Native Mass Spectrometry Dissects the Structural Dynamics of an Allosteric Heterodimer of SARS-CoV-2 Nonstructural Proteins.

Structure-based drug design, which relies on precise understanding of the target protein and its interaction with the drug candidate, is dramatically expedited by advances in computational methods for candidate prediction. Yet, the accuracy needs to be improved with more structural data from high throughput experiments, which are challenging to generate, especially for dynamic and weak associations. Herein, we applied native mass spectrometry (native MS) to rapidly characterize ligand binding of an allosteric heterodimeric complex of SARS-CoV-2 nonstructural proteins (nsp) nsp10 and nsp16 (nsp10/16), a complex essential for virus survival in the host and thus a desirable drug target. Native MS showed that the dimer is in equilibrium with monomeric states in solution. Consistent with the literature, well characterized small cosubstrate, RNA substrate, and product bind with high specificity and affinity to the dimer but not the free monomers. Unsuccessfully designed ligands bind indiscriminately to all forms. Using neutral gas collision, the nsp16 monomer with bound cosubstrate can be released from the holo dimer complex, confirming the binding to nsp16 as revealed by the crystal structure. However, we observed an unusual migration of the endogenous zinc ions bound to nsp10 to nsp16 after collisional dissociation. The metal migration can be suppressed by using surface collision with reduced precursor charge states, which presumably resulted in minimal gas-phase structural rearrangement and highlighted the importance of complementary techniques. With minimal sample input (∼µg), native MS can rapidly detect ligand binding affinities and locations in dynamic multisubunit protein complexes, demonstrating the potential of an "all-in-one" native MS assay for rapid structural profiling of protein-to-AI-based compound systems to expedite drug discovery.

Identification of Unique Fragmentation Patterns of Fentanyl Analog Protomers Using Structures for Lossless Ion Manipulations Ion Mobility-Orbitrap Mass Spectrometry.

The opioid crisis in the United States is being fueled by the rapid emergence of new fentanyl analogs and precursors that can elude traditional library-based screening methods, which require data from known reference compounds. Since reference compounds are unavailable for new fentanyl analogs, we examined if fentanyls (fentanyl + fentanyl analogs) could be identified in a reference-free manner using a combination of electrospray ionization (ESI), high-resolution ion mobility (IM) spectrometry, high-resolution mass spectrometry (MS), and higher-energy collision-induced dissociation (MS/MS). We analyzed a mixture containing nine fentanyls and W-15 (a structurally similar molecule) and found that the protonated forms of all fentanyls exhibited two baseline-separated IM distributions that produced different MS/MS patterns. Upon fragmentation, both IM distributions of all fentanyls produced two high intensity fragments, resulting from amine site cleavages. The higher mobility distributions of all fentanyls also produced several low intensity fragments, but surprisingly, these same fragments exhibited much greater intensities in the lower mobility distributions. This observation demonstrates that many fragments of fentanyls predominantly originate from one of two different gas-phase structures (suggestive of protomers). Furthermore, increasing the water concentration in the ESI solution increased the intensity of the lower mobility distribution relative to the higher mobility distribution, which further supports that fentanyls exist as two gas-phase protomers. Our observations on the IM and MS/MS properties of fentanyls can be exploited to positively differentiate fentanyls from other compounds without requiring reference libraries and will hopefully assist first responders and law enforcement in combating new and emerging fentanyls.

Bacille Calmette-Guérin preparation and intravesical administration to patients with bladder cancer: Risks to healthcare personnel and patients, and mitigation strategies.

Intravesical Bacillus Calmette-Guérin (BCG) is a standard therapy for non-muscle-invasive bladder cancer used in urology clinics and inpatient settings. We present a review of infection risks to patients receiving intravesical BCG, healthcare personnel who prepare and administer BCG, and other patients treated in facilities where BCG is prepared and administered. Knowledge of these risks and relevant regulations informs appropriate infection prevention measures.

Discovering novel barriers to eBook implementation.

BACKGROUND: Medical errors when managing inpatient paediatric patients with diabetes mellitus can lead to significant morbidity and mortality, necessitating improvement in training of medical trainees. Previous educational interventions have focused on time and resource intensive methods, restricting their applicability to teaching in time-limited scenarios. We chose a blended learning approach to create and implement a novel eBook for use by trainees and then aimed to determine the efficacy and potential barriers to that implementation. APPROACH: We grounded our work in complex adaptive systems theory and used the framework of complex adaptive blended learning system (CABLS) to shape both our implementation and evaluation. We utilised an embedded mixed methodology to quantitatively evaluate efficacy via knowledge acquisition and self-reported confidence and to qualitatively evaluate barriers via open-ended questions. EVALUATION: Between 2022 and 2023, the study enrolled 72 learners and 12 educators. There was no statistically significant difference in change in knowledge acquisition and self-reported confidence between learners who did and did not have access to the eBook. Learners and educators identified several unique and novel barriers to eBook implementation, which were mapped to the CABLS framework. IMPLICATIONS: Our results on the efficacy of implementation are inconclusive, possibly due to the small participant size and implying a need for multi-institutional evaluation. Our qualitative results demonstrated previously unknown barriers to eBook implementation. This knowledge can assist other medical specialties as they implement their own eBooks. We also hope to use these barriers to improve implementation in our next iteration: the outpatient setting.

Integrated data-driven and experimental approaches to accelerate lead optimization targeting SARS-CoV-2 main protease.

Identification of potential therapeutic candidates can be expedited by integrating computational modeling with domain aware machine learning (ML) models followed by experimental validation in an iterative manner. Generative deep learning models can generate thousands of new candidates, however, their physiochemical and biochemical properties are typically not fully optimized. Using our recently developed deep learning models and a scaffold as a starting point, we generated tens of thousands of compounds for SARS-CoV-2 Mpro that preserve the core scaffold. We utilized and implemented several computational tools such as structural alert and toxicity analysis, high throughput virtual screening, ML-based 3D quantitative structure-activity relationships, multi-parameter optimization, and graph neural networks on generated candidates to predict biological activity and binding affinity in advance. As a result of these combined computational endeavors, eight promising candidates were singled out and put through experimental testing using Native Mass Spectrometry and FRET-based functional assays. Two of the tested compounds with quinazoline-2-thiol and acetylpiperidine core moieties showed IC[Formula: see text] values in the low micromolar range: [Formula: see text] [Formula: see text]M and 3.41±0.0015 [Formula: see text]M, respectively. Molecular dynamics simulations further highlight that binding of these compounds results in allosteric modulations within the chain B and the interface domains of the Mpro. Our integrated approach provides a platform for data driven lead optimization with rapid characterization and experimental validation in a closed loop that could be applied to other potential protein targets.

AI-Accelerated Design of Targeted Covalent Inhibitors for SARS-CoV-2.

Direct-acting antivirals for the treatment of the COVID-19 pandemic caused by the SARS-CoV-2 virus are needed to complement vaccination efforts. Given the ongoing emergence of new variants, automated experimentation, and active learning based fast workflows for antiviral lead discovery remain critical to our ability to address the pandemic's evolution in a timely manner. While several such pipelines have been introduced to discover candidates with noncovalent interactions with the main protease (Mpro), here we developed a closed-loop artificial intelligence pipeline to design electrophilic warhead-based covalent candidates. This work introduces a deep learning-assisted automated computational workflow to introduce linkers and an electrophilic "warhead" to design covalent candidates and incorporates cutting-edge experimental techniques for validation. Using this process, promising candidates in the library were screened, and several potential hits were identified and tested experimentally using native mass spectrometry and fluorescence resonance energy transfer (FRET)-based screening assays. We identified four chloroacetamide-based covalent inhibitors of Mpro with micromolar affinities (KI of 5.27 µM) using our pipeline. Experimentally resolved binding modes for each compound were determined using room-temperature X-ray crystallography, which is consistent with the predicted poses. The induced conformational changes based on molecular dynamics simulations further suggest that the dynamics may be an important factor to further improve selectivity, thereby effectively lowering KI and reducing toxicity. These results demonstrate the utility of our modular and data-driven approach for potent and selective covalent inhibitor discovery and provide a platform to apply it to other emerging targets.

Creation and impact of containment units with high-risk zones during the coronavirus disease 2019 (COVID-19) pandemic.

BACKGROUND: The rapid spread of coronavirus disease 2019 (COVID-19) required swift preparation to protect healthcare personnel (HCP) and patients, especially considering shortages of personal protective equipment (PPE). Due to the lack of a pre-existing biocontainment unit, we needed to develop a novel approach to placing patients in isolation cohorts while working with the pre-existing physical space. OBJECTIVES: To prevent disease transmission to non-COVID-19 patients and HCP caring for COVID-19 patients, to optimize PPE usage, and to provide a comfortable and safe working environment. METHODS: An interdisciplinary workgroup developed a combination of approaches to convert existing spaces into COVID-19 containment units with high-risk zones (HRZs). We developed standard workflow and visual management in conjunction with updated staff training and workflows. The infection prevention team created PPE standard practices for ease of use, conservation, and staff safety. RESULTS: The interventions resulted in 1 possible case of patient-to-HCP transmission and zero cases of patient-to-patient transmission. PPE usage decreased with the HRZ model while maintaining a safe environment of care. Staff on the COVID-19 units were extremely satisfied with PPE availability (76.7%) and efforts to protect them from COVID-19 (72.7%). Moreover, 54.8% of HCP working in the COVID-19 unit agreed that PPE monitors played an essential role in staff safety. CONCLUSIONS: The HRZ model of containment unit is an effective method to prevent the spread of COVID-19 with several benefits. It is easily implemented and scaled to accommodate census changes. Our experience suggests that other institutions do not need to modify existing physical structures to create similarly protective spaces.

The Impact of a Gamified Curriculum Using Kahoot! on Musculoskeletal Knowledge and Skill Acquisition Among Pediatric Residents.

OBJECTIVE: To determine whether a musculoskeletal curriculum involving gamification via Kahoot! (an online classroom response system) was acceptable and more effective at teaching pediatric residents musculoskeletal knowledge and skills than a nongamified curriculum. METHODS: A prospective, randomized controlled trial was conducted at an urban, academic pediatric clinic. All participants received a curriculum that included brief didactics and knowledge questions. The knowledge questions were delivered via Kahoot! to the intervention group and administered via paper to the control group. The primary outcome was knowledge and skill acquisition following curriculum participation. RESULTS: A total of 73 of 85 (86%) residents completed the study (intervention group: 46; control group: 27). Following participation in the curriculum, intervention and control residents demonstrated an improvement in musculoskeletal knowledge (P < .05) measured via questionnaire, as well as an improvement in physical exam skills during a standardized patient encounter (P < .05). There was no difference in knowledge or skill improvement between groups. Intervention participants indicated positive attitudes toward Kahoot!. CONCLUSIONS: Our musculoskeletal curriculum demonstrated improvements in knowledge and skills among residents, though inclusion of Kahoot! did not enhance the experimental effect. Further research is needed to identify strategies to optimize gamification for learning.

Strategies utilized to prevent and control SARS-CoV-2 transmission in two congregate, psychiatric healthcare settings during the pandemic.

BACKGROUND: The COVID-19 pandemic has had a substantial effect on the delivery of psychiatric health care. Inpatient psychiatric health care facilities have experienced outbreaks of COVID-19, making these areas particularly vulnerable. METHODS: Our facility used a multidisciplinary approach to implement enhanced infection prevention and control (IPC) interventions in our psychiatric health care areas. RESULTS: In a 16-month period during the COVID-19 pandemic, our 2 facilities provided >29,000 patient days of care to 1,807 patients and identified only 47 COVID-19 positive psychiatric health inpatients (47/1,807, or 2.6%). We identified the majority of these cases by testing all patients at admission, preventing subsequent outbreaks. Twenty-one psychiatric health care personnel were identified as COVID+ during the same period, with 90% linked to an exposure other than a known positive case at work. DISCUSSION: The IPC interventions we implemented provided multiple layers of safety for our patients and our staff. Ultimately, this resulted in low SARS-CoV-2 infection rates within our facilities. CONCLUSIONS: Psychiatric health care facilities are uniquely vulnerable to COVID-19 outbreaks because they are congregate units that promote therapeutic interactions in shared spaces. IPC interventions used in acute medical care settings can also work effectively in psychiatric health care, but often require modifications to ensure staff and patient safety.

Building a personal protective equipment monitor team as part of a comprehensive COVID-19 prevention strategy.

We instituted Personal Protective Equipment (PPE) Monitors as part of our care of COVID-19 patients in high-risk zones. PPE Monitors aided health care personnel (HCP) in donning and doffing, which contributed to nearly zero transmission of COVID-19 to HCP, despite their care of over 1400 COVID-19 patients.

Implementation of a virtual ward as a response to the COVID-19 pandemic.

Objective The aim of this study was to describe and evaluate the implementation of a virtual ward as a COVID-19 hospital avoidance response strategy and identify opportunities for improvement and future applicability. Methods A mixed-method observational study was conducted of a centralised virtual ward, which operated in a large metropolitan Australian health service from 23 March to 1 June 2020. Results In total, 238 unique patients were admitted to the virtual ward, accounting for 264 individual admission episodes and 2451 virtual bed days. Twenty (7.6%) episodes resulted in transfer to hospital and 136 patients provided responses to feedback surveys and reported their experience as very good (61.7%, n=87) or good (34.8%, n=49). Implementation success was high, with the model widely accepted and adopted across the health service. The service delivery model was considered to be low-cost in comparison to inpatient hospital-based care. Conclusions Overall, as a rapidly developed and implemented low-tech model of care, the virtual ward was found to provide an effective, accessible and low-cost solution to managing low-acuity COVID-19-positive patients in the community. This model should be considered in future pandemics as a hospital-avoidance response, with the ability to minimise patient-to-healthcare worker transmission, reduce personal protective equipment use and enhance patient adherence with isolation requirements. Targeted remote telemonitoring should be considered as a future modification to improve patient care. What is known about this topic? Virtual wards aim to reduce hospital demand by providing hospital-level care in community settings such as the patients' home. The COVID-19 pandemic has seen a rapid increase in the utilisation of virtual wards as an acute healthcare response that facilitates contactless care of infectious patients. Despite this rapid adoption, there is limited literature on the effectiveness of virtual ward models of care in a pandemic context. What does this paper add? This study provides a detailed description of the implementation of a virtual ward in a large metropolitan health service. It evaluates the effectiveness of the virtual ward as a COVID-19 response strategy and identifies opportunities for improvement and future applicability. This study contributes to the growing body of literature on the COVID-19 healthcare response and virtual wards. What are the implications for practitioners? This study details the implementation of a virtual ward and highlights potential facilitators and barriers to successful implementation and sustained applicability. Findings provide a comparative benchmark for other health services implementing virtual wards as a pandemic response strategy.

To Flip or Not to Flip: Learning Style Preferences among Millennial Physician Assistant Students.

INTRODUCTION: Presenting material in a manner that is most palatable to students is important to improve the learning process. We evaluated the efficacy of different teaching styles including the flipped classroom and assessed the learning style preferences of a cohort of medical learners in a preclinical obstetrics and gynecology course. METHODS: We conducted three teaching sessions with 35 physician assistant students. A different teaching style was implemented for each session including a traditional lecture with interactive learning technology augmentation, a flipped classroom, and a hybrid approach incorporating lecture and group work. Students were surveyed using a Likert scale regarding the efficacy of the format, clinical relevance of the material, and their learning preference for future sessions. RESULTS: Students rated the traditional approach as the most effective, most relevant, and most preferred method. Students preferred the flipped classroom least, but they rated it as slightly more effective and relevant than the hybrid approach. CONCLUSION: The teaching style of various coursework including the preclinical obstetrics and gynecology curriculum may not need to be altered for millennial learners. This study showed the flipped classroom was the least favored teaching style and that there was a marked preference by students for a more traditional didactic lecture.

Ligand- and Structure-Based Analysis of Deep Learning-Generated Potential α2a Adrenoceptor Agonists.

The α2a adrenoceptor is a medically relevant subtype of the G protein-coupled receptor family. Unfortunately, high-throughput techniques aimed at producing novel drug leads for this receptor have been largely unsuccessful because of the complex pharmacology of adrenergic receptors. As such, cutting-edge in silico ligand- and structure-based assessment and de novo deep learning methods are well positioned to provide new insights into protein-ligand interactions and potential active compounds. In this work, we (i) collect a dataset of α2a adrenoceptor agonists and provide it as a resource for the drug design community; (ii) use the dataset as a basis to generate candidate-active structures via deep learning; and (iii) apply computational ligand- and structure-based analysis techniques to gain new insights into α2a adrenoceptor agonists and assess the quality of the computer-generated compounds. We further describe how such assessment techniques can be applied to putative chemical probes with a case study involving proposed medetomidine-based probes.

Application and assessment of deep learning for the generation of potential NMDA receptor antagonists.

Uncompetitive antagonists of the N-methyl d-aspartate receptor (NMDAR) have demonstrated therapeutic benefit in the treatment of neurological diseases such as Parkinson's and Alzheimer's, but some also cause dissociative effects that have led to the synthesis of illicit drugs. The ability to generate NMDAR antagonists in silico is therefore desirable for both new medication development and preempting and identifying new designer drugs. Recently, generative deep learning models have been applied to de novo drug design as a means to expand the amount of chemical space that can be explored for potential drug-like compounds. In this study, we assess the application of a generative model to the NMDAR to achieve two primary objectives: (i) the creation and release of a comprehensive library of experimentally validated NMDAR phencyclidine (PCP) site antagonists to assist the drug discovery community and (ii) an analysis of both the advantages conferred by applying such generative artificial intelligence models to drug design and the current limitations of the approach. We apply, and provide source code for, a variety of ligand- and structure-based assessment techniques used in standard drug discovery analyses to the deep learning-generated compounds. We present twelve candidate antagonists that are not available in existing chemical databases to provide an example of what this type of workflow can achieve, though synthesis and experimental validation of these compounds are still required.

"What Do You Mean It's Not Cancer?" A Review of Autoimmune and Systemic Inflammatory Diseases Involving the Breast.

Autoimmune and systemic inflammatory diseases represent a heterogeneous group of immune-mediated conditions with a wide range of clinical presentations and various affected organs. Autoimmune diseases can present in the breast as localized disease or as part of systemic involvement. Although breast involvement is uncommon, the spectrum of imaging findings can include breast masses, axillary adenopathy, calcifications, and skin changes, the appearance of which can mimic breast cancer. Common etiologies include diabetic mastopathy, systemic lupus erythematosus, scleroderma, rheumatoid arthritis, idiopathic granulomatous mastitis, sarcoidosis, and Immunoglobulin-G4 related mastopathy. This educational review will present multimodality imaging findings of breast manifestations of systemic inflammatory and autoimmune diseases and coexisting complications. It will also review how these disorders may affect breast cancer risk and breast cancer treatment options, including radiation therapy.

Trends in Gender Authorship and Collaborations: A 30-Year Comparative Bibliometric Analysis of Manuscripts from The Journal of Bone and Joint Surgery and The Bone and Joint Journal.

Publishing original peer-reviewed research is essential for advancement through all career stages. Fewer women than men hold senior-level positions in academic medicine and, therefore, examining publication trends relative to gender is important. The goal of this study was to examine and compare publication trends in The Journal of Bone and Joint Surgery (JBJS) and The Bone and Joint Journal (BJJ) with a particular emphasis on trends regarding author gender. Data was collected and analyzed for manuscripts published in JBJS and BJJ over the past 30 years. For manuscripts published in 1986, 1996, 2006, and 2016, we recorded the numbers of authors, manuscript pages, references, collaborating institutions, the position in the byline of the corresponding author, the country of the corresponding author, and the names of the first and corresponding author. We also calculated the normalized number of citations and corresponding author position. The number of authors, institutions, and countries collaborating on manuscripts published in both JBJS and BJJ increased over time. JBJS published more manuscripts from North America and BJJ published more manuscripts from Europe. In both journals, the percentage of women as first and/or corresponding author increased over time. Trends over the past 30 years have shown increased collaborations with greater citations in manuscripts published in JBJS and BJJ. In the same time period, both journals demonstrated a rise in the percentage of manuscripts with women first and/or corresponding authors, suggesting a decrease in the gender gap.

Preventable Patient Harm: a Multidisciplinary, Bundled Approach to Reducing Clostridium difficile Infections While Using a Glutamate Dehydrogenase/Toxin Immunochromatographic Assay/Nucleic Acid Amplification Test Diagnostic Algorithm.

Health care facility-onset Clostridium difficile infections (HO-CDI) are an important national problem, causing increased morbidity and mortality. HO-CDI is an important metric for the Center for Medicare and Medicaid Service's (CMS) performance measures. Hospitals that fall into the worst-performing quartile in preventing hospital-acquired infections, including HO-CDI, may lose millions of dollars in reimbursement. Under pressure to reduce CDI and without a clear optimal method for C. difficile detection, health care facilities are questioning how best to use highly sensitive nucleic acid amplification tests (NAATs) to aid in the diagnosis of CDI. Our institution has used a two-step glutamate dehydrogenase (GDH)/toxin immunochromatographic assay/NAAT algorithm since 2009. In 2016, our institution set an organizational goal to reduce our CDI rates by 10% by July 2017. We achieved a statistically significant reduction of 42.7% in our HO-CDI rate by forming a multidisciplinary group to implement and monitor eight key categories of infection prevention interventions over a period of 13 months. Notably, we achieved this reduction without modifying our laboratory algorithm. Significant reductions in CDI rates can be achieved without altering sensitive laboratory testing methods.