Functional Acrylic Surfaces Obtained by Scratching

By using sandpaper of different grit, we have scratched up smooth sheets of acrylic to cover their surfaces with disordered but near parallel micro-grooves. This procedure allowed us to transform the acrylic surface into a functional surface; measuring the capillary rise of silicone oil up to an average height h¯, we found that h¯ evolves as a power law of the form h¯∼tn, where t is the elapsed time from the start of the flow and n takes the values 0.40 or 0.50, depending on the different inclinations of the sheets. Such behavior can be understood alluding to the theoretical predictions for the capillary rise in very tight, open capillary wedges. We also explore other functionalities of such surfaces, as the loss of mass of water sessile droplets on them and the generic role of worn surfaces, in the short survival time of SARS-CoV-2, the virus that causes COVID-19.

Sarcopenia Measured by Temporalis Muscle Thickness Independently Predicts Early Relapse and Short Survival in Primary CNS Lymphoma

Introduction: Primary CNS lymphoma (PCNSL) outcomes diverge between a majority of patients who achieve long term remission and a smaller minority who have aggressive disease course and die in the first year. Sarcopenia is increasingly recognized as a powerful predictor of mortality in brain and systemic cancers. Temporalis muscle thickness (TMT) is a validated radiographic measure of sarcopenia. We hypothesized that patients with TMT less than one standard deviation below the mean (“very thin TMT”) would go on to have shorter survival. Methods: Two blinded operators retrospectively measured TMT in 99 consecutive pretreatment brain MRIs from patients that were subsequently diagnosed with PCNSL. Results: On univariate analysis TMT predicted early progression (HR 4.25, 95% CI 1.95 – 9.29, p<0.001) and early mortality (HR 4.38, 95% CI 2.25 – 8.53, p<0.001), and these effects were maintained in subgroups of patients both <65 and ³65 years of age. Very thin TMT predicted mortality more robustly than IELSG or MSKCC scores. Patients with very thin TMT received fewer cycles of high-dose methotrexate (HD-MTX) and were less likely to receive consolidation. On multivariate analysis which included the covariates age, sex, TMT, ECOG, BMI, lifetime doses of HD-MTX, and consolidation, very thin TMT was independently associated with both early progression (HR = 7.87, 95% CI = 3.55 – 17.45, p<0.001) and short survival (HR 4.49, 95% CI = 1.94 – 10.40, p<0.001). Conclusions: We conclude that PCNSL patients with very thin TMT are at high risk for relapse and early mortality. Future trials should stratify patients by TMT to avoid potential confounding.

Edge-Based Detection and Classification of Malicious Contents in Tor Darknet Using Machine Learning

With the increase of data in the network, the load of servers and communication links becomes heavier and heavier. Edge computing can alleviate this problem. Due to a sea of malicious contents in Darknet, it is of high research value to combine edge computing with content detection and analysis. Therefore, this paper illustrates an intelligent classification system based on machine learning and Scrapy that can detect and judge fleetly categories of services with malicious contents. Because of the nondisclosure and short survival time of Tor Darknet domain names, obtaining uniform resource locators (URLs) and resources of the network is challenging. In this paper, we focus on a network based on the Onion Router (tor) anonymous communication system. We designed a crawler program to obtain the contents of the Tor network and label them into six classes. We also construct a dataset which contains URLs, categories, and keywords. Edge computing is used to judge the category of websites. The accuracy of the classifier based on a machine learning algorithm is as high as 89%. The classifier will be used in an operational system which can help researchers quickly obtain malicious contents and categorize hidden services.

Determining Clinical Course of Diffuse Large B-Cell Lymphoma Using Targeted Transcriptome and Machine Learning Algorithms

Introduction: Multiple studies have demonstrated that diffuse large B-cell lymphoma (DLBCL) can be divided into subgroups based on their biology. However, these biological subgroups overlap clinically. While R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) remains the standard of care for treating patients with DLBCL, predicting which patients will not benefit from such therapy is important so that alternative therapy or clinical trials can be considered. Most of the studies stratifying patients select biomarkers first, then explore how these biomarkers can stratify patients based on outcome. We explored the potential of using machine learning to first group patients with DLBCL based on survival, then isolating the biomarkers necessary for predicting these survival subgroups. Methods: RNA was extracted from tissue paraffin blocks from 379 R-CHOP treated patients with de novo DLBCL, and from 247 patients with extranodal DLBCL. A targeted hybrid capture RNA panel of 1408 genes was used for next generation sequencing (NGS). Sequencing was performed using an Illumina NextSeq 550 System platform. Ten million reads per sample in a single run were required, and the read length was 2 × 150 bp. An expression profile was generated from the sequencing coverage profile of each individual sample using Cufflinks. A machine learning system was developed to classify patients into four groups based on their overall survival. This machine learning approach based on Naïve Bayesian algorithm was also used to discover the relevant subset of genes with which to classify patients into each of the four survival groups. To eliminate the underflow problem commonly associated with the standard Naïve Bayesian classifiers, we applied Geometric Mean Naïve Bayesian (GMNB) as the classifier to predict the survival group for each patient. Results: Using machine learning, patients were first divided into two groups: short survival (S) and long survival (L). To refine this model, we used the same approach and divided the patients in each group into two subgroups, generating four groups: long survival in the long group (LL), short survival in the long group (LS), long survival in the short group (SL), and short survival in the short group (SS). The hazard ratio for this model was 0.174 (confidence interval: 0.120-0.251), and P-value &lt;0.0001. After defining these four groups, a machine learning algorithm was used to discover the biomarkers from the expression data of the 1408 genes from NGS data. To reduce the effects of noise and avoid overfitting, we employed a 12-step cross validation to obtain a robust measure. For an individual gene, a generalized Naïve Bayesian classifier was constructed on the training of one of the 12 subsets and tested on the other 11 testing subsets. This allowed us to limit the prediction process to 60 genes for each separation step. Using the selected biomarkers, we classified the patients in the original set (379 patients) into LL, LS, SL, and SS groups and then evaluated the survival pattern of these groups. As shown in Fig. 1A, the selected biomarkers predicted survival as expected in the overall survival groups prior to biomarker selection. For additional validation of the system, we used the selected biomarkers to classify a completely new set of 247 samples of patients with extranodal DLBCL. As shown in Fig. 1B, these selected biomarkers successfully predicted the overall survival in this group of patients with an HR of 0.530 (confidence interval: 0.234-1.197, P=0.005). This classification correlated with cell of origin classification, TP53 mutation status, MYC expression, and IRF4 expression. However, in a multivariate analysis, only TP53 mutation was independent in predicting prognosis (P=0.005) and age (below or over 60) (P=0.01) along with the survival grouping (P&lt;0.000001). Conclusions: Using a novel machine learning approach with the expression levels of 180 genes, we developed a model that can reliably stratify patients with DLBCL treated with R-CHOP into four survival subgroups. This model can be used to identify patients who may not respond well to R-CHOP to be considered for alternative therapy and clinical trials. Figure 1 Figure 1. Disclosures Hsi: AbbVie Inc, Eli Lilly: Research Funding. Ferreri: Ospedale San Raffaele srl: Patents & Royalties; BMS: Research Funding; Pfizer: Research Funding; Beigene: Research Funding; Hutchison Medipharma: Research Funding; Amgen: Research Funding; Genmab: Research Funding; ADC Therapeutics: Research Funding; Gilead: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Membership on an entity's Board of Directors or advisory committees, Research Funding; PletixaPharm: Membership on an entity's Board of Directors or advisory committees; x Incyte: Membership on an entity's Board of Directors or advisory committees; Adienne: Membership on an entity's Board of Directors or advisory committees. Piris: Millenium/Takeda, EUSA, Jansen, NanoString, Kyowa Kirin, Gilead and Celgene.: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Winter: BMS: Other: Husband: Data and Safety Monitoring Board; Actinium Pharma: Consultancy; Janssen: Other: Husband: Consultancy; Agios: Other: Husband: Consultancy; Gilead: Other: Husband: Consultancy; Epizyme: Other: Husband: Data and Safety Monitoring Board; Ariad/Takeda: Other: Husband: Data and Safety Monitoring Board; Merck: Consultancy, Honoraria, Research Funding; Novartis: Other: Husband: Consultancy, Data and Safety Monitoring Board; Karyopharm (Curio Science): Honoraria.

When SUV Matters: FDG PET/CT at Baseline Correlates with Survival in Soft Tissue and Ewing Sarcoma

Introduction: The role of positron-emission tomography/computed-tomography (PET/CT) in the management of sarcomas and as a prognostic tool has been studied. However, it remains unclear which metric is the most useful. We aimed to investigate if volume-based PET metrics (Tumor volume (TV) and total lesions glycolysis (TLG)) are superior to maximal standardized uptake value (SUVmax) and other metrics in predicting survival of patients with soft tissue and bone sarcomas. Materials and Methods: In this retrospective cohort study, we screened over 52′000 PET/CT scans to identify patients diagnosed with either soft tissue, bone or Ewing sarcoma and had a staging scan at our institution before initial therapy. We used a Wilcoxon signed-rank to assess which PET/CT metric was associated with survival in different patient subgroups. Receiver-Operating-Characteristic curve analysis was used to calculate cutoff values. Results: We identified a total of 88 patients with soft tissue (51), bone (26) or Ewing (11) sarcoma. Median age at presentation was 40 years (Range: 9–86 years). High SUVmax was most significantly associated with short survival (defined as <24 months) in soft tissue sarcoma (with a median and range of SUVmax 12.5 (8.8–16.0) in short (n = 18) and 5.5 (3.3–7.2) in long survival (≥24 months) (n = 31), with (p = 0.001). Similar results were seen in Ewing sarcoma (with a median and range of SUVmax 12.1 (7.6–14.7) in short (n = 6) and 3.7 (3.5–5.5) in long survival (n = 5), with (p = 0.017). However, no PET-specific metric but tumor-volume was significantly associated (p = 0.035) with survival in primary bone sarcomas (with a median and range of 217 cm3 (186–349) in short survival (n = 4) and 60 cm3 (22–104) in long survival (n = 19), with (p = 0.035). TLG was significantly inversely associated with long survival only in Ewing sarcoma (p = 0.03). Discussion: Our analysis shows that the outcome of soft tissue, bone and Ewing sarcomas is associated with different PET/CT metrics. We could not confirm the previously suggested superiority of volume-based metrics in soft tissue sarcomas, for which we found SUVmax to remain the best prognostic factor. However, bone sarcomas should probably be evaluated with tumor volume rather than FDG PET activity.

Lenvatinib in Anaplastic Thyroid Carcinoma (ATC) in a Tertiary Caner Hospital- a Single Institute Experience

Anaplastic Thyroid Carcinoma (ATC) is an aggressive rare form of caner with limited treatment options and short survival. In view of initial case reports have shown some good clinical response with lenvatinib, we used the same in our institute. We are presenting a retrospective series of 4 cases between 2018-2021. It showed very promising results with 75% showing clinically meaningful regression of tumor. Hypertension is the most common side effect, which should be aggressively managed. We feel that, lenvatinib remains a safe and effective option to explore in patients with refractory anaplastic thyroid carcinoma.

Aberrant acquisition of T-cell associated markers in plasma cell neoplasms: An aggressive disease with extramedullary involvement and very short survival

Background: Plasma cell neoplasms can show aberrant expression of a different lineage-related antigens, however, co-expression of T-cell associated markers on malignant plasma cells is extremely rare. Material and methods: This is a report of clinicopathologic characteristics of three myeloma patients with emergent plasmablastic morphology and aberrant acquisition of T‐cell associated markers. An extensive literature search for similar cases was conducted and the relevant pathologic, clinical and prognostic characteristics were summarized. Results: A total of 22 cases of plasma cell neoplasm, showed aberrant co-expression of T-cell markers. We found an evident association between aberrant expression of T-cell markers on malignant plasma cells and extramedullary involvement, aggressive morphologic features, high proliferative index ki67 >90%, aggressive clinical course, adverse outcome with short survival. Conclusion: Due to rarity of this aberrant phenotype and scarcity of the published data, the precise causative mechanism and its clinical implications have not yet been elucidated.