A phase I study of intraperitoneal nanoparticulate paclitaxel (Nanotax®) in patients with peritoneal malignancies

PURPOSE: This multicenter, open-label, dose-escalating, phase I study evaluated the safety, tolerability, pharmacokinetics and preliminary tumor response of a nanoparticulate formulation of paclitaxel (Nanotax®) administered intraperitoneally for multiple treatment cycles in patients with solid tumors predominantly confined to the peritoneal cavity for whom no other curative systemic therapy treatment options were available. METHODS: Twenty-one patients with peritoneal malignancies received Nanotax® in a modified dose-escalation approach utilizing an accelerated titration method. All patients enrolled had previously received chemotherapeutics and undergone surgical procedures, including 33 % with optimal debulking. Six doses (50–275 mg/m2) of Cremophor-free Nanotax® were administered intraperitoneally for one to six cycles (every 28 days). RESULTS: Intraperitoneal (IP) administration of Nanotax® did not lead to increases in toxicity over that typically associated with intravenous (IV) paclitaxel. No patient reported ≥Grade 2 neutropenia and/or ≥Grade 3 neurologic toxicities. Grade 3 thrombocytopenia unlikely related to study medication occurred in one patient. The peritoneal concentration–time profile of paclitaxel rose during the 2 days after dosing to peritoneal fluid concentrations 450–2900 times greater than peak plasma drug concentrations and remained elevated through the entire dose cycle. Best response assessments were made in 16/21 patients: Four patients were assessed as stable or had no response and twelve patients had increasing disease. Five of 21 patients with advanced cancers survived longer than 400 days after initiation of Nanotax® IP treatment. CONCLUSIONS: Compared to IV paclitaxel administration, Cremophor-free IP administration of Nanotax® provides higher and prolonged peritoneal paclitaxel levels with minimal systemic exposure and reduced toxicity

Measure it, See it, Manage it: Using Real Time Data to Benchmark, Optimize, and Sustain System Energy Efficiency

Even after years of training and awareness building at the state and national level, industrial cross-cutting systems (motor-driven, steam, process heating) continue to offer significant opportunities for energy savings. The US Department of Energy estimates these remaining savings at more than 7 percent of all industrial energy use. This paper presents a different approach to promoting industrial system energy efficiency -- providing plant personnel with ready access to data upon which to base energy management decisions.In 2005, a Del Monte Foods fruit processing plant in Modesto, California worked with Lawrence Berkeley National Laboratory (LBNL) to specify and purchase permanent instrumentation for monitoring their compressed air system. This work, completed as part of a demonstration project under a State Technologies Advancement Collaborative (STAC) grant, was designed to demonstrate the effectiveness of enterprise energy management (EEM), which is predicated on the assumption that the energy efficiency of existing, cross-cutting industrial systems (motor-driven, steam) can be improved by providing management and operating personnel with real-time data on energy use. The initial STAC grant provided for the installation and some initial analyses, but did not address the larger issue of integrating these new data into an ongoing energy management program for the compressed air system.The California Energy Commission (CEC) decided to support further analysis to identify potential for air system optimization. Through the CEC's Energy in Agriculture Program, a compressed air system audit was performed by Tom Taranto to: Measure and document the system's baseline and CASE Index of present operation; Establish methods to sustain an ongoing CASE Index measure of performance; Use AIRMaster+ to analyze supply side performance as compared to the CASE Index; Identify demand side opportunities for efficiency and performance improvement; Assess supply / demand balance and energy reduction opportunities; Evaluate the present air compressor control strategy and potential improvement, and Collect data to benchmark parameters for compressed air systems at similar facilities.This paper addresses the benefits and limitations of both continuous and targeted measurement in benchmarking, optimizing, and sustaining an efficient compressed air system. Included are methods used in applying both of these measurements to a complex industrial system. Further, this paper will describe the results of these additional analyses and the plant response to them

Measure it, See it, Manage it: Using Real Time Data to Benchmark, Optimize, and Sustain System Energy Efficiency

Even after years of training and awareness building at the state and national level, industrial cross-cutting systems (motor-driven, steam, process heating) continue to offer significant opportunities for energy savings. The US Department of Energy estimates these remaining savings at more than 7 percent of all industrial energy use. This paper presents a different approach to promoting industrial system energy efficiency -- providing plant personnel with ready access to data upon which to base energy management decisions.In 2005, a Del Monte Foods fruit processing plant in Modesto, California worked with Lawrence Berkeley National Laboratory (LBNL) to specify and purchase permanent instrumentation for monitoring their compressed air system. This work, completed as part of a demonstration project under a State Technologies Advancement Collaborative (STAC) grant, was designed to demonstrate the effectiveness of enterprise energy management (EEM), which is predicated on the assumption that the energy efficiency of existing, cross-cutting industrial systems (motor-driven, steam) can be improved by providing management and operating personnel with real-time data on energy use. The initial STAC grant provided for the installation and some initial analyses, but did not address the larger issue of integrating these new data into an ongoing energy management program for the compressed air system.The California Energy Commission (CEC) decided to support further analysis to identify potential for air system optimization. Through the CEC's Energy in Agriculture Program, a compressed air system audit was performed by Tom Taranto to: Measure and document the system's baseline and CASE Index of present operation; Establish methods to sustain an ongoing CASE Index measure of performance; Use AIRMaster+ to analyze supply side performance as compared to the CASE Index; Identify demand side opportunities for efficiency and performance improvement; Assess supply / demand balance and energy reduction opportunities; Evaluate the present air compressor control strategy and potential improvement, and Collect data to benchmark parameters for compressed air systems at similar facilities.This paper addresses the benefits and limitations of both continuous and targeted measurement in benchmarking, optimizing, and sustaining an efficient compressed air system. Included are methods used in applying both of these measurements to a complex industrial system. Further, this paper will describe the results of these additional analyses and the plant response to them

Outcomes of pirtobrutinib for relapsed/refractory mantle cell lymphoma in compassionate use program in Europe.

BACKGROUND Mantle cell lymphoma (MCL) is a type of B-cell lymphoma that is currently incurable. Pirtobrutinib shows promising response rates in heavily pretreated MCL patients according to the approval study, but the real-world data are scarce. METHODS In this study, we retrospectively analyzed the efficacy and safety profile of pirtobrutinib in 10 relapsed/refractory MCL patients from compassionate use program (CUP). RESULTS On average, the patients underwent three lines of systemic therapy prior to pirtobrutinib and were predominantly BTKi exposed (9/10). The best overall response rate (BORR) was 67%. In a median follow-up of 8.6 months, the mean duration of response (DOR), progression-free survival (PFS), and overall survival (OS) were not reached. No new safety signals were documented. CONCLUSIONS In summary, pirtobrutinib represented a safe and effective treatment option in a small real-world population

Chimeric antigen receptor-T cell therapy shows similar efficacy and toxicity in patients with diffuse large B-cell lymphoma aged 70 and older compared to younger patients: A multicenter cohort study.

CD19-directed chimeric antigen receptor (CAR)-T cell therapy has become a standard treatment for relapsed/refractory diffuse large B-cell lymphoma (r/r DLBCL). While the benefits of CAR-T cell treatment are clear in the general patient population, there remains a relative scarcity of real-world evidence regarding its efficacy and toxicity in patients (pts) aged ≥70 years with DLBCL. We conducted a multicenter retrospective analysis including 172 r/r DLBCL pts with CAR-T cell treatment, axicabtagene ciloleucel or tisagenlecleucel, between 2019 and 2023 at three tertiary centers. Pts were grouped by age at CAR-T infusion (<70 vs. ≥70 years). Subsequently, descriptive and survival analyses, including propensity score matching, were performed to compare outcomes between both age groups. We identified 109 pts aged <70 and 63 pts aged ≥70 years. Overall response rates for both age groups were comparable (77.7% vs. 78.3%; p = 0.63). With a median follow-up of 8.3 months, median progression-free survival was 10.2 months (95% confidence interval [CI]: 6.5-21.8) and 11.1 months (95% CI: 4.9-NR) (p = 0.93) for both cohorts. Median overall survival reached 21.8 months (95% CI: 11.8-NR) and 34.4 months (95% CI: 10.1-NR) (p = 0.97), respectively. No significant differences in the incidence of cytokine release syndrome (p = 0.53) or grade ≥3 neurotoxicity (p = 0.56) were observed. Relapse and nonrelapse mortality were not significantly different between both groups. Our findings provide additional support that CAR-T cell therapy is feasible and effective in patients with r/r DLBCL aged 70 years or older, demonstrating outcomes comparable to those observed in younger patients. CAR-T cell therapy should be not withheld for elderly patients with r/r DLBCL

The dark matter of the cancer genome: aberrations in regulatory elements, untranslated regions, splice sites, non-coding RNA and synonymous mutations

Cancer is a disease of the genome caused by oncogene activation and tumor suppressor gene inhibition. Deep sequencing studies including large consortia such as TCGA and ICGC identified numerous tumor‐specific mutations not only in protein‐coding sequences but also in non‐coding sequences. Although 98% of the genome is not translated into proteins, most studies have neglected the information hidden in this “dark matter” of the genome. Malignancy‐driving mutations can occur in all genetic elements outside the coding region, namely in enhancer, silencer, insulator, and promoter as well as in 5′‐UTR and 3′‐UTR. Intron or splice site mutations can alter the splicing pattern. Moreover, cancer genomes contain mutations within non‐coding RNA, such as microRNA, lncRNA, and lincRNA. A synonymous mutation changes the coding region in the DNA and RNA but not the protein sequence. Importantly, oncogenes such as TERT or miR‐21 as well as tumor suppressor genes such as TP53/p53,APC,BRCA1, or RB1 can be affected by these alterations. In summary, coding‐independent mutations can affect gene regulation from transcription, splicing, mRNA stability to translation, and hence, this largely neglected area needs functional studies to elucidate the mechanisms underlying tumorigenesis. This review will focus on the important role and novel mechanisms of these non‐coding or allegedly silent mutations in tumorigenesis

The dark matter of the cancer genome: aberrations in regulatory elements, untranslated regions, splice sites, non-coding RNA and synonymous mutations

This paper deals with the use of reported speech (RS) in Spanish criminal lawsuits (querellas) and police reports (denuncias) and argues about the most suitable strategies to translate such RS into Italian. In line of principle, the written record of the statements made by the individual(s) who filed the complaint is often the result of an oral cooperation between the plaintiff and the police officer and/or their attorney, whose subjectivity is reflected on the texts in a quite different fashion in the two legal cultures. The so-called ‘verbatim assumption’ of quotations in direct speech (DS) turns out to be a fallacy in the discussed genres, insofar as the locutor (i.e. the police officer or the attorney responsible for the drafting of the document) often normalizes the original utterances of the enunciator (i.e. the plaintiff whose point of view is represented in the report) in terms of cohesion, register and sentence length. Usually, these texts are translated following a strictly ‘interlinear approach’, so much so as to result almost illegible. An adequate command  of genre conventions – both in the source and in the target language – and the abidance by the translation universals of simplification and explicitation may help the translator produce a more efficient and readable target text, consistent with the expectations of a jurist in the target culture

Validation of an internationally derived patient severity phenotype to support COVID-19 analytics from electronic health record data.

ObjectiveThe Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing coronavirus disease 2019 (COVID-19) with federated analyses of electronic health record (EHR) data. We sought to develop and validate a computable phenotype for COVID-19 severity.Materials and methodsTwelve 4CE sites participated. First, we developed an EHR-based severity phenotype consisting of 6 code classes, and we validated it on patient hospitalization data from the 12 4CE clinical sites against the outcomes of intensive care unit (ICU) admission and/or death. We also piloted an alternative machine learning approach and compared selected predictors of severity with the 4CE phenotype at 1 site.ResultsThe full 4CE severity phenotype had pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of individual code categories for acuity had high variability-up to 0.65 across sites. At one pilot site, the expert-derived phenotype had mean area under the curve of 0.903 (95% confidence interval, 0.886-0.921), compared with an area under the curve of 0.956 (95% confidence interval, 0.952-0.959) for the machine learning approach. Billing codes were poor proxies of ICU admission, with as low as 49% precision and recall compared with chart review.DiscussionWe developed a severity phenotype using 6 code classes that proved resilient to coding variability across international institutions. In contrast, machine learning approaches may overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold-standard outcomes, possibly owing to heterogeneous pandemic conditions.ConclusionsWe developed an EHR-based severity phenotype for COVID-19 in hospitalized patients and validated it at 12 international sites

J Am Med Inform Assoc

INTRODUCTION: The Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing COVID-19 with federated analyses of electronic health record (EHR) data. OBJECTIVE: We sought to develop and validate a computable phenotype for COVID-19 severity. METHODS: Twelve 4CE sites participated. First we developed an EHR-based severity phenotype consisting of six code classes, and we validated it on patient hospitalization data from the 12 4CE clinical sites against the outcomes of ICU admission and/or death. We also piloted an alternative machine-learning approach and compared selected predictors of severity to the 4CE phenotype at one site. RESULTS: The full 4CE severity phenotype had pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of individual code categories for acuity had high variability - up to 0.65 across sites. At one pilot site, the expert-derived phenotype had mean AUC 0.903 (95% CI: 0.886, 0.921), compared to AUC 0.956 (95% CI: 0.952, 0.959) for the machine-learning approach. Billing codes were poor proxies of ICU admission, with as low as 49% precision and recall compared to chart review. DISCUSSION: We developed a severity phenotype using 6 code classes that proved resilient to coding variability across international institutions. In contrast, machine-learning approaches may overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold-standard outcomes, possibly due to heterogeneous pandemic conditions. CONCLUSION: We developed an EHR-based severity phenotype for COVID-19 in hospitalized patients and validated it at 12 international sites