Assessing Consistency of Respondent-driven Sampling Estimators by Using Repeated Surveys among People Who Inject Drugs (PWID) in New Jersey.

Respondent-driven sampling (RDS) is widely used to sample populations with higher risk of HIV infection for whom no sampling frames exist. However, few studies have been done to assess the consistency of RDS estimators in real world settings. We conducted an assessment study on the consistency of RDS estimators using data from the National HIV Behavioral Surveillance - People Who Inject Drugs surveys in Newark, New Jersey from 2005 to 2018. Population parameter estimates based on RDS-I, RDS-II, Gile's SS, and HCG were compared longitudinally and cross-sectionally. Population homophily statistics and differential recruitment statistics were estimated and compared. Convergence plots were used for RDS diagnosis. Sensitivity analyses were conducted on population size estimates and seeds biases. By comparing time-insensitive population parameters and population homophily statistics estimated by four RDS estimators, the study found that RDS-II and Gile's SS could provide longitudinally and cross-sectionally consistent estimates and population homophily statistics on gender and sexual orientation. Cross-sectional comparison of time-sensitive population parameter estimates also supported the consistency of RDS-II and Gile's SS. However, RDS-I and HCG did not perform well in those comparisons. In conclusion, RDS estimators may not address all inconsistencies, but RDS-II and Gile's SS are recommended to weight RDS samples given enough consistency was observed in them.

Accurate and efficient open-source implementation of domain-based local pair natural orbital (DLPNO) coupled-cluster theory using a t1-transformed Hamiltonian.

We present an efficient, open-source formulation for coupled-cluster theory through perturbative triples with domain-based local pair natural orbitals [DLPNO-CCSD(T)]. Similar to the implementation of the DLPNO-CCSD(T) method found in the ORCA package, the most expensive integral generation and contraction steps associated with the CCSD(T) method are linear-scaling. In this work, we show that the t1-transformed Hamiltonian allows for a less complex algorithm when evaluating the local CCSD(T) energy without compromising efficiency or accuracy. Our algorithm yields sub-kJ mol-1 deviations for relative energies when compared with canonical CCSD(T), with typical errors being on the order of 0.1 kcal mol-1, using our TightPNO parameters. We extensively tested and optimized our algorithm and parameters for non-covalent interactions, which have been the most difficult interaction to model for orbital (PNO)-based methods historically. To highlight the capabilities of our code, we tested it on large water clusters, as well as insulin (787 atoms).

A molecular analysis of meropenem-vaborbactam non-susceptible KPC-producing Klebsiella pneumoniae.

We characterized the molecular determinants of meropenem-vaborbactam (MV) non-susceptibility among non-metallo-ß-lactamase-producing KPC-Klebsiella pneumoniae (KPC-KP). Whole-genome sequencing was performed to identify mutations associated with MV non-susceptibility. Isolates with elevated MV MICs were found to have mutations encoding truncated or altered OmpK36 porins and increased blaKPC copy numbers. KPC-KP isolates with decreased susceptibility to MV were detected among a collection of isolates predating the availability of MV.

Comparative Study of Neutral and Cationic Sn2H2: Toward Laboratory Detection of the Cation.

Group 14 M2H2 isomers (M = Si, Ge, Sn, and Pb) have attracted interest due to their radically differing electronic structures from acetylene. To better understand the Sn-H interactions of the neutral and cationic Sn2H2 structures, we present the most rigorous study of these systems to date. CCSD(T)/cc-pwCVTZ harmonic frequencies are presented as the first predictions for the neutral and cationic species to date. CCSDT(Q)/CBS relative energies are reported using the focal point approach, confirming the butterfly isomer as the global minimum on the potential energy surface for both the neutral and cationic species. In all, there exist 7 minima and 15 transition states. NBO analysis is also performed to elucidate the changes in bond order going from neutral to cation across all isomers of Sn2H2. Our results provide insights into the important Sn-H interaction and provide guidance for future work that may detect Sn2H2+ in the laboratory for the first time.

Teeth with vital pulps and stage III periodontitis unresponsive to therapy exhibit a pulpal inflammatory profile similar to symptomatic irreversible pulpitis.

AIM: The aim of this study is to investigate the expression of inflammatory biomarkers (TNF-α, IL-10, IL-1ß) and the pulpitis-associated miRNA (miR-30a-5p and miR-128-3p) in pulp tissue samples from unrestored teeth with a vital normal pulp (NP), teeth with symptomatic irreversible pulpitis (IP) and in unrestored teeth with periodontal disease, unresponsive to periodontal therapy, and a vital pulp (EP). METHODOLOGY: Thirty patients were included in this observational study (10 teeth with NP, 10 teeth with IP, 10 teeth with EP). Dental pulp tissues samples were collected from patients during root canal treatment (RCT). RNA was extracted and qRT-PCR of target genes (tumour necrosis factor [TNF]-α, interleukin [IL]-1ß, IL-10) and miRNAs (has-miR-30a-5p, has-miR-128-3p) performed to assess the expression profile. Fold-change in expression was calculated using the formula 2-(ΔCt(Exp)-ΔCt(Ctrl)). One-way anova with post-hoc Tukey's was used to determine significant differences between groups. The significance level was set at 5% (p < .05). All teeth were also followed up clinically for 1 year and evaluated for a range of endodontic and periodontal-related outcomes. RESULTS: All investigated genes significantly increased in expression and miRNAs significantly decreased in expression in the IP and EP groups compared with the NP group (p < .05). With regards to TNF-α and IL-1ß there were no significant differences in expression between the IP and EP groups (p > .05), whereas IL-10 expression levels were significantly reduced in the EP compared with the IP group (p < .05). Both miR-30a-5p and miR-128-3p showed significantly reduced expression in both IP and EP lesions, compared with NP (p < .05); however, no significant differences in miRNA expression were observed between IP and EP groups (p > .05). One year after root canal treatment and periodontal maintenance, tooth mobility and probing depth were significantly reduced in the EP group (p < .05). CONCLUSION: Pulp tissues from teeth with IP and EP presented similar levels of altered inflammatory markers compared with NP. TNF-α, IL-10, IL-1ß cytokines and miRNAs (miR-30a-5p and miR-128-3p) are potential objective biomarkers to indicate pulpal inflammatory status, aiding diagnosis and directing clinical decision-making. RCT may be beneficial to improve stage III periodontitis unresponsive to non-surgical periodontal treatment, but further research is required.

Tissue-based T cell activation and viral RNA persist for up to 2 years after SARS-CoV-2 infection.

The mechanisms of postacute medical conditions and unexplained symptoms after SARS-CoV-2 infection [Long Covid (LC)] are incompletely understood. There is growing evidence that viral persistence, immune dysregulation, and T cell dysfunction may play major roles. We performed whole-body positron emission tomography imaging in a well-characterized cohort of 24 participants at time points ranging from 27 to 910 days after acute SARS-CoV-2 infection using the radiopharmaceutical agent [18F]F-AraG, a selective tracer that allows for anatomical quantitation of activated T lymphocytes. Tracer uptake in the postacute COVID-19 group, which included those with and without continuing symptoms, was higher compared with prepandemic controls in many regions, including the brain stem, spinal cord, bone marrow, nasopharyngeal and hilar lymphoid tissue, cardiopulmonary tissues, and gut wall. T cell activation in the spinal cord and gut wall was associated with the presence of LC symptoms. In addition, tracer uptake in lung tissue was higher in those with persistent pulmonary symptoms specifically. Increased T cell activation in these tissues was also observed in many individuals without LC. Given the high [18F]F-AraG uptake detected in the gut, we obtained colorectal tissue for in situ hybridization of SARS-CoV-2 RNA and immunohistochemical studies in a subset of five participants with LC symptoms. We identified intracellular SARS-CoV-2 single-stranded spike protein-encoding RNA in rectosigmoid lamina propria tissue in all five participants and double-stranded spike protein-encoding RNA in three participants up to 676 days after initial COVID-19, suggesting that tissue viral persistence could be associated with long-term immunologic perturbations.

A Feasibility Study of [18F]F-AraG Positron Emission Tomography (PET) for Cardiac Imaging-Myocardial Viability in Ischemia-Reperfusion Injury Model.

PURPOSE: Myocardial infarction (MI) with subsequent inflammation is one of the most common heart conditions leading to progressive tissue damage. A reliable imaging marker to assess tissue viability after MI would help determine the risks and benefits of any intervention. In this study, we investigate whether a new mitochondria-targeted imaging agent, 18F-labeled 2'-deoxy-2'-18F-fluoro-9-ß-d-arabinofuranosylguanine ([18F]F-AraG), a positron emission tomography (PET) agent developed for imaging activated T cells, is suitable for cardiac imaging and to test the myocardial viability after MI. PROCEDURE: To test whether the myocardial [18F]-F-AraG signal is coming from cardiomyocytes or immune infiltrates, we compared cardiac signal in wild-type (WT) mice with that of T cell deficient Rag1 knockout (Rag1 KO) mice. We assessed the effect of dietary nucleotides on myocardial [18F]F-AraG uptake in normal heart by comparing [18F]F-AraG signals between mice fed with purified diet and those fed with purified diet supplemented with nucleotides. The myocardial viability was investigated in rodent model by imaging rat with [18F]F-AraG and 2-deoxy-2[18F]fluoro-D-glucose ([18F]FDG) before and after MI. All PET signals were quantified in terms of the percent injected dose per cc (%ID/cc). We also explored [18F]FDG signal variability and potential T cell infiltration into fibrotic area in the affected myocardium with H&E analysis. RESULTS: The difference in %ID/cc for Rag1 KO and WT mice was not significant (p = ns) indicating that the [18F]F-AraG signal in the myocardium was primarily coming from cardiomyocytes. No difference in myocardial uptake was observed between [18F]F-AraG signals in mice fed with purified diet and with purified diet supplemented with nucleotides (p = ns). The [18F]FDG signals showed wider variability at different time points. Noticeable [18F]F-AraG signals were observed in the affected MI regions. There were T cells in the fibrotic area in the H&E analysis, but they did not constitute the predominant infiltrates. CONCLUSIONS: Our preliminary preclinical data show that [18F]F-AraG accumulates in cardiomyocytes indicating that it may be suitable for cardiac imaging and to evaluate the myocardial viability after MI.

Artificial intelligence in endodontics: Data preparation, clinical applications, ethical considerations, limitations, and future directions.

Artificial intelligence (AI) is emerging as a transformative technology in healthcare, including endodontics. A gap in knowledge exists in understanding AI's applications and limitations among endodontic experts. This comprehensive review aims to (A) elaborate on technical and ethical aspects of using data to implement AI models in endodontics; (B) elaborate on evaluation metrics; (C) review the current applications of AI in endodontics; and (D) review the limitations and barriers to real-world implementation of AI in the field of endodontics and its future potentials/directions. The article shows that AI techniques have been applied in endodontics for critical tasks such as detection of radiolucent lesions, analysis of root canal morphology, prediction of treatment outcome and post-operative pain and more. Deep learning models like convolutional neural networks demonstrate high accuracy in these applications. However, challenges remain regarding model interpretability, generalizability, and adoption into clinical practice. When thoughtfully implemented, AI has great potential to aid with diagnostics, treatment planning, clinical interventions, and education in the field of endodontics. However, concerted efforts are still needed to address limitations and to facilitate integration into clinical workflows.

Artificial intelligence in endodontics: Fundamental principles, workflow, and tasks.

The integration of artificial intelligence (AI) in healthcare has seen significant advancements, particularly in areas requiring image interpretation. Endodontics, a specialty within dentistry, stands to benefit immensely from AI applications, especially in interpreting radiographic images. However, there is a knowledge gap among endodontists regarding the fundamentals of machine learning and deep learning, hindering the full utilization of AI in this field. This narrative review aims to: (A) elaborate on the basic principles of machine learning and deep learning and present the basics of neural network architectures; (B) explain the workflow for developing AI solutions, from data collection through clinical integration; (C) discuss specific AI tasks and applications relevant to endodontic diagnosis and treatment. The article shows that AI offers diverse practical applications in endodontics. Computer vision methods help analyse images while natural language processing extracts insights from text. With robust validation, these techniques can enhance diagnosis, treatment planning, education, and patient care. In conclusion, AI holds significant potential to benefit endodontic research, practice, and education. Successful integration requires an evolving partnership between clinicians, computer scientists, and industry.

Histamine H3 Receptor Isoforms: Insights from Alternative Splicing to Functional Complexity.

Alternative splicing significantly enhances the diversity of the G protein-coupled receptor (GPCR) family, including the histamine H3 receptor (H3R). This post-transcriptional modification generates multiple H3R isoforms with potentially distinct pharmacological and physiological profiles. H3R is primarily involved in the presynaptic inhibition of neurotransmitter release in the central nervous system. Despite the approval of pitolisant for narcolepsy (Wakix®) and daytime sleepiness in adults with obstructive sleep apnea (Ozawade®) and ongoing clinical trials for other H3R antagonists/inverse agonists, the functional significance of the numerous H3R isoforms remains largely enigmatic. Recent publicly available RNA sequencing data have confirmed the expression of multiple H3R isoforms in the brain, with some isoforms exhibiting unique tissue-specific distribution patterns hinting at isoform-specific functions and interactions within neural circuits. In this review, we discuss the complexity of H3R isoforms with a focus on their potential roles in central nervous system (CNS) function. Comparative analysis across species highlights evolutionary conservation and divergence in H3R splicing, suggesting species-specific regulatory mechanisms. Understanding the functionality of H3R isoforms is crucial for the development of targeted therapeutics. This knowledge will inform the design of more precise pharmacological interventions, potentially enhancing therapeutic efficacy and reducing adverse effects in the treatment of neurological and psychiatric disorders.

Binuclear Lantern Complexes of All First-Row 3d Metals Scandium to Zinc: Metal-Metal Bond Lengths and Bond Orders, with Measures of Intrinsic Metal-Metal Bond Strength.

The B3LYP and M06-L functionals with the cc-pVTZ basis set are used to study lantern-type binuclear complexes of all the first-row (3d block) metals scandium to zinc in various low-energy spin states, out of which the ground states are predicted. These complexes are studied as models using mostly the unsubstituted formamidinate ligand. For each metal, metal-metal (MM) bond lengths are related to the formal MM bond orders (zero to five), derived by MO analysis and by electron counting. The predicted ground-state spin multiplicities and MM bond lengths of the model complexes generally agree fairly well with available experimental results on substituted analogues. Finally, values of the formal shortness ratio and Wiberg index for the MM bonds in all of these complexes in all spin states studied are categorized into ranges according to the MM bond orders 0 to 5 in steps of 0.5. The trends shown validate their use in estimating intrinsic metal-metal bond strength regardless of the metal.

Ethynyl Radical Hydrogen Abstraction Energetics and Kinetics Utilizing High-Level Theory.

The ethynyl radical, C2H, is found in a variety of different environments ranging from interstellar space and planetary atmospheres to playing an important role in the combustion of various alkynes under fuel-rich conditions. Hydrogen-atom abstraction reactions are common for the ethynyl radical in these contrasting environments. In this study, the C2H + HX → C2H2 + X, where HX = HNCO, trans-HONO, cis-HONO, C2H4, and CH3OH, reactions have been investigated at rigorously high levels of theory, including CCSD(T)-F12a/cc-pVTZ-F12. For the stationary points thus located, much higher levels of theory have been used, with basis sets as large as aug-cc-pV5Z and methods up to CCSDT(Q), and core correlation was also included. These molecules were chosen because they can be found in either interstellar or combustion environments. Various additive energy corrections have been included to converge the relative enthalpies of the stationary points to subchemical accuracy (≤0.5 kcal mol-1). Barriers predicted here (2.19 kcal mol-1 for the HNCO reaction and 0.47 kcal mol-1 for C2H4) are significantly lower than previous predictions. Reliable kinetics were acquired over a wide range of temperatures (50-5000 K), which may be useful for future experimental studies of these reactions.

[18F]F-AraG imaging reveals association between neuroinflammation and brown- and bone marrow adipose tissue.

Brown and brown-like adipose tissues have attracted significant attention for their role in metabolism and therapeutic potential in diabetes and obesity. Despite compelling evidence of an interplay between adipocytes and lymphocytes, the involvement of these tissues in immune responses remains largely unexplored. This study explicates a newfound connection between neuroinflammation and brown- and bone marrow adipose tissue. Leveraging the use of [18F]F-AraG, a mitochondrial metabolic tracer capable of tracking activated lymphocytes and adipocytes simultaneously, we demonstrate, in models of glioblastoma and multiple sclerosis, the correlation between intracerebral immune infiltration and changes in brown- and bone marrow adipose tissue. Significantly, we show initial evidence that a neuroinflammation-adipose tissue link may also exist in humans. This study proposes the concept of an intricate immuno-neuro-adipose circuit, and highlights brown- and bone marrow adipose tissue as an intermediary in the communication between the immune and nervous systems. Understanding the interconnectedness within this circuitry may lead to advancements in the treatment and management of various conditions, including cancer, neurodegenerative diseases and metabolic disorders.

Top 100 most-cited scientific articles in regenerative endodontics 2019-2023: A bibliometric analysis.

BACKGROUND: Bibliometric analysis is a critical indicator of the influence and relevance of scientific papers, whilst also highlighting key contributors and gaps in knowledge in a scientific field. OBJECTIVES: To update and analyse the 100 most-cited papers in regenerative endodontics from 2019 to 2023. METHODS: A search of the most-cited recent papers focusing on regenerative endodontics using journals included in the category, 'Dentistry, Oral Surgery & Medicine', in the Clarivate Web of Science database from 2019 to 2023 was performed. Three researchers conducted the study selection and data extraction. Data extraction included publication title and year, authors, number and mean number of citations, institution, country and continent, study design, journal title, keywords and research topic. Citation counts were also collected in Google Scholar and Scopus databases. Graphical bibliometric networks were created using VOSviewer software. RESULTS: The number of citations of the 100 most-cited articles ranged from 6 to 85. Most were published in 2020 (n = 48), principally in the Journal of Endodontics (47%), followed by International Endodontic Journal (13%), Journal of Dental Research (6%) and Dental Materials (6%). Laboratory study was the most common study design amongst the included papers (n = 47), followed by narrative reviews (n = 17) and observational studies (n = 16). The most frequent first author on the top three most-cited papers was Hacer Aksel, whilst Adham A. Azim (n = 6; 89 citations) contributed most to the top 100 articles. The institution from which most articles originated was the University of Hong Kong (China) (n = 5; 81 citations), whereas the corresponding authors were predominantly from the United States of America (USA) (n = 31; 560 citations). The VOSviewer map of co-authorship demonstrated research collaborative clusters. 'Regenerative endodontics' and 'stem-cells' were the most employed keywords (37 and 36 occurrences respectively). DISCUSSION: The current study was designed not only to showcase the most influential papers in regenerative endodontics since 2019 but also to provide a better understanding of global research in this area over the last five years. CONCLUSIONS: This bibliometric analysis highlighted papers, authors, institutions and keywords in regenerative endodontics. The 100 most-cited papers primarily consisted of laboratory studies published in the USA, focusing on evaluating biomaterials and scaffold design strategies in contact with stem cells. Clinical studies and systematic reviews representing higher levels of scientific evidence are currently not the most influential in the regenerative endodontic field.

Tissue engineering approaches for dental pulp regeneration: The development of novel bioactive materials using pharmacological epigenetic inhibitors.

The drive for minimally invasive endodontic treatment strategies has shifted focus from technically complex and destructive root canal treatments towards more conservative vital pulp treatment. However, novel approaches to maintaining dental pulp vitality after disease or trauma will require the development of innovative, biologically-driven regenerative medicine strategies. For example, cell-homing and cell-based therapies have recently been developed in vitro and trialled in preclinical models to study dental pulp regeneration. These approaches utilise natural and synthetic scaffolds that can deliver a range of bioactive pharmacological epigenetic modulators (HDACis, DNMTis, and ncRNAs), which are cost-effective and easily applied to stimulate pulp tissue regrowth. Unfortunately, many biological factors hinder the clinical development of regenerative therapies, including a lack of blood supply and poor infection control in the necrotic root canal system. Additional challenges include a need for clinically relevant models and manufacturing challenges such as scalability, cost concerns, and regulatory issues. This review will describe the current state of bioactive-biomaterial/scaffold-based engineering strategies to stimulate dentine-pulp regeneration, explicitly focusing on epigenetic modulators and therapeutic pharmacological inhibition. It will highlight the components of dental pulp regenerative approaches, describe their current limitations, and offer suggestions for the effective translation of novel epigenetic-laden bioactive materials for innovative therapeutics.

MolSym: A Python package for handling symmetry in molecular quantum chemistry.

A consideration of the point group symmetry of molecules is often advantageous from a computational efficiency standpoint and sometimes necessary for the correct treatment of chemical physics problems. Many modern electronic structure software packages include a treatment of symmetry, but these are sometimes incomplete or unusable outside of that program's environment. Therefore, we have developed the MolSym package for handling molecular symmetry and its associated functionalities to provide a platform for including symmetry in the implementation and development of other methods. Features include point group detection, molecule symmetrization, arbitrary generation of symmetry element sets and character tables, and symmetry adapted linear combinations of real spherical harmonic basis functions, Cartesian displacement coordinates, and internal coordinates. We present some of the advantages of using molecular symmetry as achieved by MolSym, particularly with respect to Hartree-Fock theory, and the reduction of finite difference displacements in gradient/Hessian computations. This package is designed to be easily integrated into other software development efforts and may be extended to further symmetry applications.

The fate of 17ß-estradiol in snowmelt from a field with a history of manure application: A laboratory simulation and field study.

17ß-estradiol is a naturally occurring estrogen, and livestock manure applied to agricultural fields is a major source to the environment. Liquid swine manure is widely applied to agricultural fields in the Canadian Prairies, a region where the majority of the annual runoff occurs during a brief snowmelt period over frozen soil. Transport of estrogens from manure amendments to soil during this important hydrological period is not well understood but is critical to mitigating the snowmelt-driven offsite transport of estrogens. This study quantified the concentration and load of 17ß-estradiol in snowmelt from an agricultural field with a history of manure application under manure application methods: no manure applied, manure applied on the sub-surface, and on the surface, using a laboratory simulation study with flooded intact soil cores and a field study during snowmelt. A higher concentration of 17ß-estradiol was in the laboratory simulation than in the field (mean laboratory pore water = 1.65 ± 1.2 µg/L; mean laboratory flood water = 0.488 ± 0.58 µg/L; and mean field snowmelt = 0.0619 ± 0.048 µg/L). There were no significant differences among manure application methods for 17ß-estradiol concentration. Laboratory pore water concentrations significantly increased over time, corresponding with changes in pH. In contrast, there was no significant change in the field snowmelt concentrations of 17ß-estradiol over time. However, for both laboratory simulation experiments and field-based snowmelt experiments, mean concentrations of 17ß-estradiol were higher with subsurface than surface-applied manure, and the cumulative load of 17ß-estradiol was significantly higher in the sub-surface than in surface applied. The mean cumulative load from the field study across all treatments (6.91 ± 3.7 ng/m2) approximates the magnitude of 17ß-estradiol that could be mobilized from manured fields. The sub-surface application of manure seems to increase the persistence of 17ß-estradiol in soil, thus enhancing the potential loss to snowmelt runoff.

A high-affinity, cis-on photoswitchable beta blocker to optically control ß2-adrenergic receptors in vitro and in vivo.

This study introduces (S)-Opto-prop-2, a second-generation photoswitchable ligand designed for precise modulation of ß2-adrenoceptor (ß2AR). Synthesised by incorporating an azobenzene moiety with propranolol, (S)-Opto-prop-2 exhibited a high PSScis (photostationary state for cis isomer) percentage (∼90 %) and a favourable half-life (>10 days), facilitating diverse bioassay measurements. In vitro, the cis-isomer displayed substantially higher ß2AR binding affinity than the trans-isomer (1000-fold), making (S)-Opto-prop-2 one of the best photoswitchable GPCR (G protein-coupled receptor) ligands reported so far. Molecular docking of (S)-Opto-prop-2 in the X-ray structure of propranolol-bound ß2AR followed by site-directed mutagenesis studies, identified D1133.32, N3127.39 and F2896.51 as crucial residues that contribute to ligand-receptor interactions at the molecular level. In vivo efficacy was assessed using a rabbit ocular hypertension model, revealing that the cis isomer mimicked propranolol's effects in reducing intraocular pressure, while the trans isomer was inactive. Dynamic optical modulation of ß2AR by (S)-Opto-prop-2 was demonstrated in two different cAMP bioassays and using live-cell confocal imaging, indicating reversible and dynamic control of ß2AR activity using the new photopharmacology tool. In conclusion, (S)-Opto-prop-2 emerges as a promising photoswitchable ligand for precise and reversible ß2AR modulation with light. The new tool shows superior cis-on binding affinity, one of the largest reported differences in affinity (1000-fold) between its two configurations, in vivo efficacy, and dynamic modulation. This study contributes valuable insights into the evolving field of photopharmacology, offering a potential avenue for targeted therapy in ß2AR-associated pathologies.

Machine learning of dissection photographs and surface scanning for quantitative 3D neuropathology.

We present open-source tools for three-dimensional (3D) analysis of photographs of dissected slices of human brains, which are routinely acquired in brain banks but seldom used for quantitative analysis. Our tools can: (1) 3D reconstruct a volume from the photographs and, optionally, a surface scan; and (2) produce a high-resolution 3D segmentation into 11 brain regions per hemisphere (22 in total), independently of the slice thickness. Our tools can be used as a substitute for ex vivo magnetic resonance imaging (MRI), which requires access to an MRI scanner, ex vivo scanning expertise, and considerable financial resources. We tested our tools on synthetic and real data from two NIH Alzheimer's Disease Research Centers. The results show that our methodology yields accurate 3D reconstructions, segmentations, and volumetric measurements that are highly correlated to those from MRI. Our method also detects expected differences between post mortem confirmed Alzheimer's disease cases and controls. The tools are available in our widespread neuroimaging suite 'FreeSurfer' (https://surfer.nmr.mgh.harvard.edu/fswiki/PhotoTools).

Every year, thousands of human brains are donated to science. These brains are used to study normal aging, as well as neurological diseases like Alzheimer's or Parkinson's. Donated brains usually go to 'brain banks', institutions where the brains are dissected to extract tissues relevant to different diseases. During this process, it is routine to take photographs of brain slices for archiving purposes. Often, studies of dead brains rely on qualitative observations, such as 'the hippocampus displays some atrophy', rather than concrete 'numerical' measurements. This is because the gold standard to take three-dimensional measurements of the brain is magnetic resonance imaging (MRI), which is an expensive technique that requires high expertise ­ especially with dead brains. The lack of quantitative data means it is not always straightforward to study certain conditions. To bridge this gap, Gazula et al. have developed an openly available software that can build three-dimensional reconstructions of dead brains based on photographs of brain slices. The software can also use machine learning methods to automatically extract different brain regions from the three-dimensional reconstructions and measure their size. These data can be used to take precise quantitative measurements that can be used to better describe how different conditions lead to changes in the brain, such as atrophy (reduced volume of one or more brain regions). The researchers assessed the accuracy of the method in two ways. First, they digitally sliced MRI-scanned brains and used the software to compute the sizes of different structures based on these synthetic data, comparing the results to the known sizes. Second, they used brains for which both MRI data and dissection photographs existed and compared the measurements taken by the software to the measurements obtained with MRI images. Gazula et al. show that, as long as the photographs satisfy some basic conditions, they can provide good estimates of the sizes of many brain structures. The tools developed by Gazula et al. are publicly available as part of FreeSurfer, a widespread neuroimaging software that can be used by any researcher working at a brain bank. This will allow brain banks to obtain accurate measurements of dead brains, allowing them to cheaply perform quantitative studies of brain structures, which could lead to new findings relating to neurodegenerative diseases.

Trauma-Informed Undergraduate Medical Education: A Pathway to Flourishing with Adversity by Enhancing Psychological Safety.

We describe the Life Experiences Curriculum (LEC), which attempts to integrate medical student well-being with trauma-informed medical education. The long-term goal of LEC is to help medical students flourish with adversity and trauma, where flourishing refers to having a sense of purpose that arises from awareness of one's strengths and limitations, shaped by life experiences. The short-term goal of LEC is to develop students' relational capacities, such as acceptance and awareness of self and others, while building and maintaining students' psychological safety. We describe the conceptual rationale for these goals and the curriculum's development, implementation, evaluation, and limitations. The curriculum extends over four years and involves a preclinical seminar and students' individual and group reflection sessions with LEC faculty. The seminar addresses the coexistence of trauma and flourishing across life experiences, as well as how safety in relationships is impaired by traumatic experiences and must be restored for healing and growth. The physician faculty have no role in student evaluation and co-lead all LEC activities. LEC is intended to provide students with new language for understanding the process of trauma and flourishing in both individuals and systems and to build and sustain students' relational capacities. There are ongoing efforts to re-imagine self-care as communal-care in which care and support are given and received in a community of students and faculty. Such a model may help build the relational capacities needed to deliver trauma-informed care and also promote flourishing with adversity in healers and in those seeking to be healed.