Citations

Abstract Background Deep brain stimulation (DBS) is a widely used therapy for neurologic and psychiatric disorders. Conventional DBS delivers highly regular stimulation patterns that suppress pathological activity but can induce stimulation-related side effects, limiting the therapeutic window. Introducing controlled temporal variability through stochastic pulse timing may represent an alternative programming dimension to improve tolerability while preserving clinical benefit. Methods An adult in their 60’s with bilateral Vim DBS underwent evaluation of tonic, pink-noise, and white-noise stimulation patterns delivered through his chronically implanted Boston Scientific Genus system using the Chronos research platform. We assessed tremor and stimulation-induced side effects using accelerometry, spiral drawing tasks, standardized speech recordings, and patient-reported paresthesias. Results Pink noise stimulation preserved meaningful tremor suppression while improving tolerability compared with conventional tonic 130 Hz stimulation. Under tonic stimulation, dysarthria and paresthesias were prominent at 2.0 mA, narrowing the usable therapeutic window. In contrast, pink noise maintained tremor control across the same amplitude range with reduced side-effect burden. White noise stimulation demonstrated intermediate effects, providing improved tolerability relative to tonic stimulation but less tremor suppression than pink noise. Findings were consistent across accelerometry and functional drawing tasks. Conclusion This study provides first-in-human evidence that temporally structured stochastic pulse timing can preserve therapeutic benefit while expanding the tolerable stimulation range relative to tonic DBS. These findings suggest that temporal structure represents a clinically meaningful programming dimension that may broaden the DBS therapeutic window using software based updates to existing hardware. Further evaluation in larger cohorts is warranted.

Abstract Provided herein are novel compounds as orexin 2 receptor agonists, pharmaceutical compositions, use of such compounds in treating narcolepsy, and processes for preparing such compounds.

Abstract Immune checkpoint inhibitors (ICIs) which target immune checkpoint proteins like programmed cell death protein 1 (PD-1) and programmed cell death ligand 1 (PD-L1) have revolutionized cancer therapy. Skeletal toxicities are emerging immune-related adverse events for patients treated with ICIs, and PD-1 blockade in pre-clinical mouse models significantly reduces bone mass. However, the effect of PD-L1 inhibition on the bone throughout development is not well understood. We investigated the role of PD-L1 loss on bone microarchitecture with genetic PD-L1 knockout in myeloid cells using LysM-Cre;PD-L1Flox/Flox mice and pharmacologic inhibition with a PD-L1 neutralizing antibody in adult and aged mice. We report that neither PD-L1 deletion in myeloid cells nor inhibition with α-PD-L1 significantly impacts femoral trabecular bone microarchitecture; however, α-PD-L1 treatment increases vertebral trabecular bone volume. Additionally, PD-L1 blockade influences T cell expansion by decreasing naïve T cells and increasing effector memory T cells in the bone marrow, consistent with previous studies on the effects of PD-1 inhibition. The trabecular bone-sparing effect of PD-L1 inhibition is strikingly different from the bone loss observed with PD-1 blockade. Clinical studies are necessary and justified to determine if PD-L1 may be a less bone destructive alternative for cancer patients treated with ICIs who are at high risk of fracture.

Abstract Background Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome comprising multiple pathophysiological phenotypes. HFpEF trials have largely enrolled diverse populations and reported average treatment effects, consistently yielding neutral results that may obscure drug-specific benefits within distinct subgroups. To address this issue, we employ an interaction-based that incorporates treatment-by-variable interactions to uncover drug-specific responses. Methods We leveraged four HFpEF clinical trials (TOPCAT, RELAX, NEAT-HFpEF, INDIE-HFpEF) and developed a framework comprising two complementary approaches. The first employed a prognostic responder model to evaluate whether conventional responder definitions reflect treatment-specific benefit or instead capture favorable clinical trajectories common to both treatment and placebo groups. The second used an interaction-based individual treatment effect (ITE) modeling to identify baseline variables that modify therapy effect, distinguishing drug-specific response from prognostic phenotypes. Results Although the prognostic responder model demonstrated good discrimination, further analisys suggested it primarily captured a prognostic signal associated with favorable clinical trajectories common to both treatment and placebo arms. In contrast, the ITE model identified distinct, drug-specific effect modifiers across trials (cardiorenal-inflammatory for spironolactone (TOPCAT), NO-mediated anti-inflammatory for isosorbide mononitrate (NEAT-HFpEF), afterload-reducing for inorganic nitrite (INDIE-HFpEF), and anti–volume-overload for sildenafil (RELAX). Each ITE model demonstrated significance only within its own trial suggesting drug-specific signal. Conclusions The proposed method identifies mechanism-specific effect modifiers, and uncovers clinically meaningful heterogeneity in treatment response, which is not captured by conventional MCID-based approaches. Although exploratory, these findings support phenotype-guided therapy in HFpEF and argue for phenotype-informed trial design to enhance treatment-effect detection and therapy targeting. Graphical Abstract

Tackling hospital emergency department (ED) overcrowding is a paramount challenge for healthcare systems. To combat this issue, an innovative approach is to identify patients who can be served vertically (i.e., in a seated position) and route them to a dedicated area termed the vertical processing pathway (VPP). Successfully implementing this design requires understanding which patients should be routed to the VPP and when. Currently, the decision to leverage the VPP is made in an ad hoc fashion. To assist our partner hospital and other EDs in capturing the value of the VPP, we develop a machine learning model that provides personalized risk scores predicting whether each arriving patient needs an ED bed. We use these scores as input to a stochastic patient flow model and analytically characterize the optimal VPP policy that minimizes length of stay. We then combine our results to derive an interpretable VPP patient streaming protocol suitable for implementation in practice and conduct a before-and-after experiment in which we leverage empirical analyses to evaluate the impact of integrating it into practice. The implemented protocol led to an 11-minute (4.2%) reduction in ED length of stay in our partner hospital without any adverse effect on quality-of-care outcomes. This effect was statistically significant and remained robust after controlling for various confounding factors and potential sources of endogeneity. Our VPP protocol can also be utilized in other EDs, offering operational improvements without requiring additional resources. This paper was accepted by Stefan Scholtes, healthcare management. Funding: S. Saghafian received financial support from the NSF [Grant CMMI-15 62645], “Data-Driven Management of Post-Transplant Medications,” which partially enabled this work, as well as funding and support from the Harvard Data Science Initiative and the Harvard Mossavar-Rahmani Center for Business and Government. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.07517 .