搜索结果：powerful

ConspectusSuper-resolution microscopy has fundamentally transformed our ability to observe biological structures, allowing nanoscale observation into biological samples such as fixed and live cells and tissues, and has been notably recognized by the 2014 Nobel Prize in Chemistry. Single-molecule localization microscopy (SMLM) stands out as a highly successful and widely accessible method for super-resolution microscopy. However, because SMLM relies on computationally reconstructing a single image from thousands of sparse frames, it suffers from significant algorithmic bottlenecks, particularly when dealing with high emitter densities or three-dimensional data. Deep learning has emerged as an exceptionally effective solution to bypass these computational challenges, enabling fast, parameter-free reconstruction. The application of neural networks for SMLM analysis is uniquely powerful because such networks can be trained entirely on simulated data; since the optical point spread function is well-understood, and SMLM images are fundamentally simple, consisting of a collection of point-spread functions, it is relatively easy to numerically simulate the vast quantities of highly accurate training images required to train reconstruction nets.In this Account, we summarize our contributions to localization microscopy by applying neural nets to address its limitations and bottlenecks. Specific challenges and applications include dense molecule fitting in 2D and in 3D, single-channel multicolor imaging, super spatiotemporal resolution microscopy, optical genome mapping, large field-of-view (FOV) imaging, and accurate background simulation. The fact that the training of the neural net is based on simulated images enables powerful and interesting capabilities. For example, the optical setup itself can be algorithmically designed together with the decoding neural net; we have used this concept for tasks such as designing optimal phase masks for depth encoding, for spectral encoding, and even for both simultaneously.

Chemical derivatization has long been employed to enhance the structural characterization of lipids by mass spectrometry (MS). In recent years, olefin aziridination has emerged as a powerful and versatile strategy in lipidomics, driven by its ability to selectively target carbon-carbon double bonds (C═C bonds) and to introduce nitrogen-containing functionalities that facilitate both structural elucidation and quantitative analysis. Aziridination-enabled MS approaches provide reliable C═C bond localization through diagnostic fragmentation, while simultaneously improving ionization efficiency, particularly for nonpolar lipid classes. A diverse range of aziridination chemistry has been developed, each offering distinct advantages for lipid analysis. In this review, we summarize recent advances in aziridination-enabled MS methodologies, with an emphasis on reaction development and analytical performance. We further highlight applications across biological and complex sample systems. These developments highlight aziridination-assisted MS as a powerful strategy for precision lipidomics with isomer-resolved capability and accurate quantification.

Mammalian telomeric DNA comprises long tracts of tandem TTAGGG repeats. The same repeats are also found at internal chromosomal regions called interstitial telomeric sequences (ITSs). Telomeres are transcribed into UUAGGG-containing transcripts, named TERRA, which serve multiple functions in maintaining telomere integrity. Complementary RNAs containing C-rich telomeric repeats, named ARIA, have also been identified in yeast mutants and mammalian cells with dysfunctional telomeres. The molecular features and functions of ARIA remain understudied, mainly due to its low abundance and the lack of suitable cellular systems. Here, we show that Chinese hamster ovary (CHO) cells produce abundant TERRA and ARIA transcripts, predominantly originating from ITSs. Both RNAs are extensively polyadenylated, exhibit relatively short half-lives and form large cellular foci. We also show that ARIA depletion leads to exposure of single-stranded (ss) DNA at ITSs and that ssDNA exposure increases when ITS DNA is damaged. SsDNA formation does not strictly require the DNA damage signaling kinases ATM and ATR, nor the exonucleases DNA2 and EXO1; however, ATM prevents excessive ssDNA accumulation when ARIA function is inhibited. These findings establish CHO cells as a powerful model to dissect telomeric RNA functions and reveal ARIA as a key regulator of telomeric repeat DNA integrity.

To characterize the prevalence, severity, and correlates of hearing loss and tinnitus in U.S. Veterans enrolled in the Million Veteran Program, and, secondarily, to contextualize audiometric patterns relative to an age-matched civilian cohort. Retrospective cross-sectional cohort study. Audiometric thresholds and word recognition scores were analyzed for 267,395 Veterans (1999-2021) with linked demographics and self-reported health data. Findings were compared descriptively with 40,912 adults from a clinical cohort at Massachusetts Eye and Ear. Primary outcomes included the prevalence of hearing loss and tinnitus, age- and sex-specific audiometric thresholds, word recognition in quiet, and associations between tinnitus and selected comorbidities. Among 267,395 Veterans (94% male), 86% had hearing loss and 47% reported tinnitus. In multivariable models, word recognition was independently associated with older age and worse hearing thresholds. Because Veterans with tinnitus were younger for a given degree of hearing loss, word recognition scores were similar between groups when adjusting for hearing thresholds alone; however, after accounting for age, Veterans with tinnitus showed slightly poorer word recognition. Tinnitus prevalence was higher among Veterans with traumatic brain injury or concussion and among those reporting anxiety or depression, with smaller increases associated with sleep disturbance and alcohol use. Tobacco use showed no association with tinnitus prevalence. African American Veterans had better hearing thresholds and lower tinnitus prevalence than other racial groups, whereas Asian Veterans showed greater hearing loss at older ages. Compared with the civilian cohort, Veterans showed earlier and greater hearing loss, particularly at 4 kHz. Men showed greater high-frequency loss, whereas women showed steeper low-frequency declines at older ages. Hearing loss and tinnitus are highly prevalent among VA healthcare-using Veterans and appear to occur earlier and with greater severity than in a clinic-based civilian cohort. These patterns, including the occurrence of tinnitus in some individuals with preserved audiometric thresholds and subtle suprathreshold deficits, are consistent with mechanisms such as cochlear nerve degeneration that are not captured by conventional audiometry. The findings highlight the need for improved diagnostic approaches for suprathreshold auditory dysfunction, objective measures of tinnitus, and integrated clinical care addressing both auditory and neuropsychiatric comorbidities. The Million Veteran Program cohort provides a powerful platform for future studies examining the biological and genetic determinants of hearing loss and tinnitus.

Insect antimicrobial peptides (AMPs) are classically viewed as terminal effectors of innate immunity, but emerging evidence suggests that some can also shape defined neural states. In this Review, we argue that insect systems provide a powerful framework for resolving immune-brain communication at the level of individual peptide effectors, because genetically tractable innate-immune pathways allow pathway activation to be distinguished from peptide-specific effector function. Rather than surveying AMP families exhaustively, we focus on representative cases in which peptide identity, source, and timing can be linked to sleep, memory-related plasticity, and responses to acute injury. These studies show that the neural consequences of AMP induction cannot be inferred from pathway activation alone, but require peptide-level analysis of effector identity, cellular context, and exposure logic. This perspective also raises the question of translational potential. At present, direct biomedical development of endogenous insect AMPs in neural contexts remains limited, whereas more tangible applied interest has centered on insect venom peptides that share AMP-like physicochemical features. We therefore discuss insect venoms separately from endogenous AMP physiology. Venom peptides are not physiological equivalents of endogenous insect AMPs, but represent evolutionarily diversified AMP-like templates for scaffold discovery, mechanistic probing, and therapeutic engineering. Together, this review develops a peptide-level perspective on insect neuroimmune biology while highlighting insect venoms as a valuable, but highly constrained, source of templates for biomedical discovery.

Pathogenic bacterial infections remain a persistent global public health crisis. However, traditional clinical detection methods-such as culture-based assays and polymerase chain reaction (PCR)-are often time-consuming and labor-intensive, and they lack sufficient sensitivity for low-abundance pathogens, hindering rapid point-of-care diagnosis. With label-free, ultra-sensitive molecular fingerprinting, Surface-Enhanced Raman Scattering (SERS) has emerged as a powerful tool for rapid pathogen identification. This review summarizes the evolution of SERS-based bacterial detection from fundamental nanomaterials to integrated clinical diagnostic platforms. The article explores four core dimensions: functional integration and enrichment strategies of colloidal probes; structural design and multifaceted capture mechanisms of solid substrates; synergistic advantages of microfluidic systems in enabling automated "sample-to-answer" architectures; and the translational potential of SERS-based lateral flow assays (LFAs) for robust point-of-care testing (POCT). This review reveals a research shift from maximizing electromagnetic enhancement toward overcoming matrix effects in clinical samples and ensuring robustness. In synergy with microfluidics, LFAs, and AI, SERS technology is bridging the bench-to-bedside gap, offering a roadmap for next-generation decentralized, high-precision diagnostics.

Mendelian randomization (MR) has become a powerful method for using genetic variations as instrumental variables to identify the causal relationship between risk factors and diseases. To investigate the causal relationship between gastroesophageal reflux disease (GERD) and allergic asthma using the Mendelian randomization (MR) method. Suitable single-nucleotide polymorphisms (SNPs) were extracted from genome-wide association study (GWAS) data on GERD as genetic instrumental variables. The causal association between GERD and allergic asthma risk was explored using the Inverse Variance-Weighted, Weighted Median, Simple Mode, Weighted Mode, and MR-Egger methods. The results of the Inverse Variance-Weighted method (OR= 1.47, 95% CI for OR: 1.25-1.73) show a causal relationship between GERD and allergic asthma. Gastroesophageal reflux disease is a risk factor for the development of allergic asthma. The weighted median method (OR= 1.31, 95% CI for OR: 1.02-1.66) further confirmed GERD as a risk factor for the development of allergic asthma. Tests for horizontal pleiotropy, heterogeneity, and leave-one-out analysis demonstrated that the instrumental variables did not bias the results. In contrast, the results of reverse MR showed no significant causal relationship between allergic asthma and GERD (OR=1.02, 95% CI for OR: 0.93-1.11, P=0.653), and allergic asthma was not a risk factor for the development of GERD. A positive causal relationship exists between GERD and allergic asthma. However, there is no direct effect of GERD-independent allergic asthma.

Biocatalyst-controlled regioselective C(sp)-H functionalization of carboxylic acid derivatives provides a powerful method for the synthesis of noncanonical α- and β-amino acids. Herein, we report a nonheme Fe enzyme-catalyzed, regiodivergent, and enantioselective nitrogen migration, enabled by directed evolution of 1-aminocyclopropane-1-carboxylic acid oxidase from Petunia hybrida (PhyACCO). Through systematic evaluation of azanyl ester N-protecting groups and nonheme Fe enzymes, as well as iterative rounds of protein engineering, we developed two complementary nitrogen migratases, ACCONimα and ACCONimβ, that enabled biocatalyst-controlled 1,3- and 1,4-nitrogen migration with excellent regioselectivity. ACCONimα catalyzed the efficient enantioselective synthesis of α-amino acids via amidation of unactivated (nonbenzylic) secondary C(sp)-H bonds with up to 1900 total turnover numbers (TTN) and a kcat of 1560 min-1, affording diverse noncanonical α-amino acids. ACCONimα further allowed the enantioconvergent synthesis of challenging α,α-disubstituted amino acids from racemic substrates via tertiary C(sp)-H bond amidation. In contrast, ACCONimβ enabled the regio- and enantioselective synthesis of β-amino acids via a catalyst-controlled 1,6-hydrogen atom transfer pathway that remains largely underexplored. Kinetic and intramolecular hydrogen-deuterium competition studies indicated rate-determining HAT, with the more exergonic 1,6-HAT showing a smaller KIE than 1,5-HAT. ACCONimα and ACCONimβ exhibited similar kinetic isotope effects for both 1,5- and 1,6-HAT pathways, suggesting that enzyme engineering controls regio- and enantioselectivity but does not alter the intrinsic transition-state properties of the HAT event. Together, these results further established engineered nonheme Fe enzymes as an excellent platform for the development of stereoselective, synthetically useful non-native biocatalytic transformations.

Polymorphism in covalent organic frameworks (COFs) offers a unique platform to decipher structure-property relationships, yet its impact on excited-state dynamics remains unexplored. Herein, we construct two chemically identical but topologically distinct 1D and 2D COF polymorphs to correlate framework architecture with photocatalytic performance. Impressively, in H2O2 photosynthesis coupled with furfuryl alcohol valorization, the 1D-TBPP-COF showed an exceptional H2O2 generation rate (18.75 mmol g-1 h-1) and selective oxidation of furfuryl alcohol to high-value 6-hydroxy-2H-pyran-3(6H)-one (PN) with PN formation rate of 28.14 mmol·g-1·h-1, substantially outperforming the 2D-TBPP-COF counterpart. Mechanistic investigations revealed that the intercalated dual-chain edges in 1D-TBPP-COF impose steric constraints on aromatic ring rotation, effectively suppressing vibrational relaxation losses and prolonging the charge-transfer state lifetime. In contrast, the conformationally flexible 2D-TBPP-COF permits greater rotational freedom, leading to non-radiative energy dissipation. This work establishes polymorphism engineering as a powerful strategy to manipulate excited-state dynamics in COFs for photocatalysis.

The combination of chiral phosphoric acid with a biomimetic hydrogen source (Hantzsch esters) constitutes a powerful system for asymmetric reduction of unsaturated compounds. In contrast, asymmetric hydrogenolysis of C-O single bonds remains an elusive challenge owing to the high C-O bond energy and the weak intermolecular affinity between the substrate and the catalyst. In this study, we report a hydrogen-bond-activation strategy, driven by aromatization, for asymmetric hydrogenolysis of C-O bonds using the chiral phosphoric acid/Hantzsch ester system. This protocol enables kinetic resolution of o-quinone monoketals, affording axially chiral compounds and chiral spirocycles with selectivity factors up to 1773. Moreover, this methodology provides an efficient route to axially chiral cannabidiol (axCBD) analogs. Preliminary mechanistic experiments and DFT calculations suggested that asymmetric hydrogenolysis proceeded via an enantioselective 1,4-transfer hydrogenation initiation step, followed by an aromatization-driven remote proton transfer and skeletal rearrangement.

Reactive extrusion (REx) is emerging as a powerful technology for the continuous and solventless production of modified lignins. However, the optimization of REx-based processes for modifying lignin relies on offline lignin analytics, which are time-consuming and heavily influenced by sample preparation. This study integrated near-infrared (NIR) spectroscopy into a twin-screw extruder to monitor in real time the modification of softwood kraft lignin via esterification with octenyl succinic anhydride (OSA). The NIR data was processed by means of chemometric methods. Temperature and screw configuration were found to influence the esterification of lignin. Combining these results with offline lignin analytics, 120 °C was selected as the optimal temperature in conjunction with the integration of kneading elements into the screw profile, to yield OSA-lignin esters with ≥ 50% degree of modification. The product output was successfully scaled up sevenfold while using NIR spectroscopy to monitor the extrusion. In addition, the broader applicability of this inline monitoring method was demonstrated by using a biorefinery lignin from hardwood. The target degree of modification was achieved with minimal recalibration of process parameters. A space-time yield of up to 7 x 104kg∙m-3∙day-1 was realized, indicating the potential of this REx process for industrial adoption. Overall, this work provides a foundation for the development of a process analytical technology for monitoring lignin modification during REx that can be expanded to other lignin chemistries, thus providing scalable and adaptable solutions for adding value to lignin.

The invasive front and peritumoral region of colorectal cancers are replete with powerful prognostic biomarkers that can predict oncologic outcomes with far greater precision than achievable by tumor, node, metastasis (TNM) stage alone. This article considers both established and emerging invasive front/peritumoral prognostic factors with an emphasis on recent developments, practical considerations, diagnostic challenges, and the potential role of automated assessment in the integration of emerging prognostic factors into future clinical practice.

Apical periodontitis (AP) is an inflammatory disease of the periapical tissues driven by host responses to endodontic infection, and systemic metabolic disturbances, such as high-fat diet (HFD)-induced dysmetabolism, may exacerbate tissue destruction. This study aimed to determine how chronic HFD exposure alters periapical myeloid cell states and lesion characteristics in a mouse model of AP using spatially resolved transcriptomics. Male C57BL/6 mice (n = 20; 6-8 weeks, specific pathogen-free) were randomly assigned to normal chow (NC) or 60% HFD for 18 weeks, with AP induced by pulp exposure in first molars after 14 weeks. Body weight and fasting blood glucose were monitored longitudinally; apical lesion volume was quantified by high-resolution micro-CT and histology at 28 days post-induction; and periapical tissues from AP and contralateral control sites underwent 10× Genomics Xenium in situ spatial transcriptomics focused on neutrophils and macrophages. Compared with NC, HFD mice developed greater body weight gain, higher fasting glucose (approximately 9-11 mM vs. 6-8 mM) and significantly larger apical lesion volumes by micro-CT (p ≤ 0.05), confirming an obese, dysmetabolic background with exacerbated AP-associated bone loss. Spatial transcriptomics revealed that in non-lesion tissues, HFD-conditioned neutrophils and macrophages were biased toward lipid-handling, stress-adaptive and inflammasome-associated programs, whereas NC maintained antimicrobial and oxidative effector signatures. Within AP lesions, HFD was associated with foam cell-like macrophage phenotypes and neutrophils with reduced degranulation and bactericidal gene expression, while NC lesions preserved antigen presentation, neutrophil effector pathways and structured tissue-remodelling responses. Chronic high-fat feeding reprograms periapical innate immunity from antimicrobial defence toward lipid-driven fibro-inflammatory states, thereby amplifying apical bone loss in AP. These findings highlight spatial transcriptomics as a powerful approach to map immunometabolic circuits in endodontic disease and suggest potential targets for managing AP in metabolically compromised patients.

Regulation of gene transcription based on clustered regularly interspaced short palindromic repeats (CRISPR) is a powerful tool for constructing synthetic gene circuits in Saccharomyces cerevisiae. The current CRISPR-based regulatory approaches primarily focus on inhibiting the binding of dCas9 protein to single guide RNA (sgRNA) or blocking target site recognition. However, these regulation strategies are often at a single level, and their sensitivity still needs to be improved. In this study, the gene regulatory approaches at the translational and post-translational levels were integrated with optogenetic control patterns to attain very sensitive multi-level precision regulation of the dCas9 protein, thereby facilitating flexible regulation of transcription levels of target genes. This strategy was used to regulate the transcription levels of fluorescent proteins, resulting in up to 2.58-fold increase in the fluorescence intensity of mCherry compared to that without regulation. This CRISPR-based multi-level optogenetic system should be extremely helpful in understanding gene regulatory networks and in designing robust genetic circuits for synthetic biology.

A molecular-level understanding of electrolyte solvation structure and ion-ion correlations is critical to developing next-generation battery chemistries. Atomistic simulation capabilities with sufficient accuracy, speed, and transferability to deliver reliable structural insights while avoiding arduous system-specific reparameterization are thus highly desirable. Machine learning interatomic potentials (MLIPs) trained on large, chemically diverse data sets are revolutionizing computational chemistry, enabling molecular dynamics simulations of battery electrolytes with near-DFT accuracy over 10,000× faster than DFT. While previous MLIP training data sets with suitable elemental coverage for electrolytes have been based on inorganic materials, the Open Molecules 2025 (OMol25) data set provides large-scale molecular DFT MLIP training data with broad elemental coverage and specifically samples tens of millions of electrolyte configurations. Here, we integrate computational modeling with experimental validation to systematically assess the ability of large-scale MLIPs pretrained on materials data or on OMol25 to accurately resolve nanoscale structural organization and ion-solvation characteristics in Na-ion battery electrolytes across diverse physicochemical conditions and compositional regimes. We find that the OMol25-trained Universal Model of Atoms (UMA-OMol) predicts experimentally measured densities and X-ray structure factors in substantially better agreement compared to state-of-the-art models trained only on inorganic materials data. Using UMA-OMol, we further analyze systematic trends in solvation structure as a function of cation identity, anion chemistry, salt concentration, and solvent topology. We observe that increasing system temperature amplifies the heterogeneity within the solvation environment, perturbing cation-solvent interactions and promoting the formation of contact ion pairs (CIPs). Moreover, subtle variations in the solvent topology of glyme-based electrolytes cause pronounced changes in ion correlations and solvation structure. The experimental agreement and microscopic insights shown here position OMol25-trained MLIPs as a practical route to predictive, high-throughput electrolyte simulations beyond the limits of classical force fields and direct DFT molecular dynamics, serving as a powerful tool for accelerating the design of next-generation Na-ion battery electrolytes and beyond.

Phase engineering in two-dimensional materials provides a powerful route to tuning electronic states and enabling functional devices. TaSe2 is a representative transition metal dichalcogenide with distinct correlated phases associated with its 1T and 2H polytypes. However, experimentally realizing and unambiguously verifying a complete 1T-to-2H transition in TaSe2 remains a major challenge. Here, we report a complete thermally driven 1T-to-2H transition in TaSe2. The two phases are distinguished by their electronic and vibrational fingerprints, as revealed by angle-resolved photoemission spectroscopy and Raman spectroscopy. The transition is tracked in situ through the evolution of Raman-active phonon modes, accompanied by pronounced changes in the electronic structure and Ta core-level spectral features. Photoluminescence and electrical transport measurements before and after annealing further confirm the completeness of the phase transition. Our findings deepen the understanding of structural phase transitions in TaSe2 and support phase engineering in other two-dimensional materials.

The aging global population has led to a steep rise in numbers of adults living with dementia, with a parallel expanded demand for healthcare and social service professionals. These trends, in conjunction with a relative dearth of students selecting dementia-focused educational paths, have led to a critical dementia workforce gap. Service learning (SL) is an educational paradigm that is well situated to potentially address this issue. The purpose of this scoping review was to identify sources investigating SL in the context of dementia care to inform best practices in recruitment and education of the future dementia care workforce. A search across five databases identified 34 relevant studies from 2000 to 2025. Program characteristics including key SL elements and outcome measures across students and service recipients were mapped. Results revealed strong use of key SL elements but variation in the scope, methodology, and rigor of student outcomes measures, with underrepresentation of service recipient perspectives and outcomes. This scoping review highlights next steps for educators to maximize determination of SL feasibility and value across project collaborators and participants. These practices will serve to further augment dementia-based SL as a powerful pedagogical intervention to serve students, the future dementia care workforce, and society.

Traditional cancer drug discovery encounters challenges, including lengthy synthesis durations, high costs, and a 90% failure rate in clinical trials, primarily due to inadequate chemical design and drug properties. Artificial intelligence (AI) provides powerful computational tools to overcome these issues by speeding up target identification, predicting properties, and optimizing leads. This review emphasizes the influence of new AI platforms like AlphaFold3, molecular interactions are structurally optimized (MISATO), and ZairaChem on the discovery of oncology drugs. We specifically examine how AI reconciles chemical design with pharmacological feasibility. In addition to evaluating these advancements, we meticulously evaluate methodological challenges, including dataset bias, overfitting, insufficient external validation, and reproducibility issues. Furthermore, the development of complex and targeted modalities, such as antibody-drug conjugates (ADCs), aptamer-drug conjugates (Ap‑DCs), and proteolysis-targeting chimeras (PROTACs), is being explored for cancer treatment using AI. Following a detailed review of regulatory and clinical translation issues, this review presents practical tips for improving model validation, data sharing, and incorporation into medicinal chemistry workflows. By examining successes and persistent limitations, this review article offers a strategic roadmap for leveraging AI to provide clinically translatable cancer therapies with enhanced chemical and pharmacological balance.

Youth-onset type 2 diabetes mellitus (T2DM) is a rapidly growing pediatric metabolic disorder that parallels the global childhood obesity epidemic. Despite multiple population-based registries and a global meta-analysis documenting near-universal obesity at the time of a T2DM diagnosis, no integrated synthesis spanning 25 years and including diverse populations has been performed. Here we conducted a systematic review of PubMed and Scopus (January 2000 to March 2025) using longitudinal data from population-based registries in the USA (SEARCH), Canada (Manitoba), England and Wales (National Paediatric Diabetes Audit), Israel, Australia, and New Zealand as well as cross-sectional burden from a global meta-analysis (53 studies, n=8,942) and national audits. Study quality was appraised using the Newcastle-Ottawa Scale and A Measurement Tool to Assess Systematic Reviews v2. All registries showed a temporally parallel increase in the incidence of pediatric obesity and youth-onset T2DM. In the USA, the T2DM incidence rose 79% (3.8 → 6.8 per 100,000/yr, 2002-2018); in Manitoba, it nearly doubled (16.0 → 31.1 per 100,000/yr, 2009-2018); and Israel showed a 441% rise (0.63 → 3.41 per 100,000/yr, 2008-2019). The global pooled obesity prevalence at the T2DM diagnosis was 75.3% (95% confidence interval, 72.1%-78.5%), with approximately 41,600 new youth cases annually worldwide. Once-weekly semaglutide 2.4 mg (STEP TEENS trial) reduced the average body mass index by 16.1% at 68 weeks with concurrent glycemic improvement, while bariatric surgery achieved 95% T2DM remission at 3 years in adolescents (Teen-LABS cohort). Multicontinental evidence confirmed a robust temporally consistent obesity-T2DM link; obesity precedes T2DM at the population level and is present in approximately 75% of affected youths at diagnosis worldwide. Weight-reduction interventions, particularly glucagon-like peptide-1 receptor agonists and bariatric surgery, offer meaningful glycemic benefits; however, primary prevention of childhood obesity remains the most powerful strategy to arrest this epidemic.