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Primary sources are the foundations of historical research because they are artifacts representing first-hand accounts or connections to the event, time, or place being investigated. The reality is that many IDT researchers do not have training in historical source analysis, nor do many researchers know where to find primary sources in the field of IDT, or how to conduct historical research if interested. Thus, this article retraces the history of the Association for Educational Communication and Technology's archive, and reveals where primary sources are located. The article details the history of the AECT archive to understand its significance in research. Next, a description on how to begin an archival visit, how archives function, and how to research within the archive is given -- including examples. Last, the authors discuss the importance of historical archive preservation and research to the field of IDT.

BACKGROUND: Patients with relapsed/refractory stage 4 high-risk neuroblastoma were enrolled on a phase I study (NANT2004-03) of intravenous fenretinide emulsion. Pharmacokinetic samples were collected during and after the infusion, and the levels were measured using an HPLC system. A likely case of a fatal drug interaction between fenretinide, ceftriaxone, and acetaminophen is described, including the pharmacokinetics of fenretinide, laboratory data, and post-mortem autopsy in a pediatric neuroblastoma patient treated on this study. CASE PRESENTATION: On Day 4 of a scheduled 5-day-infusion of intravenous fenretinide, the patient developed a fever, acetaminophen was started, ceftriaxone initiated for possible bacteremia, and fenretinide level doubled from 56 to 110 μM. Over the next three days, although blood cultures remained negative, the patients condition deteriorated rapidly. Acute liver failure was diagnosed on Day 7, and the patient expired on Day 20 of fulminant hepatic failure with associated renal, cardiac, and hemorrhagic/coagulation toxicities. Autopsy showed extensive hemorrhagic necrosis of the liver, marked bile duct proliferation, and abundant hemosiderin, consistent with cholestasis and drug toxicity. CONCLUSIONS: After extensive review of patient data, the clinical course, and the literature, we conclude that observed hepatic toxicity was likely due to a drug interaction between fenretinide and concomitant ceftriaxone and acetaminophen. None of the other 16 patients treated on this study experienced significant hepatic toxicity. Although the prevalence of cholestasis with ceftriaxone usage is relatively high, the potential drug interaction with these concomitant medications has not been previously reported. Concomitant use of fenretinide, ceftriaxone, and acetaminophen should be avoided.

This paper will outline the functionality available in the CovRegpy package which was written for actuarial practitioners, wealth managers, fund managers, and portfolio analysts in the language of Python 3.11. The objective is to develop a new class of covariance regression factor models for covariance forecasting, along with a library of portfolio allocation tools that integrate with this new covariance forecasting framework. The novelty is in two stages: the type of covariance regression model and factor extractions used to construct the covariates used in the covariance regression, along with a powerful portfolio allocation framework for dynamic multi-period asset investment management. The major contributions of package CovRegpy can be found on the GitHub repository for this library in the scripts: CovRegpy.py , CovRegpy_DCC.py , CovRegpy_RPP.py , CovRegpy_SSA.py , CovRegpy_SSD.py , and CovRegpy_X11.py. These six scripts contain implementations of software features including multivariate covariance time series models based on the regularized covariance regression (RCR) framework, dynamic conditional correlation (DCC) framework, risk premia parity (RPP) weighting functions, singular spectrum analysis (SSA), singular spectrum decomposition (SSD), and X11 decomposition framework, respectively. These techniques can be used sequentially or independently with other techniques to extract implicit factors to use them as covariates in the RCR framework to forecast covariance and correlation structures and finally apply portfolio weighting strategies based on the portfolio risk measures based on forecasted covariance assumptions. Explicit financial factors can be used in the covariance regression framework, implicit factors can be used in the traditional explicit market factor setting, and RPP techniques with long/short equity weighting strategies can be used in traditional covariance assumption frameworks. We examine, herein, two real-world case studies for actuarial practitioners. The first of these is a modification (demonstrating the regularization of covariance regression) of the original example from Hoff & Niu ((2012). Statistica Sinica , 22(2), 729–753) which modeled the covariance and correlative relationship that exists between forced expiratory volume (FEV) and age and FEV and height. We examine this within the context of making probabilistic predictions about mortality rates in patients with chronic obstructive pulmonary disease. The second case study is a more complete example using this package wherein we present a funded and unfunded UK pension example. The decomposition algorithm isolates high-, mid-, and low-frequency structures from FTSE 100 constituents over 20 years. These are used to forecast the forthcoming quarter's covariance structure to weight the portfolio based on the RPP strategy. These fully funded pensions are compared against the performance of a fully unfunded pension using the FTSE 100 index performance as a proxy. [ABSTRACT FROM AUTHOR]

PurposeA phase I, dose-finding study of vorinostat in combination with temozolomide (TMZ) was conducted to determine the maximum tolerated dose (MTD), safety, and pharmacokinetics in patients with high-grade glioma (HGG).Experimental designThis phase I, dose-finding, investigational study was conducted in two parts. Part 1 was a dose-escalation study of vorinostat in combination with TMZ 150 mg/m(2)/day for 5 days every 28 days. Part 2 was a dose-escalation study of vorinostat in combination with TMZ 150 mg/m(2)/day for 5 days of the first cycle and 200 mg/m(2)/day for 5 days of the subsequent 28-day cycles.ResultsIn part 1, the MTD of vorinostat administered on days 1 to 7 and 15 to 21 of every 28-day cycle, in combination with TMZ, was 500 mg daily. Dose-limiting toxicities (DLT) included grade 3 anorexia, grade 3 ALT, and grade 5 hemorrhage in the setting of grade 4 thrombocytopenia. In part 2, the MTD of vorinostat on days 1 to 7 and 15 to 21 of every 28-day cycle, combined with TMZ, was 400 mg daily. No DLTs were encountered, but vorinostat dosing could not be escalated further due to thrombocytopenia. The most common serious adverse events were fatigue, lymphopenia, thrombocytopenia, and thromboembolic events. There were no apparent pharmacokinetic interactions between vorinostat and TMZ. Vorinostat treatment resulted in hyperacetylation of histones H3 and H4 in peripheral mononuclear cells.ConclusionVorinostat in combination with temozolomide is well tolerated in patients with HGG. A phase I/II trial of vorinostat with radiotherapy and concomitant TMZ in newly diagnosed glioblastoma is underway.

This work presents a $\textsf{Python}$ EMD package named AdvEMDpy that is both more flexible and generalises existing empirical mode decomposition (EMD) packages in $\textsf{Python}$ , $\textsf{R}$ , and $\textsf{MATLAB}$. It is aimed specifically for use by the insurance and financial risk communities, for applications such as return modelling, claims modelling, and life insurance applications with a particular focus on mortality modelling. AdvEMDpy both expands upon the EMD options and methods available, and improves their statistical robustness and efficiency, providing a robust, usable, and reliable toolbox. Unlike many EMD packages, AdvEMDpy allows customisation by the user, to ensure that a broader class of linear, non-linear, and non-stationary time series analyses can be performed. The intrinsic mode functions (IMFs) extracted using EMD contain complex multi-frequency structures which warrant maximum algorithmic customisation for effective analysis. A major contribution of this package is the intensive treatment of the EMD edge effect which is the most ubiquitous problem in EMD and time series analysis. Various EMD techniques, of varying intricacy from numerous works, have been developed, refined, and, for the first time, compiled in AdvEMDpy. In addition to the EMD edge effect, numerous pre-processing, post-processing, detrended fluctuation analysis (localised trend estimation) techniques, stopping criteria, spline methods, discrete-time Hilbert transforms (DTHT), knot point optimisations, and other algorithmic variations have been incorporated and presented to the users of AdvEMDpy. This paper and the supplementary materials provide several real-world actuarial applications of this package for the user's benefit. [ABSTRACT FROM AUTHOR]

Small intestine neuroendocrine tumors (SI-NETs) are the most common malignancy of the small bowel. Several clinical trials target PI3K/Akt/mTOR signaling; however, it is unknown whether these or other genes are [...]

The study aims to investigate and establish whether variations in CYP2D6 polymorphisms are associated with clinical outcomes in women with early stage breast cancer being treated with tamoxifen. The results indicate that a relationship did exist between CYP2D6 variations and clinical outcomes.

The purpose of this study was to develop and validate a population pharmacokinetic model for Z‐endoxifen in patients with advanced solid tumors and to identify clinical variables that influence pharmacokinetic parameters. Z‐endoxifen‐HCl was administered orally once a day on a 28‐day cycle (±3 days) over 11 dose levels ranging from 20 to 360 mg. A total of 1256 Z‐endoxifen plasma concentration samples from 80 patients were analyzed using nonlinear mixed‐effects modeling to develop a population pharmacokinetic model for Z‐endoxifen. A 2‐compartment model with oral depot and linear elimination adequately described the data. The estimated apparent total clearance, apparent central volume of distribution, and apparent peripheral volume of distribution were 4.89 L/h, 323 L, and 39.7 L, respectively, with weight‐effect exponents of 0.75, 1, and 1, respectively. This model was used to explore the effects of clinical and demographic variables on Z‐endoxifen pharmacokinetics. Weight, race on clearance, and aspartate aminotransferase on the absorption rate constant were identified as significant covariates in the final model. This novel population pharmacokinetic model provides insight regarding factors that may affect the pharmacokinetics of Z‐endoxifen and may assist in the design of future clinical trials. [ABSTRACT FROM AUTHOR]