Antifungal mechanism of nanosilver biosynthesized with Trichoderma longibrachiatum and its potential to control muskmelon Fusarium wilt.

Fusarium oxysporum (Schl.) f.sp. melonis, which causes muskmelon wilt disease, is a destructive filamentous fungal pathogen, attracting more attention to the search for effective fungicides against this pathogen. In particular, Silver nanoparticles (AgNPs) have strong antimicrobial properties and they are not easy to develop drug resistance, which provides new ideas for the prevention and control of muskmelon Fusarium wilt (MFW). This paper studied the effects of AgNPs on the growth and development of muskmelon, the control efficacy on Fusarium wilt of muskmelon and the antifungal mechanism of AgNPs to F. oxysporum. The results showed that AgNPs could inhibit the growth of F. oxysporum on the PDA and in the PDB medium at 100-200 mg/L and the low concentration of 25 mg/L AgNPs could promote the seed germination and growth of muskmelon seedlings and reduce the incidence of muskmelon Fusarium wilt. Further studies on the antifungal mechanism showed that AgNPs could impair the development, damage cell structure, and interrupt cellular metabolism pathways of this fungus. TEM observation revealed that AgNPs treatment led to damage to the cell wall and membrane and accumulation of vacuoles and vessels, causing the leakage of intracellular contents. AgNPs treatment significantly hampered the growth of mycelia in the PDB medium, even causing a decrease in biomass. Biochemical properties showed that AgNPs treatment stimulated the generation of reactive oxygen species (ROS) in 6 h, subsequently producing malondialdehyde (MDA) and increasing protective enzyme activity. After 6 h, the protective enzyme activity decreased. These results indicated that AgNPs destroy the cell structure and affect the metabolisms, eventually leading to the death of fungus.

Amur Tiger Individual Identification Based on the Improved InceptionResNetV2.

Accurate and intelligent identification of rare and endangered individuals of flagship wildlife species, such as Amur tiger (Panthera tigris altaica), is crucial for understanding population structure and distribution, thereby facilitating targeted conservation measures. However, many mathematical modeling methods, including deep learning models, often yield unsatisfactory results. This paper proposes an individual recognition method for Amur tigers based on an improved InceptionResNetV2 model. Initially, the YOLOv5 model is employed to automatically detect and segment facial, left stripe, and right stripe areas from images of 107 individual Amur tigers, achieving a high average classification accuracy of 97.3%. By introducing a dropout layer and a dual-attention mechanism, we enhance the InceptionResNetV2 model to better capture the stripe features of individual tigers at various granularities and reduce overfitting during training. Experimental results demonstrate that our model outperforms other classic models, offering optimal recognition accuracy and ideal loss changes. The average recognition accuracy for different body part features is 95.36%, with left stripes achieving a peak accuracy of 99.37%. These results highlight the model's excellent recognition capabilities. Our research provides a valuable and practical approach to the individual identification of rare and endangered animals, offering significant potential for improving conservation efforts.

Multi-FusNet: fusion mapping of features for fine-grained image retrieval networks.

As the diversity and volume of images continue to grow, the demand for efficient fine-grained image retrieval has surged across numerous fields. However, the current deep learning-based approaches to fine-grained image retrieval often concentrate solely on the top-layer features, neglecting the relevant information carried in the middle layer, even though these information contains more fine-grained identification content. Moreover, these methods typically employ a uniform weighting strategy during hash code mapping, risking the loss of critical region mapping-an irreversible detriment to fine-grained retrieval tasks. To address the above problems, we propose a novel method for fine-grained image retrieval that leverage feature fusion and hash mapping techniques. Our approach harnesses a multi-level feature cascade, emphasizing not just top-layer but also intermediate-layer image features, and integrates a feature fusion module at each level to enhance the extraction of discriminative information. In addition, we introduce an agent self-attention architecture, marking its first application in this context, which steers the model to prioritize on long-range features, further avoiding the loss of critical regions of the mapping. Finally, our proposed model significantly outperforms existing state-of-the-art, improving the retrieval accuracy by an average of 40% for the 12-bit dataset, 22% for the 24-bit dataset, 16% for the 32-bit dataset, and 11% for the 48-bit dataset across five publicly available fine-grained datasets. We also validate the generalization ability and performance stability of our proposed method by another five datasets and statistical significance tests. Our code can be downloaded from https://github.com/BJFU-CS2012/MuiltNet.git.

Leveraging a Longitudinally Linked Dataset to Assess Recurrence of Severe Maternal Morbidity.

OBJECTIVES: Among those with a severe maternal morbidity (SMM) event and a subsequent birth, we examined how the risk of a second SMM event varied by patient characteristics and intrapartum hospital utilization. METHODS: We used a Massachusetts population-based dataset that longitudinally linked in-state births, hospital discharge records, prior and subsequent births, and non-birth-related hospital utilizations for birthing individuals and their children from January 1, 1999, to December 31, 2018, representing 1,460,514 births by 907,530 birthing people. We restricted our study sample to 2,814 people who had their first SMM event associated with a singleton birth and gave birth a second time within the study period. Our outcome measure was recurrence of SMM in the second birth. We calculated the prevalence of SMM at second birth, compared SMM conditions between births, and estimated the adjusted risk ratios and 95% confidence intervals for having an SMM event at second birth among those who had an SMM at the first birth. We also examined overall hospital utilization including inpatient admissions, emergency room visits, and observational stays, and hospital utilization by interpregnancy intervals (IPIs) between the first and second birth. RESULTS: There were 2,814 birthing people with at least one birth after the first SMM singleton birth. Among those, 198 (7.0%) had a subsequent SMM. The percentage of people with a second SMM event varied by age, race/ethnicity, insurance, IPI, and history of hypertension at first case of SMM (all p < .05). Between births, people with a second SMM event had significantly higher proportions of inpatient admissions (60.1% vs. 33.2.0%; p < .001), emergency room visits (71.7% vs. 57.7%; p < .001), and observational stays (35.4% vs. 19.5%; p < .001) compared with those who did not experience a second SMM event. CONCLUSION: Hospital utilization after a birth with SMM might indicate an elevated risk of a second SMM event. Providers should counsel their patients about prevention and warning signs.

SIG: Graph-Based Cancer Subtype Stratification With Gene Mutation Structural Information.

Somatic tumors have a high-dimensional, sparse, and small sample size nature, making cancer subtype stratification based on somatic genomic data a challenge. Current methods for improving cancer clustering performance focus on dimension reduction, integrating multi-omics data, or generating realistic samples, yet ignore the associations between mutated genes within the patient-gene matrix. We refer to these associations as gene mutation structural information, which implicitly includes cancer subtype information and can enhance subtype clustering. We introduce a novel method for cancer subtype clustering called SIG(Structural Information within Graph). As cancer is driven by a combination of genes, we establish associations between mutated genes within the same patient sample, pair by pair, and use a graph to represent them. An association between two mutated genes corresponds to an edge in the graph. We then merge these associations among all mutated genes to obtain a structural information graph, which enriches the gene network and improves its relevance to cancer clustering. We integrate the somatic tumor genome with the enriched gene network and propagate it to cluster patients with mutations in similar network regions. Our method achieves superior clustering performance compared to SOTA methods, as demonstrated by clustering experiments on ovarian and LUAD datasets. The code is available at https://github.com/ChangSIG/SIG.git.

CircGPC3 promotes hepatocellular carcinoma progression and metastasis by sponging miR-578 and regulating RAB7A/PSME3 expression.

CircRNAs are a class of highly stable noncoding RNAs that play an important role in the progression of many diseases, especially cancer. In this study, high-throughput sequencing was used to screen for abnormally expressed circRNAs, and we found that circGPC3 was overexpressed in HCC tissues. However, the underlying mechanism of circGPC3 in the development and metastasis of hepatocellular carcinoma (HCC) remains unknown. In our study, we found that circGPC3 was significantly upregulated in HCC tissues and cells and that its overexpression was positively correlated with overall survival, TNM stage and lymph node metastasis. In vivo and in vitro experiments showed that circGPC3 knockdown repressed HCC cell migration, invasion and proliferation and promoted apoptosis. Mechanistically, circGPC3 promoted HCC proliferation and metastasis through the miR-578/RAB7A/PSME3 axis. Our results demonstrate that circGPC3 contributes to the progression of HCC and provides an intervention target for HCC.

The Application of Artificial Intelligence and Big Data in the Food Industry.

Over the past few decades, the food industry has undergone revolutionary changes due to the impacts of globalization, technological advancements, and ever-evolving consumer demands. Artificial intelligence (AI) and big data have become pivotal in strengthening food safety, production, and marketing. With the continuous evolution of AI technology and big data analytics, the food industry is poised to embrace further changes and developmental opportunities. An increasing number of food enterprises will leverage AI and big data to enhance product quality, meet consumer needs, and propel the industry toward a more intelligent and sustainable future. This review delves into the applications of AI and big data in the food sector, examining their impacts on production, quality, safety, risk management, and consumer insights. Furthermore, the advent of Industry 4.0 applied to the food industry has brought to the fore technologies such as smart agriculture, robotic farming, drones, 3D printing, and digital twins; the food industry also faces challenges in smart production and sustainable development going forward. This review articulates the current state of AI and big data applications in the food industry, analyses the challenges encountered, and discusses viable solutions. Lastly, it outlines the future development trends in the food industry.

Genome-Wide Identification of PAP1 Direct Targets in Regulating Seed Anthocyanin Biosynthesis in Arabidopsis.

Anthocyanins are widespread water-soluble pigments in the plant kingdom. Anthocyanin accumulation is activated by the MYB-bHLH-WD40 (MBW) protein complex. In Arabidopsis, the R2R3-MYB transcription factor PAP1 activates anthocyanin biosynthesis. While prior research primarily focused on seedlings, seeds received limited attention. This study explores PAP1's genome-wide target genes in anthocyanin biosynthesis in seeds. Our findings confirm that PAP1 is a positive regulator of anthocyanin biosynthesis in Arabidopsis seeds. PAP1 significantly increased anthocyanin content in developing and mature seeds in Arabidopsis. Transcriptome analysis at 12 days after pollination reveals the upregulation of numerous genes involved in anthocyanin accumulation in 35S:PAP1 developing seeds. Chromatin immunoprecipitation and dual luciferase reporter assays demonstrate PAP1's direct promotion of ten key genes and indirect upregulation of TT8, TTG1, and eight key genes during seed maturation, thus enhancing seed anthocyanin accumulation. These findings enhance our understanding of PAP1's novel role in regulating anthocyanin accumulation in Arabidopsis seeds.

Prior Hospitalization, Severe Maternal Morbidity, and Pregnancy-Associated Deaths in Massachusetts From 2002 to 2019.

OBJECTIVE: To examine demographic and clinical precursors to pregnancy-associated deaths overall and when pregnancy-related deaths are excluded. METHODS: We conducted a retrospective cohort study based on a Massachusetts population-based data system linking data from live birth and fetal death certificates to corresponding delivery hospital discharge records and a birthing individual's nonbirth hospital contacts and associated death records. Exposures included maternal demographics, severe maternal morbidity (without transfusion), hospitalizations in the 3 years before pregnancy, comorbidities during pregnancy, and opioid use. In cases of postpartum deaths, hospitalization between delivery and death was examined. The primary outcome measure was pregnancy-associated death , defined as death during pregnancy or up to 1 year postpartum. RESULTS: There were 1,291,626 deliveries between 2002 and 2019, of which 384 were linked to pregnancy-associated deaths. Pregnancy-associated but not pregnancy-related deaths (per 100,000 deliveries) were highest for birthing people with opioid use before pregnancy (498.3), severe maternal morbidity (387.3), a comorbidity (106.3), or a prior hospitalization (88.9). In multivariable analysis, the adjusted risk ratios associated with severe maternal morbidity (9.37, 95% CI, 6.14-14.31) and opioid use (6.49, 95%, CI, 3.71-11.35) were highest. Individuals with pregnancy-associated deaths were also more likely to have been hospitalized before or during pregnancy (2.30, 95% CI, 1.62-3.26). Among postpartum deaths, more than two-thirds (69.9%) of birthing people had a hospital contact after delivery and before their death. CONCLUSION: Severe maternal morbidity and opioid use disorder were precursors to pregnancy-associated deaths. Individuals with pregnancy-associated but not pregnancy-related deaths experienced a history of hospital contacts during and after pregnancy before death.

A case report of Vibrio vulnificus sepsis in a diabetic patient.

Vibrio vulnificus is a facultative anaerobic, alkalophilic, halophilic, mesophilic, Gram-negative bacterium that can cause severe wound infection, sepsis and diarrhea. This paper reported a case of 85-year-old male patient infected with Vibrio vulnificus due to being stabbed by a sea shrimp. This patient also had diabetes with a long history of alcoholism. Due to bacterial pathogenicity and the patient's underlying diseases, his condition deteriorated rapidly. Based on the rapid diagnosis of Vibrio vulnificus using the next-generation sequencing（NGS）technology and blood culture method, as well as the selection of the most effective antibiotics via drug sensitivity test, this patient underwent precise antimicrobial treatment, thorough debridement and drainage within the shortest possible time, and thus the prognosis of this patient was greatly improved. In this paper, we have systematically explored the epidemiology, clinical features, diagnosis and treatment of Vibrio vulnificus infection, thus providing a practical reference for the clinicians to quickly identify and treat possible Vibrio vulnificus infection in diabetic patients after contacting with sea water or seafood.

Identifying Influential Nodes in Complex Networks Based on Information Entropy and Relationship Strength.

Identifying influential nodes is a key research topic in complex networks, and there have been many studies based on complex networks to explore the influence of nodes. Graph neural networks (GNNs) have emerged as a prominent deep learning architecture, capable of efficiently aggregating node information and discerning node influence. However, existing graph neural networks often ignore the strength of the relationships between nodes when aggregating information about neighboring nodes. In complex networks, neighboring nodes often do not have the same influence on the target node, so the existing graph neural network methods are not effective. In addition, the diversity of complex networks also makes it difficult to adapt node features with a single attribute to different types of networks. To address the above problems, the paper constructs node input features using information entropy combined with the node degree value and the average degree of the neighbor, and proposes a simple and effective graph neural network model. The model obtains the strength of the relationships between nodes by considering the degree of neighborhood overlap, and uses this as the basis for message passing, thereby effectively aggregating information about nodes and their neighborhoods. Experiments are conducted on 12 real networks, using the SIR model to verify the effectiveness of the model with the benchmark method. The experimental results show that the model can identify the influence of nodes in complex networks more effectively.

Preconception and perinatal hospitalizations as indicators of risk for severe maternal morbidity in primiparas.

BACKGROUND: Severe maternal morbidity includes unexpected outcomes of labor and delivery that result in significant short- or long-term consequences to a woman's health. A statewide longitudinally linked database was used to examine hospitalization during and before pregnancy for birthing people with severe maternal morbidity at delivery. OBJECTIVE: This study aimed to examine the association between hospital visits during pregnancy and 1 to 5 years before pregnancy and severe maternal morbidity at delivery. STUDY DESIGN: This study was a retrospective, population-based cohort analysis of the Massachusetts Pregnancy to Early Life Longitudinal database between January 1, 2004, and December 31, 2018. Nonbirth hospital visits, including emergency department visits, observational stays, and hospital admissions during pregnancy and 5 years before pregnancy, were identified. The diagnoses for hospitalizations were categorized. We compared medical conditions leading to antecedent, nonbirth hospital visits among primiparous birthing individuals with singleton births with and without severe maternal morbidity, excluding transfusions. RESULTS: Of 235,398 birthing individuals, 2120 had severe maternal morbidity, a rate of 90.1 cases per 10,000 deliveries, and 233,278 did not have severe maternal morbidity. Compared with 4.3% of patients without severe maternal morbidity, 10.4% of patients with severe maternal morbidity were hospitalized during pregnancy. In multivariable analysis, there was a 31% increased risk of hospital admission during the prenatal period, a 60% increased risk of hospital admission in the year before pregnancy, and a 41% increased risk of hospital admission in 2 to 5 years before pregnancy. Compared with 9.8% of non-Hispanic White birthing people, 14.9% of non-Hispanic Black birthing people with severe maternal morbidity experienced a hospital admission during pregnancy. For those with severe maternal morbidity, prenatal hospitalization was most common for those with endocrine (3.6%) or hematologic (3.3%) conditions, with the largest differences between those with and without severe maternal morbidity for musculoskeletal (relative risk, 9.82; 95% confidence interval, 7.06-13.64) and cardiovascular (relative risk, 9.73; 95% confidence interval, 7.26-13.03) conditions. CONCLUSION: This study found a strong association between previous nonbirth hospitalizations and the likelihood of severe maternal morbidity at delivery.

Application of Three-Dimensional Digital Photogrammetry to Quantify the Surface Roughness of Milk Powder.

The surface appearance of milk powders is a crucial quality property since the roughness of the milk powder determines its functional properties, and especially the purchaser perception of the milk powder. Unfortunately, powder produced from similar spray dryers, or even the same dryer but in different seasons, produces powder with a wide variety of surface roughness. To date, professional panelists are used to quantify this subtle visual metric, which is time-consuming and subjective. Consequently, developing a fast, robust, and repeatable surface appearance classification method is essential. This study proposes a three-dimensional digital photogrammetry technique for quantifying the surface roughness of milk powders. A contour slice analysis and frequency analysis of the deviations were performed on the three-dimensional models to classify the surface roughness of milk powder samples. The result shows that the contours for smooth-surface samples are more circular than those for rough-surface samples, and the smooth-surface samples had a low standard deviation; thus, milk powder samples with the smoother surface have lower Q (the energy of the signal) values. Lastly, the performance of the nonlinear support vector machine (SVM) model demonstrated that the technique proposed in this study is a practicable alternative technique for classifying the surface roughness of milk powders.

AFG1-induced TNF-α-mediated inflammation enhances gastric epithelial cell injury via CYP2E1.

Aflatoxin G1 (AFG1), a member of the aflatoxin family with cytotoxic and carcinogenic properties, is one of the most common mycotoxins occurring in various agricultural products, animal feed, and human foods and drinks worldwide. Epithelial cells in the gastrointestinal tract are the first line of defense against ingested mycotoxins. However, the toxicity of AFG1 to gastric epithelial cells (GECs) remains unclear. In this study, we explored whether and how AFG1-induced gastric inflammation regulates cytochrome P450 to contribute to DNA damage in GECs. Oral administration of AFG1 induced gastric inflammation and DNA damage in mouse GECs associated with P450 2E1 (CYP2E1) upregulation. Treatment with the soluble TNF-α receptor sTNFR:Fc inhibited AFG1-induced gastric inflammation, and reversed CYP2E1 upregulation and DNA damage in mouse GECs. TNF-α-mediated inflammation plays an important role in AFG1-induced gastric cell damage. Using the human gastric cell line GES-1, AFG1 upregulated CYP2E1 through NF-κB, causing oxidative DNA damage in vitro. The cells were also treated with TNF-α and AFG1 to mimic AFG1-induced TNF-α-mediated inflammation. TNF-α activated the NF-κB/CYP2E1 pathway to promote AFG1 activation, which enhanced DNA cellular damage in vitro. In conclusion, AFG1 ingestion induces TNF-α-mediated gastric inflammation, which upregulates CYP2E1 to promote AFG1-induced DNA damage in GECs.

Neutral amino acid transporter SLC38A2 protects renal medulla from hyperosmolarity-induced ferroptosis.

Hyperosmolarity of the renal medulla is essential for urine concentration and water homeostasis. However, how renal medullary collecting duct (MCD) cells survive and function under harsh hyperosmotic stress remains unclear. Using RNA-Seq, we identified SLC38A2 as a novel osmoresponsive neutral amino acid transporter in MCD cells. Hyperosmotic stress-induced cell death in MCD cells occurred mainly via ferroptosis, and it was significantly attenuated by SLC38A2 overexpression but worsened by Slc38a2-gene deletion or silencing. Mechanistic studies revealed that the osmoprotective effect of SLC38A2 is dependent on the activation of mTORC1. Moreover, an in vivo study demonstrated that Slc38a2-knockout mice exhibited significantly increased medullary ferroptosis following water restriction. Collectively, these findings reveal that Slc38a2 is an important osmoresponsive gene in the renal medulla and provide novel insights into the critical role of SLC38A2 in protecting MCD cells from hyperosmolarity-induced ferroptosis via the mTORC1 signalling pathway.

ELAA: An Ensemble-Learning-Based Adversarial Attack Targeting Image-Classification Model.

The research on image-classification-adversarial attacks is crucial in the realm of artificial intelligence (AI) security. Most of the image-classification-adversarial attack methods are for white-box settings, demanding target model gradients and network architectures, which is less practical when facing real-world cases. However, black-box adversarial attacks immune to the above limitations and reinforcement learning (RL) seem to be a feasible solution to explore an optimized evasion policy. Unfortunately, existing RL-based works perform worse than expected in the attack success rate. In light of these challenges, we propose an ensemble-learning-based adversarial attack (ELAA) targeting image-classification models which aggregate and optimize multiple reinforcement learning (RL) base learners, which further reveals the vulnerabilities of learning-based image-classification models. Experimental results show that the attack success rate for the ensemble model is about 35% higher than for a single model. The attack success rate of ELAA is 15% higher than those of the baseline methods.

Advances in High-Performance Non-Ferrous Materials.

Non-ferrous metallic materials are considered to be fundamental materials for manufacturing in-dustries, i [...].

IFL-GAN: Improved Federated Learning Generative Adversarial Network With Maximum Mean Discrepancy Model Aggregation.

The generative adversarial network (GAN) is usually built from the centralized, independent identically distributed (i.i.d.) training data to generate realistic-like instances. In real-world applications, however, the data may be distributed over multiple clients and hard to be gathered due to bandwidth, departmental coordination, or storage concerns. Although existing works, such as federated learning GAN (FL-GAN), adopt different distributed strategies to train GAN models, there are still limitations when data are distributed in a non-i.i.d. manner. These studies suffer from convergence difficulty, producing generated data with low quality. Fortunately, we found that these challenges are often due to the use of a federated averaging strategy to aggregate local GAN models' updates. In this article, we propose an alternative approach to tackling this problem, which learns a globally shared GAN model by aggregating locally trained generators' updates with maximum mean discrepancy (MMD). In this way, we term our approach improved FL-GAN (IFL-GAN). The MMD score helps each local GAN hold different weights, making the global GAN in IFL-GAN getting converged more rapidly than federated averaging. Extensive experiments on MNIST, CIFAR10, and SVHN datasets demonstrate the significant improvement of our IFL-GAN in both achieving the highest inception score and producing high-quality instances.

DCU-Net: a dual-channel U-shaped network for image splicing forgery detection.

The detection and location of image splicing forgery are a challenging task in the field of image forensics. It is to study whether an image contains a suspicious tampered area pasted from another image. In this paper, we propose a new image tamper location method based on dual-channel U-Net, that is, DCU-Net. The detection framework based on DCU-Net is mainly divided into three parts: encoder, feature fusion, and decoder. Firstly, high-pass filters are used to extract the residual of the tampered image and generate the residual image, which contains the edge information of the tampered area. Secondly, a dual-channel encoding network model is constructed. The input of the model is the original tampered image and the tampered residual image. Then, the deep features extracted from the dual-channel encoding network are fused for the first time, and then the tampered features with different granularity are extracted by dilation convolution, and then, the secondary fusion is carried out. Finally, the fused feature map is input into the decoder, and the predicted image is decoded layer by layer. The experimental results on Casia2.0 and Columbia datasets show that DCU-Net performs better than the latest algorithm and can accurately locate tampered areas. In addition, the attack experiments show that DCU-Net model has good robustness and can resist noise and JPEG recompression attacks.

Trends and inequities in severe maternal morbidity in Massachusetts: A closer look at the last two decades.

It is estimated that 50,000-60,000 pregnant people in the United States (US) experience severe maternal morbidity (SMM). SMM includes life-threatening conditions, such as acute myocardial infarction, acute renal failure, amniotic fluid embolism, disseminated intravascular coagulation, or sepsis. Prior research has identified both rising rates through 2014 and wide racial disparities in SMM. While reducing maternal death and SMM has been a global goal for the past several decades, limited progress has been made in the US in achieving this goal. Our objectives were to examine SMM trends from 1998-2018 to identify factors contributing to the persistent and rising rates of SMM by race/ethnicity and describe the Black non-Hispanic/White non-Hispanic rate ratio for each SMM condition. We used a population-based data system that links delivery records to their corresponding hospital discharge records to identify SMM rates (excluding transfusion) per 10, 000 deliveries and examined the trends by race/ethnicity. We then conducted stratified analyses separately for Black and White birthing people. While the rates of SMM during the same periods steadily increased for all racial/ethnic groups, Black birthing people experienced the greatest absolute increase compared to any other race/ethnic group going from 69.4 in 1998-2000 to 173.7 per 10,000 deliveries in 2016-2018. In addition, we found that Black birthing people had higher rates for every individual condition compared to White birthing people, with rate ratios ranging from a low of 1.11 for heart failure during surgery to a high of 102.4 for sickle cell anemia. Obesity was not significantly associated with SMM among Black birthing people but was associated with SMM among White birthing people [aRR 1.18 (95% CI: 1.02, 1.36)]. An unbiased understanding of how SMM has affected different race/ethnicity groups is key to improving maternal health and preventing SMM and mortality among Black birthing people. SMM needs to be addressed as both a medical and public health challenge.