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Tropical ecosystems face escalating global change. These shifts can disrupt tropical forests' carbon (C) balance and impact root dynamics. Since roots perform essential functions such as resource acquisition and tissue protection, root responses can inform about the strategies and vulnerabilities of ecosystems facing present and future global changes. However, root trait dynamics are poorly understood, especially in tropical ecosystems. We analyzed existing research on tropical root responses to key global change drivers: warming, drought, flooding, cyclones, nitrogen (N) deposition, elevated (e) CO2, and fires. Based on tree species- and community-level literature, we obtained 266 root trait observations from 93 studies across 24 tropical countries. We found differences in the proportion of root responsiveness to global change among different global change drivers but not among root categories. In particular, we observed that tropical root systems responded to warming and eCO2 by increasing root biomass in species-scale studies. Drought increased the root: shoot ratio with no change in root biomass, indicating a decline in aboveground biomass. Despite N deposition being the most studied global change driver, it had some of the most variable effects on root characteristics, with few predictable responses. Episodic disturbances such as cyclones, fires, and flooding consistently resulted in a change in root trait expressions, with cyclones and fires increasing root production, potentially due to shifts in plant community and nutrient inputs, while flooding changed plant regulatory metabolisms due to low oxygen conditions. The data available to date clearly show that tropical forest root characteristics and dynamics are responding to global change, although in ways that are not always predictable. This synthesis indicates the need for replicated studies across root characteristics at species and community scales under different global change factors.
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Cambio Climático , Sequías , Raíces de Plantas , Clima Tropical , Raíces de Plantas/crecimiento & desarrollo , Raíces de Plantas/metabolismo , Árboles/crecimiento & desarrollo , Biomasa , Nitrógeno/metabolismo , Bosques , Inundaciones , IncendiosRESUMEN
PURPOSE: To synthesize the current literature regarding patients with inherited bleeding disorders and suggest comprehensive evaluation and preoperative recommendations for these patients before undergoing corneal refractive surgery. METHODS: The literature search was conducted through PubMed, Embase, and Google Scholar databases for publications through January 4, 2024 for reports of ocular bleeding manifestations in patients with inherited bleeding disorders and reports of patients without a history of bleeding disorders who had intraoperative or postoperative hemorrhagic complications with corneal refractive surgery. Additional cases from the literature and a retrospective chart review at a corneal practice were obtained describing patients with inherited bleeding disorders who underwent corneal refractive surgery. RESULTS: Four articles were found detailing ocular bleeding manifestations in patients with inherited bleeding disorders who underwent ocular surgery other than corneal refractive surgery. Thirty articles were found detailing intraoperative and postoperative bleeding manifestations in patients without a history of inherited bleeding disorders who underwent corneal refractive surgery. Eight cases (3 patients from the literature search and 5 patients from a retrospective chart review) were found regarding patients with inherited bleeding disorders who underwent corneal refractive surgery. CONCLUSIONS: For corneal refractive surgery with topical anesthesia, the perioperative risk and need for any hemostasis intervention in individuals with an inherited bleeding disorder depends on the type of disorder, status of preoperative factor level concentrations, or a prior history of bleeding. If required, clotting factor optimization should be tailored to each candidate on a case-by-case basis.
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The field of computational pathology[1,2] has witnessed remarkable progress in the development of both task-specific predictive models and task-agnostic self-supervised vision encoders[3,4]. However, despite the explosive growth of generative artificial intelligence (AI), there has been limited study on building general purpose, multimodal AI assistants and copilots[5] tailored to pathology. Here we present PathChat, a vision-language generalist AI assistant for human pathology. We build PathChat by adapting a foundational vision encoder for pathology, combining it with a pretrained large language model and finetuning the whole system on over 456,000 diverse visual language instructions consisting of 999,202 question-answer turns. We compare PathChat against several multimodal vision language AI assistants and GPT4V, which powers the commercially available multimodal general purpose AI assistant ChatGPT-4[7]. PathChat achieved state-of-the-art performance on multiple-choice diagnostic questions from cases of diverse tissue origins and disease models. Furthermore, using open-ended questions and human expert evaluation, we found that overall PathChat produced more accurate and pathologist-preferable responses to diverse queries related to pathology. As an interactive and general vision-language AI Copilot that can flexibly handle both visual and natural language inputs, PathChat can potentially find impactful applications in pathology education, research, and human-in-the-loop clinical decision making.
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Despite increasing numbers of regulatory approvals, deep learning-based computational pathology systems often overlook the impact of demographic factors on performance, potentially leading to biases. This concern is all the more important as computational pathology has leveraged large public datasets that underrepresent certain demographic groups. Using publicly available data from The Cancer Genome Atlas and the EBRAINS brain tumor atlas, as well as internal patient data, we show that whole-slide image classification models display marked performance disparities across different demographic groups when used to subtype breast and lung carcinomas and to predict IDH1 mutations in gliomas. For example, when using common modeling approaches, we observed performance gaps (in area under the receiver operating characteristic curve) between white and Black patients of 3.0% for breast cancer subtyping, 10.9% for lung cancer subtyping and 16.0% for IDH1 mutation prediction in gliomas. We found that richer feature representations obtained from self-supervised vision foundation models reduce performance variations between groups. These representations provide improvements upon weaker models even when those weaker models are combined with state-of-the-art bias mitigation strategies and modeling choices. Nevertheless, self-supervised vision foundation models do not fully eliminate these discrepancies, highlighting the continuing need for bias mitigation efforts in computational pathology. Finally, we demonstrate that our results extend to other demographic factors beyond patient race. Given these findings, we encourage regulatory and policy agencies to integrate demographic-stratified evaluation into their assessment guidelines.
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Glioma , Neoplasias Pulmonares , Humanos , Sesgo , Negro o Afroamericano , Población Negra , Demografía , Errores Diagnósticos , Glioma/diagnóstico , Glioma/genética , BlancoRESUMEN
Background: Mortality due to immune-mediated thrombotic thrombocytopenic purpura (iTTP) remains significant. Predicting mortality risk may potentially help individualize treatment. The French Thrombotic Microangiopathy (TMA) Reference Score has not been externally validated in the United States. Recent advances in machine learning technology can help analyze large numbers of variables with complex interactions for the development of prediction models. Objectives: To validate the French TMA Reference Score in the United States Thrombotic Microangiopathy (USTMA) iTTP database and subsequently develop a novel mortality prediction tool, the USTMA TTP Mortality Index. Methods: We analyzed variables available at the time of initial presentation, including demographics, symptoms, and laboratory findings. We developed our model using gradient boosting machine, a machine learning ensemble method based on classification trees, implemented in the R package gbm. Results: In our cohort (n = 419), the French score predicted mortality with an area under the receiver operating characteristic curve of 0.63 (95% CI: 0.50-0.77), sensitivity of 0.35, and specificity of 0.84. Our gradient boosting machine model selected 8 variables to predict acute mortality with a cross-validated area under the receiver operating characteristic curve of 0.77 (95% CI: 0.71-0.82). The 2 cutoffs corresponded to sensitivities of 0.64 and 0.50 and specificities of 0.76 and 0.87, respectively. Conclusion: The USTMA Mortality Index was acceptable for predicting mortality due to acute iTTP in the USTMA registry, but not sensitive enough to rule out death. Identifying patients at high risk of iTTP-related mortality may help individualize care and ultimately improve iTTP survival outcomes. Further studies are needed to provide external validation. Our model is one of many recent examples where machine learning models may show promise in clinical prediction tools in healthcare.
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The accelerated adoption of digital pathology and advances in deep learning have enabled the development of robust models for various pathology tasks across a diverse array of diseases and patient cohorts. However, model training is often difficult due to label scarcity in the medical domain, and a model's usage is limited by the specific task and disease for which it is trained. Additionally, most models in histopathology leverage only image data, a stark contrast to how humans teach each other and reason about histopathologic entities. We introduce CONtrastive learning from Captions for Histopathology (CONCH), a visual-language foundation model developed using diverse sources of histopathology images, biomedical text and, notably, over 1.17 million image-caption pairs through task-agnostic pretraining. Evaluated on a suite of 14 diverse benchmarks, CONCH can be transferred to a wide range of downstream tasks involving histopathology images and/or text, achieving state-of-the-art performance on histology image classification, segmentation, captioning, and text-to-image and image-to-text retrieval. CONCH represents a substantial leap over concurrent visual-language pretrained systems for histopathology, with the potential to directly facilitate a wide array of machine learning-based workflows requiring minimal or no further supervised fine-tuning.
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Lenguaje , Aprendizaje Automático , Humanos , Flujo de TrabajoRESUMEN
Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks, requiring the objective characterization of histopathological entities from whole-slide images (WSIs). The high resolution of WSIs and the variability of morphological features present significant challenges, complicating the large-scale annotation of data for high-performance applications. To address this challenge, current efforts have proposed the use of pretrained image encoders through transfer learning from natural image datasets or self-supervised learning on publicly available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using more than 100 million images from over 100,000 diagnostic H&E-stained WSIs (>77 TB of data) across 20 major tissue types. The model was evaluated on 34 representative CPath tasks of varying diagnostic difficulty. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient artificial intelligence models that can generalize and transfer to a wide range of diagnostically challenging tasks and clinical workflows in anatomic pathology.
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Inteligencia Artificial , Flujo de TrabajoRESUMEN
INTRODUCTION: Guidelines on the management of pregnant individuals with von Willebrand disease (VWD) at the time of delivery recommend that von Willebrand factor (VWF) and factor VIII:C (FVIII:C) levels be ≥50% to prevent postpartum haemorrhage (PPH). Yet, high PPH rates persist despite these levels or with prophylactic factor replacement therapy to achieve these levels. AIMS: The current practice at our centre has been to target peak plasma VWF and FVIII:C levels of ≥100 IU/dL at time of delivery. The objective of this study was to describe obstetric outcomes in pregnant individuals with VWD who were managed at our centre. METHODS: Demographics and outcomes on pregnant individuals with VWD who delivered between January 2015 and April 2023 were collected. RESULTS: Forty-seven singleton deliveries (among 41 individuals) resulting in 46 live births and one foetal death were included. Twenty-one individuals had at least one prior birth by the start date of this study, of which 11 (52.4%) self-reported a history of PPH. Early PPH occurred in 12.8% (6/47) of deliveries. Two individuals required blood transfusion, of which one also had an unplanned hysterectomy and transfer to ICU. There were no thrombotic events reported. CONCLUSION: The strategy of targeting higher peak plasma VWF and FVIII:C levels (≥100 IU/dL) at the time of delivery may be effective in reducing the risk of delivery-associated bleeding complications in VWD patients. Yet, the rate of early PPH remains unsatisfactory compared to the non-VWD population.
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Hemostáticos , Hemorragia Posparto , Enfermedades de von Willebrand , Embarazo , Femenino , Humanos , Enfermedades de von Willebrand/complicaciones , Factor de von Willebrand , Estudios de Cohortes , Factor VIII , Hemorragia Posparto/etiologíaRESUMEN
ABSTRACT: Antiphospholipid syndrome (APS) is a rare autoimmune disease characterized by arterial, venous, or microvascular thrombosis, pregnancy morbidities, or nonthrombotic manifestations in patients with persistently positive antiphospholipid antibodies. These antibodies bind cellular phospholipids and phospholipid-protein complexes resulting in cellular activation and inflammation that lead to the clinical features of APS. Our evolving understanding of APS has resulted in more specific classification criteria. Patients meeting these criteria should be treated during pregnancy according to current guidelines. Yet, despite treatment, those positive for lupus anticoagulant have at least a 30% likelihood of adverse pregnancy outcomes. Patients with recurrent early miscarriage or fetal death in the absence of preeclampsia or placental insufficiency may not meet current classification criteria for APS. Patients with only low titer anticardiolipin or anti-ß(2)-glycoprotein I antibodies or immunoglobulin M isotype antibodies will not meet current classification criteria. In such cases, clinicians should implement management plans that balance potential risks and benefits, some of which involve emotional concerns surrounding the patient's reproductive future. Finally, APS may present in pregnancy or postpartum as a thrombotic microangiopathy, a life-threatening condition that may initially mimic preeclampsia with severe features but requires a very different treatment approach.
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Síndrome Antifosfolípido , Preeclampsia , Humanos , Femenino , Embarazo , Síndrome Antifosfolípido/diagnóstico , Síndrome Antifosfolípido/terapia , Placenta , Anticuerpos Antifosfolípidos , Fosfolípidos , Resultado del Embarazo , Autoanticuerpos , Anticuerpos AnticardiolipinaRESUMEN
INTRODUCTION: Gene therapy is now a reality for individuals with haemophilia, yet little is known regarding the quality-of-life impact of factor correction. As few data exist, and recognizing the analogy to liver transplantation (OLTX), we identified OLTX+ and OLTX- men in the ATHNdataset to compare post-OLTX factor VIII and IX on quality of life (QoL) by Haem-A-QoL and PROMIS-29. METHODS: OLTX- were matched to OLTX+ by age, race, and haemophilia type and severity. Deidentified demographic data, including post-transplant factor levels, genotype and target joint disease were analysed by descriptive statistics. Haem-A-Qol and PROMIS-29 were compared in OLTX+ and OLTX- by student's t-test and univariate regression models. RESULTS: Of 86 people with haemophilia A (HA) or haemophilia B (HB) cared for at 10 haemophilia treatment centers (HTCs), 21 (24.4%) OLTX+ and 65 (75.6%) OLTX- were identified. OLTX+ and OLTX- had a similar frequency of target joint disease (p = .806), HA genotypes, null versus non-null (p = .696), and HIV infection (p = .316). At a median 9.2 years post-OLTX, median FVIII, .63 IU/mL [IQR 0.52-0.97] and FIX, .91 IU/mL [IQR .63-1.32], Haem-A-QoL, PROMIS-29, and HOT scores were comparable. Severe HA/HB had lower post-OLTX 'dealing with haemophilia' scores (p = .022) and higher 'sports and leisure' (p = .010) and 'view of yourself' scores (p = .024) than OLTX+ non-severe participants. Non-caucasian OLTX+ had significantly lower scores in sports and leisure (p = .042), future expectations (p = .021) and total score (p = .010). CONCLUSION: Nine years after OLTX, QoL is comparable to OLTX-, but significantly better in OLTX+ with severe than non-severe disease and in caucasians than non-caucasians.
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Infecciones por VIH , Hemofilia A , Hemofilia B , Artropatías , Trasplante de Hígado , Masculino , Humanos , Hemofilia A/terapia , Calidad de Vida , Estudios de Cohortes , HemoRESUMEN
Background: Heparin-induced thrombocytopenia (HIT) is a relatively uncommon condition characterized by 2 exceedingly common phenomena in hospitalized patients: thrombocytopenia and heparin exposure. Consequently, HIT is frequently overdiagnosed and inappropriately treated. These issues are the focus of many quality improvement (QI) initiatives. Objectives: In this scoping review, we identified and characterized all published QI studies on improving the diagnosis and management of HIT. Methods: We conducted a systematic literature search through April 2022 for studies reporting on QI interventions regarding the diagnosis, treatment, and/or prevention of HIT. Results: Thirty studies were included in the final review. Studies were separated into 5 groups based on the focus of the interventions: increasing HIT recognition, reducing HIT incidence, reducing HIT overdiagnosis, promoting safer HIT management, and creating HIT task forces. Nine studies focused on the implementation of 4Ts score calculator into electronic medical record orders for HIT testing, while only 1 evaluated the impact of reducing unfractionated heparin use in favor of low-molecular-weight heparin. Six studies focused on the implementation of direct thrombin inhibitor management protocols, while none evaluated the use of alternative anticoagulants in HIT management. Conclusion: The bulk of published HIT QI research focused on reducing overdiagnosis and promoting safer direct thrombin inhibitor therapy, while minimal attention has been devoted to HIT prevention and the use of evidence-based alternative HIT therapies.
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Graphical representation of increasing percentage of female patients seen at HTCs, percentage of females by diagnosis, number of clinics in existence, and absolute number of female patients seen over a 10-year period (top left then clockwise).
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Hemofilia A , Humanos , Femenino , Hemofilia A/epidemiología , Hemofilia A/terapiaRESUMEN
Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology. However, whole-slide imaging (WSI) poses a complex computer vision problem in which the large-scale image resolutions of WSIs and the enormous diversity of morphological phenotypes preclude large-scale data annotation. Current efforts have proposed using pretrained image encoders with either transfer learning from natural image datasets or self-supervised pretraining on publicly-available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using over 100 million tissue patches from over 100,000 diagnostic haematoxylin and eosin-stained WSIs across 20 major tissue types, and evaluated on 33 representative CPath clinical tasks in CPath of varying diagnostic difficulties. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree code classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient AI models that can generalize and transfer to a gamut of diagnostically-challenging tasks and clinical workflows in anatomic pathology.
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BACKGROUND: Up to 40% of vulvar cancer patients present with local recurrence within 10 years of follow-up. An inguinofemoral lymphadenectomy (IFL) is indicated if not performed at primary treatment. The incidence and risk factors for lymph node metastases (LNM) at first local recurrence, however, are unclear. Our aim was to determine the incidence of LNM at first local recurrence, in relation to previous groin treatment and clinicopathological factors. METHODS: A multicenter cohort study including vulvar cancer patients with a first macroinvasive local recurrence after primary surgical treatment between 2000 and 2015 was conducted in the Netherlands. Groin status at local recurrence was defined as positive (N+), negative (N-) or unknown (N?) and based on histology, imaging and follow-up. Patient-, tumour- and treatment characteristics of primary and recurrent disease were analysed. RESULTS: Overall, 16.3% (66/404) had a N+ groin status at first local recurrence, 66.4% (268/404) N- and 17.3% (70/404) N? groin status. The incidence of a N+ groin status was comparable after previous SLN and IFL, 11.5% and 13.8%, respectively. A N+ groin status was related to tumour size (25 vs.12 mm; P < 0.001), depth of invasion (5 vs. 3 mm; P < 0.001) and poorly differentiated tumours (22.9 vs. 11.9%; P = 0.050) at local recurrence. CONCLUSIONS: The incidence of LNM at first local recurrence in vulvar cancer patients was 16.3%, and independent of previous type of groin surgery. In accordance with primary diagnosis, tumour size, depth of invasion, and tumour grade were significantly associated with a positive groin status.
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Neoplasias de la Vulva , Femenino , Humanos , Metástasis Linfática/patología , Neoplasias de la Vulva/epidemiología , Neoplasias de la Vulva/cirugía , Neoplasias de la Vulva/patología , Estudios de Cohortes , Incidencia , Recurrencia Local de Neoplasia/patología , Escisión del Ganglio Linfático/efectos adversos , Ganglios Linfáticos/cirugía , Ganglios Linfáticos/patología , Biopsia del Ganglio Linfático Centinela , Estadificación de NeoplasiasRESUMEN
In healthcare, the development and deployment of insufficiently fair systems of artificial intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models stratified across subpopulations have revealed inequalities in how patients are diagnosed, treated and billed. In this Perspective, we outline fairness in machine learning through the lens of healthcare, and discuss how algorithmic biases (in data acquisition, genetic variation and intra-observer labelling variability, in particular) arise in clinical workflows and the resulting healthcare disparities. We also review emerging technology for mitigating biases via disentanglement, federated learning and model explainability, and their role in the development of AI-based software as a medical device.
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Inteligencia Artificial , Medicina , Humanos , Programas Informáticos , Aprendizaje Automático , Atención a la SaludRESUMEN
INTRODUCTION: Reproductive-age women with bleeding disorders (BDs) are underdiagnosed and understudied, despite their increased risk for adverse health outcomes and pregnancy complications. AIM: This study examines pregnancy outcomes and obstetric complications of Utah women with BDs. METHODS: This retrospective cohort study utilized linked birth records and clinical billing data from two large Utah healthcare systems. Utah residents who had their first birth at > 20 weeks gestation (2008-2015) and who received non-emergent care within either system before delivery were included (n = 61 226). Multivariable logistic regression models were used to examine relationships between BDs and neonatal and obstetric outcomes. RESULTS: A total of 295 women (.48%) were included in the BD study population. Women with BDs had significantly increased odds of preterm birth (aOR 1.85, 95% CI 1.32-2.60), Caesarean delivery (aOR 1.38, 95% CI 1.06-1.79), postpartum blood transfusion (aOR 2.55, 95% CI 1.05-6.22), unplanned postpartum hysterectomy (aOR 33.96, 95% CI 7.30-157.89) and transfer to an intensive care unit (aOR 18.18, 95% CI 7.17-46.08). All of the women with BDs who experienced these serious complications were not diagnosed with a BD until the year of their first birth. Additionally, those with BDs were more likely to experience maternal and infant mortality. CONCLUSION: Women with BDs had an increased risk for preterm birth, Caesarean delivery, blood transfusion, unplanned hysterectomy, intensive care unit admission, maternal and infant mortality. Those who were not diagnosed with a BD before the year of their first birth were at an increased risk for serious pregnancy complications.
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Trastornos de la Coagulación Sanguínea , Trastornos Hemorrágicos , Complicaciones del Embarazo , Nacimiento Prematuro , Embarazo , Lactante , Recién Nacido , Humanos , Femenino , Nacimiento Prematuro/epidemiología , Nacimiento Prematuro/etiología , Estudios Retrospectivos , Parto , Cesárea/efectos adversos , Complicaciones del Embarazo/epidemiología , Trastornos de la Coagulación Sanguínea/complicaciones , Trastornos Hemorrágicos/complicacionesRESUMEN
The most common thrombotic microangiopathy (TMA) of pregnancy is the well-recognized syndrome of preeclampsia with hemolysis, elevated liver enzymes, and low platelet count (HELLP) syndrome. However, rare TMAs, including thrombotic thrombocytopenic purpura, complement-mediated hemolytic-uremic syndrome, and catastrophic antiphospholipid syndrome, may occur during pregnancy or postpartum and present with features similar to those of preeclampsia with severe features. Early recognition and treatment of these infrequently encountered conditions are key for avoiding serious maternal morbidities with long-term sequelae and possible maternal or fetal death. Differentiating between preeclampsia with severe features and these rare TMAs is diagnostically challenging as there is significant overlap in their clinical and laboratory presentation. Given the rarity of these TMAs, high-quality evidence-based recommendations on diagnosis and management during pregnancy are lacking. Using current objective information and recommendations from working groups, this report provides practical clinical approaches to diagnose and manage these rare TMAs. This report also discusses how to manage individuals with a history of these rare TMAs who are planning to conceive. To optimize favorable outcomes, a multidisciplinary approach including obstetricians, maternal-fetal medicine specialists, hematologists, and nephrologists alongside close clinical and laboratory monitoring is vital.