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This study presents direct in situ observational evidence of a cyclonic circulation cell east of Cape Santa María (CSM), in the northern Gulf of Cádiz (GoC). Three Lagrangian drifters deployed in October 2022 revealed a coherent cyclonic circulation cell with a characteristic diameter of approximately 40-45 km, consistently estimated from drifter trajectories and relative vorticity. Their trajectories showed alternating eastward and westward flows, modulated by wind variability, bathymetry, and mesoscale dynamics. Satellite observations showed a cooler, chlorophyll-a enriched core, consistent with upwelling and retention of enriched surface water masses within the circulation cell. High-resolution WRF atmospheric simulations indicated alternating easterly and westerly wind regimes that were associated with reversals in coastal circulation. Westerly winds between 18th and 24th October produced positive Ekman pumping east of CSM, creating conditions favourable to the intensification of cyclonic circulation. The IBI ocean model reproduced the main structure of the circulation cell structure, including flow accelerations near the shelf edge, and indicated upward vertical motions within the cyclonic circulation cell. A Lagrangian particle experiment suggested that the cyclonic cell favours both retention and offshore export of surface waters, with residence times of up to approximately 16 days. Overall, this study highlights the role of interactions between atmospheric forcing, bathymetry, and mesoscale dynamics in controlling small-scale surface circulation in the GoC, and underscores the value of integrating drifter observations, satellite data, and numerical models to characterize coastal dynamics.

High-Intensity Interval Cross Training (HIICT) combines sprinting, plyometric, and functional strength exercises, but its effects compared with classical sprint training in male junior sprinters remain unclear. This study examined the effects of HIICT and Classical Training (CT) on physical and sprint performance in male junior sprinters. Sixty-four male junior sprinters were randomly assigned to an HIICT group or a CT group for an 8-week intervention. Both groups completed similar overall internal training loads. Assessments before and after the intervention included sprint performance, jump performance, ground contact time, strength-related outcomes, anaerobic power, and body composition. Internal load was monitored using session rating of perceived exertion. Training load was comparable between groups throughout the intervention. Compared with CT, HIICT showed more favorable changes in 100-m sprint performance, first-60-m split time, ground contact time, countermovement jump performance, and relative peak power. Squat jump performance improved in both groups, with a greater tendency toward improvement following HIICT. Changes in body composition and several strength-related outcomes were broadly similar between groups, whereas power-clean performance showed a more favorable response in the HIICT group. These findings suggest that, under comparable internal training loads, HIICT may provide additional benefits for sprint-related explosive qualities and acceleration-related performance in male junior sprinters. From a practical perspective, HIICT may be considered as a supplementary training option within junior sprint training programs, particularly when the aim is to target sprint-specific neuromuscular qualities. However, given the specific sample and methodological limitations, these findings should be interpreted cautiously and require confirmation in future studies.

Hyaluronic acid (HA) is widely used for intra-articular injection to improve joint lubrication and mobility, but its influence on microbiologically influenced corrosion of joint implants remains unclear. Here, we investigated how HA affects the corrosion behavior of TiZr alloy in simulated body fluid (SBF) under sterile and Staphylococcus aureus (S. aureus) biofilm conditions. Surface observations and chemical analysis indicated that HA-related surface coverage was formed on the TiZr surface and reduced surface damage under sterile conditions, suggesting a protective role of HA. However, in the presence of S. aureus biofilm, HA showed contrasting effects at different immersion stages, slightly reducing corrosion at the early stage compared with the S. aureus group but aggravating biofilm-associated localized damage after prolonged exposure. Contact angle analysis showed decreases of 68.1% and 57.1% within 1 min in the S. aureus + HA group on days 7 and 14, respectively, indicating that HA promoted rapid surface wetting and altered the interfacial wetting behavior in the biofilm environment. At these time points, electrochemical tests further confirmed corrosion acceleration, with the S. aureus + HA group showing the highest corrosion current densities among all groups, reaching 7.972 × 10-6 A cm-2 and 2.715 × 10-6 A cm-2, respectively. These results demonstrate that the effect of HA on TiZr alloy corrosion shifts from protection under sterile conditions to corrosion aggravation during prolonged S. aureus biofilm exposure. This finding suggests that HA-related effects on biofilm formation and implant corrosion should be considered when assessing the corrosion risk of TiZr joint implants under infection-related conditions.

This study develops and validates a smartphone-based framework for automatically detecting emergency maneuvers, strong jolts, and crashes involving electric scooters and electric bicycles. Detection criteria were established through controlled track experiments and subsequently evaluated using data collected during a naturalistic riding study involving 119 participants and more than 26,000 km and 1,600 h of riding, combining accelerometer, gyroscope, GPS, and video recordings. Threshold-based detection criteria were defined using variables selected for their physical relevance and ability to discriminate between target and non-target situations. Hard braking, sharp turns, strong jolts, and crash-related events were identified using combinations of acceleration, jerk, rotational dynamics, and post-event vehicle motion. Video review showed that 74% of hard-braking detections corresponded to harsh-braking maneuvers, 64% of sharp-turn detections reflected genuine avoidance maneuvers, and 91% of strong-jolt detections were associated with infrastructure features. Video verification of collision candidates confirmed several reported and previously unreported impacts, including collisions with other road users and single-vehicle falls. Application of the framework to the naturalistic dataset revealed marked differences between vehicle types. E-scooter users experienced higher rates of hard braking and strong jolts than e-bicycle users, reflecting behavioral differences and vehicle characteristics. Illustrative mapping examples showed that detected events and rider-reported hazardous situations could occur in close proximity, suggesting opportunities for future spatial analyses of micromobility safety. Although additional validation on larger crash datasets is required, the results demonstrate that threshold-based approaches can provide meaningful indicators of rider safety, support large-scale monitoring of micromobility risks, and contribute to infrastructure and transport-safety assessment.

To examine the effect of chronic alcohol exposure on the activity of CYP3A enzymes in human liver, we studied the metabolism of CYP3A-specific substrates 7-benzyloxyquinoline (7-BQ) and ivermectin in 23 preparations of human liver microsomes (HLM) obtained from donors with documented alcohol exposure, from non-drinkers to heavy alcoholics. All HLM samples were characterized for the composition of the cytochrome P450 pool by global proteomics. Our studies revealed a significant increase in the activities of CYP3A enzymes by alcohol exposure. This effect is not associated with CYP3A enzyme levels, which do not correlate with alcohol exposure. Instead, the rates of 7-BQ and ivermectin metabolism correlate with the content of alcohol-inducible CYP2E1. However, this enzyme does not metabolize ivermectin, and its activity with 7-BQ is negligible. A significant increase in the rate of ivermectin demethylation was also observed in CYP3A4-containing Supersomes® and pooled HLM upon incorporation of purified CYP2E1 into their membrane. These results suggest that the reported acceleration of the elimination of drugs metabolized by CYP3A enzymes by alcohol exposure is due to functional effects of the interaction between CYP3A and CYP2E1. To elucidate the potential mechanism of this effect, we studied the formation of CYP2E1-CYP3A4 complexes in CYP3A4-containing Supersomes with co-incorporated CYP2E1 using tag-transfer chemical crosslinking mass spectrometry (CX-MS). These experiments confirmed physical interactions between the proteins and allowed the identification of CYP3A4 residues at the sites of contact. This information was used to build structural models of the CYP2E1-CYP3A4 complex and to propose possible mechanisms for the observed effects.

Emerging organic contaminants (EOCs), including per- and polyfluoroalkyl substances (PFASs), polybrominated diphenyl ethers (PBDEs), polycyclic aromatic hydrocarbons (PAHs), and antibiotics, persist in China's surface seawater, yet their nationwide occurrence and community-level impacts remain unclear. We integrated a systematic review (125 studies) with laboratory bioassays and field surveys to screen priority EOCs, identify drivers, and assess impacts on phytoplankton. Spatially, prevalent contaminants included perfluorooctanoic acid (PFOA) and its short-chain alternatives, naphthalene (Nap), tetrabromodiphenyl ether (BDE-47), decabromodiphenyl ether (BDE-209), oxytetracycline (OTC), and norfloxacin. EOC composition varied among the four seas. Temporally, PFASs and PBDEs declined (driven by regulations and substitutes) PAHs showed a fall-and-rise pattern (linked to energy shifts), while antibiotics kept rising (from mariculture). Under the lenient risk scenario, several PAHs showed 27-38% high-risk exceedance; OTC and BDE-47 had > 10% medium-risk, and PFOA had 3% medium-risk. PFAS distributions correlated with waste treatment and sewage volume, whereas PAHs were driven by direct marine pollution discharge and industrial intensity. Multi-species toxicity tests ranked BDE-47 and sulfamethoxazole as highly toxic; Nap, acenaphthylene and BDE-209 as moderately toxic. In Laizhou Bay, field measurements detected Nap (101.0 ng/L) and PFOA (91.3 ng/L); Nap, together with dissolved oxygen and pH, drove phytoplankton community. Co-culture experiments showed that environmental concentrations did not alter interspecific competition, but elevated exposures did. This work links EOC exposure to regulatory effectiveness and ecological impacts in Chinese seawater, highlights phytoplankton vulnerability, and supports targeted management: accelerating PFAS alternatives, promoting green industrial transition and controlling aquaculture antibiotics, with priority on the Bohai Sea.

Predicted deleterious mutations (SNPs) have different distributions of effects compared to random SNPs based on population composition. Variant prioritization of markers based on deleterious scores can improve the prediction of yield. Favoring mating schemes between parents with fewer highly deleterious mutations can increase the rate of genetic gain. The study of mutations is fundamental to understanding evolution, domestication, and genetics. Characterizing mutations has potential to accelerate breeding programs through selection and purging deleterious mutations (DelMut). We investigated how predicting DelMut in breeding populations informs genomic prediction (GP) increasing the rate of genetic gain. DelMut were annotated in three independent common bean populations using a previously developed random forest (RF) model for common bean incorporating phylogenetic and protein information. Deleterious scores from the RF model were around 0.25, with the top 1% (highly DelMut) of variants scoring between 0.78 and 0.82 among populations. All populations showed variation in the number of highly DelMut per line (max. 13-197) and in genetic load. We assessed the impact of incorporating a priori information on DelMut for variant prioritization and weighting in GP models for yield and flowering time. Stochastic simulations were conducted to evaluate how designing mating schemes based on variable numbers of DelMut per parent can affect genetic gain. Variants with higher predicted scores had significantly different effect distributions compared to random or lower-scored markers. Simulated breeding cycles showed that selecting parents with fewer highly DelMut consistently increases the rate of genetic gain, and depending on the population, can be superior to phenotypic selection. These results highlight the potential of DelMut information for variant prioritization and the optimization of common bean breeding programs. The approaches we developed can be applied to other species to improve the efficacy of crop improvement.

Antibody-drug conjugates (ADCs)-composed of a monoclonal antibody linked to a payload-were designed to deliver high concentrations of cytotoxic agents to cancer cells. Since their inception, a deeper understanding of the mechanisms of action and resistance to ADCs in patients has generated a wealth of advances in the chemistry of ADC constructs, together with rational therapeutic combinations. However, the pace of innovation now exceeds clinical trial capacity. Also, any single modification or combination is unlikely to generate clinically meaningful benefit on its own. In this context, there is a need to integrate multiple chemistry advances into individual ADCs and to develop frameworks, infrastructures and tools to accelerate and de-risk the preclinical and early clinical development of these agents. In addition, the development of multidimensional molecular tools to predict ADC sensitivity, together with the optimal use of new ADCs in early-stage cancers, should contribute to improved outcomes for patients. We discuss these opportunities and challenges and predict that in the longer term, the development of diversified ADC libraries incorporating distinct constructs and drug-to-antibody ratios will enable personalized treatment strategies aligned with individual tumor biology.

Microwave (MW) drying offers rapid processing but poses challenges in controlling thermal exposure, which can degrade heat-sensitive bioactive compounds such as ascorbic acid (AA) and anthocyanins. Although conventional and hybrid MW configurations have been explored, real-time temperature-based power modulation remains underexplored as a strategy to balance drying efficiency and bioactive retention. This study evaluated a temperature-controlled MW drying mode (Tcon), in which magnetron power was dynamically regulated via infrared thermography surface feedback, and compared it against conventional MW, fluidized-bed (FB), and hybrid FB-MW configurations for drying acerola (Malpighia emarginata). Drying kinetics, drying rate, and thermal histories were assessed alongside the retention of total polyphenol content (TPC), antioxidant capacity (2,2-diphenyl-1-picrylhydrazyl), anthocyanins, and AA. The Tcon mode achieved the shortest drying time among the treatments evaluated while maintaining moderate surface temperatures, and showed higher retention of TPC, antioxidant capacity, and AA compared to fixed-power MW and hybrid processes. Hybrid configurations accelerated drying relative to FB alone; however, longer residence times led to greater cumulative thermal exposure and reduced bioactive retention. These findings suggest that real-time infrared-based power modulation is a promising strategy for improving bioactive preservation during MW drying of thermally sensitive fruit products.

Polyvinyl alcohol (PVA) is a widely used polymer in many applications. The nanoparticles' incorporation into the polyvinyl alcohol structure can significantly modify the electrical characteristics by benefiting the properties of both polymer and nanofillers. Although the dielectric constant of PVA-based nanocomposites has been comprehensively measured, no general predictive model currently exists for its estimation. So, this study develops a robust, data-driven framework to predict the dielectric constant of PVA nanocomposites as a function of nanofiller type, filler concentration, temperature, and frequency. First, a comprehensive dataset of 1698 experimental measurements was gathered from the literature, covering nanofillers including CuO, TiO2, ZnO, Al2O3, GO, V2O5, SrTiO3, and PbO. Feature relevance was first analyzed using multiple linear regression, followed by the implementation and comparison of six machine learning models, i.e., categorical boosting, gradient boosting (GradBoost), extreme gradient boosting, least-squares support vector regression, multilayer perceptron neural network, and adaptive neuro-fuzzy inference system. Among these, the GradBoost model demonstrated superior predictive performance and generalization capability. The GradBoost model achieved high accuracy in cross-validation (1359 samples) with AARD = 3.38%, RMSE = 3.49, MAE = 0.83, and R = 0.99926, and maintained strong performance on the external test set (339 samples) with AARD = 9.32%, RMSE = 4.24, MAE = 1.76, and R = 0.99877. The proposed model represents the first generalized predictive tool for estimating the dielectric constant of PVA-based nanocomposites across multiple nanofillers and operating conditions. This approach provides a practical and accurate alternative to experimental study, enabling accelerated design and optimization of polymer nanocomposites for electronic and dielectric applications.

Reentrant arrhythmias are life-threatening cardiac events that are difficult to study due to limited experimental control over the complex circuit dynamics. We present a real-time coupled cardiac system that allows in real-time dynamic manipulation of reentrant pathways in vitro using physiologically relevant simulations. We designed a closed-feedback loop system that couples a cultured cardiac monolayer with a two-dimensional computational simulation of cardiac tissue. The simulation, based on GPU-accelerated models (e.g., cellular automata), predicts wave propagation in real-time using the Abubu.js library. Optical mapping captures monolayer activation patterns, and simulation outputs are converted into light-based stimulation via optogenetics, using LEDs and microcontrollers to depolarize cardiac tissue. Our platform is capable of accurately detecting and responding to electrical waves in real-time, enabling interactive modulation of reentrant circuits. The system replaces traditional fixed-delay stimulation protocols with computationally guided interventions, better mimicking physiological conduction dynamics. This coupled system provides a novel and responsive method to study reentrant arrhythmias. Its integration of optical stimulation, real-time modeling, and tissue feedback enables the construction of user-defined reentry pathways and dynamic interaction with reentrant circuit behavior. By merging computational and biological systems, this work introduces a versatile experimental framework for investigating arrhythmias. Built from inexpensive and accessible components, it lowers technical and financial barriers, increasing accessibility across a broad range of researchers and research environments. The platform may inform future control and anti-arrhythmic strategies and pave the way for personalized cardiac electrophysiology studies.

Antibiotic contamination demands efficient remediation technologies. Although three-dimensional Electro-Fenton (3D-EF) systems show promise, conventional granular electrodes suffer from rapid deactivation and poor conductivity. Herein, we report a ternary Fe-Mn composite granular electrode for tetracycline (TC) degradation. The electrode integrates three synergistic functionalities: (i) iron-manganese oxides as the primary active phase, where Fe-Mn redox synergy accelerates Fe(II) regeneration; (ii) silica incorporation to fortify the oxide layer and enhance structural stability; and (iii) conductive carbon black doping to improve electrical conductivity and electron transfer. The system achieved 95% TC removal within 120 min. Characterization confirmed the structural and electrochemical advantages, while mechanistic studies identified singlet oxygen (1O₂) as the dominant reactive species. This work provides a durable, high-performance granular electrode for practical 3D-EF treatment of antibiotic pollutants.

Prelithiation is a pivotal strategy for enhancing the initial coulombic efficiency (ICE) and energy density of lithium-ion batteries, yet its practical application is impeded by the pronounciked sensitivity of prelithiated electrodes to ambient moisture and oxygen during storage. Herein, we rationally devise a targeted design for a fluorine-rich acrylate copolymer-poly (tridecafluorooctyl methacrylate-co-methyl methacrylate) (PFMMA)-and introduce it as a multifunctional protective coating, with Li13Si4-prelithiated SiOC electrodes (preSiOC) employed as a proof of concept. Fluorinated side chains impart strong hydrophobicity, while methyl methacrylate units retain electrolyte affinity; the two moieties act synergistically to stabilize electrodes in air and preserve unimpeded interfacial ion/charge transport during redox reactions. Consequently, the preSiOC/PFMMA electrode with a 540 nm-thick PFMMA coating retains 97.4% capacity and 95.3% ICE after 48 h air exposure at 50% relative humidity (RH), alongside robust cycling stability (677.5 mAh·g-1 after 100 cycles). These results outperform both unprotected preSiOC and other reported conventionally protected prelithiated electrodes. Furthermore, the electrode shows exceptional environmental adaptability, maintaining functionality under extreme scenarios (10% RH for 100 days or 90% RH for 3 days). This study establishes a rational copolymer design paradigm for fabricating durable, electrolyte-compatible interfaces, thereby accelerating the development of ambient-stable prelithiated electrodes.

Chromosome-scale genome assemblies in gymnosperms have lagged behind those of angiosperms, likely due to their large genomes. Coniferous tree species, which belong to the gymnosperms, are important resources for wood production in the forestry industry. To elucidate the evolution and speciation of these species and establish genome resources for breeding, we integrated draft assemblies with optical and genetic mapping to construct chromosome-scale genomes for Japanese cypress (Chamaecyparis obtusa, 8.7 Gb), Japanese cedar (Cryptomeria japonica, 9.6 Gb), and Chinese fir (Cunninghamia lanceolata, 13.4 Gb). Additionally, we assembled and annotated their chloroplast and mitochondrial genomes. Comparative analysis of the nuclear genomes revealed that while synteny is largely conserved, distinct translocations and inversions occurred in chromosomes 2, 6, and 9. Notably, the significantly larger genome of Cu. lanceolata was associated with frequent tandem gene duplications rather than transposon expansion. These findings suggest that chromosomal rearrangements and segmental duplications played key roles in the divergence of these species. The genomic resources presented here including chromosome-scale sequences, gene annotations, and genetic maps will facilitate advanced conifer genetics and accelerate forest tree breeding programmes.

The accelerating global crisis of antibiotic resistance demands new therapeutic paradigms, and antimicrobial peptides (AMPs) have emerged as promising candidates owing to their broad activity and reduced propensity for resistance development. However, despite rapid progress in AMP discovery and generation, the accurate prediction of antimicrobial potency and activity spectrum remains a major bottleneck for clinical translation. In this Review, we examine how recent advances in machine learning are reshaping AMP research, driving a shift from large-scale discovery toward precision-guided prediction and design. We first summarize the molecular mechanisms underlying AMP function and critically assess existing AMP databases from the perspective of machine learning readiness, highlighting limitations in quantitative and spectrum-resolved annotations. We then review recent developments in peptide representation learning, describing how modern models encode sequence, structure, and dynamic features to capture antimicrobial activity. Building on this foundation, we discuss progress in de novo AMP design and emerging frameworks for quantitative minimum inhibitory concentration prediction and strain-specific spectrum profiling. Finally, we outline future directions for the field, emphasizing integrated generative-predictive pipelines, interpretable models, and closed-loop experimental validation as key enablers for the development of potent, selective, and clinically viable antimicrobial therapeutics.

Conventional antibody discovery approaches that do not account for enrichment-driven biases, such as epitope immunogenicity, PCR amplification bias, or protein expression efficiency, may result in under-representation of rare yet functionally relevant clones, necessitating labor-intensive in vitro screening to identify agonistic antibodies among a large number of dominant clones. Thus, efficient screening methods for agonistic antibodies are urgently needed. OX40 is a promising target for cancer immunotherapy due to its role in enhancing T-cell activation and survival. However, effective anti-OX40 agonistic antibodies have not yet been developed. We developed a novel screening strategy that involves the selection of nanobody clone pools enriched by biopanning against gp34-engaged and non-engaged OX40-expressing cells, next-generation sequencing, and computational clustering and subtraction analysis to identify clones recognizing the ligand-receptor interface. Representative nanobody clones underwent in vitro validation, including epitope mapping, binding affinity measurements, and functional assessments. Furthermore, we engineered the selected nanobody to enhance its in vivo efficacy. We also performed structural analysis of the nanobody-OX40 complex. Our epitope-directed approach efficiently identified nanobody clones recognizing functionally relevant epitopes distinct from dominant immunogenic regions. Notably, clone Nb479 demonstrated robust agonistic activity, closely mimicking the natural ligand gp34 with extensive OX40-binding interactions. Trimerization of Nb479 facilitated potent OX40 activation without the need for a cross-linking scaffold. Conjugation of the Nb479 trimer with an anti-serum albumin nanobody exhibited significantly improved pharmacokinetics in vivo and enhanced antitumor activity in a mouse model treated with CD19 chimeric antigen receptor T cells. This study presents an innovative epitope-directed approach that greatly accelerates the discovery of functionally potent agonistic nanobodies by effectively circumventing enrichment-driven epitope bias. Our approach and engineered multivalent anti-OX40 nanobody offer a powerful platform to advance immunotherapeutic strategies for cancer treatment.

Commissioning of a synchrotron hard X-ray nanoprobe beamline traditionally requires months of iterative alignment after hardware installation, during which operational knowledge accumulates but remains inaccessible to non-specialist users. To address this, we present a browser-based virtual commissioning platform for the Korea Light Source ID10 Hard X-ray Nanoprobe beamline (first light 2029) that allows beamline scientists to design, test and refine alignment procedures, scan plans and experimental workflows years before the first photon arrives. Specifically, the platform integrates a Monte Carlo ray-tracing engine, a standard Experimental Physics and Industrial Control System (EPICS)/Bluesky control stack, and a multilingual natural-language interface within a single deployable package, which we name HANBIT (Hybrid Agent-driven Natural-language Beamline Interactive Toolkit). Users can interactively explore parameter trade-offs, such as the effect of the secondary source aperture on beam size versus photon flux. The Monte Carlo engine reproduces the overall Shadow4 beam-profile shape and is validated against SPECTRA undulator spectra, source size and divergence. The natural language processing (NLP) agent achieves 98.2% automated action-identification accuracy across 228 test cases in Korean, English and Japanese, whereas expert review of the same responses yields an acceptance rate of 67.3%. We identify this 30.9 percentage-point gap as a central finding: automated accuracy does not guarantee operational acceptability, and closing it is the key challenge for deployment-grade natural-language beamline control. We further validate the zero-change hardware transition strategy that the beamline pursues on three real hardware subsystems, confirming that at the validated device layers the control code and scan plans operate unchanged on the real devices; the integration of the remaining parts, such as high-rate area detectors, which awaits the detector hardware, is also discussed.

Hypertensive disorders of pregnancy affect approximately 9% of pregnancies in the United States and are a leading cause of maternal morbidity and mortality. For nephrologists, these conditions sit at a critical intersection: chronic kidney disease is among the strongest risk factors for preeclampsia, and pregnancy itself can unmask previously unrecognized kidney disease. The physiologic demands of gestation stress renal and vascular reserve in ways that make the pregnant and immediately postpartum patient particularly vulnerable. Preeclampsia, driven by placental release of anti-angiogenic factors and widespread maternal endothelial dysfunction, produces acute cardiorenal injury that does not entirely resolve with delivery. Postpartum hypertension peaks between days 3 to 6 after delivery and is under-recognized, yet this period carries the greatest risk of preventable maternal death, with cardiovascular causes accounting for more than one third of pregnancy-related deaths. Beyond the acute phase, women with hypertensive pregnancies face accelerated trajectories toward chronic hypertension, chronic kidney disease, and overall cardiometabolic risks that compound with each affected pregnancy. The postpartum visit represents a critical and underutilized opportunity to identify women who warrant nephrology evaluation, initiate renoprotective therapy, and interrupt a long-term cardiorenal disease course. Nephrologists are well-positioned to recognize cardiorenal sequelae of hypertensive pregnancy and ensure that the postpartum period is not a missed opportunity for intervention. This review addresses the diagnosis, pathophysiology, and management of hypertensive disorders across the full peripartum continuum, with particular attention to the role of nephrologists in recognizing kidney disease that pregnancy has brought to light and in ensuring that delivery is not treated as a clinical endpoint.

To examine associations between social drivers of health and veteran-level reach of STRIDE, a supervised walking program implemented in the Veterans Health Administration (VA). We included 2527 patients across 5 facilities. We tested whether housing insecurity, rurality, race, and neighborhood deprivation were associated with reach, defined as any STRIDE walk during hospitalization. We used the first 6 months of postimplementation data from a stepped-wedge implementation trial. During the implementation period, 197 veterans (7.8%) received at least 1 STRIDE walk. In unadjusted models, patients residing rurally (vs. urban) and those who were White, non-Hispanic/Latino (NH) (vs. Black NH) were significantly more likely to receive ≥1 STRIDE walk. Unadjusted models showed large site differences in STRIDE reach, ranging from 3% to 26% probability by site. Reach differences coincided somewhat with facility-level racial composition: the lowest reach was observed at a site with a majority of Black NH admitted patients, whereas the highest reach was found at a site with a majority of White NH patients. In adjusted models, patient-level rurality remained associated with greater reach (probability 9% for rural veterans vs. 6% for urban veterans), but race was no longer associated (probability 6% among White NH veterans vs. 7% among Black NH veterans). Patient-level rurality was consistently positively associated with implementation outcomes. Patient-level race was associated with implementation outcomes in unadjusted but not adjusted models. Secondary analyses in implementation trials may help assess how social drivers of health are associated with implementation outcomes.