurement environment, noise is inevitably introduced into the recorded signals. This research presents a denoising approach based on Improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN) and Composite Multiscale Permutation Entropy (CMPE). First, the noisy blast vibration signal is decomposed into different intrinsic mode functions using ICEEMDAN; then multiple intrinsic mode functions (IMFs) are separated into pure and noisy using CMPE, the noisy IMFs are denoised using wavelet thresholding; finally the blast wave is reconstructed using the pure and denoised mixed IMFs. The proposed approach was compared with four other approaches (CEEMDAN-CMPE, VMD-CMPE, SVMD-CMPE, and WST). The results indicate that the proposed approach has better performance and can be considered as an effective denoising method for building blast vibration signals.

CT-Guided Online Adaptive Radiotherapy Delivered via Personalized Ultrafractionated Stereotactic Adaptive Radiotherapy (PULSAR) for a Bulky Thoracic and Abdominal Mass in Oligometastatic Renal Cell Carcinoma.

In the context of oligometastatic renal cell carcinoma (RCC), local treatment with stereotactic body radiotherapy (SBRT) may improve oncologic outcomes. However, the location and size can often pose a technical challenge in standard SBRT delivery, and the dose is potentially limited by nearby organs at risk (OARs). Online adaptive radiotherapy (oART) improves radiation delivery by personalizing high-dose fractions to account for daily stochastic variations in patient anatomy or setup. The oART process aims to maximize tumor control and enhances precision by tailoring to a more accurate representation of a patient in near-real time. The proceeding re-optimization can mitigate the uncertainty inherent in the traditional radiation delivery workflow and precludes the need for larger margins that account for anatomical variations and setup errors. Here, we describe a case of oligometastatic RCC with a bulky (>300 cm3) pleural-based left lower lobe mass extending into the upper abdomen treated via personalized ultrafractionated stereotactic adaptive radiotherapy (PULSAR). Three fractions were delivered four weeks apart allowing for tumor shrinkage of these bulky lesions, and oART permitted on-table adaptation of the plan without traditional re-simulation and re-planning required during off-line adaptive radiotherapy. The plan was designed for the Ethos linear accelerator (Varian Medical Systems, Inc., Palo Alto, CA, USA). The prescription dose was 36 Gray (Gy) in three fractions, and the adapted plan was selected in each treatment over the scheduled plan due to better target coverage and reversal of OAR dose violations. The adapted plan met all OAR dose constraints, and it achieved higher target coverage in the first two PULSAR fractions compared to the scheduled plan. In the third fraction, the cumulative point dose was approaching the maximum heart tolerance, and target coverage was accordingly compromised based on clinical judgment. There was evidence of tumor regression throughout the course of treatment, and the patient did not develop any significant radiation-related toxicities. Follow-up imaging has demonstrated the overall stable size of her lesion without any evidence of disease progression. Our case reflects the benefit of adaptive SBRT delivery to a bulky mass near multiple OARs in the setting of oligometastatic RCC. The adapted plan allowed for prioritization of critical structures on a fraction-by-fraction basis while preserving the therapeutic intent of SBRT. Further integration of advanced imaging techniques, optimal disease-specific systemic immunotherapies or targeted therapies, and refinement of patient selection will be crucial in identifying which patients would most benefit from an adaptive approach.

On Route to Chimeric Antigen Receptor T-cell (CAR T) Therapy, Less Is More: Adaptive Bridging Radiotherapy in Large B-cell Lymphoma.

CD19-targeted chimeric antigen receptor (CAR) T-cell therapy has appreciably advanced treatment for relapsed or refractory large B-cell lymphoma (LBCL). During the critical interim of four to six weeks, until CAR T-cells are ready, radiation therapy (RT) can be used to control the disease. We present the case of a 64-year-old female with relapsed/refractory diffuse large B-cell lymphoma (DLBCL) who received adaptive RT for bilateral adrenal masses as a bridging strategy before undergoing CAR T-cell therapy and enrolled in an adaptive RT clinical trial. A plan was developed to deliver up to five once-weekly fractions (5 Gy per fraction) of CT-based online adaptive RT (Varian Ethos with HyperSight imaging, Varian Medical Systems, Palo Alto, CA). The patient experienced rapid symptomatic relief, with no RT-related toxicities. The patient received RT at only half of the sessions (two out of four sessions) due to excellent tumor shrinkage on cone-beam CT (CBCT). As such, the patient was treated at a lower total dose (10 Gy) than she otherwise would have received with standard RT. Post-RT PET/CT showed significant disease regression, compatible with partial response, prior to CAR T-cell infusion. This case shows the successful application of adaptive RT as bridging therapy prior to CAR T-cell therapy, and we expect the results of this adaptive RT trial to guide the future of adaptive RT in relapsed/refractory B-cell lymphomas.

Making the most out of timeseries symptom data: A machine learning study on symptom predictions of internet-based CBT.

Objective: Predicting who will not benefit enough from Internet-Based Cognitive Behavioral (ICBT) Therapy early on can assist in better allocation of limited mental health care resources. Repeated measures of symptoms during treatment is the strongest predictor of outcome, and we want to investigate if methods that explicitly account for time-dependency are superior to methods that do not, with data from (a) only two pre-treatment timepoints and (b) the pre-treatment timepoints and three timepoints during initial treatment. Methods: We use 1) commonly used time-independent methods (i.e., Linear Regression and Random Forest models) and 2) time-dependent methods (i.e., multilevel model regression, mixed-effects random forest, and a Long Short-Term Memory model) to predict symptoms during treatment, including the final outcome. This is done with symptom scores from 6436 ICBT patients from regular care, using robust multiple imputation and nested cross-validation methods. Results: The models had a 14 %-12 % root mean squared error (RMSE) in predicting the post-treatment outcome, corresponding to a balanced accuracy of 67-74 %. Time-dependent models did not have higher accuracies. Using data for the initial treatment period (b) instead of only from before treatment (a) increased prediction results by 1.3 % percentage points (12 % to 10.7 %) RMSE and 6 % percentage points BACC (69 % to 75 %). Conclusion: Training prediction models on only symptom scores of the first few weeks is a promising avenue for symptom predictions in treatment, regardless of which model is used. Further research is necessary to better understand the interaction between model complexity, dataset length and width, and the prediction tasks at hand.

Low CD3 level is a risk factor for amyotrophic lateral sclerosis: a Mendelian randomization study.

Amyotrophic lateral sclerosis (ALS) is a progressive and fatal disease characterized by neuronal degeneration of the spinal cord and brain and believed to be related to the immune system. In this study, our aim is to use Mendelian randomization (MR) to search for immune markers related to ALS. A total of 731 immune cell traits were included in this study. MR analysis was used to identify the causality between 731 immune cell traits (with 3,757 Europeans) and ALS (with 138,086 Europeans). Colocalization analysis was used to verify the found causality, protein-protein interaction prediction was used to look for the interacting proteins that are known to be involved in ALS. We found low expression levels of CD3 on central memory CD8+ T cell is risk factor for ALS (OR = 0.90, 95% CI: 0.86-0.95, P = 0.0000303). CD3 can interact with three ALS-related proteins: VCP, HLA-DRA and HLA-DRB5, which are associated with adaptive immune response. Our study reported for the first time that low-level CD3 is a risk factor for ALS and the possible mechanism, which could provide a potential strategy for ALS diagnosis and therapy.