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Background: To construct and validate the CT-based radiomics model for predicting the tyrosine kinase inhibitors (TKIs) effects in osteosarcoma (OS) patients with pulmonary metastasis. Methods: OS patients with pulmonary metastasis treated with TKIs were randomly separated into training and testing cohorts (2:1 ratio). Radiomic features were extracted from the baseline unenhanced chest CT images. The random survival forest (RSF) and Kaplan-Meier survival analyses were performed to construct and evaluate radiomics signatures (R-model-derived). The univariant and multivariant Cox regression analyses were conducted to establish clinical (C-model) and combined models (RC-model). The discrimination abilities, goodness of fit and clinical benefits of the three models were assessed and validated in both training and testing cohorts. Results: A total of 90 patients, 57 men and 33 women, with a mean age of 18 years and median progression-free survival (PFS) of 7.2 months, were enrolled. The R-model was developed with nine radiomic features and demonstrated significant predictive and prognostic values. In both training and testing cohorts, the time-dependent area under the receiver operating characteristic curves (AUC) of the R-model and RC-model exhibited obvious superiority over C-model. The calibration and decision curve analysis (DCA) curves indicated that the accuracy of the R-model was comparable to RC-model, which exhibited significantly better performance than C-model. Conclusions: The R-model showed promising potential as a predictor for TKI responses in OS patients with pulmonary metastasis. It can potentially identify pulmonary metastatic OS patients most likely to benefit from TKIs treatment and help guide optimized clinical decisions.

We introduce a novel continual learning method based on multifidelity deep neural networks. This method learns the correlation between the output of previously trained models and the desired output of the model on the current training dataset, limiting catastrophic forgetting. On its own the multifidelity continual learning method shows robust results that limit forgetting across several datasets. Additionally, we show that the multifidelity method can be combined with existing continual learning methods, including replay and memory aware synapses, to further limit catastrophic forgetting. The proposed continual learning method is especially suited for physical problems where the data satisfy the same physical laws on each domain, or for physics-informed neural networks, because in these cases we expect there to be a strong correlation between the output of the previous model and the model on the current training domain.

Abstract Hot rolling and annealing are critical intermediate steps for controlling microstructures and thickness variations when fabricating uranium alloyed with 10% molybdenum (U-10Mo), which is highly relevant to worldwide nuclear non-proliferation efforts. This work proposes a machine-learning surrogate model combined with sensitivity analysis to identify and predict U-10Mo microstructure development during thermomechanical processing. Over 200 simulations were collected using physics-based microstructure models covering a wide range of thermomechanical processing routes and initial alloy grain features. Based on the sensitivity analysis, we determined that an increase in rolling reduction percentage at each processing pass has the strongest effect in reducing the grain size. Multi-pass rolling and annealing can significantly improve recrystallization regardless of the reduction percentage. With a volume fraction below 2%, uranium carbide particles were found to have marginal effects on the average grain size and distribution. The proposed stratified stacking ensemble surrogate predicts the U-10Mo grain size with a mean square error four times smaller than a standard single deep neural network. At the same time, with a significant speedup (1000×) compared to the physics-based model, the machine learning surrogate shows good potential for U-10Mo fabrication process optimization.

Lung cancer is one of the leading causes of cancer-related death. Lung cancer mortality has decreased over the past decade, which is partly attributed to improved treatments. Curative surgery for patients with early-stage lung cancer is the standard of care, but not all surgical treatments have a good prognosis. Adjuvant and neoadjuvant chemotherapy are used to improve the prognosis of patients with resectable lung cancer. Immunotherapy, an epoch-defining treatment, has improved curative effects, prognosis, and tolerability compared with traditional and ordinary cytotoxic chemotherapy, providing new hope for patients with non-small cell lung cancer (NSCLC). Immunotherapy-related clinical trials have reported encouraging clinical outcomes in their exploration of different types of perioperative immunotherapy, from neoadjuvant immune checkpoint inhibitor (ICI) monotherapy, neoadjuvant immune-combination therapy (chemoimmunotherapy, immunotherapy plus antiangiogenic therapy, immunotherapy plus radiotherapy, or concurrent chemoradiotherapy), adjuvant immunotherapy, and neoadjuvant combined adjuvant immunotherapy. Phase 3 studies such as IMpower 010 and CheckMate 816 reported survival benefits of perioperative immunotherapy for operable patients. This review summarizes up-to-date clinical studies and analyzes the efficiency and feasibility of different neoadjuvant therapies and biomarkers to identify optimal types of perioperative immunotherapy for NSCLC.

Abstract Immunotherapy has become the standard of care for non-small cell lung cancer (NSCLC), either in combination or monotherapy. However, there are still some patients who cannot benefit from it. Immunization strategies for NSCLC are based on the expression of PD-L1 on tumor cells and TMB, and although these indicators have a certain predictive effect, their predictive performance is not good. Therefore, clinicians must make adjustments to recognize markers. This is a review article that summarized immunotherapeutic biomarkers according to the “seed-soil-environment”, generalizes primary resistance to immunotherapy, and summarizes the integration of markers.

Angiogenesis has been recognized as a pivotal contributor to tumorigenesis and progression. However, the role of angiogenesis-related genes (ARGs) in vessel state, immune infiltration, and prognosis remains unknown in osteosarcoma (OS). Bulk RNA sequencing data of osteosarcoma patients were obtained from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database, and patients were divided into two angiogenesis subgroups according to the expression of ARGs. We compared their vessel state and used two independent algorithms to evaluate the tumor microenvironment (TME) in the two subgroups. Furthermore, hub genes of differentially expressed genes (DEGs) in the two subgroups were selected to perform LASSO regression and multivariate Cox stepwise regression, and two prognostic hub genes were found. An ARG_score based on prognostic hub genes was calculated and proved to be reliable in the overall survival prediction in OS patients. Furthermore, the ARG_score was significantly associated with ARGs, immune infiltration, response to immunotherapy, and drug sensitivity. To make our prediction model perform well, clinical features were added and a highly accurate interactive nomogram was constructed. Immunohistochemistry and qRT-PCR were utilized to verify the expression of prognostic hub genes. GSE21257 from the Gene Expression Omnibus (GEO) database was used as a validation dataset to verify its robustness. In conclusion, our comprehensive analysis of angiogenesis subgroups in OS illustrated that angiogenesis may lead to different vessel states and further affect immune infiltration and prognosis of OS patients. Our findings may bring a novel perspective for the immunotherapy strategies for OS patients.

Background: Apoptosis played vital roles in the formation and progression of osteosarcoma. However, no studies elucidated the prognostic relationships between apoptosis-associated genes (AAGs) and osteosarcoma. Methods: The differentially expressed genes associated with osteosarcoma metastasis and apoptosis were identified from GEO and MSigDB databases. The apoptosis-associated prognostic signature was established through univariate and multivariate cox regression analyses. The Kaplan–Meier (KM) survival curve, ROC curve and nomogram were constructed to investigate the predictive value of this signature. CIBERSORT algorithm and ssGSEA were used to explore the relationships between immune infiltration and AAG signature. The above results were validated in another GEO dataset and the expression of AAGs was also validated in osteosarcoma patient samples by immunohistochemistry. Results: HSPB1 and IER3 were involved in AAG signature. In training and validation datasets, apoptosis-associated risk scores were negatively related to patient survival rates and the AAG signature was regarded as the independent prognostic factor. ROC and calibration curves demonstrated the signature and nomogram were reliable. GSEA revealed the signature related to immune-associated pathways. ssGSEA indicated that one immune cell and three immune functions were significantly dysregulated. The immunohistochemistry analyses of patients’ samples revealed that AAGs were significantly differently expressed between metastasis and non-metastasis osteosarcomas. Conclusions: The present study identified and validated a novel apoptosis-associated prognostic signature related to osteosarcoma metastasis. It could serve as the potential biomarker and therapeutic targets for osteosarcoma in the future.

(1) Background: The use of antiangiogenic TKIs (AA-TKIs) has recently emerged as a major paradigm shift in the treatment of advanced sarcoma. However, the feasibility of drug holidays for patients demonstrating a very favorable response remains unknown. (2) Methods: We aim to explore the outcomes of patients with advanced sarcoma who discontinued AA-TKIs after a (near-) complete remission or were long-term responders. Patients with advanced disease were included if they had bilateral or multiple lung metastases, extrapulmonary recurrence, a short disease-free interval, etc., at the initiation of AA-TKIs. (3) Results: A total of 22 patients with AA-TKI discontinuation were analyzed, with a median follow-up of 22.3 months post-discontinuation. Prior to discontinuation, there were four drug-induced complete remissions (CRs), twelve surgical CRs, and six long-term responders. Disease progression was observed in 17/22 (77.3%) patients, with a median of 4.2 months. However, since the majority were still sensitive to the original AA-TKIs and amenable to a second surgical remission, 7 out of these 17 patients achieved a second CR after disease progression and were thus considered as relapse-free post-discontinuation (pd-RFS). Therefore, the pd-RFS and post-discontinuation overall survival (pd-OS) in the last follow-up were 12/22 (54.5%) and 16/22 (72.7%), respectively. Remarkably, surgical CR and drug tapering off (versus abrupt stopping) were associated with a greater pd-RFS and pd-OS (p < 0.05). Furthermore, higher necrosis rates (p = 0.040) and lower neutrophil-to-lymphocyte ratios (NLR) (p = 0.060) before discontinuation tend to have a better pd-RFS. (4) Conclusions: Our results suggest that AA-TKI discontinuation with a taper-off strategy might be safe and feasible in highly selected patients with advanced sarcoma. Surgical CR, NLR, and tumor necrosis rates before discontinuation were potential biomarkers for AA-TKI withdrawal.

Heat sealing is one of the most common techniques for sealing food packages. In this study, the effects of sealer parameters such as dwell time (0.5–2 s), jaw temperature (225–250°C) and pressure (69–345 KPa) and the seal contaminants (coffee particles [0.70–0.85 mm]) were studied on polymer materials used in coffee capsules: (1) lidding, poly(ethylene terephthalate) (PET)/Al/linear low‐density poly(ethylene) (LLDPE); (2) wall, polystyrene (PS)/ethylene‐vinyl alcohol copolymer (EVOH)/PS; and non‐woven filter, NW1 (mono‐component fibre) or NW2 (bicomponent fibre [core/sheath]). The presence of NW1 and NW2 non‐woven in the seal structures decreased the interfacial temperature by 5°C and 11°C, respectively in comparison to seal structures without non‐woven. The degradation of the seal bonding caused a decline in the seal strength at elevated temperature (NW: >240°C without NW: >235°C) and longer dwell time (NW: >1 s, without NW: >1.5 s). The core and sheath structure of NW2 (969 N/mm) was responsible for higher seal strength in comparison to NW1 and without NW, as the core remained intact during the sealing, increasing the material strength. The minimum pressure of 207 KPa was required for creating enough contact between films to achieve proper sealing. The coffee particles buried under the opaque layer of lidding films created visible thermal artefacts when observed under thermal camera. The contaminated region differed by 30–38°C from the surrounding sound region after cooling for 3.5 s. The seal strength was stronger when a single coffee particle was in the middle compared to a sample with multiple particles. [ABSTRACT FROM AUTHOR]