Professionalism in healthcare education can be challenging to define, implement, and assess across students, faculty, staff, and administration. michigan state university's College of Osteopathic Medicine created ...
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Professionalism in healthcare education can be challenging to define, implement, and assess across students, faculty, staff, and administration. michigan state university's College of Osteopathic Medicine created the Common Ground Professionalism Initiative (CGPI) to address these challenges through a unified approach. By breaking down hierarchical barriers and fostering collaboration among all stakeholders, CGPI promotes adherence to universally defined CORE values: collaboration, opportunity, respect, and expertise. This initiative emerged as part of the college's strategic response to institutional challenges, emphasizing shared accountability and a collective commitment to professionalism as a foundation for future growth and community trust moving forward.
Despite guidelines for pathology undergraduate medical education set forth by the Association of American Medical Colleges, American Medical Association, Liaison Committee on Medical Education, and Commission on Osteo...
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Despite guidelines for pathology undergraduate medical education set forth by the Association of American Medical Colleges, American Medical Association, Liaison Committee on Medical Education, and Commission on Osteopathic College Accreditation, there is sparse literature regarding differences in pathology curricula between allopathic and osteopathic institutions. As programs alter curricula to adapt to the ever-increasing breadth and depth of medical knowledge, there is concern for lost educational opportunities in pathology and a growing need for research on the landscape of pathology undergraduate medical education in medical schools nationwide. An Institutional Review Board approved, voluntary 22-item survey regarding pathology curricula was distributed to allopathic and osteopathic medical school students at michigan state university from July 2022 to January 2023. The total number of responses was 363 (n = 363;22.6% allopathic, 77.4% osteopathic). We present data on pathology education at a university that features both an allopathic and osteopathic college of medicine while focusing on factors that influence medical students' perceptions of pathology. Statistically significant differences (P<0.05) in responses-favoring michigan state university osteopathic students over their allopathic counterparts-were observed in several areas: the perception of pathology as a medical versus surgical specialty (P 0.014), acknowledgement of a dedicated pathology course (P 0.002), and awareness of pathology-specific content (P < 0.001). Allopathic students expressed a greater desire for pathology exposure (P 0.003). This study highlights the variable exposure of pathology between two different curriculums and suggests that, while traditionally primary-care-focused, osteopathic medical programs may offer stronger pathology education and exposure.
In soft tissue, growth-induced instability is increasingly being viewed as a key agent in certain morphological developments, such as the folding of the cerebral cortex. As such, the study of growth-induced instabilit...
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In soft tissue, growth-induced instability is increasingly being viewed as a key agent in certain morphological developments, such as the folding of the cerebral cortex. As such, the study of growth-induced instability in the context of morphoelastic large deformation continuum mechanics is a burgeoning field of study. In this work, we examine some of the connections between these new developments and earlier work that can be viewed as the conventional-or no-growth-hyperelastic theory. By a systematic examination of a standard problem, certain direct correspondences are clarified which effectively permit results of a very general nature in the conventional theory to inform ongoing work in the morphoelastic theory.
Class learning outcomes (CLOs) supporting the interdisciplinary curriculum of the School of Packaging (SoP) at michigan state university (MSU) were mapped to competency-based, programmatic learning outcomes (CPLOs) un...
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Class learning outcomes (CLOs) supporting the interdisciplinary curriculum of the School of Packaging (SoP) at michigan state university (MSU) were mapped to competency-based, programmatic learning outcomes (CPLOs) undergirded by broad learning goals of the university (MSU-LGs). Six CPLOs related to the core curriculum, including the ability to design and evaluate packaging systems, as well as students' professionalism and teamwork skills, were developed using consensus building with all teaching faculty. Relationships, from specific to broad (i.e., CLOs to CPLOs to MSU-LGs), were mapped with the SoP faculty. This mapping scheme (class-specific CLOs supporting broader program CPLOs and, ultimately, MSU-LGs) was developed to guarantee alignment of expectations for learning from the course to the packaging program to the MSU-LGs. From Fall 2018 until Fall 2019, assessment tools, including rubrics and assignments intended to evaluate learning, were developed to assess core and elective courses offered by the SoP. Data collection of each student's performance was conducted utilizing Watermark's VIA software from Fall 2018 until Fall 2021 and continuous. Assessment of student performance provided evidence of learning across the SoP curriculum as well as how CLOs, delivered and assessed at the individual student level, translate into competence achieved at the programmatic and university levels.
More and more industrial businesses leverage AI to optimize operations and introduce new and innovative business models for competitive advantage. Businesses are collecting huge amounts of data from their business pro...
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More and more industrial businesses leverage AI to optimize operations and introduce new and innovative business models for competitive advantage. Businesses are collecting huge amounts of data from their business processes and plan to utilize them for better customer experience and insights. However, industrial managers are still determining the AI capabilities they need to analyze the data and develop customer-centric business models. Based on the discussions with twenty-five elite informants from five large industrial businesses, in this study, we propose a set of AI capabilities that can help managers develop data-driven business models. We offer a conceptual framework of AI capabilities and their influence on data-driven business model innovation that can guide managers in their transformation journeys.
We report the first study using active-orbital-based and adaptive CC(P;Q) approaches to describe excited electronic states. These CC(P;Q) methodologies are applied, alongside their completely renormalized (CR) coupled...
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We report the first study using active-orbital-based and adaptive CC(P;Q) approaches to describe excited electronic states. These CC(P;Q) methodologies are applied, alongside their completely renormalized (CR) coupled-cluster (CC) and equation-of-motion (EOM) CC counterparts, to recover the ground- and excited-state potential cuts of the water molecule along the O-H bond-breaking coordinate obtained in the parent CC/EOMCC calculations with a full treatment of singles, doubles, and triples (CCSDT/EOMCCSDT). We demonstrate that the active-orbital-based and adaptive CC(P;Q) approaches closely approximate the CCSDT/EOMCCSDT data using significantly reduced computational costs while improving the CR-CC and CR-EOMCC energetics in stretched regions of the O-H bond-breaking potentials.
The discovery of two-dimensional van der Waals (vdW) magnetic materials has propelled advancements in technological devices. The Nernst effect, which generates a transverse electric voltage in the presence of a longit...
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The discovery of two-dimensional van der Waals (vdW) magnetic materials has propelled advancements in technological devices. The Nernst effect, which generates a transverse electric voltage in the presence of a longitudinal thermal gradient, shows great promise for thermoelectric applications. In this work, we report the electronic and thermoelectric transport properties of Cr1.25Te2, a layered self-intercalated vdW material which exhibits an antiferromagnetic ordering at T-N similar to 191 K followed by a ferromagnetic-like phase transition at T-C similar to 171 K. We observe a prominent topological Hall effect and topological Nernst effect between T-C and T-N, which is ascribable to non-coplanar spin textures inducing a real-space Berry phase due to competing ferromagnetic and antiferromagnetic interactions. Furthermore, we show that Cr1.25Te2 exhibits a substantial anomalous Nernst effect, featuring a giant Nernst angle of similar to 37 % near T-C and a maximum Nernst thermoelectric coefficient of 0.52 mu V/K. These results surpass those of conventional ferromagnets and other two-dimensional vdW materials, highlighting Cr1.25Te2 as a promising candidate for advanced thermoelectric devices based on the Nernst effect.
In the rapidly evolving field of nanomedicine, understanding the interactions between nanoparticles (NPs) and biological systems is crucial. A pivotal aspect of these interactions is the formation of a protein corona ...
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In the rapidly evolving field of nanomedicine, understanding the interactions between nanoparticles (NPs) and biological systems is crucial. A pivotal aspect of these interactions is the formation of a protein corona when NPs are exposed to biological fluids (e.g., human plasma), which significantly influences their behavior and functionality. This study introduces an advanced capillary isoelectric focusing tandem mass spectrometry (cIEF-MS/MS) platform designed to enable high-throughput and reproducible top-down proteomic analysis of protein corona. Our cIEF-MS/MS technique completed each analysis within 30 min. It produced reproducible proteoform measurements of protein corona for at least 50 runs regarding the proteoforms' migration time [relative standard deviations (RSDs) <4%], the proteoforms' intensity (Pearson's correlation coefficients between any two runs >0.90), the number of proteoform identifications (71 +/- 10), and the number of proteoform-spectrum matches (PrSMs) (196 +/- 30). Of the 53 identified genes, 33 are potential biomarkers of various diseases (e.g., cancer, cardiovascular disease, and Alzheimer's disease). We identified 1-102 proteoforms per potential protein biomarker, containing various sequence variations or post-translational modifications. Delineating proteoforms in protein corona by our cIEF-MS/MS in a reproducible and high-throughput fashion will benefit our understanding of nanobiointeractions and advance both diagnostic and therapeutic nanomedicine technologies.
The ability of deep image prior (DIP) to recover high-quality images from incomplete or corrupted measurements has made it popular in inverse problems in image restoration and medical imaging, including magnetic reson...
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The ability of deep image prior (DIP) to recover high-quality images from incomplete or corrupted measurements has made it popular in inverse problems in image restoration and medical imaging, including magnetic resonance imaging (MRI). However, conventional DIP suffers from severe overfitting and spectral bias effects. In this work, we first provide an analysis of how DIP recovers information from undersampled imaging measurements by analyzing the training dynamics of the underlying networks in the kernel regime for different architectures. This study sheds light on important underlying properties for DIP-based recovery. Current research suggests that incorporating a reference image as network input can enhance DIP's performance in image reconstruction compared to using random inputs. However, obtaining suitable reference images requires supervision and raises practical difficulties. In an attempt to overcome this obstacle, we further introduce a self-driven reconstruction process that concurrently optimizes both the network weights and the input while eliminating the need for training data. Our method incorporates a novel denoiser regularization term which enables robust and stable joint estimation of both the network input and reconstructed image. We demonstrate that our self-guided method surpasses both the original DIP and modern supervised methods in terms of MR image reconstruction performance and outperforms previous DIP-based schemes for image inpainting.
BackgroundThe design of smart, photoactivated nanomaterials for targeted drug delivery systems (DDS) has garnered significant research interest due in part to the ability of light to precisely control drug release in ...
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BackgroundThe design of smart, photoactivated nanomaterials for targeted drug delivery systems (DDS) has garnered significant research interest due in part to the ability of light to precisely control drug release in specific cells or tissues with high spatial and temporal resolution. The development of effective light-triggered DDS involves mechanisms including photocleavage, photoisomerization, photopolymerization, photosensitization, photothermal phenomena, and photorearrangement, which permit response to ultraviolet (UV), visible (Vis), and/or Near Infrared (NIR) light. This review explores recent advancements in light-responsive small molecules, polymers, and nanocarriers, detailing their underlying mechanisms and utility for drug delivery and/or imaging. Furthermore, it highlights key challenges and future perspectives in the development of light-triggered DDS, emphasizing the potential of these systems to revolutionize targeted *** systematic literature search was performed using Google Scholar as the primary database and information source. We searched the recently published literature (within 15 years) with the following keywords individually and in relevant combinations: light responsive, nanoparticle, drug release, mechanism, photothermal, photosensitization, photopolymerization, photocleavage, and *** selected 117 scientific articles to assess the strength of evidence after screening titles and abstracts. We found that six mechanisms (photocleavage, photoisomerization, photopolymerization, photosensitization, photothermal phenomena, and photorearrangement) have primarily been used for light-triggered drug release and categorized our review accordingly. Azobenzene/spiropyran-based derivatives and o-nitrobenzyl/Coumarin derivatives are often used for photoisomerization and photocleavage-enabled drug delivery, while free radical polymerization and cationic polymerization comprise two main mechanisms of photopolymerizat
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