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Healthcare providers (HCPs) face high rates of distress, experienced as burnout, moral distress, compassion fatigue, and grief. HCPs are also experiencing a crisis in meaning whereby distress is associated with disconnection from meaning in work and, in turn, a lack of meaning in work can further perpetuate distress for HCPs. Although scalable systems-level solutions are needed to tackle multidimensional HCP distress, it is also necessary to address HCP suffering at individual, team, and institutional levels. Targeted interventions to alleviate HCP distress are limited. Meaning-centered psychotherapy (MCP), a brief, evidence-based, intervention first developed for persons with advanced cancer, holds promise to mitigate HCP distress. This study adapted MCP for HCPs through feedback from a multidisciplinary sample of clinicians trained in MCP and working in healthcare settings. A survey was distributed electronically between November and December 2023 to HCPs previously trained in MCP assessing quantitative and qualitative feedback on the appropriateness of MCP for HCPs, the relevance of MCP session topics and exercises, and implementation barriers and facilitators. Descriptive statistics on relevant participant ratings were calculated; a matrix analysis approach was used for qualitative data. Forty participants, primarily mental health providers, expressed that MCP principles were highly relevant for HCPs and offered key insights on appropriate intervention modifications, including the need for a primary focus on meaning in professional life, reduced intervention length, and delivery in group format. Feedback informed critical adjustments to promote appropriateness and acceptability of MCP-HCP which is poised for pilot testing to determine its feasibility and preliminary efficacy for HCPs., (© Society of Behavioral Medicine 2024. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Meaning-Centered Psychotherapy (MCP) is a manualized, evidence-based intervention designed to help cancer patients to find meaning and alleviate distress. Meaning-Centered Psychotherapy Training (MCPT) is a multicomponent program for cancer care clinicians that consists of didactics, group experiential learning, and role-plays with simulated patients to learn MCP and acquire skills to deliver it in real-world oncology settings. The efficacy and impact of MCPT for multidisciplinary cancer care clinicians to learn and disseminate MCP is described and evaluated. A multilevel evaluation based on the RE-AIM framework was utilized to assess the efficacy of the MCPT program over the initial 5 years of the program. The outcomes of the evaluation supported MCPT goals. Three hundred forty-two participants attended MCPT. Overall satisfaction measured in the post-training assessment was high. Significant increases in MCP skills were demonstrated by participants over the course of the role-play sessions, and participants showed significant improvements in pre/post-training MCP knowledge assessment scores, as well as significant increases in self-reported overall MCP skills and core competencies. Follow-up survey responses indicate that MCP trainees were utilizing MCP, had made changes to their clinical practice, and progressed on individual implementation goals. During the first 5 years, the MCPT program was successfully developed, established, implemented, and shown to be effective in the dissemination of MCP across the RE-AIM domains. Future directions for training and implementation research include increasing diversity of providers and investigating the impact of the program on patient outcomes., (© Society of Behavioral Medicine 2024. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Swimming microrobots are increasingly developed with complex materials and dynamic shapes and are expected to operate in complex environments in which the system dynamics are difficult to model and positional control of the microrobot is not straightforward to achieve. Deep reinforcement learning is a promising method of autonomously developing robust controllers for creating smart microrobots, which can adapt their behavior to operate in uncharacterized environments without the need to model the system dynamics. This article reports the development of a smart helical magnetic hydrogel microrobot that uses the soft actor critic reinforcement learning algorithm to autonomously derive a control policy which allows the microrobot to swim through an uncharacterized biomimetic fluidic environment under control of a time varying magnetic field generated from a three-axis array of electromagnets. The reinforcement learning agent learned successful control policies from both state vector input and raw images, and the control policies learned by the agent recapitulated the behavior of rationally designed controllers based on physical models of helical swimming microrobots. Deep reinforcement learning applied to microrobot control is likely to significantly expand the capabilities of the next generation of microrobots., Competing Interests: Competing Interests The authors declare that they have no competing interests.

An evolving understanding of disease pathogenesis has compelled the development of new drug delivery approaches. Recently, bioinspired microrobots have gained traction as drug delivery systems. By leveraging the microscale phenomena found in physiological systems, these microrobots can be designed with greater maneuverability, which enables more precise, controlled drug release. Their function could be further improved by testing their efficacy in physiologically relevant model systems as part of their development. In parallel with the emergence of microscale robots, organ-on-a-chip technologies have become important in drug discovery and physiological modeling. These systems reproduce organ-level functions in microfluidic devices, and can also incorporate specific biological, chemical, and physical aspects of a disease. This review highlights recent developments in both microrobotics and organ-on-a-chip technologies and envisions their combined use for developing future drug delivery systems.

Microfluidic technologies are frequently employed as point-of-care diagnostic tools for improving time-to-diagnosis and improving patient outcomes in clinical settings. These microfluidic devices often are designed to operate with peripheral equipment for liquid handling that increases the cost and complexity of these systems and reduces their potential for widespread adoption in low resource healthcare applications. Here, we present a low-cost (~$120), open-source peristaltic pump constructed with a combination of three dimensional (3D)-printed parts and common hardware, which is amenable to deployment with microfluidic devices for point-of-care diagnostics. This pump accepts commonly available silicone rubber tubing in a range of sizes from 1.5 to 3 mm, and is capable of producing flow rates up to 1.6 mL min -1 . This device is programmed with an Arduino microcontroller, allowing for custom flow profiles to fit a wide range of low volume liquid handling applications including precision liquid aliquoting, flow control within microfluidics, and generation of physiologically relevant forces for studying cellular mechanobiology within microfluidic systems.

The advent of biotechnology-derived, herbicide-resistant crops has revolutionized farming practices in many countries. Facile, highly effective, environmentally sound, and profitable weed control methods have been rapidly adopted by crop producers who value the benefits associated with biotechnology-derived weed management traits. But a rapid rise in the populations of several troublesome weeds that are tolerant or resistant to herbicides currently used in conjunction with herbicide-resistant crops may signify that the useful lifetime of these economically important weed management traits will be cut short. We describe the development of soybean and other broadleaf plant species resistant to dicamba, a widely used, inexpensive, and environmentally safe herbicide. The dicamba resistance technology will augment current herbicide resistance technologies and extend their effective lifetime. Attributes of both nuclear- and chloroplast-encoded dicamba resistance genes that affect the potency and expected durability of the herbicide resistance trait are examined.