Citations

Abstract Frailty predicts adverse outcomes in multiple myeloma (MM), yet an objective frailty biomarker is lacking. Circulating senescence-associated secretory phenotype (SASP) factors correlate with frailty and adverse outcomes in chronic diseases, but their relevance in MM is unclear. In this study, we investigated the relationship between 36 plasma SASP factors, frailty, and clinical outcomes in: (1) a historical cohort using stored specimens from 59 patients with newly diagnosed MM, and (2) a prospective cohort of 36 patients initiating new MM treatment. Several SASP factors were significantly associated with frailty after adjusting for age, sex, and body mass index; GDF-15, TNF-RI, IL-6, and IL-8 showed positive correlations with both cumulative-deficit based and physical frailty indices. Additionally, significant age-adjusted associations were observed between SASP factors and treatment outcomes, including overall survival (OS), high-grade toxicity, and healthcare utilization. We developed a 5-factor SASP model that demonstrated superior predictive performance for OS compared to chronological age, R-ISS stage, and clinical frailty, and TNF-RI alone (5-year OS AUC: 0.90). These findings highlight the association between senescence biomarkers and frailty and OS, and support further investigation of SASP factors as objective frailty biomarkers in MM.

The intersection between radiomics, the computational analysis of imaging data, and machine learning (ML) may lead to new developments in the diagnosis, prognosis, and management of diseases. For spinal tumors specifically, applications of these fields appear promising. In this educational narrative review, we provide a summary of the current advancements in radiomics and artificial intelligence (AI), as well as applications of both fields in the diagnosis and management of spinal tumors. We also provide a suggested workflow of radiomics and machine learning analysis of spinal tumors for researchers, including a list and description of commonly used radiomic features. Future directions in the field of radiomics and machine learning applications to spinal tumors may involve validating already proposed algorithms with larger datasets, ensuring that all computational applications to patient care maintain high ethical standards, and continuing work in developing novel and highly accurate computational techniques to enhance patient outcomes.

Recurrent bowel obstruction secondary to angioedema is uncommon, particularly when due to C1 esterase inhibitor (C1-INH) deficiency. We present a case of a 56 year old, male patient with recurrent abdominal pain, nausea and vomiting secondary to bowel angioedema resulting in multiple hospitalisations and surgical interventions. This case underscores the importance of considering hereditary and acquired C1-INH deficiency in the differential diagnosis of recurrent abdominal pain and bowel oedema despite late onset and lack of concomitant facial or peripheral angioedema. We review the clinical presentation, laboratory evaluation and management of C1-INH deficiency, including the hereditary and acquired forms.

Abstract There is a growing number of cancer survivors given improvement in early cancer diagnosis, therapeutics, and supportive care strategies. Cognitive impairment is among the most common and clinically significant symptoms managed by cancer survivors, which negatively impacts quality of life, functional and occupational status for patients and their care partners. Cancer-related cognitive impairment is often multifactorial—impacted by patient and cancer-specific factors. For systemic (non-CNS) malignancies, cognitive impairment has been identified in up to 75%, depending on cohort, which persists in up to 35% of patients in the years following initial treatment. The most common areas of impairment include memory and processing speed, and executive function. These adverse effects of cancer treatment have been reported after chemotherapy, hormone therapy, targeted therapy, and immunotherapy. This review will summarize existing knowledge regarding the nature and pattern of cognitive changes, mechanisms and risk factors underlying these changes, and biomarkers to support identification of this adverse consequence of cancer therapies.

Introduction/Objective: As fewer physicians enter primary care, Advanced Practice Providers (APPs) play an increasingly important role in meeting the demand for patient care. Factors contributing to work status changes among primary care APPs are not well studied. We aimed to evaluate differences in primary care APP work status across electronic health record (EHR) workload measures. Methods: We conducted a retrospective cohort study within a Midwestern healthcare system from 2020 to 2022, evaluating the relationship between primary care APP work status defined as retention at current full-time equivalent (FTE), reduction of FTE within primary care, or relocation outside of primary care and 3 time-based EHR workload metrics. Descriptive statistics, univariate, and multivariable analyses were reported. Results: There were no differences between work status (retained, reduced, or relocated) across the EHR workload measurements of in-basket time per appointment, system time per appointment and note time per appointment ( P > .05) on univariate analysis. With multivariable analysis, there was no difference in likelihood of combined relocation/reduction adjusting for EHR workload variables ( P > .05). Conclusion: The EHR workload was not associated with work status changes for primary care APPs in our study. Further exploration is needed to identify factors that may contribute to reduction in primary care APP workforce capacity.

A deep learning–based neural network enabled virtual monoenergetic imaging at energy-integrating detector CT, reduced blooming artifacts, and improved stenosis assessment in heavily calcified coronary plaques, with performance comparable to ultrahigh-resolution photon-counting detector CT.