Demographic Disparities in Acute Myeloid Leukemia Mortality Trends in the United States.

BACKGROUND/AIM: Acute myeloid leukemia (AML) is a hematological malignancy with an overall poor prognosis; however, survival rates vary widely by clinical and demographic characteristics. This study sought to identify historical trends in AML mortality in the US and to identify any disparities by sex, race or ethnicity. PATIENTS AND METHODS: For each demographic population, the age adjusted mortality rate (AAMR) per 1,000,000 for AML-related deaths from 1999 to 2020 in the United States was accessed from the CDC Wonder Database. These values were then used to calculate the average Annual Percent Change (AAPC) from 1999 to 2020 using the National Cancer Institute (NCI)'s Joinpoint Regression Program (Joinpoint V 4.9.0.0, NCI) with log-linear regression models. Statistical significance for all reported findings was determined using a 2-tailed t-test or parallel pairwise comparison with significance defined as p<0.05. RESULTS: The overall population had a significant downtrend in mortality rate between 2011 and 2020 with an APC of -0.61% [95% confidence interval (CI)=-1 to -0.2]. In 2020, the AAMR due to AML for males was 32 and for females was 20.2. Females did not have a significant overall AAPC from 1999 to 2020. Males had a significant AAPC of 0.5% (95%CI=0-0.9) from 1999 to 2020, signifying an overall uptrend. In 2020, the White population had the greatest mortality rate (29.6), followed by the Black or African American population (20.9), Asian or Pacific Islander (AAPI) population (18.6), and the American Indian/Alaska Native population (8.8). American Indian and Alaska Native population data could not be reliably compared. No race/ethnic group had a significant AAPC trend from 1999 to 2020. However, parallel pairwise comparison found a significant difference in the trend of mortality rates between the Black or African American and AAPI, Black or African American and White, and White and AAPI populations. CONCLUSION: Our findings highlight disparities in mortality due to AML and underscore the need for additional resources and support in affected populations and areas.

Disparities in Mortality Trends of Breast Cancer by Racial and Ethnic Status in the United States.

BACKGROUND AND AIM: Breast cancer is the most commonly diagnosed malignancy for women and is a leading cause of mortality in women worldwide and in the United States. Recently, new interventions have been developed to improve its prognosis. The aim of this study was to assess the impact of new therapies on racial and ethnic groups in the United States for demographic-based disparities. We assessed the impact of these developments from 1999 to 2020 on age adjusted mortality rate (AAMR), mortality rate trend from 1999 to 2020, average annual percent change (AAPC), and temporal trends, by annual percent change (APC) in the United States for various demographic groups. PATIENTS AND METHODS: We queried the CDC Wonder database to retrieve mortality rates by race and ethnic group from 1999 to 2020 with breast malignancy as a contributing cause of death. RESULTS: Between 1999 to 2020, all racial groups presented a significant overall decline in mortality rates: AI/AN [AAPC, -1.6% (95% CI=-2.2% to -1.0%); p<0.01], AAPI [AAPC, -0.5% (95% CI=-1.00% to -0.1%); p<0.01], Black/African American [AAPC, -1.4% (95% CI=-1.6% to -1.2%); p<0.01], and the white population [AAPC, -1.7% (95% CI=-1.8% to -1.5%); p<0.01]. The Black/African American population had a significant lower rate of decline compared to the white population (p<0.01) and Hispanic/Latinx populations had a lower rate of decline compared to those who are non-Hispanic/Latinx (p<0.01). CONCLUSION: We found that Black/African American population had a significant lower rate of decline compared to the white population and Hispanic/Latinx populations had a lower rate of decline compared to those who are non-Hispanic/Latinx. These differences in mortality trend rates in breast cancer emphasize the need for targeted interventions and resources tailored to specific demographic needs.

Trends of liver cell carcinoma mortality in the United States by demographics and geography.

BACKGROUND: Liver cancer is a complex disease that presents many challenges in its diagnosis, treatment, and prevention. It's mortality rate in the United States is a significant and warrants attention. OBJECTIVE: To assess the trend of mortality rate due to HCC in the US from 1999 to 2020 by demographic groups for differences in trend of mortality. METHODS: We used the CDC wonder database to collect mortality rate data due to HCC as a multiple cause of death in the US from 1999 to 2020 by sex, race, age, and state of residence. The SEER Joinpoint program was used to calculate trends, defined as average annual percent change (AAPC) and to identify disparities between groups. All age-adjusted rates (AAMR) are reported per 100,000. RESULTS: From 1999 to 2020, we found that women observed an uptrend (AAPC1.6%) and men observed a slightly higher uptrend in mortality (AAPC 1.8%). In addition, AI/AN population had a significant uptrend (AAPC 2.3%). The AAPI population observed a downtrend (AAPC -2.6%). The Black or African American population observed an uptrend (AAPC 1.8%) The white population also observed an uptrend (AAPC 2.2%). In the 2010 to 2020 time period, Mississippi had the lowest AAMR of any state with 15.2, while Hawaii had with the highest with 38.8. CONCLUSION: This investigation assesses mortality rates and trends due to HCC cancer in the US and found significant differences in mortality rates and mortality rate trends due to HCC by demographic status in the US. Addressing the disparities in HCC incidence and mortality by race, ethnicity, state, and region, as well as improving access to screening, surveillance, and effective treatments, can reduce the burden of HCC and improve outcomes for patients.

Assessing appropriate responses to ACR urologic imaging scenarios using ChatGPT and Bard.

Artificial intelligence (AI) has recently become a trending tool and topic regarding productivity especially with publicly available free services such as ChatGPT and Bard. In this report, we investigate if two widely available chatbots chatGPT and Bard, are able to show consistent accurate responses for the best imaging modality for urologic clinical situations and if they are in line with American College of Radiology (ACR) Appropriateness Criteria (AC). All clinical scenarios provided by the ACR were inputted into ChatGPT and Bard with result compared to the ACR AC and recorded. Both chatbots had an appropriate imaging modality rate of of 62% and no significant difference in proportion of correct imaging modality was found overall between the two services (p>0.05). The results of our study found that both ChatGPT and Bard are similar in their ability to suggest the most appropriate imaging modality in a variety of urologic scenarios based on ACR AC criteria. Nonetheless, both chatbots lack consistent accuracy and further development is necessary for implementation in clinical settings. For proper use of these AI services in clinical decision making, further developments are needed to improve the workflow of physicians.

Plant Parasitic Nematode Identification in Complex Samples with Deep Learning.

Plant parasitic nematodes are significant contributors to yield loss worldwide, causing devastating losses to every crop species, in every climate. Mitigating these losses requires swift and informed management strategies, centered on identification and quantification of field populations. Current plant parasitic nematode identification methods rely heavily on manual analyses of microscope images by a highly trained nematologist. This mode is not only expensive and time consuming, but often excludes the possibility of widely sharing and disseminating results to inform regional trends and potential emergent issues. This work presents a new public dataset containing annotated images of plant parasitic nematodes from heterologous soil extractions. This dataset serves to propagate new automated methodologies or speedier plant parasitic nematode identification using multiple deep learning object detection models and offers a path towards widely shared tools, results, and meta-analyses.

Demographic disparities in trend of gynecological cancer in the United States.

PURPOSE: This study aimed to analyze the age-adjusted mortality rates (AAMR) per 100,000 for gynecological cancer-related deaths in the United States from 1999 to 2020. We compare trends by different demographic groups to identify significant disparities in these rates between populations within the United States. METHODS: The National Cancer Institute's Joinpoint Regression Program was used to calculate the average Annual Percent Change (AAPC) to identify trends over the study period using data from the CDC Wonder database, which comprises of demographic information for all causes of mortality in the United States from death certificate records. RESULTS: From 1999 to 2020, the African American population exhibited a significant downtrend (AAPC, -0.8% [95% CI, - 1.0% to - 0.6%]; p < 0.01), while the white population also demonstrated a notable downtrend (AAPC, - 1.0% [95% CI, - 1.2% to - 0.8%]; p < 0.01). Similarly, the AI/AN population experienced a decline (AAPC, - 1.6% [95% CI, - 2.4% to - 0.9%]; p < 0.01). The AAPI population did not observe a significant trend (AAPC, - 0.2% [95% CI, - 0.5% to 0.5%]; p = 0.127). In addition, the Hispanic/LatinX population experiencing a lower rate of decline compared to non-Hispanics (p = 0.025). CONCLUSIONS: We found that the AI/AN population to observe the greatest downtrend in mortality rates, while the AAPI observed the least and that the African American population observed a smaller downtrend when compared to the white population. In addition, the Hispanic/LatinX community are significantly being underserved by developing therapies compared to the non-Hispanic/LatinX population. These findings provide valuable insights into the impact of gynecological cancers on specific demographic groups, emphasizing the urgency of targeted interventions to address disparities and improve outcomes.

First Description of the Nuclear and Mitochondrial Genomes and Associated Host Preference of Trichopoda pennipes, a Parasitoid of Nezara viridula.

Trichopoda pennipes is a tachinid parasitoid of several significant heteropteran agricultural pests, including the southern green stink bug, Nezara viridula, and leaf-footed bug, Leptoglossus phyllopus. To be used successfully as a biological control agent, the fly must selectively parasitize the target host species. Differences in the host preference of T. pennipes were assessed by assembling the nuclear and mitochondrial genomes of 38 flies reared from field-collected N. viridula and L. phyllopus. High-quality de novo draft genomes of T. pennipes were assembled using long-read sequencing. The assembly totaled 672 MB distributed among 561 contigs, having an N50 of 11.9 MB and a GC of 31.7%, with the longest contig at 28 MB. The genome was assessed for completeness using BUSCO in the Insecta dataset, resulting in a score of 99.4%, and 97.4% of the genes were single copy-loci. The mitochondrial genomes of the 38 T. pennipes flies were sequenced and compared to identify possible host-determined sibling species. The assembled circular genomes ranged from 15,345 bp to 16,390 bp and encode 22 tRNAs, two rRNAs, and 13 protein-coding genes (PCGs). There were no differences in the architecture of these genomes. Phylogenetic analyses using sequence information from 13 PCGs and the two rRNAs individually or as a combined dataset resolved the parasitoids into two distinct lineages: T. pennipes that parasitized both N. viridula and L. phyllopus, and others that parasitized only L. phyllopus.

Temporally Regulated Plant-Nematode Gene Networks Implicate Metabolic Pathways.

Root-knot nematodes (RKN) (Meloidogyne spp.) constantly communicate with their host to establish and maintain specialized feeding cells. They likely regulate this interaction by monitoring host biology. As plant host biology is influenced by light and gene expression varies correspondingly, RKN gene transcription and biology likely follow similar patterns. We profiled RKN transcripts over a period of 24 h and identified approximately 1,000 differentially expressed genes (DEG) in nematode and model host Medicago truncatula, with the majority of DEG occurring in the middle of the dark period. Many of the plant DEG are involved in defense-response pathways, while the nematode DEG are involved in establishing infection, suggesting a strong host-nematode interaction occurring during the dark. To identify interacting genes, we developed a plant-nematode gene network based on DEG signals. The phenylpropanoid pathway was identified as a significant plant-nematode interacting pathway, representing four of 33 genes in the network. We further examined if this pathway interacts similarly in another host, tomato, by quantifying phenolic and flavonoid compounds produced by this pathway. Phenolic compounds showed a significant increase in production during the day in uninoculated plants as compared with during the night. However, during the dark period, there was an increase in flavonoid content in infected plants when compared with uninfected controls, indicating potential host defense mechanisms active during the height of nematode activity at night. This study elucidated cross-species interacting pathways that could be targeted to develop novel management strategies to these important pests.[Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.