Dilemmas in a pregnant woman with myelofibrosis secondary to signet ring adenocarcinoma: a case report

Abstract Background We describe the first reported case of myelofibrosis as an extremely rare complication of gastric cancer during pregnancy; the clinical diagnosis and treatment of which is highly challenging due to nonspecific symptoms coupled with the conflicting needs of immediate disease control and continuation of pregnancy. Case presentation We report a 36-year-old pregnant woman who presented with cytopenia, fatigue, vomiting, and diarrhea for 20 days on the background of newly diagnosed myelofibrosis secondary to gastric signet ring adenocarcinoma. She accepted palliative care and died several months after the delivery of a healthy newborn. Conclusion Signet ring gastric adenocarcinoma is an unusual cause of myelofibrosis during pregnancy. Treatment remains a great challenge as clinicians have to consider the needs of immediate treatment against fetal well-being while taking into account patient preference and fetus rights

Smooth muscle α-actin missense variant promotes atherosclerosis through modulation of intracellular cholesterol in smooth muscle cells

AIMS: The variant p.Arg149Cys in ACTA2, which encodes smooth muscle cell (SMC)-specific α-actin, predisposes to thoracic aortic disease and early onset coronary artery disease in individuals without cardiovascular risk factors. This study investigated how this variant drives increased atherosclerosis. METHODS AND RESULTS: Apoe−/− mice with and without the variant were fed a high-fat diet for 12 weeks, followed by evaluation of atherosclerotic plaque formation and single-cell transcriptomics analysis. SMCs explanted from Acta2R149C/+ and wildtype (WT) ascending aortas were used to investigate atherosclerosis-associated SMC phenotypic modulation. Hyperlipidemic Acta2R149C/+Apoe−/− mice have a 2.5-fold increase in atherosclerotic plaque burden compared to Apoe−/− mice with no differences in serum lipid levels. At the cellular level, misfolding of the R149C α-actin activates heat shock factor 1, which increases endogenous cholesterol biosynthesis and intracellular cholesterol levels through increased HMG-CoA reductase (HMG-CoAR) expression and activity. The increased cellular cholesterol in Acta2R149C/+ SMCs induces endoplasmic reticulum stress and activates PERK-ATF4-KLF4 signaling to drive atherosclerosis-associated phenotypic modulation in the absence of exogenous cholesterol, while WT cells require higher levels of exogenous cholesterol to drive phenotypic modulation. Treatment with the HMG-CoAR inhibitor pravastatin successfully reverses the increased atherosclerotic plaque burden in Acta2R149C/+Apoe−/− mice. CONCLUSION: These data establish a novel mechanism by which a pathogenic missense variant in a smooth muscle-specific contractile protein predisposes to atherosclerosis in individuals without hypercholesterolemia or other risk factors. The results emphasize the role of increased intracellular cholesterol levels in driving SMC phenotypic modulation and atherosclerotic plaque burden

Enzyme- and pH-Sensitive Branched Polymer–Doxorubicin Conjugate-Based Nanoscale Drug Delivery System for Cancer Therapy

Owing to their dendritic architectural features, branched copolymers have been investigated as drug delivery systems. In this paper, an enzyme- and pH-sensitive branched poly­[<i>N</i>-(2-hydroxypropyl)­methacrylamide] (polyHPMA) copolymer–doxorubicin (DOX) conjugate possessing a molecular weight (MW) of 165 kDa was designed and prepared via a one-pot reaction and drug conjugation. This conjugate’s potential as a smart, nanoscale drug delivery system (NDDS) is also investigated. The branched conjugate was capable of forming nanoparticles with a negative surface charge. The self-assembled nanoparticles were 102 nm in diameter as measured by dynamic light scattering (DLS) and 95 nm in diameter via scanning electron microscopy, respectively. The nanoparticles were degraded to low-MW products (23∼25 kDa) in the presence of papain or cathepsin B, and the degradation was monitored via DLS and size-exclusion chromatography. The nanoparticles demonstrated pH-sensitive drug release, as the DOX was attached to the branched copolymer via a hydrazone bond. In comparison to free DOX, the conjugate-based nanoparticles exhibited greater accumulation in breast tumors, resulting in enhanced antitumor therapeutic indexes. Furthermore, widespread dissemination of the conjugate among breast tumor cells was confirmed by immunohistochemical assay. Finally, no obvious systemic toxicities were observed in vivo in normal mice. Thus, the branched HPMA copolymer–DOX conjugate may be employed as a safe and efficient pH- and enzyme-responsive NDDS for cancer therapy

Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico evaluation, but few have yet demonstrated real benefit to patient care. Early-stage clinical evaluation is important to assess an AI system's actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use and pave the way to further large-scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multi-stakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two-round, modified Delphi process to collect and analyze expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 pre-defined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. In total, 123 experts participated in the first round of Delphi, 138 in the second round, 16 in the consensus meeting and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI-specific reporting items (made of 28 subitems) and ten generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we developed a guideline comprising key items that should be reported in early-stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings