IMPORTANCE Financial toxicity resulting from cancer care poses a substantial public health concern, leading some patients to turn to online crowdfunding. However, the practice may exacerbate existing socioeconomic cancer disparities by privileging those with access to interpersonal wealth and digital media literacy. OBJECTIVE To test the hypotheses that higher county-level socioeconomic status and the presence (vs absence) of text indicators of beneficiary worth in campaign descriptions are associated with amount raised from cancer crowdfunding.

Background Patients with microsatellite instability–high (MSI‐H)/mismatch repair–deficient (dMMR) metastatic colorectal cancer (mCRC) show a significant response to checkpoint inhibitor therapies, but the economic impact of these therapies is unknown. A decision analytic model was used to explore the effectiveness and cost burden of MSI‐H/dMMR mCRC treatment. Methods The treatment of hypothetical patients with MSI‐H/dMMR mCRC was simulated in 2 treatment scenarios: a third‐line treatment and an exploratory first‐line treatment. The treatments compared were nivolumab, ipilimumab and nivolumab, trifluridine and tipiracil (third‐line treatment), and mFOLFOX6 and cetuximab (first‐line treatment). Disease progression, drug toxicity, and survival rates were based on the CheckMate 142, study of TAS‐102 in patients with metastatic colorectal cancer refractory to standard chemotherapies (RECOURSE), and Cancer and Leukemia Group B/Southwest Oncology Group 80405 trials. The analyzed outcomes included survival (life‐years), quality‐adjusted life‐years (QALYs), and incremental cost‐effectiveness ratios (ICERs). Results Ipilimumab with nivolumab was the most effective strategy (10.69 life‐years and 9.25 QALYs for the third line; 10.69 life‐years and 9.44 QALYs for the first line) in comparison with nivolumab (8.21 life‐years and 6.76 QALYs for the third line; 8.21 life‐years and 7.00 QALYs for the first line), trifluridine and tipiracil (0.74 life‐years and 0.07 QALYs), and mFOLFOX6 and cetuximab (2.72 life‐years and 1.63 QALYs). However, neither checkpoint inhibitor therapy was cost‐effective in comparison with trifluridine and tipiracil (nivolumab ICER, $153,000; ipilimumab and nivolumab ICER, $162,700) or mFOLFOX6 and cetuximab (nivolumab ICER, $150,700; ipilimumab and nivolumab ICER, $158,700). Conclusions This modeling analysis found that both single and dual checkpoint blockade could be significantly more effective for MSI‐H/dMMR mCRC than chemotherapy, but they were not cost‐effective, largely because of drug costs. Decreases in drug pricing and/or the duration of maintenance nivolumab could make ipilimumab and nivolumab cost‐effective. Prospective clinical trials should be performed to explore the optimal duration of maintenance nivolumab.

Bacterial surfaces are complex structures with nontrivial adhesive properties. The physics of bacterial adhesion deviates from that of ideal colloids as a result of cell-surface roughness and because of the mechanical properties of the polymers covering the cell surface. In the present study, we develop a simple multiscale model for the interplay between the potential energy functions that characterize the cell surface biopolymers and their interaction with the extracellular environment. We then use the model to study a discrete network of bonds in the presence of significant length heterogeneities in cell-surface polymers. The model we present is able to generate force curves (both approach and retraction) that closely resemble those measured experimentally. Our results show that even small-length-scale heterogeneities can lead to macroscopically nonlinear behavior that is qualitatively and quantitatively different from the homogeneous case. We also report on the energetic consequences of such structural heterogeneity.

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Opioids are the preferred medications for the treatment of pain in the intensive care unit. While undertreatment leads to unrelieved pain and poor clinical outcomes, excessive use of opioids puts patients at risk of experiencing multiple adverse effects. In this work, we present a sequential decision making framework for opioid dosing based on deep reinforcement learning. It provides real-time clinically interpretable dosing recommendations, personalized according to each patient's evolving pain and physiological condition. We focus on morphine, one of the most commonly prescribed opioids. To train and evaluate the model, we used retrospective data from the publicly available MIMIC-3 database. Our results demonstrate that reinforcement learning may be used to aid decision making in the intensive care setting by providing personalized pain management interventions.