Infarcts Due to Large Vessel Occlusions Continue to Grow Despite Near-Complete Reperfusion After Endovascular Treatment.

BACKGROUND AND PURPOSE: Infarcts in acute ischemic stroke (AIS) patients may continue to grow even after reperfusion, due to mechanisms such as microvascular obstruction and reperfusion injury. We investigated whether and how much infarcts grow in AIS patients after near-complete (expanded Thrombolysis in Cerebral Infarction [eTICI] 2c/3) reperfusion following endovascular treatment (EVT), and to assess the association of post-reperfusion infarct growth with clinical outcomes. METHODS: Data are from a single-center retrospective observational cohort study that included AIS patients undergoing EVT with near-complete reperfusion who received diffusion-weighted magnetic resonance imaging (MRI) within 2 hours post-EVT and 24 hours after EVT. Association of infarct growth between 2 and 24 hours post-EVT and 24-hour National Institutes of Health Stroke Scale (NIHSS) as well as 90-day modified Rankin Scale score was assessed using multivariable logistic regression. RESULTS: Ninety-four of 155 (60.6%) patients achieved eTICI 2c/3 and were included in the analysis. Eighty of these 94 (85.1%) patients showed infarct growth between 2 and 24 hours post-reperfusion. Infarct growth ≥5 mL was seen in 39/94 (41.5%) patients, and infarct growth ≥10 mL was seen in 20/94 (21.3%) patients. Median infarct growth between 2 and 24 hours post-reperfusion was 4.5 mL (interquartile range: 0.4-9.2 mL). Post-reperfusion infarct growth was associated with the 24-hour NIHSS in multivariable analysis (odds ratio: 1.16 [95% confidence interval 1.09-1.24], P<0.01). CONCLUSION: Infarcts continue to grow after EVT, even if near-complete reperfusion is achieved. Investigating the underlying mechanisms may inform future therapeutic approaches for mitigating the process and help improve patient outcome.

MRI-Based Prediction of Clinical Improvement Following Ventricular Shunt Placement for Normal Pressure Hydrocephalus (NPH): Development and Evaluation of an Integrated Multi-Sequence Machine Learning Algorithm.

BACKGROUND AND PURPOSE: Symptoms of normal pressure hydrocephalus (NPH) are sometimes refractory to shunt placement, with limited ability to predict improvement for individual patients. We evaluated an MRI-based artificial intelligence method to predict post-shunt NPH symptom improvement. MATERIALS AND METHODS: NPH patients who underwent magnetic resonance imaging (MRI) prior to shunt placement at a single center (2014-2021) were identified. Twelve-month post-shunt improvement in modified Rankin Scale (mRS), incontinence, gait, and cognition were retrospectively abstracted from clinical documentation. 3D deep residual neural networks were built on skull stripped T2-weighted and fluid attenuated inversion recovery (FLAIR) images. Predictions based on both sequences were fused by additional network layers. Patients from 2014-2019 were used for parameter optimization, while those from 2020-2021 were used for testing. Models were validated on an external validation dataset from a second institution (n=33). RESULTS: Of 249 patients, n=201 and n=185 were included in the T2-based and FLAIR-based models according to imaging availability. The combination of T2-weighted and FLAIR sequences offered the best performance in mRS and gait improvement predictions relative to models trained on imaging acquired using only one sequence, with AUROC values of 0.7395 [0.5765-0.9024] for mRS and 0.8816 [0.8030-0.9602] for gait. For urinary incontinence and cognition, combined model performances on predicting outcomes were similar to FLAIR-only performance, with AUROC values of 0.7874 [0.6845-0.8903] and 0.7230 [0.5600-0.8859]. CONCLUSIONS: Application of a combined algorithm using both T2-weighted and FLAIR sequences offered the best image-based prediction of post-shunt symptom improvement, particularly for gait and overall function in terms of mRS. ABBREVIATIONS: NPH = normal pressure hydrocephalus; iNPH = idiopathic NPH; sNPH = secondary NPH; AI = artificial intelligence; ML = machine learning; CSF = cerebrospinal fluid; AUROC = area under the receiver operating characteristic; FLAIR = fluid attenuated inversion recovery; BMI = body mass index; CCI = Charlson Comorbidity Index; SD = standard deviation; IQR = interquartile range.

Multi-modality Regional Alignment Network for Covid X-Ray Survival Prediction and Report Generation.

In response to the worldwide COVID-19 pandemic, advanced automated technologies have emerged as valuable tools to aid healthcare professionals in managing an increased workload by improving radiology report generation and prognostic analysis. This study proposes a Multi-modality Regional Alignment Network (MRANet), an explainable model for radiology report generation and survival prediction that focuses on high-risk regions. By learning spatial correlation in the detector, MRANet visually grounds region-specific descriptions, providing robust anatomical regions with a completion strategy. The visual features of each region are embedded using a novel survival attention mechanism, offering spatially and risk-aware features for sentence encoding while maintaining global coherence across tasks. A cross-domain LLMs-Alignment is employed to enhance the image-to-text transfer process, resulting in sentences rich with clinical detail and improved explainability for radiologists. Multi-center experiments validate the overall performance and each module's composition within the model, encouraging further advancements in radiology report generation research emphasizing clinical interpretation and trustworthiness in AI models applied to medical studies.

A global meta-analysis on the effects of organic and inorganic fertilization on grasslands and croplands.

A central role for nature-based solution is to identify optimal management practices to address environmental challenges, including carbon sequestration and biodiversity conservation. Inorganic fertilization increases plant aboveground biomass but often causes a tradeoff with plant diversity loss. It remains unclear, however, whether organic fertilization, as a potential nature-based solution, could alter this tradeoff by increasing aboveground biomass without plant diversity loss. Here we compile data from 537 experiments on organic and inorganic fertilization across grasslands and croplands worldwide to evaluate the responses of aboveground biomass, plant diversity, and soil organic carbon (SOC). Both organic and inorganic fertilization increase aboveground biomass by 56% and 42% relative to ambient, respectively. However, only inorganic fertilization decreases plant diversity, while organic fertilization increases plant diversity in grasslands with greater soil water content. Moreover, organic fertilization increases SOC in grasslands by 19% and 15% relative to ambient and inorganic fertilization, respectively. The positive effect of organic fertilization on SOC increases with increasing mean annual temperature in grasslands, a pattern not observed in croplands. Collectively, our findings highlight organic fertilization as a potential nature-based solution that can increase two ecosystem services of grasslands, forage production, and soil carbon storage, without a tradeoff in plant diversity loss.

Updates on coding in dermatology.

Correct coding is an important component of effective dermatology practice management. Over the past several years there have been updates to many commonly used codes within dermatology. This review highlights many of these updates, such as: the skin biopsy codes have been subdivided to reflect the different biopsy techniques. The definition of complex linear repairs has been updated and clarified. Outpatient and inpatient evaluation and management visits have new coding guidelines to determine level of care. Dermatopathology consultation codes have been updated and category III codes related to digital pathology have been created. Understanding the details and nuances of each of these categories of codes is vital to ensuring appropriate coding is performed.

De-Biased Disentanglement Learning for Pulmonary Embolism Survival Prediction on Multimodal Data.

Health disparities among marginalized populations with lower socioeconomic status significantly impact the fairness and effectiveness of healthcare delivery. The increasing integration of artificial intelligence (AI) into healthcare presents an opportunity to address these inequalities, provided that AI models are free from bias. This paper aims to address the bias challenges by population disparities within healthcare systems, existing in the presentation of and development of algorithms, leading to inequitable medical implementation for conditions such as pulmonary embolism (PE) prognosis. In this study, we explore the diverse bias in healthcare systems, which highlights the demand for a holistic framework to reducing bias by complementary aggregation. By leveraging de-biasing deep survival prediction models, we propose a framework that disentangles identifiable information from images, text reports, and clinical variables to mitigate potential biases within multimodal datasets. Our study offers several advantages over traditional clinical-based survival prediction methods, including richer survival-related characteristics and bias-complementary predicted results. By improving the robustness of survival analysis through this framework, we aim to benefit patients, clinicians, and researchers by enhancing fairness and accuracy in healthcare AI systems.

Expanding the flexibility of base editing for high-throughput genetic screens in bacteria.

Genome-wide screens have become powerful tools for elucidating genotype-to-phenotype relationships in bacteria. Of the varying techniques to achieve knockout and knockdown, CRISPR base editors are emerging as promising options. However, the limited number of available, efficient target sites hampers their use for high-throughput screening. Here, we make multiple advances to enable flexible base editing as part of high-throughput genetic screening in bacteria. We first co-opt the Streptococcus canis Cas9 that exhibits more flexible protospacer-adjacent motif recognition than the traditional Streptococcus pyogenes Cas9. We then expand beyond introducing premature stop codons by mutating start codons. Next, we derive guide design rules by applying machine learning to an essentiality screen conducted in Escherichia coli. Finally, we rescue poorly edited sites by combining base editing with Cas9-induced cleavage of unedited cells, thereby enriching for intended edits. The efficiency of this dual system was validated through a conditional essentiality screen based on growth in minimal media. Overall, expanding the scope of genome-wide knockout screens with base editors could further facilitate the investigation of new gene functions and interactions in bacteria.

Sheet Models for Methylaluminoxane (MAO) Activators? A Theoretical Case Study involving rac-Me2Si(η5-C9H6)2Zr (SBIZr) Complexes.

Activation of SBIZrMe2 or SBIZrMeCl and a sheet model for an active component of hydrolytic MAO, (MeAlO)16(Me3Al)6, (16,6) has been studied by DFT. Contact ion-pair formation occurs through the intermediacy of SBIZrMe(Cl) or SBIZrMe2 reacting with sheet 16,6 to furnish SBIZrMe-µ-X(MeAlO)16(Me3Al)6 (2, X=Me, Cl). Contact ion-pairs 2 would be in equilibrium with heterodinuclear catalyst precursors [SBIZrMe2AlMe2][(MeAlO)16(Me3Al)6X] (3 (X=Me, Cl) through reversible binding of Me3Al at higher Al : Zr ratios. Calculations show that formation of ion-pairs 3 from contact ion-pairs 2 is more favourable for the SBIZr compared with the parent Cp2Zr complexes. TD-DFT calculations were conducted on relevant SBIZr complexes to relate the results to earlier spectroscopic studies of catalyst activation using UV-Vis spectroscopy. Finally, propene insertion into ion-pairs 2, SBIZrMe-µ-MeB(C6F5)3 (6) and [SBIZrMe][B(C6F5)4] (7) was studied at M06-2X/TZVP level of theory. These studies suggest that contact ion-pairs 2 are significantly less reactive towards insertion than 6 or 7, in disagreement with experiment.

Development of a Novel DNA Mono-alkylator Platform for Antibody-Drug Conjugates.

Although microtubule inhibitors (MTI) remain a therapeutically valuable payload option for antibody-drug conjugates (ADC), some cancers do not respond to MTI-based ADCs. Efforts to fill this therapeutic gap have led to a recent expansion of the ADC payload "toolbox" to include payloads with novel mechanisms of action such as topoisomerase inhibition and DNA cross-linking. We present here the development of a novel DNA mono-alkylator ADC platform that exhibits sustained tumor growth suppression at single doses in MTI-resistant tumors and is well tolerated in the rat upon repeat dosing. A phosphoramidate prodrug of the payload enables low ADC aggregation even at drug-to-antibody ratios of 5:1 while still delivering a bystander-capable payload that is effective in multidrug resistant (MDR)-overexpressing cell lines. The platform was comparable in xenograft studies to the clinical benchmark DNA mono-alkylator ADC platform DGN459 but with a significantly better tolerability profile in rats. Thus, the activity and tolerability profile of this new platform make it a viable option for the development of ADCs.

Modeling diffusion-weighted imaging lesion expansion between 2 and 24 h after endovascular thrombectomy in acute ischemic stroke.

PURPOSE: Diffusion-weighted imaging (DWI) lesion expansion after endovascular thrombectomy (EVT) is not well characterized. We used serial diffusion-weighted magnetic resonance imaging (MRI) to measure lesion expansion between 2 and 24 h after EVT. METHODS: In this single-center observational analysis of patients with acute ischemic stroke due to large vessel occlusion, DWI was performed post-EVT (< 2 h after closure) and 24-h later. DWI lesion expansion was evaluated using multivariate generalized linear mixed modeling with various clinical moderators. RESULTS: We included 151 patients, of which 133 (88%) had DWI lesion expansion, defined as a positive change in lesion volume between 2 and 24 h. In an unadjusted analysis, median baseline DWI lesion volume immediately post-EVT was 15.0 mL (IQR: 6.6-36.8) and median DWI lesion volume 24 h post-EVT was 20.8 mL (IQR: 9.4-66.6), representing a median change of 6.1 mL (IQR: 1.5-17.7), or a 39% increase. There were no significant associations among univariable models of lesion expansion. Adjusted models of DWI lesion expansion demonstrated that relative lesion expansion (defined as final/initial DWI lesion volume) was consistent across eTICI scores (0-2a, 0.52%; 2b, 0.49%; 2c-3, 0.42%, p = 0.69). For every 1 mL increase in lesion volume, there was 2% odds of an increase in 90-day mRS (OR: 1.021, 95%CI [1.009, 1.034], p < 0.001). CONCLUSION: We observed substantial lesion expansion post-EVT whereby relative lesion expansion was consistent across eTICI categories, and greater absolute lesion expansion was associated with worse clinical outcome. Our findings suggest that alternate endpoints for cerebroprotectant trials may be feasible.

Extreme drought impacts have been underestimated in grasslands and shrublands globally.

Climate change is increasing the frequency and severity of short-term (~1 y) drought events-the most common duration of drought-globally. Yet the impact of this intensification of drought on ecosystem functioning remains poorly resolved. This is due in part to the widely disparate approaches ecologists have employed to study drought, variation in the severity and duration of drought studied, and differences among ecosystems in vegetation, edaphic and climatic attributes that can mediate drought impacts. To overcome these problems and better identify the factors that modulate drought responses, we used a coordinated distributed experiment to quantify the impact of short-term drought on grassland and shrubland ecosystems. With a standardized approach, we imposed ~a single year of drought at 100 sites on six continents. Here we show that loss of a foundational ecosystem function-aboveground net primary production (ANPP)-was 60% greater at sites that experienced statistically extreme drought (1-in-100-y event) vs. those sites where drought was nominal (historically more common) in magnitude (35% vs. 21%, respectively). This reduction in a key carbon cycle process with a single year of extreme drought greatly exceeds previously reported losses for grasslands and shrublands. Our global experiment also revealed high variability in drought response but that relative reductions in ANPP were greater in drier ecosystems and those with fewer plant species. Overall, our results demonstrate with unprecedented rigor that the global impacts of projected increases in drought severity have been significantly underestimated and that drier and less diverse sites are likely to be most vulnerable to extreme drought.

Interrogating two extensively self-targeting Type I CRISPR-Cas systems in Xanthomonas albilineans reveals distinct anti-CRISPR proteins that block DNA degradation.

CRISPR-Cas systems store fragments of invader DNA as spacers to recognize and clear those same invaders in the future. Spacers can also be acquired from the host's genomic DNA, leading to lethal self-targeting. While self-targeting can be circumvented through different mechanisms, natural examples remain poorly explored. Here, we investigate extensive self-targeting by two CRISPR-Cas systems encoding 24 self-targeting spacers in the plant pathogen Xanthomonas albilineans. We show that the native I-C and I-F1 systems are actively expressed and that CRISPR RNAs are properly processed. When expressed in Escherichia coli, each Cascade complex binds its PAM-flanked DNA target to block transcription, while the addition of Cas3 paired with genome targeting induces cell killing. While exploring how X. albilineans survives self-targeting, we predicted putative anti-CRISPR proteins (Acrs) encoded within the bacterium's genome. Screening of identified candidates with cell-free transcription-translation systems and in E. coli revealed two Acrs, which we named AcrIC11 and AcrIF12Xal, that inhibit the activity of Cas3 but not Cascade of the respective system. While AcrF12Xal is homologous to AcrIF12, AcrIC11 shares sequence and structural homology with the anti-restriction protein KlcA. These findings help explain tolerance of self-targeting through two CRISPR-Cas systems and expand the known suite of DNA degradation-inhibiting Acrs.

Grassland sensitivity to drought is related to functional composition across East Asia and North America.

Plant traits can be helpful for understanding grassland ecosystem responses to climate extremes, such as severe drought. However, intercontinental comparisons of how drought affects plant functional traits and ecosystem functioning are rare. The Extreme Drought in Grasslands experiment (EDGE) was established across the major grassland types in East Asia and North America (six sites on each continent) to measure variability in grassland ecosystem sensitivity to extreme, prolonged drought. At all sites, we quantified community-weighted mean functional composition and functional diversity of two leaf economic traits, specific leaf area and leaf nitrogen content, in response to drought. We found that experimental drought significantly increased community-weighted means of specific leaf area and leaf nitrogen content at all North American sites and at the wetter East Asian sites, but drought decreased community-weighted means of these traits at moderate to dry East Asian sites. Drought significantly decreased functional richness but increased functional evenness and dispersion at most East Asian and North American sites. Ecosystem drought sensitivity (percentage reduction in aboveground net primary productivity) positively correlated with community-weighted means of specific leaf area and leaf nitrogen content and negatively correlated with functional diversity (i.e., richness) on an intercontinental scale, but results differed within regions. These findings highlight both broad generalities but also unique responses to drought of community-weighted trait means as well as their functional diversity across grassland ecosystems.

Prospective Margin Estimates Predict Local Tumor Progression Following Microwave Ablation of Small Renal Masses.

PURPOSE: To evaluate the relationship between prospectively generated ablative margin estimates and local tumor progression (LTP) among patients undergoing microwave ablation (MWA) of small renal masses (SRMs). MATERIALS AND METHODS: Between 2017 and 2020, patients who underwent MWA for SRM were retrospectively identified. During each procedure, segmented kidney and tumor shapes were coregistered with intraprocedural helical CT images obtained after microwave antenna placement. Predicted ablation zone shape and size were then overlaid onto the resultant model, and a model-to-model distance algorithm was employed to calculate multiple ablative margin estimates. LTP was modeled as a function of each margin estimate by hazard regression. Models were evaluated using hazard ratios and Akaike information criterion. Receiver operating characteristic curve area under the curve was also estimated using Harrell's and Uno's C indices (HI and UI, respectively). RESULTS: One hundred and twenty-eight patients were evaluated (median age 72.1 years). Mean tumor diameter was 2.4 ± 0.9 cm. LTP was observed in nine (7%) patients. Analysis showed that decreased estimated margin size as measured by first quartile (Q1; 25th percentile), maximum, and average ablative margin metrics was significantly associated with risk of LTP. For every one millimeter increase in Q1, maximum, and mean ablative margin, the hazard of LTP increased 67% (HR: 1.67; 95% CI = 1.25-2.20, UI = 0.93, HI = 0.77), 32% (HR: 1.32; 95% CI 1.09-1.60; UI = 0.93; HI = 0.76), and 48% (HR: 1.48; 95% CI 1.18-1.85; UI = 0.83; HI = 0.75), respectively. CONCLUSION: Prospectively generated ablative margin estimates can be used to predict the risk of local tumor progression following microwave ablation of small renal masses. LEVEL OF EVIDENCE 3: Retrospective cohort study.

A Microfabricated, Flow-Driven Grinding Mill for Mechanical Cell Lysing.

This paper presents the design, microfabrication, and demonstration of a novel microfluidic grinding mill for the lysis of the dinoflagellate, Alexandrium, a neurotoxin-producing genus of algae that is responsible for red tide and paralytic shellfish poisoning. The mill consists of a high-speed, hydrodynamically driven microrotor coupled to a micro grinding mill that lyses robust algal cells by mechanical abrasion with single-pass efficiencies as high as 97%. These efficiencies are comparable to, or better than, current mechanical and chemical lysing methods without adding complications associated with harsh chemical additives that can interfere with subsequent downstream bioanalysis. Release of cytoplasm from lysed algae was confirmed using polymerase chain reaction (PCR) amplification of Alexandrium DNA using dinoflagellate primers.

The positive effect of plant diversity on soil carbon depends on climate.

Little is currently known about how climate modulates the relationship between plant diversity and soil organic carbon and the mechanisms involved. Yet, this knowledge is of crucial importance in times of climate change and biodiversity loss. Here, we show that plant diversity is positively correlated with soil carbon content and soil carbon-to-nitrogen ratio across 84 grasslands on six continents that span wide climate gradients. The relationships between plant diversity and soil carbon as well as plant diversity and soil organic matter quality (carbon-to-nitrogen ratio) are particularly strong in warm and arid climates. While plant biomass is positively correlated with soil carbon, plant biomass is not significantly correlated with plant diversity. Our results indicate that plant diversity influences soil carbon storage not via the quantity of organic matter (plant biomass) inputs to soil, but through the quality of organic matter. The study implies that ecosystem management that restores plant diversity likely enhances soil carbon sequestration, particularly in warm and arid climates.

Accounting for herbaceous communities in process-based models will advance our understanding of "grassy" ecosystems.

Grassland and other herbaceous communities cover significant portions of Earth's terrestrial surface and provide many critical services, such as carbon sequestration, wildlife habitat, and food production. Forecasts of global change impacts on these services will require predictive tools, such as process-based dynamic vegetation models. Yet, model representation of herbaceous communities and ecosystems lags substantially behind that of tree communities and forests. The limited representation of herbaceous communities within models arises from two important knowledge gaps: first, our empirical understanding of the principles governing herbaceous vegetation dynamics is either incomplete or does not provide mechanistic information necessary to drive herbaceous community processes with models; second, current model structure and parameterization of grass and other herbaceous plant functional types limits the ability of models to predict outcomes of competition and growth for herbaceous vegetation. In this review, we provide direction for addressing these gaps by: (1) presenting a brief history of how vegetation dynamics have been developed and incorporated into earth system models, (2) reporting on a model simulation activity to evaluate current model capability to represent herbaceous vegetation dynamics and ecosystem function, and (3) detailing several ecological properties and phenomena that should be a focus for both empiricists and modelers to improve representation of herbaceous vegetation in models. Together, empiricists and modelers can improve representation of herbaceous ecosystem processes within models. In so doing, we will greatly enhance our ability to forecast future states of the earth system, which is of high importance given the rapid rate of environmental change on our planet.

Dryland sensitivity to climate change and variability using nonlinear dynamics.

Primary productivity response to climatic drivers varies temporally, indicating state-dependent interactions between climate and productivity. Previous studies primarily employed equation-based approaches to clarify this relationship, ignoring the state-dependent nature of ecological dynamics. Here, using 40 y of climate and productivity data from 48 grassland sites across Mongolia, we applied an equation-free, nonlinear time-series analysis to reveal sensitivity patterns of productivity to climate change and variability and clarify underlying mechanisms. We showed that productivity responded positively to annual precipitation in mesic regions but negatively in arid regions, with the opposite pattern observed for annual mean temperature. Furthermore, productivity responded negatively to decreasing annual aridity that integrated precipitation and temperature across Mongolia. Productivity responded negatively to interannual variability in precipitation and aridity in mesic regions but positively in arid regions. Overall, interannual temperature variability enhanced productivity. These response patterns are largely unrecognized; however, two mechanisms are inferable. First, time-delayed climate effects modify annual productivity responses to annual climate conditions. Notably, our results suggest that the sensitivity of annual productivity to increasing annual precipitation and decreasing annual aridity can even be negative when the negative time-delayed effects of annual precipitation and aridity on productivity prevail across time. Second, the proportion of plant species resistant to water and temperature stresses at a site determines the sensitivity of productivity to climate variability. Thus, we highlight the importance of nonlinear, state-dependent sensitivity of productivity to climate change and variability, accurately forecasting potential biosphere feedback to the climate system.

Discovery and Optimization of a STING Agonist Platform for Application in Antibody Drug Conjugates.

While STING agonists have proven to be effective preclinically as anti-tumor agents, these promising results have yet to be translated in the clinic. A STING agonist antibody-drug conjugate (ADC) could overcome current limitations by improving tumor accessibility, allowing for systemic administration as well as tumor-localized activation of STING for greater anti-tumor activity and better tolerability. In line with this effort, a STING agonist ADC platform was identified through systematic optimization of the payload, linker, and scaffold based on multiple factors including potency and specificity in both in vitro and in vivo evaluations. The platform employs a potent non-cyclic dinucleotide STING agonist, a cleavable ester-based linker, and a hydrophilic PEG8-bisglucamine scaffold. A tumor-targeted ADC built with the resulting STING agonist platform induced robust and durable anti-tumor activity and demonstrated high stability and favorable pharmacokinetics in nonclinical species.