Rigid Poly(phenylene ethynylene)s as Gd(III) Contrast Agents for Magnetic Resonance Imaging

Rigid poly(phenylene ethynylene) (PPE) polymers and dendrimers are composed of alternating phenyl rings and alkynebonds. Despite their inherent rigidity they have not yet been used as scaffolds for Gd(III) MRI contrast agents. This thesis examines the attachment of Gd(III) contrast agents to water-soluble PPE polymers in order to increase the contrast agent’s relaxivity. Two PPE polymers having Gd(III) chelates were synthesized but one showed poor water solubility and was not tested for its effects on relaxivity. The other’s relaxivity was tested, but was found to be lower than anticipated. This was attributed to the assembly of the polymer into micrometer sized particles that inhibited the contrast agent’s relaxivity. Steps toward a Gd(III) contrast agent attached to the periphery of a PPE dendrimer are also reported in this work

Small Molecule Inhibition of HIV-1–Induced MHC-I Down-Regulation Identifies a Temporally Regulated Switch in Nef Action

Nef assembles a multi-kinase complex triggering MHC-I down-regulation. We identify an inhibitor that blocks MHC-I down-regulation, identifying a temporally regulated switch in Nef action from directing MHC-I endocytosis to blocking cell surface delivery. These findings challenge current dogma and reveal a regulated immune evasion program

Feasibility and acceptability of ChatGPT generated radiology report summaries for cancer patients

Objective Patients now have direct access to their radiology reports, which can include complex terminology and be difficult to understand. We assessed ChatGPT's ability to generate summarized MRI reports for patients with prostate cancer and evaluated physician satisfaction with the artificial intelligence (AI)-summarized report. Methods We used ChatGPT to summarize five full MRI reports for patients with prostate cancer performed at a single institution from 2021 to 2022. Three summarized reports were generated for each full MRI report. Full MRI and summarized reports were assessed for readability using Flesch-Kincaid Grade Level (FK) score. Radiation oncologists were asked to evaluate the AI-summarized reports via an anonymous questionnaire. Qualitative responses were given on a 1–5 Likert-type scale. Fifty newly diagnosed prostate cancer patient MRIs performed at a single institution were additionally assessed for physician online portal response rates. Results Fifteen summarized reports were generated from five full MRI reports using ChatGPT. The median FK score for the full MRI reports and summarized reports was 9.6 vs. 5.0, ( p  < 0.05), respectively. Twelve radiation oncologists responded to our questionnaire. The mean [SD] ratings for summarized reports were factual correctness (4.0 [0.6], understanding 4.0 [0.7]), completeness (4.1 [0.5]), potential for harm (3.5 [0.9]), overall quality (3.4 [0.9]), and likelihood to send to patient (3.1 [1.1]). Current physician online portal response rates were 14/50 (28%) at our institution. Conclusions We demonstrate a novel application of ChatGPT to summarize MRI reports at a reading level appropriate for patients. Physicians were likely to be satisfied with the summarized reports with respect to factual correctness, ease of understanding, and completeness. Physicians were less likely to be satisfied with respect to potential for harm, overall quality, and likelihood to send to patients. Further research is needed to optimize ChatGPT's ability to summarize radiology reports and understand what factors influence physician trust in AI-summarized reports

Weight Regain After Bariatric Surgery: Scope of the Problem, Causes, Prevention, and Treatment

Purpose of reviewAlthough bariatric surgery is the most effective treatment of severe obesity, a proportion of patients experience clinically significant weight regain (WR) with further out from surgery. The purpose of this review is to summarize the prevalence, predictors, and causes of weight regain.Recent findingsEstimating the prevalence of WR is limited by a lack of consensus on its definition. While anatomic failures such as dilated gastric fundus after sleeve gastrectomy and gastro-gastric fistula after Roux-en-Y gastric bypass can lead to WR, the most common causes appear to be dysregulated/maladaptive eating behaviors, lifestyle factors, and physiological compensatory mechanisms. To date, dietary, supportive, behavioral, and exercise interventions have not demonstrated a clinically meaningful impact on WR, and there is limited evidence for pharmacotherapy. Future studies should be aimed at better defining WR to begin to understand the etiologies. Additionally, there is a need for non-surgical interventions with demonstrated efficacy in rigorous randomized controlled trials for the prevention and reversal of WR after bariatric surgery

Elevated Coronary Artery Calcium Quantified by a Validated Deep Learning Model From Lung Cancer Radiotherapy Planning Scans Predicts Mortality

PURPOSE: Coronary artery calcium (CAC) quantified on computed tomography (CT) scans is a robust predictor of atherosclerotic coronary disease; however, the feasibility and relevance of quantitating CAC from lung cancer radiotherapy planning CT scans is unknown. We used a previously validated deep learning (DL) model to assess whether CAC is a predictor of all-cause mortality and major adverse cardiac events (MACEs). METHODS: Retrospective analysis of non-contrast-enhanced radiotherapy planning CT scans from 428 patients with locally advanced lung cancer is performed. The DL-CAC algorithm was previously trained on 1,636 cardiac-gated CT scans and tested on four clinical trial cohorts. Plaques ≥ 1 cubic millimeter were measured to generate an Agatston-like DL-CAC score and grouped as DL-CAC = 0 (very low risk) and DL-CAC ≥ 1 (elevated risk). Cox and Fine and Gray regressions were adjusted for lung cancer and cardiovascular factors. RESULTS: The median follow-up was 18.1 months. The majority (61.4%) had a DL-CAC ≥ 1. There was an increased risk of all-cause mortality with DL-CAC ≥ 1 versus DL-CAC = 0 (adjusted hazard ratio, 1.51; 95% CI, 1.01 to 2.26; P = .04), with 2-year estimates of 56.2% versus 45.4%, respectively. There was a trend toward increased risk of major adverse cardiac events with DL-CAC ≥ 1 versus DL-CAC = 0 (hazard ratio, 1.80; 95% CI, 0.87 to 3.74; P = .11), with 2-year estimates of 7.3% versus 1.2%, respectively. CONCLUSION: In this proof-of-concept study, CAC was effectively measured from routinely acquired radiotherapy planning CT scans using an automated model. Elevated CAC, as predicted by the DL model, was associated with an increased risk of mortality, suggesting a potential benefit for automated cardiac risk screening before cancer therapy begins

Elevated Coronary Artery Calcium Quantified by a Validated Deep Learning Model From Lung Cancer Radiotherapy Planning Scans Predicts Mortality

PURPOSE: Coronary artery calcium (CAC) quantified on computed tomography (CT) scans is a robust predictor of atherosclerotic coronary disease; however, the feasibility and relevance of quantitating CAC from lung cancer radiotherapy planning CT scans is unknown. We used a previously validated deep learning (DL) model to assess whether CAC is a predictor of all-cause mortality and major adverse cardiac events (MACEs). METHODS: Retrospective analysis of non-contrast-enhanced radiotherapy planning CT scans from 428 patients with locally advanced lung cancer is performed. The DL-CAC algorithm was previously trained on 1,636 cardiac-gated CT scans and tested on four clinical trial cohorts. Plaques ≥ 1 cubic millimeter were measured to generate an Agatston-like DL-CAC score and grouped as DL-CAC = 0 (very low risk) and DL-CAC ≥ 1 (elevated risk). Cox and Fine and Gray regressions were adjusted for lung cancer and cardiovascular factors. RESULTS: The median follow-up was 18.1 months. The majority (61.4%) had a DL-CAC ≥ 1. There was an increased risk of all-cause mortality with DL-CAC ≥ 1 versus DL-CAC = 0 (adjusted hazard ratio, 1.51; 95% CI, 1.01 to 2.26; P = .04), with 2-year estimates of 56.2% versus 45.4%, respectively. There was a trend toward increased risk of major adverse cardiac events with DL-CAC ≥ 1 versus DL-CAC = 0 (hazard ratio, 1.80; 95% CI, 0.87 to 3.74; P = .11), with 2-year estimates of 7.3% versus 1.2%, respectively. CONCLUSION: In this proof-of-concept study, CAC was effectively measured from routinely acquired radiotherapy planning CT scans using an automated model. Elevated CAC, as predicted by the DL model, was associated with an increased risk of mortality, suggesting a potential benefit for automated cardiac risk screening before cancer therapy begins

A HIF-regulated VHL-PTP1B-Src signaling axis identifies a therapeutic target in renal cell carcinoma

Metastatic renal cell carcinoma (RCC) is a molecularly heterogeneous disease that is intrinsically resistant to chemotherapy and radiotherapy. Although therapies targeted to the molecules vascular endothelial growth factor and mammalian target of rapamycin have shown clinical effectiveness, their effects are variable and short-lived, underscoring the need for improved treatment strategies for RCC. Here, we used quantitative phosphoproteomics and immunohistochemical profiling of 346 RCC specimens and determined that Src kinase signaling is elevated in RCC cells that retain wild-type von Hippel-Lindau (VHL) protein expression. RCC cell lines and xenografts with wild-type VHL exhibited sensitivity to the Src inhibitor dasatinib, in contrast to cell lines that lacked the VHL protein, which were resistant. Forced expression of hypoxia-inducible factor (HIF) in RCC cells with wild-type VHL diminished Src signaling output by repressing transcription of the Src activator protein tyrosine phosphatase 1B (PTP1B), conferring resistance to dasatinib. Our results suggest that a HIF-regulated VHL-PTP1B-Src signaling pathway determines the sensitivity of RCC to Src inhibitors and that stratification of RCC patients with antibody-based profiling may identify patients likely to respond to Src inhibitors in RCC clinical trials

Associations of combined physical activity and body mass index groups with colorectal cancer survival outcomes

Abstract Background Physical activity and BMI have been individually associated with cancer survivorship but have not yet been studied in combinations in colorectal cancer patients. Here, we investigate individual and combined associations of physical activity and BMI groups with colorectal cancer survival outcomes. Methods Self-reported physical activity levels (MET hrs/wk) were assessed using an adapted version of the International Physical Activity Questionnaire (IPAQ) at baseline in 931 patients with stage I-III colorectal cancer and classified into ‘highly active’ and’not-highly active’(≥ / < 18 MET hrs/wk). BMI (kg/m2) was categorized into ‘normal weight’, ‘overweight’, and ‘obese’. Patients were further classified into combined physical activity and BMI groups. Cox-proportional hazard models with Firth correction were computed to assess associations [hazard ratio (HR), 95% profile HR likelihood confidence interval (95% CI) between individual and combined physical activity and BMI groups with overall and disease-free survival in colorectal cancer patients. Results ‘Not-highly active’ compared to ‘highly active’ and ‘overweight’/ ‘obese’ compared to ‘normal weight’ patients had a 40–50% increased risk of death or recurrence (HR: 1.41 (95% CI: 0.99–2.06), p = 0.03; HR: 1.49 (95% CI: 1.02–2.21) and HR: 1.51 (95% CI: 1.02–2.26), p = 0.04, respectively). ‘Not-highly active’ patients had worse disease-free survival outcomes, regardless of their BMI, compared to ‘highly active/normal weight’ patients. ‘Not-highly active/obese’ patients had a 3.66 times increased risk of death or recurrence compared to ‘highly active/normal weight’ patients (HR: 4.66 (95% CI: 1.75–9.10), p = 0.002). Lower activity thresholds yielded smaller effect sizes. Conclusion Physical activity and BMI were individually associated with disease-free survival among colorectal cancer patients. Physical activity seems to improve survival outcomes in patients regardless of their BMI

Associations of combined physical activity and body mass index groups with colorectal cancer survival outcomes

Background: Physical activity and BMI have been individually associated with cancer survivorship but have not yet been studied in combinations in colorectal cancer patients. Here, we investigate individual and combined associations of physical activity and BMI groups with colorectal cancer survival outcomes. Methods: Self-reported physical activity levels (MET hrs/wk) were assessed using an adapted version of the International Physical Activity Questionnaire (IPAQ) at baseline in 931 patients with stage I-III colorectal cancer and classified into ‘highly active’ and ’not-highly active’ (≥/ < 18 MET hrs/wk). BMI (kg/m2) was categorized into ‘normal weight’, ‘overweight’, and ‘obese’. Patients were further classified into combined physical activity and BMI groups. Cox-proportional hazard models with Firth correction were computed to assess associations [hazard ratio (HR), 95% profile HR likelihood confidence interval (95% CI) between individual and combined physical activity and BMI groups with overall and disease-free survival in colorectal cancer patients. Results: ‘Not-highly active’ compared to ‘highly active’ and ‘overweight’/‘obese’ compared to ‘normal weight’ patients had a 40–50% increased risk of death or recurrence (HR: 1.41 (95% CI: 0.99–2.06), p=0.03; HR: 1.49 (95% CI: 1.02–2.21) and HR: 1.51 (95% CI: 1.02–2.26), p= 0.04, respectively). ‘Not-highly active’ patients had worse disease-free survival outcomes, regardless of their BMI, compared to ‘highly active/normal weight’ patients. ‘Not-highly active/obese’ patients had a 3.66 times increased risk of death or recurrence compared to ‘highly active/normal weight’ patients (HR: 4.66 (95% CI: 1.75–9.10), p=0.002). Lower activity thresholds yielded smaller effect sizes. Conclusion: Physical activity and BMI were individually associated with disease-free survival among colorectal cancer patients. Physical activity seems to improve survival outcomes in patients regardless of their BMI