Kaufman, David L.

Abstract Background: mTOR activation is associated with chronic inflammation in ME/CFS. Previous studies have shown that sustained mTOR activation can cause chronic muscle fatigue by inhibiting ATG13-mediated autophagy. This highlights the pivotal role of mTOR in the pathogenesis of ME/CFS. Methods: We conducted a decentralized, uncontrolled trial of rapamycin in 86 patients with ME/CFS to evaluate its safety and efficacy. Low-dose rapamycin (6 mg/week) was administered, and core ME/CFS symptoms were assessed on days 30 (T1), 60 (T2), and 90 (T3). Plasma levels of autophagy metabolites, such as pSer258-ATG13 and BECLIN-1, were measured and correlated with clinical outcomes, specifically MFI. Results: Rapamycin (6 mg/week) was tolerated without any SAEs. Of the 40 patients, 29 (72.5%) showed strong recovery in PEM, fatigue, and OI, along with improvements in MFI fatigue domains and SF-36 aspects. High levels of BECLIN-1 were detected in T3. Plasma pSer258-ATG13 levels were strongly downregulated at T1. Spearman’s correlation analysis indicated an association between autophagy impairment and reduced activity. Conclusions: Low-dose rapamycin effectively reduced PEM and other key symptoms in patients with ME/CFS, as measured by BAS, SSS, MFI, and SF-36. Future studies should encompass dose optimization and develop a diagnostic tool to identify responders with mTOR-mediated autophagy disruption.

The prevailing first‐come‐first‐served approach to outpatient appointment scheduling ignores differing urgency levels, leading to unnecessarily long waits for urgent patients. In data from a partner healthcare organization, we found in some departments that urgent patients were inadvertently waiting longer for an appointment than non‐urgent patients. This study develops a capacity allocation optimization methodology that reserves appointment slots based on urgency in a complicated, integrated care environment where multiple specialties serve multiple types of patients. This optimization reallocates network capacity to limit access delays (indirect waiting times) for initial and downstream appointments differentiated by urgency. We formulate this problem as a queueing network optimization and approximate it via deterministic linear optimization to simultaneously smooth workloads and guarantee access delay targets. In a case study of our industry partner we demonstrate the ability to (i) reduce urgent patient mean access delay by 27% with only a 7% increase in mean access delay for non‐urgent patients, and (ii) increase throughput by 31% with the same service levels and overtime.