Epigenetic and oncogenic inhibitors cooperatively drive differentiation and kill KRAS-mutant colorectal cancers.

Current treatments for KRAS-mutant colorectal cancers (CRCs) are often limited by cellular plasticity and rewiring responses. Here we describe a promising therapeutic strategy that simultaneously targets epigenetic and oncogenic signals. Specifically, we show that inhibitors of the histone methyltransferase, EZH2, synergize with various RAS pathway inhibitors and promote dramatic tumor regression in vivo. Together these agents cooperatively suppress WNT-driven transcription and drive CRCs into a more differentiated cell state by inducing the Groucho/TLE corepressor, TLE4, along with a network of WNT pathway inhibitors and intestinal differentiation proteins. However, these agents also induce the pro-apoptotic protein BMF, which subsequently kills these more differentiated cells. Accordingly, cell death can be prevented by activating ß-catenin, blocking differentiation, or by ablating BMF expression. Collectively, these studies reveal a new therapeutic approach for treating KRAS-mutant CRCs and illustrate a critical convergence of EZH2 and RAS on oncogenic WNT signals, intestinal differentiation, and apoptosis.

The incidence of acute pancreatitis with GLP-1 receptor agonist therapy in individuals with a known history of pancreatitis.

Glucagon-like peptide-1 receptor agonists (GLP-1RA) have been reported to increase the risk of acute pancreatitis (AP). This real-world study did not observe a higher frequency of AP with GLP-1RA exposure in adults with T2D and a prior history of AP regardless of etiology.

A neurodevelopmental disorder mutation locks G proteins in the transitory pre-activated state.

Many neurotransmitter receptors activate G proteins through exchange of GDP for GTP. The intermediate nucleotide-free state has eluded characterization, due largely to its inherent instability. Here we characterize a G protein variant associated with a rare neurological disorder in humans. GαoK46E has a charge reversal that clashes with the phosphate groups of GDP and GTP. As anticipated, the purified protein binds poorly to guanine nucleotides yet retains wild-type affinity for G protein ßγ subunits. In cells with physiological concentrations of nucleotide, GαoK46E forms a stable complex with receptors and Gßγ, impeding effector activation. Further, we demonstrate that the mutant can be easily purified in complex with dopamine-bound D2 receptors, and use cryo-electron microscopy to determine the structure, including both domains of Gαo, without nucleotide or stabilizing nanobodies. These findings reveal the molecular basis for the first committed step of G protein activation, establish a mechanistic basis for a neurological disorder, provide a simplified strategy to determine receptor-G protein structures, and a method to detect high affinity agonist binding in cells.

Laue-DIALS: open-source software for polychromatic X-ray diffraction data.

Most X-ray sources are inherently polychromatic. Polychromatic ("pink") X-rays provide an efficient way to conduct diffraction experiments as many more photons can be used and large regions of reciprocal space can be probed without sample rotation during exposure-ideal conditions for time-resolved applications. Analysis of such data is complicated, however, causing most X-ray facilities to discard >99% of X-ray photons to obtain monochromatic data. Key challenges in analyzing polychromatic diffraction data include lattice searching, indexing and wavelength assignment, correction of measured intensities for wavelength-dependent effects, and deconvolution of harmonics. We recently described an algorithm, Careless, that can perform harmonic deconvolution and correct measured intensities for variation in wavelength when presented with integrated diffraction intensities and assigned wavelengths. Here, we present Laue-DIALS, an open-source software pipeline that indexes and integrates polychromatic diffraction data. Laue-DIALS is based on the dxtbx toolbox, which supports the DIALS software commonly used to process monochromatic data. As such, Laue-DIALS provides many of the same advantages: an open-source, modular, and extensible architecture, providing a robust basis for future development. We present benchmark results showing that Laue-DIALS, together with Careless, provides a suitable approach to the analysis of polychromatic diffraction data, including for time-resolved applications.

Passively sensing smartphone use in teens with rates of use by sex and across operating systems.

Youth screen media activity is a growing concern, though few studies include objective usage data. Through the longitudinal, U.S.-based Adolescent Brain Cognitive Development (ABCD) Study, youth (mage = 14; n = 1415) self-reported their typical smartphone use and passively recorded three weeks of smartphone use via the ABCD-specific Effortless Assessment Research System (EARS) application. Here we describe and validate passively-sensed smartphone keyboard and app use measures, provide code to harmonize measures across operating systems, and describe trends in adolescent smartphone use. Keyboard and app-use measures were reliable and positively correlated with one another (r = 0.33) and with self-reported use (rs = 0.21-0.35). Participants recorded a mean of 5 h of daily smartphone use, which is two more hours than they self-reported. Further, females logged more smartphone use than males. Smartphone use was recorded at all hours, peaking on average from 8 to 10 PM and lowest from 3 to 5 AM. Social media and texting apps comprised nearly half of all use. Data are openly available to approved investigators ( https://nda.nih.gov/abcd/ ). Information herein can inform use of the ABCD dataset to longitudinally study health and neurodevelopmental correlates of adolescent smartphone use.

The mode of action of IL-23 in experimental inflammatory arthritic pain and disease.

OBJECTIVES: We have previously reported using gene-deficient mice that the interleukin (IL)-23p19 subunit is required for the development of innate immune-driven arthritic pain and disease. We aimed to explore here, using a number of in vivo approaches, how the IL-23p19 subunit can mechanistically control arthritic pain and disease in a T- and B- lymphocyte-independent manner. METHODS: We used the zymosan-induced arthritis (ZIA) model in wild-type and Il23p19-/- mice, by a radiation chimera approach, and by single cell RNAseq and qPCR analyses, to identify the IL23p19-expressing and IL-23-responding cell type(s) in the inflamed joints. This model was also utilized to investigate the efficacy of IL-23p19 subunit blockade with a neutralizing monoclonal antibody (mAb). A novel IL-23-driven arthritis model was established, allowing the identification of putative downstream mediators of IL-23 in the control of pain and disease. Pain and arthritis were assessed by relative static weight distribution and histology, respectively. RESULTS: We present evidence that (i) IL-23p19+ non-bone marrow-derived macrophages are required for the development of ZIA pain and disease, (ii) prophylactic and therapeutic blockade of the IL-23p19 subunit ameliorate ZIA pain and disease and (iii) systemically administered IL-23 can induce arthritic pain and disease in a manner dependent on TNF, GM-CSF, CCL17 and cyclooxygenase activity, but independently of lymphocytes, CGRP, NGF and substance P. CONCLUSIONS: The data presented should aid IL-23 targeting both in the choice of inflammatory disease to be treated and the design of clinical trials.

A developmental neurotoxicity adverse outcome pathway (DNT-AOP) with voltage gate sodium channel (VGSC) inhibition as a molecular initiating event (MiE).

The adverse outcome pathway (AOP) framework serves as a practical tool for organising scientific knowledge that can be used to infer cause-effect relationships between stressor events and toxicity outcomes in intact organisms. However, a major challenge in the broader application of the AOP concept within regulatory toxicology is the development of a robust AOPs that can withstand peer review and acceptance. This is mainly due to the considerable amount of work required to substantiate the modular units of a complete AOP, which can take years from inception to completion. The methodology used here consisted of an initial assessment of a single chemical hazard using the Integrated Approach to Testing and Assessment (IATA) framework. An evidence-based approach was then used to gather empirical evidence combining systematic literature review methods with expert knowledge to ensure the effectiveness of the AOP development methodology. The structured framework used assured transparency, objectivity and comprehensiveness, and included expert knowledge elicitation for the evaluation of key event relationships (KERs). This stepwise approach led to the development of an AOP that begins with binding of chemicals to Voltage Gate Sodium Channels (VGSC/Nav) during mammalian development leading to adverse consequences in neurodevelopment evidenced as deficits in cognitive functions. Disruption of the formation of precise neural circuits by alterations in VGSC kinetics during the perinatal stages of brain development may also underlie neurodevelopmental disorders. Gaps in our understanding include the specific critical developmental windows and the quantitative relationship of binding to VGSC and subsequent disruption and cognitive function. Despite the limited quantitative information at all KER levels, regulatory applications of this AOP for DNT assessment have been identified.

Improving prediction of progression of idiopathic scoliosis based on curve size and skeletal maturity.

PURPOSE: To define the risk of curve progression of idiopathic scoliosis (IS) to 35°, 40°, 45°, and 50° based on current curve magnitude and Sanders stage for boys and girls, using a large cohort of patients and encounters, to improve granularity and allow more accurate estimations to guide treatment. METHODS: Retrospective analysis of a prospectively collected scoliosis database. Generalized estimation equation logistic regression models estimated probabilities of curve progression to 35°, 40°, 45°, and 50° based on starting curve size and Sanders stage. Probabilities and their 95% confidence intervals were calculated for each combination of variables to each endpoint separately for boys and girls. RESULTS: A total of 309 patients (80% girls) were included. Starting curve size and Sanders stage were significant predictors for progression in both sexes (all P ≤ 0.04). Higher starting curve sizes and lower Sanders stages were associated with greater odds of progression. Risk of progression was still present even at higher Sanders stages. CONCLUSION: IS curves follow a predictable pattern, having more risk for progression when curves are larger and Sanders stages are smaller. Risk of curve progression is a spectrum based on these factors, indicating some risk of progression exists even for many smaller curves with higher Sanders stages. The improved granularity of this analysis compared to prior efforts may be useful for counseling patients about the risks of curve progression to various curve size endpoints and may aid shared decision-making regarding treatments. LEVEL OF EVIDENCE OR CLINICAL RELEVANCE: Level III: retrospective cohort study.

Sex Differences in the Etiology of Victimization in Adulthood.

Until relatively recently, the study of victimization has been largely outside the purview of behavioral geneticists and evolutionary psychologists. Recent victimology research, however, has shown that genetic and evolutionary forces are connected to the risk of victimization. The current study expands on these findings by examining whether genetic influences differentially explain victimization in males and females. To do so, we use a sample of sibling pairs drawn from the National Longitudinal Study of Adolescent to Adult Health (Add Health; N = 4,244). The analyses revealed no significant quantitative sex differences in the etiology of adult victimization. However, the results of this study do highlight the importance of accounting for genetic factors when studying the etiology of specific types of adult victimization. We conclude by discussing the implications of the current study for future research.

Scaling and Merging Time-Resolved Laue Data with Variational Inference.

Time-resolved X-ray crystallography (TR-X) at synchrotrons and free electron lasers is a promising technique for recording dynamics of molecules at atomic resolution. While experimental methods for TR-X have proliferated and matured, data analysis is often difficult. Extracting small, time-dependent changes in signal is frequently a bottleneck for practitioners. Recent work demonstrated this challenge can be addressed when merging redundant observations by a statistical technique known as variational inference (VI). However, the variational approach to time-resolved data analysis requires identification of successful hyperparameters in order to optimally extract signal. In this case study, we present a successful application of VI to time-resolved changes in an enzyme, DJ-1, upon mixing with a substrate molecule, methylglyoxal. We present a strategy to extract high signal-to-noise changes in electron density from these data. Furthermore, we conduct an ablation study, in which we systematically remove one hyperparameter at a time to demonstrate the impact of each hyperparameter choice on the success of our model. We expect this case study will serve as a practical example for how others may deploy VI in order to analyze their time-resolved diffraction data.

Sustained mucosal colonization and fecal metabolic dysfunction by Bacteroides associates with fecal microbial transplant failure in ulcerative colitis patients.

Fecal microbial transplantation (FMT) offers promise for treating ulcerative colitis (UC), though the mechanisms underlying treatment failure are unknown. This study harnessed longitudinally collected colonic biopsies (n = 38) and fecal samples (n = 179) from 19 adults with mild-to-moderate UC undergoing serial FMT in which antimicrobial pre-treatment and delivery mode (capsules versus enema) were assessed for clinical response (≥ 3 points decrease from the pre-treatment Mayo score). Colonic biopsies underwent dual RNA-Seq; fecal samples underwent parallel 16S rRNA and shotgun metagenomic sequencing as well as untargeted metabolomic analyses. Pre-FMT, the colonic mucosa of non-responsive (NR) patients harbored an increased burden of bacteria, including Bacteroides, that expressed more antimicrobial resistance genes compared to responsive (R) patients. NR patients also exhibited muted mucosal expression of innate immune antimicrobial response genes. Post-FMT, NR and R fecal microbiomes and metabolomes exhibited significant divergence. NR metabolomes had elevated concentrations of immunostimulatory compounds including sphingomyelins, lysophospholipids and taurine. NR fecal microbiomes were enriched for Bacteroides fragilis and Bacteroides salyersiae strains that encoded genes capable of taurine production. These findings suggest that both effective mucosal microbial clearance and reintroduction of bacteria that reshape luminal metabolism associate with FMT success and that persistent mucosal and fecal colonization by antimicrobial-resistant Bacteroides species may contribute to FMT failure.

Robust methodology for the EBSD local misorientation analysis of surface cold work.

The amount of cold work induced by a surface hardening technique and the depth to which it is produced within a metallic material are both important parameters within the field of surface engineering. In this paper a methodology of establishing reliable estimates of the depth and magnitude of cold work in surface hardened nickel-based superalloy single crystals from a dataset (map) of electron backscattered diffraction images through the analysis of local misorientations is described in detail. The impact of varying a number of acquisition parameters within the scanning electron microscope and the impact of the various post-acquisition analysis parameters on the outcome of the analysis are both described and discussed in detail. The Python script used to perform this analysis is published in full. The principles and processes underlying this methodology, as well as the published script, can be readily adapted for the analysis of datasets of electron backscattered diffraction images from other surface hardening techniques and other surface-hardened materials.

Case Series of Acral and Mucosal Melanoma in a Diverse Patient Population.

Melanomas affecting acral and mucosal sites have distinct features and are associated with poorer prognosis. Patients of color may be disproportionately affected. Herein we discuss six ethnically diverse cases of acral and mucosal melanoma (AMM). More data on clinical, genetic, and environmental features of AMM are needed, but thorough physical examination can reduce the burden of disease now. J Drugs Dermatol. 2024;23(8):683-685. doi:10.36849/JDD.8311.

Predictors of a positive attitude towards rural practice in female osteopathic medical students.

CONTEXT: Women outnumber men in medical school. Given the growing need for rural physicians and the underrepresentation of women in rural medicine, understanding predictors of rural practice among female students is imperative for addressing future healthcare workforce shortages in rural areas. Utilizing current evidence, medical schools should seek to admit students with a rural background and interest in pursuing a primary care specialty; however, whether this holds true for female osteopathic medical students is unknown. OBJECTIVES: This study aims to determine if a rural background, a plan to practice family medicine, and motivation toward civic duty are associated with attitudes toward eventual practice of rural medicine in female osteopathic medical students. METHODS: An anonymous online survey was administered to actively enrolled female students at Arkansas Colleges of Health Education College of Osteopathic Medicine. The survey assessed the respondents' presence of a rural background, degree of civic-mindedness, intention to practice family medicine, and attitudes to rural work and life. Demographics of ethnicity and year of study in medical school were also collected. In total, 129 students responded to the survey, with 97 complete responses for analysis. Hierarchical regression was utilized to compare nested models and interpret interactions. RESULTS: Of the 97 survey respondents, 34.0 % described themselves as first-year students, 37.1 % as second-year students, 16.5 % as third-year students, and 12.4 % as fourth-year students. Linear regression modeling indicated that for female medical students at Arkansas Colleges of Health Education, having a rural background had no relationship with their attitude toward practicing in a rural setting, ß=-0.97, standard error (SE)=1.96, t=-0.49, p>0.05, CI [-4.86, 2.92]. Civic-mindedness had a positive relationship with their attitude toward practicing in a rural setting, ß=0.17, SE=0.07, t=2.44, p=0.016, CI [0.03, 0.31], accounting for 4.57 % of its variance. Plan to practice family medicine was positively associated with a student's attitude toward practicing in a rural setting, ß=4.38, SE=0.85, t=5.15, p<0.001, CI [2.69, 6.07], accounting for 20.25 % of its variance. Civic-mindedness appeared to moderate the relationship between planning to practice family medicine and attitudes toward practicing in a rural setting, F(1, 91) = 3.91, p=0.05, R 2=0.31. The p value for this interaction term was 0.05, but its effect size measure and graphical representation revealed a substantive effect. CONCLUSIONS: The regression analysis showed that, for the women in this study, the greater the student's civic-mindedness, the more favorable her attitude toward practicing in a rural setting. Additionally, the stronger the student's plan to practice family medicine, the more favorable her attitude toward eventual rural practice was. Lastly, the student's civic-mindedness influenced the predictive value of having a plan to practice family medicine.

Correction to Conceptual Process Design and Technoeconomic Analysis of an e-Methanol Plant with Direct Air-Captured CO2 and Electrolytic H2.

[This corrects the article DOI: 10.1021/acs.energyfuels.3c04147.].

Sensitive Detection of Structural Differences using a Statistical Framework for Comparative Crystallography.

Chemical and conformational changes underlie the functional cycles of proteins. Comparative crystallography can reveal these changes over time, over ligands, and over chemical and physical perturbations in atomic detail. A key difficulty, however, is that the resulting observations must be placed on the same scale by correcting for experimental factors. We recently introduced a Bayesian framework for correcting (scaling) X-ray diffraction data by combining deep learning with statistical priors informed by crystallographic theory. To scale comparative crystallography data, we here combine this framework with a multivariate statistical theory of comparative crystallography. By doing so, we find strong improvements in the detection of protein dynamics, element-specific anomalous signal, and the binding of drug fragments.

Geometric deep learning for molecular property predictions with chemical accuracy across chemical space.

Chemical engineers heavily rely on precise knowledge of physicochemical properties to model chemical processes. Despite the growing popularity of deep learning, it is only rarely applied for property prediction due to data scarcity and limited accuracy for compounds in industrially-relevant areas of the chemical space. Herein, we present a geometric deep learning framework for predicting gas- and liquid-phase properties based on novel quantum chemical datasets comprising 124,000 molecules. Our findings reveal that the necessity for quantum-chemical information in deep learning models varies significantly depending on the modeled physicochemical property. Specifically, our top-performing geometric model meets the most stringent criteria for "chemically accurate" thermochemistry predictions. We also show that by carefully selecting the appropriate model featurization and evaluating prediction uncertainties, the reliability of the predictions can be strongly enhanced. These insights represent a crucial step towards establishing deep learning as the standard property prediction workflow in both industry and academia.Scientific contributionWe propose a flexible property prediction tool that can handle two-dimensional and three-dimensional molecular information. A thermochemistry prediction methodology that achieves high-level quantum chemistry accuracy for a broad application range is presented. Trained deep learning models and large novel molecular databases of real-world molecules are provided to offer a directly usable and fast property prediction solution to practitioners.

Flash-within-flash synthesis of gram-scale solid-state materials.

Sustainable manufacturing that prioritizes energy efficiency, minimal water use, scalability and the ability to generate diverse materials is essential to advance inorganic materials production while maintaining environmental consciousness. However, current manufacturing practices are not yet equipped to fully meet these requirements. Here we describe a flash-within-flash Joule heating (FWF) technique-a non-equilibrium, ultrafast heat conduction method-to prepare ten transition metal dichalcogenides, three group XIV dichalcogenides and nine non-transition metal dichalcogenide materials, each in under 5 s while in ambient conditions. FWF achieves enormous advantages in facile gram scalability and in sustainable manufacturing criteria when compared with other synthesis methods. Also, FWF allows the production of phase-selective and single-crystalline bulk powders, a phenomenon rarely observed by any other synthesis method. Furthermore, FWF MoSe2 outperformed commercially available MoSe2 in tribology, showcasing the quality of FWF materials. The capability for atom substitution and doping further highlights the versatility of FWF as a general bulk inorganic materials synthesis protocol.