Explore the vast range of titles available.

To assess patient characteristics of users and new initiators of triple therapy for chronic obstructive pulmonary disease (COPD) in Germany. Retrospective cohort study of patients with COPD and ≥1 prescription for single-inhaler triple therapy (SITT; fluticasone furoate/umeclidinium/vilanterol [FF/UMEC/VI] or beclomethasone dipropionate/glycopyrronium bromide/formoterol [BDP/GLY/FOR]) or multiple-inhaler triple therapy (MITT), using data from the AOK PLUS German sickness fund (1 January 2015-31 December 2019). The index date was the first date of prescription for FF/UMEC/VI or BDP/GLY/FOR (SITT users), or the first date of overlap of inhaled corticosteroid, long-acting β -agonist, and long-acting muscarinic antagonist (MITT users). Two cohorts were defined: the prevalent cohort included all identified triple therapy users; the incident cohort included patients newly initiating triple therapy for the first time (no prior use of MITT or SITT in the last 2 years). Patient characteristics and treatment patterns were assessed on the index date and during the 24-month pre-index period. In total, 18,630 patients were identified as prevalent triple therapy users (MITT: 17,945; FF/UMEC/VI: 700; BDP/GLY/FOR: 908; non-mutually exclusive) and 2932 patients were identified as incident triple therapy initiators (MITT: 2246; FF/UMEC/VI: 311; BDP/GLY/FOR: 395; non-mutually exclusive). For both the prevalent and incident cohorts, more than two-thirds of patients experienced ≥1 moderate/severe exacerbation in the preceding 24 months; in both cohorts more BDP/GLY/FOR users experienced ≥1 moderate/severe exacerbation, compared with FF/UMEC/VI and MITT users. Overall, 97.9% of prevalent triple therapy users and 86.4% of incident triple therapy initiators received maintenance treatment in the 24-month pre-index period. In a real-world setting in Germany, triple therapy was most frequently used after maintenance therapy in patients with recent exacerbations, in line with current treatment recommendations.

For over a century, the structures and reactivities of strained organic compounds have captivated the chemical community. Whereas triple-bond-containing strained intermediates have been well studied, cyclic allenes have received far less attention. Additionally, studies of cyclic allenes that bear heteroatoms in the ring are scarce. We report an experimental and computational study of azacyclic allenes, which features syntheses of stable allene precursors, the mild generation and Diels–Alder trapping of the desired cyclic allenes, and explanations of the observed regio- and diastereoselectivities. Furthermore, we show that stereochemical information can be transferred from an enantioenriched silyl triflate starting material to a Diels–Alder cycloadduct by way of a stereochemically defined azacyclic allene intermediate. These studies demonstrate that heteroatom-containing cyclic allenes, despite previously being overlooked as valuable synthetic intermediates, may be harnessed for the construction of complex molecular scaffolds bearing multiple stereogenic centres.

Premise Recently, plant science has seen transformative advances in scalable data collection for sequence and chemical data. These large datasets, combined with machine learning, have demonstrated that conducting plant metabolic research on large scales yields remarkable insights. A key next step in increasing scale has been revealed with the advent of accessible large language models, which, even in their early stages, can distill structured data from the literature. This brings us closer to creating specialized databases that consolidate virtually all published knowledge on a topic. Methods Here, we first test different combinations of prompt engineering techniques and language models in the identification of validated enzyme–product pairs. Next, we evaluate the application of automated prompt engineering and retrieval‐augmented generation to identify compound–species associations. Finally, we build and determine the accuracy of a multimodal language model–based pipeline that transcribes images of tables into machine‐readable formats. Results When tuned for each specific task, these methods perform with high (80–90%) or modest (50%) accuracies for enzyme–product pair identification and table image transcription, but with lower false‐negative rates than previous methods (decreasing from 55% to 40%) for compound–species pair identification. Discussion We enumerate several suggestions for researchers working with language models, among which is the importance of the user's domain‐specific expertise and knowledge.

Introduction: Traumatic brain injury (mTBI) is a leading cause of disability in the US. Angiotensin 1-7, an endogenous peptide, acts at MAS receptors to inhibit inflammatory mediators and decrease reactive oxygen species within the CNS. Few studies have identified whether Ang-(1-7) decreases cognitive impairment following closed mTBI. Materials and Methods: Twenty-four male mice underwent a closed-skull, controlled cortical impact injury. Two hours after injury, mice were administered either Ang-(1-7) (n=12) or vehicle (n=12), continuing through day-5 post-TBI, and tested for cognitive impairment on days 1-5 and 18. pTau, Tau, GFAP, and serum cytokines were measured at multiple time points. Animals were observed daily for cognition and motor coordination via novel object recognition. Brain sections were stained and evaluated for neuronal injury. Results: Administration of Ang-(1-7) daily for five days post-mTBI significantly increased cognitive function as compared to saline control-treated animals. Cortical hippocampal structures of mice showed less damage in the presence of Ang-(1-7). Ang-(1-7) administration significantly changed the expression of pTau and GFAP in cortical and hippocampal regions as compared to control. Discussion: These are among the first studies to demonstrate that sustained administration of Ang-(1-7) following a closed-skull single impact mTBI significantly improves outcomes, potentially offering a novel therapy to prevent long-term CNS impairment.

There is a limited understanding of the uptake of pharmacological treatments and incidence of clinical events in newly diagnosed heart failure (HF) patients, stratified by left-ventricular ejection fraction (LVEF) in contemporary clinical settings in Japan. A retrospective cohort study was conducted using a nationwide Japanese hospital database to evaluate the patterns of HF medications and clinical events in adult patients with a first confirmed HF diagnosis from January 1, 2020-July 31, 2023 (n = 16,001). Fine-Gray sub-distribution hazard models were applied to assess factors associated with HF medication initiations and clinical events. Overall, 5473 (34.2%), 3053 (19.1%), and 7475 (46.7%) patients with HF with reduced ejection fraction, mildly reduced ejection fraction (HFmrEF), and preserved ejection fraction (HFpEF) were included, respectively. Within the first 6 months, prescription rates of HF medications were: angiotensin-converting enzyme inhibitors, angiotensin-receptor blockers, or angiotensin receptor blocker-neprilysin inhibitor (65.6%), beta-blockers (57.6%), mineralocorticoid antagonists (55.7%), and sodium-glucose cotransporter-2 inhibitors (33.2%), with the lowest rates in HFpEF patients. The increase in prescription rates between 6 and 12 months was modest across all medication classes. One-year incidence rates per 100 person-years (95% confidence interval) of in-hospital all-cause mortality and in-hospital cardiovascular death were 23.0 (22.1-24.0) and 16.6 (15.8-17.4), respectively. HFmrEF and HFpEF were associated with lower hazards of treatment initiation for most HF medications, whereas the risks of in-hospital all-cause mortality and in-hospital cardiovascular death did not differ significantly across all LVEF subtypes. The findings underscore the suboptimal uptake of pharmacological treatments, despite the poor prognosis of newly diagnosed HF patients. The disparities in initiations of recommended treatments reaffirm the importance of properly implementing evidence-based therapies within each LVEF subtype.

ABSTRACT In Escherichia coli, DNA cytosine methyltransferase (Dcm) methylates the second cytosine in the sequence 5′CCWGG3′ generating 5-methylcytosine. Dcm is not associated with a cognate restriction enzyme, suggesting Dcm impacts facets of bacterial physiology outside of restriction-modification systems. Other than gene expression changes, there are few phenotypes that have been identified in strains with natural or engineered Dcm loss, and thus Dcm function has remained an enigma. Herein, we demonstrate that Dcm does not impact bacterial growth under optimal and selected stress conditions. However, Dcm does impact viability in long-term stationary phase competition experiments. Dcm+ cells outcompete cells lacking dcm under different conditions. Dcm knockout cells have more RpoS-dependent HPII catalase activity than wild-type cells. Thus, the impact of Dcm on stationary phase may involve changes in RpoS activity. Overall, our data reveal a new role for Dcm during long-term stationary phase. Dcm promotes viability during long-term stationary phase and represses RpoS activity.

Development of an efficacious, non-addicting analgesic has been challenging. Discovery of novel mechanisms underlying addiction may present a solution. Here we target the neurokinin system, which is involved in both pain and addiction. Morphine exerts its rewarding actions, at least in part, by inhibiting GABAergic input onto substance P (SP) neurons in the ventral tegmental area (VTA), subsequently increasing SP release onto dopaminergic neurons. Genome editing of the neurokinin 1 receptor (NK1R) in the VTA renders morphine non-rewarding. Complementing our genetic approach, we demonstrate utility of a bivalent pharmacophore with dual activity as a μ/δ opioid agonist and NK1R antagonist in inhibiting nociception in an animal model of acute pain while lacking any positive reinforcement. These data indicate that dual targeting of the dopaminergic reward circuitry and pain pathways with a multifunctional opioid agonist–NK1R antagonist may be an efficacious strategy in developing future analgesics that lack abuse potential.

This dissertation describes the development of reaction methodologies that utilize unconventional building blocks in chemical synthesis. One major effort involves the nickel-catalyzed net hydrolysis of traditionally inert amide C–N bonds to give carboxylic acids. Additionally, the development of synthetic routes to afford structurally complex bioactive compounds are reported. Specifically, these include the synthesis of a small library of furanoindoline compounds for structure-activity relationship studies related to the treatment of Alzheimer’s disease and an alternative synthesis of the nucleobase found in the FDA-approved COVID-19 antiviral remdesivir. Finally, investigations into strained heterocyclic allenes are described. These studies have allowed for highly reactive cyclic allene intermediates to be utilized strategically in the regioselective and enantiospecific synthesis of a diverse array of densely functionalized heterocycles. Furthermore, a synthetic approach toward the synthesis of alstilobanine A is reported, where the key step hinges on a cycloaddition of an azacyclic allene intermediate. Each of the new strategies presented are expected to expand the synthetic toolbox by leveraging unique reactivity.Chapter one describes the development of a nickel-catalyzed net hydrolysis of amides. The methodology strategically employs a nickel-catalyzed esterification using 2-(trimethylsilyl)-ethanol, followed by a fluoride-mediated deprotection in a single-pot operation. The selectivity and mildness of this transformation are demonstrated through competition experiments and the net-hydrolysis of a complex valine-derived substrate. This strategy addresses a limitation in the field with regard to functional groups accessible from amides using transition metal-catalyzed C–N bond activation.Chapters two and three detail the synthesis of bioactive compounds. Chapter two specifically describes the synthesis of a small library of furanoindoline analogs for structure-activity relationship studies on the inhibition of neutral sphingomyelinase-2 and acetylcholinesterase, enzymes implicated in Alzheimer’s disease. The syntheses employ a key interrupted Fischer indolization reaction where the furanoindoline product is elaborated to generate a number of analogs. Identification of the dual inhibitors represents a promising new therapeutic approach to Alzheimer’s disease. Chapter three describes an alternative approach to the unnatural nucleobase fragment found in remdesivir (Veklury®), an FDA-approved antiviral for the treatment of COVID-19. The route relies on the formation of a cyanoamidine intermediate, which undergoes a Lewis acid-mediated cyclization to yield the desired nucleobase. The approach is strategically distinct from prior routes and could further enable the synthesis of remdesivir and other small-molecule therapeutics.Chapters four and five are concerned with the investigation of cyclic allene intermediates. Chapter four describes an experimental and computational study of azacyclic allenes, including the synthesis of several substituted azacyclic allene precursors, subsequent allene generation, and trapping in cycloadditions. Additionally, the computational studies performed provide insight into the underlying reasons for the observed regioselectivities and enantiospecificities. Chapter five details experimental studies of oxacyclic. Specifically, the development of a precursor to 3,4-oxacyclohexadiene and subsequent allene trapping in (4+2), (3+2), and (2+2) cycloadditions is disclosed. Additionally, the first asymmetric synthesis of a silyl triflate cyclic allene precursor was achieved, as well as enantiospecific trapping of the allene. These studies highlighted the potential for cyclic allenes to be valuable building blocks the asymmetric synthesis of heterocycles.Chapter six illustrates the development of an alternative precursor toward strained cyclic allenes and alkynes. Our studies of strained cyclic allenes revealed that, in some cases, silyl triflate precursors were inaccessible. This study shows that silyl tosylates can serve as alternative precursors to strained cyclic allenes and alkynes.Chapter seven details a strategy for the total synthesis of alstilobanine A, a monoterpene indole alkaloid. Our approach hinges on a key (4+2) Diels–Alder reaction between an acetoxy-substituted azacyclic allene intermediate and a pyrone. This cycloaddition forms two key C–C bonds and sets three of the four stereocenters found in the natural product. Current efforts to synthesize the natural product are detailed. If successful, these studies should provide efficient access to alstilobanine A and demonstrate the utility of cyclic allenes in complex molecule synthesis.Finally, chapter eight is a contribution to chemical education. The chapter outlines a new course centered around transition-metal catalysis in modern drug discovery. The course was designed to illustrate the central role of organic chemistry in driving small-molecule drug development and was taught by graduate students with mentorship from a faculty member. Additionally, experts in the fields of catalysis and drug discovery served as guest lecturers throughout the duration of the course. This chapter reflects on the experience of creating and developing the course, and aims to motivate the creation of future courses that unify fundamental concepts with applications and career outcomes.

The quantification of observed stand density relative to a desired density, a measure known as relative density, is a critical component of many silvicultural treatments. Calculating stand density is more complicated when there are multiple species involved. I compared four regionally appropriate relative density measures to frequently used absolute density measures such as biomass, basal area, trees per area and stand density indices. I found absolute measures inferior to relative measures of stand density in that they lack an accepted reference point that allows for the comparison of one stand to the next in a meaningful and biologically accurate way. Focusing on the four relative density measures I explored the effects of species groups, specific gravity, plot size and definition of maximum density in mixed-species forests of the northeastern United States (New England and New York). The comparison of the relative density measures considered here resulted in conclusions similar to Curtis (1970) in that the choice among the measures is, in part, a matter of available information and convenience of computation. The cluster analysis implied measures form clusters based on the lumping viewpoint versus splitting viewpoint. Rater agreement analysis, used as a novel method of comparing relative density models, suggests that Ducey and Knapp (2010) density estimates fall in between other density model estimates and thus if a single relative density model needs to be used in this region the Ducey and Knapp one seems most appropriate. Although on average FOXDEN2.1 (Desmaris, 2001) and Stout and Nyland (1986) provide higher density estimates than Ducey and Knapp (2010) and Woodall et al. (2006) when density estimates are assigned to categories, Ducey and Knapp (2010) and Woodall et al. (2006) consistently place more plots in higher density categories than FOXDEN2.1 (Desmaris, 2001) and Stout and Nyland (1986). The small FIA plot size used to estimate model coefficients may explain why Ducey and Knapp (2010) and Woodall et al. (2006) behave this way.

Premise: Recently, plant science has seen transformative advances in scalable data collection for sequence and chemical data. These large datasets, combined with machine learning, revealed that conducting plant metabolic research on large scales yields remarkable insights. A key next step in increasing scale has been revealed with the advent of accessible large language models, which, even in their early stages, can distill structured data from literature. This brings us closer to creating specialized databases that consolidate virtually all published knowledge on a topic. Methods: Here, we first test different prompt engineering technique / language model combinations in the identification of validated enzyme-product pairs. Next, we evaluate automated prompt engineering and retrieval augmented generation applied to identifying compound-species associations. Finally, we build and determine the accuracy of a multimodal language model-based pipeline that transcribes images of tables into machine-readable formats. Results: When tuned for each specific task, these methods perform with high accuracies (80-90 percent for enzyme-product pair identification and table image transcription), or with modest accuracies (50 percent) but lower false-negative rates than previous methods (down to 40 percent from 55 percent) for compound-species pair identification. Discussion: We enumerate several suggestions for working with language models as researchers, among which is the importance of the user's domain-specific expertise and knowledge.Competing Interest StatementThe authors have declared no competing interest.Footnotes* https://github.com/thebustalab/ai_in_phytochemistry